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

Saved by Alex Dobrenko and

RelatedCollections

Although the practical success of neural networks is still undeniable, it must be observed that their most powerful applications are in domains where rules are set in advance and don’t change over time — where the goals are clearly defined. The way you win a game of chess or Go is fixed and unambiguous. Protein folding is constrained by the laws of... See more

Nameless Feeling

The essential lesson here is that, when datasets are large enough, the knowledge encapsulated in all that data will often trump the efforts of even the best programmers.

Martin Ford • Rise of the Robots: Technology and the Threat of a Jobless Future

A second, and equally serious, concern is whether—even if a predictive model measured exactly what it claimed to—we are in practice using it for its intended purpose, or for something else.

Brian Christian • The Alignment Problem

The important training is not STEM, coding or how AI works. (The market will take care of that.) It’s about how people work—and how businesses so often make money by manipulating people to buy things they might not need. Instead, people can learn how to manipulate themselves .

Rather than compete with computers, people need to learn how to use and m... See more

Esther Dyson • Don’t Fuss About Training AIs. Train Our Kids

Rich Sutton's "The Bitter Lesson" reveals a fascinating pattern in AI research:

For decades, AI researchers have tried to encode human knowledge and expertise into their systems. They believed this would lead to better results. They were wrong. Time and time again, the approaches that won out were simple, general methods that leveraged raw computat

If there is one myth regarding computer technology that ought to be swept into the dustbin it is the pervasive believe that computers can do only what they are specifically programmed to do. As we’ve seen, machine learning algorithms routinely churn through data, revealing statistical relationships and, in essence, writing their own programs on the

Nameless Feeling

Martin Ford • Rise of the Robots: Technology and the Threat of a Jobless Future

Brian Christian • The Alignment Problem

Esther Dyson • Don’t Fuss About Training AIs. Train Our Kids

Martin Ford • Rise of the Robots: Technology and the Threat of a Jobless Future

Nameless Feeling

Ernest Davis • Rebooting AI: Building Artificial Intelligence We Can Trust