Computers have been around for more than 50 years. Why is machine learning suddenly so important?

It’s been said that the greatest failing of the human mind is the inability to understand the exponential function. Daniela Rus—the chair of the Computer Science and Artificial Intelligence Lab at MIT—thinks that, if anything, our projections about how rapidly machine learning will become mainstream are too pessimistic. It’ll happen even faster. And that’s the way it works with exponential trends: they’re slower than we expect, then they catch us off guard and soar ahead.

There’s a passage from a Hemingway novel about a man going broke in two ways: “gradually and then suddenly.” And that characterizes the progress of digital technologies. It was really slow and gradual and then, boom—suddenly, it’s right now. The difference here is each thing builds on each other thing. The data and the computational capability are increasing exponentially, and the more data you give these deep-learning networks and the more computational capability you give them, the better the result becomes because the results of previous machine-learning exercises can be fed back into the algorithms. That means each layer becomes a foundation for the next layer of machine learning, and the whole thing scales in a multiplicative way every year. There’s no reason to believe that has a limit.

With the foundational layers we now have in place, you can take a prior innovation and augment it to create something new. This is very different from the common idea that innovations get used up like low-hanging fruit. Now each innovation actually adds to our stock of building blocks and allows us to do new things.  People are massively underestimating the impact, on both their organizations and on society, of the combination of data plus modern analytical techniques. The reason for that is very clear: these techniques are growing exponentially in capability, and the human brain just can’t conceive of that.

There is no organisation that shouldn’t be thinking about leveraging these approaches, because either you do—in which case you’ll probably surpass the competition—or somebody else will. And by the time the competition has learned to leverage data really effectively, it’s probably going to be too late for you to try to catch up. Your competitors will be on the exponential path, and you’ll still be on that linear path.

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