[mjn]

That's a good point; this system certainly didn't come from nowhere! The protein datasets they used also mostly came out of various NIH-funded projects.

What I meant to focus on was that I think DeepMind has less of a pure money/scale advantage in this area than in some others. In something like Go or Atari game-playing, there are many academic groups researching similar things, but their resources are laughably small compared to what DeepMind threw at it. So you might argue that they got good results there in part because they directed 1000x the personnel and compute at the problem compared to what any academic group could afford. In biomed though, their peers in academia and industry are also pretty well-funded.

[dekhn]

Personally I think a major part of the secret sauce is Google's internal compute infrastructure. When I was an academic, 50% of my time went to building infra to do my science. At Google, petabytes of storage, millions of cores, algorithms, and brains were all easily tappable within a common software repo and cluster infrastructure. That immediately translates to higher scientific productivity.

[MaxBarraclough]

Has cloud computing changed this?

[dekhn]

Mostly? I left google to work at a biotech startup working in a related area and found that the big three cloud providers have built systems that greatly improve computational science. That said, it's still a lot of work to get productive, many in the field are really resistant to changes like version control, continuous integration, testing, and architecting distributed systems for handling complex lab production environments.

Here's an exemplar of how I think it evolved well in a cloud world: https://gnomad.broadinstitute.org/

that project adopts many concepts from google and others and greatly improved our analytic capabilities for large-scale genomics.

[asah]

Having recently experienced both, 1000x this.

[t_serpico]

You hit the nail on the head here.