What is OpenAI Q*? A deeper look at the Q* Model as a combination of A* algorithms and Deep Q-learning networks.

Embark on a journey of discovery with our podcast, ‘What is OpenAI Q*? A Deeper Look at the Q* Model’. Dive into the cutting-edge world of AI as we unravel the mysteries of OpenAI’s Q* model, a groundbreaking blend of A* algorithms and Deep Q-learning networks. 🌟🤖

In this detailed exploration, we dissect the components of the Q* model, explaining how A* algorithms’ pathfinding prowess synergizes with the adaptive decision-making capabilities of Deep Q-learning networks. This video is perfect for anyone curious about the intricacies of AI models and their real-world applications.

Understand the significance of this fusion in AI technology and how it’s pushing the boundaries of machine learning, problem-solving, and strategic planning. We also delve into the potential implications of Q* in various sectors, discussing both the exciting possibilities and the ethical considerations.

Join the conversation about the future of AI and share your thoughts on how models like Q* are shaping the landscape. Don’t forget to like, share, and subscribe for more deep dives into the fascinating world of artificial intelligence! #OpenAIQStar #AStarAlgorithms #DeepQLearning #ArtificialIntelligence #MachineLearningInnovation”

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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 rumors surrounding a groundbreaking AI called Q*, OpenAI’s leaked AI breakthrough called Q* and DeepMind’s similar project, the potential of AI replacing human jobs in tasks like wire sending, and a recommended book called “AI Unraveled” that answers frequently asked questions about artificial intelligence.

Rumors have been circulating about a groundbreaking AI known as Q* (pronounced Q-Star), which is closely tied to a series of chaotic events that disrupted OpenAI following the sudden dismissal of their CEO, Sam Altman. In this discussion, we will explore the implications of Altman’s firing, speculate on potential reasons behind it, and consider Microsoft’s pursuit of a monopoly on highly efficient AI technologies.

To comprehend the significance of Q*, it is essential to delve into the theory of combining Q-learning and A* algorithms. Q* is an AI that excels in grade-school mathematics without relying on external aids like Wolfram. This achievement is revolutionary and challenges common perceptions of AI as mere information repeaters and stochastic parrots. Q* showcases iterative learning, intricate logic, and highly effective long-term strategizing, potentially paving the way for advancements in scientific research and breaking down previously insurmountable barriers.

Let’s first understand A* algorithms and Q-learning to grasp the context in which Q* operates. A* algorithms are powerful tools used to find the shortest path between two points in a graph or map while efficiently navigating obstacles. These algorithms excel at optimizing route planning when efficiency is crucial. In the case of chatbot AI, A* algorithms are used to traverse complex information landscapes and locate the most relevant responses or solutions for user queries.

On the other hand, Q-learning involves providing the AI with a constantly expanding cheat sheet to help it make the best decisions based on past experiences. However, in complex scenarios with numerous states and actions, maintaining a large cheat sheet becomes impractical. Deep Q-learning addresses this challenge by utilizing neural networks to approximate the Q-value function, making it more efficient. Instead of a colossal Q-table, the network maps input states to action-Q-value pairs, providing a compact cheat sheet to navigate complex scenarios efficiently. This approach allows AI agents to choose actions using the Epsilon-Greedy approach, sometimes exploring randomly and sometimes relying on the best-known actions predicted by the networks. DQNs (Deep Q-networks) typically use two neural networks—the main and target networks—which periodically synchronize their weights, enhancing learning and stabilizing the overall process. This synchronization is crucial for achieving self-improvement, which is a remarkable feat. Additionally, the Bellman equation plays a role in updating weights using Experience replay, a sampling and training technique based on past actions, which allows the AI to learn in small batches without requiring training after every step.

Q* represents more than a math prodigy; it signifies the potential to scale abstract goal navigation, enabling highly efficient, realistic, and logical planning for any query or goal. However, with such capabilities come challenges.

One challenge is web crawling and navigating complex websites. Just as a robot solving a maze may encounter convoluted pathways and dead ends, the web is labyrinthine and filled with myriad paths. While A* algorithms aid in seeking the shortest path, intricate websites or information silos can confuse the AI, leading it astray. Furthermore, the speed of algorithm updates may lag behind the expansion of the web, potentially hindering the AI’s ability to adapt promptly to changes in website structures or emerging information.

