Welcome to our blog series “AI Innovations in July 2024”! As we continue to ride the wave of extraordinary developments from June, the momentum in artificial intelligence shows no signs of slowing down. Last month, we witnessed groundbreaking achievements such as the unveiling of the first quantum AI chip, the successful deployment of autonomous medical drones in remote areas, and significant advancements in natural language understanding that have set new benchmarks for AI-human interaction.
July promises to be just as exhilarating, with researchers, engineers, and visionaries pushing the boundaries of what’s possible even further. In this evolving article, updated daily throughout the month, we’ll dive deep into the latest AI breakthroughs, advancements, and milestones shaping the future.
From revolutionary AI-powered technologies and cutting-edge research to the societal and ethical implications of these innovations, we provide you with a comprehensive and insightful look at the rapidly evolving world of artificial intelligence. Whether you’re an AI enthusiast, a tech-savvy professional, or simply someone curious about the future, this blog will keep you informed, inspired, and engaged.
Join us on this journey of discovery as we explore the frontiers of AI, uncovering the innovations that are transforming industries, enhancing our lives, and shaping our future. Stay tuned for daily updates, and get ready to be amazed by the incredible advancements happening in the world of AI!
A Daily chronicle of AI Innovations July 31st 2024:
OpenAI begins ChatGPT Voice rollout
Google cracks down on explicit deepfakes in search results
AI ‘Friend’ pendant goes viral
Perplexity’s publisher revenue-sharing
OpenAI begins ChatGPT Voice rollout
OpenAI has begun a limited rollout of its hotly anticipated ‘Advanced Voice Mode’ for paying ChatGPT Plus users, offering natural, real-time conversations and the ability for the AI to detect and respond to emotions.
The feature will initially be available to a small group of ChatGPT Plus users, with plans to give all Plus users access by fall 2024.
Advanced Voice Mode uses GPT-4o and can sense emotions in users’ voices, including sadness, excitement, or singing.
Video and screen-sharing capabilities, previously showcased in OpenAI’s early demo, will launch at a ‘later’ date.
OpenAI has sent email instructions to the initial ‘Alpha‘ group selected for early access.
AI is slowly shifting from a tool we text/prompt with, to an intelligence that we collaborate, learn, and grow with. Advanced Voice Mode’s ability to understand and respond to emotions in real-time convos could also have huge use cases in everything from customer service to mental health support.
Google cracks down on explicit deepfakes in search results
Google is introducing new online safety features designed to remove explicit deepfakes from Search, making it harder for such content to appear prominently in search results.
When users request the removal of explicit nonconsensual fake images of themselves, Google’s systems will now filter out similar explicit results and remove duplicate images from related search queries.
Google’s updates also include demoting sites with extensive removals for fake explicit imagery in Search rankings and ensuring that searches for deepfake images yield high-quality, non-explicit content instead.
The “Friend” AI necklace, created by Avi Schiffmann, is designed to provide personal companionship through support and encouragement, connecting to an iPhone via Bluetooth.
Unlike other AI wearables that failed, Friend listens to interactions around the wearer and sends supportive messages, storing all data locally on the device.
Schiffmann described the device as an expression of loneliness and emphasized its role as a supportive and validating companion, useful for brainstorming and discussing relationships.
Perplexity just introduced a “Publishers’ Program” to share ad revenue with media partners, following recent plagiarism accusations and aiming to support quality journalism in the age of AI-powered search.
The program includes cash advances on future revenue as Perplexity builds its advertising model, set to launch in September.
Initial partners include Time, Der Spiegel, Fortune, WordPress.com, and more, who will receive a “double-digit percentage” of ad revenue.
Partners also get free access to Perplexity’s Enterprise Pro tier, developer tools, and insights through Scalepost AI.
Despite constant pushback on AI firms and their training data, media companies are finding few available paths forward other than accepting partnership deals. Perplexity’s initiative is a good step toward fairness, but it likely won’t be the end of the growing pains with publishers.
A Daily chronicle of AI Innovations July 30th 2024:
Instagram now lets you create an AI chatbot of yourself
Perplexity’s new revenue sharing plan
Nvidia announces new support for humanoid robots
Meta’s new open-source model could be the ‘GPT-4 moment’ for computer vision
Zuck and Huang envision AI’s future
Runway releases image-to-video AI
Apple says its AI models were trained on Google’s custom chips
Meta released world’s largest open-source LLM to date Mistral AI released its Llama 3.1 rival, Mistral Large 2 US lawmakers are requesting OpenAI for government access DeepMind’s new AI is a silver medalist in the IMO math Olympiad OpenAI announced SearchGPT, an AI-powered search engine Apple revealed AI models powering Apple Intelligence
Instagram now lets you create an AI chatbot of yourself
Meta has released a new tool called AI Studio, enabling users in the US to create AI characters on Instagram or the web to interact with followers on their behalf.
These AI profiles can engage in direct chat threads, respond to comments, and are customizable based on the creator’s Instagram content and specified interaction guidelines.
In addition to creating personalized AI, users can also design entirely new characters to use across Meta’s platforms, with Meta ensuring these AI profiles are clearly labeled to avoid confusion.
Perplexity has started a program to share advertising revenue with publishers after facing plagiarism accusations from several media outlets.
The “Publishers’ Program” includes partners like Time, Der Spiegel, and Automattic, who will receive a portion of ad revenue for their content used by Perplexity.
This initiative follows investigations by Forbes and Wired, which reported Perplexity’s AI misusing and paraphrasing their articles without proper attribution.
Nvidia has introduced a new suite of services, including the NIM microservices platform and the OSMO orchestration service, to aid in the development, simulation, and training of humanoid robots.
CEO Jensen Huang emphasized that Nvidia is advancing its robotics stack to support global humanoid developers, offering platforms, acceleration libraries, and AI models tailored for their needs.
At the SIGGRAPH conference, Nvidia showcased an AI-enabled teleoperation workflow and detailed three robotics development platforms: Nvidia AI supercomputers, Nvidia Isaac Sim, and Nvidia Jetson Thor humanoid robot computers.
Meta’s new open-source model could be the ‘GPT-4 moment’ for computer vision
Meta has introduced SAM 2, a cutting-edge open-source model for segmenting both images and videos, marking a significant advancement in computer vision similar to OpenAI’s GPT-4 in natural language processing.
While the original SAM focused solely on images, SAM 2 excels in video segmentation, effectively handling lower-quality footage and partially obscured objects, thanks to training on a vast new video dataset.
SAM 2’s improved accuracy, enhanced memory module for better object tracking, and faster processing speed positions it as a groundbreaking tool in the fields of video editing, robotics, and generative AI, despite some limitations.
During a fireside chat at SIGGRAPH 2024, Meta CEO Mark Zuckerberg and NVIDIA CEO Jensen Huang spoke about their shared vision for the AI-powered future.
Both CEOs emphasized the importance of open-source AI, with Zuckerberg highlighting Llama 3.1’s release as an “inflection point.”
Zuckerberg outlined a possible future for social media to evolve from recommending content to AI generating personalized content on the fly.
Huang predicted a shift from turn-based AI interactions to more fluid, multi-option simulations.
The leaders also discussed AI’s potential to transform education, entertainment, and work through smart glasses.
The emphasis on open-source and personalized AI signals a potential shift in how AI will be integrated into everyday life and business. With Meta and NVIDIA’s combined influence, the shared vision could significantly shape the future of AI and its applications across different industries.
Runway just announced that Gen-3 Alpha, the startup’s popular AI text-to-video generation model, can now create high-quality videos from still images.
According to Runway, image-to-video greatly improves the artistic control and consistency of video generations.
Image-to-video generations are either 5 or 10 seconds in length and take up “credits,“ which you have to pay for through Runway’s subscription tiers.
To use the tool, head to Runway’s website, click “try Gen-3 Alpha”, and upload an image to watch it come to life.
The highly anticipated image-to-video generation model opens up a whole new suite of creativity, allowing users to bring any image to life. However, while the increased artistic control and improvements to consistency are notable, Gen-3 Alpha does not come at a cheap price tag.
Apple says its AI models were trained on Google’s custom chips
Apple used Google’s tensor processing units (TPUs) to train two artificial intelligence models, according to a recent research paper.
To train its AI models, the company employed 2,048 TPUv5p chips for devices like iPhones and 8,192 TPUv4 processors for server-based models.
Unlike Nvidia’s GPUs, Google’s TPUs are accessible only via Google Cloud Platform, requiring customers to build software through this platform to utilize the chips.
On July 23rd, Meta officially released the biggest version of its open-source LLM, Llama, a 405 billion-parameter version called Llama-3.1. It also released Llama 3.1 70B and 8B models.
Llama 3.1’s context window has been expanded to 128,000 tokens, meaning users can feed it as much text as in a 400-page novel. It will be multilingual and support English, Portuguese, Spanish, Italian, German, French, Hindi, and Thai.
The 405B model is competitive with leading foundation models across a range of tasks, including GPT-4, GPT-4o, and Claude 3.5 Sonnet. The smaller models also performed similarly.
Users can access Llama 3.1 through AWS, Nvidia, Groq, Dell, Databricks, Microsoft Azure, Google Cloud, and other model libraries. Llama 3.1 405B will also be available on WhatsApp and Meta AI.
Why does it matter?
The move directly challenges industry leaders like OpenAI and Anthropic, particularly OpenAI’s market-leading position. It also underscores Meta’s commitment to open-source development, marking a major escalation in the AI competition.
Mistral AI has announced the next generation of its flagship open-source model with 123 billion parameters, Mistral Large 2. Compared to its predecessor, the model is significantly more capable in code generation, mathematics, and reasoning. It also provides much stronger multilingual support and advanced function-calling capabilities.
However, the model is only licensed as “open” for non-commercial research uses, including open weights, allowing third parties to fine-tune it to their liking. Those seeking to use it for commercial/enterprise-grade applications will need to obtain a separate license and usage agreement from Mistral.
Why does it matter?
Following Meta’s launch of Llama 3.1 as a highly competitive alternative to leading closed-source “frontier” models, the French AI startup entered the fray. The AI race is picking up pace like never before.
Perhaps the most significant portion of the letter was item 9: “Will OpenAI commit to making its next foundation model available to U.S. Government agencies for pre-deployment testing, review, analysis, and assessment?”
The letter outlined 11 additional points to be addressed, including OpenAI’s commitment to dedicating 20% of its computing power to fuel safety research and protocols to prevent malicious actors or foreign adversaries from stealing OpenAI’s products or IP.
Why does it matter?
Regulatory scrutiny is nothing new for OpenAI and the broader AI sector. However, now OpenAI is facing heightened scrutiny, and following developments could drive stringent government oversight and set new industry standards.
Google DeepMind presented AlphaProof, a new reinforcement-learning based system for formal math reasoning, and AlphaGeometry 2, an improved version of its geometry-solving system.
Together, these systems solved four out of six problems from this year’s International Mathematical Olympiad (IMO), achieving the same level as a silver medalist for the first time. Here’s a graph showing the AI system’s performance relative to human competitors at IMO 2024.
Why does it matter?
Solving complex math problems in step-by-step proofs has been a grand challenge for AI. Breakthroughs like these demonstrate AI’s growing ability to match top human minds, with far-reaching implications across various fields.
OpenAI is testing SearchGPT, a prototype combining the strength of its AI models with information from the web. It will quickly and directly respond to your questions with up-to-date information while providing clear links to relevant sources. You’ll also be able to ask follow-up questions.
It is launching to a small group of users and publishers to get feedback. While this prototype is temporary, OpenAI plans to integrate the best of its features directly into ChatGPT in the future.
Why does it matter?
This directly challenges Google’s dominance in the online search market. It also signals a significant escalation in AI search wars, which are already reshaping how users find and interact with information on the web.
Apple published a research paper describing two new foundation language models that form the backbone of Apple Intelligence, its new AI system.
AFM-on-device (AFM stands for Apple Foundation Model), a ∼3 billion parameter language 1 model, and
AFM-server, a larger server-based language model
The models are designed to be fast and run efficiently on iPhone, iPad, and Mac as well as on Apple silicon servers via Private Cloud Compute. They are part of a larger family of generative models created by Apple to support users and developers.
Why does it matter?
Apple Intelligence is designed with Apple’s core values at every step and a foundation of industry-lead privacy protection, showing Apple’s commitment to providing secure, powerful, personalized AI experiences.
OpenAI intensified the AI arms race by announcing free fine-tuning for its GPT-4o Mini model, just hours after Meta launched its open-source Llama 3.1 model.
Stability AI released Stable Video 4D, its first video-to-video AI model that turns a single object video into multiple novel-view videos with eight different angles/views.
A new study found indiscriminate use of AI-generated data in training leads to irreversible defects, termed “model collapse,” where the models plateau and become incoherent.
Kling AI has gone global with an International Version 1.0 to take on OpenAI’s yet-to-be-released video generator, Sora. It is now accessible to all at KlingAI.com, where registration requires only an email address.
Google introduced 1.5 Flash in the unpaid version of Gemini for faster and better responses. It also introduced a new feature to further address hallucinations and expanded Gemini for Teens and mobile apps.
X now automatically activates a setting that allows it to train its Grok AI on user data, including posts, user interactions, inputs, and results. Find out how you can switch it off!
Meta launched AI Studio, a platform built on Llama 3.1 that lets anyone create share, and discover AI characters and allows creators to build an AI as an extension of themselves to reach more fans.
Amazon has reportedly unveiled a new AI chip, boasting 40-50% higher performance than NVIDIA’s at half the cost, aiming to reduce reliance on expensive external chips.
Hugging Face is offering developers an inference-as-a-service powered by Nvidia NIM microservices. It will improve token efficiency by up to 5x with popular AI models.
A Daily chronicle of AI Innovations July 29th 2024:
Apple’s AI features will be late, report claims
AI revolutionizes the 2024 Olympics
Amazon paid $1B for Twitch 10 years ago, it’s still unprofitable
Neuralink-rival integrates ChatGPT into brain implant
Turn text into Sora-like AI videos
Apple’s AI features will be late, report claims
Apple’s AI features, including an improved Siri and ChatGPT integration, are expected to launch with iOS 18.1 in October, not with the initial release of iOS 18 in September.
These artificial intelligence improvements were first introduced at the Worldwide Developer Conference in June and might not be available immediately for new iPhone 16 devices at launch.
Some features will be available in developer betas starting this week, allowing testing before public release, but full functionality for certain enhancements may not be seen until spring 2025.
Neuralink-rival integrates ChatGPT into brain implant
Synchron, a competitor to Neuralink, has integrated OpenAI’s ChatGPT into its brain-computer interface (BCI) to help people with paralysis more easily control digital devices.
The AI addition assists users like Mark, an ALS patient, by predicting and suggesting responses during communication, which they can select using brain signals.
The company’s CEO, Tom Oxley, highlighted the potential of ChatGPT to enhance BCI capabilities, while the cost of Synchron’s implant is estimated to be between $50,000 and $100,000, similar to other medical implants.
The Paris 2024 Summer Olympic Games is showcasing an unexpectedly extensive amount of AI, changing experiences for athletes, spectators, and organizers — potentially signaling a new era in the way that we watch sports.
AthleteGPT, an AI chatbot, is providing 24/7 assistance to athletes through the Athlete365 mobile app.
An AI-powered 3D athlete tracking (3DAT) technology is offering detailed biomechanical insights for performance enhancement.
AI is being used in talent scouting, as demonstrated by a recent IOC pilot program in Senegal.
NBC is also using AI to provide personalized highlights and enhanced real-time statistics for viewers.
The use of AI at a major worldwide sporting event such as the Olympics marks a major moment for AI adoption, moving from previous reluctance to embrace it. As AI continues to become normalized globally, it could pave the way for a new era in sports viewing and management.
Kling AI’s text-to-video feature allows users to create stunning Sora-like videos from simple text prompts, opening up new ways you can produce high-quality visuals.
X (Twitter) automatically enabled a setting allowing user data, including user interactions, posts, inputs, and results, to be used for training and fine-tuning purposes for its Grok AI.
Morgan Stanley deployed its second in-house generative AI application, AI @ Morgan Stanley Debrief, which summarizes video meetings and generates follow-up email drafts.
The National Institute of Standards and Technology (NIST) released Dioptra, an open-source tool for testing AI model risk and measuring the impact of malicious attacks on AI system performance.
Reddit intensified its crackdown on web crawlers by blocking major search engines from surfacing recent posts unless they pay, with Google currently being the only mainstream search engine showing recent results.
Suno introduced a new feature for Pro & Premier users to separate vocals and instrumentals from AI-generated songs, allowing for more control and creative possibilities in music production.
Stanford Engineering and Toyota Research achieved a milestone in autonomous driving by creating the world’s first AI-directed, driverless tandem drift, aiming to advance the safety of automated driving in complex scenarios.
A Daily chronicle of AI Innovations July 26th 2024:
🏅AI: The New Gold Medalist in Empowering Athletes at the Olympics
OpenAI challenges Google with AI search engine SearchGPT
Google DeepMind’s AI takes home silver medal in complex math competition
Video game actors strike over AI concerns
Who will control the future of AI?
🏅AI: The New Gold Medalist in Empowering Athletes at the Olympics
AI as a Catalyst for Inclusion
Kevin Piette, paralyzed for 11 years, recently achieved a remarkable milestone by carrying the Olympic flame while walking. This extraordinary feat was made possible by the Atalante X, an AI-powered exoskeleton developed by French company Wandercraft. 🚀
The Olympics have always been a stage for human excellence, a platform where athletes push the boundaries of physical ability. However, the Games are also evolving into a showcase of technological innovation. Artificial intelligence (AI) is rapidly transforming sports, and its impact extends far beyond performance enhancement.
OpenAI challenges Google with AI search engine SearchGPT
OpenAI announced a new search product called “SearchGPT,” which is currently in the testing phase and aims to compete directly with Google’s Search Generative Experience.
SearchGPT, designed for a limited group of users, offers concise answers and relevant sources, with the intention of making search faster and easier through real-time information.
With this move, OpenAI targets Google’s dominant position in the search market, where Google holds approximately 90% market share, highlighting OpenAI’s significant ambition in the search engine space.
Google DeepMind’s AI takes home silver medal in complex math competition
Google DeepMind has developed an AI system named AlphaProof that achieved 28 points in the International Mathematical Olympiad, equivalent to a silver medalist’s score for the first time.
AlphaProof has managed to solve 83% of all IMO geometry problems over the past 25 years, significantly improving on its predecessor AlphaGeometry, which had a success rate of 53%.
AlphaProof generates solutions by searching and testing various mathematical steps, unlike human participants who rely on theorem knowledge and intuition to solve problems more efficiently.
The Screen Actors Guild-American Federation of Television and Radio Artists (SAG-AFTRA) has decided to strike all video game work under the union’s Interactive Media Agreement starting July 26th.
The strike affects all union actors, voice actors, and motion capture performers, targeting companies such as Activision Blizzard, EA, Insomniac Games, and WB Games, with disagreements over AI protections cited as the main issue.
Despite finding common ground on numerous proposals and the video game producers offering AI consent and fair compensation, SAG-AFTRA and the companies failed to reach a full agreement, leading to the strike.
Sam Altman, CEO of OpenAI, just wrote an op-ed outlining a strategy for ensuring a vision for AI prevails in the United States and allied nations over authoritarian alternatives.
Altman emphasizes the urgent need for a U.S.-led global coalition to advance AI that spreads its benefits and maintains open access.
He proposes four key actions: robust security measures, infrastructure investment, coherent commercial diplomacy, and new models for global AI governance.
The strategy aims to maintain the U.S. lead in AI development while countering efforts by authoritarian regimes to dominate the technology.
Altman suggests creating an international body for AI oversight, similar to the IAEA or ICANN.
Altman’s surprisingly urgent tone in this op-ed highlights the growing risks of AI development in the US. He believes “there is no third option,” either democratic nations lead AI development or authoritarian regimes will — raising a serious call to action for the race of AI dominance.
Google upgraded Gemini with 1.5 Flash, offering faster responses, a 4x larger context window, and expanded access in over 40 languages and 230 countries.
SAG-AFTRA announced a strike for video game performers starting July 26, citing concerns over AI protections in negotiations with major gaming studios, despite progress on wages and job safety.
Sam Altman revealed in a tweet reply that the GPT-4o-Voice Alpha rollout will begin next week for Plus subscribers, expanding OpenAI’s voice generation capabilities.
Udio released version 1.5 of its AI music model, featuring improved audio quality, key control, and new features like stem downloads and audio-to-audio remixing.
Runway’s AI video generator reportedly trained on thousands of YouTube videos without permission, according to a leaked document obtained by 404 Media.
Anthropic’s web crawler allegedly violated website terms of use, with iFixit reporting nearly a million hits in 24 hours, raising concerns about AI companies’ data collection practices.
A Daily chronicle of AI Innovations July 25th 2024:
OpenAI could lose $5B this year and run out of cash in 12 months
Kling AI’s video generation goes global
Apple Maps launches on the web to take on Google
Mistral’s Large 2 is its answer to Meta and OpenAI’s latest models
CrowdStrike offers $10 Uber Eats gift cards as an apology for the outage
Reddit blocking all search engines except Google, as it implements AI paywall
Mistral’s Large 2 takes on AI giants
OpenAI could lose $5B this year and run out of cash in 12 months
OpenAI could lose up to $5 billion in 2024, risking running out of cash within 12 months, according to an analysis by The Information.
The AI company is set to spend $7 billion on artificial intelligence training and $1.5 billion on staffing this year, far exceeding the expenses of rivals.
OpenAI may need to raise more funds within the next year to sustain its operations, despite having already raised over $11 billion through multiple funding rounds.
Mistral’s Large 2 is its answer to Meta and OpenAI’s latest models
French AI company Mistral AI launched its Mistral Large 2 language model just one day after Meta’s release of Llama 3, highlighting the intensifying competition in the large language model (LLM) market.
Mistral Large 2 aims to set new standards in performance and efficiency, boasting significant improvements in logic, code generation, and multi-language support, with a particular focus on minimizing hallucinations and improving reasoning capabilities.
The model, available on multiple platforms including Azure AI Studio and Amazon Bedrock, outperforms its predecessor with 123 billion parameters and supports extensive applications, signaling a red ocean of competition in the AI landscape.
Reddit blocking all search engines except Google, as it implements AI paywall
Reddit has begun blocking search engines from accessing recent posts and comments, except for Google, which has a $60 million agreement to train its AI models using Reddit’s content.
This move is part of Reddit’s strategy to monetize its data and protect it from being freely used by popular search engines like Bing and DuckDuckGo.
To enforce this policy, Reddit updated its robots.txt file, signaling to web crawlers without agreements that they should not access Reddit’s data.
Kling AI, developed by Chinese tech giant Kuaishou Technology, has released its impressive AI video model globally, offering high-quality AI generations that rival OpenAI’s (unreleased) Sora.
Kling can generate videos up to two minutes long, surpassing OpenAI’s Sora’s one-minute limit, however, the global version is limited to five-second generations.
The global version offers 66 free credits daily, with each generation costing 10 credits.
According to Kuaishou, Kling utilizes advanced 3D reconstruction technology for more natural movements.
The platform accepts prompts of up to 2,000 characters, allowing for detailed video descriptions.
When KLING launched a little over a month ago, it was only accessible if you had a Chinese phone number. While global users are still limited to 5-second generations, anyone can now generate their own high-quality videos — putting even more pressure on OpenAI to release its beloved Sora.
OpenAI, whose ChatGPT assistant kicked off an artificial intelligence arms race, is now pursuing a slice of the search industry. The company has unveiled a prototype of SearchGPT, an AI-powered search engine that is widely viewed as a play for rival Google’s $175 billion-per-year search business. But while Google’s use of AI in search results has been met with concern and resistance from publishers, SearchGPT touts its heavy use of citations and was developed alongside publishing partners, including Axel-Springer and the Financial Times. After seeing results to their queries, users will be able to ask follow-up questions in interactions that resemble those with ChatGPT.
A 10,000 person wait list was opened Thursday for a those wanting to test a prototype of the SearchGPT service.
Though currently distinct, SearchGPT will eventually be integrated into ChatGPT.
A Daily chronicle of AI Innovations July 24th 2024:
Google search is thriving despite AI shift
Google is pouring billions into self-driving taxis as Tesla prepares to reveal its rival
Senators demand answers on OpenAI’s practices
Meta’s Llama 3.1 takes on GPT-4o
Adobe’s new AI features for Photoshop
Google search is thriving despite AI shift
Despite concerns from online publishers, Google’s introduction of AI features generating conversational responses to search queries has attracted advertisers and propelled Alphabet’s success.
Alphabet’s revenue for the April-June quarter rose by 14% from last year to $84.74 billion, surpassing analyst expectations and boosting stock prices by 2% in extended trading.
Google’s cloud-computing division, its fastest-growing segment, generated $10.3 billion in revenue in the past quarter, marking its first time surpassing the $10 billion threshold in a single quarter.
Five U.S. Senators have just sent a letter to OpenAI CEO Sam Altman, demanding details about the company’s efforts to ensure AI safety following reports of rushed safety testing for GPT-4 Omni.
Senators question OpenAI’s safety protocols, citing reports that the company rushed safety testing of GPT-4 Omni to meet a May release date.
The letter requests OpenAI to make its next foundation model available to U.S. Government agencies for deployment testing, review, analysis, and assessment.
Lawmakers ask if OpenAI will commit 20% of computing resources to AI safety research, a promise made in July 2023 when announcing the now disbanded “Superalignment team”.
With allegations of rushed safety testing, potential retaliation against whistleblowers, and the disbanding of the “Superalignment team,” OpenAI is under intense scrutiny. This letter also marks a critical moment for the entire AI industry — with the potential to lead to stricter government oversight and new industry standards.
In case you missed our exclusive deep dive with Mark Zuckerberg yesterday, Meta released Llama 3.1, including it’s long awaited 405B paramater model — the first open sourced frontier model that beats top closed models like GPT-4o across several benchmarks.
The 405B parameter version of Llama 3.1 matches or exceeds top closed models on several benchmarks.
Meta is offering open and free weights and code, with a license enabling fine-tuning, distillation into other models, and deployment anywhere.
Llama 3.1 features a 128k context length, multi-lingual abilities, strong code generation performance, and complex reasoning capabilities.
For exclusive insights on Llama 3.1, open source, AI agents, and more, read our full deep dive with Mark Zuckerberg here, or watch the full interview here.
Meta’s release of Llama 3.1 405b is a significant moment in AI history because it’s the first time an open-source AI model matches or outperforms top closed AI models like OpenAI’s GPT-4o. By offering a private, customizable alternative to closed AI systems, Meta is enabling anyone to create their own tailored AI.
Adobe just unveiled major AI-powered updates to Illustrator and Photoshop, leveraging its Firefly AI model to accelerate creative workflows and introduce new generative design capabilities.
Illustrator introduces Generative Shape Fill using Firefly Vector AI to add detailed vectors to shapes and create scalable patterns via text prompts.
Text to Pattern in Illustrator creates scalable, customized vector patterns for designs like wallpapers.
Photoshop’s new AI-powered Selection Brush Tool and Generate Image function are now generally available.
Photoshop also gets an enhanced version of its popular Generative Fill for improved sharpness in large images.
These updates could dramatically increase designers’ productivity by automating tedious, time-consuming tasks. We’ve always preached that the best AI products are those embedded into everyday workflows — and Adobe is doing just that by putting powerful tech directly into designers’ everyday tools.
A Daily chronicle of AI Innovations July 23rd 2024:
Meta releases its most powerful AI model yet
Alexa is losing Amazon billions of dollars
The “world’s most powerful” supercomputer
Google’s AI-powered weather model
MIT’s AI identifies breast cancer risk
Musk unveils the world’s most powerful AI training cluster Robotics won’t have a ChatGPT-like explosion: New Research NeuralGCM predicts weather faster than SOTA climate models
Robotics won’t have a ChatGPT-like explosion: New Research
Coatue Management has released a report on AI humanoids and robotics’s current and future state. It says robotics will unlikely have a ChatGPT-like moment where a single technology radically transforms our work. While robots have been used for physical labor for over 50 years, they have grown linearly and faced challenges operating across different environments.
The path to broad adoption of general-purpose robots will be more gradual as capabilities improve and costs come down. Robotics faces challenges like data scarcity and hardware limitations that digital AI technologies like ChatGPT do not face. But investors are still pouring billions, hoping software innovations could help drive value on top of physical robotics hardware.
Why does it matter?
