121–130 of 268 posts
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This is a good take. People overwhelmingly fit a narrative to how they make decisions rather than make decisions according to a chain of reasoning.
That’s just conflating reasoning and “parallel reconstruction” - there is such thing as reasoning, and I agree it’s probably less common in decision making, but it’s fundamental to many tasks where we figure out a solution, like writing an algorithm, math proofs, etc.
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> There is a long history of bad naming conventions in the field of AI, including “artificial intelligence” itself imo. It's all marketing. AI companies repurpose the terms of AI researchers to exaggerate their products. The public has an idea of what AI means and the companies exploit it. AI, in public perception, is what the Terminator movies are about. It's exactly like all the "hover boards" you can buy.
The specific terms I gave as examples weren’t created by marketers. They were created by researchers. It’s just that “attention” is a lot easier to say than “similarity-weighted representation mixing”.
The marketing is responsible for all of this confusion because they want to associate their product with something that it isn't.
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Holy run-on sentence batman, maybe you do need AI. Your employers are (likely, some assumption here) paying for the productivity of a full time employee. You have figured out that they're satisfied with a third of that productivity. You are the reason why there are so many return-to-office mandates, because you're projecting the idea that remote employees cannot be trusted. You're making it worse for everyone. Good l…
You sound very upset and angry but there is no need to use curse words and shift all the blame on me. I just figured out a way to make AI work for me, I am qualified to work the jobs, that doesn't mean I am gate keeping it. More importantly I am not responsible for other's employment situation, that's on them, there are plenty of remote jobs out there, just keep your head up, and I'm sure you will land on your feet.
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That’s just conflating reasoning and “parallel reconstruction” - there is such thing as reasoning, and I agree it’s probably less common in decision making, but it’s fundamental to many tasks where we figure out a solution, like writing an algorithm, math proofs, etc.
Sure, but the same must be true for these models as well. They must be doing some form of reasoning, even if it's not formally correct and it relies instead on all kind of half-assed heuristics which only work inside certain unexamined boundaries, which leads to wrong conclusions when those boundaries are being trespassed, etc. And even if it's not the reasoning they report when asked. But all this can't be that diff…
Surely it is worthwhile to attempt to understand the details of that? And if we seek human equivalent performance then it is reasonable to wonder if the reasoning achieved to date is the "correct" sort.
An intuitive explanation for why reasoning tokens help is to remember that LLMs are just mathmatical functions f() that take in an input sequence x and produces the next token f(x). Without reasoning tokens, you require the function f() to immediately take you from x to the start of an output sequence that is a correct answer. With reasoning tokens, this is much relaxed, allowing for many repeated applications of f()…
There is a long history of bad naming conventions in the field of AI, including “artificial intelligence” itself imo. (What is “intelligence” here? It’s more like “automation” or “automated problem solving”.) What really happens is that we figure out something that works, sometimes inspired by some biological thing or neuroscience thing. Examples: neural network, attention, reasoning, hallucination, agents, experts i…
There are two things occurring here. One is the AI effect, once something mysterious becomes creatable it is not intelligent at all. The other isn't a problem of the field of AI, but the total fields of intelligence together. There is no formal agreed upon definition of what intelligence actually is across disciplines. It would be slightly hypocritical to point out the field of AI on this, when biology and psychology…
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> I'll admit that I find this discussion a bit navel-gazy. It has become a question of semantics not a question of actual functionality Ah, so you're more in the Investor mindset than the Scientist mindset. All you care about is results, not how it got there. There's a whiff of "hey, it's magic!" to that.
No, that's stupid. The question of how LLMs (and "LRM"'s, an unfortunate and by the article's own admission, already outdated term) accomplish what they accomplish is fascinating. The question of whether they reason is meaningless. Most of the article is about the latter, with tiny tiny tiny bits of the former.
It is often (but not always) meaningless (or at least largely pointless) to quibble about which particular semantic bin a concrete object or process ought to be placed in.
Asking how one thing differs from another is not that. Confusingly, the two activities often appear the same when glanced at superficially.
The question "do current LLMs reason" is ambiguous in this regard. It might be a question of submarines swimming or it might be a question about how the mechanisms underlying the process or the overall performance or some other aspect compare to those of human reasoning.
Also even the semantic question can occasionally have some value. Such as attempting to articulate where we ought to place the goalposts which necessarily leads to hammering out the specifics of various definitions which is an exercise in furthering our understanding of a given concept. (Basically arguing about goalposts can be productive if the field in which they are being placed isn't yet well understood. It becomes increasingly pointless as understanding increases, gradually devolving into nothing more than word games and a contest of wills.)
I'll admit that I find this discussion a bit navel-gazy. It has become a question of semantics not a question of actual functionality. The question has become "what do we mean when we use the word 'reasoning'" which is uninteresting. Dijkstra said[1] "... the question whether computers can think. The question is just as relevant and just as meaningful as the question whether submarines can swim." I don't see a clear…
In order to properly engineer things, we must know how they work. We cannot just jump from emergent property to more convincing emergent property as if the rocks on the surface of a lake. "Reasoning" is an important part in said framework: yes, we must understand how it works, how it works properly, how its simulations work, how they work properly...
We don't have to understand the resulting product at all. Genetic algorithms are an example - we understand the evolution function, not what evolves from it.
Back in the day it was a bit of a cliche to bring up “clever Hans”, the horse that could do math, when talking about machine learning. He couldn’t do math but he read some cues from his handler of pick the write answers, the handler iirc wasn’t in on it. The point of the story was that classifiers can be right for the wrong reasons and almost inevitably are. At least there’s zero guarantee that the reason for making…
This sounds like a tautological argument that doesn't really mean anything. Hypothetically, in a near future a super-powerful AI could bring something truly astonishing (say, practical fusion power, or cure all cancer) and one could still argue "They do what their handler wants to see, that's all."
So what does that even mean.
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There are two things occurring here. One is the AI effect, once something mysterious becomes creatable it is not intelligent at all. The other isn't a problem of the field of AI, but the total fields of intelligence together. There is no formal agreed upon definition of what intelligence actually is across disciplines. It would be slightly hypocritical to point out the field of AI on this, when biology and psychology…
I’d go a step further and say your first point is a consequence of the second. Because “intelligence” is poorly defined, anyone can slap “AI” on their product, so it becomes a nebulous moving target.
That way we can judge systems that are just a repeat of previous systems with dubious claims attached to their capabilities.
It can be somewhat difficult to do this properly and generally enough to ensure the tests aren't benchmaxxed.