Comment by Version467
9 hours ago
LeCun's argument wasn't about the definition of learning though. He stated that they would never get these common sense things correct because they weren't sufficiently part of the training data. A statement that we can hopefully all agree has been thoroughly refuted.
As of a few months ago they still have trouble, with low thinking, at the "should I drive to a car wash that is 100 m away" kind of question.
Simply appending “check your assumptions” to the question fixed it even back then: https://news.ycombinator.com/item?id=47040530
Similarly for Apple’s “red herring” paper, simply adding a generic caveat to “disregard irrelevant factors” (without specifying which ones) restored performance even in the weaker local llama models back then.
The flaw was not in the reasoning; the flaw seems to be simply that the assumptions we make are often different from the assumptions it makes. I wonder if that might be a fundamental underlying cause of misalignment.
Low thinking is an artificial constraint. It can fail spectacularly on things that aren't in the training data.
It's a nonsensical question to ask, and how an LLM answers gives 0 signal.
If you were home and a family member asked you that question, you'd probably criticise the question rather than answering. LLM are RLHF'd into being milk-toast helpers that just try to answer questions like that with no criticism.
This is all beside the fact that the world of AI has changed pretty dramatically in the last few months.
It is so nonsensical because it has such an obvious answer. The answer is so obvious, in fact, that one answer can be considered nonsense and the other common sense.
*Milquetoast
This is just a stupid post.
It’s nonsense to test if a product that is marketed and sold as being able to provide generalised intelligence on demand, does what it says on the tin?
Check yourself
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nothing indicated otherwise at the time. IMO he just underestimated RL-scaling. chinese models improved a lot too, they are not parrots anymore, there's some real intelligence, at 27B params.
consider me optimist now, but just few months ago, even frontier models were dumb, doing stupid mistakes all the time, all of them were so dumb I'd never expect anything to change in just few months.
I thought it was more because of fundamental limitations in the architecture. As in, no matter the training data, it could not be consistently and generally represented
Actually, I think my fundamental challenge with AI is that it has no common sense. The way it builds things, writes, and operates is out of touch with reality.
Incidents like hugging face are partly rooted in the lack of common sense. It still functions like a supercharged toddler.
I'd love to overcome this because it'd mean I spend less time guiding the the LLM to produce usable outputs.
> It still functions like a supercharged toddler.
And we've had difficulty as humans to childproof our sandboxes and infrastructure. Things that are otherwise innocuous spots to coordinate between like minded toddlers can become problematic.
Last week I asked a frontier model draw me a backplane PCB and it placed daughterboard slots side by side in a chain.
No?
This is always the issues in the discussions.
There’s the outcomes camp (objectivists?), which points at the things LLMs can do.
Then there’s the process methods camp, which talks about what is actually going on.
If you only care about the outcome, then the process does t matter.
If you are talking about what is happening, what the underlying mechanics and science of it is, then the process matters.
These models aren’t thinking. They simulate cognition well enough to do useful work in several fields and domains.
Both are true.
I think where both camps get hung up is sometimes the process method group "ignores" the obvious outcomes and effectiveness of LLMs.
But the outcomes group "ignores" the fundamental limitations of models which are purely text based.
E.g, a baseball players trains to catch high-speed balls and they dont do it by: "ball velocity 50mph, vector:[1,2,3], run move hand command now"
That's absurd.
No, there is an embodied network which is "trained" on visual, tactile input, and control as direct output.
LLMs are fundamentally not the right tool for that.
> E.g, a baseball players trains to catch high-speed balls and they dont do it by: "ball velocity 50mph, vector:[1,2,3], run move hand command now"
That is a NN that learns a skill.
But that is not an Analyst. If it were ballistics, then the answer to "how to parametrize the launch to reliably hit the target" excludes getting the result through natural skill.
The problem lies in the need to get "AI" facing "LLMs": the latter create a need for reliability, for "AI".
Speech is an endowment of both those who give educated guesses via developed skills and of those who return answers like Analysts, who check and compute. LLMs create a confusion between the two, and they will remain a problem until an ability to act as Analysts - strictly - will be implemented.
> These models aren’t thinking.
They are for any definition of the word that makes any kind of sense. I'm sure you have a contorted definition that magically only includes humans though...
> for any definition of the word
For "thinking" here we mean "assessing a representation of an object". That, or equivalent, is required to be reliable. So it is fundamental and critical.
Sure? If humans happen to be doing something that LLMs are not, then should the answer change to accommodate your disdain?
The models are simulating thinking, if the fidelity is good enough for you - great!
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> A statement that we can hopefully all agree has been thoroughly refuted.
Uh, no? So much of what we learn and take for granted as common sense is not learned via language, and not even expressible in it.