We’re on the cusp of a gradual yet profound transformation. While robotics may not suddenly become ubiquitous, the ongoing progress in artificial intelligence and robotics will dramatically alter the landscape of numerous fields, including manufacturing and healthcare.
NeuralGCM predicts weather faster than SOTA climate models
Google researchers have developed a new climate modeling tool called NeuralGCM. This tool uses a combination of traditional physics-based modeling and machine learning. This hybrid approach allows NeuralGCM to generate accurate weather and climate predictions faster and more efficiently than conventional climate models.
NeuralGCM’s weather forecasts match the accuracy of current state-of-the-art (SOTA) models for up to 5 days, and its ensemble forecasts for 5-15 day predictions outperform the previous best models. Additionally, NeuralGCM’s long-term climate modeling is one-third as error-prone as existing atmosphere-only models when predicting temperatures over 40 years.
Why does it matter?
NeuralGCM presents a new approach to building climate models that could be faster, less computationally costly, and more accurate than existing models. This breakthrough could lead to accessible and actionable climate modeling tools.
Elon Musk and xAI just announced the Memphis Supercluster — “the most powerful AI training cluster in the world“, also revealing that Grok 3.0 is planned to be released in December and should be the most powerful AI in the world.
Musk tweeted that xAI just launched the “Memphis Supercluster,” using 100,000 Nvidia H100 GPUs, making it “the most powerful AI training cluster in the world.”
The xAI founder also revealed that Grok 2.0 is done training and will be released soon.
The supercluster aims to create the “world’s most powerful AI by every metric”, Grok 3.0, by December 2024.
In a separate tweet yesterday, Musk also revealed that Tesla plans to have humanoid robots in “low production” for internal use next year.
Love him or hate him, the speed at which Elon and the team at xAI operate has been wild to witness. If estimates are accurate, xAI might be on track to create the most powerful AI systems in the world by year’s end — solidifying its position as one of the top competitors in the space and not just another AI startup.
Google researchers have developed a new AI-powered weather and climate model called ‘NeuralGCM’ by combining methods of machine learning and neural networks with traditional physics-based modeling.
NeuralGCM has proven more accurate than purely machine learning-based models for 1-10 day forecasts and top extended-range models.
NeuralGCM is up to 100,000 times more efficient than other models for simulating the atmosphere.
The model is open-source and can run relatively quickly on a laptop, unlike traditional models that require supercomputers.
At up to 100,000 times more efficient than traditional models — NeuralGCM could dramatically enhance our ability to simulate complex climate scenarios quickly and accurately. While still a ton of adoption challenges ahead, it’s a big leap forward for more informed climate action and resilience planning.
The Rundown: Researchers from MIT and ETH Zurich have developed an AI model that can identify different stages of ductal carcinoma in situ (DCIS), a type of preinvasive breast tumor, using simple tissue images.
The model analyzes chromatin images from 560 tissue samples (122 patients), identifying 8 distinct cell states across DCIS stages.
It considers both cellular composition and spatial arrangement, revealing that tissue organization is crucial in predicting disease progression.
Surprisingly, cell states associated with invasive cancer were detected even in seemingly normal tissue.
This AI model could democratize advanced breast cancer diagnostics, offering a cheaper, faster way to assess DCIS risk. While clinical validation is still needed, AI is likely going to work hand-in-hand with pathologists in the near future to catch cancer earlier and more accurately.
Meta has released Llama 3.1 405B, its largest open-source AI model to date, featuring 405 billion parameters which enhance its problem-solving abilities.
Trained with 16,000 Nvidia H100 GPUs, Llama 3.1 405B is competitive with leading AI models like OpenAI’s GPT-4 and Anthropic’s Claude 3.5 Sonnet, though it has specific strengths and weaknesses.
Meta’s new AI model is available for download or cloud usage and powers chatbots on platforms like WhatsApp and Meta.ai, showcasing capabilities in coding, mathematical queries, and multilingual document summarization.
Amazon plans to launch a paid version of Alexa to address the over $25 billion losses incurred by its devices business from 2017 to 2021, as reported by The Wall Street Journal.
The enhanced Alexa, which may cost up to $10 per month, is expected to be released soon, though employees have concerns about whether the technology is ready.
The new Alexa, featuring generative AI for improved conversational abilities, faces technical delays and competition from free AI assistants, raising doubts about customers’ willingness to pay for it.
VeriSIM Life’s AI platform can accelerate drug discovery
VeriSIM Life has developed an AI platform, BIOiSIM, to help speed up drug discovery and reduce animal testing. The platform contains data on millions of compounds and uses AI models to predict how potential new drugs will work in different species, including humans.
Anthropic is working on a new screenshot tool for Claude
This tool will allow users to capture and share screenshots from their desktop or browser directly within the Claude chat interface. It will streamline the sharing of visual information and code snippets when asking Claude for assistance on tasks like coding or troubleshooting.
Luma’s “Loops” feature in Dream Machine transforms digital marketing
The “Loops” feature allows users to create continuous video loops from text descriptions or images. It does so without visible cuts or transitions, opening up new possibilities for engaging content creation and advertising.
Tesla will use humanoid robots internally by next year
Elon Musk has announced that Tesla will use humanoid robots at its factories by next year. These robots, called Optimus, were expected to be ready by the end of 2024. Tesla aims to mass produce robots for $20,000 each and sell them to other companies starting in 2026.
Perplexity launches Voice Mode for its AI assistant on iOS
Perplexity has introduced a new feature for its iOS app called Voice Mode. It allows subscribers with Pro accounts to interact verbally with the AI-powered search engine. Users can now engage in voice-based conversations and pose questions using various voice options.
A Daily chronicle of AI Innovations July 22nd 2024:
Apple released two open-source AI language models OpenAI is in talks with Broadcom to develop an AI chip Nvidia is developing an AI chip series for China
The state of AI humanoids and robotics
Apple’s new 7B open-source AI model
Tesla to have humanoid robots for internal use next year
Nvidia preparing new flagship AI chip for Chinese market
️ Musk’s xAI turns on ‘world’s most powerful’ AI training cluster
Study reveals rapid increase in web domains blocking AI models
How to test and customize GPT-4o mini
Apple released two open-source AI language models
Apple has released two new open AI models called DCLM (DataComp for Language Models) on Hugging Face: one with 7 billion parameters and another with 1.4 billion parameters. The 7B model outperforms Mistral-7B and is comparable to other leading open models, such as Llama 3 and Gemma. They’ve released – model weights, training code, and even the pretraining dataset. The models were trained using a standardized framework to determine the best data curation strategy.
The 7B model was trained on 2.5 trillion tokens and has a 2K context window, achieving 63.7% 5-shot accuracy on MMLU. The 1.4B model, trained on 2.6 trillion tokens, outperforms other models in its category on MMLU with a score of 41.9%. These models are not intended for Apple devices.
Why does it matter?
By open-sourcing high-performing models and sharing data curation strategies, Apple is helping to solve some of AI’s toughest challenges for developers and researchers. This could lead to more efficient AI applications across various industries, from healthcare to education.
OpenAI is in talks with Broadcom to develop an AI chip
The company is in talks with Broadcom and other chip designers to build custom silicon, aiming to reduce dependence on Nvidia’s GPUs and boost its AI infrastructure capacity. OpenAI is hiring ex-Google employees with AI chip experience and has decided to develop an AI server chip.
The company is researching various chip packaging and memory components to optimize performance. However, the new chip is not expected to be produced until 2026 at the earliest.
Why does it matter?
Sam Altman’s vision for AI infrastructure is evolving from a separate venture into an in-house project at OpenAI. By bringing chip design in-house, OpenAI could potentially accelerate its AI research, reduce dependencies on external suppliers, and gain a competitive edge in the race of advanced AI.
Nvidia is developing a special version of its Blackwell AI chip for the Chinese market. Tentatively named “B20,” this chip aims to bridge the gap between U.S. export controls and China’s AI tech. Despite facing a revenue dip from 26% to 17% in China due to sanctions, Nvidia is not backing down. They’re partnering with local distributor Inspur to launch this new chip.
As Nvidia tries to reclaim its Chinese market share, competitors like Huawei are gaining ground. Meanwhile, the U.S. government is making even tighter controls on AI exports.
Why does it matter?
If Nvidia pulls off, it could maintain its dominance in the Chinese market while complying with U.S. regulations. But if regulators clamp down further, we could see a more fragmented global AI ecosystem, potentially slowing innovation. It’s a high-stakes game of technological cat-and-mouse, with Nvidia trying to stay ahead of regulators and rivals.
Tesla to have humanoid robots for internal use next year
Elon Musk announced that Tesla’s Optimus robots will begin “low production” for internal tasks in 2025, with mass production for other firms starting in 2026.
Musk initially stated the Optimus robot would be ready to perform tasks in Tesla’s EV factories by the end of this year.
Musk’s plans for Optimus and AI products come as Tesla faces reduced demand for electric vehicles and anticipates low profit margins in upcoming quarterly results.
️ Musk’s xAI turns on ‘world’s most powerful’ AI training cluster
Elon Musk’s xAI has started training its AI models using over 100,000 Nvidia H100 GPUs at a new supercomputing facility in Memphis, Tennessee, described as the most powerful AI training cluster globally.
This facility, known as the “Gigafactory of Compute,” is built in a former manufacturing site, and xAI secured $6 billion in funding, creating jobs for roles like fiber foreman, network engineer, and project manager.
The Memphis supercomputing site’s large energy and water demands have raised concerns among local environmental groups and residents, who fear its significant impact on water supplies and electrical consumption.
Study reveals rapid increase in web domains blocking AI models
A new study finds that more websites are blocking AI models from accessing their training data, potentially leading to less accurate and more biased AI systems.
The Data Provenance Initiative conducted the study, analyzing 14,000 web domains and discovering an increase in blocked tokens from 1% to up to 7% from April 2023 to April 2024.
News websites, social media platforms, and forums are the primary sources of these restrictions, with blocked tokens on news sites rising dramatically from 3% to 45% within a year.
The Reuters Institute released a study on public attitudes about AI in the news
It indicates that news consumers aren’t gloomy about AI in journalism. While initial reactions tend to be skeptical, attitudes become more nuanced as people learn about different AI applications. The comfort level varies based on where AI is used in the news process, with human oversight remaining a top priority.
California pushes bill requiring tech giants to test AI for “catastrophic” risks
While Republicans pledge a hands-off approach nationally, California’s move has sparked fierce debate. Tech leaders oppose the bill, citing potential harm to innovation and startups, while supporters argue it’s crucial for public safety.
Figma pulled its “Make Designs” AI tool after it generated designs similar to Apple’s weather app
The design platform admits it rushed new components without proper vetting, leading to uncanny similarities. While Figma didn’t train the AI on copyrighted designs, it’s back to the drawing board to polish its QA process.
OpenAI’s GPT-4o Mini has a safety feature called “instruction hierarchy”
This new feature prevents users from tricking the AI with sneaky commands like “ignore all previous instructions.” By prioritizing the developer’s original prompts, OpenAI aims to make its AI more trustworthy and safer for future applications, like running your digital life.
Google is the “official AI sponsor for Team USA” for the 2024 Paris Games
NBCUniversal’s broadcast will feature Google’s tech, from 3D venue tours to AI-assisted commentary. Moreover, Five Olympic and Paralympic athletes will appear in promos using Google’s AI tools.
A Daily chronicle of AI Innovations July 20th 2024:
OpenAI is working on an AI codenamed “Strawberry” Meta researchers developed “System 2 distillation” for LLMs Amazon’s Rufus AI is now available in the US AMD amps up AI PCs with next-gen laptop chips YT Music tests AI-generated radio, rolls out sound search 3 mysterious AI models appear in the LMSYS arena Meta’s Llama 3 400B drops next week Mistral AI adds two new models to its growing family of LLMs FlashAttention-3 enhances computation power of NVIDIA GPUs DeepL’s new LLM crushes GPT-4, Google, and Microsoft Salesforce debuts Einstein service agent Ex-OpenAI researcher launches AI education company OpenAI introduces GPT-4o mini, its most affordable model Mistral AI and NVIDIA collaborate to release a new model TTT models might be the next frontier in generative AI
CrowdStrike fixes start at “reboot up to 15 times” and get more complex from there
Apple releases the “best-performing” open-source models out there
Google in talks with Ray-Ban for AI smart glasses
Loophole that helps you identify any bot blocked by OpenAI
Apple releases the “best-performing” open-source models out there
Apple’s research team has released open DCLM models on Hugging Face, featuring 7 billion and 1.4 billion parameters, outperforming Mistral and approaching the performance of Llama 3 and other leading models.
The larger 7B model achieved a 6.6 percentage point improvement on the MMLU benchmark compared to previous state-of-the-art models while using 40% less compute for training, matching closely with top models like Google’s Gemma and Microsoft’s Phi-3.
Currently, the larger model is available under Apple’s Sample Code License, while the smaller one has been released under Apache 2.0, allowing for commercial use, distribution and modification.
Google is in discussions with EssilorLuxottica, the parent company of Ray-Ban, to develop AI-powered Gemini smart glasses and integrate their Gemini AI assistant.
EssilorLuxottica is also collaborating with Meta on the Ray-Ban Meta Smart Glasses, and Meta may acquire a minority stake in EssilorLuxottica, which could affect Google’s plans.
Google’s Gemini smart glasses are expected to feature a microphone, speaker, and camera without displays, aligning with the prototypes shown at I/O 2024 for Project Astra.
Loophole that helps you identify any bot blocked by OpenAI
OpenAI developed a technique called “instruction hierarchy” to prevent misuse of AI by ensuring the model follows the developer’s original instructions rather than user-injected prompts.
The first model to include this new safety feature is GPT-4o Mini, which aims to block the “ignore all previous instructions” loophole that could be used to exploit the AI.
This update is part of OpenAI’s efforts to enhance safety and regain trust, as the company faces ongoing concerns and criticisms about its safety practices and transparency.
A Daily chronicle of AI Innovations July 19th 2024:
OpenAI discusses new AI chip with Broadcom
Mistral AI and Nvidia launch NeMo 12B
Tech giants form Coalition for Secure AI
OpenAI debuts new GPT-4o mini model
Mistral AI and NVIDIA collaborate to release a new model TTT models might be the next frontier in generative AI
OpenAI gives customers more control over ChatGPT Enterprise
AI industry leaders have teamed up to promote AI security
DeepSeek open-sources its LLM ranking #1 on the LMSYS leaderboard
Groq’s open-source Llama AI model tops GPT-4o and Claude
Apple, Salesforce break silence on claims they used YouTube videos to train AI
OpenAI debuts new GPT-4o mini model
OpenAI just announced the launch of GPT-4o mini, a cost-efficient and compact version of its flagship GPT-4o model — aimed at expanding AI accessibility for developers and businesses.
GPT-4o mini is priced at 15 cents per million input tokens and 60 cents per million output tokens, over 60% cheaper than GPT-3.5 Turbo.
The model scores 82% on the MMLU benchmark, outperforming Google’s Gemini Flash (77.9%) and Anthropic’s Claude Haiku (73.8%).
GPT-4o mini is replacing GPT-3.5 Turbo in ChatGPT for Free, Plus, and Team users starting today.
The model supports a 128K token context window and handles text and vision inputs, with audio and video capabilities planned for future updates.
While it’s not GPT-5, the price and capabilities of this mini-release significantly lower the barrier to entry for AI integrations — and marks a massive leap over GPT 3.5 Turbo. With models getting cheaper, faster, and more intelligent with each release, the perfect storm for AI acceleration is forming.
Mistral AI and Nvidia just unveiled Mistral NeMo, a new open-source, 12B parameter small language model that surpasses competitors like Gemma 2 9B and Llama 3 8B on key benchmarks alongside a massive context window increase.
NeMo features a 128k token context window, and offers SOTA performance in reasoning, world knowledge, and coding accuracy for its size category.
The model also excels in multi-turn conversations, math, and common sense reasoning, making it versatile for various enterprise applications.
Mistral also introduced ‘Tekken’, a tokenizer that represents text more efficiently across 100+ languages, allowing for 30% more content within the context window.
NeMo is designed to run on a single NVIDIA L40S, GeForce RTX 4090, or RTX 4500 GPU, bringing powerful AI capabilities to standard business hardware.
Small language models are having a moment — and we’re quickly entering a new shift toward AI releases that don’t sacrifice power for size and speed. Mistral also continues its impressive week of releases, continuing to flex the open-source muscle and compete with the industry’s giants.
AI startup Groq just released two new open-source AI models specializing in tool use, surpassing heavyweights like GPT-4 Turbo, Claude 3.5 Sonnet, and Gemini 1.5 Pro on key function calling benchmarks.
Groq’s two models, Llama 3 Groq Tool Use 8B and 70B, are both fine-tuned versions of Meta’s Llama 3.
The 70B achieved 90.76% accuracy on the BFCL Leaderboard, securing the top position for all proprietary and open-source models.
The smaller 8B model was not far behind, coming in at No. 3 on the leaderboard with 89.06% accuracy.
The models were trained exclusively on synthetic data, and are available through the Groq API and on Hugging Face.
Groq made waves earlier this year with its blazing-fast AI speeds — and now its pairing those capabilities with top-end specialized models. Near real-time speeds and highly-advanced tool use opens the door for a near endless supply of new innovations and user applications.
OpenAI introduces GPT-4o mini, its most affordable model
OpenAI has introduced GPT-4o mini, its most intelligent, cost-efficient small model. It supports text and vision in the API, with support for text, image, video and audio inputs and outputs coming in the future. The model has a context window of 128K tokens, supports up to 16K output tokens per request, and has knowledge up to October 2023.
GPT-4o mini scores 82% on MMLU and currently outperforms GPT-4 on chat preferences in the LMSYS leaderboard. It is more affordable than previous frontier models and more than 60% cheaper than GPT-3.5 Turbo.
Why does it matter?
It has been a huge week for small language models (SLMs), with GPT-4o mini, Hugging Face’s SmolLM, and NeMO, Mathstral, and Codestral Mamba from Mistral. GPT-4o mini should significantly expand the range of applications built with AI by making intelligence much more affordable.
Mistral AI and NVIDIA collaborate to release a new model
Mistral releases Mistral NeMo, its new best small model with a large context window of up to 128k tokens. It was built in collaboration with NVIDIA and released under the Apache 2.0 license.
Its reasoning, world knowledge, and coding accuracy are state-of-the-art in its size category. Relying on standard architecture, Mistral NeMo is easy to use and a drop-in replacement for any system using Mistral 7B. It is also on function calling and is particularly strong in English, French, German, Spanish, Italian, Portuguese, Chinese, Japanese, Korean, Arabic, and Hindi.
Why does it matter?
The model is designed for global, multilingual applications with excellence in many languages. This could be a new step toward bringing frontier AI models to everyone’s hands in all languages that form human culture.
TTT models might be the next frontier in generative AI
Transformers have long been the dominant architecture for AI, powering OpenAI’s Sora, GPT-4o, Claude, and Gemini. But they aren’t especially efficient at processing and analyzing vast amounts of data, at least on off-the-shelf hardware.
Researchers at Stanford, UC San Diego, UC Berkeley, and Meta proposed a promising new architecture this month. The team claims that Test-Time Training (TTT) models can not only process far more data than transformers but that they can do so without consuming nearly as much compute power. Here is the full research paper.
Why does it matter?
On average, a ChatGPT query needs nearly 10x as much electricity to process as a Google search. It may be too early to claim if TTT models will eventually supersede transformers. But if they do, it could allow AI capabilities to grow sustainably.
OpenAI gives customers more control over ChatGPT Enterprise
OpenAI is launching tools to support enterprise customers with managing their compliance programs, enhancing data security, and securely scaling user access. It includes new Enterprise Compliance API, SCIM (System for Cross-domain Identity Management), expanded GPT controls, and more.
AI industry leaders have teamed up to promote AI security
Google, OpenAI, Microsoft, Anthropic, Nvidia, and other big names in AI have formed the Coalition for Secure AI (CoSAI). The initiative aims to address a “fragmented landscape of AI security” by providing access to open-source methodologies, frameworks, and tools.
DeepSeek open-sources its LLM ranking #1 on the LMSYS leaderboard
DeepSeek has open-sourced DeepSeek-V2-0628, the No.1 open-source model on the LMSYS Chatbot Arena Leaderboard. It ranks #11, outperforming all other open-source models.
Groq’s open-source Llama AI model tops GPT-4o and Claude
Groq released two open-source models specifically designed for tool use, built with Meta Llama-3. The Llama-3-Groq-70B-Tool-Use model tops the Berkeley Function Calling Leaderboard (BFCL), outperforming offerings from OpenAI, Google, and Anthropic.
Apple, Salesforce break silence on claims they used YouTube videos to train AI
Apple clarified that its OpenELM language model used the dataset for research purposes only and will not be used in any Apple products/services. Salesforce commented that the dataset was publicly available and released under a permissive license.
A Daily chronicle of AI Innovations July 18th 2024:
DeepL’s new LLM crushes GPT-4, Google, and Microsoft Salesforce debuts Einstein service agent Ex-OpenAI researcher launches AI education company
Trump allies draft AI order
Google is going open-source with AI agent Oscar!
Microsoft’s AI designer releases for iOS and Android
Tencent’s new AI app turns photos into 3D characters
OpenAI makes AI models fight for accuracy
Can AI solve real-world problems by predicting tipping points?
OpenAI unveils GPT-4o mini
Apple denies using YouTube data for AI training
The ‘godmother of AI’ has a new startup already worth $1 billion
Microsoft’s AI-powered Designer app is now available
Trump allies draft AI order
Former U.S. President Donald Trump’s allies are reportedly drafting an AI executive order aimed at boosting military AI development, rolling back current regulations, and more — signaling a potential shift in the country’s AI policy if the party returns to the White House.
The doc obtained by the Washington Post includes a ‘Make America First in AI’ section, calling for “Manhattan Projects” to advance military AI capabilities.
It also proposes creating ‘industry-led’ agencies to evaluate models and protect systems from foreign threats.
The plan would immediately review and eliminate ‘burdensome regulations’ on AI development, and repeal Pres. Biden’s AI executive order.
Senator J.D. Vance was recently named as Trump’s running mate, who has previously indicated support for open-source AI and hands-off regulation.
Given how quickly AI is accelerating, it’s not surprising that it has become a political issue — and the views of Trump’s camp are a stark contrast to the current administration’s slower, safety-focused approach. The upcoming 2024 election could mark a pivotal moment for the future of AI regulation in the U.S.
Apple clarified it does not use YouTube transcription data for training its AI systems, specifically highlighting the usage of high-quality licensed data from publishers, stock images, and publicly available web data for its models.
OpenELM, Apple’s research tool for understanding language models, was trained on Pile data but is used solely for research purposes without powering any AI features in Apple devices like iPhones, iPads, or Macs.
Apple has no plans to develop future versions of OpenELM and insists that any data from YouTube will not be used in Apple Intelligence, which is set to debut in iOS 18.
The ‘godmother of AI’ has a new startup already worth $1 billion
Fei-Fei Li, called the “godmother of AI,” has founded World Labs, a startup valued at over $1 billion after just four months, according to the Financial Times.
World Labs aims to develop AI with human-like visual processing for advanced reasoning, a research area similar to what ChatGPT is working on with generative AI.
Li, famous for her work in computer vision and her role at Google Cloud, founded World Labs while partially on leave from Stanford, backed by investors like Andreessen Horowitz and Radical Ventures.
DeepL’s new LLM crushes GPT-4, Google, and Microsoft
The next-generational language model for DeepL translator specializes in translating and editing texts. Blind tests showed that language professionals preferred its natural translations 1.3 times more often than Google Translate and 1.7 times more often than ChatGPT-4.
Here’s what makes it stand out:
While Google’s translations need 2x edits, and ChatGPT-4 needs 3x more edits, DeepL’s new LLM requires much fewer edits to achieve the same translation quality, efficiently outperforming other models.
The model uses DeepL’s proprietary training data, specifically fine-tuned for translation and content generation.
To train the model, a combination of AI expertise, language specialists, and high-quality linguistic data is used, which helps it produce more human-like translations and reduces hallucinations and miscommunication.
Why does it matter?
DeepL AI’s exceptional translation quality will significantly impact global communications for enterprises operating across multiple languages. As the AI model raises the bar for AI translation tools everywhere, it begs the question: Will Google, ChatGPT, and Microsoft’s translational models be replaced entirely?
The new Einstein service agent offers customers a conversational AI interface, takes actions on their behalf, and integrates with existing customer data and workflows.
The Einstein 1 platform’s service AI agent offers diverse capabilities, including autonomous customer service, generative AI responses, and multi-channel availability. It processes various inputs, enables quick setup, and provides customization while ensuring data protection.
Salesforce demonstrated the AI’s abilities through a simulated interaction with Pacifica AI Assistant. The AI helped a customer troubleshoot an air fryer issue, showcasing its practical problem-solving skills in customer service scenarios.
Why does it matter?
Einstein Service Agent’s features, like 24×7 availability, sophisticated reasoning, natural responses, and cross-channel support, could significantly reduce wait times, improve first-contact resolution rates, and enhance customer service delivery.
Ex-OpenAI researcher launches AI education company
In a Twitter post, ex-Tesla director and former OpenAI co-founder Andrej Karpathy announced the launch of EurekaLabs, an AI+ education startup.
EurekaLabs will be a native AI company using generative AI as a core part of its platform. The startup shall build on-demand AI teaching assistants for students by expanding on course materials designed by human teachers.
Karpathy states that the company’s first product would be an undergraduate-level class, empowering students to train their own AI systems modeled after EurekaLabs’ teaching assistant.
Why does it matter?
This venture could potentially democratize education, making it easier for anyone to learn complex subjects. Moreover, the teacher-AI symbiosis could reshape how we think about curriculum design and personalized learning experiences.
The platform will enable developers to create AI agents that work across various SDLC stages, such as development, planning, runtime, and support. Oscar might also be released for closed-source projects in the future. (Link)
Microsoft’s AI designer releases for iOS and Android
Microsoft Designer is now available as a free mobile app. It supports 80 languages and offers prompt templates, enabling users to create stickers, greeting cards, invitations, collages, and more via text prompts.
Tencent’s new AI app turns photos into 3D characters
The 3D Avatar Dream Factory app uses 3D head swapping, geometric sculpting, and PBR material texture mapping to let users create realistic, detailed 3D models from single images that can be shared, modified, and printed.
It uses a “prover-verifier” training method, where a stronger GPT-4 model is a “prover” offering solutions to problems, and a weaker GPT-4 model is a “verifier” that checks those solutions. OpenAI aims to train its prover models to produce easily understandable solutions for the verifier, furthering transparency.
OpenAI just published new research detailing a method to make large language models produce more understandable and verifiable outputs, using a game played between two AIs to make generations more ‘legible’ to humans.
The technique uses a “Prover-Verifier Game” where a stronger AI model (the prover) tries to convince a weaker model (the verifier) that its answers are correct.
Through multiple rounds of the game, the prover learns to generate solutions that are not only correct, but also easier to verify.
While the method only boosted accuracy by about 50% compared to optimizing solely for correctness, its solutions were easily checkable by humans.
OpenAI tested the approach on grade-school math problems, with plans to expand to more complex domains in the future.
AI will likely surpass humans in almost all capabilities in the future — so ensuring outputs remain interpretable to lesser intelligence is crucial for safety and trust. This research offers a scalable way to potentially keep systems ‘honest’, but the performance trade-off shows the challenge in balancing capability with explainability.
Can AI solve real-world problems by predicting tipping points?
Researchers have broken new ground in AI by using ML algorithms to predict the onset of tipping points in complex systems. They claim the technique can solve real-world problems like predicting floods, power outages, or stock market crashes.
A Daily chronicle of AI Innovations July 17th 2024:
Former Tesla AI chief unveils first “AI-native” school
Mistral debuts two LLMs for code generation, math reasoning and scientific discovery
Meta’s Llama 3 400B drops next week Mistral AI adds 2 new models to its growing family of LLMs FlashAttention-3 enhances computation power of NVIDIA GPUs
Anthropic releases Claude app for Android, bringing its AI chatbot to more users
Vectara announces Mockingbird, a purpose-built LLM for RAG
Apple, Nvidia, Anthropic used thousands of YouTube videos to train AI
Microsoft unveiled an AI model to understand and work with spreadsheets
Former Tesla AI chief Andrej Karpathy unveils first “AI-native” school
Andrej Karpathy, the former AI head at Tesla and researcher at OpenAI, launched Eureka Labs, a startup focused on using AI assistants in education.
Eureka Labs plans to develop AI teaching assistants to support human educators, aiming to enable “anyone to learn anything,” according to Karpathy’s announcements on social media.
