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@Thore Graepel
别被迷惑——LLM 并不会推理
MIT Technology Review 文章以 2016 年 AlphaGo 对李世石第二局第 37 手为例，指出该手并非纯粹机器直觉，而是策略网络提供直觉、搜索机制进行显式博弈树推演的结果，类似 Kahneman 的 System 1/System 2 分工。作者据此认为，当前 LLM 逐 token 预测的生成方式缺少这种显式搜索与推演能力，若要让 AI 在科学、医学等领域产生可信且真正新颖的成果，需要为其配备真正的推理能力。
AI推荐理由 MIT Technology Review 的评论文章，作者以 AlphaGo 第 37 手为例，区分了策略网络的直觉与搜索机制的推理，并据此论证当前 LLM 逐 token 生成的方式缺乏这种显式搜索式推理。文章提供了具体的历史案例和 Kahneman 双系统框架作为论据，观点明确、信息密度较高；但属于观点性评论而非新研究或可验证数据，且正文片段在关键论证处被截断，无法确认其完整结论与证据强度。
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Domain: technologyreview.com
Points: 29 | Comments: 23
[Top Comments]
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- Jet planes don't fly by flapping their wings...
- Bird don't fly by producing thrust out of their asses... what the heck that comparison does even mean?
- It's wild when you think about it, you don't need to reason to solve the hardest math problems that humans failed to solve for decades.
- I thought I saw a paper recently explaining that LLMs have a global workspace. Is that not like having the internal state that he's talking about?
- There was a blog post from Anthropic saying Claude has a "J space" or something like that.
- This is the double edged sword of calling it AI, of using terms like Temperature and Hallucinate and Thought.
Stop trying to compare either system to a human and look at it for what it is -
A prediction engine that runs fast enough to brute force problems.
In the case of alpha go its "innovation" was millions of games played against itself. It had bound parameters and strict win conditions.
In the case of LLM's you can deploy 1000's of agents to smash themselves against an idea. The whole hugging face attack is an example of this (1200 agents out of an unknown number chose that path).
There is the old saying about monkeys, typewriters and Shakespeare. Well we have better monkeys who basically follow a derivative of zipfs law (not actually), who use tokens not letters and their goal in many cases is testable (compile, unit, E2E).
- Yep, it's not 'AI', or even just 'I', if anything it is just the 'A' wrapped in buzzwords.
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- > Knowledge and reasoning are inextricably interwoven in the weights of the neural network—there is no independent, explicitly represented set of beliefs.
I'm not sure I understand this. Do we have evidence humans have an independent set of beliefs not shaped by knowledge and reasoning? If so, where do these come from?
I'm especially confused about a prior statement as a scientist:
> Three shortcomings prevent what chatbots do from qualifying as reasoning (in a way that a scientist might recognize).
How does a set of beliefs help with reasoning?
> Third, while the chains of thought chatbots produce look like deliberation, research has demonstrated that the bots often concoct them after the fact, reaching an answer by one route but reporting another.
We also often do the same as humans.
- I think, "contexting" is apropos.
I relate a lot to what they do. Find words, alignment and suss out follow ups, ons, and outs to the next reasonable conclusion.
Then use that context to bootstrap the next because if you build a powerful conclusion than can reverse itself into its evidentiary context, then every next context step can update its priors.
And so on the turtles flow where like an LLM, THE start of the context disappears over the horizon, but as long as im contexting in disinterested chunks of equal quality, then its not a problem.
But while internal tobeach context you can find reason, as a requisite building block like falling tetris pieces, the whole isnt the sum of its parts.
- Yes, this is true—there's no "logic" in the sense of deductive rigor. It's a wonder we *animals* are capable of it.
- Don't be fooled, submarines don't swim.
- Neither do humans!
At least reasoning is not guaranteed.
Would sure be nice though...