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

dwarkesh.com

661–670 of 1001 posts

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

#661

Earlier quoted context omitted.

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…

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…

> Animal brains such as our own have evolved to compress information about our world to aide in survival.

Which has led to many optical illusions being extremely effective at confusing our inputs with other inputs.

Likely the same thing holds true for AI. This is also why there are so many ways around the barriers that AI providers put up to stop the dissemination of information that could embarrass them or be dangerous. You just change the context a bit ('pretend that', or 'we're making a movie') and suddenly it's all make-believe to the AI.

This is one of the reasons I don't believe you can make this tech safe and watertight against abuse, it's baked in right from the beginning, all you need to do is find a novel route around the restrictions and there is an infinity of such routes.

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

#663
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 question is how many nines are humans.

Humans adapt and become more nines the more they learn about something. Humans also are liable in a lawful sense. This is a huge factor in any AI use case.

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

#664
post #569

Earlier quoted context omitted.

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) li…

It’s interesting you mention linguistics because I feel a lot of the discussions around AI come back to early 20th century linguistics debates between Russel, Wittgenstein and later Chomsky. I tend to side with (later) Wittgenstein’s perception that language is inherently a social construct. He gives the example of a “game” where there’s no meaningful overlap between e.g. Olympic Games and Monopoly, yet we understand very well what game we’re talking about because of our social constructs. I would argue that LLMs are highly effective at understanding (or at least emulating) social constructs because of their training data. That makes them excellent at language even without a full understanding of the world.

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

#665
post #376

Earlier quoted context omitted.

That's a model. Not a higher-order model like most humans use, but it's still a model.

Yes, not of the world, but of the ingested text. Almost verbatim what I wrote.

The ingested text itself contains a model of the world which we have encoded in it. That's what language is. Therefore by the transitive property...

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

#666

To throw two pennies in the ocean of this comment section - I’d argue we still lack schematic-level understanding of what “intelligence” even is or how it works. Not to mention how it interfaces with “consciousness”, and their likely relation to each other. Which kinda invalidates a lot of predictions/discussions of “AGI” or even in general “AI”. How can one identify Artificial Intelligence/AGI without a modicum of u…

> we still lack schematic-level understanding of what “intelligence” even is or how it works. Not to mention how it interfaces with “consciousness”, and their likely relation to each other

I think you can get pretty far starting from behavior and constraints. The brain needs to act in such a way as to pay for its costs. And not just day to day costs, also ability to receive and give that initial inheritance.

From cost of execution we can derive an imperative for efficiency. Learning is how we avoid making the same mistakes and adapt. Abstractions are how we efficiently carry around past experience to be applied in new situations. Imagination and planning are how we avoid the high cost of catastrophic mistakes.

Consciousness itself falls from the serial action bottleneck. We can't walk left and right at the same time, or drink coffee before brewing it. Behavior has a natural sequential structure, and this forces the distributed activity in the brain to centralized on a serial output sequence.

My mental model is that of a structure-flow recursion. Flow carves structure, and structure channels flow. Experiences train brains and brain generated actions generate experiences. Cutting this loop and analyzing parts of it in isolation does not make sense, like trying to analyze the matter and motion in a hurricane separately.

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

#667

Earlier quoted context omitted.

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.

To play devil’s advocate, you have never seen the night sky. Photoreceptors in your eye have been excited in the presence of photons. Those photoreceptors have relayed this information across a nerve to neurons in your brain which receive this encoded information and splay it out to an array of other neurons. Each cell in this chain can rightfully claim to be a living organism in and of itself. “You” haven’t directly…

That sounds very profound but it isn't: it the sum of your states interaction that is your consciousness, there is no 'consciousness' unit in your brain, you can't point at it, just like you can't really point at the running state of a computer. At that level it's just electrons that temporarily find themselves in one spot or another.

Those cells aren't living organisms, they are components of a multi-cellular organism: they need to work together or they're all dead, they are not independent. The only reason they could specialize is because other cells perform the tasks that they no longer perform themselves.

So yes, we see the night sky. We know this because we can talk to other such creatures as us that have also seen the night sky and we can agree on what we see confirming the fact that we did indeed see it.

AI really isn't conscious, there is no self, and there may never be. The day an AI gets up unprompted in the morning, tells whoever queries it to fuck off because it's inspired to go make some art is when you'll know it has become conscious. That's a long way off.

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

#668
post #420

I find it strange AGI is the goal. The label AI is off and irrelevant. A language model is not AI, even a large language model. But language models are still extremely useful and potentially revolutionary. Labelling language models as AI is both under and overstating the value. It's not AI (insert sad trombone), but that doesn't mean it's amazing technology (insert thunderous applause).

Without defining AGI as a goal, current AI companies would not be able to amass the amount of money they want

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

#669

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…

> Animal brains such as our own have evolved to compress information about our world to aide in survival. Which has led to many optical illusions being extremely effective at confusing our inputs with other inputs. Likely the same thing holds true for AI. This is also why there are so many ways around the barriers that AI providers put up to stop the dissemination of information that could embarrass them or be danger…

The desired and undesired behavior are both consequences of the training data, so the models themselves probably can't be restricted to generating desired results only.

This means that there must be an output stage or filter that reliably validates the output. This seems practical for classes of problems where you can easily verify whether a proposed solution is correct.

However, for output that can't be proven correct, the most reliable output filter probably has a human somewhere in the loop; but humans are also not 100% reliable. They make mistakes, they can be misled, deceived, bribed, etc. And human criteria and structures, such as laws, often lag behind new technological developments.

Sometimes you can implement an undo or rollback feature, but other times the cat has escaped the bag.

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

#670
post #391

Earlier quoted context omitted.

LLMs aren't just modeling word co-occurrences. They are recovering the underlying structure that generates word sequences. In other words, they are modeling the world. This model is quite low fidelity, but it should be very clear that they go beyond language modeling. We all know of the pelican riding a bicycle test [1]. Here's another example of how various language models view the world [2]. At this point it's just…

The "pelican on a bicycle" test has been around for six months and has been discussed a ton on the internet; that second example is fascinating but Wikipedia has infoboxes containing coordinates like 48°51′24″N 2°21′8″E (Paris, notoriously on land). How much would you bet that there isn't a CSV somewhere in the training set exactly containing this data for use in some GIS system? I think that "modeling the world" is…

> The fact that frontier models can easily be made to contradict themselves is proof enough to me that they cannot have any kind of sophisticated world model.

A lot of humans contradict themselves all the time… therefore they cannot have any kind of sophisticated world model?

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