Open AI Q* (Q-STAR) Exposed - NEW Hidden Details Of Q*

1. Confirmation of Q* leak by Sam Alman.

🥈85 00:27

Sam Alman confirmed that the Q* leak was true, indicating that the leaked information about Q* is indeed accurate.

• This confirmation adds credibility to the leaked information.
• The leaked information may have significant implications for AI technology.

2. Q* leak suggests a major breakthrough at Open AI.

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The Q* leak indicates that there was a significant breakthrough at Open AI, pushing the boundaries of what is possible with AI technology.

• Open AI is known for constantly pushing the frontiers of AI technology.
• The leaked information suggests that Open AI is making rapid progress in this area.

3. Possible connection between Q* leak and Meta's Llama leak.

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The Q* leak and Meta's Llama leak both involved powerful AI language models and were leaked in a similar manner.

• The similarity in the leaks raises questions about the authenticity of the Q* leak.
• The Q* leak may have some validity if the Llama leak was indeed true.

4. Implications of Q* leak on encryption and security.

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If the Q* leak is true, it could have significant implications for encryption and security.

• The leak suggests that AI may have advanced to a level where it can crack encryption algorithms.
• This poses a grave threat to the security of sensitive computer data.

5. AI's potential to surpass human mathematicians.

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If AI is able to crack major encryption algorithms, it would demonstrate a level of mathematical understanding beyond human capabilities.

• AI's ability to analyze mathematics and come up with new formulas could have far-reaching implications.
• This could lead to breakthroughs and advancements that surpass human capabilities.

6. OpenAI collaborated with DARPA on cyber security.

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OpenAI collaborated with DARPA on a cyber security challenge called The Dara AI cyber security Challenge.

• OpenAI trained a special version of Q* on Dara's computers.
• This collaboration suggests that OpenAI has expertise in reinforcement learning and cryptography.

7. Q* combines math and Q learning for reinforcement learning.

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Q* is an LLM trained on math combined with Q learning, a reinforcement learning technique.

• OpenAI has expertise in reinforcement learning and has used it to achieve superhuman capabilities in various fields.
• Q* uses optimal policies and aligns with what people speculate about qstar.

8. OpenAI's research on cryptography is plausible.

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OpenAI's research on cryptography is not completely impossible, given their expertise in AI algorithms and pattern prediction.

• OpenAI's AI algorithms are good at pattern selection and prediction.
• Their collaboration with DARPA and expertise in reinforcement learning make it plausible that they have worked on cryptography.

9. The leaked letter strengthens the claims about qstar.

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The leaked letter, despite being intended to disprove the claims, actually strengthens the claims about qstar.

• The letter shows an expert level of understanding of AI research and cryptography.
• The references to Project Tundra and the to analysis technique support the claims about qstar.

10. The leak about qstar is credible and hard to disprove.

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The leak about qstar is credible and has not been successfully disproven, despite attempts to do so.

• The leak contains niche and specialized information that is difficult to fake.
• The timing of the leak and the lack of earlier mentions of qstar on the internet add to its credibility.

11. OpenAI's breakthrough in qstar is significant.

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OpenAI's breakthrough in qstar is significant, even if previous breakthroughs have not lived up to initial expectations.

• Breakthroughs in AI often work in specific contexts and may not generalize.
• OpenAI's qstar breakthrough is different from previous ones and has potential implications for encryption and information systems.

12. Improving gameplay through self-play and look ahead planning.

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An agent can enhance its gameplay by playing against slightly different versions of itself. Look ahead planning involves using a model of the world to reason into the future and produce better actions or outputs.

• These techniques have been used in AlphaGo and other AI systems.
• Model predictive control is often used for continuous state, while Monte Carlo tree search works on discrete actions and states.

13. Modular reasoning and tree of thoughts in language models.

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Language models like GPT-4 use modular reasoning with tree of thoughts and other methods of prompting to improve their base systems.

• These techniques are important for enhancing large language models.
• The article suggests that QAR uses PRMs to score the tree of thoughts reasoning data, which is then optimized with offline reinforcement learning.

14. Reinforcement learning in a multi-step fashion.

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Reinforcement learning can be done in a multi-step fashion, using a sequence of reasoning steps instead of contextual bandits.

• This approach is an interesting hypothesis for the future of reinforcement learning.
• It may have implications for AGI and the development of GPT-5.