A few weeks ago, a friend and colleague (Alex G.) asked me this question. He was aware of recent breakthroughs in the world of Machine Learning, esp. where it came to computer vision. Popular media picked up these relatively recent successes and brought them to the center of attention and then started projecting a future where machines are going to render humans obsolete. So he asked me: “Other than the recent breakthroughs in computer vision, what are the other success stories for machine learning?”
At first I was quite surprised by the question. I have been using ML techniques for several years now and was thoroughly convinced of their usefulness and feasibility, I thought: surely, everyone must see it the same way! But as I started formulating my answer, I was struck by how hard I found it to come up with a satisfying answer. I though it must be one of these brain-freeze moments where you can’t seem to capture the simplest of words or ideas, but at the moment, my own answer sounded unconvincing to my own ears. Being a generous man, he didn’t call me out on my terrible answer, but went on to ask: “… and what is it that makes a particular problem more or less amenable to ML techniques?”
So what is machine learning good for? What problems has it solved for us? In what fields has it proven itself worth the investment and the hype? And should we expect it to succeed in the future at solving more problems in other domains, or is this just naive optimism? Where are we in the hype cycle of “Deep Learning”? Are we heading towards a new AI winter?
I’ll try to make the case that machine learning has already paid off, and that optimism in its future is well warranted (even if it is currently somewhat overhyped).
Spam or ham?
One of the early successes of machine learning was in the domain of text “understanding”, or more precisely, document classification. The simple task of filtering out spam email, without which email would not really be viable and the world as we know today would not exist. Most of the techniques used for document classification don’t actually rely on any kind of language understanding, but rely instead on the occurrence of various words and phrases.