The Ultimate Learning Machines

Last July, I went to the Defense Advanced Research Projects Agency (DARPA), the blue-sky government research lab that helped to invent the computer and the internet. I was there, strange as it may seem, to talk about babies. The latest big DARPA research project, Machine Common Sense, is funding collaborations between child psychologists like me and computer scientists. This year I also talked about children’s minds at Google, Facebook and Apple.

Why are quintessentially geeky places like DARPA and Google suddenly interested in talking about something as profoundly ungeeky as babies? It turns out that understanding babies and young children may be one key to ensuring that the current “AI spring” continues—despite some chilly autumnal winds in the air.

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Children, on the other hand, can learn new categories from just a small number of examples. A few storybook pictures can teach them not only about cats and dogs but jaguars and rhinos and unicorns.

The kind of data that children learn from is also very different from the data AI needs. The pictures that feed the AI algorithms have been curated by people, so they generally provide good examples and clear categories. (Nobody posts that messed-up smartphone shot where the cat ran halfway out of the picture.) Games like chess and Go provide curated data in another way, since people designed these games to have clearly defined rules and a restricted range of possibilities.

Read the whole story (subscription may be required): The Wall Street Journal