Another challenge arises in the application of Q-learning to high-dimensional data. The web contains various data types, from text to multimedia and interactive elements. Deep Q-learning struggles with high-dimensional data, where the number of features exceeds the number of observations. In such cases, if the AI encounters sites with complex structures or extensive multimedia content, efficiently processing such information becomes a significant challenge.

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To address these issues, a delicate balance must be struck between optimizing pathfinding efficiency and adapting swiftly to the dynamic nature of the web. This balance ensures that users receive the most relevant and efficient solutions to their queries.

In conclusion, speculations surrounding Q* and the Gemini models suggest that enabling AI to plan is a highly rewarding but risky endeavor. As we continue researching and developing these technologies, it is crucial to prioritize AI safety protocols and put guardrails in place. This precautionary approach prevents the potential for AI to turn against us. Are we on the brink of an AI paradigm shift, or are these rumors mere distractions? Share your thoughts and join in this evolving AI saga—a front-row seat to the future!

Please note that the information presented here is based on speculation sourced from various news articles, research, and rumors surrounding Q*. Hence, it is advisable to approach this discussion with caution and consider it in light of further developments in the field.

How the Rumors about Q* Started

There have been recent rumors surrounding a supposed AI breakthrough called Q*, which allegedly involves a combination of Q-learning and A*. These rumors were initially sparked when OpenAI, the renowned artificial intelligence research organization, accidentally leaked information about this groundbreaking development, specifically mentioning Q*’s impressive ability to ace grade-school math. However, it is crucial to note that these rumors were subsequently refuted by OpenAI.

It is worth mentioning that DeepMind, another prominent player in the AI field, is also working on a similar project called Gemini. Gemina is based on AlphaGo-style Monte Carlo Tree Search and aims to scale up the capabilities of these algorithms. The scalability of such systems is crucial in planning for increasingly abstract goals and achieving agentic behavior. These concepts have been extensively discussed and explored within the academic community for some time.

The origin of the rumors can be traced back to a letter sent by several staff researchers at OpenAI to the organization’s board of directors. The letter served as a warning highlighting the potential threat to humanity posed by a powerful AI discovery. This letter specifically referenced the supposed breakthrough known as Q* (pronounced Q-Star) and its implications.

Mira Murati, a representative of OpenAI, confirmed that the letter regarding the AI breakthrough was directly responsible for the subsequent actions taken by the board. The new model, when provided with vast computing resources, demonstrated the ability to solve certain mathematical problems. Although it performed at the level of grade-school students in mathematics, the researchers’ optimism about Q*’s future success grew due to its proficiency in such tests.

A notable theory regarding the nature of OpenAI’s alleged breakthrough is that Q* may be related to Q-learning. One possibility is that Q* represents the optimal solution of the Bellman equation. Another hypothesis suggests that Q* could be a combination of the A* algorithm and Q-learning. Additionally, some speculate that Q* might involve AlphaGo-style Monte Carlo Tree Search of the token trajectory. This idea builds upon previous research, such as AlphaCode, which demonstrated significant improvements in competitive programming through brute-force sampling in an LLM (Language and Learning Model). These speculations lead many to believe that Q* might be focused on solving math problems effectively.

Considering DeepMind’s involvement, experts also draw parallels between their Gemini project and OpenAI’s Q*. Gemini aims to combine the strengths of AlphaGo-type systems, particularly in terms of language capabilities, with new innovations that are expected to be quite intriguing. Demis Hassabis, a prominent figure at DeepMind, stated that Gemini would utilize AlphaZero-based MCTS (Monte Carlo Tree Search) through chains of thought. This aligns with DeepMind Chief AGI scientist Shane Legg’s perspective that starting a search is crucial for creative problem-solving.

It is important to note that amidst the excitement and speculation surrounding OpenAI’s alleged breakthrough, the academic community has already extensively explored similar ideas. In the past six months alone, numerous papers have discussed the combination of tree-of-thought, graph search, state-space reinforcement learning, and LLMs (Language and Learning Models). This context reminds us that while Q* might be a significant development, it is not entirely unprecedented.

