Has the term AI become meaningless? How about MI instead

Ian Bogost writing for The Atlantic says that in too many cases today “artificial intelligence” is just another name for a fancy computer program. I don’t see it that way. I know from experience that what most data scientists are building is entirely different from what rank-and-file software developers are building. We use different tools and different approaches. And the data-driven learning algorithms we deploy at their best solve an entirely different class of problems than regular computer programs do.

Personally I like to call what we build “machine intelligence” rather than “artificial intelligence” because machine intelligence is really an alternative kind of intelligence, not an artificial version of human intelligence.

No, it’s not “Making computers act like they do in the movies” as Bogost quotes AI researcher Charles Isbell. That is too glib indeed. Why not let machines do what they do best rather than just serve as poor imitators of humans?

Part of what makes “artificial intelligence” feel a bit underwhelming is that we’ve barely begun to see what we might achieve with machine intelligence. Yes, self-driving cars are pretty amazing. I don’t have one myself (can’t afford a Tesla, darn) but I do adore the parking sensors on my SUV. They allow me to navigate around the dangerously-placed porch jutting out by the attached garage set back to the rear of my house. If I didn’t have them I probably would have hit the porch at least once already. The car can parallel park itself too but I’ve only tried that once, before I bought the car, with the salesperson sitting next to me.

I have faith that we are going to see many more amazing machine intelligence capabilities come out, as startups and big companies start focusing on vertical artificial intelligence in specific domains rather than continuing to build out horizontal machine learning capabilities for use by data scientists. Vertical AI (or MI) is tough. That’s where you have to get domain experts and data scientists together and figure out how to encode domain expertise and capabilities into machine learning models. It’s tough and slow work. I know. I’ve been doing it for a few years now in the temporary workforce management space. We’re beginning to see the payoff though, and that is truly exciting.

If you want to hear about it, I’m going to be at VMSA Live in Phoenix in early April talking about Machine Intelligence in Talent during the Executive Gateway session on Wednesday, April 5th. If you’ll be there, stop by.


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