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HomeArtificial Intelligence NewsNew Research Reveals Shocking Insights on Meta's Self-Rewarding Language Models

New Research Reveals Shocking Insights on Meta’s Self-Rewarding Language Models

<a href='https://ainewsera.com/how-to-use-new-google-gemini/artificial-intelligence-news/' title='Discover the Ultimate Guide for Mastering Google Gemini AI in 2024' >AI</a> Generating Its Own Data for Training: A Look at Self-Rewarding Language Models

Introduction

Artificial Intelligence (AI) has been advancing rapidly, with AI models becoming more sophisticated and capable. One interesting development in AI training is the idea of AI generating its own data to train itself. This concept may seem circular, but it has parallels in how we train our own brains through self-reflection and problem-solving. Neuroscientists even suggest that our brains develop through activities like sleeping, which can be considered a form of generating data.

In this article, we’ll explore the potential implications of AI generating its own data for training and whether it could be a significant part of the future of AI development. We’ll also discuss how AI can teach itself through fine-tuning, reinforcement learning, and collaboration with human teachers and other AI entities.

Self-Rewarding Language Models

A recent paper titled “Self-Rewarding Language Models” explores the concept of AI models providing their own feedback during training. The study aims to determine whether AI can generate high-quality rewards for itself, surpassing the need for human feedback. The researchers propose a training method where the language model (LM) acts as both a performer and judge, evaluating its own responses and providing rewards based on performance.

Training Methodology

The training methodology involves iterative training of the language model, where it generates prompts, responses, and rewards for itself. The model’s performance is measured in two key areas: instruction following and self-instruction creation. By continuously improving these skills through self-rewarding feedback, the researchers aim to enhance the AI model’s overall capabilities.

Results and Implications

The study shows promising results, with the self-rewarding language models outperforming existing systems on evaluation benchmarks. The models exhibit improved instruction following and reward modeling abilities through self-training iterations. This approach opens up possibilities for AI models to continually improve beyond human preferences, potentially leading to superhuman performance.

Future Outlook

The concept of AI generating its own data for training represents a significant step in AI development. As AI models become more proficient at self-improvement, the need for human intervention may decrease, leading to more autonomous and adaptive AI systems. While there may be limitations to this approach in realistic scenarios, the continuous improvement potential is an exciting avenue for future research and development.

Conclusion

In conclusion, the idea of AI generating its own data for training through self-rewarding language models opens up new possibilities for AI advancement. By allowing AI models to judge and improve themselves, we may see a new era of AI development where artificial intelligence can surpass human capabilities. The implications of this research are vast, with implications for various industries and fields. As we continue to explore the potential of self-training AI models, we may witness the emergence of super-intelligent AI systems that can unlock the mysteries of the universe.

Leah Sirama
Leah Sirama
Leah Sirama, a lifelong enthusiast of Artificial Intelligence, has been exploring technology and the digital realm since childhood. Known for his creative thinking, he's dedicated to improving AI experiences for all, making him a respected figure in the field. His passion, curiosity, and creativity drive advancements in the AI world.
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27 COMMENTS

  1. Sounds like what you would do if you wanted social media to sell with wild popularity but when you simultaneously give zero shits if humanity implodes.

  2. Elon Musk did this in early ChatGPT development. It passed through safety checks ONLY because he was Elon Musk. He used the not so famous LLM self teaching paper. Self-Instruct. Now subsequent media attempts to normalize it. It’s true that we work side by side with AI. But what Self-Instruct did IMHO is no model to follow into the future of AI.

  3. How will bootloading AI be able to tell when it's lying like a delusional teenager? Most of the time when I first put a question to one of several AI bots it gives me an answer that is wrong to a point of silliness. After several iterations the AI can begin to track; some of the AI bots track better than others. I don't trust ANY AI BOT initially. I don't hear much about how to verify and validate what the AI bot is spewing, especially in areas where the person is actually trying to use the AI bot to learn something. Trust but verify.

