Great insights from someone in the field. AlphaFold improvement is equivalent to combining past 2 CASP improvements. Ruminations on huge advantage that industri

Great insights from someone in the field. AlphaFold improvement is equivalent to combining past 2 CASP improvements. Ruminations on huge advantage that industrial labs have with top engineering talent and compute resources makes author wonder if its worth continue in academic lab. “If I were to pick, I think about half of the performance improvement we see in AlphaFold comes from the simple ideas above, and about half from the sophisticated engineering of the distance-predicting neural network.” “...with DeepMind’s entry I will have to reconsider, and from conversations with others this appears to be a nearly universal concern. Just like in machine learning, for some of us it will make sense to go into industrial labs, while for others it will mean staying in academia but shifting to entirely new problems or structure-proximal problems that avoid head-on competition with DeepMind.” “...competitively-compensated research engineers with software and computer science expertise are almost entirely absent from academic labs, despite the critical role they play in industrial research labs. Much of AlphaFold’s success likely stems from the team’s ability to scale up model training to large systems, which in many ways is primarily a software engineering challenge. “ “For DeepMind’s group of ~10 researchers, with primarily (but certainly not exclusively) ML expertise, to so thoroughly route everyone surely demonstrates the structural inefficiency of academic science.”