OpenAI Employee ACCIDENTALLY REVEALS Q* Details! (Open AI Q*)
TLDRThe video discusses a deleted tweet from Noan Brown, an AI expert at OpenAI, which has led to speculation about its connection to the secretive Qstar model. Brown's work on AI in imperfect information games and his recent focus on planning models suggest a potential link to Qstar. The video also explores the concept of synthetic data and its role in training AI models, as well as the potential for AI systems to perform better with increased inference time, hinting at the future capabilities of AI like Qstar.
Takeaways
- 🧠 Noan Brown, a prominent AI researcher known for his work in AI systems capable of playing poker at superhuman levels, made a tweet that sparked speculation about its relation to OpenAI's infamous Q* model.
- 🤯 The tweet suggested that superhuman performance isn't achieved by simply improving imitation learning on human data, hinting at a potential breakthrough in AI training methodologies.
- 🚀 Brown's previous tweets and interviews emphasize the potential of AI models that can engage in 'planning' and 'reasoning' over extended periods, potentially leading to significant advancements in AI capabilities.
- 🔍 The community speculates that Brown's deleted tweet might be related to OpenAI's Q* model, which is rumored to involve planning and the use of synthetic data for training.
- 🧩 Brown's work at OpenAI focuses on generalizing methods used in imperfect information games like poker and diplomacy, which could have broad applications in real-world scenarios such as negotiation and cybersecurity.
- 🌟 Brown's vision of future AI models suggests the possibility of models a thousand times more capable than GPT-4, albeit with potentially higher inference costs.
- 🔎 The discussion around planning in AI highlights the importance of accuracy over speed in certain tasks, where giving models more time to 'think' can significantly improve performance.
- 💡 The concept of scaling up inference costs rather than model size presents a novel approach to enhancing AI capabilities, which could be valuable for safety research and other high-stakes applications.
- 🔄 The industry trend towards agentic AI and planning is evident in recent demos and developments, showcasing the potential for AI systems to perform complex tasks with multi-step reasoning.
- 🚀 The anticipation for OpenAI's Q* model and its potential to integrate planning and multi-step thinking has the AI community excited about the future of AI technology and its applications.
Q & A
What is the main speculation surrounding the deleted tweet from an OpenAI employee?
-The main speculation is that the deleted tweet might be related to OpenAI's infamous Qstar model, which they refuse to discuss openly. The tweet in question suggested that superhuman performance is not achieved by simply improving imitation learning on human data, leading to theories that it might be linked to the Qstar model's use of synthetic data and planning mechanisms.
Who is Noam Brown and what is his significance in the AI field?
-Noam Brown is a prominent figure in the field of artificial intelligence, known for his contributions to developing AI systems capable of playing poker at superhuman levels. His work has significantly advanced the standing and capabilities of AI in imperfect information games, which include not just poker but also potential real-world applications like negotiation, cybersecurity, and strategic decision-making.
What did Noam Brown say in his earlier tweets about joining OpenAI?
-In his earlier tweets, Noam Brown expressed excitement about joining OpenAI. He mentioned that he has researched AI self-play and reinforcing in games like poker and diplomacy, and he aimed to investigate how to make these methods truly general. He also suggested the possibility of seeing AI models a thousand times better than GPT-4 in the future.
What is the significance of AlphaGo's victory over Lee Sedol in 2016?
-AlphaGo's victory over Lee Sedol in 2016 was a milestone for AI. The key to AlphaGo's success was its ability to ponder for one minute before each move, which significantly improved its performance. This is equivalent to scaling pre-training by 100,000 times, demonstrating the potential for substantial improvements in AI capabilities through strategic planning and decision-making.
How does Noam Brown's research on planning and inference costs relate to the development of AI models?
-Noam Brown's research focuses on the potential of scaling up AI models not just through pre-training but also through inference costs. He suggests that by allowing models more time to think and plan, their accuracy can be significantly improved. This approach could be applied in various tasks where immediate responses are not necessary, and higher inference costs could be justified for achieving better outcomes, such as in drug discovery or proving scientific hypotheses.
What is the Qstar model and its significance in AI research?
-The Qstar model is an AI model that OpenAI is allegedly working on, which is believed to involve planning and the use of synthetic data. The model is significant because it suggests a breakthrough in AI's ability to plan and reason, potentially leading to more effective and strategic AI systems. The Qstar model is expected to be a major advancement in the field, improving AI's capabilities beyond current models like GPT-4.
How do recent AI demonstrations show the effectiveness of planning in AI systems?
-Recent AI demonstrations, such as Mesa's KPU and Devon, showcase the effectiveness of planning in AI systems. These systems are built on top of the GPT-4 stack and are able to reason and plan effectively, leading to more accurate and reliable performance. They demonstrate the potential of AI systems to reduce hallucinations and perform tasks more effectively through multi-step reasoning and planning.
What is the potential impact of AI models with planning capabilities on various industries?
-AI models with planning capabilities can have a significant impact on various industries by improving efficiency, accuracy, and strategic decision-making. In fields like drug discovery, strategic planning, and even creative tasks like writing, these models could potentially offer higher quality outcomes by spending more on inference costs to achieve deeper and more thoughtful results.
