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.

Exactly, all technology has both good an bad uses. Some technologies are more easily pressed into service for one or the other, but there is no black-and-white. The comment I was responding to implied there were no downsides of ML, or none of significance.

for me its not that ML is uniquely inherently bad, its more that I was really excited about its applications but realized that, in practice, it's usually pretty simple algorithms, and the more complicated applications dont have much direct application to socially positive stuff. prove me wrong!

Perhaps the reason ML isn't used for socially positive stuff is because it's really not all that good, and while it can be profitable to sell ML tools and skills, the actual benefits to the organizations buying it are marginal. In an era when budgets for socially positive things have been cut to the bare minimums, and schools, libraries, social services, museums, and the visual and musical arts are crumbling, why would they spend a lot of money on something that doesn't work all that well, compared to simpler, cheaper, existing methods?

exactly. Im not saying ML should be used in more places. I'm saying that there's almost never a real need for it, despite how hyped it can be and how cool the math/algorithms are in theory

The comment you were responding to (mine) was responding to a comment implying there were no significant upsides of ML.

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

and some of those nails don’t need to be hammered!