A research team led by Dr. Sun-Mi Hwang from the Clean Air Research Laboratory at the Korea Institute of Energy Research (KIER) has developed catalysts as an all-in-one solution to address exhaust emissions from ammonia-fueled ships. This technology can simultaneously and cost-effectively remove various greenhouse gases from ammonia combustion and is expected to contribute to achieving carbon neutrality in the maritime industry.
Have you ever imagined that your mind could be connected to the whole universe? Well, our brains are one of the most complex systems, arguably more complicated than any other known system in every galaxy, and that includes different economies and the internet.
The human brain is made up of interconnected neurons that have the ability to process information in a very unique and dynamic way, suggesting the highest degree of complexity. Up to this day, it is the only organ that can not be transplanted from one person to the other.
All of these are reasons why scientists have formed a lot of theories about where our consciousness comes from, or if there’s a possibility that it could be connected to something bigger than one living organism, namely, to this entire universe.
As the BMW test vehicle approaches the intersection, the artificial intelligence system has already gathered information from all relevant sensors in the vicinity. These include infrastructure sensors mounted on the sign gantries at the intersection, as well as cameras, radar and a lidar unit on the roof of the test vehicle.
The in-car display shows all road users approaching the intersection from the right, left, front and rear, with a positional accuracy of up to 10 centimeters (4 inches). The AI continuously predicts where the cars, bicycles and pedestrians are likely to move next.
The advantage of a digital, real-time representation of traffic is clear: “Drivers can now see into areas that would otherwise be obscured, allowing them to react more quickly to potential hazards or be warned of them,” explains professor Alois Knoll, head of the Chair of Robotics, Artificial Intelligence and Real-Time Systems at TUM.
The ocean beneath Saturn’s moon Enceladus lies under kilometers of ice, but some of its contents do reach the surface. Jets near the south pole spray out water vapor and icy particles, offering a way to investigate the hidden ocean without drilling down to it.
The challenge is knowing which material preserves the most useful clues. A new laboratory study points to a possible target: salt crystals that trap tiny pockets of water as they grow during freezing.
Liam Perera of Diamond Light Source in the United Kingdom and colleagues found these pockets inside natron, a sodium carbonate mineral expected to form on Enceladus. Their findings, published in Communications Earth & Environment, suggest that the crystals could preserve information about the ocean’s chemistry—and potentially traces of life, if any exists there.
Joscha Bach is a cognitive scientist and the founder of the California Institute for Machine Consciousness.
SPONSOR: – 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… –
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.
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.