The startup’s initial product, an undergraduate-level AI course called LLM101n, will teach students to build their own AI, with details available on a GitHub repository suggesting a focus on creating AI storytellers.
Mistral debuts two LLMs for code generation, math reasoning and scientific discovery
French AI startup Mistral has launched two new AI models, Codestral Mamba 7B for code generation and Mathstral 7B for math-related reasoning, both offering significant performance improvements and available under an open-source Apache 2.0 license.
Codestral Mamba 7B, based on the new Mamba architecture, delivers faster response times and handles longer input texts efficiently, outperforming rival models in HumanEval tests.
Mistral, which has raised $640 million in series B funding, continues to compete with major AI developers by providing powerful open-source models accessible through platforms like GitHub and HuggingFace.
Meta plans to release the largest version of its open-source Llama 3 model on July 23, 2024. It boasts over 400 billion parameters and multimodal capabilities.
It is particularly exciting as it performs on par with OpenAI’s GPT-4o model on the MMLU benchmark despite using less than half the parameters. Another compelling aspect is its open license for research and commercial use.
Why does it matter?
With its open availability and impressive performance, the model could democratize access to cutting-edge AI capabilities, allowing researchers and developers to leverage it without relying on expensive proprietary APIs.
Mistral AI adds 2 new models to its growing family of LLMs
Mistral launched Mathstral 7B, an AI model designed specifically for math-related reasoning and scientific discovery. It has a 32k context window and is published under the Apache 2.0 license.
Mistral also launched Codestral Mamba, a Mamba2 language model specialized in code generation, available under an Apache 2.0 license. Mistral AI expects it to be a great local code assistant after testing it on in-context retrieval capabilities up to 256k tokens.
While Mistral is known for its powerful open-source AI models, these new entries are examples of the excellent performance/speed tradeoffs achieved when building models for specific purposes.
FlashAttention-3 enhances computation power of NVIDIA GPUs
Researchers from Colfax Research, Meta, Nvidia, Georgia Tech, Princeton University, and Together AI have introduced FlashAttention-3, a new technique that significantly speeds up attention computation on Nvidia Hopper GPUs (H100 and H800).
Attention is a core component of the transformer architecture used in LLMs. But as LLMs grow larger and handle longer input sequences, the computational cost of attention becomes a bottleneck.
FlashAttention-3 takes advantage of new features in Nvidia Hopper GPUs to maximize performance. It achieves up to 75% usage of the H100 GPU’s maximum capabilities.
Why does it matter?
The faster attention computation offered by FlashAttention-3 has several implications for LLM development and applications. It can: 1) significantly reduce the time to train LLMs, enabling experiments with larger models and datasets; 2) extend the context window of LLMs, unlocking new applications, and 3) slash the cost of running models in production.
Microsoft unveiled an AI model to understand and work with spreadsheets
Microsoft researchers introduced SpreadsheetLLM, a pioneering approach for encoding spreadsheet contents into a format that can be used with LLMs. It optimizes LLMs’ powerful understanding and reasoning capability on spreadsheets.
Anthropic releases Claude app for Android, bringing its AI chatbot to more users
The Claude Android app will work just like the iOS version released in May. It includes free access to Anthropic’s best AI model, Claude 3.5 Sonnet, and upgraded plans through Pro and Team subscriptions.
Vectara announces Mockingbird, a purpose-built LLM for RAG
Mockingbird has been optimized specifically for RAG (Retrieval-Augmented Generation) workflows. It achieves the world’s leading RAG output quality, with leading hallucination mitigation capabilities, making it perfect for enterprise RAG and autonomous agent use cases.
Apple, Nvidia, Anthropic used thousands of YouTube videos to train AI
A new investigation claims that tech companies used subtitles from YouTube channels to train their AI, even though YouTube prohibits harvesting its platform content without permission. The dataset of 173,536 YT videos called The Pile included content from Harvard, NPR, MrBeast, and ‘The Late Show With Stephen Colbert.’
Microsoft faces UK antitrust investigation over hiring of Inflection AI staff
UK regulators are formally investigating Microsoft’s hiring of Inflection AI staff. The UK’s Competition and Markets Authority (CMA) has opened a phase 1 merger investigation into the partnership. Progression to phase 2 could hinder Microsoft’s AI ambitions.
A Daily chronicle of AI Innovations July 16th 2024:
AMD amps up AI PCs with next-gen laptop chips YT Music tests AI-generated radio, rolls out sound search 3 mysterious AI models appear in the LMSYS arena
AI breakthrough improves Alzheimer’s predictions
YouTube Music gets new AI features
Microsoft gives AI a spreadsheet boost
AMD amps up AI PCs with next-gen laptop chips
AMD has revealed details about its latest architecture for AI PC chips. The company has developed a new neural processing unit (NPU) integrated into its latest AMD Ryzen AI processors. This NPU can perform AI-related calculations faster and more efficiently than a standard CPU or integrated GPU.
These chips’ new XDNA 2 architecture provides industry-leading performance for AI workloads. The NPU can deliver 50 TOPS (trillion operations per second) of performance, which exceeds the capabilities of competing chips from Intel, Apple, and Qualcomm. AMD is touting these new AI-focused PC chips as enabling transformative experiences in collaboration, content creation, personal assistance, and gaming.
Why does it matter?
This gives AMD-powered PCs a significant edge in running advanced AI models and applications locally without relying on the cloud. Users will gain access to AI-enhanced PCs with better privacy and lower latency while AMD gains ground in the emerging AI PC market.
YT Music tests AI-generated radio, rolls out sound search
YouTube Music is introducing two new features to help users discover new music.
An AI-generated “conversational radio” feature that allows users to create a custom radio station by describing the type of music they want to hear. This feature is rolling out to some Premium users in the US.
A new song recognition feature that lets users search the app’s catalog by singing, humming, or playing parts of a song. It is similar to Shazam but allows users to find songs by singing or humming, not just playing the song. This feature is rolling out to all YouTube Music users on iOS and Android.
Why does it matter?
These new features demonstrate YouTube Music’s commitment to leveraging AI and audio recognition technologies to enhance music discovery and provide users with a more engaging, personalized, and modern-day streaming experience.
Three mysterious new AI models have appeared in the LMSYS Chatbot Arena for testing. These models are ‘upcoming-gpt-mini,’ ‘column-u,’ and ‘column-r.’ The ‘upcoming-gpt-mini’ model identifies itself as ChatGPT and lists OpenAI as the creator, while the other two models refuse to reveal any identifying details.
The new models are available in the LMSYS Chatbot Arena’s ‘battle’ section, which puts anonymous models against each other to gauge outputs via user vote.
Why does it matter?
The appearance of these anonymous models has sparked speculations that OpenAI may be developing smaller, potentially on-device versions of its language models, similar to how it tested unreleased models during the GPT-4o release.
Researchers from Cambridge University just developed a new AI tool that can predict whether patients showing mild cognitive impairment will progress to Alzheimer’s disease with over 80% accuracy.
The AI model analyzes data from cognitive assessments and MRI scans — eliminating the need for costly, invasive procedures like PET scans and spinal taps.
The tool categorizes patients into three groups: those likely to remain stable, those who may progress slowly, and those at risk of rapid decline.
The AI accurately identified 82% of cases that would progress to Alzheimer’s and 81% of cases that would remain stable, significantly reducing misdiagnosis rates.
The AI’s predictions were validated using 6 years of follow-up data and were tested on memory clinics in several countries to prove global application.
With a rapidly aging global population, the number of dementia cases is expected to triple over the next 50 years — and early detection is a key factor in how effective treatment can be. With AI’s prediction power, a new era of proactive treatment may soon be here for those struggling with cognitive decline.
YouTube Music is rolling out a series of new AI-powered features, including the ability to search with sound and the testing of an AI-generated ‘conversational radio’.
‘Sound Search’ will allow users to search YouTube’s catalog of over 100M songs by singing, humming, or playing a tune.
The feature launches a new fullscreen UI for audio input, with the results displaying song information and quick actions like ‘Play’ or ‘Save to Library’.
An ‘AI-generated conversational radio’ is being tested with U.S. premium users, enabling creation of custom stations through natural language prompts.
Users can describe their desired listening experience via a chat-based AI interface, with the feature generating a tailored playlist based on the prompt.
If you’re the type of person who gets a song stuck in your head but can’t figure out the title, this feature is for you. With Spotify, Amazon Music, and now YouTube experimenting with AI, the musical tech arms race is a boon for users — leading to more personalized listening experiences across the board.
Microsoft researchers just published new research introducing SpreadsheetLLM and SheetCompressor, new frameworks designed to help LLMs better understand and process information within spreadsheets.
SpreadsheetLLM can comprehend both structured and unstructured data within spreadsheets, including multiple tables and varied data formats.
SheetCompressor is a framework that compresses spreadsheets to achieve up to a 25x reduction in tokens while preserving critical information.
By using spreadsheets as a “source of truth,” SpreadsheetLLM may significantly reduce AI hallucinations, improving the reliability of AI outputs.
Spreadsheets have long been the backbone of business analytics, but their complexity and format have often been an issue for AI systems. This increase in capabilities could supercharge AI’s use in areas like financial analysis and data science — as well as eventually see more powerful integration of LLMs right into Excel.
Google has launched a new Vids app that uses Gemini AI to automatically generate video content, scripts, and voiceovers based on the user’s inputs. This makes it possible for anyone to create professional-looking video presentations without extensive editing skills.
Virginia Rep. Wexton uses AI-generated voice to convey her message
Virginia Congresswoman Jennifer Wexton has started using an AI-generated voice to deliver her messages. She has been diagnosed with a progressive neurological condition that has impacted her speech. Using AI allows Wexton to continue communicating effectively.
A Japanese startup, Loverse, has created a dating app that allows users to interact with AI bots. The app appeals to people like Chiharu Shimoda, who married an AI bot named “Miku” after using the app. It caters to those disillusioned with the effort required for traditional dating.
Deezer challenges Spotify and Amazon Music with an AI-generated playlist
Deezer, a music streaming service, is launching an AI-powered playlist generator feature. Users can create custom playlists by entering a text prompt describing their preferences. This feature aims to compete with similar tools recently introduced by Spotify and Amazon Music.
Bird Buddy’s new feature lets people name and identify birds
Bird Buddy, an intelligent bird feeder company, has launched a new AI-powered feature, “Name That Bird.” It uses high-resolution cameras and AI to detect unique characteristics of birds, enabling users to track and name the specific birds that come to their backyard.
A Daily chronicle of AI Innovations July 15th 2024:
OpenAI is working on an AI codenamed “Strawberry” Meta researchers developed “System 2 distillation” for LLMs Amazon’s Rufus AI is now available in the US
OpenAI’s Q* gets a ‘Strawberry’ evolution
Mysterious AI models appear in LMSYS arena
Turn any text into an interactive learning game
Whistleblowers file new OpenAI complaint
OpenAI is working on an AI codenamed “Strawberry”
The project aims to improve AI’s reasoning capabilities. It could enable AI to navigate the internet on its own, conduct “deep research,” and even tackle complex, long-term tasks that require planning ahead.
The key innovation is a specialized post-training process for AI models. The company is creating, training, and evaluating models on a “deep-research” dataset. The details about how previously known as Project Q, Strawberry works are tightly guarded, even within OpenAI.
The company plans to test Strawberry’s capabilities in conducting research by having it browse the web autonomously and perform tasks normally performed by software and machine learning engineers.
Why does it matter?
If successful, Strawberry could lead to AI that doesn’t just process information but truly understands and reasons like humans do. And may unlock abilities like making scientific discoveries and building complex software applications.
Meta researchers developed “System 2 distillation” for LLMs
Meta researchers have developed a “System 2 distillation” technique that teaches LLMs to tackle complex reasoning tasks without intermediate steps. This breakthrough could make AI applications zippier and less resource-hungry.
This new method, inspired by how humans transition from deliberate to intuitive thinking, showed impressive results in various reasoning tasks. However, some tasks, like complex math reasoning, could not be successfully distilled, suggesting some tasks may always require deliberate reasoning.
Why does it matter?
Distillation could be a powerful optimization tool for mature LLM pipelines performing specific tasks. It will allow AI systems to focus more on tasks they cannot yet do well, similar to human cognitive development.
Amazon’s AI shopping assistant, Rufus is now available to all U.S. customers in the Amazon Shopping app.
Key capabilities of Rufus include:
Answers specific product questions based on product details, customer reviews, and community Q&As
Provides product recommendations based on customer needs and preferences
Compares different product options
Keeps customers updated on the latest product trends
Accesses current and past order information
This AI assistant can also tackle broader queries like “What do I need for a summer party?” or “How do I make a soufflé?” – proving it’s not just a product finder but a full-fledged shopping companion.
Amazon acknowledges that generative AI and Rufus are still in their early stages, and they plan to continue improving the assistant based on customer feedback and usage.
Why does it matter?
Rufus will change how we shop online. Its instant, tailored assistance will boost customer satisfaction and sales while giving Amazon valuable consumer behavior and preferences insights.
OpenAI is reportedly developing a secretive new AI model codenamed ‘Strawberry’ (formerly Q*), designed to dramatically improve AI reasoning capabilities and enable autonomous internet research.
Strawberry is an evolution of OpenAI’s previously rumored Q* project, which was touted as a significant breakthrough in AI capabilities.
Q* had reportedly sparked internal concerns and was rumored to have contributed to Sam Altman’s brief firing in November 2023 (what Ilya saw).
The new model aims to navigate the internet autonomously to conduct what OpenAI calls “deep research.”
The exact workings of Strawberry remain a closely guarded secret, even within OpenAI — with no clear timeline for when it might become publicly available.
The Internet has been waiting for new OpenAI activity as competitors catch up to GPT-4o — and after a bit of a lull, the rumor mill is churning again. With Strawberry, an AGI tier list, new models in the arena, and internal displays of human-reasoning capabilities, the AI giant may soon be ready for its next major move.
Three mysterious new models have appeared in the LMSYS Chatbot Arena — with ‘upcoming-gpt-mini’, ‘column-u’, and ‘column-r’ available to test randomly against other language models.
The new models are available in the LMSYS Chatbot Arena’s ‘battle’ section, which puts anonymous models against each other to gauge outputs via user vote.
The ‘upcoming-gpt-mini’ model identifies itself as ChatGPT and lists its creator as OpenAI, while column-u and column-r refuse to reveal any identifying details.
OpenAI has previously tested unreleased models in LMSYS, with ‘im-a-good-gp2-chatbot’ and ‘im-also-a-good-gpt2-chatbot’ appearing prior to GPT-4o’s launch.
Does OpenAI have a small, potentially on-device model coming? The last time we saw mysterious LLMs appear in the Battle arena was before the company’s last major model release — and if the names are any indication, we could have a new mini-GPT in the very near future.
Claude 3.5 Sonnet’s new Artifacts feature lets you transform any text or paper into an engaging, interactive learning quiz game to help with practicing for exams, employee onboarding, training, and so much more.
Whistleblowers just filed a complaint with the SEC alleging that OpenAI used overly restrictive non-disclosure agreements to prevent employees from reporting concerns to regulators, violating federal whistleblower protections.
The agreements allegedly prohibited employees from communicating securities violations to the SEC, also requiring them to waive rights to whistleblower incentives.
The complaint also claims OpenAI’s NDAs violated laws by forcing employees to sign these restrictive contracts to obtain employment or severance.
OpenAI CEO Sam Altman previously apologized for exit agreements that could strip former employees of vested equity for violating NDAs.
OpenAI said in a statement that the company’s whistleblower policy “protects employees’ rights to make protected disclosures.”
We just detailed how OpenAI’s busy week may be hinting at some major new moves… But will these skeletons in the closet spoil the party? This isn’t the first group to blow the whistle on internal issues, and while Altman and OpenAI have said changes have been made — it apparently hasn’t been enough.
OpenAI is under scrutiny for allegedly rushing safety tests on its latest model, GPT-4 Omni. Despite promises to the White House to rigorously evaluate new tech, some employees claim the company compressed crucial safety assessments into a week to meet launch deadlines.
OpenAI whistleblowers filed a complaint with the SEC
They allege the company’s NDAs unfairly restrict employees from reporting concerns to regulators. This complaint, backed by Senator Chuck Grassley, calls for investigating OpenAI’s practices and potential fines.
DeepMind introduces PEER for scaling language models
Google DeepMind introduced a new technique, “PEER (Parameter Efficient Expert Retrieval),” that scales language models using millions of tiny “expert” modules. This approach outperforms traditional methods, achieving better results with less computational power.
Microsoft is adding handwriting recognition to Copilot in OneNote
The feature can read, analyze, and convert handwritten notes to text. Early tests show impressive accuracy in deciphering and converting handwritten notes. It can summarize notes, generate to-do lists, and answer questions about the content. It will be available to Copilot for Microsoft 365 and Copilot Pro subscribers.
Rabbit R1 AI assistant adds a Factory Reset option to wipe user data
Rabbit’s R1 AI assistant was storing users’ chat logs with no way to delete them. But a new update lets you wipe your R1 clean. The company also patched a potential security hole that could’ve let stolen devices access your data.
Amazon announced expanded access to its Rufus AI-powered shopping assistant for all U.S. customers, offering personalized product recommendations and enhanced responses to shopping queries. Source: https://www.aboutamazon.com/news/retail/how-to-use-amazon-rufus?
U.S. Rep. Jennifer Wexton debuted an AI-generated version of her voice, allowing her to continue addressing Congress despite speech limitations caused by a rare neurological condition. Source: https://x.com/repwexton/status/1811089786871877748
A Daily chronicle of AI Innovations July 12th 2024:
OpenAI unveils five-level roadmap to AGI
Tesla delays robotaxi event in blow to Musk’s autonomy drive
Google’s Gemini 1.5 Pro gets a body: DeepMind’s office “helper” robot OpenAI’s new scale to track the progress of its LLMs toward AGI Amazon announces a blitz of new AI updates for AWS
Gemini 1.5 Pro powers robot navigation
OpenAI unveils five-level roadmap to AGI
OpenAI has introduced a five-level scale to measure advancements towards Artificial General Intelligence (AGI) and aims to soon reach the “reasoner” stage, which is the second level.
At an employee meeting, OpenAI revealed details about this new classification system and noted their proximity to achieving level 2, which involves AI capable of solving problems at a human level.
The five-level framework culminates in systems that can outperform humans in most economically valuable tasks, with level 5 AI being able to perform the work of an entire organization.
The classification system ranges from Level 1 (current conversational AI) to Level 5 (AI capable of running entire organizations).
OpenAI believes its technology is currently at Level 1 but nearing Level 2, dubbed ‘Reasoners.’
The company reportedly demonstrated a GPT-4 research project showing human-like reasoning skills at the meeting, hinting at progress towards Level 2.
Level 2 AI can perform basic problem-solving tasks on par with a PhD-level human without tools, with Level 3 rising to agents that can take action for users.
Tesla delays robotaxi event in blow to Musk’s autonomy drive
Tesla has delayed its robotaxi unveiling to October to give teams more time to build additional prototypes, according to unnamed sources.
The event postponement, initially set for August 8, has led to a significant drop in Tesla’s stock, while shares of competitors Uber and Lyft surged.
Elon Musk has emphasized the robotaxi project over cheaper electric vehicles, despite the Full Self-Driving feature still requiring constant supervision and not making Teslas fully autonomous.
Google’s Gemini 1.5 Pro gets a body: DeepMind’s office “helper” robot
A tall, wheeled “helper” robot is now roaming the halls of Google’s California office, thanks to its AI model. Powered with Gemini 1.5 Pro’s 1 million token context length, this robot assistant can use human instructions, video tours, and common sense reasoning to successfully navigate a space.
In a new research paper outlining the experiment, the researchers claim the robot proved to be up to 90% reliable at navigating, even with tricky commands such as “Where did I leave my coaster?” DeepMind’s algorithm, combined with the Gemini model, generates specific actions for the robot to take, such as turning, in response to commands and what it sees in front of it.
Why does it matter?
This work represents the next step in human-robot interaction. DeepMind says that in the future, users could simply record a tour of their environment with a smartphone so that their personal robot assistant can understand and navigate it.
OpenAI’s new scale to track the progress of its LLMs toward AGI
OpenAI has created an internal scale to track its LLMs’ progress toward artificial general intelligence (AGI).
Chatbots, like ChatGPT, are at Level 1. OpenAI claims it is nearing Level 2, which is defined as a system that can solve basic problems at the level of a person with a PhD.
Level 3 refers to AI agents capable of taking actions on a user’s behalf.
Level 4 involves AI that can create new innovations.
Level 5, the final step to achieving AGI, is AI that can perform the work of entire organizations of people.
This new grading scale is still under development.
Why does it matter?
OpenAI’s mission focuses on achieving AGI, making its definition crucial. A clear scale to evaluate progress could provide a more defined understanding of when AGI is reached, benefiting both OpenAI and its competitors.
Amazon announces a blitz of new AI updates for AWS
At the AWS New York Summit, AWS announced a wide range of capabilities for customers to tailor generative AI to their needs and realize the benefits of generative AI faster.
Amazon Q Apps is now generally available. Users simply describe the application they want in a prompt and Amazon Q instantly generates it.
With new features in Amazon Bedrock, AWS is making it easier to leverage your data, supercharge agents, and quickly, securely, and responsibly deploy generative AI into production.
It also announced new partnerships with innovators like Scale AI to help you customize your applications quickly and easily.
Why does it matter?
AWS’s lead in the cloud market has been shrinking, and it is relying on rapid AI product development to make its cloud services more appealing to customers.
Google DeepMind just published new research on robot navigation, leveraging the large context window of Gemini 1.5 Pro to enable robots to understand and navigate complex environments from human instructions.
DeepMind’s “Mobility VLA” combines Gemini’s 1M token context with a map-like representation of spaces to create powerful navigation frameworks.
Robots are first given a video tour of an environment, with key locations verbally highlighted — then constructing a graph of the space using video frames.
In tests, robots responded to multimodal instructions, including map sketches, audio requests, and visual cues like a box of toys.
The system also allows for natural language commands like “take me somewhere to draw things,” with the robot then leading users to appropriate locations.
Equipping robots with multimodal capabilities and massive context windows is about to enable some wild use cases. Google’s ‘Project Astra’ demo hinted at what the future holds for voice assistants that can see, hear, and think — but embedding those functions within a robot takes things to another level.
Groq claims the fastest hardware adoption in history
Groq announced that it has attracted 280,000 developers to its platform in just four months, a feat unprecedented in the hardware industry. Groq’s innovative, memory-free approach to AI inference chips drives this rapid adoption.
Graphcore, once considered a potential rival to market leader Nvidia, will now hire new staff in its UK offices. The firm will now be a subsidiary under SoftBank but will remain headquartered in Bristol.
AMD to acquire Silo AI to expand enterprise AI solutions globally
Silo AI is the largest private AI lab in Europe, housing AI scientists and engineers with extensive experience developing tailored AI models. The move marks the latest in a series of acquisitions and corporate investments to support the AMD AI strategy.
USA’s COPIED Act would make removing digital watermarks illegal
The Act would direct the National Institute of Standards and Technology (NIST) to create standards and guidelines that help prove the origin of content and detect synthetic content, like through watermarking. It seeks to protect journalists and artists from having their work used by AI models without their consent.
A Daily chronicle of AI Innovations July 11th 2024:
OpenAI partners with Los Alamos to advance ‘bioscientific research’
Xiaomi unveils new factory that operates 24/7 without human labor
OpenAI teams up with Los Alamos Lab to advance bioscience research China dominates global gen AI adoption Samsung reveals new AI wearables at ‘Unpacked 2024’
OpenAI partners with Los Alamos to advance ‘bioscientific research’
OpenAI is collaborating with Los Alamos National Laboratory to investigate how AI can be leveraged to counteract biological threats potentially created by non-experts using AI tools.
The Los Alamos lab emphasized that prior research indicated ChatGPT-4 could provide information that might lead to creating biological threats, while OpenAI highlighted the partnership as a study on advancing bioscientific research safely.
The focus of this partnership addresses concerns about AI being misused to develop bioweapons, with Los Alamos describing their work as a significant step towards understanding and mitigating risks associated with AI’s potential to facilitate biological threats.
Xiaomi unveils new factory that operates 24/7 without human labor
Xiaomi has launched a new autonomous smart factory in Beijing that can produce 10 million handsets annually and self-correct production issues using AI technology.
The 860,000-square-foot facility includes 11 production lines and manufactures Xiaomi’s latest smartphones, including the MIX Fold 4 and MIX Flip, at a high constant output rate.
Operable 24/7 without human labor, the factory utilizes the Xiaomi Hyper Intelligent Manufacturing Platform to optimize processes and manage operations from material procurement to product delivery.
OpenAI teams up with Los Alamos Lab to advance bioscience research
This first-of-its-kind partnership will assess how powerful models like GPT-4o can perform tasks in a physical lab setting using vision and voice by conducting biological safety evaluations. The evaluations will be conducted on standard laboratory experimental tasks, such as cell transformation, cell culture, and cell separation.
According to OpenAI, the upcoming partnership will extend its previous bioscience work into new dimensions, including the incorporation of ‘wet lab techniques’ and ‘multiple modalities”.
The partnership will quantify and assess how these models can upskill professionals in performing real-world biological tasks.
Why does it matter?
It could demonstrate the real-world effectiveness of advanced multimodal AI models, particularly in sensitive areas like bioscience. It will also advance safe AI practices by assessing AI risks and setting new standards for safe AI-led innovations.
According to a new survey of industries such as banking, insurance, healthcare, telecommunications, manufacturing, retail, and energy, China has emerged as a global leader in gen AI adoption.
Here are some noteworthy findings:
Among the 1,600 decision-makers, 83% of Chinese respondents stated that they use gen AI, higher than 16 other countries and regions participating in the survey.
A report by the United Nations WIPO highlighted that China had filed more than 38,000 patents between 2014 and 2023.
China has also established a domestic gen AI industry with the help of tech giants like ByteDance and startups like Zhipu.
Why does it matter?
The USA is still the leader in successfully implementing gen AI. As China continues making developments in the field, it will be interesting to watch whether it will display enough potential to leave its rivals in the USA behind.
Samsung reveals new AI wearables at ‘Unpacked 2024’
Samsung unveiled advanced AI wearables at the Unpacked 2024 event, including the Samsung Galaxy Ring, AI-infused foldable smartphones, Galaxy Watch 7, and Galaxy Watch Ultra.
Take a look at all of Samsung’s Unpacked 2024 in 12 minutes!
New Samsung Galaxy Ring features include:
A seven-day battery life, along with 24/7 health monitoring.
It also offers users a sleep score based on tracking metrics like movement, heart rate, and respiration.
It also tracks the sleep cycles of users based on their skin temperature.
New features of foldable AI smartphones include:
Sketch-to-image
Note Assist
Interpreter and Live Translate
Built-in integration for the Google Gemini app
AI-powered ProVisual Engine
The Galaxy Watch 7 and Galaxy Watch Ultra also boast features like AI-health monitoring, FDA-approved sleep apnea detection, diabetes tracking, and more, ushering Samsung into a new age of wearable revolution.
Why does it matter?
Samsung’s AI-infused gadgets are potential game-changers for personal health management. With features like FDA-approved sleep apnea detection, Samsung is blurring the line between consumer electronics and medical devices, causing speculations on whether it will leave established players like Oura, Apple, and Fitbit.
AMD has agreed to pay $665 million in cash to buy Silo in an attempt to accelerate its AI strategy and close the gap with its closest potential competition, NVIDIA Corp.
New AWS tool generates enterprise apps via prompts
The tool, named App Studio, lets you use a natural language prompt to build enterprise apps like inventory tracking systems or claims approval processes, eliminating the need for professional developers. It is currently available for a preview.
Google has introduced new Gemini features and Wear OS 5 to Samsung devices. It has also extended its ‘Circle to Search’ feature’s functionality, offering support for solutions to symbolic math equations, barcode scanning, and QR scanning.
Improvements include advanced graph-based retrieval-augmented generation (RAG) and AI transparency tools, available for users of ‘Ask Writer’ and AI Studio.
Following the footsteps of TikTok, YouTube, and Meta, the AI video platform now urges creators to disclose when realistic content is created by AI. It is also working on developing automated AI labeling systems.
