231–240 of 341 posts
Earlier quoted context omitted.
it's actually the end of the beginning, there's walls that self-improving models hit that are never overcome even when given vast amounts of time.
I don't think anyone has ever tried having a model fully autonomously train a model that is better than it.
Have you tried asking people instead of not thinking?
I've been at two startups that have done genuine world first fundamental research. The first tried to publish novel results for 3 years in tier 1 journals before finally doing a preprint and telling the tier one publishers to jump in a fire. The second, and ongoing, isn't publishing anything because of my experience with the first. That and avoiding openAI and Anthropic copying our results and leaving us with nothing…
Publishing CS papers at the top venues requires using in-group language and formalism that is pretty much inaccessible to someone who has not done a PhD in that specific narrow field. LLMs are pretty good at this, hilariously. This means a genuinely good paper by an outsider has significantly less chance of getting good reviews than AI slop.
And when that prejudice degrades the quality of your own thinking and writing, you're producing "human slop", which is considerably more tragic: you ought to be capable of doing better than the AI.
You're holding it wrong.
Earlier quoted context omitted.
Plenty of research is performed by non PhDs. The main difference is reproducibility and peer review process.
outside physics/math a lot of published research isn't reproducible at all. There's been several studies that show this.
Were they peer reviewed and reproducable?
And what's the point of claiming there are peer reviewed reproducable studies that show this, if you don't bother citing those studies for your peers to review?
Earlier quoted context omitted.
good.
The downside is frontier computer science in several domains is decades beyond the state-of-the-art from academic literature. This is the situation that patents were designed to disincentivize. There is not an obvious path to making this research public again. That's the reality. For open source to be competitive it needs to be approximately as efficient as the best systems we design.
Listing on stock exchange of ai product requires open sourcing parts or all of your publicly trained stack.
Double or triple taxes on companies that do not provide for public good.
Will this happen, probably when the market crashes and people want companies and individuals to pay. Really matters if it’s a 2008 crash or a 1929 crash where the bankers and stock brokers were jumping out of upper stories.
And it makes sense. If you publish some state-of-the-art algorithm then you're basically helping your competition, and you don't get anything back from them. So you publish bare minimum in small articles on your website, only to gain people's/investors' interest, and just hope that you didn't share too much. And it'll always be like that, unless the root cause changes - intentives. And the main incentives right now a…
And it makes sense. If you publish some state-of-the-art algorithm then you're basically helping your competition, and you don't get anything back from them. So you publish bare minimum in small articles on your website, only to gain people's/investors' interest, and just hope that you didn't share too much. And it'll always be like that, unless the root cause changes - intentives. And the main incentives right now a…
The least these companies can do is to publish everything they own