Too many AI researchers think real-world problems are not relevant | Artificial intelligence - MIT Technology Review

Hannah Kerner, assistant research professor at the University of Maryland in College Park argues, The community’s hyperfocus on novel methods ignores what's really important.

Too many AI researchers think real-world problems are not relevant
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Any researcher who’s focused on applying machine learning to real-world problems has likely received a response like this one: “The authors present a solution for an original and highly motivating problem, but it is an application and the significance seems limited for the machine-learning community.” 

These words are straight from a review I received for a paper I submitted to the NeurIPS (Neural Information Processing Systems) conference, a top venue for machine-learning research. I’ve seen the refrain time and again in reviews of papers where my coauthors and I presented a method motivated by an application, and I’ve heard similar stories from countless others.  

This makes me wonder: If the community feels that aiming to solve high-impact real-world problems with machine learning is of limited significance, then what are we trying to achieve?

The goal of artificial intelligence (pdf) is to push forward the frontier of machine intelligence...

As neuroscientist and AI thought leader Gary Marcus once wrote (pdf): “AI’s greatest contributions to society … could and should ultimately come in domains like automated scientific discovery, leading among other things towards vastly more sophisticated versions of medicine than are currently possible. But to get there we need to make sure that the field as whole doesn’t first get stuck in a local minimum.”
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Source: MIT Technology Review