Demis Hassabis on AGI, consciousness, and the nature of intelligence — Andrew Ng interview

In this DeepLearning.AI Pie & AI interview, Hassabis answers the questions Andrew Ng poses most directly and practically: What does AGI actually mean? What's the path? When will we get there? Hassabis's definition of AGI: a system that can learn to do any task that a human can do, with human-level or better performance, through experience and training rather than explicit programming. He explicitly excludes systems that are pre-programmed or hard-coded for specific tasks — performance alone is not sufficient; the learning mechanism matters. He argues DeepMind's approach distinguishes itself by building systems that can learn new skills rapidly from limited data (few-shot learning) — a capability he considers more fundamental to AGI than raw performance on any single benchmark. On consciousness: Hassabis says he doesn't know whether future AI systems will be conscious, but he thinks the question is scientifically tractable (unlike the 'hard problem' framing, which he considers philosophically confused). His view: consciousness is likely a functional property that emerges from certain types of information processing, not a mysterious additional ingredient. If that's right, sufficiently capable AI systems will probably develop functional consciousness as a side effect of modeling the world accurately.