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Monday, 14 December 2015

Meet the Military-Funded AI that Learns as Fast as a Human

Today, it recognizes handwriting; tomorrow, it may vastly improve the military’s surveillance and targeting efforts. 

A computer program, funded in large part by the U.S. military, has displayed the ability to learn and generate new ideas as quickly and accurately as can a human. While the scope of the research was limited to understanding handwritten characters, the breakthrough could have big consequences for military’s ability to collect, analyze and act on image data, according to the researchers and military scientists. That, in turn, could lead to far more capable drones, far faster intelligence collection, and far swifter targeting through artificial intelligence.

You could be forgiven for being surprised that computers are only now catching up to humans in their ability to learn. Every day, we are reminded that computers can process information of enormous volume at the speed of light, while we are reliant on slow, chemical synaptic connections. But take the simple task of recognizing an object: a face. Facebook’s DeepFace program can recognize faces about as well a human, but in order to do that, it had to learn from a dataset of more than 4 million images of 4,000 faces. Humans, generally speaking, have the ability to remember a face after just one encounter. We learn after “one shot,” so to speak.

In their paper, “Human-level Concept Learning Through Probabilistic Program Induction,” published today in the journal Science, Brenden M. Lake, Ruslan Salakhutdinov, and Joshua B. Tenenbaum, present a model that they call the Bayesian Program Learning framework. BPL, they write, can classify objects and generate concepts about them using a tiny amount of data — one single instance.

To test it, they showed several people —and BPL — 20 handwritten letters from 10 different alphabets, then asked them to match the letter to the same character written by someone else. BPL scored 97%, about as well as the humans and far better than other algorithms. For comparison, a deep (convolutional) learning model scored about 77%, while a model t designed for “one-shot” learning reached 92% — still around twice the error rate of humans and BPL.

BPL also passed a visual form of the Turing Test by drawing letters that most humans couldn’t distinguish from a human’s handwriting. (Named after British mathematician Alan Turing, a Turing Test challenges an program’s ability to produce an intellectual product — teletype communication in the most traditional sense — that is indistinguishable from what a human could produce.)

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