A new AI framework called THOR is transforming how scientists calculate the behavior of atoms inside materials. Instead of relying on slow simulations that take weeks of supercomputer time, the system uses tensor network mathematics and machine-learning models to solve the problem directly. The approach can compute key thermodynamic properties hundreds of times faster while preserving accuracy. Researchers say this could accelerate discoveries in materials science, physics, and chemistry.
As companies pour billions into sprawling industrial campuses for cloud and AI computing, some data center operators are experimenting with four-legged bots — about the size of large dogs — that can patrol fences, inspect equipment, and flag any issues before they turn into costly outages.
“I was literally at a data center this week,” Merry Frayne, senior director of product management at Boston Dynamics, the maker of Spot, told Business Insider. “We’ve seen a huge, huge uptick in interest from data centers in the last year, I’d say, which is probably not surprising given the investment in that space.”
Robot dogs have already been deployed by first responders, the military, and in other industrial sectors such as oil and mining. But the rapid pace of data center buildouts is creating another niche for the mechanical quadrupeds.
From abiogenesis to AI, we rank the top Great Filter candidates and test them against the data to see which best explains the Fermi Paradox. Is the universe empty, or just dangerous? We explore ten filters—cosmic, biological, and civilizational—that could silence civilizations before they spread.
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Could We Accidentally Destroy the Universe?
Written, Produced & Narrated by: Isaac Arthur
Select imagery/video supplied by Getty Images
Music Courtesy of Epidemic Sound http://epidemicsound.com/creator.
Chapters
0:00 Intro
5:08 #10 The Fine-Tuned Universe & Rare Earth
12:55 #9 Abiogenesis (The Origin of Life)
16:29 #8 Complex Cells & Eukaryotes
20:14 #7 Multicellularity and Specialization
22:39 #6 Sexual Reproduction & Genetic Innovation
23:54 #5 Complex Animal Life
25:24 Curiosity
26:39 #4 Extended Childhood & Cooperative Rearing
29:17 #3 Long-Term Climate Stability
31:40 #2 Intelligence That Produces Technology
35:11 #1 The Late Filters: Surviving Technology, Ourselves, and Expanding Beyond the Home System.
The New Hampshire House continued to embrace a hands-off attitude toward technology regulation during two jam-packed session days on Wednesday and Thursday.
Representatives passed a bill proposing a broad “right to compute,” while killing other safeguard proposals aimed at data centers and artificial intelligence.
House Bill 1124, sponsored by New Boston Republican Keith Ammon, proposes enshrining the right of New Hampshire residents to “make use of computational tools” for any purpose, with carve-outs for cases where the government deems such use dangerous to the public.
From the article:
“Each equation [for calculating π ] seemed unrelated to the others. But in late 2025, a team of seven AI researchers at the Technion–Israel Institute of Technology found a previously unknown mathematical structure underlying hundreds of pi formulas, including those of Archimedes, Euler and Ramanujan. “It’s not every day that you get to cite Archimedes,” says Ph.D. student Michael Shalyt, part of the team. The structure, called a conservative matrix field, or CMF, acts as a kind of mathematical common ancestor, showing how formulas that look nothing alike turn out to be different expressions of the same underlying object.”
A mixture of AI and algorithms uncovered a hidden structure spanning 2,000 years of equations for pi.
By Lyndie Chiou edited by Clara Moskowitz.
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.
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.
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.
