A scientific benefit (and cost) of AI innovation


What changed was that the cost of preliminary exploration collapsed. I could sketch an argument, identify the first serious objections, test whether they were fatal, and reach a provisional verdict in an afternoon rather than a fortnight. This sounds like a simple acceleration, and the more profound effect was on what I was willing to abandon. Dropping a question after an afternoon’s work feels nothing like dropping one after three weeks. When the exploration costs are low, the sunk cost attachment disappears, and you find yourself dropping bad questions earlier and more often, which means the questions you keep are better. I explored far more ideas, and my working portfolio became both larger and better curated. I arrived at this outcome not through any deliberate plan but simply through sustained engagement with a tool that changed what exploration cost.

The skill that improved most, and the one I would never have thought to look for, was something I can only describe as question-identification – the ability to find problems that are both tractable and important. This is the thing an academic career is substantially built on and which nobody, so far as I know, has ever tried to teach directly.

I want to be honest about the costs. My ability to hold together a complex position verbally, under pressure, in a seminar or a conversation, has probably not improved and may have declined somewhat. When preliminary exploration is cheap, you spend less time grinding through arguments from first principles, a grinding that builds fluency that shows up in live exchange. Friends have pressed me on this, and they are right to worry.

That is from Carlo Cordasco, and there is more, via Conor Friedersdorf.

The post A scientific benefit (and cost) of AI innovation appeared first on Marginal REVOLUTION.



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