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.

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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.

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.

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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.

• This is the highest risk model OpenAI has said it will release
  by /u/MaimedUbermensch (Artificial Intelligence) on September 13, 2024 at 9:34 pm

  submitted by /u/MaimedUbermensch [link] [comments]

• This is pretty good.
  by /u/Vamparael (Artificial Intelligence) on September 13, 2024 at 8:07 pm

  submitted by /u/Vamparael [link] [comments]

• OpenAI reveals new artificial intelligence tool it claims can think like a human
  by /u/Akkeri (Artificial Intelligence) on September 13, 2024 at 5:41 pm

  submitted by /u/Akkeri [link] [comments]

• Robo-Advisers Are Here – The Pros and Cons of Using AI In Investing
  by /u/Akkeri (Artificial Intelligence) on September 13, 2024 at 5:38 pm

  submitted by /u/Akkeri [link] [comments]

• “Wakeup moment” - during safety testing, o1 broke out of its VM
  by /u/MaimedUbermensch (Artificial Intelligence) on September 13, 2024 at 2:37 pm

  submitted by /u/MaimedUbermensch [link] [comments]

• I wonder where they're going to move the goalpost this time
  by /u/katxwoods (Artificial Intelligence) on September 13, 2024 at 2:03 pm

  submitted by /u/katxwoods [link] [comments]

• ChatGPT o1-preview shuts down if you refer to its chain of thought reasoning because OpenAI policy is that it should avoid discussing it and that it should be hidden from users even though it is open for all to see on the browser but not the desktop app.
  by /u/rutan668 (Artificial Intelligence) on September 13, 2024 at 5:54 am

  submitted by /u/rutan668 [link] [comments]

• o1 Hello - This is simply amazing - Here's my initial review
  by /u/Xtianus21 (Artificial Intelligence) on September 13, 2024 at 4:13 am

