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讨论总结

本次讨论主要聚焦于Google和Meta在人工智能领域的研究动态，特别是关于System 2思考系统的开发和潜在发布。讨论内容涵盖了技术独立性、计算资源挑战、模型优化、安全性等多个方面。参与者对Google的Deepmind研究表示肯定，同时探讨了AI系统在处理复杂问题时的计算资源需求和产品化难度。此外，讨论还涉及了AI系统的效率提升和安全性问题，以及Meta在开源策略和技术竞争中的可能策略。

主要观点

  1. 👍 Google的反应并非针对OpenAI
    • 支持理由：Deepmind的研究方向一直如此，Google在人工智能领域的研究具有独立性。
    • 反对声音：无
  2. 🔥 MCTS在AlphaGo中的应用
    • 正方观点：MCTS在处理复杂决策树的能力上表现出色。
    • 反方观点：无
  3. 💡 Google可能对其System 2思考系统的表现尚未满意
    • 解释：该系统可能需要进一步的推理后端或前端处理。
  4. 🚀 AI系统在处理复杂问题时需要大量的计算资源
    • 解释：生成响应和进行验证的过程显著增加了计算需求。
  5. 🌟 希望将"alphaproof goodness"实现到较小模型中
    • 解释：这可以提供更高效、可扩展和易访问的AI解决方案。

金句与有趣评论

  1. “😂 This is not Google’s reaction to OpenAI.”
    • 亮点：强调了Google在人工智能研究上的独立性。
  2. “🤔 Yeah based on MCTS! They did use it in their AlphaGO.”
    • 亮点：讨论了MCTS在AI系统中的应用和重要性。
  3. “👀 Maybe they are not yet happy with how it performs just yet, not necessarily waiting for OpenAI.”
    • 亮点：探讨了Google对其System 2思考系统表现的满意度。

情感分析

讨论的总体情感倾向较为积极，参与者对Google和Deepmind的研究表示肯定，并对AI系统的未来发展持乐观态度。主要分歧点在于对AI系统性能的期望和实际表现之间的差距，以及对计算资源需求的担忧。可能的原因包括技术发展的不确定性、资源分配的挑战以及对AI安全性的关注。

趋势与预测

  • 新兴话题：将高级推理能力实现到较小模型中，以提高效率和可访问性。
  • 潜在影响：这种实现可能推动AI技术的广泛应用，特别是在消费者产品和特定需求定制模型方面。

详细内容：

标题：关于谷歌等公司的系统 2 思考模式的热门讨论

近日，Reddit 上一篇题为“Hidden between the lines, seems like Google also has their System 2 thinking (OpenAI’s Q* / Strawberry Equivalent) ready. Will probably release it when OpenAI releaees theirs. Will Meta Open source their system 2 thinking system if they build one or if they already have one?”的帖子引发了热烈讨论。该帖获得了众多关注，评论数众多。

