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(In case people miss that before discussion)
Updated, thanks!
This is still a great paper, but it's missing the second axis of the quadric -- if the only two options are thinking fast or thinking about thinking, that leaves no room for thinking slow yet deliberately, AKA selfconsciousness. See https://www.gutenberg.org/cache/epub/4280/pg4280-images.html for details
I do wonder if any of these folks ever got a chance to try this at one of the big labs, tho...
It looks like a lot like how data bases query optimizers work, with the exception that in the paper there is also a learning/memory component that conditions the evaluation of the answer provided by the first model.
It reminds me of "At the time we drew boxes labeled 'perception', 'cognition' with arrows between them." An imprecise quote that I can't place.
I guess my box labelled 'subconsciousness' is trying to say that low-level mechanisms that give rise to the observed cognitive phenomena might have nothing to do with neat boxes.
You mean "Artificial Intelligence meets Natural Stupidity" by Drew McDermott
https://dl.acm.org/doi/pdf/10.1145/1045339.1045340
McDermott's A Critique of Pure Reason pretty much captured all of the misgivings I had about "Good Old-Fashioned Artificial Intelligence", which was slightly unfortunate as I was trying to complete a PhD in that very area at the time (around 1990 or so...)
Did you complete it?
2021. Please remember the rule of HN to add the year if it’s not actual.
This has already been solved by GPT 5 Adaptive reasoning. A single model that knows when to reason or not based on a thinking parameter we provide (like xhigh). What’s the relevancy to post it today?
edit: why is this downvoted?
Surely that is obvious
Tell me you didn't read Daniel Khaneman's book without telling me you didn't read Daniel Khaneman's book.
Asking earnestly, I don’t know what you mean by this reply. I know what system 1 and 2 is. But this has already been solved using same model.
No, it hasn't. Maybe you have a different definition of System 1 and System 2. I last read the book well over a decade ago (2011, maybe? 2012?), but System 1 and System 2 are different systems. IOW, System 2 is not a more computational version of System 1.
The argument you made implies that System 2 is just a more capable System 1, which is not what the book (nor this paper, AIUI) proposes.
In computery terms, System 1 runs in O(1) time, System 2 runs in O(log n) (or maybe just O(n)) time.
This means that any System 1 will run the input once through the heuristics, using the same computational power and taking the same time whether the input is 100 tokens or 1 million tokens, for quick but perhaps wrong decision (not "answer"). We don't have LLMs that do that. We have System 2 - run in O(log n) time and produce an answer.
System 1 is completely bereft of thought.
No, system 2 is the emergent capability to reason and increase the space of places to find the answer. Forget the paper's proposal, and look at the problem it is trying to solve. Ability to give quick answers, ability to give thought out answers, and the ability to know when to choose what. Adaptive reasoning does all three.
No, I don't think we humans use o(1) to for understanding 1000 tokens or 2 tokens. I simply don't think that's the case. There's a new model called "Jev" and it is literally named System 1 (from the book) and even it is billed per input token.
It's being downvoted, I think, for a few reasons:
* The person who posted it likely posted it not as an out-of-date paper but as an interesting idea. Your comment ignores the idea and focuses on what you're calling its out-of-dateness.
* You say "this has been solved" without defining what "this" is.
* Your description of the solution -- different effort levels -- seems to indicate that you misunderstand the idea that the paper is proposing. If I understand their proposal, it's that the system itself decides how to reason based on the nature of the problem it faces, given the model's world model and past experience. "Effort" isn't so much the issue as types of effort using different systems, modeled specifically after Kahneman's idea of fast and slow thinking.
* The title is an allusion to a book by Daniel Kahneman. The brisk dismissal without acknowledging the idea or the history doesn't leave a good impression, even if I'm mistaken and you're right.
In short, Hacker News readers tend to reward depth and detail (the FAQ specifically encourages thoughtful contributions and explicitly discourages dismissal). Your comment doesn't provide them, and it appears to make a mistake that further undermines its value as a contribution to discussion.
do you even know how adaptive reasoning works?
Godspeed to you in your efforts to contribute productively to Hacker News threads.
https://openai.com/index/gpt-5-1/
It says literally the thing you wanted from system 2. Its almost exactly that.
This is what you said btw:
"it's that the system itself decides how to reason based on the nature of the problem it faces"
At least this is written before ChatGPT.
How relevant is this fast/slow thinking thing with regards to current frontier models?
I know a large organization who's built their AI framework completely around this concept, and I feel that it's not really meaningful concept with the capabilities of current models.
You can ask a model for output directly and stop, or you can recursively ask it to keep refining the output.
That seems to fit the fast vs slow model of human thought reasonably well.
That’s still several orders of magnitudes too slow to fit fast vs slow. Think of 30ms vs 3-4 seconds to get an idea of what we’re talking about here
That's because an LLM thinks in terms of language, while we think in a different way, then convert the ideas to language. It can be said that language is a tool for the serialization (writing) and deserialization (reading) of human ideas. It is also an incredible useful and powerful tool by itself. This last sentence has been proved true by LLMs themselves. However, since it is working on the serialized version of ideas, I agree with you in that's not the optimal way to think and something not serialized (maybe world models) can be invented that's better for thinking. All this in no way diminishes the usefulness of language and of automated language generation.
Trying to get LLMs to 'think about their thinking' is my daily struggle. This paper nails why it's so critical.
If I recall correctly, all that fast and slow business has been debunked as yet more non-replicable pop psychology.
I shouldn't be surprised that it shows up in a screed on AI