Picture a tray. On the tray is an assortment of shapes: Some cubes, others spheres. The shapes are made from a variety of different materials and represent an assortment of sizes. In total, there are, perhaps, eight objects. My question: “Looking at the objects, are there an equal number of large things and metal spheres?”
It’s not a trick question. The fact that it sounds as if it is is proof positive of just how simple it actually is. It’s the kind of question that a preschooler could most likely answer with ease. But it’s next to impossible for today’s state-of-the-art neural networks. This needs to change. And it needs to happen by reinventing artificial intelligence as we know it.
That’s not my opinion; it’s the opinion of David Cox, director of the MIT-IBM Watson A.I. Lab in Cambridge, MA. In a previous life, Cox was a professor at Harvard University, where his team used insights from neuroscience to help build better brain-inspired machine-learning computer systems. In his current role at IBM, he oversees a unique partnership between MIT and IBM that is advancing A.I. research, including IBM’s Watson A.I. platform. Watson, for those who don’t know, was the A.I. which famously defeated two of the top game show players in history at TV quiz show Jeopardy. Watson also happens to be a primarily machine-learning system, trained using masses of data as opposed to human-derived rules.