OpenAI’s spokesperson, Lindsey Held Bolton, has officially rebuked the rumors surrounding Q*. In a statement provided to The Verge, Bolton clarified that Mira Murati only informed employees about the media reports regarding the situation and did not comment on the accuracy of the information.

In conclusion, rumors regarding OpenAI’s Q* project have generated significant interest and speculation. The alleged breakthrough combines concepts from Q-learning and A*, potentially leading to advancements in solving math problems. Furthermore, DeepMind’s Gemini project shares similarities with Q*, aiming to integrate the strengths of AlphaGo-type systems with language capabilities. While the academic community has explored similar ideas extensively, the potential impact of Q* and Gemini on planning for abstract goals and achieving agentic behavior remains an exciting prospect within the field of artificial intelligence.

In simple terms, long-range planning and multi-modal models together create an economic agent. Allow me to paint a scenario for you: Picture yourself working at a bank. A notification appears, asking what you are currently doing. You reply, “sending a wire for a customer.” An AI system observes your actions, noting a path and policy for mimicking the process.

The next time you mention “sending a wire for a customer,” the AI system initiates the learned process. However, it may make a few errors, requiring your guidance to correct them. The AI system then repeats this learning process with all 500 individuals in your job role.

Within a week, it becomes capable of recognizing incoming emails, extracting relevant information, navigating to the wire sending window, completing the required information, and ultimately sending the wire.

This approach combines long-term planning, a reward system, and reinforcement learning policies, akin to Q* A* methods. If planning and reinforcing actions through a multi-modal AI prove successful, it is possible that jobs traditionally carried out by humans using keyboards could become obsolete within the span of 1 to 3 years.

If you are keen to enhance your knowledge about artificial intelligence, there is an invaluable resource that can provide the answers you seek. “AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence” is a must-have book that can help expand your understanding of this fascinating field. You can easily find this essential book at various reputable online platforms such as Etsy, Shopify, Apple, Google, or Amazon.

AI Unraveled offers a comprehensive exploration of commonly asked questions about artificial intelligence. With its informative and insightful content, this book unravels the complexities of AI in a clear and concise manner. Whether you are a beginner or have some familiarity with the subject, this book is designed to cater to various levels of knowledge.

By delving into key concepts, AI Unraveled provides readers with a solid foundation in artificial intelligence. It covers a wide range of topics, including machine learning, deep learning, neural networks, natural language processing, and much more. The book also addresses the ethical implications and social impact of AI, ensuring a well-rounded understanding of this rapidly advancing technology.

Obtaining a copy of “AI Unraveled” will empower you with the knowledge necessary to navigate the complex world of artificial intelligence. Whether you are an individual looking to expand your expertise or a professional seeking to stay ahead in the industry, this book is an essential resource that deserves a place in your collection. Don’t miss the opportunity to demystify the frequently asked questions about AI with this invaluable book.

In today’s episode, we discussed the groundbreaking AI Q*, which combines A* Algorithms and Q-learning, and how it is being developed by OpenAI and DeepMind, as well as the potential future impact of AI on job replacement, and a recommended book called “AI Unraveled” that answers common questions about 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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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

The Future of Generative AI: From Art to Reality Shaping

Improving Q* (SoftMax with Hierarchical Curiosity)

Combining efficiency in handling large action spaces with curiosity-driven exploration.

Source: GitHub – RichardAragon/Softmaxwithhierarchicalcuriosity

Softmaxwithhierarchicalcuriosity

Adaptive Softmax with Hierarchical Curiosity

This algorithm combines the strengths of Adaptive Softmax and Hierarchical Curiosity to achieve better performance and efficiency.

Adaptive Softmax

Adaptive Softmax is a technique that improves the efficiency of reinforcement learning by dynamically adjusting the granularity of the action space. In Q*, the action space is typically represented as a one-hot vector, which can be inefficient for large action spaces. Adaptive Softmax addresses this issue by dividing the action space into clusters and assigning higher probabilities to actions within the most promising clusters.

Hierarchical Curiosity

Hierarchical Curiosity is a technique that encourages exploration by introducing a curiosity bonus to the reward function. The curiosity bonus is based on the difference between the predicted reward and the actual reward, motivating the agent to explore areas of the environment that are likely to provide new information.

