[proofofstake]

An example of what I was aiming at is to use ML methods like deep nets to detect early-onset diabetic retinopathy. Diabetic retinopathy is the leading cause of blindness and at least 90% of new cases could be reduced with early detection and proper treatment. [1]

Especially in third world countries where there is no eye doctor in sight, these cheap automated methods can be deployed on a mobile phone and achieve near-human expert level accuracy. [2]

Then organizations like Watsi can use data science and predictive modeling to reduce fraud and get both detection and treatments to those most in need. [3]

[1] https://en.wikipedia.org/wiki/Diabetic_retinopathy

[2] http://blog.kaggle.com/2015/09/09/diabetic-retinopathy-winne...

[3] https://dssg.uchicago.edu/

About IBM Watson, the entire thing is unfortunate, I completely agree. Their marketing department upsold IBM Watson for cancer treatment. But I know that a lot of great research scientists worked at IBM on Watson. What they were doing was legit advancing machine learning too. That's the thing about marketing: if IBM were to deploy 10.000 phones with a neural net to improve early detection of disease in a third world country, I probably won't even hear about it, and I work with ML. But for IBM Watson, everybody and their grandmother goes: That's the AI that beat Jeopardy. It's a thin line between being majorly successful in marketing and crossing the line into damaging your reputation and goodwill (or in this case: the entire ML industry).