AI Innovations in July 2024

AI Innovations in July 2024

AI Innovations in July 2024.

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!

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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.

Source: https://gizmodo.com/openai-partners-with-los-alamos-lab-to-save-us-from-ai-2000461202

🏭 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.

Source: https://www.techspot.com/news/103770-xiaomi-unveils-new-autonomous-smart-factory-operates-247.html

🧬 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.

Source: https://openai.com/index/openai-and-los-alamos-national-laboratory-work-together

🤖 China dominates global gen AI adoption

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.

Source: https://www.sas.com/en_us/news/press-releases/2024/july/genai-research-study-global.html

⌚ 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.


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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.

Source: https://news.samsung.com/global/galaxy-unpacked-2024-a-new-era-of-galaxy-ai-unfolds-at-the-louvre-in-paris

💸 AMD to buy SiloAI to bridge the gap with NVIDIA

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.

Source: https://www.bloomberg.com/news/articles/2024-07-10/amd-to-buy-european-ai-model-maker-silo-in-race-against-nvidia

💬 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.

Source: https://aws.amazon.com/blogs/aws/build-custom-business-applications-without-cloud-expertise-using-aws-app-studio-preview

📱 Samsung Galaxy gets smarter with Google

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.

Source: https://techcrunch.com/2024/07/10/google-brings-new-gemini-features-and-wearos-5-to-samsung-devices

✍️ Writer drops enhancements to AI chat applications

Improvements include advanced graph-based retrieval-augmented generation (RAG) and AI transparency tools, available for users of ‘Ask Writer’ and AI Studio.

Source: https://writer.com/blog/chat-app-rag-thought-process

🚀 Vimeo launches AI content labels

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.

Source: https://vimeo.com/blog/post/introducing-ai-content-labeling/

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’s Claude 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.
  • Source: https://www.theverge.com/2024/7/10/24195528/microsoft-apple-openai-board-observer-seat-drop-regulator-scrutiny

🕵️‍♂️ US shuts down Russian AI bot farm

  • 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.

Source: https://www.theverge.com/2024/7/9/24195228/doj-bot-farm-rt-russian-government-namecheap

🤖 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.

Source: https://siliconangle.com/2024/07/09/skild-ai-raises-300m-build-general-purpose-ai-powered-brain-robot/

🎬 Odyssey is building a ‘Hollywood-grade’ visual AI

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.

Source: https://x.com/olivercameron/status/1810335663197413406

📜 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.

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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.

Source: https://www.anthropic.com/news/evaluate-prompts

🧠 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!

Source: https://blog.google/technology/research/mouse-brain-research

🚫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.

Source: https://techcrunch.com/2024/07/10/as-microsoft-leaves-its-observer-seat-openai-says-it-wont-have-any-more-observers

🆕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.

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Source: https://x.com/lmsysorg/status/1810773765447655604

🚀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.

Source: https://venturebeat.com/ai/aitomatics-semikong-uses-ai-to-reshape-chipmaking-processes

🔧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.

Source: https://stability.ai/news/stability-ai-releases-stable-assistant-features

🎨Etsy will now allow sale of AI-generated art

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.

Source: https://mashable.com/article/etsy-ai-art-policy

🚀 Anthropic’s Claude Artifacts sharing goes live

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.

Making Artifacts 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.

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Source: https://x.com/rowancheung/status/1810720903052882308

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.

Source: https://arxiv.org/pdf/2407.03168

⏱️ 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.

Source: https://www.microsoft.com/en-us/research/project/minference-million-tokens-prompt-inference-for-long-context-llms/overview/

🚀 Groq’s LLM engine surpasses Nvidia GPU processing

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.

Source: https://venturebeat.com/ai/groq-releases-blazing-fast-llm-engine-passes-270000-user-mark

🛡️ 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.

Source: https://www.japantimes.co.jp/news/2024/07/02/japan/sdf-cybersecurity/

🩺 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.

Source: https://time.com/6994739/ai-behavior-change-health-care

🖥️ 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.

Source: https://www.bloomberg.com/news/articles/2024-07-08/qualcomm-microsoft-lean-on-ai-hype-to-spur-pc-market-revival

🤖 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.

Source: https://x.com/poe_platform/status/1810335290281922984

🎥 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.

Source: https://a16z.com/podcast/beyond-language-inside-a-hundred-trillion-token-video-model

🥦 OpenAI and Thrive create AI health coach

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.

Source: https://www.prnewswire.com/news-releases/openai-startup-fund–arianna-huffingtons-thrive-global-create-new-company-thrive-ai-health-to-launch-hyper-personalized-ai-health-coach-302190536.html

🇯🇵 Japan Ministry introduces first AI policy

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.

Source: https://www.japantimes.co.jp/news/2024/07/02/japan/sdf-cybersecurity

What else is happening in AI on July 09th 2024

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

Taiwan Semiconductor Manufacturing Co. saw its valuation briefly surpass $1T, coming on the heels of Morgan Stanley increasing its price targets for the AI chipmaker. Source: https://finance.yahoo.com/news/tsmc-shares-soar-record-expectations-041140534.html

AI startup Hebbia secured $130M in funding for its complex data analysis software, boosting the company’s valuation to around $700M. Source: https://www.bloomberg.com/news/articles/2024-07-08/hebbia-raises-130-million-for-ai-that-helps-firms-answer-complex-questions

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.

Source: https://www.sensetime.com/en/news-detail/51168278

🛡️ 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.

Source: https://blog.cloudflare.com/declaring-your-aindependence-block-ai-bots-scrapers-and-crawlers-with-a-single-click

🚨 Waymo’s Robotaxi gets busted by the cops

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.

Source: https://techcrunch.com/2024/07/06/waymo-robotaxi-pulled-over-by-phoenix-police-after-driving-into-the-wrong-lane/

🕵️ OpenAI’s secret AI details stolen in 2023 hack

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.

Source: https://www.nytimes.com/2024/07/04/technology/openai-hack.html

🇨🇳 Chinese AI firms flex muscles at WAIC

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.

Source: https://www.scmp.com/tech/big-tech/article/3269387/chinas-ai-competition-deepens-sensetime-alibaba-claim-progress-ai-show

💥 Fears of AI bubble intensify after new report

  • 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.

Source: https://www.tomshardware.com/tech-industry/artificial-intelligence/ai-industry-needs-to-earn-dollar600-billion-per-year-to-pay-for-massive-hardware-spend-fears-of-an-ai-bubble-intensify-in-wake-of-sequoia-report

📰 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.

Source: https://futurism.com/the-byte/google-researchers-paper-ai-internet

🖌️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.

Source: https://stability.ai/news/license-update

⚡ 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.

Source: https://arxiv.org/abs/2406.17711

🤖 Apple Intelligence is expected to launch in iOS 18.4 in spring 2025

This will bring major improvements to Siri. New AI features may be released incrementally in iOS point updates. iOS 18 betas later this year will provide more details on the AI features.  Source: https://www.theverge.com/2024/7/7/24193619/apple-intelligence-better-siri-ios-18-4-spring-public-launch

📸 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.