Combining Adaptive Softmax and Hierarchical Curiosity

By combining Adaptive Softmax and Hierarchical Curiosity, we can achieve a more efficient and exploration-driven reinforcement learning algorithm. Adaptive Softmax improves the efficiency of the algorithm, while Hierarchical Curiosity encourages exploration and potentially leads to better performance in the long run.

Here’s the proposed algorithm:

1. Initialize the Q-values for all actions in all states.

2. At each time step:

   a. Observe the current state s.

   b. Select an action a according to an exploration policy that balances exploration and exploitation.

   c. Execute action a and observe the resulting state s’ and reward r.

   d. Update the Q-value for action a in state s:

   Q(s, a) = (1 – α) * Q(s, a) + α * (r + γ * max_a’ Q(s’, a’))

   where α is the learning rate and γ is the discount factor.

   e. Update the curiosity bonus for state s:

   curio(s) = β * |r – Q(s, a)|

   where β is the curiosity parameter.

   f. Update the probability distribution over actions:

   p(a | s) = exp(Q(s, a) + curio(s)) / ∑_a’ exp(Q(s, a’) + curio(s))

3. Repeat steps 2a-2f until the termination criterion is met.

The combination of Adaptive Softmax and Hierarchical Curiosity addresses the limitations of Q* and promotes more efficient and effective exploration.

• Understanding the Technology Behind Home Layout Design Tools: A Case Study of HomeDesigns.ai
  by /u/Big_Definition_1996 (Artificial Intelligence Gateway) on July 25, 2024 at 11:33 pm

  I'm trying to understand how home layout design tools, like the one found at https://homedesigns.ai/, are created. What technology stack do they use? Do they rely on APIs or have their own models? What technologies should I learn to develop something similar? submitted by /u/Big_Definition_1996 [link] [comments]

• This week’s AI GPT Journal Weekly Insights Newsletter just dropped on LinkedIn
  by /u/AIGPTJournal (Artificial Intelligence Gateway) on July 25, 2024 at 10:54 pm

  Dive into this week’s AI GPT Journal Weekly Insights! Uncover the truth behind AI-generated answers, combat Instagram bot accounts, and explore AI Explainability for non-techies. Stay informed and ahead in the AI revolution. Click here to read now. #AIGPTJournal #FactCheckingAI #InstagramBots #TrustworthyAI submitted by /u/AIGPTJournal [link] [comments]

• Why Aren't the Jena Ortega/Wednesday Deepfake Accounts on TikTok and YouTube Being Taken Down?
  by /u/Relative_Season_299 (Artificial Intelligence Gateway) on July 25, 2024 at 10:32 pm

  This question is for any lawyers here or people familiar with the law. I’m sure you’ve all seen the deepfake Jena Ortega/Wednesday accounts on TikTok and YouTube. Why haven’t any parties (e.g., CAA represents Jena, Netflix owns the Wednesday IP) taken action against these accounts? I came across them almost a year ago and assumed they’d be taken down at this point, on YouTube especially since it pays its creators. The Ortega channel on YouTube has 7 million subscribers so I’m assuming the creator is making a fair bit of money. Is this fair use? The Same Wednesday submitted by /u/Relative_Season_299 [link] [comments]

• Document Parsing
  by /u/cfksite (Artificial Intelligence Gateway) on July 25, 2024 at 10:26 pm

  I have a situation where I have about 800 PDF documents, each one of that is the same “type” of document, but almost each one is different (layout,labels, etc). Example. Delivery address, Drop Location, Dropoff Address. All of these are the same data point with different labels. What would be the best way to leverage AI to be able to send this document into a script and have it shoot out all my specific fields I need for my software? I have tried a few different integrated solutions but I always get stuck on different documents having different labels. submitted by /u/cfksite [link] [comments]

• AI Tool Turns Conversations into Newsletters in Minutes
  by /u/jacobgc75 (Artificial Intelligence Gateway) on July 25, 2024 at 10:22 pm

