I think the next step for developers is to combine existing technologies. For example, in the medical imaging space that are thousands of published algorithms that were implemented once (by the researcher who published the paper), but never used. AI can now read the paper and one-shot an implementation. I do wonder if there are breakthroughs to be had by combining such algorithms.
It's coming for everyone: there's a startup that introduced robot arms that promise to learn anything 1 shot: if it holds - and there's little wiggle room for one shot - it would be a good indicator that manual labor is also soon gone.
The only question that remains is if 99.9% of the world population will let themselves be fooled by UBI & VR to keep the 'masses content; while the 0.1% hold all the wealth and AI power.
And this is decided now, because it is too late once AI is consolidated under one bi/tri-llionaire.
Is the equivalent of ants trying to outsmart Google that wants to build a datacenter on their land. ASI will unfathomably outsmart the wealthy and intelligent humans like Stockfish 13 does Magnus Carlsen
If AI is a verifiable domain, how do you get AIs to construct benchmarks to apply to themselves?
It seems that the only benchmarks that could be automatically constructed are those for tasks that are themselves verifiable (theorem proving, coding, etc).
Non-verifiable tasks require humans to define what exactly a good job is.
The RSI hypothesis is dependent on an assumption that progress on verifiable tasks generalizes to non-verifiable tasks.
I feel like this is why more distributed control or open ownership of AI models is such an important goal for regulators. As AI accelerates its own development, the private companies that are initially maybe only a few months ahead of open-source will widen the gap more and more.
It seems likely we end up with an even more extreme tech oligarchy than we already have when superintelligence arrives. And that's only if we manage to solve alignment and it doesn't do something more horrible.
Dwarkesh has been skeptical about AI claims for a long time. In his interview with Dario earlier this year, he pressed him hard. So this is a fairly recent change of opinion.
What he says still doesn't map to my daily practice. I use Astra and Fable extensively and they need an incredible amount of guidance and harnessing to produce fairly simple systems. Any decent code is produced with tons of static tooling, agent files, harnessing, and even then, there are myriad mistakes.
As far as I'm concerned Dwarkesh could be prey of AI psychosis... I don't think a proxy metric like the average daily spend of AI researchers going up proves anything.
The technology has undoubtedly advanced but for me it's still a coin toss on whether it will be an exponential or an s-function.
A lot of AI research can be already automated. The experimental part of work can be tonally outsourced to agents (at least for the topics that I am interested in). I had some success with outsourcing theoretical work as well. These days I use quite advanced orchestration. Interestingly, the most difficult part still is writing good stories for research papers. There I can still beat any AI. And when it comes to research we have been already covered by an avalanche of AI slop. There was always slop in AI research but now it is the fully automated slop. A few years ago there was some human-made slop, now it is being factory produced. AI can do good research on its own, but good research is rare in general.
>majority of this acceleration today is likely attributable to Anthropic’s extensive use of AI to do R&D
Lots of companies think they are reaping similar rewards from ai coding and research but it has failed to lift bottomlines or create any new products. My last two employers are gungho on ai and ppl have been producing unfathomable amount of ai output. To a causal observer it would look like we have really accelerated but this has had no visible impact on any of the products that employers produce other than make them shittier.
Whats different about product development at athropic that ai is particular suited for not generating slop.
TLDR; someone used some fake graphs and probabilities pulled out of thin air to justify an unwarranted conclusion based on a false analogy to a situation that is far from clear.
Isn't Sam Altman accused of being a habitual liar by a lot of people, including the richest man on the planet, the arguably best (certainly longest-lasting) AI researcher, the most famous AI ethics researcher, and half of his own board?
Strange how this guy can be CEO of anything. All you hear management types ever blubber about is integrity ... Clearly there's a problem here.
The only question that remains is if 99.9% of the world population will let themselves be fooled by UBI & VR to keep the 'masses content; while the 0.1% hold all the wealth and AI power.
And this is decided now, because it is too late once AI is consolidated under one bi/tri-llionaire.
Is the equivalent of ants trying to outsmart Google that wants to build a datacenter on their land. ASI will unfathomably outsmart the wealthy and intelligent humans like Stockfish 13 does Magnus Carlsen
But we've all seen robots fold clothes to do that sort of thinking easily. Oh maybe we need to hangout on ai twitter more.
It seems that the only benchmarks that could be automatically constructed are those for tasks that are themselves verifiable (theorem proving, coding, etc).
Non-verifiable tasks require humans to define what exactly a good job is.
The RSI hypothesis is dependent on an assumption that progress on verifiable tasks generalizes to non-verifiable tasks.
It seems likely we end up with an even more extreme tech oligarchy than we already have when superintelligence arrives. And that's only if we manage to solve alignment and it doesn't do something more horrible.
What he says still doesn't map to my daily practice. I use Astra and Fable extensively and they need an incredible amount of guidance and harnessing to produce fairly simple systems. Any decent code is produced with tons of static tooling, agent files, harnessing, and even then, there are myriad mistakes.
As far as I'm concerned Dwarkesh could be prey of AI psychosis... I don't think a proxy metric like the average daily spend of AI researchers going up proves anything.
The technology has undoubtedly advanced but for me it's still a coin toss on whether it will be an exponential or an s-function.
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And that does not include Substack bulk subscriptions.
Lots of companies think they are reaping similar rewards from ai coding and research but it has failed to lift bottomlines or create any new products. My last two employers are gungho on ai and ppl have been producing unfathomable amount of ai output. To a causal observer it would look like we have really accelerated but this has had no visible impact on any of the products that employers produce other than make them shittier.
Whats different about product development at athropic that ai is particular suited for not generating slop.
Trump's mistake is using the word to denote AI in general when it already had an established meaning.
Strange how this guy can be CEO of anything. All you hear management types ever blubber about is integrity ... Clearly there's a problem here.