Comment by gotostatement
5 years ago
I came out of college super excited about machine learning because the math was so interesting and there was a sense of boundless utility and applicability. I came to realize that, at least in industry, the utility is mostly ad targeting and other stuff that doesn't really matter, and other than that ML is mostly overpowered for the task. There is not much socially positive application of machine learning, mostly socially neutral or negative. Some positive, sure, like there's some in medicine, but my understanding is the hardness there is not algorithmic.
> There is not much socially positive application of machine learning, mostly socially neutral or negative.
How do you arrive at this conclusion? What about Computer Vision? Speech recognition and synthesis? Fraud detection? Sentiment analysis?
Computer Vision can be used for facial recognition in surveillance applications. Sentiment analysis for ad targeting. Fraud detection to harass and profile the poor and groups the state considers enemies. And all of them gather massive datasets that can be stolen or sold and used for less than savory purposes. https://algorithmwatch.org/en/syri-netherlands-algorithm/
You basically described all or most technological innovation. It’s not specific to machine learning.
5 replies →
Higher productivity increases the scale of good and evil equally.
I'm not saying every application is bad. It's just that most of the positive applications seem to mostly be data aggregation and cleaning problems, not hard ML problems. Most of the time it's really a simple algorithm with a lot of good data that you need, there's not much interesting work to do besides plugging things together