Currently: software engineer at Desmos. Previously: engineering at Development Seed, math teaching and curriculum development at Park School of Baltimore
roottwo
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Currently: software engineer at Desmos. Previously: engineering at Development Seed, math teaching and curriculum development at Park School of Baltimore
I think this is a good question. I’d love to hear from people with experience building frontier models have to say about it.
Meanwhile, my first pass at decomposing “activities that go into creating better models” into some distinct components that might be relevant in this discussion:
Core algorithmic R&D: choose research questions, design & execute experiments, interpret findings
ML engineering: build & maintain distributed training setup, along with the infra and dev ops that go along with a complex software system
Data acquisition and curation: collect, filter, clean datasets; hire humans to produce/QA; generate synthetic data
Safety research and evaluation: red-teaming, interpretability, safety-specific evals, AI-assisted oversight, etc.
External productization: product UX and design, UX-driven performance optimization, legal compliance and policy, marketing, and much more.
Physical compute infrastructure: GPU procurement, data center building and management, power procurement, likely various physical logistics.
(I wonder what’s missing from this?)
Eli suggested above that we should bracket the issue of data. And I think it’s also reasonable to set aside 4 and 5 if we’re trying to think about how quickly a lab could iterate internally.
If we do that, we’re left with 1, 2, and 6. I think 1 and 2 are covered even by a fairly narrow definition of “superhuman (AI researcher + coder)”. I’m uncertain what to make of 6, besides having a generalized “it’s probably messier and more complicated than I think” kind of feeling about it.