What does the future hold for AI models like GPT-5 in terms of planning and agentic behavior?
-The future of AI models like GPT-5 is likely to involve the integration of planning and agentic behavior. It is expected that OpenAI will continue to develop these capabilities, potentially offering models that natively possess planning capabilities or separate versions of the GPT series with enhanced strategic reasoning. This will lead to AI systems that can achieve long-term goals and perform tasks more effectively.
How does the concept of synthetic data play a role in overcoming data limitations for AI training?
-Synthetic data, which is data generated by AI itself, can help overcome limitations in obtaining enough high-quality data to train new models. By using computer-generated data, AI systems can be trained more effectively without relying solely on real-world data from the internet, thus addressing a major obstacle in developing next-generation models.
What are the implications of Noam Brown's tweet for the broader AI research community?
-Noam Brown's tweet has sparked speculation and discussion within the AI research community about the direction of AI development, particularly in relation to planning and the use of synthetic data. It highlights the importance of these concepts in pushing the boundaries of AI capabilities and has led to increased interest and research in these areas.
Outlines
🧠 AI and the Mystery of the Deleted Tweet
The first paragraph discusses a recent deleted tweet from an OpenAI employee, Noan Brown, which has sparked speculation within the AI community. The tweet hinted at the possibility of achieving superhuman AI performance not through better imitation learning on human data, but by other means. The community is intrigued, especially in relation to OpenAI's infamous Qstar model. Noan Brown is known for his work on AI systems capable of playing poker at a superhuman level, contributing significantly to AI advancements in imperfect information games. His current work at OpenAI involves investigating how to make these methods truly general, with the potential of creating AI models far superior to GPT-4. The paragraph also references a clip where Brown discusses the importance of not needing immediate responses from AI models and the benefits of allowing them more time to think, which can significantly improve their performance.
🚀 Scaling AI Models: The Future and Challenges
The second paragraph delves into the challenges and future prospects of scaling AI models. It discusses the limitations of scaling up models through pre-training and the potential of increasing inference costs to achieve better performance. The paragraph highlights Brown's thoughts on the possibility of waiting longer for AI responses to get higher quality outcomes, such as writing a contract or a novel. It also touches on the concept of synthetic data, which is data generated by AI itself, and how it could be a game-changer in training new models. The paragraph further explores the idea of planning in AI and how it could be the next breakthrough in the field, with top labs like OpenAI working on incorporating planning into their models.
🤖 Agentic AI and the Impact of Planning on Performance
The third paragraph focuses on the impact of planning and reasoning in AI systems. It showcases examples of AI systems, like Mesa's CPU and Devon, that are capable of planning and executing tasks more effectively. The paragraph emphasizes the reduction in hallucinations and improved task performance through multi-step reasoning. It also speculates on the potential of OpenAI's GPT-5 model and the possibility of it being natively agentic or having separate versions with planning capabilities. The paragraph concludes with excitement for the future of AI systems that can perform multi-step reasoning and planning, and invites the audience to share their thoughts on the deleted tweet and its implications.
Mindmap
Keywords
💡OpenAI
💡Noan Brown
💡Q* model
💡Imperfect information games
💡Artificial Intelligence
💡Superhuman performance
💡Planning
💡Synthetic data
💡Inference cost
💡Agentic AI
💡Multi-step reasoning
Highlights
The tweet in question is from Noam Brown, a prominent figure in AI known for his contributions to AI systems capable of playing poker at superhuman levels.
Noam Brown's work has significantly advanced the standing and capabilities of AI in imperfect information games, which include poker and have potential real-world applications.
Brown's tweet suggested that superhuman performance is not achieved by better imitation learning on human data, which could be related to OpenAI's infamous Qstar model.
In 2023, Brown shared his excitement about joining OpenAI to investigate how to make AI methods truly general, hinting at the possibility of models a thousand times better than GPT-4.
Brown referenced the 2016 AlphaGo victory as a milestone for AI, highlighting the importance of the AI's ability to ponder for a minute before each move.
The concept of scaling pre-training by 100,000x through pondering time was discussed, which significantly increased AlphaGo's abilities.
Brown suggests that a general version of AI methods could yield huge benefits, even if it slows down inference time.
The idea of spending more on inference to see what a more capable future model might look like is presented as a valuable research tool for safety.
In certain tasks, accuracy is preferred over speed, and allowing models more time to think can significantly improve their performance.
Noam Brown expands on the concept of planning in AI, drawing parallels to how adding planning in games like Go and poker increases model effectiveness.
The potential of language models to scale up inference cost rather than pre-training size is discussed as a way to achieve more powerful AI systems.
Brown's theories from 2023 may have influenced the development of Qstar, which is rumored to involve planning and synthetic data generation.
Qstar is speculated to be OpenAI's attempt at incorporating planning into AI, with Brown being a likely lead researcher on this project.
The industry is moving towards more agentic AI, with many top labs working on planning capabilities for AI systems.
Recent demos have shown AI systems with planning capabilities achieving orders of magnitude more effective results in tasks.
The potential of GPT-5 and its possible incorporation of planning capabilities is discussed, indicating a significant leap forward in AI technology.
The transcript discusses the importance of planning and multi-step reasoning in AI, suggesting that these will be key features of future AI systems like Qstar.