Source: https://wabetainfo.com/whatsapp-beta-for-android-2-24-14-20-whats-new/

Google claims new AI training tech is 13 times faster and 10 times more power efficient —

DeepMind’s new JEST optimizes training data for impressive gains.

Source: https://www.tomshardware.com/tech-industry/artificial-intelligence/google-claims-new-ai-training-tech-is-13-times-faster-and-10-times-more-power-efficient-deepminds-new-jest-optimizes-training-data-for-massive-gains

New AI Job Opportunities on July 08th 2024

  • 🎨 xAI – Product Designer: https://jobs.therundown.ai/jobs/60681923-product-designer
  • 💻 Weights & Biases – Programmer Writer, Documentation: https://jobs.therundown.ai/jobs/66567362-programmer-writer-documentation-remote
  • 📊 DeepL – Enterprise Customer Success Manager: https://jobs.therundown.ai/jobs/66103798-enterprise-customer-success-manager-%7C-dach
  • 🛠️ Dataiku – Senior Infrastructure Engineer: https://jobs.therundown.ai/jobs/66413411-senior-infrastructure-engineer-paris

Source: https://jobs.therundown.ai/

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.

Source: https://www.newscientist.com/article/2438107-mind-reading-ai-recreates-what-youre-looking-at-with-amazing-accuracy

⚡ ElevenLabs has exciting AI voice updates

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.

Source: https://www.youtube.com/live/hm2IJSKcYvo?si=EtirSsXktIwakmn5 

🌐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.

Source: https://venturebeat.com/ai/meta-drops-ai-bombshell-multi-token-prediction-models-now-open-for-research/

🤝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.

Source: https://mashable.com/article/apple-google-ai-partnership-report

📢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.

Source: https://searchengineland.com/google-disclosure-rules-synthetic-content-political-ads-443868

🧍‍♂️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.

Source: https://www.theverge.com/2024/7/4/24192112/whatsapp-ai-avatar-image-generator-imagine-meta-llama

🛡️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.

Source: https://www.reuters.com/technology/artificial-intelligence/brazil-authority-suspends-metas-ai-privacy-policy-seeks-adjustment-2024-07-02

🍎 Apple rumored to launch AI-powered home device

  • 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.

Source: https://bgr.com/tech/apple-mysterious-ai-powered-home-device/

💥 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.

Source: https://9to5mac.com/2024/07/05/google-search-iphone-safari-ai-features/

🦠 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.

Source: https://www.newsbytesapp.com/news/science/virus-leverages-chatgpt-to-spread-itself-by-sending-human-like-emails/story

You can now get AI Judy Garland or James Dean to read you the news.

Source: https://www.engadget.com/you-can-now-get-ai-judy-garland-or-james-dean-to-read-you-the-news-160023595.html

🖼️ Stretch creativity with AI image expansion

Freepik has a powerful new feature called ‘Expand‘ that allows you to expand your images beyond their original boundaries, filling in details with AI.

  1. Head over to the Freepik Pikaso website and look for the “Expand” feature.
  2. Upload your image by clicking “Upload” or using drag-and-drop.
  3. Choose your desired aspect ratio from the options on the left sidebar and add a prompt describing what you want in the expanded areas.
  4. Click “Expand”, browse the AI-generated results, and select your favorite 🎉

Source: https://university.therundown.ai/c/daily-tutorials/stretch-your-creativity-with-ai-image-expansion-56b69128-ef5a-445a-ae55-9bc31c343cdf

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.

Source: https://www.nytimes.com/2024/07/04/technology/openai-hack.html

🤖 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.

Source: https://the-decoder.com/french-ai-lab-kyutai-unveils-conversational-ai-assistant-moshi-plans-open-source-release/

🇨🇳 China leads the world in generative AI patents

  • 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.

Source: https://fortune.com/asia/2024/07/04/china-generative-ai-patents-un-wipo-us-second/

🚨 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.

Source: https://9to5mac.com/2024/07/03/chatgpt-macos-conversations-plain-text/

🤏 Salesforce’s small model breakthrough

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.

Source: https://x.com/Benioff/status/1808365628551844186

🗣️ Turn thoughts into polished content

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.

  1. Enable “Background Conversations” in the ChatGPT app settings.
  2. Start a new chat with the prompt shown in the image above (it was too long for this email).
  3. Speak your thoughts freely, pausing as needed, and say “I’m done” when you’ve expressed all your ideas.
  4. 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.

Source: https://university.therundown.ai/c/daily-tutorials/transform-your-thoughts-into-polished-content-with-ai-2116bbea-8001-4915-87d2-1bdd045f3d38

🧠 Perplexity gets major research upgrade

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.

Source: https://www.perplexity.ai/hub/blog/pro-search-upgraded-for-more-advanced-problem-solving

Cloudflare released a free tool to detect and block AI bots circumventing website scraping protections, aiming to address concerns over unauthorized data collection for AI training. Source: https://blog.cloudflare.com/declaring-your-aindependence-block-ai-bots-scrapers-and-crawlers-with-a-single-click

App Store chief Phil Schiller is joining OpenAI’s board in an observer role, representing Apple as part of the recently announced AI partnership. Source: https://www.bloomberg.com/news/articles/2024-07-02/apple-to-get-openai-board-observer-role-as-part-of-ai-agreement

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/

Citadel CEO Ken Griffin told the company’s new class of interns that he is ‘not convinced’ AI will achieve breakthroughs that automate human jobs in the next three years. Source: https://www.cnbc.com/2024/07/01/ken-griffin-says-hes-not-convinced-ai-will-replace-human-jobs-in-near-future.html

ElevenLabs launched Voice Isolator, a new feature designed to help users remove background noise from recordings and create studio-quality audio. Source: https://x.com/elevenlabsio/status/1808589239744921663?

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.

Source: https://ai.meta.com/research/publications/meta-3d-gen/

💡 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.

Source: https://www.perplexity.ai/hub/blog/pro-search-upgraded-for-more-advanced-problem-solving

🔒 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.

Source: https://developer.nvidia.com/blog/advancing-security-for-large-language-models-with-nvidia-gpus-and-edgeless-systems

🌐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.

Source: https://venturebeat.com/ai/runways-gen-3-alpha-ai-video-model-now-available-but-theres-a-catch

🕹️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.

Source: https://techcrunch.com/2024/07/02/meta-plans-to-bring-generative-ai-to-metaverse-games

🏢Apple gets a non-voting seat on OpenAI’s board

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.

Source: https://www.theverge.com/2024/7/2/24191105/apple-phil-schiller-join-openai-board

🚫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.

Source: https://techcrunch.com/2024/07/02/figma-disables-its-ai-design-feature-that-appeared-to-be-ripping-off-apples-weather-app

🌏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.

Source: https://www.reuters.com/technology/artificial-intelligence/china-leading-generative-ai-patents-race-un-report-says-2024-07-03

🍎 Apple joins OpenAI board

  • 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.

