I enjoyed this Karpathy post about how there is absolutely no extant solution to training language models to reliably solve open ended problems.
I preferred Zitron’s point* that we would need to invent several branches of science to solve this problem, but it’s good to see the point made tweet-sized.
I read the article you linked. I feel like I wasted my time.
The article has a single point it repeats over and over again: OpenAI (and "generative AI as a whole"/"transformer-based models") are too expensive to run, and it's "close to impossible" for them to either limit costs or increase revenue. This is because "only 5% of businesses report using the technology in production", and that the technology had no impact on "productivity growth". It's also because "there's no intelligence in it", and the "models can't reason". Oh, also, ChatGPT is "hard to explain to a layman".
All that is liberally sprinkled with "I don't know, but"s and absolutely devoid of any historical context other than in financial terms. No technical details. Just some guesses and an ironclad belief that it's impossible to improve GPTs without accessing more data than there is in existence. Agree or disagree; the article is not worth wading through so many words: others made arguments on both sides much better and, crucially, shorter.
> The article has a single point it repeats over and over again: [7 distinct points]
I don’t think have a single overall thesis is the same thing as repeating oneself. For example “models can’t reason” has nothing at all to do with cost.
7 distinct points in the number of words that would suffice for 70 points...
Anyway, it's just my opinion: to me, the length of the article was artificially increased to the point where it wasn't worth my time to read it. As such, unfortunately, I'm not inclined to spend any more time discussing it - I just posted my takeaways and a warning for people like me. If you liked the article, good for you.
> "models can't reason" has nothing at all to do with cost.
Yeah, that one falls under "no technical details".
> Yeah, that one falls under "no technical details".
Technical details about cost? Or technical details about how models can’t reason?
For example I don’t really need “technical details” to confidently assert that Quicken cannot do ray tracing. Everyone is welcome to try it, and exactly zero people will report being able to use Quicken’s ray tracing abilities in an automated business-critical way.
There isn’t an enormous burden of proof required to back up a description of software that is consistent with the limitations that normal users run into. I don’t know anyone, for example, that would trust it to do basic math in the same way that they would trust other software or even a normal person to do it. Do you?
You can't tell me something "can't reason" or "has no intelligence" without also telling me what reasoning or intelligence is. Or what you think it is. That's the technical detail that's missing. From what I know, whether LLMs reason or not is an open question - are they stochastic parrots? are they not? I dunno, but since you evidently do, so please show your reasoning and evidence. Just because a claim is repeated over and over doesn't mean it's true (or false).
> even a normal person to do it. Do you?
There are intelligent people capable of reasoning with dyscalculia. Sorry, but being unable to do arithmetic is not enough of an argument.
I have no idea what Quicken is, an honestly, ray-tracing (using the definition I'm familiar with) is a mechanical process that doesn't require any intelligence.
EDIT: Here's an article that has the technical details (and it's not a 15-minute read): https://www.theguardian.com/technology/article/2024/aug/06/a... The original article is 80% PR fluff aimed at emotional influence, the one from Guardian is just good journalism. I have an allergy to the former.
That's a great writeup, and great references too.
> OpenAI needs at least $5 billion in new capital a year to survive. This would require it to raise more money than has ever been raised by any startup in history
They were probably toast before, but after Zuck decided to take it personally and made free alternatives for most use cases they definitely are, since if they had any notable revenue from selling API access it will just keep dropping.
I really would not be surprised to see OpenAI collapse - founders leaving like rats from a sinking ship is not a good sign, and not indicative of any AGI breakthrough on the horizon. Would not be surprised to see Brockman's "sabbatical" become permanent.
I have to wonder how long Anthropic can remain independent too, although at least they don't have toxic workplace and founder exodus issues to deal with. Maybe they get acquired by Amazon?