Artificial intelligence systems have beaten humans at chess, poker, Jeopardy, Go, and countless other games. But machines still falter when it comes to understanding some basic rules about the physical world. Building a machine-learning system based on how babies’ brain works could be a step towards making machine learning systems more efficient thinkers -- like humans, write, Susan Hespos and Brendan Dalton.

Computers have come a long way. From punch-card behemoths to hand-held voice-activated smartphones, advances in miniaturisation and computing power have supported the development of Artificial Intelligence (AI) from smart marketing algorithms, incredible image recognition capabilities, operating within the global financial market, efficient search engines and achievements like beating humans at games, considered to represent the apogee of human intelligence like chess or Go. Despite these achievements, AI is falling short.

In 1950 Alan Turing threw down a gauntlet when he said, “Instead of trying to produce a programme to simulate the adult mind, why not rather try to produce one which simulates the child’s?” In the seven decades that have passed since this challenge was issued, we have yet to build an artificial intelligence model that can rival the infant cognition of a typically developing 1-year-old.