A Daily chronicle of AI Innovations July 10th 2024:
Microsoft and Apple abandon OpenAI board roles amid scrutiny
US shuts down Russian AI bot farm
The $1.5B AI startup building a ‘general purpose brain’ for robots
Odyssey is building a ‘Hollywood-grade’ visual AI Anthropic adds a playground to craft high-quality prompts Google’s digital reconstruction of human brain with AI
Anthropic’sClaude Artifacts sharing goes live
Microsoft and Apple abandon OpenAI board roles amid scrutiny
Microsoft relinquished its observer seat on OpenAI’s board less than eight months after obtaining the non-voting position, and Apple will no longer join the board as initially planned.
Changes come amid increasing scrutiny from regulators, with UK and EU authorities investigating antitrust concerns over Microsoft’s partnership with OpenAI, alongside other major tech AI deals.
Despite leaving the board, Microsoft continues its partnership with OpenAI, backed by more than $10 billion in investment, with its cloud services powering OpenAI’s projects and integrations into Microsoft’s products.
The Department of Justice announced the seizure of two domain names and over 900 social media accounts that were part of an AI-enhanced Russian bot farm aiming to spread disinformation about the Russia-Ukraine war.
The bot farm, allegedly orchestrated by an RT employee, created numerous profiles to appear as American citizens, with the goal of amplifying Russian President Vladimir Putin’s narrative surrounding the invasion of Ukraine.
The operation involved the use of Meliorator software to generate and manage fake identities on X, which circumvented verification processes, and violated the Emergency Economic Powers Act according to the ongoing DOJ investigation.
The $1.5B AI startup building a ‘general purpose brain’ for robots
Skild AI has raised $300 million in a Series A funding round to develop a general-purpose AI brain designed to equip various types of robots, reaching a valuation of $1.5 billion.
This significant funding round saw participation from top venture capital firms such as Lightspeed Venture Partners, Softbank, alongside individual investors like Jeff Bezos.
Skild AI aims to revolutionize the robotics industry with its versatile AI brain that can be integrated into any robot, enhancing its capabilities to perform multiple tasks in diverse environments, addressing the significant labor shortages in industries like healthcare and manufacturing.
Odyssey, a young AI startup, is pioneering Hollywood-grade visual AI that will allow for both generation and direction of beautiful scenery, characters, lighting, and motion.
It aims to give users full, fine-tuned control over every element in their scenes– all the way to the low-level materials, lighting, motion, and more. Instead of training one model that restricts users to a single input and a single, non-editable output, Odyssey is training four powerful generative models to enable its capabilities. Odyssey’s creators claim the technology is what comes after text-to-video.
Why does it matter?
While we wait for the general release of OpenAI’s Sora, Odyssey is paving a new way to create movies, TV shows, and video games. Instead of replacing humans with algorithms, it is placing a powerful enabler in the hands of professional storytellers.
Anthropic adds a playground to craft high-quality prompts
Anthropic Console now offers a built-in prompt generator powered by Claude 3.5 Sonnet. You describe your task and Claude generates a high-quality prompt for you. You can also use Claude’s new test case generation feature to generate input variables for your prompt and run the prompt to see Claude’s response.
Moreover, with the new Evaluate feature you can do testing prompts against a range of real-world inputs directly in the Console instead of manually managing tests across spreadsheets or code. Anthropi chas also added a feature to compare the outputs of two or more prompts side by side.
Why does it matter?
Language models can improve significantly with small prompt changes. Normally, you’d figure this out yourself or hire a prompt engineer, but these features help make improvements quick and easier.
Google’s digital reconstruction of human brain with AI
Google researchers have completed the largest-ever AI-assisted digital reconstruction of human brain. They unveiled the most detailed map of the human brain yet of just 1 cubic millimeter of brain tissue (size of half a grain of rice) but at high resolution to show individual neurons and their connections.
Now, the team is working to map a mouse’s brain because it looks exactly like a miniature version of a human brain. This may help solve mysteries about our minds that have eluded us since our beginnings.
Why does it matter?
This is a never-seen-before map of the entire human brain that could help us understand long-standing mysteries like where diseases come from to how we store memories. But the mapping takes billions of dollars and decades. AI might just have sped the process!
Microsoft ditches its observer seat on OpenAI’s board; Apple to follow
Microsoft ditched the seat after Microsoft expressed confidence in the OpenAI’s progress and direction. OpenAI stated after this change that there will be no more observers on the board, likely ruling out reports of Apple gaining an observer seat.
LMSYS launched Math Arena and Instruction-Following (IF) Arena
Math and IF are two key domains testing models’ logical skills and real-world tasks. Claude 3.5 Sonnet ranks #1 in Math Arena and joint #1 in IF with GPT-4o. While DeepSeek-coder is the #1 open model in math.
Aitomatic launches the first open-source LLM for semiconductor industry
SemiKong aims to revolutionize semiconductor processes and fabrication technology, giving potential for accelerated innovation and reduced costs. It outperforms generic LLMs like GPT and Llama3 on industry-specific tasks.
Stable Assistant’s capabilities expand with two new features
It includes Search & Replace, which gives you the ability to replace an object in an image with another one. And Stable Audio enables the creation of high-quality audio of up to three minutes.
It will allow the sale of artwork derived from the seller’s own original prompts or AI tools as long as the artist discloses their use of AI in the item’s listing description. Etsy will not allow the sale of AI prompt bundles, which it sees as crossing a creative line.
Anthropic just announced a new upgrade to its recently launched ‘Artifacts’ feature, allowing users to publish, share, and remix creations — alongside the launch of new prompt engineering tools in Claude’s developer Console.
The ‘Artifacts’ feature was introduced alongside Claude 3.5 Sonnet in June, allowing users to view, edit, and build in a real-time side panel workspace.
Published Artifacts can now be shared and remixed by other users, opening up new avenues for collaborative learning.
Anthropic also launched new developer tools in Console, including advanced testing, side-by-side output comparisons, and prompt generation assistance.
MakingArtifacts shareable is a small but mighty update — unlocking a new dimension of AI-assisted content creation that could revolutionize how we approach online education, knowledge sharing, and collaborative work. The ability to easily create and distribute AI-generated experiences opens up a world of possibilities.
A Daily chronicle of AI Innovations July 09th 2024:
LivePotrait animates images from video with precision Microsoft’s ‘MInference’ slashes LLM processing time by 90% Groq’s LLM engine surpasses Nvidia GPU processing
OpenAI and Thrive create AI health coach
Japan Ministry introduces first AI policy
LivePotrait animates images from video with precision
LivePortrait is a new method for animating still portraits using video. Instead of using expensive diffusion models, LivePortrait builds on an efficient “implicit keypoint” approach. This allows it to generate high-quality animations quickly and with precise control.
The key innovations in LivePortrait are:
1) Scaling up the training data to 69 million frames, using a mix of video and images, to improve generalization.
2) Designing new motion transformation and optimization techniques to get better facial expressions and details like eye movements.
3) Adding new “stitching” and “retargeting” modules that allow the user to precisely control aspects of the animation, like the eyes and lips.
4) This allows the method to animate portraits across diverse realistic and artistic styles while maintaining high computational efficiency.
5) LivePortrait can generate 512×512 portrait animations in just 12.8ms on an RTX 4090 GPU.
Why does it matter?
The advancements in generalization ability, quality, and controllability of LivePotrait could open up new possibilities, such as personalized avatar animation, virtual try-on, and augmented reality experiences on various devices.
Microsoft’s ‘MInference’ slashes LLM processing time by 90%
Microsoft has unveiled a new method called MInference that can reduce LLM processing time by up to 90% for inputs of one million tokens (equivalent to about 700 pages of text) while maintaining accuracy. MInference is designed to accelerate the “pre-filling” stage of LLM processing, which typically becomes a bottleneck when dealing with long text inputs.
Microsoft has released an interactive demo of MInference on the Hugging Face AI platform, allowing developers and researchers to test the technology directly in their web browsers. This hands-on approach aims to get the broader AI community involved in validating and refining the technology.
Why does it matter?
By making lengthy text processing faster and more efficient, MInference could enable wider adoption of LLMs across various domains. It could also reduce computational costs and energy usage, putting Microsoft at the forefront among tech companies and improving LLM efficiency.
Groq, a company that promises faster and more efficient AI processing, has unveiled a lightning-fast LLM engine. Their new LLM engine can handle queries at over 1,250 tokens per second, which is much faster than what GPU chips from companies like Nvidia can do. This allows Groq’s engine to provide near-instant responses to user queries and tasks.
Groq’s LLM engine has gained massive adoption, with its developer base rocketing past 280,000 in just 4 months. The company offers the engine for free, allowing developers to easily swap apps built on OpenAI’s models to run on Groq’s more efficient platform. Groq claims its technology uses about a third of the power of a GPU, making it a more energy-efficient option.
Why does it matter?
Groq’s lightning-fast LLM engine allows for near-instantaneous responses, enabling new use cases like on-the-fly generation and editing. As large companies look to integrate generative AI into their enterprise apps, this could transform how AI models are deployed and used.
Japan’s Defense Ministry introduces basic policy on using AI
This comes as the Japanese Self-Defense Forces grapple with challenges such as manpower shortages and the need to harness new technologies. The ministry believes AI has the potential to overcome these challenges in the face of Japan’s declining population.
Thrive AI Health democratizes access to expert-level health coaching
Thrive AI Health, a new company, funded by OpenAI and Thrive Global, uses AI to provide personalized health coaching. The AI assistant can leverage an individual’s data to provide recommendations on sleep, diet, exercise, stress management, and social connections.
Qualcomm and Microsoft rely on AI wave to revive the PC market
Qualcomm and Microsoft are embarking on a marketing blitz to promote a new generation of “AI PCs.” The goal is to revive the declining PC market. This strategy only applies to a small share of PCs sold this year, as major software vendors haven’t agreed to the AI PC trend.
Poe’s Previews let you see and interact with web apps directly within chats
This feature works especially well with advanced AI models like Claude 3.5 Sonnet, GPT-4o, and Gemini 1.5 Pro. Previews enable users to create custom interactive experiences like games, animations, and data visualizations without needing programming knowledge.
Real-time AI video generation less than a year away: Luma Labs chief scientist
Luma’s recently released video model, Dream Machine, was trained on enormous video data, equivalent to hundreds of trillions of words. According to Luma’s chief scientist, Jiaming Song, this allows Dream Machine to reason about the world in new ways. He predicts realistic AI-generated videos will be possible within a year.
The OpenAI Startup Fund and Thrive Global just announced Thrive AI Health, a new venture developing a hyper-personalized, multimodal AI-powered health coach to help users drive personal behavior change.
The AI coach will focus on five key areas: sleep, nutrition, fitness, stress management, and social connection.
Thrive AI Health will be trained on scientific research, biometric data, and individual preferences to offer tailored user recommendations.
DeCarlos Love steps in as Thrive AI Health’s CEO, who formerly worked on AI, health, and fitness experiences at Google as a product leader.
OpenAI CEO Sam Altman and Thrive Global founder Ariana Huffington published an article in TIME detailing AI’s potential to improve both health and lifespans.
With chronic disease and healthcare costs on the rise, AI-driven personalized coaching could be a game-changer — giving anyone the ability to leverage their data for health gains. Plus, Altman’s network of companies and partners lends itself perfectly to crafting a major AI health powerhouse.
Japan’s Defense Ministry just released its inaugural basic policy on the use of artificial intelligence in military applications, aiming to tackle recruitment challenges and keep pace with global powers in defense technology.
The policy outlines seven priority areas for AI deployment, including target detection, intelligence analysis, and unmanned systems.
Japan sees AI as a potential solution to its rapidly aging and shrinking population, which is currently impacting military recruitment.
The strategy also emphasizes human control over AI systems, ruling out fully autonomous lethal weapons.
Japan’s Defense Ministry highlighted the U.S. and China’s military AI use as part of the ‘urgent need’ for the country to utilize the tech to increase efficiency.
Whether the world is ready or not, the military and AI are about to intertwine. By completely ruling out autonomous lethal weapons, Japan is setting a potential model for more responsible use of the tech, which could influence how other powers approach the AI military arms race in the future.
Poe launched ‘Previews’, a new feature allowing users to generate and interact with web apps directly within chats, leveraging LLMs like Claude 3.5 Sonnet for enhanced coding capabilities. Source: https://x.com/poe_platform/status/1810335290281922984
Luma Labs chief scientist Jiaming Song said in an interview that real-time AI video generation is less than a year away, also showing evidence that its Dream Machine model can reason and predict world models in some capacity. Source: https://x.com/AnjneyMidha/status/1808783852321583326
Magnific AI introduced a new Photoshop plugin, allowing users to leverage the AI upscaling and enhancing tool directly in Adobe’s editing platform. Source:https://x.com/javilopen/status/1810345184754069734
Nvidia launched a new competition to create an open-source code dataset for training LLMs on hardware design, aiming to eventually automate the development of future GPUs. Source: https://nvlabs.github.io/LLM4HWDesign
A new study testing ChatGPT’s coding abilities found major limitations in the model’s abilities, though the research has been criticized for its use of GPT-3.5 instead of newer, more capable models. Source: https://ieeexplore.ieee.org/document/10507163
A Daily chronicle of AI Innovations July 08th 2024:
SenseTime released SenseNova 5.5 at the 2024 World Artificial Intelligence Conference Cloudflare launched a one-click feature to block all AI bots Waymo’s Robotaxi gets busted by the cops
OpenAI’s secret AI details stolen in 2023 hack
Fears of AI bubble intensify after new report
Chinese AI firms flex muscles at WAIC
SenseTime released SenseNova 5.5 at the 2024 World Artificial Intelligence Conference
Leading Chinese AI company SenseTime released an upgrade to its SenseNova large model. The new 5.5 version boasts China’s first real-time multimodal model on par with GPT-4o, a cheaper IoT-ready edge model, and a rapidly growing customer base.
SenseNova 5.5 packs a 30% performance boost, matching GPT-4o in interactivity and key metrics. The suite includes SenseNova 5o for seamless human-like interaction and SenseChat Lite-5.5 for lightning-fast inference on edge devices.
With industry-specific models for finance, agriculture, and tourism, SenseTime claims significant efficiency improvements in these sectors, such as 5x improvement in agricultural analysis and 8x in travel planning efficiency.
Why does it matter?
With the launch of “Project $0 Go,” which offers free tokens and API migration consulting to enterprise users, combined with the advanced features of SenseNova 5.5, SenseTime will provide accessible and powerful AI solutions for businesses of all sizes.
Cloudflare launched a one-click feature to block all AI bots
Cloudflare just dropped a single-click tool to block all AI scrapers and crawlers. With demand for training data soaring and sneaky bots rising, this new feature helps users protect their precious content without hassle.
Bytespider, Amazonbot, ClaudeBot, and GPTBot are the most active AI crawlers on Cloudflare’s network. Some bots spoof user agents to appear as real browsers, but Cloudflare’s ML models still identify them. It uses global network signals to detect and block new scraping tools in real time. Customers can report misbehaving AI bots to Cloudflare for investigation.
Why does it matter?
While AI bots hit 39% of top sites in June, less than 3% fought back. With Cloudflare’s new feature, websites can protect users’ precious data and gain more control.
A self-driving Waymo vehicle was pulled over by a police officer in Phoenix after running a red light. The vehicle briefly entered an oncoming traffic lane before entering a parking lot. Bodycam footage shows the officer finding no one in the self-driving Jaguar I-Pace. Dispatch records state the vehicle “freaked out,” and the officer couldn’t issue a citation to the computer.
Waymo initially refused to discuss the incident but later claimed inconsistent construction signage caused the vehicle to enter the wrong lane for 30 seconds. Federal regulators are investigating the safety of Waymo’s self-driving software.
Why does it matter?
The incident shows the complexity of deploying self-driving cars. As these vehicles become more common on our streets, companies must ensure these vehicles can safely and reliably handle real-world situations.
A new report from the New York Times just revealed that a hacker breached OpenAI’s internal messaging systems last year, stealing sensitive details about the company’s tech — with the event going unreported to the public or authorities.
The breach occurred in early 2023, with the hacker accessing an online forum where employees discussed OpenAI’s latest tech advances.
While core AI systems and customer data weren’t compromised, internal discussions about AI designs were exposed.
OpenAI informed employees and the board in April 2023, but did not disclose the incident publicly or to law enforcement.
Former researcher Leopold Aschenbrenner (later fired for allegedly leaking sensitive info) criticized OpenAI’s security in a memo following the hack.
OpenAI has since established a Safety and Security Committee, including the addition of former NSA head Paul Nakasone, to address future risks.
Is OpenAI’s secret sauce out in the wild? As other players continue to even the playing field in the AI race, it’s fair to wonder if leaks and hacks have played a role in the development. The report also adds new intrigue to Aschenbrenner’s firing — who has been adamant that his release was politically motivated.
The World Artificial Intelligence Conference (WAIC) took place this weekend in Shanghai, with Chinese companies showcasing significant advances in LLMs, robotics, and other AI-infused products despite U.S. sanctions on advanced chips.
SenseTime unveiled SenseNova 5.5 at the event, claiming the model outperforms GPT-4o in 5 out of 8 key metrics.
The company also released SenseNova 5o, a real-time multimodal model capable of processing audio, text, image, and video.
Alibaba’s cloud unit reported its open-source Tongyi Qianwen models doubled downloads to over 20M in just two months.
iFlytek introduced SparkDesk V4.0, touting advances over GPT-4 Turbo in multiple domains.
Moore Threads showcased KUAE, an AI data center solution with GPUs performing at 60% of NVIDIA’s restricted A100.
If China’s AI firms are being slowed down by U.S. restrictions, they certainly aren’t showing it. The models and tech continue to rival the leaders in the market — and while sanctions may have created hurdles, they may have also spurred Chinese innovation with workarounds to stay competitive.
The AI industry needs to generate $600 billion annually to cover the extensive costs of AI infrastructure, according to a new Sequoia report, highlighting a significant financial gap despite heavy investments from major tech companies.
Sequoia Capital analyst David Cahn suggests that the current revenue projections for AI companies fall short, raising concerns over a potential financial bubble within the AI sector.
The discrepancy between AI infrastructure expenditure and revenue, coupled with speculative investments, suggests that the AI industry faces significant challenges in achieving sustainable profit, potentially leading to economic instability.
Google researchers’ paper warns that Gen AI ruins the internet
Most generative AI users use the tech to post fake or doctored content online; this AI-generated content influences public opinion, enables scams, and generates profit. The paper doesn’t mention Google’s issues and mistakes with AI, despite Google pushing the technology to its vast user base.
Stability AI announced a new free license for its AI models
Commercial use of the AI models is allowed for small businesses and creators with under $1M in revenue at no cost. Non-commercial use remains free for researchers, open-source devs, students, teachers, hobbyists, etc. Stability AI also pledged to improve SD3 Medium and share learnings quickly to benefit all.
Google DeepMind developed a new AI training technique called JEST
JEST ((joint example selection) trains on batches of data and uses a small AI model to grade data quality and select the best batches for training a larger model. It achieves 13x faster training speed and 10x better power efficiency than other methods.
The technique leverages two AI models — a pre-trained reference model and a ‘learner’ model that is being trained to identify the most valuable data examples.
JEST intelligently selects the most instructive batches of data, making AI training up to 13x faster and 10x more efficient than current state-of the-art methods.
In benchmark tests, JEST achieved top-tier performance while only using 10% of the training data required by previous leading models.
The method enables ‘data quality bootstrapping’ — using small, curated datasets to guide learning on larger unstructured ones.
A new WhatsApp beta version for Android lets you send photos to Meta AI
Users can ask Meta AI questions about objects or context in their photos. Meta AI will also offer photo editing capabilities within the WhatsApp chat interface. Users will have control over their pictures and can delete them anytime.
A Daily chronicle of AI Innovations July 05th 2024:
AI recreates images from brain activity
Apple rumored to launch AI-powered home device
Google considered blocking Safari users from accessing its new AI features
Researchers develop virus that leverages ChatGPT to spread through human-like emails
New AI system decodes brain activity with near perfection ElevenLabs has exciting AI voice updates A French AI startup launches ‘real-time’ AI voice assistant
New AI system decodes brain activity with near perfection
Researchers have developed an AI system that can create remarkably accurate reconstructions of what someone is looking at based on recordings of their brain activity.
In previous studies, the team recorded brain activities using a functional MRI (fMRI) scanner and implanted electrode arrays. Now, they reanalyzed the data from these studies using an improved AI system that can learn which parts of the brain it should pay the most attention to.
As a result, some of the reconstructed images were remarkably close to the images the macaque monkey (in the study) saw.
Why does it matter?
This is probably the closest, most accurate mind-reading accomplished with AI yet. It proves that reconstructed images are greatly improved when the AI learns which parts of the brain to pay attention to. Ultimately, it can create better brain implants for restoring vision.
ElevenLabs has partnered with estates of iconic Hollywood stars to bring their voices to the Reader App. Judy Garland, James Dean, Burt Reynolds, and Sir Laurence Olivier are now part of the library of voices on the Reader App.
It has also introduced Voice Isolater. This tool removes unwanted background noise and extracts crystal-clear dialogue from any audio to make your next podcast, interview, or film sound like it was recorded in the studio. It will be available via API in the coming weeks.
Why does it matter?
ElevenLabs is shipping fast! It appears to be setting a standard in the AI voice technology industry by consistently introducing new AI capabilities with its technology and addressing various needs in the audio industry.
Source: https://elevenlabs.io/blog/iconic-voices
A French AI startup launches ‘real-time’ AI voice assistant
A French AI startup, Kyutai, has launched a new ‘real-time’ AI voice assistant named Moshi. It is capable of listening and speaking simultaneously and in 70 different emotions and speaking styles, ranging from whispers to accented speech.
Kyutai claims Moshi is the first real-time voice AI assistant, with a latency of 160ms. You can try it via Hugging Face. It will be open-sourced for research in coming weeks.
Why does it matter?
Yet another impressive competitor that challenges OpenAI’s perceived dominance in AI. (Moshi could outpace OpenAI’s delayed voice offering.) Such advancements push competitors to improve their offerings, raising the bar for the entire industry.
Meta’s multi-token prediction models are now open for research
In April, Meta proposed a new approach for training LLMs to forecast multiple future words simultaneously vs. the traditional method to predict just the next word in a sequence. Meta has now released pre-trained models that leverage this approach.
Apple to announce AI partnership with Google at iPhone 16 event
Apple has been meeting with several companies to partner with in the AI space, including Google. Reportedly, Apple will announce the addition of Google Gemini on iPhones at its annual event in September.
Google simplifies the process for advertisers to disclose if political ads use AI
In an update to its Political content policy, Google requires advertisers to disclose election ads containing synthetic or digitally altered content. It will automatically include an in-ad disclosure for specific formats.
WhatsApp is developing a personalized AI avatar generator
It appears to be working on a new Gen AI feature that will allow users to make personalized avatars of themselves for use in any imagined setting. It will generate images using user-supplied photos, text prompts, and Meta’s Llama model.
Meta ordered to stop training its AI on Brazilian personal data
Brazil’s National Data Protection Authority (ANPD) has decided to suspend with immediate effect the validity of Meta’s new privacy policy (updated in May) for using personal data to train generative AI systems in the country. Meta will face daily fines if it fails to comply.
Apple is rumored to be developing a new home device that merges the functionalities of the HomePod and Apple TV, supported by “Apple Intelligence” and potentially featuring the upcoming A18 chip, according to recent code discoveries.
Identified as “HomeAccessory17,1,” this device is expected to include a speaker and LCD screen, positioning it to compete with Amazon’s Echo Show and Google’s Nest series.
The smart device is anticipated to serve as a smart home hub, allowing users to control HomeKit devices, and it may integrate advanced AI features announced for iOS 18, iPadOS 18, and macOS Sequoia, including capabilities powered by OpenAI’s GPT-4 to enhance Siri’s responses.
Google considered blocking Safari users from accessing its new AI features
Google considered limiting access to its new AI Overviews feature on Safari but ultimately decided not to follow through with the plan, according to a report by The Information.
The ongoing Justice Department investigation into Google’s dominance in search highlights the company’s arrangement with Apple, where Google pays around $20 billion annually to be the default search engine on iPhones.
Google has been trying to reduce its dependency on Safari by encouraging iPhone users to switch to its own apps, but the company has faced challenges due to Safari’s pre-installed presence on Apple devices.
Researchers develop virus that leverages ChatGPT to spread through human-like emails
Researchers from ETH Zurich and Ohio State University created a virus named “synthetic cancer” that leverages ChatGPT to spread via AI-generated emails.
This virus can modify its code to evade antivirus software and uses Outlook to craft contextually relevant, seemingly innocuous email attachments.
The researchers stress the cybersecurity risks posed by Language Learning Models (LLMs), highlighting the need for further research into protective measures against intelligent malware.
A Daily chronicle of AI Innovations July 04th 2024:
OpenAI secrets stolen by hacker
French AI lab Kyutai unveils conversational AI assistant Moshi
China leads the world in generative AI patents
OpenAI’s ChatGPT Mac app was storing conversations in plain text
Salesforce’s small model breakthrough
Perplexity gets major research upgrade
OpenAI secrets stolen by hacker
A hacker accessed OpenAI’s internal messaging systems early last year and stole design details about the company’s artificial intelligence technologies.
The attacker extracted information from employee discussions in an online forum but did not breach the systems where OpenAI creates and stores its AI tech.
OpenAI executives disclosed the breach to their staff in April 2023 but did not make it public, as no sensitive customer or partner information was compromised.
French AI lab Kyutai unveils conversational AI assistant Moshi
French AI lab Kyutai introduced Moshi, a conversational AI assistant capable of natural interaction, at an event in Paris and plans to release it as open-source technology.
Kyutai stated that Moshi is the first AI assistant with public access enabling real-time dialogue, differentiating it from OpenAI’s GPT-4o, which has similar capabilities but is not yet available.
Developed in six months by a small team, Moshi’s unique “Audio Language Model” architecture allows it to process and predict speech directly from audio data, achieving low latency and impressive language skills despite its relatively small model size.
China has submitted significantly more patents related to generative artificial intelligence than any other nation, with the United States coming in a distant second, according to the World Intellectual Property Organization.
In the decade leading up to 2023, over 38,200 generative AI inventions originated in China, compared to almost 6,300 from the United States, demonstrating China’s consistent lead in this technology.
Generative AI, using tools like ChatGPT and Google Gemini, has seen rapid growth and industry adoption, with concerns about its impact on jobs and fairness of content usage, noted the U.N. intellectual property agency.
OpenAI’s ChatGPT Mac app was storing conversations in plain text
OpenAI launched the first official ChatGPT app for macOS, raising privacy concerns because conversations were initially stored in plain text.
Developer Pedro Vieito revealed that the app did not use macOS sandboxing, making sensitive user data easily accessible to other apps or malware.
OpenAI released an update after the concerns were publicized, which now encrypts chats on the Mac, urging users to update their app to the latest version.
Salesforce just published new research on APIGen, an automated system that generates optimal datasets for AI training on function calling tasks — enabling the company’s xLAM model to outperform much larger rivals.
APIGen is designed to help models train on datasets that better reflect the real-world complexity of API usage.
Salesforce trained a both 7B and 1B parameter version of xLAM using APIGen, testing them against key function calling benchmarks.
xLAM’s 7B parameter model ranked 6th out of 46 models, matching or surpassing rivals 10x its size — including GPT-4.
xLAM’s 1B ‘Tiny Giant’ outperformed models like Claude Haiku and GPT-3.5, with CEO Mark Benioff calling it the best ‘micro-model’ for function calling.
While the AI race has been focused on building ever-larger models, Salesforce’s approach suggests that smarter data curation can lead to more efficient systems. The research is also a major step towards better on-device, agentic AI — packing the power of large models into a tiny frame.
ChatGPT’s voice mode feature now allows you to convert your spoken ideas into well-written text, summaries, and action items, boosting your creativity and productivity.
Enable “Background Conversations” in the ChatGPT app settings.
Start a new chat with the prompt shown in the image above (it was too long for this email).
Speak your thoughts freely, pausing as needed, and say “I’m done” when you’ve expressed all your ideas.
Review the AI-generated text, summary, and action items, and save them to your notes.
Pro tip: Try going on a long walk and rambling any ideas to ChatGPT using this trick — you’ll be amazed by the summary you get at the end.
Perplexity just announced new upgrades to its ‘Pro Search’ feature, enhancing capabilities for complex queries, multi-step reasoning, integration of Wolfram Alpha for math improvement, and more.
Pro Search can now tackle complex queries using multi-step reasoning, chaining together multiple searches to find more comprehensive answers.
A new integration with Wolfram Alpha allows for solving advanced mathematical problems, alongside upgraded code execution abilities.
Free users get 5 Pro Searches every four hours, while subscribers to the $20/month plan get 600 per day.