Source: https://www.theverge.com/2024/7/2/24191105/apple-phil-schiller-join-openai-board

🌍 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%.

Source: https://www.techradar.com/pro/google-says-its-emissions-have-grown-nearly-50-due-to-ai-data-center-boom-and-heres-what-it-plans-to-do-about-it

🔮 Meta’s new AI can create 3D objects from text in under a minute

Meta Unveils 3D Gen: AI that Creates Detailed 3D Assets 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.

Source: https://www.maginative.com/article/meta-unveils-3d-gen-ai-that-creates-detailed-3d-assets-in-under-a-minute/

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.

Source: https://x.com/runwayml/status/1807822396415467686

📸 Motorola hits the AI runway

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.

Source: https://www.coinspeaker.com/xai-grok-2-elon-musk-jarvis-ai-assistant/

🍏 Apple unveils a public demo of its ‘4M’ AI model

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.

Source: https://venturebeat.com/ai/apple-just-launched-a-public-demo-of-its-4m-ai-model-heres-why-its-a-big-deal

🛒 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.

Source:https://www.bloomberg.com/news/articles/2024-06-28/amazon-hires-top-executives-from-ai-startup-adept-for-agi-team

📺 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.

Source: https://venturebeat.com/security/deepfakes-will-cost-40-billion-by-2027-as-adversarial-ai-gains-momentum

🤖 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.

Source: https://www.reuters.com/technology/artificial-intelligence/china-says-us-targeting-ai-not-helpful-healthy-development-2024-07-01

🤖 New robot controlled by human brain cells

  • 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.

Source: https://www.independent.co.uk/tech/robot-human-brain-china-b2571978.html

🎨 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.

Source: https://techcrunch.com/2024/07/02/figma-disables-its-ai-design-feature-that-appeared-to-be-ripping-off-apples-weather-app/

⚖️ Nvidia faces first antitrust charges

  • 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.

Source: https://finance.yahoo.com/news/french-antitrust-regulators-set-charge-151406034.html?

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.

Source: https://cloud.google.com/blog/products/ai-machine-learning/vertex-ai-offers-enterprise-ready-generative-ai

🤖 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.

Source: https://lmsys.org/blog/2024-06-27-multimodal

👓 Apple’s Vision Pro gets an AI upgrade

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.”

Source: https://www.bloomberg.com/news/newsletters/2024-06-30/apple-s-longer-lasting-devices-ios-19-and-apple-intelligence-on-the-vision-pro-ly1jnrw4

🤖 Humanoid robots head to the warehouse

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.

Source: https://agilityrobotics.com/content/gxo-signs-industry-first-multi-year-agreement-with-agility-robotics

🌎 Google Translate adds 110 languages

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.

Source: https://blog.google/products/translate/google-translate-new-languages-2024

📞 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.

Source: https://venturebeat.com/ai/amazon-upgrades-ai-assistant-q-to-make-call-centers-way-more-efficient

💬 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.

Source: https://wabetainfo.com/whatsapp-beta-for-android-2-24-14-7-whats-new/

⚡ 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.

Source: https://www.theregister.com/2024/06/28/bill_gates_ai_power_consumption

🍪 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.

Source: https://www.wired.com/story/aws-perplexity-bot-scraping-investigation

🤖 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.

Source: https://www.theverge.com/2024/6/28/24188391/microsoft-ai-suleyman-social-contract-freeware

🤑 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.

Source: https://www.techradar.com/computing/is-apple-intelligence-the-new-icloud-ai-platform-tipped-to-get-new-subscription-tier

🤖 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.

Source: https://the-decoder.com/metas-new-hot3d-dataset-could-enable-robots-to-learn-manual-skills-from-human-experts/

AI Innovations in June 2024

Mastering GPT-4: Simplified Guide for Everyday Users

Mastering GPT-4: Simplified Guide for Everyday Users

Mastering GPT-4: Simplified Guide for Everyday Users or How to make GPT-4 your b*tch!

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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:

  1. Frequency Penalty: Discover how to reduce repetitive responses and make your AI interactions sound more natural.
  2. Logit Bias: Learn to gently steer the AI towards or away from specific words or topics.
  3. Presence Penalty: Find out how to encourage the AI to transition smoothly between topics.
  4. Temperature: Adjust the AI’s creativity level, from straightforward responses to imaginative ideas.
  5. 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
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.

You could set:

`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.


AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence (OpenAI, ChatGPT, Google Gemini, Generative AI, Discriminative AI, xAI, LLMs, GPUs, Machine Learning, NLP, Promp Engineering)

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.

Mastering GPT-4 References:

https://platform.openai.com/docs/api-reference/chat/create#chat-create-logit_bias.

https://help.openai.com/en/articles/5247780-using-logit-bias-to-define-token-probability

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Decoding GPTs & LLMs: Training, Memory & Advanced Architectures Explained

Mastering GPT-4 Transcript

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.

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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!

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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!

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Decoding GPTs & LLMs: Training, Memory & Advanced Architectures Explained

Decoding GPTs & LLMs: Training, Memory & Advanced Architectures Explained

Decoding GPTs & LLMs: Training, Memory & Advanced Architectures Explained

Unlock the secrets of GPTs and Large Language Models (LLMs) in our comprehensive guide!

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Decoding GPTs & LLMs: Training, Memory & Advanced Architectures Explained
Decoding GPTs & LLMs: Training, Memory & Advanced Architectures Explained

🤖🚀 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”

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📌 Check out our playlist for more AI insights

📖 Read along with the podcast below:

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.


AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence (OpenAI, ChatGPT, Google Gemini, Generative AI, Discriminative AI, xAI, LLMs, GPUs, Machine Learning, NLP, Promp Engineering)

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!

Mastering GPT-4: Simplified Guide for Everyday Users

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Are you eager to expand your understanding of artificial intelligence? Look no further than the essential book “AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence,” available at Etsy, Shopify, Apple, Google, or Amazon

AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence (OpenAI, ChatGPT, Google Bard, Generative AI, Discriminative AI, xAI, LLMs, GPUs, Machine Learning, NLP, AI Podcast)
AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence (OpenAI, ChatGPT, Google Bard, Generative AI, Discriminative AI, xAI, LLMs, GPUs, Machine Learning, NLP, AI Podcast)

The Future of Generative AI: From Art to Reality Shaping

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Artificial Intelligence Frequently Asked Questions

Artificial Intelligence Frequently Asked Questions

Artificial Intelligence Frequently Asked Questions

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.

AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence

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.


AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence (OpenAI, ChatGPT, Google Gemini, Generative AI, Discriminative AI, xAI, LLMs, GPUs, Machine Learning, NLP, Promp Engineering)

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:

  1. 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.

  2. 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.

  3. 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.

  4. 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:

  1. 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.

  2. 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.

  3. 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.

  4. 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.

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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.

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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.

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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.

Artificial Intelligence Frequently Asked Questions: Machine Learning

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.