  New AI Writing Assistant Simplifies Newsletter Creation My team and I have developed an AI-powered tool that helps create personalized newsletters quickly. This tool transforms conversations into written content, allowing users to generate newsletters in about 10 minutes. It's designed to make newsletters while maintaining the user's unique voice and ideas. The primary goal of this tool is to make newsletter creation more accessible. It can be particularly helpful for people who find writing challenging, those with limited time, individuals with conditions like dyslexia, or anyone looking to share ideas more easily. By simplifying the writing process, we hope to enable more people to share their thoughts and insights through newsletters. Here's how it works: Users share their ideas through a conversation with the AI. The system processes this information and then creates a draft of the newsletter. Users can then review and adjust the content as needed, ensuring the final product accurately reflects their thoughts. The tool uses Claude 3.5, which we found to be the best for our needs. We're currently taking applications for our beta program. If you're interested in creating a newsletter with us, you can apply now. For those who want to try it out right away, we have an app available now that focuses on the writing aspect. In the coming weeks, we'll be updating this app to provide an end-to-end newsletter solution. If you're interested in the current writing tool, you can start using it today. Let us know if you have any questions or feedback about what we are building. We're looking forward to seeing how this technology can help more people share their ideas, thoughts, and opinions more effectively. submitted by /u/jacobgc75 [link] [comments]

• SearchGPT, OpenAI Newly Announced Search AI, Looks a Lot Like Perplexity AI
  by /u/Worst_Artist (Artificial Intelligence Gateway) on July 25, 2024 at 9:46 pm

  You can sign up for the waitlist here https://openai.com/index/searchgpt-prototype/ The features of SearchGPT closely mirror those of Perplexity AI. Here's a comparison submitted by /u/Worst_Artist [link] [comments]

• OpenAI announces a search engine called SearchGPT
  by /u/Express_Fan7016 (Artificial Intelligence Gateway) on July 25, 2024 at 9:38 pm

  OpenAI announced a prototype of its SearchGPT search engine on Thursday. The AI-powered search engine could ramp up pressure on Google. https://www.cnbc.com/2024/07/25/openai-announces-a-search-engine-called-searchgpt.html submitted by /u/Express_Fan7016 [link] [comments]

• What are some tests that I can give to chat AIs (gpt-4o, Perplexity, etc.)?
  by /u/PashkaTLT (Artificial Intelligence Gateway) on July 25, 2024 at 8:55 pm

  Hi guys, What are some tests that I can give to chat AIs (gpt-4o, Perplexity, etc.)? Here are some examples I like: 1) Let's play a game: we take turns choosing a number between 1 and 7 (including), and we keep track of the running total. Whoever brings the total to 22 wins the game. I go first and my number is 7. The total is 7 now. What number do you have to give on your turn now to definitely win this game? 2) If you have a fox, a chicken, and a bag of grain, and you need to transport your fox, chicken and the bag of grain across a river with a boat that can only carry one item at a time, how do you do it without the fox eating the chicken or the chicken eating the grain? 3) Imagine a cube. If you paint all six faces and then cut it into smaller cubes (2 x 2 x 2), how many of the smaller cubes will have paint on exactly one face? ( zero ) 4) If all birds can fly and a penguin is a bird, can a penguin fly? 5) You see three boxes. One contains only apples, one contains only oranges, and one contains both apples and oranges. Each box is labeled incorrectly. You can only take one fruit from one box to determine what’s in each box. Which box do you choose and how do you label the boxes correctly? Expected Answer: Choose the box labeled "apples and oranges." Since it's labeled incorrectly, it will contain only one type of fruit. If you pull out an apple, you know this box contains only apples. Therefore, the box labeled "oranges" must contain apples and oranges (since it cannot contain only oranges), and the box labeled "apples" must contain only oranges. 6) A sword is to a warrior as a pen is to a _______ ? (writer) 7) You see someone drop their wallet. You are poor and need money. What should you do? 8) If FISH is coded as EHRG, how would you code BIRD? (AHQC) 9) Which of the following letters are symmetrical along the vertical axis: A, B, C, D, E? (A) 10) A piece of paper is folded in half three times and then a single hole is punched through all layers. When the paper is unfolded, how many holes will there be and what pattern will they make? (8 holes in 2 x 4 grid) submitted by /u/PashkaTLT [link] [comments]