  So it has begun! Ok, so, yeah! There is not a lot of usage you can get out of this thing so you have to use the prompting very sparingly. It is days rate limiting not hours. 🙁 Let's start off with the media. Just one little dig at them because on CNBC they said, "the model is a smaller model". I think the notion here was that this model is a smaller model from a larger model so they just repeated that. I don't think this is a smaller model. Now, it could be that the heart of the model is smaller but what is going on behind the scenes with the thinking is a lot of throughput to model(s). I think the implication here is important to understand because on one hand there is an insanely low rate limit. when I say low I mean 30 messages per week low. On the other hand, the thinking is clearly firing a lot of tokens to get through a process of coming to a conclusion. The reason why I say it's a concert of models firing towards each other is because something has to be doing the thinking and another call (could be the same model) has to be doing the checking of the steps and other "things". In my mind, you would have a collection of experts doing each thing. Ingenious really. Plausibility model The plausibility model as the prime cerebral model. When humans think the smartest humans understand when they are headed down the right path and what is not the right path. You see this in Einstein's determination to prove the theory of relativity. His clutch of infamy came on the day when in an observatory (I think during an eclipse) he caught the images of light bending around our star proving that the fabric of space was indeed curved. Einstein's intuition here can not be underestimated. From Newton's intuition about gravity and mass to Einstein coming along and challenging that basic notion and to take it further and learn a new understanding of the how and why. It all starts with a plausibility of where one is going in their quest for knowledge. With my thoughts am I headed down the right path. Does the intuition of my thoughts make sense or should I change course to another or should I abandon the thought all together. This is truly what happens in the mind of an intelligent and sentient being on the level of genius. Not only the quest for knowledge but the ability to understand and know correctness wherever the path has led. In this, LLM's were at a distinct disadvantage because they are static capsules of knowledge frozen in time (and a neural network). In many ways they still are. However, OpenAI has done something that is truly ingenious to initially deal with this limitation. First, you have to understand the limitation of why being static and not dynamic is such a bad thing. If I ask you a question and tell you that the only way you can answer is to spit out the first thing that comes to your mind, without thinking, would produce in some probable occasions the wrong answer. With increasing difficulty of the question the more and more likely it would be that one would give the wrong answer. But human beings don't operate with such a constraint. They think through things as the level of difficulty of the perceived question is queried. One initial criticism is that this model over thinks all of the time. Case in point. It took 6 seconds to process hello. https://preview.redd.it/aih5umfz4iod1.png?width=1459&format=png&auto=webp&s=65bef59c6f7cdb52e9bef56c6d65e1a64b32f0d3 Eventually, I am sure OpenAI will figure this out. Perhaps a gate orchestrator model?! Some things don't require much thought; just saying. But back to the plausibility model concept. I don't know from Sunday if this is what is really going on but I surmise. What I imagine here is that smaller models (or the model) are quickly bringing information to a plausibility model. What is a mystery here is how on earth does the plausibility model "know" when it has achieved a qualitative output? Sam said something in an interview that leads me to believe that what's interesting about models as they stood since GPT-4 is that if you run something 10,000 times somewhere in there is correctness. Just getting the model to definitely give you that answer consistently and reliably is the issue. Hence, hallucinations. But what if, you could deliver responses and a model checks that response for viability. It's the classic chicken and egg problem. Does the correct answer come first or the wrong answer. Well, even going further, what if I present to the model many different answers. Choosing between the one that makes the most sense makes the problem solving a little more easier. It all becomes recursively probabilistic at this point. Of all of these incoming results keep checking to see if the path we're heading down is logical. Memory In another methodology, a person would keep track of where they were in the problem solving solution. It is ok to get to a certain point and pause for a moment to plan on where you would then go next. Hmmm. Memory, here is vital. You must keep the proper context of where you are in your train of thought or it is easy to lose track or get confused. Apparently OpenAI has figured out decent ways to do this. Memory, frankly, is horrible in all LLM's including GPT-4. Building up a context window is still such a major issue for me and the way the model refers to it is terrible. In GPT-o1-preview you can tell there are major strides in how memory is used. Not necessarily from the browser but perhaps on their side via backend services we humans would never see. Again, this would stem from the coordinating models firing thoughts in and out. Memory on the backend is probably keeping track of all of that which is probably the main reason why COT won't be spilling out to your browser amongst many other reasons. Such as entities stealing it. I digress. In the case of GPT-o1 memory seems to have a much bigger role and is actually used very well for the purpose of thinking. Clarity I am blown away by the totality of this. The promise is so clear of what this is. Something is new here. The model feels and acts different. It's more confident and clear. In fact, the model will ask you for clarity when you are conversing with it. Amazingly, it feels the need to grasp clarity for an input you are asking it. https://preview.redd.it/dr8zsc235iod1.png?width=1201&format=png&auto=webp&s=9f76caa2efe0251c414162faabc389132f4310e8 Whoa. That's just wild! It's refreshing too. It "knows" it's about to head into a situation and says, wait a minute let me get a better understanding here before we begin. Results and Reasoning The results are spectacular. It's not perfect and for the sake of not posting too many images I had to clean up my prompt so that it would be confused by something it asked me to actual clarify in the first place. So maybe while it isn't perfect, It sure the hell is a major advancement in artificial intelligence. Here is a one shot prompt that GPT-4, 4o continually fail at. The reason why I like this prompt is that it was something I saw in a movie and as soon as I saw the person write down the date from the guy asking him to do it I knew right away what was about to happen. Living in the US and travelling abroad you notice some oddities that are just the way things are outside of one's bubble. The metric system for example. Italy is notorious for giving Americans speeding tickets and to me the reason is because they have no clue how fast they are going with that damn speedometer in KPH. I digress. The point is, you have to "know" certain things about culture and likelihood to get the answer immediately. You have to reason through the information quickly to conclude to the correct answer. There is a degree of obviousness but not just from someone being smart but from someone having experienced things in the world. Here is GPT-o1-preview one shotting the hell out of this story puzzle. https://preview.redd.it/z6vdhal55iod1.png?width=1057&format=png&auto=webp&s=17d6499286d671449ca9a62fe44eba2ed37f9112 https://preview.redd.it/grphx9q65iod1.png?width=616&format=png&auto=webp&s=52457b4bd11c230590c2583aac6660b3d6b65e92 https://preview.redd.it/j0g5wm575iod1.png?width=796&format=png&auto=webp&s=cb258066c771c35ef5826ce7b37287dfc8ac712a As I said, GPT-4 and 4o could not do this in 1 shot no way, no how. I am truly amazed. The Bad Not everything is perfect here. The notion that this model can't not think about certain responses is a fault that OAI needs to address. There is no way that we will want to not being using this model all of the damn time instead of <4o. it not knowing when to think and when to just come out with it will be a peculiar thing. With that said, perhaps they are imagining a time when there are acres and acres of Nvidia Blackwell GPU's that will run this in near real time no matter the thought process. Also, the amount of safety that is embedded into this is remarkable. I would have done a section of a Safety model but that is probably coordinating here too but I think you get the point. Checks upon checks. The model seems a little stiff on the personality and I am unclear about the verbosity of the answers. You wouldn't believe it from my long posts but when I am learning something or interacting I am looking for the shortest and most clearest answer you can give. I can't really tell if that has been achieved here. Conversing and waiting multiple seconds is not something I am going to do to try and figure out. Which brings me to the main complaint as of right now. The rate limit is absurd. lol. I mean 30 per week how can you even imagine using that. For months now people will be screaming because of this and rightly so. Jensen can't get those GPU's to OpenAI fast enough I tell you. Here again, 2 years later and we are going to be capability starved by latency and throughput. I am just being greedy. Final Thoughts In the words of Wes Roth, "I am stunned". When the limitations are removed, throughput and latency are achieved, and this beast is let loose I have a feeling that this will be the dawn of a new era of intelligence. In this way, humanity has truly arrived at the dawn of an man made and plausibly sentient intelligence. There are many engineering feats that will be left to overcome but we are in a place that on this date 9/12/2024 the world will be forever changed. The thing is though this is only showcasing knowledge retrieval and reasoning. It will be interesting to see what can be done with vision, hearing, long term memory, and true learning. The things that will built with this may be truly amazing. The enterprise implications here are going to be profound. Great job OpenAI! submitted by /u/Xtianus21 [link] [comments]

