There are lots of vision applications that aren't driving or medicine. Visual inspection is a one - that's used in insurance claim management, also determining

There are lots of vision applications that aren't driving or medicine. Visual inspection is a one - that's used in insurance claim management, also determining quality/faults to save assemble reworks. Applications don't have to be "critical" to provide significant value - and alot of these cases have many disjoint users, meaning that value can be replicated. You probably won't read about many of them because "AI delivers $4.7m" doesn't make headlines, but if there are 50 end users (I have no evidence for the number btw) then that's alot of beans. I don't care what Gary Marcus says - this isn't a court of law, his opinion doesn't matter. Quoting people as authorities is not a science thing. Marcus is an academic and he has made an error saying that "deep learning has bad engineering properties", the truth is that academics and researchers are not engineers and they have used a neutral tool as academics and researchers. I have not seen a well engineered large vision model yet. Specifically the current generation of vision models have bad engineering - they have been devised with no thought about the properties of the applications that they can be used for, instead they were devised to meet academic tests like Imagenet. When you do real work you realise just what bullshit that challenge is, but the virtue of it is that it got models built and we've been able to make some hay with the results. In the next few years the knowledge of why and how the models miss the mark will propagate to folks with the time and money to build well engineered models with the appropriate properties. Then I expect we will see critical applications being built. I think medical applications may never come, along with self driving ones, but that's because there are fundamental human system problems with both of those domains, and not because it's impossible to engineer models.