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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."

"Until recently, I was deeply sceptical of the idea of AI art. I saw it as hype and casuistry, and with some cause: widely publicised efforts such as Ai-Da the robot artist obviously exaggerate the independence of the machine and play on our fascination with sentient artificial beings. But now the dream is coming true, at least in art. And art is surely one of the most inimitable expressions of the human mind."

"Google's neural networks have achieved the dream of CSI viewers everywhere: the company has revealed a new AI system capable of "enhancing" an eight-pixel square image, increasing the resolution 16-fold and effectively restoring lost data. The neural network could be used to increase the resolution of blurred or pixelated faces, in a way previously thought impossible; a similar system was demonstrated for enhancing images of bedrooms, again creating a 32x32 pixel image from an 8x8 one."

"There's a similar problem in artificial intelligence: The people who develop AI are increasingly having problems explaining how it works and determining why it has the outputs it has. Deep neural networks (DNN)-made up of layers and layers of processing systems trained on human-created data to mimic the neural networks of our brains-often seem to mirror not just human intelligence but also human inexplicability."

"A natural alternative to symbolic AI came to prominence: Instead of modeling high-level reasoning processes, why not instead model the brain? After all, brains are the only things that we know for certain can produce intelligent behavior. Why not start with them?"

"Neural networks can find telltale patterns in a person's gait that can be used to recognize and identify them with almost perfect accuracy, according to new research published in IEEE Transactions on Pattern Analysis and Machine Intelligence. The new system, called SfootBD, is nearly 380 times more accurate than previous methods, and it doesn't require a person to go barefoot in order to work. It's less invasive than other behavioral biometric verification systems, such as retinal scanners or fingerprinting, but its passive nature could make it a bigger privacy concern, since it could be used covertly."

""There's no sense in which this is a technology that will lead the science fiction walking, talking, intelligent robot", says Furber, "because they'd need a head the size of an aircraft hangar and a nuclear power station attached to it.""

"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.)"

"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."

"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."

"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."