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Why Consciousness Might Not Be Hard At All (Joscha Bach)

Joscha Bach is a cognitive scientist and the founder of the California Institute for Machine Consciousness.

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Parallel is a single platform for all your agentic research: web search built for AI agents, with Pareto-optimal quality, cost and speed.
Get started: https://parallel.ai/mlst?utm_source=c… — 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. — 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/fc708…
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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.

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:

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Collective human metabolism produces 2 billion metric tons of CO₂ a year, global study estimates

A University of Bath–led study has estimated that the carbon dioxide produced by humans as our bodies turn food into energy rose by 74% between 1985 and 2015, reaching 2.05 billion metric tons a year. Researchers say the findings could help scientists better understand where carbon comes from, where it goes and how human activity fits into the bigger picture.

Crucially, the CO₂ people breathe out is not the same as emissions from burning fossil fuels. The carbon we exhale comes from the food we eat and is largely carbon that plants have recently taken from the atmosphere. Fossil fuels, by contrast, release carbon that has been stored underground for millions of years.

Published in The Lancet Planetary Health and led by professor Javier Gonzalez, the study found that the amount of CO₂ produced by people worldwide rose from 1.18 billion metric tons in 1985 to 2.05 billion metric tons in 2015. Population growth accounted for more than 60% of the increase.

80s USB Stick Cost $500,000 #Beyond2000 #Shorts

Half a million dollars. That’s what a complete system for reading this high-density optical disc could set you back, and each disc held 40,000 pages of information. The pitch? Think of the money you’d save on paper. Printing everything on a metre-high stack of these discs would take a strip of paper 6,000 kilometres long. Today the same 40,000 pages fit on a USB stick that costs less than lunch. — Welcome to the official Beyond 2000 channel, where you’ll find weird inventions, retro technology and the future the way we imagined it back in the ’80s and ’90s: flying machines, robot jockeys, a car that folds into a suitcase, virtual reality in 1987, the smart homes of the ’80s and wearable gadgets that actually existed. Some of these came true, some never took off, and some are still waiting. Here you’ll find compilations of weird and unnecessary inventions, full episodes of Beyond 2000 and Before 2000, and clips of our favourite inventions from the archive. New videos every week. Subscribe for more of the future we imagined.

A decade of CAR-T cell therapy at Mayo Clinic: From experimental treatment to new frontiers (VIDEO)

From early clinical trials to an established CAR-T Cell Therapy Program, Mayo Clinic has delivered the transformative therapy to over 1,500 patients.

A decade ago, chimeric antigen receptor-T cell therapy, or CAR-T cell therapy, was an experimental treatment only available through clinical trials. Today, Mayo Clinic offers CAR-T cell therapy at all three sites in Arizona, Florida and Minnesota and continues to explore new frontiers for the life-changing treatment.

That progress traces back to Mayo Clinic’s early involvement in research exploring CAR-T cell therapy for patients with few remaining options.

Printed electronics turn mattress protectors into fall-risk monitors

Australian medtech company Sleeptite and RMIT University researchers have secured a U.S. patent for smart bedding technology designed to monitor residents’ well-being in health care settings and identify fall risk so caregivers can intervene.

A recent study by RMIT and Sleeptite shows the technology can be manufactured using scalable production techniques while maintaining performance and durability.

Now, Sleeptite has unveiled the technology at The MedTech Conference—a high-profile gathering of medical technology industry leaders held in Boston—as part of an Australian trade delegation led by AUScelerate. The study is published in the journal Flexible and Printed Electronics.