Two rival AI approaches combine to let machines learn about the world like a child
Over the decades since the inception of artificial intelligence, research in the field has fallen into two main camps. The “symbolists” have sought to build intelligent machines by coding in logical rules and representations of the world. The “connectionists” have sought to construct artificial neural networks, inspired by biology, to learn about the world. The two groups have historically not gotten along.
But a new paper from MIT, IBM, and DeepMind shows the power of combining the two approaches, perhaps pointing a way forward for the field. The team, led by Josh Tenenbaum, a professor at MIT’s Center for Brains, Minds, and Machines, created a computer program called a neuro-symbolic concept learner (NS-CL) that learns about the world (albeit a simplified version) just as a child might—by looking around and talking.
The system consists of several pieces. One neural network is trained on a series of scenes made up of a small number of objects. Another neural network is trained on a series of text-based question-answer pairs about the scene, such as “Q: What’s the color of the sphere?” “A: Red.” This network learns to map the natural language questions to a simple program that can be run on a scene to produce an answer.
The NS-CL system is also programed to understand symbolic concepts in text such as “objects,” “object attributes,” and “spatial relationship.” That knowledge helps NS-CL answer new questions about a different scene—a type of feat that is far more challenging using a connectionist approaches alone. The system thus recognizes concepts in new questions and can relate them visually to the scene before it.
“This is an exciting approach,” says Brenden Lake, an assistant professor at NYU. “Neural pattern recognition allows the system to see, while symbolic programs allow the system to reason. Together, the approach goes beyond what current deep learning systems can do.