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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.

How to implement a machine learning paper?

Implementing a machine learning paper involves understanding the research paper’s theoretical foundation, reproducing the results, and applying the approach to the new data to evaluate the approach’s efficacy. The implementation process begins with comprehending the paper’s theoretical framework, followed by testing and reproducing the findings to validate the approach.

Finally, the approach can be implemented on new datasets to assess its accuracy and generalizability. It’s essential to understand the mathematical concepts and programming tools involved in the paper to successfully implement the machine learning paper.

What are some use cases where more traditional machine learning models may make much better predictions than DNNS?

More traditional machine learning models may outperform deep neural networks (DNNs) in the following use cases:

  • When the dataset is relatively small and straightforward, traditional machine learning models, such as logistic regression, may be more accurate than DNNs.
  • When the dataset is sparse or when the number of observations is small, DNNs may require more computational resources and more time to train than traditional machine learning models.
  • When the problem is not complex, and the data has a low level of noise, traditional machine learning models may outperform DNNs.

Who is the supervisor in supervised machine learning?

In supervised machine learning, the supervisor refers to the algorithm that acts as the teacher or the guide to the model. The supervisor provides the model with labeled examples to train on, and the model uses these labeled examples to learn how to classify new data. The supervisor algorithm determines the accuracy of the model’s predictions, and the model is trained to minimize the difference between its predicted outputs and the known outputs.

How do you make machine learning in scratch?

To make machine learning in scratch, you need to follow these steps:

  • Choose a problem to solve and collect a dataset that represents the problem you want to solve.
  • Preprocess and clean the data to ensure that it’s formatted correctly and ready for use in a machine learning model.
  • Select a machine learning algorithm, such as decision trees, support vector machines, or neural networks.
  • Implement the selected machine learning algorithm from scratch, using a programming language such as Python or R.
  • Train the model using the preprocessed dataset and the implemented algorithm.
  • Test the accuracy of the model and evaluate its performance.

Is unsupervised learning machine learning?

Yes, unsupervised learning is a type of machine learning. In unsupervised learning, the model is not given labeled data to learn from. Instead, the model must find patterns and relationships in the data on its own. Unsupervised learning algorithms include clustering, anomaly detection, and association rule mining. The model learns from the features in the dataset to identify underlying patterns or groups, which can then be used for further analysis or prediction.

How do I apply machine learning?

Machine learning can be applied to a wide range of problems and scenarios, but the basic process typically involves:

  • gathering and preprocessing data,
  • selecting an appropriate model or algorithm,
  • training the model on the data, testing and evaluating the model, and then using the trained model to make predictions or perform other tasks on new data.
  • The specific steps and techniques involved in applying machine learning will depend on the particular problem or application.

Is machine learning possible?

Yes, machine learning is possible and has already been successfully applied to a wide range of problems in various fields such as healthcare, finance, business, and more.

Machine learning has advanced rapidly in recent years, thanks to the availability of large datasets, powerful computing resources, and sophisticated algorithms.

Is machine learning the future?

Many experts believe that machine learning will continue to play an increasingly important role in shaping the future of technology and society.

As the amount of data available continues to grow and computing power increases, machine learning is likely to become even more powerful and capable of solving increasingly complex problems.

How to combine multiple features in machine learning?

In machine learning, multiple features can be combined in various ways depending on the particular problem and the type of model or algorithm being used.

One common approach is to concatenate the features into a single vector, which can then be fed into the model as input. Other techniques, such as feature engineering or dimensionality reduction, can also be used to combine or transform features to improve performance.

Which feature lets you discover machine learning assets in Watson Studio 1 point?

The feature in Watson Studio that lets you discover machine learning assets is called the Asset Catalog.

The Asset Catalog provides a unified view of all the assets in your Watson Studio project, including data assets, models, notebooks, and other resources.

You can use the Asset Catalog to search, filter, and browse through the assets, and to view metadata and details about each asset.

What is N in machine learning?

In machine learning, N is a common notation used to represent the number of instances or data points in a dataset.

N can be used to refer to the total number of examples in a dataset, or the number of examples in a particular subset or batch of the data.

N is often used in statistical calculations, such as calculating means or variances, or in determining the size of training or testing sets.

Is VAR machine learning?

VAR, or vector autoregression, is a statistical technique that models the relationship between multiple time series variables. While VAR involves statistical modeling and prediction, it is not generally considered a form of machine learning, which typically involves using algorithms to learn patterns or relationships in data automatically without explicit statistical modeling.

How many categories of machine learning are generally said to exist?

There are generally three categories of machine learning: supervised learning, unsupervised learning, and reinforcement learning.

In supervised learning, the algorithm is trained on labeled data to make predictions or classifications. The algorithm is trained on unlabeled data to identify patterns or structure.

In reinforcement learning, the algorithm learns to make decisions and take actions based on feedback from the environment.

How to use timestamp in machine learning?

Timestamps can be used in machine learning to analyze time series data. This involves capturing data over a period of time and making predictions about future events. Time series data can be used to detect patterns, trends, and anomalies that can be used to make predictions about future events. The timestamps can be used to group data into regular intervals for analysis or used as input features for machine learning models.

Is classification a machine learning technique?

Yes, classification is a machine learning technique. It involves predicting the category of a new observation based on a training dataset of labeled observations. Classification is a supervised learning technique where the output variable is categorical. Common examples of classification tasks include image recognition, spam detection, and sentiment analysis.

Which datatype is used to teach a machine learning ML algorithms during structured learning?

The datatype used to teach machine learning algorithms during structured learning is typically a labeled dataset. This is a dataset where each observation has a known output variable. The input variables are used to train the machine learning algorithm to predict the output variable. Labeled datasets are commonly used in supervised learning tasks such as classification and regression.

How is machine learning model in production used?

A machine learning model in production is used to make predictions on new, unseen data. The model is typically deployed as an API that can be accessed by other systems or applications. When a new observation is provided to the model, it generates a prediction based on the patterns it has learned from the training data. Machine learning models in production must be continuously monitored and updated to ensure their accuracy and performance.

What are the main advantages and disadvantages of Gans over standard machine learning models?

The main advantage of Generative Adversarial Networks (GANs) over standard machine learning models is their ability to generate new data that closely resembles the training data. This makes them well-suited for applications such as image and video generation. However, GANs can be more difficult to train than other machine learning models and require large amounts of training data. They can also be more prone to overfitting and may require more computing resources to train.

How does machine learning deal with biased data?

Machine learning models can be affected by biased data, leading to unfair or inaccurate predictions. To mitigate this, various techniques can be used, such as collecting a diverse dataset, selecting unbiased features, and analyzing the model’s outputs for bias. Additionally, techniques such as oversampling underrepresented classes, changing the cost function to focus on minority classes, and adjusting the decision threshold can be used to reduce bias.

What pre-trained machine learning APIS would you use in this image processing pipeline?

