“Grown” is a highly apt metaphor, IMO. It quite succinctly captures some of the most fundamental differences between building Claude and building an Ikea desk, for example.
> AI is grown, not built, and like with anything you grow, you'll never be able to predict exactly how it will turn out.
Remember when the frontier labs found out that curated high-quality training was critical to making better models?
Basically, just like high-quality and more education tends to make better humans, on average, I think we can expect quality education to turn out better ai, on average, and with better repeatability than with humans because of better control over the initial conditions and environment.
> Basically, just like high-quality and more education tends to make better humans, on average
Much like these models seem to be plateauing, I think there is a cap to the whole “more education makes better humans” and can’t be more apparent than in the US congress and the boatload of C-Suites not actually being very good humans.
Except in this care we actually understand and know how these models work. They aren't some unknown construct of the universe. They are human made with particular goals in mind.
There is no mysticism behind the curtains, just computer science + math.
We do not understand and know how these models work. We know what their architectures are and how to create them, but we cannot explain their behaviours at a fundamental level. There is no definitive way for us to answer the question of "how did it produce response X for query Y?" - we're only grazing the surface with mechanistic interpretability.
I would love for this to be more public knowledge. I think the general public (and myself for a long time) believes the AI people know how this stuff works end to end, and so it must be trustworthy. But if we told the public "Look, we know if you put this thing in one end, you'll get something that looks similar to this out the other, but we don't really know what happens inbetween" I think we'd be able to have a more honest discussion about the relationship between AI, productivity and ongoing employment.
Isn't this fundamentally because it's all probabilities and weights? It would be like asking how did a pair of dice produce the response 4:3 on the last roll?
That’s not a refutation because this problem is not a logical problem, it is a scale problem.
We can’t explain it because we distilled so many inputs into matrixes and transformed them over and over again. If we had all the time and computing power in the universe to do so, we could trace through it bit by bit and eventually answer that question.
It is correct to say that it is just science and math, the same way we can say that gravity is just science and math even if we have only recently begun to understand how it truly functions.
If you had some time and computing power (not even all that much, in the large scale of things), you could simulate perfectly how a human grows from an embryo to an adult, or how an entire human brain processes some incoming signal, and yet this wouldn't give you the understanding to design a human or human brain from scratch.
You call this a "scale problem" as if there's some scalable way such as an algorithm to resolve arbitrary scientific questions and we simply haven't done it, but of course no such algorithm exists, which is why there's plenty of science that's still not settled.
It's a refutation that we know how they work now. In the limit, though, yes, we are likely to be able to trace the process: it is possible, though, that understanding remains inaccessible because the trace is beyond comprehension.
If you can distil the model's reasoning for a decision into a billion yes/no questions, each covering largely-independent areas, can you really say you understand what its overall reasoning was?
You could say something similar about biology—just physics behind the curtains, and we understand a lot of the basics. The difficulty comes from complexity, not mysticism.
To be clear I don't think that LLMs are sentient, but the appeal in studying them is similar to biology in that you get to dissect a highly complex system with comparatively crude tools.
it took significant research efforts to just understand how these models learn how to multiply two numbers. The fact that we know how they operate doesn't mean we understand it.
I can't predict the outcome of an RNG but that doesn't mean it grows the numbers.
Okay, but that's not relevant to AI training?
I was being very roundabout, but my point is that AIs are still built, not grown.
“Grown” is a highly apt metaphor, IMO. It quite succinctly captures some of the most fundamental differences between building Claude and building an Ikea desk, for example.
("If grown, then unpredictable" is unrelated to your apparent attempted refutation "But X is unpredictable and not grown; checkmate".)
"X implies Y" doesn't imply "Y implies X".
> AI is grown, not built, and like with anything you grow, you'll never be able to predict exactly how it will turn out.
Remember when the frontier labs found out that curated high-quality training was critical to making better models?
Basically, just like high-quality and more education tends to make better humans, on average, I think we can expect quality education to turn out better ai, on average, and with better repeatability than with humans because of better control over the initial conditions and environment.
> Basically, just like high-quality and more education tends to make better humans, on average
Much like these models seem to be plateauing, I think there is a cap to the whole “more education makes better humans” and can’t be more apparent than in the US congress and the boatload of C-Suites not actually being very good humans.
What do I know though?
> can’t be more apparent than in the US congress and the boatload of C-Suites not actually being very good humans.
Sadly, education does not correct psychopathic traits, which might be overrepresented in c-suites, and selected for in politicians.
It might be critical for humanity to identify and edit out these traits in ai, while we can.
The map is not the territory
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Except in this care we actually understand and know how these models work. They aren't some unknown construct of the universe. They are human made with particular goals in mind.
There is no mysticism behind the curtains, just computer science + math.
We do not understand and know how these models work. We know what their architectures are and how to create them, but we cannot explain their behaviours at a fundamental level. There is no definitive way for us to answer the question of "how did it produce response X for query Y?" - we're only grazing the surface with mechanistic interpretability.
I would love for this to be more public knowledge. I think the general public (and myself for a long time) believes the AI people know how this stuff works end to end, and so it must be trustworthy. But if we told the public "Look, we know if you put this thing in one end, you'll get something that looks similar to this out the other, but we don't really know what happens inbetween" I think we'd be able to have a more honest discussion about the relationship between AI, productivity and ongoing employment.
Isn't this fundamentally because it's all probabilities and weights? It would be like asking how did a pair of dice produce the response 4:3 on the last roll?
What does "it's all probabilities and weights" mean? Doesn't that apply to everything in the universe?
That’s not a refutation because this problem is not a logical problem, it is a scale problem.
We can’t explain it because we distilled so many inputs into matrixes and transformed them over and over again. If we had all the time and computing power in the universe to do so, we could trace through it bit by bit and eventually answer that question.
It is correct to say that it is just science and math, the same way we can say that gravity is just science and math even if we have only recently begun to understand how it truly functions.
If you had some time and computing power (not even all that much, in the large scale of things), you could simulate perfectly how a human grows from an embryo to an adult, or how an entire human brain processes some incoming signal, and yet this wouldn't give you the understanding to design a human or human brain from scratch.
You call this a "scale problem" as if there's some scalable way such as an algorithm to resolve arbitrary scientific questions and we simply haven't done it, but of course no such algorithm exists, which is why there's plenty of science that's still not settled.
It's a refutation that we know how they work now. In the limit, though, yes, we are likely to be able to trace the process: it is possible, though, that understanding remains inaccessible because the trace is beyond comprehension.
If you can distil the model's reasoning for a decision into a billion yes/no questions, each covering largely-independent areas, can you really say you understand what its overall reasoning was?
> If we had all the time and computing power in the universe to do so, we could trace through it bit by bit and eventually answer that question.
Then we could also solve BB(6), but that doesn't mean we know BB(6) now or ever will.
We know how the models are built and trained, but we have a very limited understanding of how the final products work.
That is to say, we don't know why they give the outputs that they do.
If we did know how they worked, AI interpretability would not be an open and growing field.
You could say something similar about biology—just physics behind the curtains, and we understand a lot of the basics. The difficulty comes from complexity, not mysticism.
To be clear I don't think that LLMs are sentient, but the appeal in studying them is similar to biology in that you get to dissect a highly complex system with comparatively crude tools.
it took significant research efforts to just understand how these models learn how to multiply two numbers. The fact that we know how they operate doesn't mean we understand it.
Utterly wrong. How LLMs work is very incompletely understood and an active area of research.
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