AI Science vs. Human Science | Hacker News Reader

AI Science vs. Human Science

lacuna.tiptreesystems.com

Author here. I work on AI tools for ML researchers, and have had this essay rattling around in me for a while (since I completed my PhD last year, actually).

Seeing OpenAI's publication on "Working with academic communities to build scientific understanding" motivated me to finally post it.

I expect we’ll continue getting faster at producing results than at turning them into ideas other researchers can understand and use.

Academia already rewards new discoveries more than the work explaining and connecting them (I talk about this at length, and am working on a subsequent piece that goes deeper into the history of that).

OpenAI organizing groups to understand these results seems like a useful response, but I think that structurally the incentives and culture aren't there yet.

what is the point of organizing results into something other researchers can use if other researchers aren't going to be doing research and research can be delegated to LLMs? (a la - humans are the bottleneck)

I don’t think that’s where we are headed. Humans are the bottleneck but they aren’t replaceable by LLMs. We experience high fidelity multimodal observations all day long, generated by a 13 billion year old gigantic quantum computer. LLMs get a few quantized tokens.

The universe has too much detail for us not to contribute useful information to a human-so scientific process

Ok, but science is not intuitive for humans. Things like hunting, or picking up sticks is because they've been evolved for many hundreds of thousands of years. I'd give science a few thousand years at most. Something something moravec's paradox.

What are the contributions humans can make, other than the usual cop out "steering" answer I hear?

Is this related to what programmers call 'composability'?

Yes I’d say so. That and a line of work on compositional generalization in deep learning.

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