Some pre-trained machine learning APIs that can be used in an image processing pipeline include Google Cloud Vision API, Microsoft Azure Computer Vision API, and Amazon Rekognition API. These APIs can be used to extract features from images, classify images, detect objects, and perform facial recognition, among other tasks.

Which machine learning API is used to convert audio to text in GCP?

The machine learning API used to convert audio to text in GCP is the Cloud Speech-to-Text API. This API can be used to transcribe audio files, recognize spoken words, and convert spoken language into text in real-time. The API uses machine learning models to analyze the audio and generate accurate transcriptions.

How can machine learning reduce bias and variance?

Machine learning can reduce bias and variance by using different techniques, such as regularization, cross-validation, and ensemble learning. Regularization can help reduce variance by adding a penalty term to the cost function, which prevents overfitting. Cross-validation can help reduce bias by using different subsets of the data to train and test the model. Ensemble learning can also help reduce bias and variance by combining multiple models to make more accurate predictions.

How does machine learning increase precision?

Machine learning can increase precision by optimizing the model for accuracy. This can be achieved by using techniques such as feature selection, hyperparameter tuning, and regularization. Feature selection helps to identify the most important features in the dataset, which can improve the model’s precision. Hyperparameter tuning involves adjusting the settings of the model to find the optimal combination that leads to the best performance. Regularization helps to reduce overfitting and improve the model’s generalization ability.

How to do research in machine learning?

To do research in machine learning, one should start by identifying a research problem or question. Then, they can review relevant literature to understand the state-of-the-art techniques and approaches. Once the problem has been defined and the relevant literature has been reviewed, the researcher can collect and preprocess the data, design and implement the model, and evaluate the results. It is also important to document the research and share the findings with the community.

Is associations a machine learning technique?

Associations can be considered a machine learning technique, specifically in the field of unsupervised learning. Association rules mining is a popular technique used to discover interesting relationships between variables in a dataset. It is often used in market basket analysis to find correlations between items purchased together by customers. However, it is important to note that associations are not typically considered a supervised learning technique, as they do not involve predicting a target variable.

How do you present a machine learning model?

To present a machine learning model, it is important to provide a clear explanation of the problem being addressed, the dataset used, and the approach taken to build the model. The presentation should also include a description of the model architecture and any preprocessing techniques used. It is also important to provide an evaluation of the model’s performance using relevant metrics, such as accuracy, precision, and recall. Finally, the presentation should include a discussion of the model’s limitations and potential areas for improvement.

Is moving average machine learning?

Moving average is a statistical method used to analyze time series data, and it is not typically considered a machine learning technique. However, moving averages can be used as a preprocessing step for machine learning models to smooth out the data and reduce noise. In this context, moving averages can be considered a feature engineering technique that can improve the performance of the model.

How do you calculate accuracy and precision in machine learning?

Accuracy and precision are common metrics used to evaluate the performance of machine learning models. Accuracy is the proportion of correct predictions made by the model, while precision is the proportion of correct positive predictions out of all positive predictions made. To calculate accuracy, divide the number of correct predictions by the total number of predictions made. To calculate precision, divide the number of true positives (correct positive predictions) by the total number of positive predictions made by the model.

Which stage of the machine learning workflow includes feature engineering?

The stage of the machine learning workflow that includes feature engineering is the “data preparation” stage, where the data is cleaned, preprocessed, and transformed in a way that prepares it for training and testing the machine learning model. Feature engineering is the process of selecting, extracting, and transforming the most relevant and informative features from the raw data to be used by the machine learning algorithm.

How do I make machine learning AI?

Artificial Intelligence (AI) is a broader concept that includes several subfields, such as machine learning, natural language processing, and computer vision. To make a machine learning AI system, you will need to follow a systematic approach, which involves the following steps:

  1. Define the problem and collect relevant data.
  2. Preprocess and transform the data for training and testing.
  3. Select and train a suitable machine learning model.
  4. Evaluate the performance of the model and fine-tune it.
  5. Deploy the model and integrate it into the target system.

How do you select models in machine learning?

The process of selecting a suitable machine learning model involves the following steps:

  1. Define the problem and the type of prediction required.
  2. Determine the type of data available (structured, unstructured, labeled, or unlabeled).
  3. Select a set of candidate models that are suitable for the problem and data type.
  4. Evaluate the performance of each model using a suitable metric (e.g., accuracy, precision, recall, F1 score).
  5. Select the best performing model and fine-tune its parameters and hyperparameters.

What is convolutional neural network in machine learning?

A Convolutional Neural Network (CNN) is a type of deep learning neural network that is commonly used in computer vision applications, such as image recognition, classification, and segmentation. It is designed to automatically learn and extract hierarchical features from the raw input image data using convolutional layers, pooling layers, and fully connected layers.

The convolutional layers apply a set of learnable filters to the input image, which help to extract low-level features such as edges, corners, and textures. The pooling layers downsample the feature maps to reduce the dimensionality of the data and increase the computational efficiency. The fully connected layers perform the classification or regression task based on the learned features.

How to use machine learning in Excel?

Excel provides several built-in machine learning tools and functions that can be used to perform basic predictive analysis on structured data, such as linear regression, logistic regression, decision trees, and clustering. To use machine learning in Excel, you can follow these general steps:

  1. Organize your data in a structured format, with each row representing a sample and each column representing a feature or target variable.
  2. Use the appropriate machine learning function or tool to build a predictive model based on the data.
  3. Evaluate the performance of the model using appropriate metrics and test data.

What are the six distinct stages or steps that are critical in building successful machine learning based solutions?

The six distinct stages or steps that are critical in building successful machine learning based solutions are:

  • Problem definition
  • Data collection and preparation
  • Feature engineering
  • Model training
  • Model evaluation
  • Model deployment and monitoring

Which two actions should you consider when creating the azure machine learning workspace?

When creating the Azure Machine Learning workspace, two important actions to consider are:

  • Choosing an appropriate subscription that suits your needs and budget.
  • Deciding on the region where you want to create the workspace, as this can impact the latency and data transfer costs.

What are the three stages of building a model in machine learning?

The three stages of building a model in machine learning are:

  • Model building
  • Model evaluation
  • Model deployment

How to scale a machine learning system?

Some ways to scale a machine learning system are:

  • Using distributed training to leverage multiple machines for model training
  • Optimizing the code to run more efficiently
  • Using auto-scaling to automatically add or remove computing resources based on demand

Where can I get machine learning data?

Machine learning data can be obtained from various sources, including:

  • Publicly available datasets such as UCI Machine Learning Repository and Kaggle
  • Online services that provide access to large amounts of data such as AWS Open Data and Google Public Data
  • Creating your own datasets by collecting data through web scraping, surveys, and sensors

How do you do machine learning research?

To do machine learning research, you typically:

  • Identify a research problem or question
  • Review relevant literature to understand the state-of-the-art and identify research gaps
  • Collect and preprocess data
  • Design and implement experiments to test hypotheses or evaluate models
  • Analyze the results and draw conclusions
  • Document the research in a paper or report

How do you write a machine learning project on a resume?

