In the same paper where Alan Turing outlined his famous criteria for determining when a computer is capable of thinking like a human being, he also shared advice on building a device that might one day pass the ‘Turing Test.’ “Instead of trying to produce a programme to simulate the adult mind,” the pioneer of computer science writes,“why not rather try to produce one which simulates the child's?”

The deep learning breakthroughs by Geoffrey Hinton and the architects of neural networks created technologies that have gone on to out-perform oncologists in cancer detection and help self-driving cars navigate our roads. However, to build systems that are capable of broadly generalizing across different use cases and experiences, researchers are looking to children for inspiration.

In this episode of Radical Talks, renowned psychologist Alison Gopnik explores how AI systems may benefit from a better understanding of the way children learn and play. Gopnik, who runs the Cognitive Development and Learning Lab at the University of California, Berkeley is also the best-selling author of The Philosophical Baby, and The Gardener & The Carpenter.

Like scientists, children constantly test hypotheses to better understand the world. Gopnik argues that the causal inference demonstrated by a child offers clues into how to build more resilient AI systems. Current AI technologies are a bit like the children of ‘helicopter’ parents – they’re task-focused and good at doing one thing well. As is the case with raising a well-rounded child, to broaden the potential of AI there may be value in nurturing and rewarding curiosity in AI models.

If researchers are successful in building an AI that demonstrates child-like curiosity, a question emerges around the need for computational caregivers to keep AIs from causing damage to themselves or others and to ensure they benefit society more broadly. As Gopnik puts it, “AIs need moms.”