"Here’s the problem with artificial intelligence today," says David Cox. Yes, it has gotten astonishingly good, from near-perfect facial recognition to driverless cars and world-champion Go-playing machines. And it’s true that some AI applications don’t even have to be programmed anymore: they’re based on architectures that allow them to learn from experience.
Yet there is still something clumsy and brute-force about it, says Cox, a neuroscientist at Harvard. “To build a dog detector, you need to show the program thousands of things that are dogs and thousands that aren’t dogs,” he says. “My daughter only had to see one dog”—and has happily pointed out puppies ever since. And the knowledge that today’s AI does manage to extract from all that data can be oddly fragile. Add some artful static to an image—noise that a human wouldn’t even notice—and the computer might just mistake a dog for a dumpster. That’s not good if people are using facial recognition for, say, security on smartphones (see “Is AI Riding a One-Trick Pony?”).
To overcome such limitations, Cox and dozens of other neuroscientists and machine-learning experts joined forces last year for the Machine Intelligence from Cortical Networks (MICrONS) initiative: a $100 million effort to reverse-engineer the brain. It will be the neuroscience equivalent of a moonshot, says Jacob Vogelstein, who conceived and launched MICrONS when he was a program officer for the Intelligence Advanced Research Projects Agency, the U.S. intelligence community’s research arm. (He is now at the venture capital firm Camden Partners in Baltimore.) MICrONS researchers are attempting to chart the function and structure of every detail in a small piece of rodent cortex.
It’s a testament to the brain’s complexity that a moonshot is needed to map even this tiny piece of cortex, a cube measuring one millimeter on a side—the size of a coarse grain of sand. But this cube is thousands of times bigger than any chunk of brain anyone has tried to detail. It will contain roughly 100,000 neurons and something like a billion synapses, the junctions that allow nerve impulses to leap from one neuron to the next.
It’s an ambition that leaves other neuroscientists awestruck. “I think what they are doing is heroic,” says Eve Marder, who has spent her entire career studying much smaller neural circuits at Brandeis University. “It’s among the most exciting things happening in neuroscience,” says Konrad Kording, who does computational modeling of the brain at the University of Pennsylvania.
The ultimate payoff will be the neural secrets mined from the project’s data—principles that should form what Vogelstein calls “the computational building blocks for the next generation of AI.” After all, he says, today’s neural networks are based on a decades-old architecture and a fairly simplistic notion of how the brain works. Essentially, these systems spread knowledge across thousands of densely interconnected “nodes,” analogous to the brain’s neurons. The systems improve their performance by adjusting the strength of the connections. But in most computer neural networks the signals always cascade forward, from one set of nodes to the next. The real brain is full of feedback: for every bundle of nerve fibers conveying signals from one region to the next, there is an equal or greater number of fibers coming back the other way. But why? Are those feedback fibers the secret to one-shot learning and so many other aspects of the brain’s immense power? Is something else going on?