Stefan Wild is leading LBNL’s contributions to ModCon, a national effort to develop core AI capabilities that will support every Genesis Mission science project.
Category: robotics/AI – Page 10
AI extracts hidden material rules from microscopic data to predict large-scale behavior
Researchers from the National University of Singapore (NUS) have developed artificial intelligence (AI) methods that learn the large-scale behavior of complex materials from microscopic data. By automatically identifying a small number of hidden variables that capture the collective behavior of a system, the methods can predict how materials evolve over time while reducing the need for costly simulations.
Understanding the behavior of materials at the macroscopic scale is essential for designing new technologies, from energy-efficient electronics to advanced alloys. However, material properties emerge from the interactions of vast numbers of atoms, and simulating every atom over long periods is often computationally impossible, even on modern supercomputers.
A major challenge in materials science is connecting these microscopic processes, such as atomic motion, to observable material properties. Existing approaches often require large-scale simulations that are prohibitively expensive.
Could A.I. Do Your Job? We Put Agents to the Test
We gave an A.I. tool full access to a laptop with pre-configured apps and sought to answer a simple question: Can artificial intelligence do an office job?
Some corporate executives seem to believe it can. More than 200 tech companies have cut roughly 120,000 jobs this year, according to Layoffs.fyi, an industry tracking site; Meta, Oracle and others have all recently made substantial cuts to their work forces, citing A.I. as the driving force; and after laying off about 1,100 employees, the chief executive of Cloudflare said recently that he expected A.I. to replace workers in middle management, finance and marketing.
In our experiment, we deployed A.I. “agents” to act as office workers and found that they were capable of performing some of the tasks we assigned, but not all of them. The agents, which can act autonomously and make decisions based on detailed instructions, excelled at problems they could solve by writing computer programs. But they struggled with understanding the nuances of human language and at navigating user interfaces like the Chrome web browser.
3D-printable material can heal the body, build better robots and recover critical minerals
A new type of 3D-printable material developed by researchers at The University of Texas at Austin mimics human tissue’s ability to sort and filter, allowing certain molecules to pass through while keeping others out. This broad functionality means the material can be used in a variety of applications across medicine, water and robotics.
Current methods for building small tissue-like materials don’t scale to sizes that can make applications possible, the researchers say. The team overcame these issues of speed and scalability by jamming billions of tiny water droplets tightly together using simple mixing and centrifuge techniques to form large, tissue-like materials in just a few minutes. Each droplet is separated by a thin membrane, allowing the membranes to link up, similar to cell organization in human tissue.
“Tissues can separate and transport ions and molecules; that’s how our kidneys or intestines work, taking only what they need and leaving the rest behind,” said Manish Kumar, professor in the Cockrell School of Engineering’s Fariborz Maseeh Department of Civil, Architectural and Environmental Engineering and the McKetta Department of Chemical Engineering. This work was recently published in Nature Materials.
AI analyzes surgical technique to improve prostate cancer care
The movements of a surgeon in a procedure—called “surgical gestures”—can be used to predict patient recovery, according to Cedars-Sinai investigators. Their findings, published in npj Digital Medicine, suggest this technology could help surgeons refine their techniques and improve patient outcomes.
The investigators have developed an AI system that analyzes surgeons’ techniques during prostate cancer surgery, helping identify the surgical movements associated with the best patient outcomes while also predicting whether patients are likely to regain sexual function.
The AI system, called Frame-to-Outcome (F2O), analyzes video recorded during the nerve-sparing portion of robot-assisted prostate surgery, when surgeons work to preserve the nerves responsible for sexual function. Rather than relying on experts to manually evaluate each procedure, the system automatically identifies patterns in a surgeon’s movements—called “surgical gestures”—and uses them to predict patient recovery.
Neuromancer: From on X:
The Matrix took its name from a 1984 novel written on a manual typewriter by a man who didn’t own a computer. 42 years later, that novel finally gets its own screen.
William Gibson typed Neuromancer on a 1927 Hermes portable. He coined “cyberspace” before he’d ever logged onto anything. The book swept the Hugo, the Nebula, and the Philip K. Dick award in the same year, the only novel ever to take all three.
Then Hollywood spent four decades strip-mining it while calling it unfilmable. The Wachowskis lifted the matrix, the jacked-in hackers, the AI pulling strings behind a corporate veil. Every razor-girl assassin in sci-fi traces back to Molly. Johnny Mnemonic, an actual Gibson adaptation, flopped so hard in 1995 that studios treated his work as radioactive for a generation.
At least five directors attached and detached over the years. Chris Cunningham in 2000. Vincenzo Natali in 2010. Tim Miller at Fox in 2017. Each attempt died the same death. By the time the technology existed to film Neuromancer, audiences had watched its ideas in a dozen movies that borrowed them first.
The original became unfilmable because it looked like a copy of its own copies.
Now the timing loops back on itself. A story about a rogue AI maneuvering to escape its corporate constraints, written before the web existed, lands in January 2027 as the least speculative thing on television.
How Meta’s AI Models Are Powering the First Wave of Genesis Mission Projects
Lawrence Berkeley National Laboratory — one of the US Department of Energy’s premier research laboratories, known for Nobel Prize-winning work in physics, chemistry, and materials science — operates some of the most advanced scientific facilities on the planet. Among them is the Advanced Light Source (ALS), a football field-sized facility that produces intensely bright beams of X-ray light, allowing researchers to study materials from the atomic and molecular scale all the way to plants. The ALS’s instruments, known as beamlines, generate enormous quantities of data — and as recent facility upgrades have dramatically increased their resolution and speed, the volume of data has exploded beyond what scientists can keep up with.
Terence Tao on AI summary
A curated summary of Terence Tao’s current thinking on AI, with practical guidance and links to source material. Positions are distilled from ~70 Mastodon posts, some sixteen interviews and talks, six long-form essays and lectures, ~55 of his own blog comments, and a direct interview; not everything he has said appears here, by design — omission is editorial. Voice is third person; scope is confined to what is obviously about AI.
Latest: his ICM 2026 public lecture “Mathematics in the age of AI” (July 24, 2026) gathers much of this into one argument — the slides are linked here now, and its content will be folded into this summary once the recording is available.
How this page was made. This summary was compiled and drafted by an AI assistant (Claude) from Terence Tao’s public writing, talks, and interviews, then reviewed and corrected by him; the companion interview was conducted by that assistant, with his answers reproduced verbatim (lightly edited). It is a living document, revised as his views develop. Like the rest of tao-web, it is maintained with AI assistance.