This is, by the way, the dirty secret of the machine learning movement: almost everything produced by ML could have been produced, more cheaply, using a very dumb heuristic you coded up by hand, because mostly the ML is trained by feeding it examples of what humans did while following a very dumb heuristic. There's no magic here. If you use ML to teach a computer how to sort through resumes, it will recommend you interview people with male, white-sounding names, because it turns out that's what your HR department already does. If you ask it what video a person like you wants to see next, it will recommend some political propaganda crap, because 50% of the time 90% of the people do watch that next, because they can't help themselves, and that's a pretty good success rate.

Forget Privacy: You're Terrible at Targeting Anyway

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all of this remains much less a science than an art, and AIs still work more like people than software.

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Today most algorithms that recommend or suppress content act purely on the basis of inferred popularity. They look at how much time people spend engaging with a piece of content, and boost it to more people if the numbers look good. The content itself is almost purely a black box. Some algorithms try to classify content with tags like “food” or “fu

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A machine-learning model, trained by data, “is by definition a tool to predict the future, given that it looks like the past. . . . That’s why it’s fundamentally the wrong tool for a lot of domains, where you’re trying to design interventions and mechanisms to change the world.”

Brian Christian • The Alignment Problem

Give a machine billions of data points. Add the power of pattern recognition. Suddenly, the mystique of creativity seems glaringly mechanical. Art, writing, and even strategic games like Go are revealed to be less divine and more like precision-based machinery.

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Is human creativity finally dead?

The first rule of machine learning: Do not start with machine learning. Unfortunately, to a hammer, everything looks like a nail. Whenever I get a call from a company to help them, they have already made their mind up: they want to use machine learning. It's always tempting to start the conversation there. Yet, machine learning is usually not th... See more

Santiago

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