In February, MIT launched Intelligence Quest, a research initiative now raising hundreds of millions of dollars to understand human intelligence in engineering terms. Such efforts, researchers hope, will result in AIs that sit somewhere between pure machine learning and pure instinct. They will boot up following some embedded rules, but will also learn as they go.
Part of the quest will be to discover what babies know and when—lessons that can then be applied to machines. That will take time, says Oren Etzioni, CEO of the Allen Institute for Artificial Intelligence (AI2) in Seattle, Washington. AI2 recently announced a $125 million effort to develop and test common sense in AI. "We would love to build on the representational structure innate in the human brain," Etzioni says, "but we don't understand how the brain processes language, reasoning, and knowledge."
Harvard University psychologist Elizabeth Spelke has argued that we have at least four "core knowledge" systems giving us a head start on understanding objects, actions, numbers, and space. We are intuitive physicists, for example, quick to understand objects and their interactions...Gary Marcus has composed a minimum list of 10 human instincts that he believes should be baked into AIs, including notions of causality, cost-benefit analysis, and types versus instances (dog versus my dog).
The debate over where to situate an AI on a spectrum between pure learning and pure instinct will continue. But that issue overlooks a more practical concern: how to design and code such a blended machine. How to combine machine learning—and its billions of neural network parameters—with rules and logic isn't clear. Neither is how to identify the most important instincts and encode them flexibly. But that hasn't stopped some researchers and companies from trying.
...researchers are working to inject their AIs with the same intuitive physics that babies seem to be born with. Computer scientists at DeepMind in London have developed what they call interaction networks. They incorporate an assumption about the physical world: that discrete objects exist and have distinctive interactions. Just as infants quickly parse the world into interacting entities, those systems readily learn objects' properties and relationships. Their results suggest that interaction networks can predict the behavior of falling strings and balls bouncing in a box far more accurately than a generic neural network.
Vicarious, a robotics software company in San Francisco, California, is taking the idea further with what it calls schema networks. Those systems, too, assume the existence of objects and interactions, but they also try to infer the causality that connects them. By learning over time, the company's software can plan backward from desired outcomes, as people do. (I want my nose to stop itching; scratching it will probably help.) The researchers compared their method with a state-of-the-art neural network on the Atari game Breakout, in which the player slides a paddle to deflect a ball and knock out bricks. Because the schema network could learn about causal relationships—such as the fact that the ball knocks out bricks on contact no matter its velocity—it didn't need extra training when the game was altered. You could move the target bricks or make the player juggle three balls, and the schema network still aced the game. The other network flailed.
Besides our innate abilities, humans also benefit from something most AIs don't have: a body. To help software reason about the world, Vicarious is "embodying" it so it can explore virtual environments, just as a baby might learn something about gravity by toppling a set of blocks. In February, Vicarious presented a system that looked for bounded regions in 2D scenes by essentially having a tiny virtual character traverse the terrain. As it explored, the system learned the concept of containment, which helps it make sense of new scenes faster than a standard image-recognition convnet that passively surveyed each scene in full. Concepts—knowledge that applies to many situations—are crucial for common sense. "In robotics it's extremely important that the robot be able to reason about new situations," says Dileep George, a co-founder of Vicarious. Later this year, the company will pilot test its software in warehouses and factories, where it will help robots pick up, assemble, and paint objects before packaging and shipping them.
One of the most challenging tasks is to code instincts flexibly, so that AIs can cope with a chaotic world that does not always follow the rules. Autonomous cars, for example, cannot count on other drivers to obey traffic laws. To deal with that unpredictability, Noah Goodman, a psychologist and computer scientist at Stanford University in Palo Alto, California, helps develop probabilistic programming languages (PPLs). He describes them as combining the rigid structures of computer code with the mathematics of probability, echoing the way people can follow logic but also allow for uncertainty: If the grass is wet it probably rained—but maybe someone turned on a sprinkler. Crucially, a PPL can be combined with deep learning networks to incorporate extensive learning. While working at Uber, Goodman and others invented such a "deep PPL," called Pyro. The ride-share company is exploring uses for Pyro such as dispatching drivers and adaptively planning routes amid road construction and game days. Goodman says PPLs can reason not only about physics and logistics, but also about how people communicate, coping with tricky forms of expression such as hyperbole, irony, and sarcasm.
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