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This is also a false dichotomy. No, it's a reality of the labor market for doctorate holders. Startups are experiencing a shortage of coders, but the supply of

This is also a false dichotomy. No, it's a reality of the labor market for doctorate holders. Startups are experiencing a shortage of coders, but the supply of CS doctoral students is robust. Just look at how quickly internships/positions at industry labs (AT&T, Yahoo, Microsoft) fill up. Not all computer vision is "low-hanging fruit," especially if you're pushing boundaries. Similarly, applying ML techniques to "other data that is relevant" is a far cry from using ML to save lives at a hospital due to misdiagnosis. I'm not talking about regressing A/B testing results.

randomdata 15y ago
> Not all computer vision is "low-hanging fruit," especially if you're pushing boundaries. Computer vision is low hanging in the sense that you already have everything you need to make positive contributions to the study. I think the same is true with ML in general, but it was specific about what type of ML, which hangs higher due to the data availability. > Similarly, applying ML techniques to "other data that is relevant" is a far cry from using ML to save lives at a hospital due to misdiagnosis. Are you saying that programmers that are not working directly on saving lives are essentially wasting their time? There are a lot of interesting ML problems that do not save lives, but they are still worth working on.