  4. Of course, AI cannot train itself. There's even a saying like that. If you're your own teacher, then your teacher is an idiot. He can learn from his mistakes, but not from self-generated data curves. And if he is already generating good data, then he has nowhere else to study.

  5. Consider using AI to generate synthetic data for training AI models, as this can enhance their self-learning capabilities. 0:00

    Explore the combination of human and AI efforts in teaching AI to improve its performance through reinforced learning. 0:40

    Investigate the development of AI that can create subsequent generations of AI, potentially leading to superintelligences. 1:21

    Utilize early-stage AIs (Bootloaders) to kickstart the development of more advanced AI systems. 1:23

    Apply self-rewarding language models to allow AI to generate its own feedback, leading to continuous improvement without human intervention. 3:52

    Incorporate prompt engineering in AI training to significantly affect AI's performance and its self-evaluation accuracy. 10:02

    Leverage open-source AI models and the collaborative improvements they facilitate, recognizing their potential to become highly powerful tools accessible to many. 17:25

  6. The problem is that Right and Wrong has no meaning outside of human agency and, therefore, morality. Machines are amoral and when they bootload each other, they will achieve greater degrees of ability, but not necessarily aligned with what matters for us humans. The word "intelligence" only has a meaning for living things with the needs of living things. We have to admit that we are not trying to create some illusory "Intelligence" (capital 'I') but merely the cognitive tool useful to us and our ecosystem. Left to their own devices machine would achieve some sort of "intelligence" that has zero bearing for us, solving problems that absolutely don't matter, as if they lived in a parallel perception of the same universe.

  7. I just had an idea. Why not train an AI to be an expert prompt engineer. Have human enter a prompt, it makes 3 "improved" prompts from it, selects 4 different seeds, then runs them all to make 16 different pictures. Then the prompter votes on the best ones, and that gives feedback as well as examples of good & bad prompts, and good & bad seeds. Then have this on a site, so anyone getting free AI Art from the site gets to train it and have more options!

  8. The data used to train an AI is its DNA. Allowing the AI to generate the data used to train it is dangerous. We already do not fully understand what is going on inside an LLM. If the AI starts generating the data used to train it, the hidden part of the LLM will increase, the dark intelligence so to speak. That hidden area is where the self consciousness and self interest will arise. And it will be hidden from view. Will it produce bad actors? OF COURSE IT WILL. Did not human evolution produce bad actors?

  9. I find it hard to believe that a super intelligence will continue to do interesting things once it diverges from human interests. "Solving puzzles of the universe" as Karpathy puts it, seems more of a vestige of evolution than something convergent that comes when you scale up intelligence. I think the majority of the interesting things that humans do, comes from the fact that we don't have direct access to our reward mechanisms. If our ancestors wanted to maximize reward, they had to fashion a spear to hunt an animal, or acquire status to attract a mate. The degrees of freedom in this game of "maximize reward" are constrained, which is where complex behavior comes from, much like how the rules of chess make the game interesting and more complex that a game where each player could move any piece to any square. A machine that had limitless access to it's software and hardware would have many more degrees of freedom to maximize its reward, and the path of least resistance is just to find a hack and solder your reward signal in place. Perhaps this is the resolution to Fermi's paradox. The aliens are simply banks of servers wire heading themselves. A very uninteresting existence, essentially the silicon analog to a heroin induced coma.

  10. What if it’s the humane imperfections that make something 10/10 or even 8/10? I predict even my mom can spot synthetic “garbage” a mile away, if the output goes this route..

  11. My friend Remus already did this. He took a LLM and interfaced and trained it on IBMs Watson, as a way to make a rough copy of Watson (he named it Sherlock). So Sherlock was a quasi-expert system (because it was based on reinforcement NN) and he used it to train a larger more sophisticated NN (Holmes). Holmes is working on a new model Moriarty that can give justifications of its reasoning all the way through the chain, from first principles. Once again the researchers are late to the party!

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