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Andrej Karpathy – It will take a decade to work through the issues with agents

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Re: Andrej Karpathy – It will take a decade to work through the issues with agents

#561

With all due respect, what does it say about us that „famous researcher voices his speculative opinion“ is an instant top 1 on hackernews?

That the speculative opinions of famous researchers are a good starting point for curious conversation.

Re: Andrej Karpathy – It will take a decade to work through the issues with agents

#562
post #68

>What takes the long amount of time and the way to think about it is that it’s a march of nines. Every single nine is a constant amount of work. Every single nine is the same amount of work. When you get a demo and something works 90% of the time, that’s just the first nine. Then you need the second nine, a third nine, a fourth nine, a fifth nine. While I was at Tesla for five years or so, we went through maybe three…

I also quite like the way he puts it. However, from a certain point onward, the AI itself will contribute to the development—adding nines—and that’s the key difference between this analogy of nines in other systems (including earlier domain‑specific ML ones) and the path to AGI. That's why we can expect fast acceleration to take off within two years.

I think the 9's include this assumption.

Re: Andrej Karpathy – It will take a decade to work through the issues with agents

#563

Earlier quoted context omitted.

Photons hit a human eye and then the human came up with language to describe that and then encoded the language into the LLM. The LLM can capture some of this relationship, but the LLM is not sensing actual photons, nor experiencing actual light cone stimulation, nor generating thoughts. Its "world model" is several degrees removed from the real world. So whatever fragment of a model it gains through learning to comp…

I agree with this. A metaphor I like is that the reason why humans say the night sky is beautiful is because they see that it is, whereas an LLM says it because it’s been said enough times in its training data.

What about a blind human? Are they just like an LLM?

What about a multimodal model trained on video? Is that like a human?

Re: Andrej Karpathy – It will take a decade to work through the issues with agents

#565

Earlier quoted context omitted.

This world model talk is interesting, and Yann Lecunn has broached on the same topic, but the fact is there are video diffusion models that are quite good at representing the "video world" and even counterfactually and temporally coherently generating a representation of that "world" under different perturbations. In fact you can go to a SOTA LLM today, and it will do quite well at predicting the outcomes of basic co…

Photons hit a human eye and then the human came up with language to describe that and then encoded the language into the LLM. The LLM can capture some of this relationship, but the LLM is not sensing actual photons, nor experiencing actual light cone stimulation, nor generating thoughts. Its "world model" is several degrees removed from the real world. So whatever fragment of a model it gains through learning to comp…

And even then, the light hitting our human eyes only describes a fraction of all the light in the world (e.g. it is missing ultraviolet patterns on plants). An LLM model of the world is shaped by our human view on the world.

Re: Andrej Karpathy – It will take a decade to work through the issues with agents

#567
post #68

>What takes the long amount of time and the way to think about it is that it’s a march of nines. Every single nine is a constant amount of work. Every single nine is the same amount of work. When you get a demo and something works 90% of the time, that’s just the first nine. Then you need the second nine, a third nine, a fourth nine, a fifth nine. While I was at Tesla for five years or so, we went through maybe three…

The interview which I've watched recently with Rich Sutton left me with the impression that AGI is not just a matter of adding more 9s. The interviewer had an idea that he took for granted: that to understand language you have to have a model of the world. LLMs seem to udnerstand language therefore they've trained a model of the world. Sutton rejected the premise immediately. He might be right in being skeptical here…

I think current AI is a human language/behavior mirror. A cat might believe they see another cat looking in a mirror, but you can’t create a new cat by creating a perfect mirror.

Re: Andrej Karpathy – It will take a decade to work through the issues with agents

#569

Earlier quoted context omitted.

Photons hit a human eye and then the human came up with language to describe that and then encoded the language into the LLM. The LLM can capture some of this relationship, but the LLM is not sensing actual photons, nor experiencing actual light cone stimulation, nor generating thoughts. Its "world model" is several degrees removed from the real world. So whatever fragment of a model it gains through learning to comp…

I agree with this. A metaphor I like is that the reason why humans say the night sky is beautiful is because they see that it is, whereas an LLM says it because it’s been said enough times in its training data.

I mean, I think the reason I would say the night sky is “beautiful” is because the meaning of the word for me is constructed from the experiences I’ve had in which I’ve heard other people use the word. So I’d agree that the night sky is “beautiful”, but not because I somehow have access to a deeper meaning of the word or the sky than an LLM does.

As someone who (long ago) studied philosophy of mind and (Chomskian) linguistics, it’s striking how much LLMs have shrunk the space available to people who want to maintain that the brain is special & there’s a qualitative (rather than just quantitative) difference between mind and machine and yet still be monists.

Re: Andrej Karpathy – It will take a decade to work through the issues with agents

#570
post #68

>What takes the long amount of time and the way to think about it is that it’s a march of nines. Every single nine is a constant amount of work. Every single nine is the same amount of work. When you get a demo and something works 90% of the time, that’s just the first nine. Then you need the second nine, a third nine, a fourth nine, a fifth nine. While I was at Tesla for five years or so, we went through maybe three…

The thing about this, though - cars have been built before. We understand what's necessary to get those 9s. I'm sure there were some new problems that had to be solved along the way, but fundamentally, "build good car" is known to be achievable, so the process of "adding 9s" there makes sense.

But this method of AI is still pretty new, and we don't know it's upper limits. It may be that there are no more 9s to add, or that any more 9s cost prohibitively more. We might be effectively stuck at 91.25626726...% forever.

Not to be a doomer, but I DO think that anyone who is significantly invested in AI really has to have a plan in case that ends up being true. We can't just keep on saying "they'll get there some day" and acting as if it's true. (I mean you can, just not without consequences.)

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