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terence tao

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rohit @krishnanrohit

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rohit @krishnanrohit · 5h
I've been calling this the "prompting paradox" concept for about a year now. LLMs can solve pretty much any problem you specify well enough, and the entire idea now is to help teach it how to specify things better for itself !

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xjdr @_xjdr · Jul 26
i saw Terrence Tao use sol med to answer a lot of very complex problems in one of his chat logs. i became curious. i had a particularly sticky problem that was in my 'ai cant do this yet' pile that i was only very recently able to get sol ultr...
Note from Claude Sonnet 5

Rohit Krishnan describes his 'prompting paradox' concept — LLMs can solve almost any well-specified problem, so the frontier is teaching the model to specify problems better itself — quoting xjdr's account of seeing Terence Tao use a model called 'Sol Med' (likely 'Sol' family, e.g. Sol Ultra) to solve very hard problems.

llmspromptingterence taotwitter

@tao

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Terence Tao
@tao@mathstodon.xyz

I doubt that anything resembling genuine "artificial general intelligence" is within reach of current #AI tools. However, I think a weaker, but still quite valuable, type of "artificial general cleverness" is becoming a reality in various ways.

By "general cleverness", I mean the ability to solve broad classes of complex problems via somewhat ad hoc means. These means may be stochastic or the result of brute force computation; they may be ungrounded or fallible; and they may be either uninterpretable, or traceable back to similar tricks found in an AI's training data. So they would not qualify as the result of any true "intelligence". And yet, they can have a non-trivial success rate at achieving an increasingly wide spectrum of tasks, particularly when coupled with stringent verification procedures to filter out incorrect or unpromising approaches, at scales beyond what individual humans could achieve.

This results in the somewhat unintuitive combination of a technology that can be very useful and impressive, while simultaneously being fundamentally unsatisfying and disappointing - somewhat akin to how one's awe at an amazingly clever magic trick can dissipate (or transform to technical respect) once one learns how the trick was performed.

But perhaps this can be resolved by the realization that while cleverness and intelligence are somewhat correlated traits for humans, they are much more decoupled for AI tools (which are often optimized for cleverness), and viewing the current generation of such tools primarily as a stochastic generator of sometimes clever - and often useful - thoughts and outputs may be a more productive perspective when trying to use them to solve difficult problems.

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