martincsweiss
10 hours ago
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.
jacquesm
6 hours ago
Is this related to what programmers call 'composability'?
martincsweiss
an hour ago
Yes I’d say so. That and a line of work on compositional generalization in deep learning.
rubikscube09
7 hours ago
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)
martincsweiss
an hour ago
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