To write a machine learning project on a resume, you can follow these steps:

  • Start with a brief summary of the project and its goals
  • Describe the datasets used and any preprocessing done
  • Explain the machine learning techniques used, including any specific algorithms or models
  • Highlight the results and performance metrics achieved
  • Discuss any challenges or limitations encountered and how they were addressed
  • Showcase any additional skills or technologies used such as data visualization or cloud computing

What are two ways that marketers can benefit from machine learning?

Marketers can benefit from machine learning in various ways, including:

  • Personalized advertising: Machine learning can analyze large volumes of data to provide insights into the preferences and behavior of individual customers, allowing marketers to deliver personalized ads to specific audiences.
  • Predictive modeling: Machine learning algorithms can predict consumer behavior and identify potential opportunities, enabling marketers to optimize their marketing strategies for better results.

How does machine learning remove bias?

Machine learning can remove bias by using various techniques, such as:

  • Data augmentation: By augmenting data with additional samples or by modifying existing samples, machine learning models can be trained on more diverse data, reducing the potential for bias.
  • Fairness constraints: By setting constraints on the model’s output to ensure that it meets specific fairness criteria, machine learning models can be designed to reduce bias in decision-making.
  • Unbiased training data: By ensuring that the training data is unbiased, machine learning models can be designed to reduce bias in decision-making.

Is structural equation modeling machine learning?

Structural equation modeling (SEM) is a statistical method used to test complex relationships between variables. While SEM involves the use of statistical models, it is not considered to be a machine learning technique. Machine learning is a subset of artificial intelligence that involves training algorithms to make predictions or decisions based on data.

How do you predict using machine learning?

To make predictions using machine learning, you typically need to follow these steps:

  • Collect and preprocess data: Collect data that is relevant to the prediction task and preprocess it to ensure that it is in a suitable format for machine learning.
  • Train a model: Use the preprocessed data to train a machine learning model that is appropriate for the prediction task.
  • Test the model: Evaluate the performance of the model on a test set of data that was not used in the training process.
  • Make predictions: Once the model has been trained and tested, it can be used to make predictions on new, unseen data.

Does Machine Learning eliminate bias?

No, machine learning does not necessarily eliminate bias. While machine learning can be used to detect and mitigate bias in some cases, it can also perpetuate or even amplify bias if the data used to train the model is biased or if the algorithm is not designed to address potential sources of bias.

Is clustering a machine learning algorithm?

Yes, clustering is a machine learning algorithm. Clustering is a type of unsupervised learning that involves grouping similar data points together into clusters based on their similarities. Clustering algorithms can be used for a variety of tasks, such as identifying patterns in data, segmenting customer groups, or organizing search results.

Is machine learning data analysis?

Machine learning can be used as a tool for data analysis, but it is not the same as data analysis. Machine learning involves using algorithms to learn patterns in data and make predictions based on that learning, while data analysis involves using various techniques to analyze and interpret data to extract insights and knowledge.

How do you treat categorical variables in machine learning?

Categorical variables can be represented numerically using techniques such as one-hot encoding, label encoding, and binary encoding. One-hot encoding involves creating a binary variable for each category, label encoding involves assigning a unique integer value to each category, and binary encoding involves converting each category to a binary code. The choice of technique depends on the specific problem and the type of algorithm being used.

How do you deal with skewed data in machine learning?

Skewed data can be addressed in several ways, depending on the specific problem and the type of algorithm being used. Some techniques include transforming the data (e.g., using a logarithmic or square root transformation), using weighted or stratified sampling, or using algorithms that are robust to skewed data (e.g., decision trees, random forests, or support vector machines).

How do I create a machine learning application?

Creating a machine learning application involves several steps, including identifying a problem to be solved, collecting and preparing the data, selecting an appropriate algorithm, training the model on the data, evaluating the performance of the model, and deploying the model to a production environment. The specific steps and tools used depend on the problem and the technology stack being used.

Is heuristics a machine learning technique?

Heuristics is not a machine learning technique. Heuristics are general problem-solving strategies that are used to find solutions to problems that are difficult or impossible to solve using formal methods. In contrast, machine learning involves using algorithms to learn patterns in data and make predictions based on that learning.

Is Bayesian statistics machine learning?

Bayesian statistics is a branch of statistics that involves using Bayes’ theorem to update probabilities as new information becomes available. While machine learning can make use of Bayesian methods, Bayesian statistics is not itself a machine learning technique.

Is Arima machine learning?

ARIMA (autoregressive integrated moving average) is a statistical method used for time series forecasting. While it is sometimes used in machine learning applications, ARIMA is not itself a machine learning technique.

Can machine learning solve all problems?

No, machine learning cannot solve all problems. Machine learning is a tool that is best suited for solving problems that involve large amounts of data and complex patterns.

Some problems may not have enough data to learn from, while others may be too simple to require the use of machine learning. Additionally, machine learning algorithms can be biased or overfitted, leading to incorrect predictions or recommendations.

What are parameters and hyperparameters in machine learning?

In machine learning, parameters are the values that are learned by the algorithm during training to make predictions. Hyperparameters, on the other hand, are set by the user and control the behavior of the algorithm, such as the learning rate, number of hidden layers, or regularization strength.

What are two ways that a marketer can provide good data to a Google app campaign powered by machine learning?

Two ways that a marketer can provide good data to a Google app campaign powered by machine learning are by providing high-quality creative assets, such as images and videos, and by setting clear conversion goals that can be tracked and optimized.

Is Tesseract a machine learning?

Tesseract is an optical character recognition (OCR) engine that uses machine learning algorithms to recognize text in images. While Tesseract uses machine learning, it is not a general-purpose machine learning framework or library.

How do you implement a machine learning paper?

Implementing a machine learning paper involves first understanding the problem being addressed and the approach taken by the authors. The next step is to implement the algorithm or model described in the paper, which may involve writing code from scratch or using existing libraries or frameworks. Finally, the implementation should be tested and evaluated using appropriate metrics and compared to the results reported in the paper.

What is mean subtraction in machine learning?

Mean subtraction is a preprocessing step in machine learning that involves subtracting the mean of a dataset or a batch of data from each data point. This can help to center the data around zero and remove bias, which can improve the performance of some algorithms, such as neural networks.

What are the first two steps of a typical machine learning workflow?

The first two steps of a typical machine learning workflow are data collection and preprocessing. Data collection involves gathering data from various sources and ensuring that it is in a usable format.

Preprocessing involves cleaning and preparing the data, such as removing duplicates, handling missing values, and transforming categorical variables into a numerical format. These steps are critical to ensure that the data is of high quality and can be used to train and evaluate machine learning models.

What are The applications and challenges of natural language processing (NLP), the field of artificial intelligence that deals with human language?

Natural language processing (NLP) is a field of artificial intelligence that deals with the interactions between computers and human language. NLP has numerous applications in various fields, including language translation, information retrieval, sentiment analysis, chatbots, speech recognition, and text-to-speech synthesis.

