In the quest to make artificial intelligence that can reason and apply knowledge flexibly, many researchers are focused on fresh insights from neuroscience. Should they be looking to psychology too?
ARTIFICIAL intelligence has come a long way. In recent years, smart machines inspired by the human brain have demonstrated superhuman abilities in games like chess and Go, proved uncannily adept at mimicking some of our language skills and mastered protein folding, a task too fiendishly difficult even for us.
But with various other aspects of what we might reasonably call human intelligence – reasoning, understanding causality, applying knowledge flexibly, to name a few – AIs still struggle. They are also woefully inefficient learners, requiring reams of data where humans need only a few examples.
Some researchers think all we need to bridge the chasm is ever larger AIs, while others want to turn back to nature’s blueprint. One path is to double down on efforts to copy the brain, better replicating the intricacies of real brain cells and the ways their activity is choreographed. But the brain is the most complex object in the known universe and it is far from clear how much of its complexity we need to replicate to reproduce its capabilities.
That’s why some believe more abstract ideas about how intelligence works can provide shortcuts. Their claim is that to really accelerate the progress of AI towards something that we can justifiably say thinks like a human, we need to emulate not the brain – but the mind.
“In some sense, they’re just different ways of looking at the same thing, but sometimes it’s profitable to do that,” says Gary Marcus at New York University and start-up Robust AI. “You don’t want a replica, what you want is to learn the principles that allow the brain to be as effective as it is.” …