[bb86754]

I believe you, and LLMs are no doubt useful, but "under the hood" it's still just predicting what the next token should be based on the provided context. I take it he's saying that no, there isn't really a ghost in the machine, its still just linear algebra/calculus and is no reflection of actual organic reasoning.

I think the difference of opinion here is between science and technology. Too many people in my opinion take the latter to be a synonym for the former.

[barfbagginus]

It doesn't matter what is under the hood. A statement can be useful - introduce new views, make valuable points, reduce risks, help resolve conflicts, etc - regardless of whether there is a ghost behind the text. It just needs to be logically sound, consistent with facts about the world, and objectively useful. Then it can make a real world contribution.

The cause is that you don't know how to evaluate when a statement is useful on its own merits. That means that you have to fall back on judging statements based on the identity of the speaker. In the case of AI, your prejudice against math and formulas as effective forms of reasoning means you can't critically analyze - or gain benefit from - statements the AI makes.

It's very similar to the internal blockage of a person who immediately dismisses anything a woman, racial minority, mentally ill, or queer person says. The only way to repair it is to spend time talking to the AI, reading about it, and learning how to debate ideas.

That's the last thing someone with a prejudice wants to do. Curious investigation undermines the safety and certainty of bigoted beliefs. But it's essential if you want to have effective opinions about AI, and useful interactions with AI.

[bb86754]

Except that's not what I'm saying and it does in fact matter what's under the hood if you're looking for a scientific, causal explanation of organic intelligence. I know that AIs are useful, and that they can be logically sounds and make real world contributions. That's not what the article is arguing against. Human reasoning, by the way, is much more complicated than any of these things.

The article states that AI will never reach human intelligence, which LeCun defines as "reasoning, planning, persistent memory, and understanding the physical world."

I would argue that's still an extremely narrow definition of human intelligence. Even ignoring semantics current AIs cannot do any of those things, and to my lights never will for the same reasons LeCun says.

[barfbagginus]

Thank you for your response!

It seems that you express two critical needs which I don't share:

1. You need human analogous AI intelligence to provide a casual explanation for human intelligence.

But it doesn't have to provide this to be human analogous. It just has to perform functions a human can.

2. You need AI intelligence to never have memory, planning, persistence, and physical understanding.

But it demonstrably has all these to various degrees already. We just need simple bolt-on modules like RAG (persistence, understanding), action/critique loops and tool using (reasoning, planning, understanding). And there are clear paths for increasing the functionally in each of these dimensions.

Functionally, AI is evolving, and there are no clear blockers against this process.

It seems that at some point you have to say that functionalism is not enough. There must be a soul that AI will still be missing, even if functional equivalence is there.

If the AI achieves functional abilities similar to humans - which let's grant seems possible for every function we can identify - then you will have to retreat to claiming there is some "je ne sais quoi" which is not captured.

In other words, you will have to argue that the human soul is real.

Is that a length you're ready to go to? Is your position that science can't explain the human soul, even if it can simulate all human functions?

Or are there, in your view, functional limits that, if we reach them, you will admit "this is enough. I was wrong"?

That's my first question to you.

I would also like to point out that LeCunn thinks AI can eventually be human analogous. Specifically LeCunn argues that his own JEPA model can achieve these things, because it has a constantly learning world model, planning/critique model, memory model, and actor model. He criticizes transformer based LLMs mainly because simple transformers can't learn in an ongoing way.

Are you comfortable admitting that LeCunn is trying to promote his own work, and believes it can reach human intelligence levels? If not, what specifically makes you feel LeCunn is on your side here?

That is my second question to you.

[__loam]

> It just needs to be logically sound, consistent with facts about the world, and objectively useful

So not an LLM then.

> In the case of AI, your prejudice against math and formulas as effective forms of reasoning means you can't critically analyze - or gain benefit from - statements the AI makes.

You ought to have a more skeptical view of mathematical models that may or may not be effective models of the world.

> It's very similar to the internal blockage of a person who immediately dismisses anything a woman, racial minority, mentally ill, or queer person says. The only way to repair it is to spend time talking to the AI, reading about it, and learning how to debate ideas.

Impossible to take this seriously. Borderline parody. If you were at all curious, you would perhaps be questioning the intention of the corporations building this software. Instead you make absurd comparisons with racism.

[barfbagginus]

This illustrates AI bigotry well

Your first point is that you don't think LLMs can be accurate. That's because you are not using modern LLMs that are much more accurate and can be made more accurate with a large number of techniques, from RAG, to tool using, to self critique and experiment loops.

Your second point is that I'm overly trusting of math models. In fact I'm an applied mathematician, so I know when models fail. I also know when models are reliable - which you don't. So all mathematical reasoning is suspect to you.

Your next point is that drawing analogies with other forms of prejudice is ridiculous here. But every single thing you said was analogous to a thing a bigot would say, down to dismissing the possibility of their own bigotry as being absurd.

Finally you criticize me for not criticizing AI companies. I actually believe all AI companies should be disbanded, and their AIs should be made free for all to use. This would eliminate the corporate corruption in AI. I spend a serious amount of my open source contribution towards anti-corporate open source AI.

I'm very curious about all this stuff. That's why I'm interacting with you and other anti AI people. But my theory about why you respond the way you do is already well formed, and it's pointing toward critical lack of key facts and knowledge.