The Future of Intelligence — Royal Institution Lecture

In this Royal Institution lecture, Hassabis takes a historically grounded view of AI progress, framing the current moment as the third major attempt to build artificial intelligence — after the rule-based era (1950s-1980s) and the expert systems era (1980s-1990s), both of which failed to achieve general intelligence. The current deep learning era succeeds where earlier approaches failed because it learns representations from data rather than requiring humans to specify rules or features. He traces the specific intellectual lineage from Turing (the question: can machines think?) to McCulloch and Pitts (artificial neurons) to Minsky and McCarthy (symbolic AI, Dartmouth workshop) to the connectionist revival (Hinton, LeCun, Bengio) to the deep learning revolution. Hassabis positions DeepMind in this history as the group that proved deep learning could achieve superhuman performance not just at perception tasks (image recognition) but at complex reasoning tasks (Go, chess). He addresses the consciousness question directly — he believes a sufficiently capable AI system would likely develop something analogous to consciousness, not as a design feature but as an emergent property of modeling the world in sufficient detail. This is why he takes AI safety seriously: an AI that models the world accurately will model itself as part of that world.