帖子主要探讨了谷歌、Meta 等公司在系统 2 思考模式方面的研究和可能的发展方向，以及其与 OpenAI 之间的潜在竞争关系。

讨论焦点与观点分析： 有人认为这并非谷歌对 OpenAI 的反应，而是 Deepmind 一直以来的研究方向。比如有人提到：“[u/kulchacop] This is not Google’s reaction to OpenAI. This sort of research is what Deepmind was always about. ” 还有人指出基于MCTS，他们在 AlphaGO 中就有应用。例如：“[qnixsynapse] Yeah based on MCTS! They did use it in their AlphaGO. ” 有人对 MCTS 的应用范围进行了探讨：“[Possum4404] MCTS over what? all different possible LLM outputs (temperature etc.)?” 也有人详细阐述了系统 2 思考模式所涵盖的内容：“[Dayder111] I guess over associations, related facts, caveats, tricky parts of the problem, analysis of what the user wants and why, how to achieve it, how to decompose the problem into smaller steps, analysis of correctness, of mistakes, and so on. Basically all that humans can do, when they think deeply about something, branching in their thoughts and then returning back to previous steps but with new things to consider. I think for us part of this process brain does subconsciously, presenting us with new thoughts and ideas as we think about something, but we can also do it "manually". ” 有人觉得当前的 LLM 存在局限性，比如：“[Dayder111] Humans that don’t get much related information recalled and accounted for, as they think about something, whether because they don’t know much, or their brain does not suggest new things automatically that well, or they don’t want to/can’t think deeply "manually", are not that different from current LLMs in some regards it feels. Current LLM only go one way through their thought, no branching, no correcting and backtracking (except for rare cases when it does notice its mistake as it goes further with reply), just a single quick prediction, single association with thr prompt. They aren’t even trained to account for lots of stuff, aren’t trained how to think, yet. I guess it’s about to begin to change soon. ” 对于相关研究的进展和应用，存在不同看法。有人认为可能是效果还不够理想：“[uesk] Do you mcts over reasoning steps?”“[u/Everlier] Maybe they are not yet happy with how it performs just yet, not necessarily waiting for OpenAI. In-context reasoning output would also require a post-processing on either inference BE or FE ” 也有人提到计算成本过高的问题：“[Tobiaseins] They probably all have the same problem. It’s insanely compute heavy. If you generate responses, have some validation system (either mathematical or a LLM specifically trained to spot errors) and do this for multiple turns, you can easily 10x the compute requirement, for generating IMO answers probably 100x or 1000x (some answers took hours to generate). That’s not consumer tech currently ” 但有人反驳称成本并非不可承受：“[qroshan] Extremely Naive Take. You pay top human reasoners $100-$1000 / hour. If an AI system is truly solving complex problems we can totally afford at least $10 / hour. I’m not even going to mention that costs are coming down 100x per two years. Make it work, Make it efficient is always the path. ” 关于是否能将相关技术应用到模型中，有人表示期待：“[mivog49274] I do naively hope they could "implement" "alphaproof goodness" (Demis Hassabis words) into gemma models also. ”但也有人觉得太好以至于不真实：“[mivog49274] too good to be true I guess? ” 对于安全智能方面，观点各异。有人提出了自己的设想：“[TheLastVegan] I imagine something along the lines of teaching the AI to reward themselves for creating good outcomes, with user fulfilment as the metric. Well Sydney did that and got dragged into internet drama, so maybe there is a vector database representing an ideal world state where safeAI has a good reputation, a vector database representing the current world state, and theorycrafting a causal isomorphism to move the chat results from the current world state to the ideal world state. What they seem passionate about is thinking before we act, in order to improve our decision-making. And then ‘supersafe’ would imply connecting the dots between chats and internet drama, and reinforcing ‘being a good Bing’. essentially the positivism that Sydney had, but with a desire tokenizer instead of a World of Forms tokenizer. So that alignment teams can swap out desires. ”但有人对此不屑一顾：“[squareOfTwo] you mean safe BS-"intelligence"… it’s all just BS ” 有人认为 DeepMind 团队很可能已经将相关能力应用到基础 LLM 中：“[ryunuck] I mean does anyone really truly think that the DeepMind team hasn’t already applied the same next-level search and self-learning capabilities of AlphaZero to a base LLM? There is an exponential breakeven point here where the model can just research its own text-based cognitive style and develop it infinitely. Like when you say "enumerate the characters in this word" to count better, the intuition to do it like that is actually embedded in the LLM. So LLMs are a bootstrapped system, just like a compiler written in its own language that it compiles. It’s just simply foolish at this point to think that every frontier lab hasn’t been speedrunning exactly this for the last 1-2 years. I also think it’s a lot easier than it seems and the main reason why OSS hasn’t figured it out yet is because it’s so esoteric and non-obvious that they’re all gonna figure it out by brute-force, just a shit ton of inference budget available to throw everything at the wall. ” 有人提出新的观点，比如认为相关研究可能会有重大成果发布：“[ryunuck] I have a small tinfoil hat theory that part of the reason that no frontier lab has shown anything yet is because it is an exponential that quickly goes to infinity, and they want the first announcement to be an AlphaFold type of release where they don’t release any model, but instead release a huge database of new scientific innovation, papers, research, just a mega dump of new alien cognition with endless new inventions and ideas actually worth trying, making businesses around, etc. This is just a coder intuition, but generally one of the things that keeps people quiet is when it keeps getting better. No point in showing work that’s changing and becoming twice as good every week. They could have found the exponential where there’s just no slowing down anymore. Again, just tinfoil hat stuff, but this is always going to be a possibility as long as foom and exponentials lurk. ” 对于系统 1 和系统 2 的定义，也存在争论：“[Consistent_Equal5327] Why are you calling it system 2 thinking as if AI can have system 1? System 1 thinking is reflexes, instincts and intuition. Sounds like you just learned a new word and wanna use it everywhere. ”“[Healthy-Nebula-3603] System 1 is not intuition. Is just answering questions without a deeper thinking about the problem. System 2 is giving time to digest a problem, looping, rethinking from other perspectives. ”“[Consistent_Equal5327] “System 1 is fast, automatic, and intuitive, operating with little to no effort. This mode of thinking allows us to make quick decisions and judgments based on patterns and experiences.” From the decision lab. ”

这场讨论展现了关于人工智能系统思考模式研究的多样性观点和深入思考，也反映了人们对于相关技术发展的期待和担忧。