I really disagree with this article. It’s basically just saying that you drank the LLM Kool-Aid. LLMs are massively overhyped. GPT-x is not the way to AGI.
This article could have been written a dozen years ago. A dozen years ago, people were saying the same thing: “we’ve given up on the Good Old-Fashioned AI / Douglas Hofstadter approach of writing algorithms and trying to find insights! it doesn’t give us commerical products, whereas the statistical / neural network stuff does!”
And our response was the same as it is today. GOFAI is hard. No one expected to make much progress on algorithms for intelligence in just a decade or two. We knew in 2005 that if you looked ahead a decade or two, we’d keep seeing impressive-looking commercial products from the statistical approach, and the GOFAI approach would be slow. And we have, but we’re no closer to AGI. GPT-x only predicts the next words based on a huge corpus, so it gives you what’s already there. An average, basically. An impressive-looking toy, but it can’t reason or set goals, which is the whole idea here. GOFAI is the only way to do that. And it’s hard, and it’s slow, but it’s the only path going in the right direction.
Once you understand that, you can see where your review errs.
cyc—it’s funny that Hanson takes what you’d expect to be Yudkowsky’s view, and vice versa. cyc is the correct approach. The only reason to doubt this is if you’re expecting commercially viable results in a few years, which no one was. Win Hanson.
AI before ems—AI does not seem well on its way, so I disagree that there’s been any evidence one way or the other. Draw.
sharing cognitive content and improvements—clear win Yudkowsky. The neural network architecture is so common for commercial reasons only, not because it “won” or is more effective. And even if you only look at neural networks, you can’t share content or improvements between one and another. How do you share content or improvements between GPT and Stable Diffusion, for instance?
algorithms:
Yudkowsky seems quite wrong here, and Hanson right, about one of the central trends—and maybe the central trend—of the last dozen years of AI.
Well, that wasn’t the question, was it? The question was about AI progress, not what the commercial trend would be. The issue is that AI progress and the commercial trend are going in opposite directions. LLMs and throwing more money, data, and training at neural networks aren’t getting us closer to actual AGI. Win Yudkowsky.
But—regardless of Yudkowsky’s current position—it still remains that you’d have been extremely surprised by the last decade’s use of comput[ing] if you had believed him
No, no you would not. Once again, the claim is that GOFAI is the slow and less commercializable path, but the only true path to AGI, and the statistical approach has and will continue to give us impressive-looking and commercializable toys and will monopolize research, but will not take us anywhere towards real AGI. The last decade is exactly what you’d expect on this trend. Not a surprise at all.
For what it’s worth, I’m at least somewhat an LLM-plateau-ist—on balance at least somewhat dubious we get AGI from models in which 99% of compute is spent on next-word prediction in big LLMs. I really think Nostalgebrists take has merit and the last few months have made me think it has more merit. Yann LeCunn’s “LLMs are an off-ramp to AGI” might come back to show his forsight. Etc etc.
But it isn’t just LLM progress which has hinged on big quantities of compute. Everything in deep learning—ResNets, vision Transformers, speech-to-text, text-to-speech, AlphaGo, EfficientZero, Dota5, VPT, and so on—has used more and more compute. I think at least some of this deep learning stuff is an important step to human-like intelligence, which is why I think this is good evidence against Yudkowsky
If you think none of the DL stuff is a step, then you can indeed maintain the compute doesn’t matter, of course, and that I am horribly wrong. But if you think the DL stuff is an important step, it becomes more difficult to maintain.
I really disagree with this article. It’s basically just saying that you drank the LLM Kool-Aid. LLMs are massively overhyped. GPT-x is not the way to AGI.
This article could have been written a dozen years ago. A dozen years ago, people were saying the same thing: “we’ve given up on the Good Old-Fashioned AI / Douglas Hofstadter approach of writing algorithms and trying to find insights! it doesn’t give us commerical products, whereas the statistical / neural network stuff does!”
And our response was the same as it is today. GOFAI is hard. No one expected to make much progress on algorithms for intelligence in just a decade or two. We knew in 2005 that if you looked ahead a decade or two, we’d keep seeing impressive-looking commercial products from the statistical approach, and the GOFAI approach would be slow. And we have, but we’re no closer to AGI. GPT-x only predicts the next words based on a huge corpus, so it gives you what’s already there. An average, basically. An impressive-looking toy, but it can’t reason or set goals, which is the whole idea here. GOFAI is the only way to do that. And it’s hard, and it’s slow, but it’s the only path going in the right direction.
Once you understand that, you can see where your review errs.
cyc—it’s funny that Hanson takes what you’d expect to be Yudkowsky’s view, and vice versa. cyc is the correct approach. The only reason to doubt this is if you’re expecting commercially viable results in a few years, which no one was. Win Hanson.
AI before ems—AI does not seem well on its way, so I disagree that there’s been any evidence one way or the other. Draw.
sharing cognitive content and improvements—clear win Yudkowsky. The neural network architecture is so common for commercial reasons only, not because it “won” or is more effective. And even if you only look at neural networks, you can’t share content or improvements between one and another. How do you share content or improvements between GPT and Stable Diffusion, for instance?
algorithms:
Well, that wasn’t the question, was it? The question was about AI progress, not what the commercial trend would be. The issue is that AI progress and the commercial trend are going in opposite directions. LLMs and throwing more money, data, and training at neural networks aren’t getting us closer to actual AGI. Win Yudkowsky.
No, no you would not. Once again, the claim is that GOFAI is the slow and less commercializable path, but the only true path to AGI, and the statistical approach has and will continue to give us impressive-looking and commercializable toys and will monopolize research, but will not take us anywhere towards real AGI. The last decade is exactly what you’d expect on this trend. Not a surprise at all.
For what it’s worth, I’m at least somewhat an LLM-plateau-ist—on balance at least somewhat dubious we get AGI from models in which 99% of compute is spent on next-word prediction in big LLMs. I really think Nostalgebrists take has merit and the last few months have made me think it has more merit. Yann LeCunn’s “LLMs are an off-ramp to AGI” might come back to show his forsight. Etc etc.
But it isn’t just LLM progress which has hinged on big quantities of compute. Everything in deep learning—ResNets, vision Transformers, speech-to-text, text-to-speech, AlphaGo, EfficientZero, Dota5, VPT, and so on—has used more and more compute. I think at least some of this deep learning stuff is an important step to human-like intelligence, which is why I think this is good evidence against Yudkowsky
If you think none of the DL stuff is a step, then you can indeed maintain the compute doesn’t matter, of course, and that I am horribly wrong. But if you think the DL stuff is an important step, it becomes more difficult to maintain.