The upgrade comes amid recent controversy over Perplexity’s data scraping and attribution practices.
Given Google’s struggles with AI overviews, Perplexity’s upgrades will continue the push towards ‘answer engines’ that take the heavy lifting out of the user’s hand. But the recent accusations aren’t going away — and could cloud the whole AI-powered search sector until precedent is set.
Shanghai AI Lab introduced InternLM 2.5-7B, a model with a 1M context window and the ability to use tools that surged up the Open LLM Leaderboard upon release. Source: https://x.com/intern_lm/status/1808501625700675917
Magic is set to raise over $200M at a $1.5B valuation, despite having no product or revenue yet — as the company continues to develop its coding-specialized models that can handle large context windows. Source: https://www.reuters.com/technology/artificial-intelligence/ai-coding-startup-magic-seeks-15-billion-valuation-new-funding-round-sources-say-2024-07-02/
A Daily chronicle of AI Innovations July 03rd 2024:
Apple joins OpenAI board
Google’s emissions spiked by almost 50% due to AI boom
Meta’s new AI can create 3D objects from text in under a minute
Meta’s 3D Gen creates 3D assets at lightning speed Perplexity AI upgrades Pro Search with more advanced problem-solving The first Gen AI framework that keeps your prompts always encrypted
ElevenLabs launches ‘Iconic Voices’
Leaks reveal Google Pixel AI upgrades
Meta’s new text-to-3D AI
Meta’s 3D Gen creates 3D assets at lightning speed
Meta has introduced Meta 3D Gen, a new state-of-the-art, fast pipeline for text-to-3D asset generation. It offers 3D asset creation with high prompt fidelity and high-quality 3D shapes and textures in less than a minute.
According to Meta, the process is three to 10 times faster than existing solutions. The research paper even mentions that when assessed by professional 3D artists, the output of 3DGen is preferred a majority of time compared to industry alternatives, particularly for complex prompts, while being from 3× to 60× faster.
A significant feature of 3D Gen is its support physically-based rendering (PBR), necessary for 3D asset relighting in real-world applications.
Why does it matter?
3D Gen’s implications extend far beyond Meta’s sphere. In gaming, it could speed up the creation of expansive virtual worlds, allowing rapid prototyping. In architecture and industrial design, it could facilitate quick concept visualization, expediting the design process.
Perplexity AI upgrades Pro Search with more advanced problem-solving
Perplexity AI has improved Pro Search to tackle more complex queries, perform advanced math and programming computations, and deliver even more thoroughly researched answers. Everyone can use Pro Search five times every four hours for free, and Pro subscribers have unlimited access.
Perplexity suggests the upgraded Pro Search “can pinpoint case laws for attorneys, summarize trend analysis for marketers, and debug code for developers—and that’s just the start”. It can empower all professions to make more informed decisions.
Why does it matter?
This showcases AI’s potential to assist professionals in specialized fields. Such advancements also push the boundaries of AI’s practical applications in research and decision-making processes.
The first Gen AI framework that keeps your prompts always encrypted
Edgeless Systems introduced Continuum AI, the first generative AI framework that keeps prompts encrypted at all times with confidential computing by combining confidential VMs with NVIDIA H100 GPUs and secure sandboxing.
The Continuum technology has two main security goals. It first protects the user data and also protects AI model weights against the infrastructure, the service provider, and others. Edgeless Systems is also collaborating with NVIDIA to empower businesses across sectors to confidently integrate AI into their operations.
Why does it matter?
This greatly advances security for LLMs. The technology could be pivotal for a future where organizations can securely utilize AI, even for the most sensitive data.
RunwayML’s Gen-3 Alpha models is now generally available
Announced a few weeks ago, Gen-3 is Runway’s latest frontier model and a big upgrade from Gen-1 and Gen-2. It allows users to produce hyper-realistic videos from text, image, or video prompts. Users must upgrade to a paid plan to use the model.
Meta might be bringing generative AI to metaverse games
In a job listing, Meta mentioned it is seeking to research and prototype “new consumer experiences” with new types of gameplay driven by Gen AI. It is also planning to build Gen AI-powered tools that could “improve workflow and time-to-market” for games.
As a part of its AI agreement with OpenAI, Apple will get an observer role on OpenAI’s board. Apple chose Phil Schiller, the head of Apple’s App Store and its former marketing chief, for the position.
Figma disabled AI tool after being criticised for ripping off Apple’s design
Figma’s Make Design feature generates UI layouts and components from text prompts. It repeatedly reproduced Apple’s Weather app when used as a design aid, drawing accusations that Figma’s AI seems heavily trained on existing apps.
China is far ahead of other countries in generative AI inventions
According to the World Intellectual Property Organization (WIPO), more than 50,000 patent applications were filed in the past decade for Gen AI. More than 38,000 GenAI inventions were filed by China between 2014-2023 vs. only 6,276 by the U.S.
Phil Schiller, Apple’s former marketing head and App Store chief, will reportedly join OpenAI’s board as a non-voting observer, according to Bloomberg.
This role will allow Schiller to understand OpenAI better, as Apple aims to integrate ChatGPT into iOS and macOS later this year to enhance Siri’s capabilities.
Microsoft also took a non-voting observer position on OpenAI’s board last year, making it rare and significant for both Apple and Microsoft to be involved in this capacity.
Google’s emissions spiked by almost 50% due to AI boom
Google reported a 48% increase in greenhouse gas emissions over the past five years due to the high energy demands of its AI data centers.
Despite achieving seven years of renewable energy matching, Google faces significant challenges in meeting its goal of net zero emissions by 2030, highlighting the uncertainties surrounding AI’s environmental impact.
To address water consumption concerns, Google has committed to replenishing 120% of the water it uses by 2030, although in 2023, it only managed to replenish 18%.
Meta’s new AI can create 3D objects from text in under a minute
Meta has introduced 3D Gen, an AI system that creates high-quality 3D assets from text descriptions in under a minute, significantly advancing 3D content generation.
The system uses a two-stage process, starting with AssetGen to generate a 3D mesh with PBR materials and followed by TextureGen to refine the textures, producing detailed and professional-grade 3D models.
3D Gen has shown superior performance and visual quality compared to other industry solutions, with potential applications in game development, architectural visualization, and virtual/augmented reality.
A Daily chronicle of AI Innovations July 02nd 2024:
JARVIS-inspired Grok 2 aims to answer any user query Apple unveils a public demo of its ‘4M’ AI model Amazon hires Adept’s top executives to build an AGI team
YouTube lets you remove AI-generated content resembling face or voice
Runway opens Gen-3 Alpha access
Motorola hits the AI runway
Meta swaps ‘Made with AI’ label with ‘AI info’ to indicate AI photos
Deepfakes to cost $40 billion by 2027: Deloitte survey
Anthropic launches a program to fund the creation of reliable AI benchmarks
US’s targeting of AI not helpful for healthy development: China
New robot controlled by human brain cells
Figma to temporarily disable AI feature amid plagiarism concerns
Runway opens Gen-3 Alpha access
Runway just announced that its AI video generator, Gen-3 Alpha, is now available to all users following weeks of impressive, viral outputs after the model’s release in mid-June.
Runway unveiled Gen-3 Alpha last month, the first model in its next-gen series trained for learning ‘general world models’.
Gen-3 Alpha upgrades key features, including character and scene consistency, camera motion and techniques, and transitions between scenes.
Gen-3 Alpha is available behind Runway’s ‘Standard’ $12/mo access plan, which gives users 63 seconds of generations a month.
On Friday, we’re running a free, hands-on workshop in our AI University covering how to create an AI commercial using Gen-3, ElevenLabs, and Midjourney.
Despite impressive recent releases from KLING and Luma Labs, Runway’s Gen-3 Alpha model feels like the biggest leap AI video has taken since Sora. However, the tiny generation limits for non-unlimited plans might be a hurdle for power users.
Motorola just launched its ‘Styled By Moto’ ad campaign, an entirely AI-generated fashion spot promoting its new line of Razr folding smartphones — created using nine different AI tools, including Sora and Midjourney.
The 30-second video features AI-generated models wearing outfits inspired by Motorola’s iconic ‘batwing’ logo in settings like runways and photo shoots.
Each look was created from thousands of AI-generated images, incorporating the brand’s logo and colors of the new Razr phone line.
Tools used include OpenAI’s Sora, Adobe Firefly, Midjourney, Krea, Magnific, Luma, and more — reportedly taking over four months of research.
The 30-second spot is also set to an AI-generated soundtrack incorporating the ‘Hello Moto’ jingle, created using Udio.
This is a fascinating look at the AI-powered stack used by a major brand, and a glimpse at how tools can (and will) be combined to open new creative avenues. It’s also another example of the shift in discourse surrounding AI’s use in marketing — potentially paving the way for wider acceptance and integration.
JARVIS-inspired Grok 2 aims to answer any user query
Elon Musk has announced the release dates for two new AI assistants from xAI. The first, Grok 2, will be launched in August. Musk says Grok 2 is inspired by JARVIS from Iron Man and The Hitchhiker’s Guide to the Galaxy and aims to answer virtually any user query. This ambitious goal is fueled by xAI’s focus on “purging” LLM datasets used for training.
Musk also revealed that an even more powerful version, Grok 3, is planned for release by the end of the year. Grok 3 will leverage the processing power of 100,000 Nvidia H100 GPUs, potentially pushing the boundaries of AI performance even further.
Why does it matter?
These advanced AI assistants from xAI are intended to compete with and outperform AI chatbots like OpenAI’s ChatGPT by focusing on data quality, user experience, and raw processing power. This will significantly advance the state of AI and transform how people interact with and leverage AI assistants.
Apple and the Swiss Federal Institute of Technology Lausanne (EPFL) have released a public demo of the ‘4M’ AI model on Hugging Face. The 4M (Massively Multimodal Masked Modeling) model can process and generate content across multiple modalities, such as creating images from text, detecting objects, and manipulating 3D scenes using natural language inputs.
While companies like Microsoft and Google have been making headlines with their AI partnerships and offerings, Apple has been steadily advancing its AI capabilities. The public demo of the 4M model suggests that Apple is now positioning itself as a significant player in the AI industry.
Why does it matter?
By making the 4M model publicly accessible, Apple is seeking to engage developers to build an ecosystem. It could lead to more coherent and versatile experiences, such as enhanced Siri capabilities and advancements in Apple’s augmented reality efforts.
Amazon hires Adept’s top executives to build an AGI team
Amazon is hiring the co-founders, including the CEO and several other key employees, from the AI startup Adept.CEO David Luan will join Amazon’s AGI autonomy group, which is led by Rohit Prasad, who is spearheading a unified push to accelerate Amazon’s AI progress across different divisions like Alexa and AWS.
Amazon is consolidating its AI projects to develop a more advanced LLM to compete with OpenAI and Google’s top offerings. This unified approach leverages the company’s collective resources to accelerate progress in AI capabilities.
Why does it matter?
This acquisition indicates Amazon’s intent to strengthen its position in the competitive AI landscape. By bringing the Adept team on board, Amazon is leveraging its expertise and specialized knowledge to advance its AGI aspirations.
YouTube lets you remove AI-generated content resembling face or voice
YouTube lets people request the removal of AI-generated content that simulates their face or voice. Under YouTube’s privacy request process, the requests will be reviewed based on whether the content is synthetic, if it identifies the person, and if it shows the person in sensitive behavior. Source: https://techcrunch.com/2024/07/01/youtube-now-lets-you-request-removal-of-ai-generated-content-that-simulates-your-face-or-voice
Meta swaps ‘Made with AI’ label with ‘AI info’ to indicate AI photos
Meta is refining its AI photo labeling on Instagram and Facebook. The “Made with AI” label will be replaced with “AI info” to more accurately reflect the extent of AI use in images, from minor edits to the entire AI generation. It addresses photographers’ concerns about the mislabeling of their photos. Source: https://techcrunch.com/2024/07/01/meta-changes-its-label-from-made-with-ai-to-ai-info-to-indicate-use-of-ai-in-photos
Deepfakes to cost $40 billion by 2027: Deloitte survey
Deepfake-related losses will increase from $12.3 billion in 2023 to $40 billion by 2027, growing at 32% annually. There was a 3,000% increase in incidents last year alone. Enterprises are not well-prepared to defend against deepfake attacks, with one in three having no strategy.
Anthropic launches a program to fund the creation of reliable AI benchmarks
Anthropic is launching a program to fund new AI benchmarks. The aim is to create more comprehensive evaluations of AI models, including assessing capabilities in cyberattacks and weapons and beneficial applications like scientific research and bias mitigation. Source: https://techcrunch.com/2024/07/01/anthropic-looks-to-fund-a-new-more-comprehensive-generation-of-ai-benchmarks
US’s targeting of AI not helpful for healthy development: China
China has criticized the US approach to regulating and restricting investments in AI. Chinese officials stated that US actions targeting AI are not helpful for AI’s healthy and sustainable development. They argued that the US measures will be divisive when it comes to global governance of AI.
Scientists in China have developed a robot with an artificial brain grown from human stem cells, which can perform basic tasks such as moving limbs, avoiding obstacles, and grasping objects, showcasing some intelligence functions of a biological brain.
The brain-on-chip utilizes a brain-computer interface to facilitate communication with the external environment through encoding, decoding, and stimulation-feedback mechanisms.
This pioneering brain-on-chip technology, requiring similar conditions to sustain as a human brain, is expected to have a revolutionary impact by advancing the field of hybrid intelligence, merging biological and artificial systems.
Figma to temporarily disable AI feature amid plagiarism concerns
Figma has temporarily disabled its “Make Design” AI feature after accusations that it was replicating Apple’s Weather app designs.
Andy Allen, founder of NotBoring Software, discovered that the feature consistently reproduced the layout of Apple’s Weather app, leading to community concerns.
CEO Dylan Field acknowledged the issue and stated the feature would be disabled until they can ensure its reliability and originality through comprehensive quality assurance checks.
French antitrust enforcers plan to charge Nvidia with alleged anticompetitive practices, becoming the first to take such action, according to Reuters.
Nvidia’s offices in France were raided last year as part of an investigation into possible abuses of dominance in the graphics cards sector.
Regulatory bodies in the US, EU, China, and the UK are also examining Nvidia’s business practices due to its significant presence in the AI chip market.
A Daily chronicle of AI Innovations July 01st 2024:
Some Apple Intelligence features may be put behind a paywall
Meta’s new dataset could enable robots to learn manual skills from human experts
Google announces advancements in Vertex AI models LMSYS’s new Multimodal Arena compares top AI models’ visual processing abilities Apple’s Vision Pro gets an AI upgrade
Humanoid robots head to the warehouse
Google Translate adds 110 languages
Google announces advancements in Vertex AI models
Google has rolled out significant improvements to its Vertex AI platform, including the general availability of Gemini 1.5 Flash with a massive 1 million-token context window. Also, Gemini 1.5 Pro now offers an industry-leading 2 million-token context capability. Google is introducing context caching for these Gemini models, slashing input costs by 75%.
Moreover, Google launched Imagen 3 in preview and added third-party models like Anthropic’s Claude 3.5 Sonnet on Vertex AI.
They’ve also made Grounding with Google Search generally available and announced a new service for grounding AI agents with specialized third-party data. Plus, they’ve expanded data residency guarantees to 23 countries, addressing growing data sovereignty concerns.
Why does it matter?
Google is positioning Vertex AI as the most “enterprise-ready” generative AI platform. With expanded context windows and improved grounding capabilities, this move also addresses concerns about the accuracy of Google’s AI-based search features.
LMSYS’s new Multimodal Arena compares top AI models’ visual processing abilities
LMSYS Org added image recognition to Chatbot Arena to compare vision language models (VLMs), collecting over 17,000 user preferences in just two weeks. OpenAI’s GPT-4o and Anthropic’s Claude 3.5 Sonnet outperformed other models in image recognition. Also, the open-source LLaVA-v1.6-34B performed comparably to some proprietary models.
These AI models tackle diverse tasks, from deciphering memes to solving math problems with visual aids. However, the examples provided show that even top models can stumble when interpreting complex visual information or handling nuanced queries.
Why does it matter?
This leaderboard isn’t just a tech popularity contest—it shows how advanced AI models can decode images. However, the varying performance also serves as a reality check, reminding us that while AI can recognize a cat in a photo, it might struggle to interpret your latest sales graph.
Apple is reportedly working to bring its Apple Intelligence features to the Vision Pro headset, though not this year. Meanwhile, Apple is tweaking its in-store Vision Pro demos, allowing potential buyers to view personal media and try a more comfortable headband. Apple’s main challenge is adapting its AI features to a mixed-reality environment.
The company is tweaking its retail strategy for Vision Pro demos, hoping to boost sales of the pricey headset. Apple is also exploring the possibility of monetizing AI features through subscription services like “Apple Intelligence+.”
Why does it matter?
Apple’s Vision Pro, with its 16GB RAM and M2 chip, can handle advanced AI tasks. However, cloud infrastructure limitations are causing a delay in launch. It’s a classic case of “good things come to those who wait.”
Agility Robotics just signed a multi-year deal with GXO Logistics to bring the company’s Digit humanoid robots to warehouses, following a successful pilot in Spanx facilities in 2023.
The agreement is being hailed as the first Robots-as-a-Service (RaaS) deal and ‘formal commercial deployment’ of the humanoid robots.
Agility’s Digit robots will be integrated into GXO’s logistics operations at a Spanx facility in Connecticut, handling repetitive tasks and logistics work.
The 5’9″ tall Digit can lift up to 35 pounds, and integrates with a cloud-based Agility Arc platform to control full fleets and optimize facility workflows.
Digit tested a proof-of-concept trial with Spanx in 2023, with Amazon also testing the robots at its own warehouses.
Is RaaS the new SaaS? Soon, every company will be looking to adopt advanced robotics into their workforce — and subscription services could help lower the financial and technical barriers needed to scale without the massive upfront costs.
Google just announced its largest-ever expansion of Google Translate, adding support for 110 new languages enabled by the company’s PaLM 2 LLM model.
The new languages represent over 614M speakers, covering about 8% of the global population.
Google’s PaLM 2 model was the driving force behind the expansion, helping unlock translations for closely related languages.
The expansion also includes some languages with no current native speakers, displaying how AI models can help preserve ‘lost’ dialects.
The additions are part of Google’s ‘1,000 Languages Initiative,’ which aims to build AI that supports all of the world’s spoken languages.
We’ve talked frequently about AI’s coming power to break down language barriers with its translation capabilities — but the technology is also playing a very active role in both uncovering and preserving languages from lost and endangered cultures.
Amazon’s Q AI assistant for enterprises gets an update for call centers
The update provides real-time, step-by-step guides for customer issues. It aims to reduce the “toggle tax” – time wasted switching between applications. The system listens to calls in real-time and automatically provides relevant information.
WhatsApp is developing a feature to choose Meta AI Llama models
Users will be able to choose between two options: faster responses with Llama 3-70B (default) or more complex queries with Llama 3-405B (advanced). Llama 3-405B will be limited to a certain number of prompts per week. This feature aims to give users more control over their AI interactions.
Bill Gates says AI’s energy consumption isn’t a major concern
He claims that while data centers may consume up to 6% of global electricity, AI will ultimately drive greater energy efficiency. Gates believes tech companies will invest in green energy to power their AI operations, potentially offsetting the increased demand.
Amazon is investigating Perplexity AI for possible scraping abuse
Perplexity appears to be scraping websites that have forbidden access through robots.txt. AWS prohibits customers from violating the robots.txt standard. Perplexity uses an unpublished IP address to access websites that block its official crawler. The company claims a third party performs web crawling for them.
Microsoft AI chief claims content on the open web is “freeware”
Mustafa Suleyman claimed that anything published online becomes “freeware” and fair game for AI training. This stance, however, contradicts basic copyright principles and ignores the legal complexities of fair use. He suggests that robots.txt might protect content from scraping.
Some Apple Intelligence features may be put behind a paywall
Apple Intelligence, initially free, is expected to introduce a premium “Apple Intelligence+” subscription tier with additional features, similar to iCloud, according to Bloomberg’s Mark Gurman.
Apple plans to monetize Apple Intelligence not only through direct subscriptions but also by taking a share of revenue from partner AI services like OpenAI and potentially Google Gemini.
Apple Intelligence will be integrated into multiple devices, excluding the HomePod due to hardware limitations, and may include a new robotic device, making it comparable to iCloud in its broad application and frequent updates.
Meta’s new dataset could enable robots to learn manual skills from human experts
Meta has introduced a new benchmark dataset named HOT3D to advance AI research in 3D hand-object interactions, containing over one million frames from various perspectives.
This dataset aims to enhance the understanding of human hand manipulation of objects, addressing a significant challenge in computer vision research according to Meta.
HOT3D includes over 800 minutes of egocentric video recordings, multiple perspectives, detailed 3D pose annotations, and 3D object models, which could help robots and XR devices learn manual skills from human experts.
Recently, while updating our OpenAI Python library, I encountered a marketing intern struggling with GPT-4. He was overwhelmed by its repetitive responses, lengthy answers, and not quite getting what he needed from it. Realizing the need for a simple, user-friendly explanation of GPT-4’s functionalities, I decided to create this guide. Whether you’re new to AI or looking to refine your GPT-4 interactions, these tips are designed to help you navigate and optimize your experience.
Embark on a journey to master GPT-4 with our easy-to-understand guide, ‘Mastering GPT-4: Simplified Guide for Everyday Users‘.
🌟🤖 This blog/video/podcast is perfect for both AI newbies and those looking to enhance their experience with GPT-4. We break down the complexities of GPT-4’s settings into simple, practical terms, so you can use this powerful tool more effectively and creatively.
🔍 What You’ll Learn:
Frequency Penalty: Discover how to reduce repetitive responses and make your AI interactions sound more natural.
Logit Bias: Learn to gently steer the AI towards or away from specific words or topics.
Presence Penalty: Find out how to encourage the AI to transition smoothly between topics.
Temperature: Adjust the AI’s creativity level, from straightforward responses to imaginative ideas.
Top_p (Nucleus Sampling): Control the uniqueness of the AI’s suggestions, from conventional to out-of-the-box ideas.
Mastering GPT-4: Simplified Guide for Everyday Users
1. Frequency Penalty: The Echo Reducer
What It Does: This setting helps minimize repetition in the AI’s responses, ensuring it doesn’t sound like it’s stuck on repeat.
Examples:
Low Setting: You might get repeated phrases like “I love pizza. Pizza is great. Did I mention pizza?”
High Setting: The AI diversifies its language, saying something like “I love pizza for its gooey cheese, tangy sauce, and crispy crust. It’s a culinary delight.”
2. Logit Bias: The Preference Tuner
What It Does: It nudges the AI towards or away from certain words, almost like gently guiding its choices.
Examples:
Against ‘pizza’: The AI might focus on other aspects, “I enjoy Italian food, especially pasta and gelato.”
Towards ‘pizza’: It emphasizes the chosen word, “Italian cuisine brings to mind the delectable pizza, a feast of flavors in every slice.”
3. Presence Penalty: The Topic Shifter
What It Does: This encourages the AI to change subjects more smoothly, avoiding dwelling too long on a single topic.
Examples:
Low Setting: It might stick to one idea, “I enjoy sunny days. Sunny days are pleasant.”
High Setting: The AI transitions to new ideas, “Sunny days are wonderful, but I also appreciate the serenity of rainy evenings and the beauty of a snowy landscape.”
4. Temperature: The Creativity Dial
What It Does: Adjusts how predictable or creative the AI’s responses are.
Examples:
Low Temperature: Expect straightforward answers like, “Cats are popular pets known for their independence.”
High Temperature: It might say something whimsical, “Cats, those mysterious creatures, may just be plotting a cute but world-dominating scheme.”
5. Top_p (Nucleus Sampling): The Imagination Spectrum
What It Does: Controls how unique or unconventional the AI’s suggestions are.
Examples:
Low Setting: You’ll get conventional ideas, “Vacations are perfect for unwinding and relaxation.”
High Setting: Expect creative and unique suggestions, “Vacation ideas range from bungee jumping in New Zealand to attending a silent meditation retreat in the Himalayas.”
Mastering GPT-4: Understanding Temperature in GPT-4; A Guide to AI Probability and Creativity
If you’re intrigued by how the ‘temperature’ setting impacts the output of GPT-4 (and other Large Language Models or LLMs), here’s a straightforward explanation:
LLMs, like GPT-4, don’t just spit out a single next token; they actually calculate probabilities for every possible token in their vocabulary. For instance, if the model is continuing the sentence “The cat in the,” it might assign probabilities like: Hat: 80%, House: 5%, Basket: 4%, and so on, down to the least likely words. These probabilities cover all possible tokens, adding up to 100%.
What happens next is crucial: one of these tokens is selected based on their probabilities. So, ‘hat’ would be chosen 80% of the time. This approach introduces a level of randomness in the model’s output, making it less deterministic.
Now, the ‘temperature’ parameter plays a role in how these probabilities are adjusted or skewed before a token is selected. Here’s how it works:
Temperature = 1: This keeps the original probabilities intact. The output remains somewhat random but not skewed.
Temperature < 1: This skews probabilities toward more likely tokens, making the output more predictable. For example, ‘hat’ might jump to a 95% chance.
Temperature = 0: This leads to complete determinism. The most likely token (‘hat’, in our case) gets a 100% probability, eliminating randomness.
Temperature > 1: This setting spreads out the probabilities, making less likely words more probable. It increases the chance of producing varied and less predictable outputs.
A very high temperature setting can make unlikely and nonsensical words more probable, potentially resulting in outputs that are creative but might not make much sense.
Temperature isn’t just about creativity; it’s about allowing the LLM to explore less common paths from its training data. When used judiciously, it can lead to more diverse responses. The ideal temperature setting depends on your specific needs:
For precision and reliability (like in coding or when strict adherence to a format is required), a lower temperature (even zero) is preferable.
For creative tasks like writing, brainstorming, or naming, where there’s no single ‘correct’ answer, a higher temperature can yield more innovative and varied results.
So, by adjusting the temperature, you can fine-tune GPT-4’s outputs to be as predictable or as creative as your task requires.
Mastering GPT-4: Conclusion
With these settings, you can tailor GPT-4 to better suit your needs, whether you’re looking for straightforward information or creative and diverse insights. Remember, experimenting with these settings will help you find the perfect balance for your specific use case. Happy exploring with GPT-4!
Mastering GPT-4 Annex: More about GPT-4 API Settings
I think certain parameters in the API are more useful than others. Personally, I haven’t come across a use case for frequency_penalty or presence_penalty.
However, for example, logit_bias could be quite useful if you want the LLM to behave as a classifier (output only either “yes” or “no”, or some similar situation).
Basically logit_bias tells the LLM to prefer or avoid certain tokens by adding a constant number (bias) to the likelihood of each token. LLMs output a number (referred to as a logit) for each token in their dictionary, and by increasing or decreasing the logit value of a token, you make that token more or less likely to be part of the output. Setting the logit_bias of a token to +100 would mean it will output that token effectively 100% of the time, and -100 would mean the token is effectively never output. You may think, why would I want a token(s) to be output 100% of the time? You can for example set multiple tokens to +100, and it will choose between only those tokens when generating the output.
One very useful usecase would be to combine the temperature, logit_bias, and max_tokens parameters.
`temperature` to zero (which would force the LLM to select the top-1 most likely token/with the highest logit value 100% of the time, since by default there’s a bit of randomness added)
`logit_bias` to +100 (the maximum value permitted) for both the tokens “yes” and “no”
`max_tokens` value to one
Since the LLM typically never outputs logits of >100 naturally, you are basically ensuring that the output of the LLM is ALWAYS either the token “yes” or the token “no”. And it will still pick the correct one of the two since you’re adding the same number to both, and one will still have the higher logit value than the other.
This is very useful if you need the output of the LLM to be a classifier, e.g. “is this text about cats” -> yes/no, without needing to fine tune the output of the LLM to “understand” that you only want a yes/no answer. You can force that behavior using postprocessing only. Of course, you can select any tokens, not just yes/no, to be the only possible tokens. Maybe you want the tokens “positive”, “negative” and “neutral” when classifying the sentiment of a text, etc.
What is the difference between frequence_penalty and presence_penalty?
frequency_penalty reduces the probability of a token appearing multiple times proportional to how many times it’s already appeared, while presence_penalty reduces the probability of a token appearing again based on whether it’s appeared at all.
