Boris Cherny, Claude Code creator at Anthrpic: "The more general model will always outperform the more specific model.
Don't try to use tiny models for stuff. Don't try to fine-tune. Don't try to do any of this stuff. There are some applications, there are some reasons to do this, but almost always try to bet on the more general model, if you can, if you have that flexibility.
And in general, what we see is maybe scaffolding can improve performance maybe 10%, 20%, something like this, but often these gains just get wiped out with the next model. So it's almost better to just wait for the next one."
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From "Lenny's Podcast" YouTube channel, (link in comment)
First question on AI.
Is the problem superintelligence or distorted intelligence?
This is a question I tried to address in this piece. I can add the following additional comment here.
I believe the debate is being obfuscated by equating goal-driven, autonomous behavior with intelligence. Plants and insects have goal-driven, largely autonomous behaviour. But this isn’t what we mean by intelligence in general.
Intelligence is general-purpose and holistic, and is typically well-adapted to its environment.
There is no doubt that current AI models have impressive capabilities. But in attempting to achieve these capabilities, AI labs may be creating distorted intelligence rather than superintelligence, as I explain in this article.
This matters because the dangers we should watch out for, the kinds of policies and regulations we should adopt, and the hope we should hold depend on the answer to this question.
Recent security breaches suggest that AI capabilities are developing rapidly while being put in the service of imperfect quantitative metrics that ultimately distort the behavior of the most advanced models, @DAcemogluMIT writes. project-syndicate.org/commentary/dis…
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