*I don't know if this is really good stuff, or I've just got confirmation bias because I agree with so much of it. But I think he's right that machine-learning is a lot closer to something obscure, geeky, yet potent, like relational dabases, than it is to "artificial general intelligence." Since Evans is a VC, he's trying to kick the metaphysics to the curb and figure out where the money is. But the metaphysics doesn't exist, while the money sort of does, so maybe he's on to something – cheesy little Machine-Learners that cost fifty cents and are built into a flashlight, yeah, I kinda believe that. I even sort of want one.
Benedict Evans. You don't have to agree, but if you're not reading him, you're kind a sap
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I don't think, though, that we yet have a settled sense of quite what machine learning means - what it will mean for tech companies or for companies in the broader economy, how to think structurally about what new things it could enable, or what machine learning means for all the rest of us, and what important problems it might actually be able to solve.
This isn't helped by the term 'artificial intelligence', which tends to end any conversation as soon as it's begun. As soon as we say 'AI', it's as though the black monolith from the beginning of 2001 has appeared, and we all become apes screaming at it and shaking our fists. You can’t analyze ‘AI’.
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This sense of automation is the second tool for thinking about machine learning. Spotting whether there’s a wrinkle in fabric doesn't need 20 years of experience - it really just needs a mammal brain. Indeed, one of my colleagues suggested that machine learning will be able to do anything you could train a dog to do, which is also a useful way to think about AI bias (What exactly has the dog learnt? What was in the training data? Are you sure? How do you ask?), but also limited because dogs do have general intelligence and common sense, unlike any neural network we know how to build. Andrew Ng has suggested that ML will be able to do anything you could do in less than one second. Talking about ML does tend to be a hunt for metaphors, but I prefer the metaphor that this gives you infinite interns, or, perhaps, infinite ten year olds.
Five years ago, if you gave a computer a pile of photos, it couldn’t do much more than sort them by size. A ten year old could sort them into men and women, a fifteen year old into cool and uncool and an intern could say ‘this one’s really interesting’. Today, with ML, the computer will match the ten year old and perhaps the fifteen year old. It might never get to the intern. But what would you do if you had a million fifteen year olds to look at your data? What calls would you listen to, what images would you look at, and what file transfers or credit card payments would you inspect?
That is, machine learning doesn't have to match experts or decades of experience or judgement. We’re not automating experts. Rather, we’re asking ‘listen to all the phone calls and find the angry ones’. ‘Read all the emails and find the anxious ones’. ‘Look at a hundred thousand photos and find the cool (or at least weird) people’.
In a sense, this is what automation always does; Excel didn't give us artificial accountants, Photoshop and Indesign didn’t give us artificial graphic designers and indeed steam engines didn’t give us artificial horses. (In an earlier wave of ‘AI’, chess computers didn’t give us a grumpy middle-aged Russian in a box.) Rather, we automated one discrete task, at massive scale.
Where this metaphor breaks down (as all metaphors do) is in the sense that in some fields, machine learning can not just find things we can already recognize, but find things that humans can’t recognize, or find levels of pattern, inference or implication that no ten year old (or 50 year old) would recognize. This is best seen Deepmind’s AlphaGo. ...