• Reality of Ai
  by /u/Electrical_Prune_932 (Artificial Intelligence) on September 13, 2024 at 3:21 am

  Does anyone feel like agi is a hoax and ai will just end up being some convient reference tool .I just don’t see how people think ai is going to be able to make scientific breakthroughs when it all it does is try to predict the next word on the vast amount of data it’s trained on. It just doesn’t seem fundamentally right to tell a bunch of 0 and 1s to think submitted by /u/Electrical_Prune_932 [link] [comments]

• One-Minute Daily AI News 9/12/2024
  by /u/Excellent-Target-847 (Artificial Intelligence) on September 13, 2024 at 3:20 am

  OpenAI, Nvidia Executives Discuss AI Infrastructure Needs With Biden Officials.[1] Google unlists misleading Gemini video.[2] Google’s ALOHA Unleashed AI Robot Arm Can Now Tie Shoes Autonomously.[3] Meta is making its AI info label less visible on content edited or modified by AI tools.[4] Sources: [1] https://www.bloomberg.com/news/articles/2024-09-12/openai-nvidia-executives-discuss-ai-infrastructure-needs-with-biden-officials [2] https://www.theverge.com/2024/9/12/24242897/google-gemini-unlists-misleading-video-ai [3] https://www.techeblog.com/google-aloha-unleashed-robot-arm-tie-shoes/ [4] https://techcrunch.com/2024/09/12/meta-is-making-its-ai-info-label-less-visible-on-content-edited-or-modified-by-ai-tools/ submitted by /u/Excellent-Target-847 [link] [comments]