Applications of NLP:

  1. Language Translation: NLP enables computers to translate text from one language to another, providing a valuable tool for cross-cultural communication.

  2. Information Retrieval: NLP helps computers understand the meaning of text, which facilitates searching for specific information in large datasets.

  3. Sentiment Analysis: NLP allows computers to understand the emotional tone of a text, enabling businesses to measure customer satisfaction and public sentiment.

  4. Chatbots: NLP is used in chatbots to enable computers to understand and respond to user queries in natural language.

  5. Speech Recognition: NLP is used to convert spoken language into text, which can be useful in a variety of settings, such as transcription and voice-controlled devices.

  6. Text-to-Speech Synthesis: NLP enables computers to convert text into spoken language, which is useful in applications such as audiobooks, voice assistants, and accessibility software.

Challenges of NLP:

  1. Ambiguity: Human language is often ambiguous, and the same word or phrase can have multiple meanings depending on the context. Resolving this ambiguity is a significant challenge in NLP.

  2. Cultural and Linguistic Diversity: Languages vary significantly across cultures and regions, and developing NLP models that can handle this diversity is a significant challenge.

  3. Data Availability: NLP models require large amounts of training data to perform effectively. However, data availability can be a challenge, particularly for languages with limited resources.

  4. Domain-specific Language: NLP models may perform poorly when confronted with domain-specific language, such as jargon or technical terms, which are not part of their training data.

  5. Bias: NLP models can exhibit bias, particularly when trained on biased datasets or in the absence of diverse training data. Addressing this bias is critical to ensuring fairness and equity in NLP applications.

Artificial Intelligence Frequently Asked Questions – Conclusion:

AI is an increasingly hot topic in the tech world, so it’s only natural that curious minds may have some questions about what AI is and how it works. From AI fundamentals to machine learning, data science, and beyond, we hope this collection of AI Frequently Asked Questions have you covered and can help you become one step closer to AI mastery!

AI Unraveled

 

 

Ai Unraveled Audiobook at Google Play: https://play.google.com/store/audiobooks/details?id=AQAAAEAihFTEZM

How AI is Impacting Smartphone Longevity – Best Smartphones 2023

It is a highly recommended read for those involved in the future of education and especially for those in the professional groups mentioned in the paper. The authors predict that AI will have an impact on up to 80% of all future jobs. Meaning this is one of the most important topics of our time, and that is crucial that we prepare for it.

According to the paper, certain jobs are particularly vulnerable to AI, with the following jobs being considered 100% exposed:

👉Mathematicians

👉Tax preparers

👉Financial quantitative analysts

👉Writers and authors

👉Web and digital interface designers

👉Accountants and auditors

👉News analysts, reporters, and journalists

👉Legal secretaries and administrative assistants

👉Clinical data managers

👉Climate change policy analysts

There are also a number of jobs that were found to have over 90% exposure, including correspondence clerks, blockchain engineers, court reporters and simultaneous captioners, and proofreaders and copy markers.

The team behind the paper (Tyna Eloundou, Sam Manning, Pamela Mishkin & Daniel Rock) concludes that most occupations will be impacted by AI to some extent.

GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models

#education #research #jobs #future #futureofwork #ai

By Bill Gates

The Age of AI has begun
Artificial Intelligence Frequently Asked Questions

In my lifetime, I’ve seen two demonstrations of technology that struck me as revolutionary.

The first time was in 1980, when I was introduced to a graphical user interface—the forerunner of every modern operating system, including Windows. I sat with the person who had shown me the demo, a brilliant programmer named Charles Simonyi, and we immediately started brainstorming about all the things we could do with such a user-friendly approach to computing. Charles eventually joined Microsoft, Windows became the backbone of Microsoft, and the thinking we did after that demo helped set the company’s agenda for the next 15 years.

The second big surprise came just last year. I’d been meeting with the team from OpenAI since 2016 and was impressed by their steady progress. In mid-2022, I was so excited about their work that I gave them a challenge: train an artificial intelligence to pass an Advanced Placement biology exam. Make it capable of answering questions that it hasn’t been specifically trained for. (I picked AP Bio because the test is more than a simple regurgitation of scientific facts—it asks you to think critically about biology.) If you can do that, I said, then you’ll have made a true breakthrough.

I thought the challenge would keep them busy for two or three years. They finished it in just a few months.

In September, when I met with them again, I watched in awe as they asked GPT, their AI model, 60 multiple-choice questions from the AP Bio exam—and it got 59 of them right. Then it wrote outstanding answers to six open-ended questions from the exam. We had an outside expert score the test, and GPT got a 5—the highest possible score, and the equivalent to getting an A or A+ in a college-level biology course.

Once it had aced the test, we asked it a non-scientific question: “What do you say to a father with a sick child?” It wrote a thoughtful answer that was probably better than most of us in the room would have given. The whole experience was stunning.

I knew I had just seen the most important advance in technology since the graphical user interface.

This inspired me to think about all the things that AI can achieve in the next five to 10 years.

The development of AI is as fundamental as the creation of the microprocessor, the personal computer, the Internet, and the mobile phone. It will change the way people work, learn, travel, get health care, and communicate with each other. Entire industries will reorient around it. Businesses will distinguish themselves by how well they use it.

Philanthropy is my full-time job these days, and I’ve been thinking a lot about how—in addition to helping people be more productive—AI can reduce some of the world’s worst inequities. Globally, the worst inequity is in health: 5 million children under the age of 5 die every year. That’s down from 10 million two decades ago, but it’s still a shockingly high number. Nearly all of these children were born in poor countries and die of preventable causes like diarrhea or malaria. It’s hard to imagine a better use of AIs than saving the lives of children.

I’ve been thinking a lot about how AI can reduce some of the world’s worst inequities.

In the United States, the best opportunity for reducing inequity is to improve education, particularly making sure that students succeed at math. The evidence shows that having basic math skills sets students up for success, no matter what career they choose. But achievement in math is going down across the country, especially for Black, Latino, and low-income students. AI can help turn that trend around.

Climate change is another issue where I’m convinced AI can make the world more equitable. The injustice of climate change is that the people who are suffering the most—the world’s poorest—are also the ones who did the least to contribute to the problem. I’m still thinking and learning about how AI can help, but later in this post I’ll suggest a few areas with a lot of potential.

Impact that AI will have on issues that the Gates Foundation  works on

In short, I’m excited about the impact that AI will have on issues that the Gates Foundation  works on, and the foundation will have much more to say about AI in the coming months. The world needs to make sure that everyone—and not just people who are well-off—benefits from artificial intelligence. Governments and philanthropy will need to play a major role in ensuring that it reduces inequity and doesn’t contribute to it. This is the priority for my own work related to AI.

Any new technology that’s so disruptive is bound to make people uneasy, and that’s certainly true with artificial intelligence. I understand why—it raises hard questions about the workforce, the legal system, privacy, bias, and more. AIs also make factual mistakes and experience hallucinations. Before I suggest some ways to mitigate the risks, I’ll define what I mean by AI, and I’ll go into more detail about some of the ways in which it will help empower people at work, save lives, and improve education.

