They dont. They have input that runs through a invisible stochastic canyon. As long as there is previous experience the stochastic canyon never ends. If there is none or isignificant one, or it runs out of tokkens, it hallucinates and the illusion falls apart. There is no reasoning, just the invisible grand canyon of all of human experience and knowledge. PS: try to get it to retell you a clichee movie or book and you can see life near the end, how the delta of all the same movies opens up into wildly different endings.
To advance further it would need the ability to abstract away the general situation shape and pattern recognize similar situations.
red75prime 7 minutes ago [-]
Stochastic gradient descent can be likened to traveling down a billion-dimensional canyon. But inference? Hardly.
12 minutes ago [-]
CrzyLngPwd 29 minutes ago [-]
My toaster doesn't reason, and neither do the current clankers.
One plausible reason I thought of that we may not understand neural nets is that by their nature their power grows with ever-more complex connections and weights.
So it is like the opposite of logical systems, in that the very design of neural net architecture is a mess of parameter "spaghetti code" which renders the entire thing a metaphorical encrypted black box. The more powerful an AI/AGI the more this would be the case, and this is analogous a complexity curve.
And so any effort to make sense of such black box computation would be like trying to reverse entropy, analogous to trying to recover information lost in waste heat. And that could be one fundamental barrier to understanding both human and artificial brains alike, relative to their internal complexity.
(Just thinking aloud my handwavy pet theory recently, I am not an expert and could be totally mistaken on this)
chrisjj 36 minutes ago [-]
Clickbait article title.
The article body does not presume they reason.
otabdeveloper4 29 minutes ago [-]
They don't reason.
JackSlateur 1 hours ago [-]
Do they ?
azakai 56 minutes ago [-]
The article answers this question, at least to the extent it can be answered, at this time.
We see some signs of reasoning, but also we understand little about how they work.
michaelchisari 42 minutes ago [-]
Do we see actual signs of reasoning or is it anthropomorphism? We have an innate tendency to do so as humans.
blooalien 36 minutes ago [-]
> Do we see signs of reasoning or is it anthropomorphism?
This is the part that so many folks just don't seem to understand (probably because it's been labeled as "thinking" or "reasoning" mode, and people assume that words have meaning). It's not reasoning or thought. It's spewing tokens pretending to "think", but it's actually just generating extra "context" to help the final answer be more coherent. The model isn't doing anything it doesn't already do. It's just doing more of it to improve the quality of the final answer displayed to the user.
Leonard_of_Q 8 minutes ago [-]
You're describing a process by which a 'thinking' entity uses cognition to refine a solution to a stated problem. That's a lot of words so usually we shorten this to 'reasoning'.
Do LLMs 'think'? I 'think' they do in a way. I don't really know how I think myself but I know I do and therefore I am (thanks, Descartes). I have a somewhat better grasp of the way LLMs 'think'. They do so sequentially, building a chain of descriptors which best fit the problem and the preceding descriptors. I suspect I do something not entirely dissimilar- i.e. I imagine 'worlds' which are like the current one changed in some way so they the problem I'm working on is reduced, then refine those until it is resolved - but in a massively parallel way.
dataflow 8 minutes ago [-]
Honestly, people need to get over this debate. It's pretty irrelevant in a lot of cases. When people ask "what is the model thinking?", they're really asking "what caused the model to produce this response (as opposed to a bunch of other plausible ones)?"
Whether it's thinking or word prediction or whatever you want to call it, people are trying to understand the causal chain.
azakai 35 minutes ago [-]
Yes, we do see signs of actual reasoning, see the papers linked in the article. (There are many others too.)
Yes, we have a tendency to anthropomorphize, but (most) researchers are aware of this.
michaelchisari 26 minutes ago [-]
The papers linked in the article discuss the mechanical operations that simulate reasoning. Intelligence is data efficiency and I don't see a strong argument that reasoning can exist if it requires a world's worth of data.
That doesn't mean that simulated reasoning isn't useful, it's wildly useful. But a thing is not its simulation.
arcanemachiner 46 minutes ago [-]
Yes, there is an LLM feature that we have anthropomorphized as "reasoning" or "thinking", where an LLM has a scratch space where it can dump tokens that help to improve the final output.
otabdeveloper4 28 minutes ago [-]
> that help to improve the final output
Do they actually help? Are you sure?
throw310822 1 hours ago [-]
Of course they do, how else do you think they manage to implement new features in large codebases, or to prove new theorems? But you don't even have to assume they do because of the results- you can read their chain of thought.
chrisjj 35 minutes ago [-]
The Eliza effect strikes.
throw310822 11 minutes ago [-]
It's indeed so powerful that even my compiler and my unit tests fell victim of this delusion.
3848499449 35 minutes ago [-]
they don't tho
ToValueFunfetti 28 minutes ago [-]
For the love of all that is sacred, please stop doing this. I'm begging you. The whole social media landscape is dying and you are creating a throwaway to participate in ruining this small corner. I assume this is not your first. And no one is convinced by this! The guidelines are there for your benefit as well. You achieve nothing but hastening the destruction of one of the last half-decent communities. Sorry for the melodrama.
Rendered at 20:11:50 GMT+0000 (Coordinated Universal Time) with Vercel.
To advance further it would need the ability to abstract away the general situation shape and pattern recognize similar situations.
So it is like the opposite of logical systems, in that the very design of neural net architecture is a mess of parameter "spaghetti code" which renders the entire thing a metaphorical encrypted black box. The more powerful an AI/AGI the more this would be the case, and this is analogous a complexity curve.
And so any effort to make sense of such black box computation would be like trying to reverse entropy, analogous to trying to recover information lost in waste heat. And that could be one fundamental barrier to understanding both human and artificial brains alike, relative to their internal complexity.
(Just thinking aloud my handwavy pet theory recently, I am not an expert and could be totally mistaken on this)
The article body does not presume they reason.
We see some signs of reasoning, but also we understand little about how they work.
This is the part that so many folks just don't seem to understand (probably because it's been labeled as "thinking" or "reasoning" mode, and people assume that words have meaning). It's not reasoning or thought. It's spewing tokens pretending to "think", but it's actually just generating extra "context" to help the final answer be more coherent. The model isn't doing anything it doesn't already do. It's just doing more of it to improve the quality of the final answer displayed to the user.
Do LLMs 'think'? I 'think' they do in a way. I don't really know how I think myself but I know I do and therefore I am (thanks, Descartes). I have a somewhat better grasp of the way LLMs 'think'. They do so sequentially, building a chain of descriptors which best fit the problem and the preceding descriptors. I suspect I do something not entirely dissimilar- i.e. I imagine 'worlds' which are like the current one changed in some way so they the problem I'm working on is reduced, then refine those until it is resolved - but in a massively parallel way.
Whether it's thinking or word prediction or whatever you want to call it, people are trying to understand the causal chain.
Yes, we have a tendency to anthropomorphize, but (most) researchers are aware of this.
That doesn't mean that simulated reasoning isn't useful, it's wildly useful. But a thing is not its simulation.
Do they actually help? Are you sure?