Richard Watson

Prof. Richard Watson discusses reimagining adaptation and Darwinism, exploring universal Darwinism beyond biology and connectionism. They tackle the dichotomy between linear thinking and vulnerable knowing, transitions in evolutionary algorithms, and the transformative power of knowing and being known.

In this engaging discussion, futurist Richard Watson and researcher Leo Caves delve into fascinating concepts like eigenforms and resonance. Richard explores how observer-dependent objects emerge through stable couplings, and introduces pilot-wave ideas that challenge traditional views of particles. Leo expands the conversation to biology, examining how organisms function as oscillatory structures and the role of harmonics in their behavior. Together, they reflect on cognition, emergent properties, and the interplay between structure and process in understanding life.

Donald Hoffman, a cognitive scientist known for his groundbreaking work on consciousness, and Richard Watson, a futurist exploring theoretical views on reality, dive into the complexities of perception and time. They discuss the distinction between living and nonliving as a construct of perception, challenge the idea of scientific theories leading to ultimate truth, and examine how concepts of time may be illusory. Their thought-provoking conversation touches on how projections shape our understanding of causation and the potential for theories beyond space-time.

Richard Watson, futurist and systems thinker, and Iain McGilchrist, psychiatrist and author on brain lateralization, join Michael Levin for a wide-ranging chat. They explore brain lateralization and why organisms defy simple mechanical explanations. They compare top-down and bottom-up processes, use musical metaphors for emergence, and debate what separates machines from living systems.

Dr. Richard Watson from the University of Southampton challenges the idea that natural selection is the sole driver of evolution. He discusses how cooperation plays a crucial role in success, using language from machine learning to explain the balance between competition and induction in evolution.