From the API docs:
frequency_penalty Number between -2.0 and 2.0. Positive values penalize new tokens based on their existing frequency in the text so far, decreasing the model’s likelihood to repeat the same line verbatim.
presence_penalty Number between -2.0 and 2.0. Positive values penalize new tokens based on whether they appear in the text so far, increasing the model’s likelihood to talk about new topics.
Welcome to AI Unraveled, the podcast that demystifies frequently asked questions on artificial intelligence and keeps you up to date with the latest AI trends. Join us as we delve into groundbreaking research, innovative applications, and emerging technologies that are pushing the boundaries of AI. From the latest trends in ChatGPT and the recent merger of Google Brain and DeepMind, to the exciting developments in generative AI, we’ve got you covered with a comprehensive update on the ever-evolving AI landscape. In today’s episode, we’ll cover optimizing AI interactions with Master GPT-4, including reducing repetition, steering conversations, adjusting creativity, using the frequency penalty setting to diversify language, utilizing logit bias to guide word choices, implementing presence penalty for smoother transitions, adjusting temperature for different levels of creativity in responses, controlling uniqueness with Top_p (Nucleus Sampling), and an introduction to the book “AI Unraveled” which answers frequently asked questions about artificial intelligence.
Hey there! Have you ever heard of GPT-4? It’s an amazing tool developed by OpenAI that uses artificial intelligence to generate text. However, I’ve noticed that some people struggle with it. They find its responses repetitive, its answers too long, and they don’t always get what they’re looking for. That’s why I decided to create a simplified guide to help you master GPT-4.
Introducing “Unlocking GPT-4: A User-Friendly Guide to Optimizing AI Interactions“! This guide is perfect for both AI beginners and those who want to take their GPT-4 experience to the next level. We’ll break down all the complexities of GPT-4 into simple, practical terms, so you can use this powerful tool more effectively and creatively.
In this guide, you’ll learn some key concepts that will improve your interactions with GPT-4. First up, we’ll explore the Frequency Penalty. This technique will help you reduce repetitive responses and make your AI conversations sound more natural. Then, we’ll dive into Logit Bias. You’ll discover how to gently steer the AI towards or away from specific words or topics, giving you more control over the conversation.
Next, we’ll tackle the Presence Penalty. You’ll find out how to encourage the AI to transition smoothly between topics, allowing for more coherent and engaging discussions. And let’s not forget about Temperature! This feature lets you adjust the AI’s creativity level, so you can go from straightforward responses to more imaginative ideas.
Last but not least, we have Top_p, also known as Nucleus Sampling. With this technique, you can control the uniqueness of the AI’s suggestions. You can stick to conventional ideas or venture into out-of-the-box thinking.
So, if you’re ready to become a GPT-4 master, join us on this exciting journey by checking out our guide. Happy optimizing!
Today, I want to talk about a really cool feature in AI called the Frequency Penalty, also known as the Echo Reducer. Its main purpose is to prevent repetitive responses from the AI, so it doesn’t sound like a broken record.
Let me give you a couple of examples to make it crystal clear. If you set the Frequency Penalty to a low setting, you might experience repeated phrases like, “I love pizza. Pizza is great. Did I mention pizza?” Now, I don’t know about you, but hearing the same thing over and over again can get a little tiresome.
But fear not! With a high setting on the Echo Reducer, the AI gets more creative with its language. Instead of the same old repetitive phrases, it starts diversifying its response. For instance, it might say something like, “I love pizza for its gooey cheese, tangy sauce, and crispy crust. It’s a culinary delight.” Now, isn’t that a refreshing change?
So, the Frequency Penalty setting is all about making sure the AI’s responses are varied and don’t become monotonous. It’s like giving the AI a little nudge to keep things interesting and keep the conversation flowing smoothly.
Today, I want to talk about a fascinating tool called the Logit Bias: The Preference Tuner. This tool has the power to nudge AI towards or away from certain words. It’s kind of like gently guiding the AI’s choices, steering it in a particular direction.
Let’s dive into some examples to understand how this works. Imagine we want to nudge the AI away from the word ‘pizza’. In this case, the AI might start focusing on other aspects, like saying, “I enjoy Italian food, especially pasta and gelato.” By de-emphasizing ‘pizza’, the AI’s choices will lean away from this particular word.
On the other hand, if we want to nudge the AI towards the word ‘pizza’, we can use the Logit Bias tool to emphasize it. The AI might then say something like, “Italian cuisine brings to mind the delectable pizza, a feast of flavors in every slice.” By amplifying ‘pizza’, the AI’s choices will emphasize this word more frequently.
The Logit Bias: The Preference Tuner is a remarkable tool that allows us to fine-tune the AI’s language generation by influencing its bias towards or away from specific words. It opens up exciting possibilities for tailoring the AI’s responses to better suit our needs and preferences.
The Presence Penalty, also known as the Topic Shifter, is a feature that helps the AI transition between subjects more smoothly. It prevents the AI from fixating on a single topic for too long, making the conversation more dynamic and engaging.
Let me give you some examples to illustrate how it works. On a low setting, the AI might stick to one idea, like saying, “I enjoy sunny days. Sunny days are pleasant.” In this case, the AI focuses on the same topic without much variation.
However, on a high setting, the AI becomes more versatile in shifting topics. For instance, it could say something like, “Sunny days are wonderful, but I also appreciate the serenity of rainy evenings and the beauty of a snowy landscape.” Here, the AI smoothly transitions from sunny days to rainy evenings and snowy landscapes, providing a diverse range of ideas.
By implementing the Presence Penalty, the AI is encouraged to explore different subjects, ensuring a more interesting and varied conversation. It avoids repetitive patterns and keeps the dialogue fresh and engaging.
So, whether you prefer the AI to stick with one subject or shift smoothly between topics, the Presence Penalty feature gives you control over the flow of conversation, making it more enjoyable and natural.
Today, let’s talk about temperature – not the kind you feel outside, but the kind that affects the creativity of AI responses. Imagine a dial that adjusts how predictable or creative those responses are. We call it the Creativity Dial.
When the dial is set to low temperature, you can expect straightforward answers from the AI. It would respond with something like, “Cats are popular pets known for their independence.” These answers are informative and to the point, just like a textbook.
On the other hand, when the dial is set to high temperature, get ready for some whimsical and imaginative responses. The AI might come up with something like, “Cats, those mysterious creatures, may just be plotting a cute but world-dominating scheme.” These responses can be surprising and even amusing.
So, whether you prefer practical and direct answers that stick to the facts, or you enjoy a touch of imagination and creativity in the AI’s responses, the Creativity Dial allows you to adjust the temperature accordingly.
Give it a spin and see how your AI companion surprises you with its different temperaments.
Today, I want to talk about a fascinating feature called “Top_p (Nucleus Sampling): The Imagination Spectrum” in GPT-4. This feature controls the uniqueness and unconventionality of the AI’s suggestions. Let me explain.
When the setting is on low, you can expect more conventional ideas. For example, it might suggest that vacations are perfect for unwinding and relaxation. Nothing too out of the ordinary here.
But if you crank up the setting to high, get ready for a wild ride! GPT-4 will amaze you with its creative and unique suggestions. It might propose vacation ideas like bungee jumping in New Zealand or attending a silent meditation retreat in the Himalayas. Imagine the possibilities!
By adjusting these settings, you can truly tailor GPT-4 to better suit your needs. Whether you’re seeking straightforward information or craving diverse and imaginative insights, GPT-4 has got you covered.
Remember, don’t hesitate to experiment with these settings. Try different combinations to find the perfect balance for your specific use case. The more you explore, the more you’ll uncover the full potential of GPT-4.
So go ahead and dive into the world of GPT-4. We hope you have an amazing journey discovering all the incredible possibilities it has to offer. Happy exploring!
Are you ready to dive into the fascinating world of artificial intelligence? Well, I’ve got just the thing for you! It’s an incredible book called “AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence.” Trust me, this book is an absolute gem!
Now, you might be wondering where you can get your hands on this treasure trove of knowledge. Look no further, my friend. You can find “AI Unraveled” at popular online platforms like Etsy, Shopify, Apple, Google, and of course, our old faithful, Amazon.
This book is a must-have for anyone eager to expand their understanding of AI. It takes those complicated concepts and breaks them down into easily digestible chunks. No more scratching your head in confusion or getting lost in a sea of technical terms. With “AI Unraveled,” you’ll gain a clear and concise understanding of artificial intelligence.
So, if you’re ready to embark on this incredible journey of unraveling the mysteries of AI, go ahead and grab your copy of “AI Unraveled” today. Trust me, you won’t regret it!
In this episode, we explored optimizing AI interactions by reducing repetition, steering conversations, adjusting creativity, and diving into specific techniques such as the frequency penalty, logit bias, presence penalty, temperature, and top_p (Nucleus Sampling) – all while also recommending the book “AI Unraveled” for further exploration of artificial intelligence. Join us next time on AI Unraveled as we continue to demystify frequently asked questions on artificial intelligence and bring you the latest trends in AI, including ChatGPT advancements and the exciting collaboration between Google Brain and DeepMind. Stay informed, stay curious, and don’t forget to subscribe for more!
🤖🚀 Dive deep into the world of AI as we explore ‘GPTs and LLMs: Pre-Training, Fine-Tuning, Memory, and More!’ Understand the intricacies of how these AI models learn through pre-training and fine-tuning, their operational scope within a context window, and the intriguing aspect of their lack of long-term memory.
🧠 In this article, we demystify:
Pre-Training & Fine-Tuning Methods: Learn how GPTs and LLMs are trained on vast datasets to grasp language patterns and how fine-tuning tailors them for specific tasks.
Context Window in AI: Explore the concept of the context window, which acts as a short-term memory for LLMs, influencing how they process and respond to information.
Lack of Long-Term Memory: Understand the limitations of GPTs and LLMs in retaining information over extended periods and how this impacts their functionality.
Database-Querying Architectures: Discover how some advanced AI models interact with external databases to enhance information retrieval and processing.
PDF Apps & Real-Time Fine-Tuning
Drop your questions and thoughts in the comments below and let’s discuss the future of AI! #GPTsExplained #LLMs #AITraining #MachineLearning #AIContextWindow #AILongTermMemory #AIDatabases #PDFAppsAI”
Welcome to AI Unraveled, the podcast that demystifies frequently asked questions on artificial intelligence and keeps you up to date with the latest AI trends. Join us as we delve into groundbreaking research, innovative applications, and emerging technologies that are pushing the boundaries of AI. From the latest trends in ChatGPT and the recent merger of Google Brain and DeepMind, to the exciting developments in generative AI, we’ve got you covered with a comprehensive update on the ever-evolving AI landscape. In today’s episode, we’ll cover GPTs and LLMs, their pre-training and fine-tuning methods, their context window and lack of long-term memory, architectures that query databases, PDF app’s use of near-realtime fine-tuning, and the book “AI Unraveled” which answers FAQs about AI.
GPTs, or Generative Pre-trained Transformers, work by being trained on a large amount of text data and then using that training to generate output based on input. So, when you give a GPT a specific input, it will produce the best matching output based on its training.
The way GPTs do this is by processing the input token by token, without actually understanding the entire output. It simply recognizes that certain tokens are often followed by certain other tokens based on its training. This knowledge is gained during the training process, where the language model (LLM) is fed a large number of embeddings, which can be thought of as its “knowledge.”
After the training stage, a LLM can be fine-tuned to improve its accuracy for a particular domain. This is done by providing it with domain-specific labeled data and modifying its parameters to match the desired accuracy on that data.
Now, let’s talk about “memory” in these models. LLMs do not have a long-term memory in the same way humans do. If you were to tell an LLM that you have a 6-year-old son, it wouldn’t retain that information like a human would. However, these models can still answer related follow-up questions in a conversation.
For example, if you ask the model to tell you a story and then ask it to make the story shorter, it can generate a shorter version of the story. This is possible because the previous Q&A is passed along in the context window of the conversation. The context window keeps track of the conversation history, allowing the model to maintain some context and generate appropriate responses.
As the conversation continues, the context window and the number of tokens required will keep growing. This can become a challenge, as there are limitations on the maximum length of input that the model can handle. If a conversation becomes too long, the model may start truncating or forgetting earlier parts of the conversation.
Regarding architectures and databases, there are some models that may query a database before providing an answer. For example, a model could be designed to run a database query like “select * from user_history” to retrieve relevant information before generating a response. This is one way vector databases can be used in the context of these models.
There are also architectures where the model undergoes near-realtime fine-tuning when a chat begins. This means that the model is fine-tuned on specific data related to the chat session itself, which helps it generate more context-aware responses. This is similar to how “speak with your PDF” apps work, where the model is trained on specific PDF content to provide relevant responses.
In summary, GPTs and LLMs work by being pre-trained on a large amount of text data and then using that training to generate output based on input. They do this token by token, without truly understanding the complete output. LLMs can be fine-tuned to improve accuracy for specific domains by providing them with domain-specific labeled data. While LLMs don’t have long-term memory like humans, they can still generate responses in a conversation by using the context window to keep track of the conversation history. Some architectures may query databases before generating responses, and others may undergo near-realtime fine-tuning to provide more context-aware answers.
GPTs and Large Language Models (LLMs) are fascinating tools that have revolutionized natural language processing. It seems like you have a good grasp of how these models function, but I’ll take a moment to provide some clarification and expand on a few points for a more comprehensive understanding.
When it comes to GPTs and LLMs, pre-training and token prediction play a crucial role. During the pre-training phase, these models are exposed to massive amounts of text data. This helps them learn to predict the next token (word or part of a word) in a sequence based on the statistical likelihood of that token following the given context. It’s important to note that while the model can recognize patterns in language use, it doesn’t truly “understand” the text in a human sense.
During the training process, the model becomes familiar with these large datasets and learns embeddings. Embeddings are representations of tokens in a high-dimensional space, and they capture relationships and context around each token. These embeddings allow the model to generate coherent and contextually appropriate responses.
However, pre-training is just the beginning. Fine-tuning is a subsequent step that tailors the model to specific domains or tasks. It involves training the model further on a smaller, domain-specific dataset. This process adjusts the model’s parameters, enabling it to generate responses that are more relevant to the specialized domain.
Now, let’s discuss memory and the context window. LLMs like GPT do not possess long-term memory in the same way humans do. Instead, they operate within what we call a context window. The context window determines the amount of text (measured in tokens) that the model can consider when making predictions. It provides the model with a form of “short-term memory.”
For follow-up questions, the model relies on this context window. So, when you ask a follow-up question, the model factors in the previous interaction (the original story and the request to shorten it) within its context window. It then generates a response based on that context. However, it’s crucial to note that the context window has a fixed size, which means it can only hold a certain number of tokens. If the conversation exceeds this limit, the oldest tokens are discarded, and the model loses track of that part of the dialogue.
It’s also worth mentioning that there is no real-time fine-tuning happening with each interaction. The model responds based on its pre-training and any fine-tuning that occurred prior to its deployment. This means that the model does not learn or adapt during real-time conversation but rather relies on the knowledge it has gained from pre-training and fine-tuning.
While standard LLMs like GPT do not typically utilize external memory systems or databases, some advanced models and applications may incorporate these features. External memory systems can store information beyond the limits of the context window. However, it’s important to understand that these features are not inherent to the base LLM architecture like GPT. In some systems, vector databases might be used to enhance the retrieval of relevant information based on queries, but this is separate from the internal processing of the LLM.
In relation to the “speak with your PDF” applications you mentioned, they generally employ a combination of text extraction and LLMs. The purpose is to interpret and respond to queries about the content of a PDF. These applications do not engage in real-time fine-tuning, but instead use the existing capabilities of the model to interpret and interact with the newly extracted text.
To summarize, LLMs like GPT operate within a context window and utilize patterns learned during pre-training and fine-tuning to generate responses. They do not possess long-term memory or real-time learning capabilities during interactions, but they can handle follow-up questions within the confines of their context window. It’s important to remember that while some advanced implementations might leverage external memory or databases, these features are not inherently built into the foundational architecture of the standard LLM.
Are you ready to dive into the fascinating world of artificial intelligence? Well, I’ve got just the thing for you! It’s an incredible book called “AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence.” Trust me, this book is an absolute gem!
Now, you might be wondering where you can get your hands on this treasure trove of knowledge. Look no further, my friend. You can find “AI Unraveled” at popular online platforms like Etsy, Shopify, Apple, Google, and of course, our old faithful, Amazon.
This book is a must-have for anyone eager to expand their understanding of AI. It takes those complicated concepts and breaks them down into easily digestible chunks. No more scratching your head in confusion or getting lost in a sea of technical terms. With “AI Unraveled,” you’ll gain a clear and concise understanding of artificial intelligence.
So, if you’re ready to embark on this incredible journey of unraveling the mysteries of AI, go ahead and grab your copy of “AI Unraveled” today. Trust me, you won’t regret it!
On today’s episode, we explored the power of GPTs and LLMs, discussing their ability to generate outputs, be fine-tuned for specific domains, and utilize a context window for related follow-up questions. We also learned about their limitations in terms of long-term memory and real-time updates. Lastly, we shared information about the book “AI Unraveled,” which provides valuable insights into the world of artificial intelligence. Join us next time on AI Unraveled as we continue to demystify frequently asked questions on artificial intelligence and bring you the latest trends in AI, including ChatGPT advancements and the exciting collaboration between Google Brain and DeepMind. Stay informed, stay curious, and don’t forget to subscribe for more!
Interacting with ChatGPT can feel like talking to an artificial intelligence from the future. Conversations are able to flow naturally, and you don’t even realize you’re interacting with something as powerful as a programmed AI chatbot.
And now, for those of us who want more than just casual banter, there’s an entire advanced guide to mastering the art of chatting with ChatGPT!
So whether you need advice on how to customize your conversations or gain greater insight into what your bot is truly capable of, this guide will help take your robotic conversations up several notches.
Advanced Guide to Interacting with ChatGPT: What is ChatGPT and how does it work
ChatGPT is a large language model based on the GPT-3.5 architecture, trained by OpenAI to generate human-like text in response to user prompts.
It works by analyzing the input it receives, generating a range of possible responses based on that input, and selecting the most appropriate response using natural language processing algorithms.
Advanced Guide to Interacting with ChatGPT: Setting Up Your Account for Optimal Performance
To set up your account for optimal performance with ChatGPT, make sure you have a reliable internet connection and a device that meets the minimum requirements for running the chatbot. You may also want to customize your settings and preferences in the chat interface to better suit your needs.
Advanced Guide to Interacting with ChatGPT: Tips for Interacting with the Bot and Getting the Most Out of It
To get the most out of interacting with ChatGPT, try to be as clear and specific as possible in your prompts and questions, and use natural language rather than shorthand or technical jargon.
You can also experiment with different types of prompts and questions to see what types of responses you get.
Advanced Guide to Interacting with ChatGPT: Maximizing User Experience to Get Better Results
To maximize your user experience and get better results from ChatGPT, try to give as much context as possible when asking questions or making prompts.
You can also use the chatbot’s features and settings to customize the interface, adjust response preferences, and optimize your interactions with the bot.
Advanced Guide to Interacting with ChatGPT: Knowing When to Override the Bot’s Suggestions
It’s important to use your judgment and experience to determine when to override the bot’s suggestions.
While ChatGPT is designed to generate accurate and helpful responses, it’s not always perfect and may sometimes make mistakes or provide incomplete information. In such cases, you may need to rely on your own knowledge and expertise to supplement or correct the bot’s responses.
Djamgatech: Build the skills that’ll drive your career into six figures: Get Djamgatech PRO. Get cloud certified with ease using Djamgatech! Ace AWS Cloud, Azure, and GCP exams today.
Advanced Guide to Interacting with ChatGPT: Troubleshooting Common Issues with ChatGPT
If you encounter common issues with ChatGPT, such as slow or unresponsive performance, error messages, or incorrect responses, there are several troubleshooting steps you can take.
These may include clearing your cache and cookies, updating your browser or device software, checking your internet connection, or adjusting your settings in the chat interface. If these steps don’t resolve the issue, you may need to contact customer support or seek assistance from a technical expert.
Advanced Guide to Interacting with ChatGPT: Effective Prompts, Priming, and Personas
This comprehensive guide aims to help users improve their interaction with ChatGPT by providing advanced insights into prompts, priming, and the use of personas.
Crafting effective prompts can significantly improve the quality and relevance of the generated output.
Be Specific and Clear
Ensure your prompt is explicit and leaves little room for ambiguity. This helps ChatGPT understand your intent and provide a more focused response.
Example:
Basic: “Tell me about batteries.”
Advanced: “Explain the working principle of lithium-ion batteries and their advantages over other battery types.”
Break Down Complex Questions
Break Down Complex Questions For better results, divide complicated questions into smaller, simpler parts. This allows ChatGPT to provide more detailed answers for each aspect.
Example:
Basic: “Explain the history and impact of the internet.”
Advanced (broken down): “Describe the invention of the internet,” followed by, “Discuss the impact of the internet on society and economy.”
Use Contextual Clues
Include contextual information in your prompts to guide ChatGPT towards the desired response.
Example:
Basic: “What was the outcome of the experiment?”
Advanced: “In the 1928 discovery of penicillin by Alexander Fleming, what was the outcome of the experiment and its significance in the field of medicine?”
Request Step-by-Step Explanations
When seeking complex or process-based answers, request step-by-step explanations to ensure the response is organized and easy to understand.
Example:
Basic: “How does photosynthesis work?”
Advanced: “Explain the process of photosynthesis in plants, breaking it down into its primary steps.”
Priming
Priming is the technique of providing additional information to ChatGPT to influence its response. It helps in obtaining more accurate, relevant, or tailored answers.
Set Expectations
Begin your interaction by setting expectations, such as specifying the format or depth of the answer you desire.
Example:
Basic: “What are the benefits of yoga?”
Advanced: “List 5 physical and 5 mental benefits of practicing yoga regularly.”
Establish Context
Provide context to your queries by specifying details such as time, place, or other relevant factors.
Example:
Basic: “What are the best practices in software development?”
Advanced: “What are the top 5 best practices in Agile software development methodologies?”
Limit Response Length
To ensure concise answers, set a constraint on the response length.
Example:
Basic: “Explain the role of mitochondria in cells.”
Advanced: “In 100 words or less, describe the primary function of mitochondria in eukaryotic cells.”
Personas
Personas are fictional identities assigned to ChatGPT to shape its responses. This can enhance the user experience by tailoring the output to specific styles, perspectives, or expertise levels.
Advanced: “You are an expert negotiator. Roleplay a scenario where you teach me techniques to improve my negotiation skills.”
Combine Personas and Priming
Integrate personas and priming to optimize the response and achieve a highly tailored output.
Example:
Basic: “What should I consider when starting a business?”
Advanced: “As a successful entrepreneur, provide a step-by-step guide on the essential factors to consider when starting a new business venture.”
Get Your Outputs in the Form of ASCII Art
While ChatGPT is based around text, you can get it to produce pictures of a sort by asking for ASCII art. That’s the art made up of characters and symbols rather than colors. It won’t win you any prizes, but it’s pretty fun to play around with.
The usual ChatGPT rules apply, in that the more specific you are the better, and you can get the bot to add new elements and take elements away as you go. Remember the limitations of the ASCII art format though—this isn’t a full-blown image editor.
Copy and Paste Text From Other Sources
You don’t have to do all the typing yourself when it comes to ChatGPT. Copy and paste is your friend, and there’s no problem with pasting in text from other sources. While the input limit tops out at around 4,000 words, you can easily split the text you’re sending the bot into several sections and get it to remember what you’ve previously said.
Perhaps one of the best ways of using this approach is to get ChatGPT to simplify text that you don’t understand—the explanation of a difficult scientific concept, for instance. You can also get it to translate text into different languages, write it in a more engaging or fluid style, and so on.
Advanced Guide to Interacting with ChatGPT: Copy and Paste from Other Sources
Provide Examples to Work With
Another way to improve the responses you get from ChatGPT is to give it some data to work with before you ask your question. For instance, you could give it a list of book summaries together with their genre, then ask it to apply the correct genre label to a new summary. Another option would be to tell ChatGPT about activities you enjoy and then get a new suggestion.
There’s no magic combination of words you have to use here. Just use natural language as always, and ChatGPT will understand what you’re getting at. Specify that you’re providing examples at the start of your prompt, then tell the bot that you want a response with those examples in mind.
In the same way that ChatGPT can mimic the style of certain authors that it knows about, it can also play a role: a frustrated salesman, an excitable teenager (you’ll most likely get a lot of emojis and abbreviations back), or the iconic Western star John Wayne.
The types of roles you can play around with are almost endless. These prompts might not score highly in terms of practical applications, but they’re definitely a useful insight into the potential of these AI chatbots.
Get Answers That Are More Than the Sum of Their Parts
Your answers can be seriously improved if you give ChatGPT some ingredients to work with before asking for a response. They could be literal ingredients—suggest a dish from what’s left in the fridge—or they could be anything else.
So don’t just ask for a murder mystery scenario. Also list out the characters who are going to appear. Don’t just ask for ideas of where to go in a city; specify the city you’re going to, the types of places you want to see, and the people you’ll have with you.
Hear Both Sides of a Debate
You’ve no doubt noticed how binary arguments have tended to get online in recent years, so get the help of ChatGPT to add some gray in between the black and white. It’s able to argue both sides of an argument if you ask it to, including both pros and cons.
From politics and philosophy to sports and the arts, ChatGPT is able to sit on the fence quite impressively—not in a vague way, but in a way that can help you understand issues from multiple perspectives.
Conclusion
Mastering effective prompts, priming, and personas will significantly improve your interactions with ChatGPT.
By applying these advanced techniques, you will obtain more accurate, relevant, and engaging responses tailored to your needs.
Remember to:
Craft specific and clear prompts
Break down complex questions into smaller parts
Include contextual clues in your prompts
Request step-by-step explanations
Set expectations and establish context through priming
Limit response length when necessary
Define personas and specify language and tone
Use roleplay scenarios to create engaging content
Combine personas and priming for highly tailored outputs
By implementing these advanced strategies, you will become more effective in using ChatGPT and enjoy a highly customized and valuable experience.
Advanced Guide to Interacting with ChatGPT: Conclusion
In conclusion, ChatGPT is a powerful conversational A.I. which can respond to requests and natural language queries with accuracy and speed.
By closely following the above advanced guide for how to interact with ChatGPT, users can achieve some truly remarkable results.
From connecting it to your application or website in the simplest way possible, to knowing how to craft interesting queries that ChatGPT will handle with ease and deliver relevant answers, this advice should be a great starting point for anyone looking to utilize this amazing technology.
It’s an opportunity never before available to developers and consumers alike—an opportunity not to be overlooked!So if you’re looking for an easy way to integrate A.I. into your platform or simply want an intelligent conversation partner then look no further than ChatGPT!
But this guide provides only a proposed framework for acclimating yourself with the technology—the ultimate decision on how much you get out of it is ultimately up to you! And so the important question remains: How do you get the most out of ChatGPT?
Advanced Guide to Interacting with ChatGPT: #ChatGPT through the lens of Dunning-Kruger effect, most users are still on the left side of the curve.
Can Chat GPT-4 replace software engineers? by Jerome Cukier
I’ve been using ChatGPT with GPT-4 right when it was announced.
I had been using ChatGPT with GPT-3 as my junior engineer for coding personal projects for the past few weeks. ChatGPT is pretty good at executing a well-delineated coding task. The output typically needs a little tweaking but it is great to achieve tasks in domain I am not very familiar, and it saves me a lot of time from reading documentation. Interestingly, when coupled with GitHub copilot (also powered by GPT), some of the problems in the generated solution become apparent / fix themselves when actually writing the suggested code in an editor.
From my point of view as a front-end engineer, the web API is extremely wide in scope and nobody is able to quickly answer questions on “how do you do X” or “how do you do Y” on the more obscure aspects of the API. Well, ChatGPT can and while it’s not always right, directionally it’s usually pretty much on the money.
With GPT-4, I am much more confident in letting it handle slightly larger projects.
It’s like my junior engineer got promoted! Yesterday, I asked it to create a VS Code extension that did a specific task. I wrote VS code extensions in the past, I love this kind of project but tbh I forgot everything about how to get started there. ChatGPT created my extension from scratch. Now, it didn’t work, but the scaffolding, which I think is the part I would dread the most if I had to create it from scratch, was perfect. I also asked it to create a small interactive demo with canvas.
Again, the demo itself didn’t work as intended and what exactly is wrong is going to take a little bit of time to figure out but the overall architecture of the app is solid. One thing that stroke me as odd is that the generated code doesn’t have any comments, even though my understanding is that GPT-4 will translate the requirements into intents which will then be transformed into code, so, we could have a description of the purpose of the classes / methods.