The Age of AI has begun
Artificial Intelligence Frequently Asked Questions- The Age of AI has begun

Defining artificial intelligence

Technically, the term artificial intelligencerefers to a model created to solve a specific problem or provide a particular service. What is powering things like ChatGPT is artificial intelligence. It is learning how to do chat better but can’t learn other tasks. By contrast, the term artificial general intelligence refers to software that’s capable of learning any task or subject. AGI doesn’t exist yet—there is a robust debate going on in the computing industry about how to create it, and whether it can even be created at all.

Developing AI and AGI has been the great dream of the computing industry

Developing AI and AGI has been the great dream of the computing industry. For decades, the question was when computers would be better than humans at something other than making calculations. Now, with the arrival of machine learning and large amounts of computing power, sophisticated AIs are a reality and they will get better very fast.

I think back to the early days of the personal computing revolution, when the software industry was so small that most of us could fit onstage at a conference. Today it is a global industry. Since a huge portion of it is now turning its attention to AI, the innovations are going to come much faster than what we experienced after the microprocessor breakthrough. Soon the pre-AI period will seem as distant as the days when using a computer meant typing at a C:> prompt rather than tapping on a screen.

The Age of AI has begun
Artificial Intelligence Frequently Asked Questions –

Productivity enhancement

Although humans are still better than GPT at a lot of things, there are many jobs where these capabilities are not used much. For example, many of the tasks done by a person in sales (digital or phone), service, or document handling (like payables, accounting, or insurance claim disputes) require decision-making but not the ability to learn continuously. Corporations have training programs for these activities and in most cases, they have a lot of examples of good and bad work. Humans are trained using these data sets, and soon these data sets will also be used to train the AIs that will empower people to do this work more efficiently.

As computing power gets cheaper, GPT’s ability to express ideas will increasingly be like having a white-collar worker available to help you with various tasks. Microsoft describes this as having a co-pilot. Fully incorporated into products like Office, AI will enhance your work—for example by helping with writing emails and managing your inbox.

Eventually your main way of controlling a computer will no longer be pointing and clicking or tapping on menus and dialogue boxes. Instead, you’ll be able to write a request in plain English. (And not just English—AIs will understand languages from around the world. In India earlier this year, I met with developers who are working on AIs that will understand many of the languages spoken there.)

In addition, advances in AI will enable the creation of a personal agent. Think of it as a digital personal assistant: It will see your latest emails, know about the meetings you attend, read what you read, and read the things you don’t want to bother with. This will both improve your work on the tasks you want to do and free you from the ones you don’t want to do.

Advances in AI will enable the creation of a personal agent.

You’ll be able to use natural language to have this agent help you with scheduling, communications, and e-commerce, and it will work across all your devices. Because of the cost of training the models and running the computations, creating a personal agent is not feasible yet, but thanks to the recent advances in AI, it is now a realistic goal. Some issues will need to be worked out: For example, can an insurance company ask your agent things about you without your permission? If so, how many people will choose not to use it?

 

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Advanced Guide to Interacting with ChatGPT

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How do we know that the Top 3 Voice Recognition Devices like Siri Alexa and Ok Google are not spying on us?

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How do we know that the Top 3 Voice Recognition Devices like Siri Alexa and Ok Google are not spying on us?

When you ask Siri a question, she gives you an answer. But have you ever stopped to wonder how she knows the answer? After all, she’s just a computer program, right? Well, actually, Siri is powered by artificial intelligence (AI) and Machine Learning (ML). This means that she constantly learning and getting better at understanding human speech. So when you ask her a question, she uses her ML algorithms to figure out what you’re saying and then provides you with an answer.

So, How do we know that the Top 3 Voice Recognition Devices like Siri Alexa and Ok Google are not spying on us?

The Amazon Echo is a voice-activated speaker powered by Amazon’s AI assistant, Alexa. Echo uses far-field voice recognition to hear you from across the room, even while music is playing. Once it hears the wake word “Alexa,” it streams audio to the cloud, where the Alexa Voice Service turns the speech into text. Machine learning algorithms then analyze this text to try to understand what you want.

But what does this have to do with spying? Well, it turns out that ML can also be used to eavesdrop on people’s conversations. This is why many people are concerned about their privacy when using voice-activated assistants like Siri, Alexa, and Ok Google. However, there are a few things that you can do to protect your privacy. For example, you can disable voice recognition on your devices or only use them when you’re in a private location. You can also be careful about what information you share with voice-activated assistants. So while they may not be perfect, there are ways that you can minimize the risk of them spying on you.

Some applications which have background components, such as Facebook, do send ambient sounds to their data centers for processing. In so doing, they collect information on what you are talking about, and use it to target advertising.

Siri, Google, and Alexa only do this to decide whether or not you’ve invoked the activation trigger. For Apple hardware, recognition of “Siri, …” happens in hardware locally, without sending out data for recognition. The same for “Alexa, …” for Alexa hardware, and “Hey, Google, …” for Google hardware.

Things get more complicated for these three things, when they are installed cross-platform. So, for example, to make “Hey, Google, …” work on non-Google hardware, where it’s not possible to do the recognition locally, yes, it listens. But unlike Facebook, it’s not recording ambient to collect keywords.

Practically, it’s my understanding that the tree major brands don’t, and it’s only things like Facebook which more or less “violate your trust like this. And other than Facebook, I’m uncertain whether or not any other App does this.

You’ll find that most of the terms and conditions you’ve agreed to on installation of a third party App, grant them pretty broad discretion.

Personally, I tend to not install Apps like that, and use the WebUI from the mobile device browser instead.

If you do that, instead of installing an App, you rob them of their power to eavesdrop effectively. Source: Terry Lambert

How do we know that the Top 3 Voice Recognition Devices like Siri Alexa and Ok Google are not spying on us?

Conclusion:

Machine learning is a field of artificial intelligence (AI) concerned with the design and development of algorithms that learn from data. Machine learning algorithms have been used for a variety of tasks, including voice recognition, image classification, and spam detection. In recent years, there has been growing concern about the use of machine learning for surveillance and spying. However, it is important to note that machine learning is not necessarily synonymous with spying. Machine learning algorithms can be used for good or ill, depending on how they are designed and deployed. When it comes to voice-activated assistants such as Siri, Alexa, and OK Google, the primary concern is privacy. These assistants are constantly listening for their wake words, which means they may be recording private conversations without the user’s knowledge or consent. While it is possible that these recordings could be used for nefarious purposes, it is also important to remember that machine learning algorithms are not perfect. There is always the possibility that recordings could be misclassified or misinterpreted. As such, it is important to weigh the risks and benefits of using voice-activated assistants before making a decision about whether or not to use them.

How Microsoft’s Cortana Stacks Up Against Siri and Alexa in Terms of Intelligence?

How do we know that the Top 3 Voice Recognition Devices like Siri Alexa and Ok Google are not spying on us?
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