I don't know dude, if there weren't such lucrative jobs for academics at places like Google and pharmaceutical research companies, the discourse wouldn't be so narrowly focused on doing blah blah neural networks on huge libraries of images and molecular hashes.

People with academic backgrounds would be less afraid to point out how much vaporware machine learning has produced. They'd take stock in that their tenure, or six year PhDs, give them an opportunity to not chase trends so aggressively. They'd shill online less, for some giant faceless corporation like Google.

I'm not going to shit on a bunch of great stuff people have made. But the scary thing is that Noam Chomsky is probably right, that AI science hasn't advanced much at all. That we're still in an AI winter where it matters, the science, despite all the money and attention.

> the discourse wouldn't be so narrowly focused on doing blah blah neural networks on huge libraries of images and molecular hashes.

Do you mind clarifying what exactly your issues are with these specific applications of machine learning? Is it that you think there isn't really any meaningful value that can be gained in working within these areas? Or is it just that you want people to spend more time researching a different topic that you think would be more interesting/compelling?

Care to expand? Prima facie this is a very reasonable stance. Further, image data has a lot of structure not present in other data and it's far from obvious what to do with ML techniques in other contexts.

This is coming from a physicist starting to investigate ML tools in my own work.

Some of the greatest advancements in research and application of machine learning have been within image recognition in the past few years. The inflection point and subsequent boom of deep learning circa 2012 is basically marked by the ImageNet competition of that year.