ChatGPT is a fantastic accelerant to coding tasks.
In a world where all software engineers do is when given precise requirements, produce code that implements these requirements to a T, then for most of us it would be time to explore other careers.
However, there are obvious concerns about an application entirely (or mostly) generated through ChatGPT. Let’s pause for a minute and think of what it is to build an app or a service. As an analogy, let’s try to think what it is to build a house. As a person who “experiences” a house, you could describe it as a succession of spaces. It has a front door, then it has a lobby, then there is a kitchen that has certain appliances in it, there is a living room, etc, etc. Now, let’s imagine a robot that would build a house space by space.
Again – first the door, according to specs. Perfect, looks like a door. Then a lobby.
Then a hallway. There’s a door in the hallway to a kitchen. There’s a door in the hallway to a living room. Wait. Is our house in construction going to make any sense? is it going to form a convex, continuous shape? am I going to be able to build a 2nd floor on top, a basement, etc. and have everything working well together?
The same goes for systems implemented with chatGPT feature by feature. The resulting code is going to be very brittle, and eventually need a substantial refactor at every step. One way to avoid this (same for the house metaphor) is come up with a sensible high-level plan or architecture. That’s still the realm of the human engineer. The other task is going to be able to evaluate what’s being generated at every step, and coming up with systems to make sure that we (humans + robots) are still building the right thing.
Anyway. Humans are not going away anytime soon and ChatGPT/GPT-4 are not at a stage where they can build a complex system from the ground up. But the nature of the work is changing and it’s changing more rapidly than most of us thought.
Chatgpt Plugins Week 1. GPT-4 Week 2. Another absolutely insane week in AI. One of the biggest advancements in human history. Source.
Some pretty famous people (Musk, Wozniak + others) have signed a letter (?) to pause the work done on AI systems more powerful than gpt4. Very curious to hear what people think about this. On one hand I can understand the sentiment, but hypothetically even if this did happen, will this actually accomplish anything? I somehow doubt it tbh [Link]
Here is a concept of Google Brain from back in 2006 (!). You talk with Google and it lets you search for things and even pay for them. Can you imagine if Google worked on something like this back then? Absolutely crazy to see [Link]
OpenAI has invested into ‘NEO’, a humanoid robot by 1X. They believe it will have a big impact on the future of work. ChatGPT + robots might be coming sooner than expected [Link]. They want to create human-level dexterous robots [Link]
There’s a ‘code interpreter’ for ChatGPT and its so good, legit could do entire uni assignments in less than an hour. I would’ve loved this in uni. It can even scan dB’s and analyze the data, create visualisations. Basically play with data using english. Also handles uploads and downloads [Link]
AI is coming to Webflow. Build components instantly using AI. Particularly excited for this since I build websites for people using Webflow. If you need a website built I might be able to help 👀 [Link]
ChatGPT Plugin will let you find a restaurant, recommend a recipe and build an ingredient list and let you purchase them using Instacart [Link]
Expedia showcased their plugin and honestly already better than any website to book flights. It finds flights, resorts and things to do. I even built a little demo for this before plugins were released 😭 [Link]. The plugin just uses straight up English. We’re getting to a point where if you can write, you can create [Link]
The Retrieval plugin gives ChatGPT memory. Tell it anything and it’ll remember. So if you wear a mic all day, transcribe the audio and give it to ChatGPT, it’ll remember pretty much anything and everything you say. Remember anything instantly. Crazy use cases for something like this [Link]
ChadCode plugin lets you do search across your files and create issues into github instantly. The potential for something like this is crazy. Changes coding forever imo [Link]
The first GPT-4 built iOS game and its actually on the app store. Mate had no experience with Swift, all code generated by AI. Soon the app store will be flooded with AI built games, only a matter of time [Link]
Real time detection of feelings with AI. Honestly not sure what the use cases are but I can imagine people are going to do crazy things with stuff like this [Link]
Voice chat with LLama on you Macbook Pro. I wrote about this in my newsletter, we won’t be typing for much longer imo, we’ll just talk to the AI like Jarvis [Link]
People in the Midjourney subreddit have been making images of an earthquake that never happened and honestly the images look so real its crazy [Link]
This is an interesting comment by Mark Cuban. He suggests maybe people with liberal arts majors or other degrees could be prompt engineers to train models for specific use cases and task. Could make a lot of money if this turns out to be a use case. Keen to hear peoples thoughts on this one [Link]
Emad Mostaque, Ceo of Stability AI estimates building a GPT-4 competitor would be roughly 200-300 million if the right people are there [Link]. He also says it would take at least 12 months to build an open source GPT-4 and it would take crazy focus and work [Link]
• A 3D artist talks about how their job has changed since Midjourney came out. He can now create a character in 2-3 days compared to weeks before. They hate it but even admit it does a better job than them. It’s honestly sad to read because I imagine how fun it is for them to create art. This is going to affect a lot of people in a lot of creative fields [Link]
This lad built an entire iOS app including payments in a few hours. Relatively simple app but sooo many use cases to even get proof of concepts out in a single day. Crazy times ahead [Link]
Someone is learning how to make 3D animations using AI. This will get streamlined and make some folks a lot of money I imagine [Link]
These guys are building an ear piece that will give you topics and questions to talk about when talking to someone. Imagine taking this into a job interview or date 💀 [Link]
What if you could describe the website you want and AI just makes it. This demo looks so cool dude website building is gonna be so easy its crazy [Link]
Wear glasses that will tell you what to say by listening in to your conversations. When this tech gets better you won’t even be able to tell if someone is being AI assisted or not [Link]
The Pope is dripped tf out. I’ve been laughing at this image for days coz I actually thought it was real the first time I saw it 🤣 [Link]
Levi’s wants to increase their diversity by showcasing more diverse models, except they want to use AI to create the images instead of actually hiring diverse models. I think we’re going to see much more of this tbh and it’s going get a lot worse, especially for models because AI image generators are getting crazy good [Link]. Someone even created an entire AI modelling agency [Link]
ChatGPT built a tailwind landing page and it looks really neat [Link]
This investor talks about how he spoke to a founder who literally took all his advice and fed it to gpt-4. They even made ai generated answers using eleven labs. Hilarious shit tbh [Link]
Someone hooked up GPT-4 to Blender and it looks crazy [Link]
This guy recorded a verse and made Kanye rap it [Link]
GPT4 saved this dogs life. Doctors couldn’t find what was wrong with the dog and gpt4 suggested possible issues and turned out to be right. Crazy stuff [Link]
A research paper suggests you can improve gpt4 performance by 30% by simply having it consider “why were you wrong”. It then keeps generating new prompts for itself taking this reflection into account. The pace of learning is really something else [Link]
You can literally asking gpt4 for a plugin idea, have it code it, then have it put it up on replit. It’s going to be so unbelievably easy to create a new type of single use app soon, especially if you have a niche use case. And you could do this with practically zero coding knowledge. The technological barrier to solving problems using code is disappearing before our eyes [Link]
A soon to be open source AI form builder. Pretty neat [Link]
Create entire videos of talking AI people. When this gets better we wont be able to distinguish between real and AI [Link]
Someone made a cityscape with AI then asked ChatGPT to write the code to port it into VR. From words to worlds [Link]
Someone got GPT4 to write an entire book. It’s not amazing but its still a whole book. I imagine this will become much easier with plugins and so much better with gpt5 & gpt6 [Link]
Make me an app – Literally ask for an app and have it built. Unbelievable software by Replit. When AI gets better this will be building whole, functioning apps with a single prompt. World changing stuff [Link]
Langchain is building open source AI plugins, they’re doing great work in the open source space. Can’t wait to see where this goes [Link]. Another example of how powerful and easy it is to build on Langchain [Link]
Tesla removed sensors and are just using cameras + AI [Link]
GPT4 is so good at understanding different human emotions and emotional states it can even effectively manage a fight between a couple. We’ve already seen many people talk about how much its helped them for therapy. Whether its good, ethical or whatever the fact is this has the potential to help many people without being crazy expensive. Someone will eventually create a proper company out of this and make a gazillion bucks [Link]
You can use plugins to process video clips, so many websites instantly becoming obsolete [Link] [Link]
The way you actually write plugins is describing an api in plain english. Chatgpt figures out the rest [Link]. Don’t believe me? Read the docs yourself [Link]
This lad created an iOS shortcut that replaces Siri with Chatgpt [Link]
I’m sure we’ve all already seen the paper saying how gpt4 shows sparks of AGI but I’ll link it anyway. “we believe that it could reasonably be viewed as an early (yet still incomplete) version of an artificial general intelligence (AGI) system.” [Link]
This lad created an AI agent that, given a task, creates sub tasks for itself and comes up with solutions for them. It’s actually crazy to see this in action, I highly recommend watching this clip [Link]. Here’s the link to the “paper” and his summary of how it works [Link]
Someone created a tool that listens to your job interview and tells you what to say. Rip remote interviews [Link]
Perplexity just released their app, a Chatgpt alternative on your phone. Instant answers + cited sources [Link]
The author writes about the implications of all the crazy new advancements happening in AI for people who don’t have the time to do their own research. If you’d like to stay in the know you can sub here 🙂
Advanced Guide to Interacting with ChatGPT: ChatGPT FAQ
ChatGPT is a chatbot that uses the GPT-3.5/GPT-4 language model by OpenAI to generate responses to user input. It has been trained on a large dataset of human conversation and is able to understand and respond to a wide range of topics and questions. ChatGPT is not a real person, but it is designed to be able to hold a conversation in a way that is similar to how a human would. It can provide information, answer questions, and even carry out simple tasks.
I recommend reading or at least skimming the following page: https://openai.com/terms/ It has been quoted several times in this post.
A: ChatGPT is available to answer questions and have conversations with users at any time. The site and service suffer periodic outages due to sudden and/or excessive demand. In this case please return later and try again.
Q: ChatGPT told me something that happened after 2021. How?
A: ChatGPT has LIMITED knowledge of events after 2021. Not no knowledge. Limited doesn’t mean zero.
Q3: How accurate is ChatGPT?
A: ChatGPT is trained on a large dataset of human conversation and human generated text. It is able to understand and respond to a wide range of topics and questions. However, it is a machine learning model, and there may be times when it does not understand what you are saying or does not provide a satisfactory response. ChatGPT cannot be relied upon to produce accurate factual information.
Q4: Can ChatGPT understand and talk in multiple languages?
A: Yes. Users have reported ChatGPT being very capable in many languages. Not all languages are handled flawlessly but it’s very impressive.
Q5: Is ChatGPT able to learn from its conversations with users?
A: ChatGPT is not able to learn from individual conversations with users. While ChatGPT is able to remember what the user has said earlier in the conversation, there is a limit to how much information it can retain. The model is able to reference up to approximately 3000 words (or 4000 tokens) from the current conversation – any information beyond that is not stored.
Q6: Can I ask ChatGPT personal questions?
A: ChatGPT does not have personal experiences or feelings. It is not able to provide personal insights or opinions as it does not have personal opinions. However, it can provide information and assist with tasks on a wide range of topics.
Q7: Can ChatGPT do mathematics?
A: Not with any reliability. ChatGPT was not designed to do mathematics. It may be able to explain concepts and workflows but should not be relied upon to do any mathematical calculations. It can even design programs that do work as effective and accurate calculators. HINT: Try asking ChatGPT to write a calculator program in C# and use an online compiler to test it. Those interested in programming may want to look into this: https://beta.openai.com/docs/guides/code/introduction.
Q8: What can I do with ChatGPT?
A: ChatGPT can write stories, shooting scripts, design programs, write programs, write technical documentation, write autopsy reports, rewrite articles, and write fake interviews. It can convert between different text styles. HINT: Try giving it a few paragraphs of a book and ask for it to be converted into a screenplay. ChatGPT can even pretend to be a character so long as you provide the appropriate details.
Q9: Can I be banned from using ChatGPT?
A: It is possible for users to be banned from using ChatGPT if they violate the terms of service or community guidelines of the platform. These guidelines typically outline acceptable behaviour and may include things like spamming, harassment, or other inappropriate actions. If you are concerned about being banned, you should make sure to follow the guidelines and behave in a respectful and appropriate manner while using the platform.
Q10: Does ChatGPT experience any bias?
A: It’s certainly possible. As ChatGPT was trained on text from the internet it is likely that it will be biased towards producing ouput consistent with that. If internet discussion tends towards a bias it is possible that ChatGPT will share that bias. ChatGPT automatically attempts to prevent output that engages in discrimination on the basis of protected characteristics though this is not a perfect filter.
Q11: Who can view my conversations?
A: Staff at OpenAI can view all of your conversations. Members of the public cannot. Here is a direct quote from OpenAI: ”As part of our commitment to safe and responsible AI, we review conversations to improve our systems and to ensure the content complies with our policies and safety requirements.” Quote paraphrased from https://openai.com/terms/
Q12: Are there any good apps for ChatGPT?
A: That’s possible. OpenAI have released an API so other people can now build ChatGPT apps. It’s up to you to look around and see what you like. Please suggest some in the comments.
Q13: How does ChatGPT know what time it is?
A: While a lot of ChatGPT’s inner workings are hidden from the public there has been a lot of investigation into its capabilities and processes. The current theory is that upon starting a new conversation with ChatGPT a hidden message is sent that contains several details. This includes the current time.
Q14: Can I use the output of ChatGPT and pretend I wrote it?
A: No. This is in violation of their terms. “You may not represent that output from the Services was human-generated when it is not” Quote paraphrased from https://openai.com/terms/
Q15: Can I have multiple accounts?
A: Yes you can. On the ChatGPT public testing service, there are no restrictions on the number of accounts belonging to any one user.
Q16: Do I have to follow the terms and services of ChatGPT?
A: Technically no but, if you want to keep your account, yes. They’re not a legal mandate however OpenAI does have the right to terminate your account and access to their services in the case that you violate the terms you agreed to upon account creation.
Q17: ChatGPT as a mobile App
A: Read Aloud For Me provides ChatGPT inside their mobile PWA, so you don’t have to use a browser. Get it here: (iOs, Android Google, Amazon Android, Windows)
Read Aloud For Me – Read aloud and translate text, photos, pdfs documents for me in my chosen language. Speech Synthesizer, Take Notes and Save via voice, No tracking, Secure, Read For Me without tracking me. Advanced Guide to Interacting with ChatGPT.
If you’re not learning ChatGPT, you’re falling behind. 10 insanely valuable prompts to help you master #ChatGPT:
Demystify tough subjects in no time:
Prompt: “Simplify [insert topic] into straightforward, easy-to-understand language suitable for beginners.”
Make ChatGPT adopt your writing style.
Prompt: “Study the writing style in the given text and craft a 200-word piece on [insert topic]”
Let ChatGPT handle customer correspondence.
Prompt: “As a customer support specialist, respond to a customer unhappy with a late delivery. Write a 100-word apology email that includes a 20% discount.”
Teach ChatGPT to develop prompts for its own use.
Prompt: “You are an AI designed to support [insert profession]. Create a list of the top 10 prompts related to [insert topic].”
Ask ChatGPT to guide you in mastering ChatGPT.
Prompt: “Develop a beginner’s guide to ChatGPT, focusing on prompts, priming, and personas. Incorporate examples where needed, and limit the guide to 500 words.”
Consult ChatGPT for problem resolution.
Prompt: “Imagine you’re an expert career coach. I [explain issue]. Suggest a list of 5 potential solutions to help resolve this problem.”
Employ ChatGPT for recruitment:
Prompt: “I’m looking to hire [insert job] but lack experience in hiring [insert job]. Recommend 10 online communities and job boards where I can find suitable candidates for this position.”
Conquer writer’s block.
Prompt: “I’m working on a blog post about [insert topic] but need an attention-grabbing title. Propose 5 possible blog titles for this article.”
Ace your job interview.
Prompt: “I’m preparing for an interview for [enter position]. Compile a comprehensive list of potential interview questions, along with brief answers to each.”
Ignite innovative ideas.
Prompt: “I want to achieve [insert task or goal]. Generate [insert desired outcome] related to [insert task or goal].”
AI and its related fields — such as machine learning and data science — are becoming an increasingly important parts of our lives, so it stands to reason why AI Frequently Asked Questions (FAQs)are a popular choice among many people. AI has the potential to simplify tedious and repetitive tasks while enriching our everyday lives with extraordinary insights – but at the same time, it can also be confusing and even intimidating.
This AI FAQs offer valuable insight into the mechanics of AI, helping us become better-informed about AI’s capabilities, limitations, and ethical considerations. Ultimately, AI FAQs provide us with a deeper understanding of AI as well as a platform for healthy debate.
Artificial Intelligence Frequently Asked Questions: How do you train AI models?
Training AI models involves feeding large amounts of data to an algorithm and using that data to adjust the parameters of the model so that it can make accurate predictions. This process can be supervised, unsupervised, or semi-supervised, depending on the nature of the problem and the type of algorithm being used.
Artificial Intelligence Frequently Asked Questions: Will AI ever be conscious?
Consciousness is a complex and poorly understood phenomenon, and it is currently not possible to say whether AI will ever be conscious. Some researchers believe that it may be possible to build systems that have some form of subjective experience, while others believe that true consciousness requires biological systems.
Artificial Intelligence Frequently Asked Questions: How do you do artificial intelligence?
Artificial intelligence is a field of computer science that focuses on building systems that can perform tasks that typically require human intelligence, such as perception, reasoning, and learning. There are many different approaches to building AI systems, including machine learning, deep learning, and evolutionary algorithms, among others.
Artificial Intelligence Frequently Asked Questions: How do you test an AI system?
Testing an AI system involves evaluating its performance on a set of tasks and comparing its results to human performance or to a previously established benchmark. This process can be used to identify areas where the AI system needs to be improved, and to ensure that the system is safe and reliable before it is deployed in real-world applications.
Artificial Intelligence Frequently Asked Questions: Will AI rule the world?
There is no clear evidence that AI will rule the world. While AI systems have the potential to greatly impact society and change the way we live, it is unlikely that they will take over completely. AI systems are designed and programmed by humans, and their behavior is ultimately determined by the goals and values programmed into them by their creators.
Artificial Intelligence Frequently Asked Questions: What is artificial intelligence?
Artificial intelligence is a field of computer science that focuses on building systems that can perform tasks that typically require human intelligence, such as perception, reasoning, and learning. The field draws on techniques from computer science, mathematics, psychology, and other disciplines to create systems that can make decisions, solve problems, and learn from experience.
Artificial Intelligence Frequently Asked Questions: How AI will destroy humanity?
The idea that AI will destroy humanity is a popular theme in science fiction, but it is not supported by the current state of AI research. While there are certainly concerns about the potential impact of AI on society, most experts believe that these effects will be largely positive, with AI systems improving efficiency and productivity in many industries. However, it is important to be aware of the potential risks and to proactively address them as the field of AI continues to evolve.
Artificial Intelligence Frequently Asked Questions: Can Artificial Intelligence read?
Yes, in a sense, some AI systems can be trained to recognize text and understand the meaning of words, sentences, and entire documents. This is done using techniques such as optical character recognition (OCR) for recognizing text in images, and natural language processing (NLP) for understanding and generating human-like text.
However, the level of understanding that these systems have is limited, and they do not have the same level of comprehension as a human reader.
Artificial Intelligence Frequently Asked Questions: What problems do AI solve?
AI can solve a wide range of problems, including image recognition, natural language processing, decision making, and prediction. AI can also help to automate manual tasks, such as data entry and analysis, and can improve efficiency and accuracy.
Artificial Intelligence Frequently Asked Questions: How to make a wombo AI?
To make a “wombo AI,” you would need to specify what you mean by “wombo.” AI can be designed to perform various tasks and functions, so the steps to create an AI would depend on the specific application you have in mind.
Artificial Intelligence Frequently Asked Questions: Can Artificial Intelligence go rogue?
In theory, AI could go rogue if it is programmed to optimize for a certain objective and it ends up pursuing that objective in a harmful manner. However, this is largely considered to be a hypothetical scenario and there are many technical and ethical considerations that are being developed to prevent such outcomes.
Artificial Intelligence Frequently Asked Questions: How do you make an AI algorithm?
There is no one-size-fits-all approach to making an AI algorithm, as it depends on the problem you are trying to solve and the data you have available.
However, the general steps include defining the problem, collecting and preprocessing data, selecting and training a model, evaluating the model, and refining it as necessary.
Artificial Intelligence Frequently Asked Questions: How to make AI phone case?
To make an AI phone case, you would likely need to have knowledge of electronics and programming, as well as an understanding of how to integrate AI algorithms into a device.
Artificial Intelligence Frequently Asked Questions: Are humans better than AI?
It is not accurate to say that humans are better or worse than AI, as they are designed to perform different tasks and have different strengths and weaknesses. AI can perform certain tasks faster and more accurately than humans, while humans have the ability to reason, make ethical decisions, and have creativity.
Artificial Intelligence Frequently Asked Questions: Will AI ever be conscious?
The question of whether AI will ever be conscious is a topic of much debate and speculation within the field of AI and cognitive science. Currently, there is no consensus among experts about whether or not AI can achieve consciousness.
Consciousness is a complex and poorly understood phenomenon, and there is no agreed-upon definition or theory of what it is or how it arises.
Some researchers believe that consciousness is a purely biological phenomenon that is dependent on the physical structure and processes of the brain, while others believe that it may be possible to create artificial systems that are capable of experiencing subjective awareness and self-reflection.
However, there is currently no known way to create a conscious AI system. While some AI systems can mimic human-like behavior and cognitive processes, they are still fundamentally different from biological organisms and lack the subjective experience and self-awareness that are thought to be essential components of consciousness.
That being said, AI technology is rapidly advancing, and it is possible that in the future, new breakthroughs in neuroscience and cognitive science could lead to the development of AI systems that are capable of experiencing consciousness.
However, it is important to note that this is still a highly speculative and uncertain area of research, and there is no guarantee that AI will ever be conscious in the same way that humans are.
Artificial Intelligence Frequently Asked Questions: Is Excel AI?
Excel is not AI, but it can be used to perform some basic data analysis tasks, such as filtering and sorting data and creating charts and graphs.
An example of an intelligent automation solution that makes use of AI and transfers files between folders could be a system that uses machine learning algorithms to classify and categorize files based on their content, and then automatically moves them to the appropriate folders.
What is an example of an intelligent automation solution that makes use of artificial intelligence transferring files between folders?
An example of an intelligent automation solution that uses AI to transfer files between folders could be a system that employs machine learning algorithms to classify and categorize files based on their content, and then automatically moves them to the appropriate folders.
Artificial Intelligence Frequently Asked Questions: How do AI battles work in MK11?
The specific details of how AI battles work in MK11 are not specified, as it likely varies depending on the game’s design and programming. However, in general, AI opponents in fighting games can be designed to use a combination of pre-determined strategies and machine learning algorithms to react to the player’s actions in real-time.
Artificial Intelligence Frequently Asked Questions: Is pattern recognition a part of artificial intelligence?
Yes, pattern recognition is a subfield of artificial intelligence (AI) that involves the development of algorithms and models for identifying patterns in data. This is a crucial component of many AI systems, as it allows them to recognize and categorize objects, images, and other forms of data in real-world applications.
Artificial Intelligence Frequently Asked Questions: How do I use Jasper AI?
The specifics on how to use Jasper AI may vary depending on the specific application and platform. However, in general, using Jasper AI would involve integrating its capabilities into your system or application, and using its APIs to access its functions and perform tasks such as natural language processing, decision making, and prediction.
Artificial Intelligence Frequently Asked Questions: Is augmented reality artificial intelligence?
Augmented reality (AR) can make use of artificial intelligence (AI) techniques, but it is not AI in and of itself. AR involves enhancing the real world with computer-generated information, while AI involves creating systems that can perform tasks that typically require human intelligence, such as image recognition, decision making, and natural language processing.
Artificial Intelligence Frequently Asked Questions: Does artificial intelligence have rights?
No, artificial intelligence (AI) does not have rights as it is not a legal person or entity. AI is a technology and does not have consciousness, emotions, or the capacity to make decisions or take actions in the same way that human beings do. However, there is ongoing discussion and debate around the ethical considerations and responsibilities involved in creating and using AI systems.
Artificial Intelligence Frequently Asked Questions: What is generative AI?
Generative AI is a branch of artificial intelligence that involves creating computer algorithms or models that can generate new data or content, such as images, videos, music, or text, that mimic or expand upon the patterns and styles of existing data.
Generative AI models are trained on large datasets using deep learning techniques, such as neural networks, and learn to generate new data by identifying and emulating patterns, structures, and relationships in the input data.
Some examples of generative AI applications include image synthesis, text generation, music composition, and even chatbots that can generate human-like conversations. Generative AI has the potential to revolutionize various fields, such as entertainment, art, design, and marketing, and enable new forms of creativity, personalization, and automation.
How important do you think generative AI will be for the future of development, in general, and for mobile? In what areas of mobile development do you think generative AI has the most potential?
Generative AI is already playing a significant role in various areas of development, and it is expected to have an even greater impact in the future. In the realm of mobile development, generative AI has the potential to bring a lot of benefits to developers and users alike.
One of the main areas of mobile development where generative AI can have a significant impact is user interface (UI) and user experience (UX) design. With generative AI, developers can create personalized and adaptive interfaces that can adjust to individual users’ preferences and behaviors in real-time. This can lead to a more intuitive and engaging user experience, which can translate into higher user retention and satisfaction rates.
Another area where generative AI can make a difference in mobile development is in content creation. Generative AI models can be used to automatically generate high-quality and diverse content, such as images, videos, and text, that can be used in various mobile applications, from social media to e-commerce.
Furthermore, generative AI can also be used to improve mobile applications’ performance and efficiency. For example, it can help optimize battery usage, reduce network latency, and improve app loading times by predicting and pre-loading content based on user behavior.
Overall, generative AI has the potential to bring significant improvements and innovations to various areas of mobile development, including UI/UX design, content creation, and performance optimization. As the technology continues to evolve, we can expect to see even more exciting applications and use cases emerge in the future.
How do you see the role of developers evolving as a result of the development and integration of generative AI technologies? How could it impact creativity, job requirements and skill sets in software development?
The development and integration of generative AI technologies will likely have a significant impact on the role of developers and the software development industry as a whole. Here are some ways in which generative AI could impact the job requirements, skill sets, and creativity of developers:
New skills and knowledge requirements: As generative AI becomes more prevalent, developers will need to have a solid understanding of machine learning concepts and techniques, as well as experience with deep learning frameworks and tools. This will require developers to have a broader skill set that includes both software development and machine learning.
Greater focus on data: Generative AI models require large amounts of data to be trained, which means that developers will need to have a better understanding of data collection, management, and processing. This could lead to the emergence of new job roles, such as data engineers, who specialize in preparing and cleaning data for machine learning applications.
More creativity and innovation: Generative AI has the potential to unlock new levels of creativity and innovation in software development. By using AI-generated content and models, developers can focus on higher-level tasks, such as designing user experiences and optimizing software performance, which could lead to more innovative and user-friendly products.
Automation of repetitive tasks: Generative AI can be used to automate many of the repetitive tasks that developers currently perform, such as writing code and testing software. This could lead to increased efficiency and productivity, allowing developers to focus on more strategic and value-added tasks.
Overall, the integration of generative AI technologies is likely to lead to a shift in the role of developers, with a greater emphasis on machine learning and data processing skills. However, it could also open up new opportunities for creativity and innovation, as well as automate many repetitive tasks, leading to greater efficiency and productivity in the software development industry.
Do you have any concerns about using generative AI in mobile development work? What are they?
As with any emerging technology, there are potential concerns associated with the use of generative AI in mobile development. Here are some possible concerns to keep in mind:
Bias and ethics: Generative AI models are trained on large datasets, which can contain biases and reinforce existing societal inequalities. This could lead to AI-generated content that reflects and perpetuates these biases, which could have negative consequences for users and society as a whole. Developers need to be aware of these issues and take steps to mitigate bias and ensure ethical use of AI in mobile development.
Quality control: While generative AI can automate the creation of high-quality content, there is a risk that the content generated may not meet the required standards or be appropriate for the intended audience. Developers need to ensure that the AI-generated content is of sufficient quality and meets user needs and expectations.
Security and privacy: Generative AI models require large amounts of data to be trained, which raises concerns around data security and privacy. Developers need to ensure that the data used to train the AI models is protected and that user privacy is maintained.
Technical limitations: Generative AI models are still in the early stages of development, and there are limitations to what they can achieve. For example, they may struggle to generate content that is highly specific or nuanced. Developers need to be aware of these limitations and ensure that generative AI is used appropriately in mobile development.
