this catches my eye because it highlights an industry mindset shift to come:
"[...] the concept of integrating power ML methods into the real-time experimental data processing loop to accelerate scientific discovery"
data scientists study the science of what the business does (laundry delivery, manufacturing TVs, tracking patient health), and the point of science is insight and understanding from data to build a theory of how it all works
what this article highlights is that ML can be an exceptional tool for discovery. this is in stark contrast to how ML is usually deployed, which is some big analytics or product effort. the obvious big reason for that is the infra is expensive, the know-how is lacking, and the data sucks. well, that all is quickly changing and we're gonna see folks weaving in ML to bolster their workflows in a much bigger way
great to see academic scientists leading the charge here too. they stand to gain a lot from that perspective
"[...] the concept of integrating power ML methods into the real-time experimental data processing loop to accelerate scientific discovery"
data scientists study the science of what the business does (laundry delivery, manufacturing TVs, tracking patient health), and the point of science is insight and understanding from data to build a theory of how it all works
what this article highlights is that ML can be an exceptional tool for discovery. this is in stark contrast to how ML is usually deployed, which is some big analytics or product effort. the obvious big reason for that is the infra is expensive, the know-how is lacking, and the data sucks. well, that all is quickly changing and we're gonna see folks weaving in ML to bolster their workflows in a much bigger way
great to see academic scientists leading the charge here too. they stand to gain a lot from that perspective