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The Singularity Needs a Navigator

In 2013, physicist Alex Wissner-Gross published a single equation for intelligence in [ITALIC] Physical Review Letters [/ITALIC]: # F = T∇Sτ

The force of an intelligent system equals its temperature — computational capacity, raw horsepower — multiplied by the gradient of its future option-space. Intelligence is not a mysterious property of carbon-based brains.

It is a physical force: the tendency of any sufficiently energetic system to maximize the number of future states accessible to it.

The equation was elegant. Correct. And incomplete.

It describes the force. It does not describe the geometry of the space through which that force navigates.

A gradient without a metric is a direction without distance — it tells the system where to push but not what distortion it will encounter on the way there.

We spent three years building the geometry. We tested it across 69 billion simulations. What we found changes everything. ## The Missing Geometry — From Force to Navigation.

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These Physicists Say They Found The Origin Of Reality

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One of the most perplexing questions in the foundations of physics is how our shared sense of reality emerges out of quantum mechanics. This is because in quantum mechanics, it seems, different observers can arrive at different conclusions about what is real and what not. A group of physicists now used an approach called “Quantum Darwinism” to solve this tricky problem. At least they say they solved it. I am not so sure. Let’s have a look.

Paper: https://journals.aps.org/pra/abstract… mugs, posters and more: ➜ https://sabines-store.dashery.com/ 💌 Support me on Donorbox ➜ https://donorbox.org/swtg 👉 Transcript with links to references on Patreon ➜ / sabine 📝 Transcripts and written news on Substack ➜ https://sciencewtg.substack.com/ 📩 Free weekly science newsletter ➜ https://sabinehossenfelder.com/newsle… 👂 Audio only podcast ➜ https://open.spotify.com/show/0MkNfXl… 🔗 Join this channel to get access to perks ➜ / @sabinehossenfelder 📚 Buy my book ➜ https://amzn.to/3HSAWJW #science #sciencenews #quantum #physics This video discusses the concept of “reality” in quantum physics, touching on how different observers can reach different conclusions. It features a presentation of a scientific paper on the “Metrological approach to the emergence of classical objectivity,” suggesting a potential solution to a long-standing problem in quantum mechanics. We explore how the “observer effect” and individual “consciousness” play a crucial role in shaping our understanding of “reality does not exist” within the realm of “quantum physics explained.” This deep dive connects the fundamental principles of “quantum mechanics” with profound questions in “philosophy.”

👕T-shirts, mugs, posters and more: ➜ https://sabines-store.dashery.com/
💌 Support me on Donorbox ➜ https://donorbox.org/swtg.
👉 Transcript with links to references on Patreon ➜ / sabine.
📝 Transcripts and written news on Substack ➜ https://sciencewtg.substack.com/
📩 Free weekly science newsletter ➜ https://sabinehossenfelder.com/newsle…
👂 Audio only podcast ➜ https://open.spotify.com/show/0MkNfXl…
🔗 Join this channel to get access to perks ➜
/ @sabinehossenfelder.
📚 Buy my book ➜ https://amzn.to/3HSAWJW

#science #sciencenews #quantum #physics.

This video discusses the concept of \.

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Markov chain Monte Carlo

In statistics, Markov chain Monte Carlo (MCMC) is a class of algorithms used to draw samples from a probability distribution. Given a probability distribution, one can construct a Markov chain whose elements’ distribution approximates it – that is, the Markov chain’s equilibrium distribution matches the target distribution. The more steps that are included, the more closely the distribution of the sample matches the actual desired distribution.

Markov chain Monte Carlo methods are used to study probability distributions that are too complex or too high dimensional to study with analytic techniques alone. Various algorithms exist for constructing such Markov chains, including the Metropolis–Hastings algorithm.

3 Questions: On the future of AI and the mathematical and physical sciences

Curiosity-driven research has long sparked technological transformations. A century ago, curiosity about atoms led to quantum mechanics, and eventually the transistor at the heart of modern computing. Conversely, the steam engine was a practical breakthrough, but it took fundamental research in thermodynamics to fully harness its power.

Today, artificial intelligence and science find themselves at a similar inflection point. The current AI revolution has been fueled by decades of research in the mathematical and physical sciences (MPS), which provided the challenging problems, datasets, and insights that made modern AI possible. The 2024 Nobel Prizes in physics and chemistry, recognizing foundational AI methods rooted in physics and AI applications for protein design, made this connection impossible to miss.

In 2025, MIT hosted a Workshop on the Future of AI+MPS, funded by the National Science Foundation with support from the MIT School of Science and the MIT departments of Physics, Chemistry, and Mathematics. The workshop brought together leading AI and science researchers to chart how the MPS domains can best capitalize on — and contribute to — the future of AI. Now a white paper, with recommendations for funding agencies, institutions, and researchers, has been published in Machine Learning: Science and Technology. In this interview, Jesse Thaler, MIT professor of physics and chair of the workshop, describes key themes and how MIT is positioning itself to lead in AI and science.

A 3D-printed swallowable robot could perform gastrointestinal procedures

Recent technological advances have opened new possibilities for the development of advanced medical devices, including tiny robots that can safely move inside the human body. Some of these systems could help to simplify complex medical procedures, including delicate surgeries and the targeted delivery of drugs to specific sites.

THE MINIMAX lab at University of Texas (UT) Austin specializes in the development of tiny robots for medical, environmental, and other applications. In a recent preprint paper on arXiv, researchers from this lab introduced a new 3Dprintable and magnetically steerable capsule robot that could potentially help to diagnose and treat some gastrointestinal (GI) conditions.

“My motivation for GI health monitoring is deeply personal,” Fangzhou Xia, director of the MINIMAX lab at UT Austin and senior author of the paper, told Medical Xpress. “In 2022, when I was a postdoc at MIT, I experienced a severe GI medical episode involving repeated gallstone-induced bile duct blockage that ultimately required gallbladder removal surgery.

Something strange is happening in the Milky Way’s magnetic field

A stunning new map of the Milky Way reveals a dramatic magnetic flip hiding in plain sight. Deep inside the Milky Way, an invisible force is quietly holding everything together — its magnetic field. Now, researchers have created one of the most detailed maps ever of this hidden structure, revealing surprising twists in how it flows through our galaxy.

For generations, scientists have studied the stars and planets to better understand how our galaxy works. Now, Dr. Jo-Anne Brown, PhD, is focused on charting something we cannot see at all: the Milky Way’s magnetic field.

“Without a magnetic field, the galaxy would collapse in on itself due to gravity,” says Brown, a professor in the Department of Physics and Astronomy at the University of Calgary.

Researcher behind major Alzheimer’s breakthrough: this is my most important advice if you would like to avoid the incurable brain disease

In a lab at Aarhus University in Denmark, researchers have made a discovery that could rewrite textbooks on the severe and fatal Alzheimer’s brain disease. And the discovery once again emphasises that you can do a lot to lower your personal risk significantly.

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