Dave Guarino

What might LLMs/generative AI mean for public benefits and the safety net/tech?

What might LLMs/generative AI mean for public benefits and the safety net/tech?

There is so much excitement about things like GPT-4, and the Executive Summary Epistemology™ of broad, sweeping summaries of things people are saying about things other people are saying and which actually have very little grounding in tactile interactions with the technology are proliferating.

In general, given just how new this wave of AI is, my perspective is that people should be spending more time experimenting with it on their problems than generating takes. But doing so does require a solid understanding of actual problems, rather than the Imagined Problems (so-high-level-to-be-meaningless) that both drive a lot of work and drive a lot of well-intentioned people to madness.

So here are some scattered thoughts to hopefully inform and encourage more experimentation. I mean them as generative (ha!) — I hope they catalyze your own thoughts for experiments; these are less predictions than provocations.

Overall: I’m cautiously optimistic. Zooming out, what are the fundamentals that make me optimistic? We have complicated programs that are largely comprised of text and a fundamental technology breakthrough about processing and reasoning about text. Of course things are more complicated; these are all complex, decentralized systems at the end of the day. Complex systems are intrinsically hazardous. Things can and will get messy. But it seems to me that these fundamentals have net positive implications for reducing burden (on many different actors) and making things better overall.

But—all of this is fairly low confidence unless and until I see these things working against concrete problems! A 90% success rate vs. a 20% success rate will have very different implications.

So my final exhortation: take these thoughts as inspiration for tests and experiments to run, not as opinions or prognostications to evaluate. (And share what you find!)

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