| ▲ | Philpax 18 days ago |
| AI is grown, not built, and like with anything you grow, you'll never be able to predict exactly how it will turn out. |
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| ▲ | halestock 18 days ago | parent | next [-] |
| I can't predict the outcome of an RNG but that doesn't mean it grows the numbers. |
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| ▲ | Philpax 18 days ago | parent | next [-] | | Okay, but that's not relevant to AI training? | | |
| ▲ | halestock 18 days ago | parent [-] | | I was being very roundabout, but my point is that AIs are still built, not grown. | | |
| ▲ | dwaltrip 18 days ago | parent [-] | | “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. |
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| ▲ | Smaug123 18 days ago | parent | prev | next [-] | | ("If grown, then unpredictable" is unrelated to your apparent attempted refutation "But X is unpredictable and not grown; checkmate".) | |
| ▲ | umanwizard 18 days ago | parent | prev [-] | | "X implies Y" doesn't imply "Y implies X". |
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| ▲ | ninjagoo 18 days ago | parent | prev | next [-] |
| > 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. |
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| ▲ | irishcoffee 18 days ago | parent [-] | | > 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? | | |
| ▲ | ninjagoo 18 days ago | parent [-] | | > 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. | | |
| ▲ | irishcoffee 17 days ago | parent [-] | | Seems to me the venn diagram of "congress and c-suites" vs "educated people" would have one circle wholly inside the other. I know people without a college education that would give you the shirt off their back, and educated people that rewrite wills while their parents are on their deathbed. What we call education today is a problem, and one need look no further than the massive amount of debt we saddle on kids. For what? So they can pay for privilege of being told what books to read, what topics to write about, and a rubber stamp? I didn't learn a _thing_ in college that I haven't learned better either at $dayjob, or from reading. Most of my math profs. didn't speak english well, and none of the TAs did. Any math I've since forgotten from college was self-taught. Calc i/ii/iii, diffew, linear, stat. College/education lost the plot. The sooner we admit it, the sooner we can fix it. | | |
| ▲ | ninjagoo 16 days ago | parent [-] | | > Sadly, education does not correct psychopathic traits, which might be overrepresented in c-suites, and selected for in politicians.
>> Seems to me the venn diagram of "congress and c-suites" vs "educated people" would have one circle wholly inside the other.
Both things can be true. > look no further than the massive amount of debt we saddle on kids.
See politicians and c-suites populated by psychopaths for the origins of this problem. > I didn't learn a _thing_ in college that I haven't learned better either at $dayjob, or from reading.
Putting it a bit bluntly, like any other activity, one gets out of it what one puts into it. I had a very different experience from yours, accents and language skills notwithstanding. But there is so much variation in a domain so broad in our country that is so big, it doesn't necessarily invalidate your experience. > College/education lost the plot. The sooner we admit it, the sooner we can fix it.
There is a long list/tradition of higher education through thousands of years of human history, with Harvard/MIT/Oxford being the pre-eminent ones today. [1][2]What alternative do you propose? For humans, and AI? [1] https://en.wikipedia.org/wiki/List_of_oldest_higher-learning_institutions
[2] https://en.wikipedia.org/wiki/List_of_oldest_universities_in_continuous_operation
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| ▲ | gensym 18 days ago | parent | prev | next [-] |
| The map is not the territory |
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| ▲ | Rekindle8090 18 days ago | parent | prev | next [-] |
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| ▲ | shimman 18 days ago | parent | prev [-] |
| 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. |
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| ▲ | Philpax 18 days ago | parent | next [-] | | 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. | | |
| ▲ | cflewis 18 days ago | parent | next [-] | | 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. | |
| ▲ | devmor 18 days ago | parent | prev | next [-] | | 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. | | |
| ▲ | stratos123 18 days ago | parent | next [-] | | 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. | |
| ▲ | Philpax 18 days ago | parent | prev | next [-] | | 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? | |
| ▲ | solomonb 18 days ago | parent | prev [-] | | > 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. |
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| ▲ | SoftTalker 18 days ago | parent | prev [-] | | 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? | | |
| ▲ | umanwizard 18 days ago | parent [-] | | What does "it's all probabilities and weights" mean? Doesn't that apply to everything in the universe? |
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| ▲ | in-silico 18 days ago | parent | prev | next [-] | | 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. | |
| ▲ | ray__ 18 days ago | parent | prev | next [-] | | 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. | |
| ▲ | j_maffe 18 days ago | parent | prev | next [-] | | 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. | |
| ▲ | umanwizard 18 days ago | parent | prev | next [-] | | Utterly wrong. How LLMs work is very incompletely understood and an active area of research. | |
| ▲ | Rekindle8090 18 days ago | parent | prev [-] | | [dead] |
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