It detects faces with 81% accuracy. That isn't bad at all...

Also, the previous best on the same dataset was 9.3%

before, there was room to double accuracy 3 times. Now, there's not. If i understood correctly their approach can take advantage of parallelism. I'm not saying they can just throw 128k cores at the problem and be done, adding 2^n resources will likely have a nice boost to results.

OTOH, it's late and i might be way off.

They just have to scale it up - more computers, more days, and the accuracy level should increase accordingly (that is my intuition and hope on this, though i could be wrong).

Haha, no.

--- last company was in computer vision.

"last company was in computer vision."

Did you sell, leave or did it fail? Why? I have some ideas that I think are novel applications of computer vision, and just within the range of what's feasible, but it seems that most computer vision applications look like that at first, and then after 90% done find out that the second 90% is exponentially harder and, realistically, infeasible. How could I test my ideas against that? Or am I asking from wrong premises?

Company was doing well and had a good idea. Product worked great, we did our job. The problem is, management didn't.

I left after all the other engineers did.

Second reply:

Email me if you want to discuss practical application.