The case against investing in machine learning: Seven reasons not to and what to do instead


The word on the street is if you don't invest in ML as a company or become an ML specialist, the industry will leave you behind. The hype has caught on at all levels, catching everyone from undergrads to VCs. Words like "revolutionary," "innovative," "disruptive," and "lucrative" are frequently used to describe ML. Allow me to share some perspective from my experiences that will hopefully temper this enthusiasm, at least a tiny bit. This essay materialized from having the same conversation several times over with interlocutors who hope ML can unlock a bright future for them. I'm here to convince you that investing in an ML department or ML specialists might not be in your best interest. That is not always true, of course, so read this with a critical eye. The names invoke a sense of extraordinary success, and for a good reason. Yet, these companies dominated their industries before Andrew Ng's launched his first ML lectures on Coursera. The difference between "good enough" and "state-of-the-art" machine learning is significant in academic publications but not in the real world. About once or twice a year, something pops into my newsfeed, informing me that someone improved the top 1 ImageNet accuracy from 86 to 87 or so. Our community enshrines state-of-the-art with almost religious significance, so this score's systematic improvement creates an impression that our field is racing towards unlocking the singularity. No-one outside of academia cares if you can distinguish between a guitar and a ukulele 1% better. Sit back and think for a minute.

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