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"At the heart of the problem that troubles Ming is the training that computer engineers receive and their uncritical faith in AI. Too often, she says, their approach to a problem is to train a neural network on a mass of data and expect the result to work fine. She berates companies for failing to engage with the problem first - applying what is already known about good employees and successful students, for example - before applying the AI."

"Researchers from MIT and Google recently showed off a machine learning algorithm capable of automatically retouching photos just like a professional photographer. Snap a photo and the neural network identifies exactly how to make it look better-increase contrast a smidge, tone down brightness, whatever-and apply the changes in less than 20 milliseconds."

"Instead, the new work shows our brains rely on the kind of maths that an algorithm might use to perform the task. In fact, Tsao and her colleague, Steven Le Chang, stumbled on their discovery while working on computer vision. The pair had initially set themselves the challenge of coming up with a way of reliably converting facial images into a numerical representation."

"So they trained a deep-learning neural net on tons of examples of deepfaked videos, and produced a model that's better than any previous automated technique at spotting hoaxery. (Their paper documenting the work is here.)"

"The results may seem similar to what you'd get if you'd searched WebMD, Mayo Clinic, etc. The difference is that Dr. A.I. pulls in many more data points than those sites do, then combines artificial intelligence with a massive database to pinpoint the most likely results in your specific case. It doesn't just find all possible ailments and list them for you to explore further on your own."