The unlikely marriage of two major artificial intelligence approaches has given rise to a new hybrid called neurosymbolic AI. It’s taking baby steps toward reasoning like humans and might one day take the wheel in self-driving cars.
(14 Oct 2020, feed from Knowable Magazine)
A few years ago, scientists learned something remarkable about mallard ducklings. If one of the first things the ducklings see after birth is two objects that are similar, the ducklings will later follow new pairs of objects that are similar, too. Hatchlings shown two red spheres at birth will later show a preference for two spheres of the same color, even if they are blue, over two spheres that are each a different color. Somehow, the ducklings pick up and imprint on the idea of similarity, in this case the color of the objects. They can imprint on the notion of dissimilarity too.
What the ducklings do so effortlessly turns out to be very hard for artificial intelligence. This is especially true of a branch of AI known as deep learning or deep neural networks, the technology powering the AI that defeated the world’s Go champion Lee Sedol in 2016. Such deep nets can struggle to figure out simple abstract relations between objects and reason about them unless they study tens or even hundreds of thousands of examples.
To build AI that can do this, some researchers are hybridizing deep nets with what the research community calls “good old-fashioned artificial intelligence,” otherwise known as symbolic AI. The offspring, which they call neurosymbolic AI, are showing duckling-like abilities and then some. “It’s one of the most exciting areas in today’s machine learning,” says Brenden Lake, a computer and cognitive scientist at New York University.
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