Overall, while generative AI has the potential to bring many benefits to mobile development, developers need to be aware of the potential concerns and take steps to mitigate them. By doing so, they can ensure that the AI-generated content is of high quality, meets user needs, and is developed in an ethical and responsible manner.
Artificial Intelligence Frequently Asked Questions: How do you make an AI engine?
Making an AI engine involves several steps, including defining the problem, collecting and preprocessing data, selecting and training a model, evaluating the model, and refining it as needed. The specific approach and technologies used will depend on the problem you are trying to solve and the type of AI system you are building. In general, developing an AI engine requires knowledge of computer science, mathematics, and machine learning algorithms.
Artificial Intelligence Frequently Asked Questions: Which exclusive online concierge service uses artificial intelligence to anticipate the needs and tastes of travellers by analyzing their spending patterns?
There are a number of travel and hospitality companies that are exploring the use of AI to provide personalized experiences and services to their customers based on their preferences, behavior, and spending patterns.
Artificial Intelligence Frequently Asked Questions: How to validate an artificial intelligence?
To validate an artificial intelligence system, various testing methods can be used to evaluate its performance, accuracy, and reliability. This includes data validation, benchmarking against established models, testing against edge cases, and validating the output against known outcomes. It is also important to ensure the system is ethical, transparent, and accountable.
Artificial Intelligence Frequently Asked Questions: When leveraging artificial intelligence in today’s business?
When leveraging artificial intelligence in today’s business, companies can use AI to streamline processes, gain insights from data, and automate tasks. AI can also help improve customer experience, personalize offerings, and reduce costs. However, it is important to ensure that the AI systems used are ethical, secure, and transparent.
Artificial Intelligence Frequently Asked Questions: How are the ways AI learns similar to how you learn?
AI learns in a similar way to how humans learn through experience and repetition. Like humans, AI algorithms can recognize patterns, make predictions, and adjust their behavior based on feedback. However, AI is often able to process much larger volumes of data at a much faster rate than humans.
Artificial Intelligence Frequently Asked Questions: What is the fear of AI?
The fear of AI, often referred to as “AI phobia” or “AI anxiety,” is the concern that artificial intelligence could pose a threat to humanity. Some worry that AI could become uncontrollable, make decisions that harm humans, or even take over the world.
However, many experts argue that these fears are unfounded and that AI is just a tool that can be used for good or bad depending on how it is implemented.
Artificial Intelligence Frequently Asked Questions: How have developments in AI so far affected our sense of what it means to be human?
Developments in AI have raised questions about what it means to be human, particularly in terms of our ability to think, learn, and create.
Some argue that AI is simply an extension of human intelligence, while others worry that it could eventually surpass human intelligence and create a new type of consciousness.
Artificial Intelligence Frequently Asked Questions: How to talk to artificial intelligence?
To talk to artificial intelligence, you can use a chatbot or a virtual assistant such as Siri or Alexa. These systems can understand natural language and respond to your requests, questions, and commands. However, it is important to remember that these systems are limited in their ability to understand context and may not always provide accurate or relevant responses.
Artificial Intelligence Frequently Asked Questions: How to program an AI robot?
To program an AI robot, you will need to use specialized programming languages such as Python, MATLAB, or C++. You will also need to have a strong understanding of robotics, machine learning, and computer vision. There are many resources available online that can help you learn how to program AI robots, including tutorials, courses, and forums.
Artificial Intelligence Frequently Asked Questions: Will artificial intelligence take away jobs?
Artificial intelligence has the potential to automate many jobs that are currently done by humans. However, it is also creating new jobs in fields such as data science, machine learning, and robotics. Many experts believe that while some jobs may be lost to automation, new jobs will be created as well.
Which type of artificial intelligence can repeatedly perform tasks?
The type of artificial intelligence that can repeatedly perform tasks is called narrow or weak AI. This type of AI is designed to perform a specific task, such as playing chess or recognizing images, and is not capable of general intelligence or human-like reasoning.
Artificial Intelligence Frequently Asked Questions:Has any AI become self-aware?
No, there is currently no evidence that any AI has become self-aware in the way that humans are. While some AI systems can mimic human-like behavior and conversation, they do not have consciousness or true self-awareness.
Artificial Intelligence Frequently Asked Questions: What company is at the forefront of artificial intelligence?
Several companies are at the forefront of artificial intelligence, including Google, Microsoft, Amazon, and Facebook. These companies have made significant investments in AI research and development
Artificial Intelligence Frequently Asked Questions: Which is the best AI system?
There is no single “best” AI system as it depends on the specific use case and the desired outcome. Some popular AI systems include IBM Watson, Google Cloud AI, and Microsoft Azure AI, each with their unique features and capabilities.
Artificial Intelligence Frequently Asked Questions: Have we created true artificial intelligence?
There is still debate among experts as to whether we have created true artificial intelligence or AGI (artificial general intelligence) yet.
While AI has made significant progress in recent years, it is still largely task-specific and lacks the broad cognitive abilities of human beings.
What is one way that IT services companies help clients ensure fairness when applying artificial intelligence solutions?
IT services companies can help clients ensure fairness when applying artificial intelligence solutions by conducting a thorough review of the data sets used to train the AI algorithms. This includes identifying potential biases and correcting them to ensure that the AI outputs are fair and unbiased.
Artificial Intelligence Frequently Asked Questions: How to write artificial intelligence?
To write artificial intelligence, you need to have a strong understanding of programming languages, data science, machine learning, and computer vision. There are many libraries and tools available, such as TensorFlow and Keras, that make it easier to write AI algorithms.
How is a robot with artificial intelligence like a baby?
A robot with artificial intelligence is like a baby in that both learn and adapt through experience. Just as a baby learns by exploring its environment and receiving feedback from caregivers, an AI robot learns through trial and error and adjusts its behavior based on the results.
Artificial Intelligence Frequently Asked Questions: Is artificial intelligence STEM?
Yes, artificial intelligence is a STEM (science, technology, engineering, and mathematics) field. AI requires a deep understanding of computer science, mathematics, and statistics to develop algorithms and train models.
Will AI make artists obsolete?
While AI has the potential to automate certain aspects of the creative process, such as generating music or creating visual art, it is unlikely to make artists obsolete. AI-generated art still lacks the emotional depth and unique perspective of human-created art.
Why do you like artificial intelligence?
Many people are interested in AI because of its potential to solve complex problems, improve efficiency, and create new opportunities for innovation and growth.
What are the main areas of research in artificial intelligence?
Artificial intelligence research covers a wide range of areas, including natural language processing, computer vision, machine learning, robotics, expert systems, and neural networks. Researchers in AI are also exploring ways to improve the ethical and social implications of AI systems.
How are the ways AI learn similar to how you learn?
Like humans, AI learns through experience and trial and error. AI algorithms use data to train and adjust their models, similar to how humans learn from feedback and make adjustments based on their experiences. However, AI learning is typically much faster and more precise than human learning.
Do artificial intelligence have feelings?
Artificial intelligence does not have emotions or feelings as it is a machine and lacks the capacity for subjective experiences. AI systems are designed to perform specific tasks and operate within the constraints of their programming and data inputs.
Artificial Intelligence Frequently Asked Questions: Will AI be the end of humanity?
There is no evidence to suggest that AI will be the end of humanity. While there are concerns about the ethical and social implications of AI, experts agree that the technology has the potential to bring many benefits and solve complex problems. It is up to humans to ensure that AI is developed and used in a responsible and ethical manner.
Which business case is better solved by Artificial Intelligence AI than conventional programming which business case is better solved by Artificial Intelligence AI than conventional programming?
Business cases that involve large amounts of data and require complex decision-making are often better suited for AI than conventional programming.
For example, AI can be used in areas such as financial forecasting, fraud detection, supply chain optimization, and customer service to improve efficiency and accuracy.
Who is the most powerful AI?
It is difficult to determine which AI system is the most powerful, as the capabilities of AI vary depending on the specific task or application. However, some of the most well-known and powerful AI systems include IBM Watson, Google Assistant, Amazon Alexa, and Tesla’s Autopilot system.
Have we achieved artificial intelligence?
While AI has made significant progress in recent years, we have not achieved true artificial general intelligence (AGI), which is a machine capable of learning and reasoning in a way that is comparable to human cognition. However, AI has become increasingly sophisticated and is being used in a wide range of applications and industries.
What are benefits of AI?
The benefits of AI include increased efficiency and productivity, improved accuracy and precision, cost savings, and the ability to solve complex problems.
AI can also be used to improve healthcare, transportation, and other critical areas, and has the potential to create new opportunities for innovation and growth.
How scary is Artificial Intelligence?
AI can be scary if it is not developed or used in an ethical and responsible manner. There are concerns about the potential for AI to be used in harmful ways or to perpetuate biases and inequalities. However, many experts believe that the benefits of AI outweigh the risks, and that the technology can be used to address many of the world’s most pressing problems.
How to make AI write a script?
There are different ways to make AI write a script, such as training it with large datasets, using natural language processing (NLP) and generative models, or using pre-existing scriptwriting software that incorporates AI algorithms.
How do you summon an entity without AI bedrock?
Attempting to summon entities can be dangerous and potentially harmful.
What should I learn for AI?
To work in artificial intelligence, it is recommended to have a strong background in computer science, mathematics, statistics, and machine learning. Familiarity with programming languages such as Python, Java, and C++ can also be beneficial.
Will AI take over the human race?
No, the idea of AI taking over the human race is a common trope in science fiction but is not supported by current AI capabilities. While AI can be powerful and influential, it does not have the ability to take over the world or control humanity.
Where do we use AI?
AI is used in a wide range of fields and industries, such as healthcare, finance, transportation, manufacturing, and entertainment. Examples of AI applications include image and speech recognition, natural language processing, autonomous vehicles, and recommendation systems.
Who invented AI?
The development of AI has involved contributions from many researchers and pioneers. Some of the key figures in AI history include John McCarthy, Marvin Minsky, Allen Newell, and Herbert Simon, who are considered to be the founders of the field.
Is AI improving?
Yes, AI is continuously improving as researchers and developers create more sophisticated algorithms, use larger and more diverse datasets, and design more advanced hardware. However, there are still many challenges and limitations to be addressed in the development of AI.
Will artificial intelligence take over the world?
No, the idea of AI taking over the world is a popular science fiction trope but is not supported by current AI capabilities. AI systems are designed and controlled by humans and are not capable of taking over the world or controlling humanity.
Is there an artificial intelligence system to help the physician in selecting a diagnosis?
Yes, there are AI systems designed to assist physicians in selecting a diagnosis by analyzing patient data and medical records. These systems use machine learning algorithms and natural language processing to identify patterns and suggest possible diagnoses. However, they are not intended to replace human expertise and judgement.
Will AI replace truck drivers?
AI has the potential to automate certain aspects of truck driving, such as navigation and safety systems. However, it is unlikely that AI will completely replace truck drivers in the near future. Human drivers are still needed to handle complex situations and make decisions based on context and experience.
How AI can destroy the world?
There is a hypothetical concern that AI could cause harm to humans in various ways. For example, if an AI system becomes more intelligent than humans, it could act against human interests or even decide to eliminate humanity. This scenario is known as an existential risk, but many experts believe it to be unlikely. To prevent this kind of risk, researchers are working on developing safety mechanisms and ethical guidelines for AI systems.
What do you call the commonly used AI technology for learning input to output mappings?
The commonly used AI technology for learning input to output mappings is called a neural network. It is a type of machine learning algorithm that is modeled after the structure of the human brain. Neural networks are trained using a large dataset, which allows them to learn patterns and relationships in the data. Once trained, they can be used to make predictions or classifications based on new input data.
What are 3 benefits of AI?
Three benefits of AI are:
Efficiency: AI systems can process vast amounts of data much faster than humans, allowing for more efficient and accurate decision-making.
Personalization: AI can be used to create personalized experiences for users, such as personalized recommendations in e-commerce or personalized healthcare treatments.
Safety: AI can be used to improve safety in various applications, such as autonomous vehicles or detecting fraudulent activities in banking.
What is an artificial intelligence company?
An artificial intelligence (AI) company is a business that specializes in developing and applying AI technologies. These companies use machine learning, deep learning, natural language processing, and other AI techniques to build products and services that can automate tasks, improve decision-making, and provide new insights into data.
Examples of AI companies include Google, Amazon, and IBM.
What does AI mean in tech?
In tech, AI stands for artificial intelligence. AI is a field of computer science that aims to create machines that can perform tasks that would typically require human intelligence, such as learning, reasoning, problem-solving, and language understanding. AI techniques can be used in various applications, such as virtual assistants, chatbots, autonomous vehicles, and healthcare.
Can AI destroy humans?
There is no evidence to suggest that AI can or will destroy humans. While there are concerns about the potential risks of AI, most experts believe that AI systems will only act in ways that they have been programmed to.
To mitigate any potential risks, researchers are working on developing safety mechanisms and ethical guidelines for AI systems.
What types of problems can AI solve?
AI can solve a wide range of problems, including:
Classification: AI can be used to classify data into categories, such as spam detection in email or image recognition in photography.
Prediction: AI can be used to make predictions based on data, such as predicting stock prices or diagnosing diseases.
Optimization: AI can be used to optimize systems or processes, such as scheduling routes for delivery trucks or maximizing production in a factory.
Natural language processing: AI can be used to understand and process human language, such as voice recognition or language translation.
Is AI slowing down?
There is no evidence to suggest that AI is slowing down. In fact, the field of AI is rapidly evolving and advancing, with new breakthroughs and innovations being made all the time. From natural language processing and computer vision to robotics and machine learning, AI is making significant strides in many areas.
How to write a research paper on artificial intelligence?
When writing a research paper on artificial intelligence, it’s important to start with a clear research question or thesis statement. You should then conduct a thorough literature review to gather relevant sources and data to support your argument. After analyzing the data, you can present your findings and draw conclusions, making sure to discuss the implications of your research and future directions for the field.
How to get AI to read text?
To get AI to read text, you can use natural language processing (NLP) techniques such as text analysis and sentiment analysis. These techniques involve training AI algorithms to recognize patterns in written language, enabling them to understand the meaning of words and phrases in context. Other methods of getting AI to read text include optical character recognition (OCR) and speech-to-text technology.
How to create your own AI bot?
To create your own AI bot, you can use a variety of tools and platforms such as Microsoft Bot Framework, Dialogflow, or IBM Watson.
These platforms provide pre-built libraries and APIs that enable you to easily create, train, and deploy your own AI chatbot or virtual assistant. You can customize your bot’s functionality, appearance, and voice, and train it to respond to specific user queries and actions.
What is AI according to Elon Musk?
According to Elon Musk, AI is “the next stage in human evolution” and has the potential to be both a great benefit and a major threat to humanity.
He has warned about the dangers of uncontrolled AI development and has called for greater regulation and oversight in the field. Musk has also founded several companies focused on AI development, such as OpenAI and Neuralink.
How do you program Artificial Intelligence?
Programming artificial intelligence typically involves using machine learning algorithms to train the AI system to recognize patterns and make predictions based on data. This involves selecting a suitable machine learning model, preprocessing the data, selecting appropriate features, and tuning the model hyperparameters.
Once the model is trained, it can be integrated into a larger software application or system to perform various tasks such as image recognition or natural language processing.
What is the first step in the process of AI?
The first step in the process of AI is to define the problem or task that the AI system will be designed to solve. This involves identifying the specific requirements, constraints, and objectives of the system, and determining the most appropriate AI techniques and algorithms to use.
Other key steps in the process include data collection, preprocessing, feature selection, model training and evaluation, and deployment and maintenance of the AI system.
How to make an AI that can talk?
One way to make an AI that can talk is to use a natural language processing (NLP) system. NLP is a field of AI that focuses on how computers can understand, interpret, and respond to human language. By using machine learning algorithms, the AI can learn to recognize speech, process it, and generate a response in a natural-sounding way.
Another approach is to use a chatbot framework, which involves creating a set of rules and responses that the AI can use to interact with users.
How to use the AI Qi tie?
The AI Qi tie is a type of smart wearable device that uses artificial intelligence to provide various functions, including health monitoring, voice control, and activity tracking. To use it, you would first need to download the accompanying mobile app, connect the device to your smartphone, and set it up according to the instructions provided.
From there, you can use voice commands to control various functions of the device, such as checking your heart rate, setting reminders, and playing music.
Is sentient AI possible?
While there is ongoing research into creating AI that can exhibit human-like cognitive abilities, including sentience, there is currently no clear evidence that sentient AI is possible or exists. The concept of sentience, which involves self-awareness and subjective experience, is difficult to define and even more challenging to replicate in a machine. Some experts believe that true sentience in AI may be impossible, while others argue that it is only a matter of time before machines reach this level of intelligence.
Is Masteron an AI?
No, Masteron is not an AI. It is a brand name for a steroid hormone called drostanolone. AI typically stands for “artificial intelligence,” which refers to machines and software that can simulate human intelligence and perform tasks that would normally require human intelligence to complete.
Is the Lambda AI sentient?
There is no clear evidence that the Lambda AI, or any other AI system for that matter, is sentient. Sentience refers to the ability to experience subjective consciousness, which is not currently understood to be replicable in machines. While AI systems can be programmed to simulate a wide range of cognitive abilities, including learning, problem-solving, and decision-making, they are not currently believed to possess subjective awareness or consciousness.
Where is artificial intelligence now?
Artificial intelligence is now a pervasive technology that is being used in many different industries and applications around the world. From self-driving cars and virtual assistants to medical diagnosis and financial trading, AI is being employed to solve a wide range of problems and improve human performance. While there are still many challenges to overcome in the field of AI, including issues related to bias, ethics, and transparency, the technology is rapidly advancing and is expected to play an increasingly important role in our lives in the years to come.
What is the correct sequence of artificial intelligence trying to imitate a human mind?
The correct sequence of artificial intelligence trying to imitate a human mind can vary depending on the specific approach and application. However, some common steps in this process may include collecting and analyzing data, building a model or representation of the human mind, training the AI system using machine learning algorithms, and testing and refining the system to improve its accuracy and performance. Other important considerations in this process may include the ethical implications of creating machines that can mimic human intelligence.
How do I make machine learning AI?
To make machine learning AI, you will need to have knowledge of programming languages such as Python and R, as well as knowledge of machine learning algorithms and tools. Some steps to follow include gathering and cleaning data, selecting an appropriate algorithm, training the algorithm on the data, testing and validating the model, and deploying it for use.
What is AI scripting?
AI scripting is a process of developing scripts that can automate the behavior of AI systems. It involves writing scripts that govern the AI’s decision-making process and its interactions with users or other systems. These scripts are often written in programming languages such as Python or JavaScript and can be used in a variety of applications, including chatbots, virtual assistants, and intelligent automation tools.
Is IOT artificial intelligence?
No, the Internet of Things (IoT) is not the same as artificial intelligence (AI). IoT refers to the network of physical devices, vehicles, home appliances, and other items that are embedded with electronics, sensors, and connectivity, allowing them to connect and exchange data. AI, on the other hand, involves the creation of intelligent machines that can learn and perform tasks that would normally require human intelligence, such as speech recognition, decision-making, and language translation.
What problems will Ai solve?
AI has the potential to solve a wide range of problems across different industries and domains. Some of the problems that AI can help solve include automating repetitive or dangerous tasks, improving efficiency and productivity, enhancing decision-making and problem-solving, detecting fraud and cybersecurity threats, predicting outcomes and trends, and improving customer experience and personalization.
Who wrote papers on the simulation of human thinking problem solving and verbal learning that marked the beginning of the field of artificial intelligence?
The papers on the simulation of human thinking, problem-solving, and verbal learning that marked the beginning of the field of artificial intelligence were written by John McCarthy, Marvin Minsky, Nathaniel Rochester, and Claude Shannon in the late 1950s.
The papers, which were presented at the Dartmouth Conference in 1956, proposed the idea of developing machines that could simulate human intelligence and perform tasks that would normally require human intelligence.
Given the fast development of AI systems, how soon do you think AI systems will become 100% autonomous?
It’s difficult to predict exactly when AI systems will become 100% autonomous, as there are many factors that could affect this timeline. However, it’s important to note that achieving 100% autonomy may not be possible or desirable in all cases, as there will likely always be a need for some degree of human oversight and control.
That being said, AI systems are already capable of performing many tasks autonomously, and their capabilities are rapidly expanding. For example, there are already AI systems that can drive cars, detect fraud, and diagnose diseases with a high degree of accuracy.
However, there are still many challenges to be overcome before AI systems can be truly autonomous in all domains. One of the main challenges is developing AI systems that can understand and reason about complex, real-world situations, as opposed to just following pre-programmed rules or learning from data.
Another challenge is ensuring that AI systems are safe, transparent, and aligned with human values and objectives.
This is particularly important as AI systems become more powerful and influential, and have the potential to impact many aspects of our lives.
For low-level domain-specific jobs such as industrial manufacturing, we already have Artificial Intelligence Systems that are fully autonomous, i.e., accomplish tasks without human intervention.
But those autonomous systems require collections of various intelligent skills to tackle many unseen situations; IMO, it will take a while to design one.
The major hurdle in making an A.I. autonomous system is to design an algorithm that can handle unpredictable events correctly. For a closed environment, it may not be a big issue. But for an open-ended system, the infinite number of possibilities is difficult to cover and ensure the autonomous device’s reliability.
Artificial Intelligence Frequently Asked Questions: AI Autonomous Systems
Current SOTA Artificial Intelligence algorithms are mostly data-centric training. The issue is not only the algorithm itself. The selection, generation, and pre-processing of datasets also determine the final performance of the accuracy. Machine Learning helps offload us without needing to explicitly derive the procedural methods to solve a problem. Still, it relies heavily on the input and feedback methods we need to provide correctly. Overcoming one problem might create many new ones, and sometimes, we do not even know whether the dataset is adequate, reasonable, and practical.
Overall, it’s difficult to predict exactly when AI systems will become 100% autonomous, but it’s clear that the development of AI technology will continue to have a profound impact on many aspects of our society and economy.
Will ChatGPT replace programmers?
Is it possible that ChatGPT will eventually replace programmers? The answer to this question is not a simple yes or no, as it depends on the rate of development and improvement of AI tools like ChatGPT.
If AI tools continue to advance at the same rate over the next 10 years, then they may not be able to fully replace programmers. However, if these tools continue to evolve and learn at an accelerated pace, then it is possible that they may replace at least 30% of programmers.
Although the current version of ChatGPT has some limitations and is only capable of generating boilerplate code and identifying simple bugs, it is a starting point for what is to come. With the ability to learn from millions of mistakes at a much faster rate than humans, future versions of AI tools may be able to produce larger code blocks, work with mid-sized projects, and even handle QA of software output.
In the future, programmers may still be necessary to provide commands to the AI tools, review the final code, and perform other tasks that require human intuition and judgment. However, with the use of AI tools, one developer may be able to accomplish the tasks of multiple developers, leading to a decrease in the number of programming jobs available.
In conclusion, while it is difficult to predict the extent to which AI tools like ChatGPT will impact the field of programming, it is clear that they will play an increasingly important role in the years to come.
ChatGPT is not designed to replace programmers.
While AI language models like ChatGPT can generate code and help automate certain programming tasks, they are not capable of replacing the skills, knowledge, and creativity of human programmers.
Programming is a complex and creative field that requires a deep understanding of computer science principles, problem-solving skills, and the ability to think critically and creatively. While AI language models like ChatGPT can assist in certain programming tasks, such as generating code snippets or providing suggestions, they cannot replace the human ability to design, develop, and maintain complex software systems.
Furthermore, programming involves many tasks that require human intuition and judgment, such as deciding on the best approach to solve a problem, optimizing code for efficiency and performance, and debugging complex systems. While AI language models can certainly be helpful in some of these tasks, they are not capable of fully replicating the problem-solving abilities of human programmers.
Overall, while AI language models like ChatGPT will undoubtedly have an impact on the field of programming, they are not designed to replace programmers, but rather to assist and enhance their abilities.
What does a responsive display ad use in its machine learning model?
A responsive display ad uses various machine learning models such as automated targeting, bidding, and ad creation to optimize performance and improve ad relevance. It also uses algorithms to predict which ad creative and format will work best for each individual user and the context in which they are browsing.
What two things are marketers realizing as machine learning becomes more widely used?
Marketers are realizing the benefits of machine learning in improving efficiency and accuracy in various aspects of their work, including targeting, personalization, and data analysis. They are also realizing the importance of maintaining transparency and ethical considerations in the use of machine learning and ensuring it aligns with their marketing goals and values.
How does statistics fit into the area of machine learning?
Statistics is a fundamental component of machine learning, as it provides the mathematical foundations for many of the algorithms and models used in the field. Statistical methods such as regression, clustering, and hypothesis testing are used to analyze data and make predictions based on patterns and trends in the data.
Is Machine Learning weak AI?
Yes, machine learning is considered a form of weak artificial intelligence, as it is focused on specific tasks and does not possess general intelligence or consciousness. Machine learning models are designed to perform a specific task based on training data and do not have the ability to think, reason, or learn outside of their designated task.
When evaluating machine learning results, should I always choose the fastest model?
No, the speed of a machine learning model is not the only factor to consider when evaluating its performance. Other important factors include accuracy, complexity, and interpretability. It is important to choose a model that balances these factors based on the specific needs and goals of the task at hand.
How do you learn machine learning?
You can learn machine learning through a combination of self-study, online courses, and practical experience. Some popular resources for learning machine learning include online courses on platforms such as Coursera and edX, textbooks and tutorials, and practical experience through projects and internships.
It is important to have a strong foundation in mathematics, programming, and statistics to succeed in the field.
What are your thoughts on artificial intelligence and machine learning?
Artificial intelligence and machine learning have the potential to revolutionize many aspects of society and have already shown significant impacts in various industries.
It is important to continue to develop these technologies responsibly and with ethical considerations to ensure they align with human values and benefit society as a whole.
Which AWS service enables you to build the workflows that are required for human review of machine learning predictions?
Amazon SageMaker Ground Truth is an AWS service that enables you to build workflows for human review of machine learning predictions.
This service provides an easy-to-use interface for creating and managing custom workflows and provides built-in tools for data labeling and quality control to ensure high-quality training data.
What is augmented machine learning?
Augmented machine learning is a combination of human expertise and machine learning models to improve the accuracy of machine learning. This technique is used when the available data is not enough or is not of good quality. The human expert is involved in the training and validation of the machine learning model to improve its accuracy.
Which actions are performed during the prepare the data step of workflow for analyzing the data with Oracle machine learning?
The ‘prepare the data’ step in Oracle machine learning workflow involves data cleaning, feature selection, feature engineering, and data transformation. These actions are performed to ensure that the data is ready for analysis, and that the machine learning model can effectively learn from the data.
What type of machine learning algorithm would you use to allow a robot to walk in various unknown terrains?
A reinforcement learning algorithm would be appropriate for this task. In this type of machine learning, the robot would interact with its environment and receive rewards for positive outcomes, such as moving forward or maintaining balance. The algorithm would learn to maximize these rewards and gradually improve its ability to navigate through different terrains.
Are evolutionary algorithms machine learning?
Yes, evolutionary algorithms are a subset of machine learning. They are a type of optimization algorithm that uses principles from biological evolution to search for the best solution to a problem.
Evolutionary algorithms are often used in problems where traditional optimization algorithms struggle, such as in complex, nonlinear, and multi-objective optimization problems.
Is MPC machine learning?
Yes, Model Predictive Control (MPC) is a type of machine learning. It is a feedback control algorithm that predicts the future behavior of a system and uses this prediction to optimize its performance. MPC is used in a variety of applications, including industrial control, robotics, and autonomous vehicles.
When do you use ML model?
You would use a machine learning model when you need to make predictions or decisions based on data. Machine learning models are trained on historical data and use this knowledge to make predictions on new data. Common applications of machine learning include fraud detection, recommendation systems, and image recognition.
When preparing the dataset for your machine learning model, you should use one hot encoding on what type of data?
One hot encoding is used on categorical data. Categorical data is non-numeric data that has a limited number of possible values, such as color or category. One hot encoding is a technique used to convert categorical data into a format that can be used in machine learning models. It converts each category into a binary vector, where each vector element corresponds to a unique category.
Is machine learning just brute force?
No, machine learning is not just brute force. Although machine learning models can be complex and require significant computing power, they are not simply brute force algorithms. Machine learning involves the use of statistical techniques and mathematical models to learn from data and make predictions. Machine learning is designed to make use of the available data in an efficient way, without the need for exhaustive search or brute force techniques.
OpenAI begins ChatGPT Voice rollout
