The Age of Open Foundation Models

Someone asked me a question a few weeks ago in anonymous Q&A: [what is a] "puzzle in the technology world you're thinking about?" It's a question that has nagged me. I became deeply interested in modern machine learning since ~2015 (sincere thanks to a now longtime friend and AI pioneer, Andrew Ng). I'm projecting, but I think it's hard to be a technologist today and not be an excited believer. The leaders of the large cloud and internet companies have an insatiable appetite for researchers, and on the weekend, most every engineer is at least lightly keeping up with the newest papers. And yet...until now there have been few tinkerers, people testing the bounds of what products you can build, of startups and side projects and cool demos. Why? It's been an exciting week (really, decade) in ML and I have a hunch the game is finally changing. The age of modern machine learning is very young. We can ruthlessly abbreviate it into three broad eras: Epoch 1: The Unreasonable Effectiveness of D

Someone asked me a question a few weeks ago in anonymous Q&A: [what is a] "puzzle in the technology world you're thinking about?" It's a question that has nagged me. I became deeply interested in modern machine learning since ~2015 (sincere thanks to a now longtime friend and AI pioneer, Andrew Ng). I'm projecting, but I think it's hard to be a technologist today and not be an excited believer. The leaders of the large cloud and internet companies have an insatiable appetite for researchers, and on the weekend, most every engineer is at least lightly keeping up with the newest papers. And yet.