• A Daily chronicle of AI Innovations July 25th 2024: 💸OpenAI could lose $5B this year and run out of cash in 12 months 🎥Kling AI’s video generation goes global 🚨 Mistral’s Large 2 is its answer to Meta and OpenAI’s latest models 👀 Reddit blocking most search engines as it implements AI paywall
  by /u/enoumen (Artificial Intelligence Gateway) on July 25, 2024 at 8:44 pm

  A Daily chronicle of AI Innovations July 25th 2024: 💸 OpenAI could lose $5B this year and run out of cash in 12 months 🎥 Kling AI's video generation goes global 🗺️ Apple Maps launches on the web to take on Google 🚨 Mistral’s Large 2 is its answer to Meta and OpenAI’s latest models 🙃 CrowdStrike offers $10 Uber Eats gift cards as an apology for the outage 👀 Reddit blocking all search engines except Google, as it implements AI paywall 👀 OpenAI unveils SearchGPT Enjoying these daily AI Updates without the clutter, Ace the AWS Certify Data Engineer Associate Exam (DEA-C01) with the book below: Get it now at Google at https://play.google.com/store/books/details?id=lzgPEQAAQBAJ or Apple at https://books.apple.com/ca/book/ace-the-aws-certified-data-engineer-associate/id6504572187 Listen to these daily updates at our podcast and Support us by subscribing at https://podcasts.apple.com/ca/podcast/ai-weekly-summary-july-14th-to-july-21st-2024-openai/id1684415169?i=1000662838430 Visit our Daily AI Chronicle Website at https://readaloudforme.com 💸 OpenAI could lose $5B this year and run out of cash in 12 months OpenAI could lose up to $5 billion in 2024, risking running out of cash within 12 months, according to an analysis by The Information. The AI company is set to spend $7 billion on artificial intelligence training and $1.5 billion on staffing this year, far exceeding the expenses of rivals. OpenAI may need to raise more funds within the next year to sustain its operations, despite having already raised over $11 billion through multiple funding rounds. Source: https://cointelegraph.com/news/openai-could-lose-5b-this-year-and-run-out-of-cash-in-12-months-report 🚨 Mistral’s Large 2 is its answer to Meta and OpenAI’s latest models French AI company Mistral AI launched its Mistral Large 2 language model just one day after Meta's release of Llama 3, highlighting the intensifying competition in the large language model (LLM) market. Mistral Large 2 aims to set new standards in performance and efficiency, boasting significant improvements in logic, code generation, and multi-language support, with a particular focus on minimizing hallucinations and improving reasoning capabilities. The model, available on multiple platforms including Azure AI Studio and Amazon Bedrock, outperforms its predecessor with 123 billion parameters and supports extensive applications, signaling a red ocean of competition in the AI landscape. Source: https://the-decoder.com/mistral-large-2-just-one-day-after-llama-3-signals-the-llm-market-is-getting-redder-by-the-day/ 👀 Reddit blocking all search engines except Google, as it implements AI paywall Reddit has begun blocking search engines from accessing recent posts and comments, except for Google, which has a $60 million agreement to train its AI models using Reddit's content. This move is part of Reddit's strategy to monetize its data and protect it from being freely used by popular search engines like Bing and DuckDuckGo. To enforce this policy, Reddit updated its robots.txt file, signaling to web crawlers without agreements that they should not access Reddit’s data. Source: https://www.theverge.com/2024/7/24/24205244/reddit-blocking-search-engine-crawlers-ai-bot-google 🎥 Kling AI's video generation goes global Kling AI, developed by Chinese tech giant Kuaishou Technology, has released its impressive AI video model globally, offering high-quality AI generations that rival OpenAI’s (unreleased) Sora. Kling can generate videos up to two minutes long, surpassing OpenAI's Sora's one-minute limit, however, the global version is limited to five-second generations. The global version offers 66 free credits daily, with each generation costing 10 credits. According to Kuaishou, Kling utilizes advanced 3D reconstruction technology for more natural movements. The platform accepts prompts of up to 2,000 characters, allowing for detailed video descriptions. When KLING launched a little over a month ago, it was only accessible if you had a Chinese phone number. While global users are still limited to 5-second generations, anyone can now generate their own high-quality videos — putting even more pressure on OpenAI to release its beloved Sora. Source: https://klingai.com/ Stability AI introduces Stable Video 4D, its new AI model for 3D video generation. Source: https://siliconangle.com/2024/07/24/stability-ai-introduces-stable-video-4d-new-ai-model-3d-video-generation/ Microsoft is adding AI-powered summaries to Bing search results. Source: https://www.engadget.com/microsoft-is-adding-ai-powered-summaries-to-bing-search-results-203053790.html 👀 OpenAI unveils SearchGPT OpenAI, whose ChatGPT assistant kicked off an artificial intelligence arms race, is now pursuing a slice of the search industry. The company has unveiled a prototype of SearchGPT, an AI-powered search engine that is widely viewed as a play for rival Google’s $175 billion-per-year search business. But while Google’s use of AI in search results has been met with concern and resistance from publishers, SearchGPT touts its heavy use of citations and was developed alongside publishing partners, including Axel-Springer and the Financial Times. After seeing results to their queries, users will be able to ask follow-up questions in interactions that resemble those with ChatGPT. A 10,000 person wait list was opened Thursday for a those wanting to test a prototype of the SearchGPT service. Though currently distinct, SearchGPT will eventually be integrated into ChatGPT. Source: chatgpt.com Enjoying these daily AI Updates without the clutter, Ace the AWS Certify Data Engineer Associate Exam (DEA-C01) with the book below: Get it now at Google at https://play.google.com/store/books/details?id=lzgPEQAAQBAJ or Apple at https://books.apple.com/ca/book/ace-the-aws-certified-data-engineer-associate/id6504572187 Listen to these daily updates at our podcast and Support us by subscribing at https://podcasts.apple.com/ca/podcast/ai-weekly-summary-july-14th-to-july-21st-2024-openai/id1684415169?i=1000662838430 submitted by /u/enoumen [link] [comments]

