Well, it came out of my frustration with my failures at Enlitic. I had this much bigger vision than radiology. It was really to transform how diagnosis and treatment planning was done throughout medicine. And it was clear I wasn't going to be able to achieve that at Enlitic. As a startup, I couldn't get access to the data that we needed. I found places that had the data and really just came down to, "we're not going to share it with the company because we don't want you profiting from our data." It also was totally incompatible with the incentives of both the investors and the staff who saw we had a product market fit with radiology. "Why do you want to go to something else? Like focus on the thing that's working great."

Definitely lean towards the latter, which is retrain existing internal people. It's not too hard to learn to become an effective deep learning practitioner. We have lots of examples of people doing that through our courses in two months. They have to be a pretty strong coder already. They have to be tenacious. It's luck. It's not some magic thing, where you don't have to work. But if you've got that, you can be an effective deep learning practitioner in a couple of months. Particularly if it's an an area where the kind of domain is somewhat well explored, like audio image, text, Tabula or collaborative filtering. Something like genomics is going to be a lot tougher because it's not really sorted yet.