Joscha Bach is a cognitive scientist and the founder of the California Institute for Machine Consciousness.
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Bach's null hypothesis is that consciousness is easy: an algorithm not much more complicated than the transformer, simple enough for an infant's brain to discover by itself. In this conversation with Tim, he lays out the Machine Consciousness Hypothesis in two parts. The first is that consciousness is a biological learning algorithm, the training algorithm that makes a brain like ours coherent. The second is that machines learning in self-organising ways might discover it in the same way. Either part can fail on its own.
From there they ask why there will never be a Turing test for consciousness, why a chess program is already a zombie, and why faking a mind can sometimes be harder than building one. The conversation runs through Leibniz's mill, the explanatory gap from John Searle to Thomas Nagel, Anil Seth's biological naturalism, integrated information theory and the letter that called it pseudoscience, Gödel and the limits of formal systems, and Mike Levin's idea that morphogenesis and the mind may run the same coherence algorithm. A tea-room interlude takes in Picbreeder and fractured, entangled representations. The episode closes on what it would take to convince us that a machine is conscious.
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TIMESTAMPS:
0:00 Cold open: Could consciousness be a learning algorithm?
1:51 Introduction
3:37 What makes an agent?
6:16 Why consciousness needs explaining
14:57 Sponsor: Parallel
15:51 Leibniz's mill and causal patterns
24:21 Perceiving that you perceive
32:04 Two hypotheses, two ways to fail
40:42 Do minds converge?
49:23 Does consciousness converge too?
56:20 When language models reshape our thinking
59:16 Can simulated water make you wet?
1:09:21 Computation as construction
1:16:03 Error-correcting patterns and the Ruliad
1:22:28 Conscious, or simulating consciousness?
1:28:23 What would count as evidence?
1:35:49 The research programme
REFERENCES:
Joscha Bach, The Machine Consciousness Hypothesis (California Institute for Machine Consciousness)
Kumar, Clune, Lehman & Stanley, "Questioning Representational Optimism in Deep Learning: The Fractured Entangled Representation Hypothesis"
Giulio Tononi, integrated information theory
Fleming et al. 2023, "The Integrated Information Theory of Consciousness as Pseudoscience"
John Searle 1980, "Minds, brains, and programs", Behavioral and Brain Sciences
Kurt Gödel 1931, "On Formally Undecidable Propositions of Principia Mathematica and Related Systems"
Alan Turing 1950, "Computing Machinery and Intelligence"
Whitehead & Russell, Principia Mathematica
Paul Thagard 2022, "Energy requirements undermine substrate independence and mind-body functionalism"
Richard Sutton 2019, "The Bitter Lesson"
Michael Graziano, attention schema theory
Bernard Baars and Stanislas Dehaene, global workspace theory
Michael Levin 2022, "Technological Approach to Mind Everywhere", Frontiers in Systems Neuroscience
Chris Olah et al. 2020, "Zoom In: An Introduction to Circuits", Distill
Adele Lopez 2025, "The Rise of Parasitic AI", LessWrong
RESCRIPT:https://app.rescript.info/share/fc7088df0dc8b84200aa6e01c9a9ab6a
Machine Learning Street Talk (MLST) 
