The difference now is that the timescales are weeks or months instead of generations. I believe we will see models that have super-human "compositional" reasoning within 1 year.
> The AI effect occurs when onlookers discount the behavior of an artificial intelligence program by arguing that it is not real intelligence.
> Author Pamela McCorduck writes: "It's part of the history of the field of artificial intelligence that every time somebody figured out how to make a computer do something—play good checkers, solve simple but relatively informal problems—there was a chorus of critics to say, 'that's not thinking'." Researcher Rodney Brooks complains: "Every time we figure out a piece of it, it stops being magical; we say, 'Oh, that's just a computation.'"
> but then when somebody builds a system
I mean this is really it. You still have to have a human to build these systems that specialist in one thing. Once you create a system that can automatically create those systems and it doesn't need humans anymore to solve novel problems, then there will be no practical difference in kind between human and AI intelligence.
Except we don't have that. We don't have one human that can create this system by themselves. We have a choice group of a handful of smart, motivated, and quite generously compensated humans working on these problems to create such system. As such, you are already surpassing the "general" intelligence level by quite a lot.
I think that is a very generous take on what we do "automatically". After all, we have millions of years of evolution to build out all the neural circuitry that helps us speech or vision -- it's not like you can throw a soup of genes on the ground and out comes intelligence. What is machine learning doing, if not selecting, out of many possible parametrizations, the ones that are suited to understand vision or speech?
Tangential to AGI, but don't we? Vegans seem to have quite a strong opinion on this assertion.
Humanity: I don’t think your intelligence matches that of a human’s.
AI: I don’t think about you at all.
Edit: Gwern has an extensive history with this so I'll let him do the talking.
https://old.reddit.com/r/TheMotte/comments/v8yyv6/somewhat_c...
Further Edits: Not to mention Scott Alexander who has directly rebutted you numerous times. Or Yann LeCunn. Not sure who exactly is backing down.
https://astralcodexten.substack.com/p/my-bet-ai-size-solves-...
https://astralcodexten.substack.com/p/somewhat-contra-marcus...
https://analyticsindiamag.com/yann-lecun-resumes-war-of-word...
Presumably you approach these arguments like Ben Shapiro and imagine you have "Dunked on the Deep Learning geeks with Facts and Logic."
i have been pretty damn consistent since me 2001 book.
edit: Maybe I made a composition error. https://imgur.com/a/Q7hHduY
Maths in and of itself doesn't require any physical resources. It's possible that doing maths in practice requires knowledge of the world to extract some kind of product from (I'm skeptical, but it's possible), but in principle a rack mounted server could demonstrate its mathematical ability to the world with nothing more than the ability to send and receive messages.
This hasn't been done so far, not because there are obvious missing prerequsites, or because nobody's tried it, or because it has no value, or because there's a prohibitively high barrier to entry for people to have a go. It hasn't been done because nobody knows how to make a machine be a mathematician, and I've seen little evidence of any progress towards it.
That's my goalpost, always has been. Reach it and I'll be overjoyed. And FWIW, I strongly believe it can be reached. I don't see the latest round of ML (or any ML, really) as a step towards it, but I'd love to be proven wrong.
When I mention this someone always points at some bit of recent research, such as [1], but it's invariably just a new way for a human mathematician to make use of a computer. If anybody knows of any progress, or serious attempts, towards a true AI mathematician I'm very curious to know.
https://dspace.mit.edu/handle/1721.1/132379.2
Is a well known project for an AI Physicist. There are plenty of other groups working on similar projects
>I don't see the latest round of ML (or any ML, really) as a step towards it, but I'd love to be proven wrong.
LLM models have been able to do basic math for quite a while now and some have been trained to solve differential equations, calculus problems, etc. Well on their way to more impressive capabilities.
Neither of the things you mention are of this nature, or working towards it. "Finding a symbolic expression that matches data from an unknown function" (Feynman) and "solv[ing] differential equations, calculus problems, etc" are not descriptions of what a research mathematician does.
It has to be maths for a specific reason. I think it's in some sense the purest form of an ability distinctive to human minds and pervasive in how they work. As I mentioned, it's an ability that can be demonstrated in the absence of any particular physical capability, and yet despite it being perhaps the oldest goal of AI it may be the one we have made least progress towards.
Anyway that's my goalpost, and it's not moving. AGI, being "general", surely should be capable of this hitherto uniquely human activity. If our attempts so far are not capable of it, then clearly they are not "general". If you know of any evidence that my goalpost has been achieved, please let me know. I'm very eager to see it happen.
Never said they were but you said:
>It hasn't been done because nobody knows how to make a machine be a mathematician, and I've seen little evidence of any progress towards it."
Which I showed is not accurate. Certainly people have ideas on how to do it and are actively making progress towards that goal.
>Finding a symbolic expression that matches data from an unknown function" (Feynman) and "solving differential equations, calculus problems, etc" are not descriptions of what a research mathematician does.
All research mathematicians started out solving calculus problems and differential equations.
Why do you expect an AI to sprint before it's learned to crawl?
Ever since computers were invented there has been a hope that you could set up a system that would just churn out interesting new theorems. Indeed it was one of the primary motivations for the invention of the computer, but it hasn't materialised yet.
You clearly consider the progress on solving problems to be progress towards being able to do mathematical research. I don't think it is, any more than progress in, say, graphics is. But maybe I will turn out to be wrong and you will turn out to be right. We won't have the answer until the problem is solved and we have our wonderful machine churning out theorems.
But I think you will probably be able to agree that since mathematical research is something human minds are capable of it's something that an AGI should be capable of, i.e. if an AI approach is inherently incapable of it, it's not AGI. You may consider it an unnecessarily stringent requirement, in that there may be other, easier challenges that AIs can perform that will convince you that they are AGI. That's fine - you think about the problem differently to me, so you find different things persuasive. If you are convinced that a given AI is AGI, though, you shouldn't be too concerned about my particular goalpost given that your AGI should be able to achieve it (and convince me) pretty soon.
We'll see what happens. I'm just explaining what I would find convincing, and pointing out that contrary to the oft-repeated accusation that started this discussion, I for one have never once "moved the goalposts".
Indeed. To be clear, I'm not saying I think any current system is remotely close to AGI. I just think that saying that no one is thinking about or making progress on a math research AI is inaccurate.
I'd settle for a demonstration that a computer has truly independently discovered/invented and proved some significant part of our existing mathematical edifice. This hasn't been achieved yet, either. However, I suspect that once we've figured out how to do this at all, surpassing human capabilities will be inevitable in a relatively short time. So I don't see much value in softening the test unless/until there's some actual candidate available that would pass the softer test.
The value in requiring genuinely new maths is that it makes it unlikely that knowledge of the result has been encoded in the algorithm or training set. Certainly, if GPT-3 were to output Euler's formula that wouldn't be at all convincing as a "discovery".