I don't care if it's "intelligent", I don't care if it "has a mind". I don't care if it is "really reasoning", I don't care if it "understands". I don't care if it is "sentient" or "conscious".
None of this matters for the practical outcome.
You'd think that this has been understood over the last 4 years, but apparently it keeps circling back to this.
[Edit: I see that it was written back in 2023. Then (2023) should be added in the submission title]
If it generates functional output that works, then it works. And it works. It's not a psychic's con when it outputs Lean-verified proofs. It isn't a con when it can find and exploit zero-days.
The OP is still in the "denial" phase. Most I see are already in "anger" (a blurry fury against everything AI-shaped, from vague reasons piling on all "bad stuff" political reasons they already hated before) or "bargaining" (mathematicians scrambling to come up with a new definition of their job and retcon that it was always the main part anyway). A few are already in "depression" and feel like spectators on the Titanic, and the tiniest sliver is at "acceptance" with some kind of well-informed plan for their future.
> The OP is still in the "denial" phase.
This was written in July 2023. ChatGPT was released November 2022. No matter your views on AI, surely you can't blame the OP for writing this after a few months ChatGPT was released.
I’m not seeing anything like a falsifiable prediction that LLMs will write math proofs in that article.
I see a thought experiment about giving GPT-2 “near-infinite training data and [compute]” but that’s not falsifiable. That’s also not how we got to modern GPT models.
He's pretty clearly saying he beilieves the technology capable of such a feat, at least in theory, which is far more than many would deign to admit even a year ago, nevermind 7. He had the right idea/model of LLM capabilities, which is more than you could say for a lot of people, even in this very thread.
If that’s your standard for what counts as prediction, Asimov beat him to it by seventy-ish years.
EDIT:
> Scott made comments on a specific emerging technology
He was speculating on what would happen if one gave GPT-2 “near-infinite training data and compute.” It’s a thought experiment, not a prediction. Near-infinite amounts of anything is a fantasy.
I acknowledge that he has predicted some things in a falsifiable way and turned out correct, but this isn’t one of them. You’re reading hindsight into the text.
>I acknowledge that he has predicted some things in a falsifiable way and turned out correct, but this isn’t one of them. You’re reading hindsight into the text.
I read that blog years ago. Believe me, my opinions are not hindsight.
>Incorrect. He was speculating on what would happen if one gave GPT-2 “near-infinite training data and compute.” It’s a thought-experiment, not a prediction. Near-infinite amounts of anything is a fantasy.
Thought experiments can generate predicitons. His claim was essentially: If you scale data and compute sufficiently, this technology can learn enough of the underlying structure of mathematics to write proofs.
This is meaningful when others around you are saying this is a dead end and that the technology is fundamentally incapable of this regardless of degree of investment and scaling. It shows a much better calibrated sense of the potential of the architecture than those who said otherwise.
If your objection is that "near infinite" makes it insufficiently quantitative to count as a falsifiable forecast, then fine. But at that point we're mostly arguing over what deserves the label "prediction" rather than whether Scott correctly identified an important capability the architecture could develop.
And i'm not trying to say this makes Scott (or the lesswrong crowd) geniuses.
bonoboTP · · focus · HN ↗
None of this matters for the practical outcome.
You'd think that this has been understood over the last 4 years, but apparently it keeps circling back to this.
[Edit: I see that it was written back in 2023. Then (2023) should be added in the submission title]
If it generates functional output that works, then it works. And it works. It's not a psychic's con when it outputs Lean-verified proofs. It isn't a con when it can find and exploit zero-days.
The OP is still in the "denial" phase. Most I see are already in "anger" (a blurry fury against everything AI-shaped, from vague reasons piling on all "bad stuff" political reasons they already hated before) or "bargaining" (mathematicians scrambling to come up with a new definition of their job and retcon that it was always the main part anyway). A few are already in "depression" and feel like spectators on the Titanic, and the tiniest sliver is at "acceptance" with some kind of well-informed plan for their future.
zkry · · focus · HN ↗
This was written in July 2023. ChatGPT was released November 2022. No matter your views on AI, surely you can't blame the OP for writing this after a few months ChatGPT was released.
bananaflag · · focus · HN ↗
<a href="https://slatestarcodex.com/2019/02/19/gpt-2-as-step-toward-general-intelligence/" rel="nofollow">https://slatestarcodex.com/2019/02/19/gpt-2-as-step-toward-g...
Of course people were skeptical:
<a href="https://www.reddit.com/r/slatestarcodex/comments/aslze7/gpt2_as_step_toward_general_intelligence/" rel="nofollow">https://www.reddit.com/r/slatestarcodex/comments/aslze7/gpt2...
sheafification · · focus · HN ↗
I see a thought experiment about giving GPT-2 “near-infinite training data and [compute]” but that’s not falsifiable. That’s also not how we got to modern GPT models.
famouswaffles · · focus · HN ↗
sheafification · · focus · HN ↗
EDIT:
> Scott made comments on a specific emerging technology
He was speculating on what would happen if one gave GPT-2 “near-infinite training data and compute.” It’s a thought experiment, not a prediction. Near-infinite amounts of anything is a fantasy.
I acknowledge that he has predicted some things in a falsifiable way and turned out correct, but this isn’t one of them. You’re reading hindsight into the text.
famouswaffles · · focus · HN ↗
I read that blog years ago. Believe me, my opinions are not hindsight.
>Incorrect. He was speculating on what would happen if one gave GPT-2 “near-infinite training data and compute.” It’s a thought-experiment, not a prediction. Near-infinite amounts of anything is a fantasy.
Thought experiments can generate predicitons. His claim was essentially: If you scale data and compute sufficiently, this technology can learn enough of the underlying structure of mathematics to write proofs.
This is meaningful when others around you are saying this is a dead end and that the technology is fundamentally incapable of this regardless of degree of investment and scaling. It shows a much better calibrated sense of the potential of the architecture than those who said otherwise.
If your objection is that "near infinite" makes it insufficiently quantitative to count as a falsifiable forecast, then fine. But at that point we're mostly arguing over what deserves the label "prediction" rather than whether Scott correctly identified an important capability the architecture could develop.
And i'm not trying to say this makes Scott (or the lesswrong crowd) geniuses.