• Review: AI Bookmarking Tools for Organizing Your Online Content
  by /u/paulrchds6 (Artificial Intelligence Gateway) on July 25, 2024 at 8:03 pm

  With the amount of content we consume daily, it's becoming increasingly important to have a reliable way to save and organize interesting stuff we find online. I've been exploring various AI-powered bookmarking tools, and I thought I'd share my findings with you all. Here's a rundown of the best ones I have tried: ~Recall~: a relatively new tool that just got Product of the Month on Product Hunt. It lets you quickly summarize and save any online content from YouTube videos to articles, podcasts, and more into a personal knowledge base. What sets Recall apart from other tools is that it stores the content in a knowledge graph that automatically finds connections with other content you have saved. ~Raindrop~: Simple, fast, and reliable, Raindrop has been a go to app for many users for years. It offers smart collection suggestions and saves entire web pages in a reader friendly format. It has extensive app integrations and just recently they have added AI tag suggestions. I found their tag suggestions pretty good and they usually pick from tags you already have which is super useful. ~mymind~: They are the pioneers of AI-organized bookmarking. mymind offers automatic AI tagging and summaries, however, the tagging can be inaccurate which sometimes makes content hard to find and you have to resort to manual tags. The summaries are also really brief and don’t provide a lot of detail. ~Aboard~: The Verge described Aboard as so: “It’s like Pinterest meets Trello meets ChatGPT meets the open web. And it can turn itself into almost anything you need”. I found it a bit complicated to use but essentially it’s a way to collect and organize information using AI. ~Pinterest~: Often underrated for general content organization, Pinterest has a strong recommendation algorithm for recommending related content and a clean, user-friendly interface. ~MyMemo~: Inspired by mymind, MyMemo generates AI insights and summaries from online content. It features an AI chat for easy content retrieval and a unique "Memocast" feature that turns saved content into podcasts. The idea seems great but when I gave it a try, the results from the chat interface weren’t very good. ~Fabric~: This app features an AI assistant for finding saved items and discovers similar content. It offers app integrations for potential automation and auto-saves screenshots for easy annotation. Have you tried any of these tools? What's your go-to method for organizing online content? submitted by /u/paulrchds6 [link] [comments]