Podcast with Joscha Bach on artificial general intelligence and deep learning

Can we build a mind, and if so, what would that tell us about who we are? AI researcher Joscha Bach argues that the path to artificial general intelligence runs through understanding the mind as a model-making system in the service of organismic regulation , and that current deep learning, while surprisingly powerful, has not yet solved the fundamental problems of grounding, abduction, and epistemic autonomy. Subscribe for more from the Convergent Science Network podcast series. Joscha Bach joins Paul Verschure and Tony Prescott for a wide-ranging debate on the nature of intelligence, the limits of current AI, and what it would take to build a system with a mind similar to our own. Bach frames intelligence as function approximation , the ability to identify meaning by discovering relationships between patterns , and sketches a progression from hand-coded algorithms through learned functions to meta-learning systems that discover how to learn. He argues that our brains are not merely learning systems but meta-learning systems, and that evolution itself can be understood as an unprincipled search for such architectures. The conversation becomes a genuine intellectual sparring match. Verschure challenges whether the recursive logic of meta-learning constitutes real progress or merely demonstrates that its proponents understand recursion. Prescott questions whether intelligence-as-function-approximation captures the full range of human cognitive abilities. Both push Bach on the epistemic autonomy problem: current AI systems learn brilliantly on human-curated data but cannot ground their knowledge independently in the world. Bach concedes that new classes of algorithms , particularly for abductive reasoning and scientific discovery , are likely needed, while maintaining that no one has proven the limits of current approaches. Key topics include why Marvin Minsky's commitment to symbolic AI set the field back, how AlphaGo's success reframes expectations about machine intelligence, the difference between intelligence, smartness, and wisdom, why consciousness might be understood as a model of attention, and whether the current wave of deep learning can carry us to general intelligence or represents a fundamental dead end. Part of the Convergent Science Network podcast series from the BCBT Summer School.