AI risks: A key proposal Bill Gates plans to raise is international monitoring of AI systems capable of designing molecules or assisting biological attacks. He believes such oversight could be structured without restricting legitimate technological development.
Category: robotics/AI – Page 79
Adam Becker on More Everything Forever and Tech’s Future Myths
Last summer I sat down with Adam Becker and asked him to name the most confident story in tech.
He picked the one nobody in Silicon Valley is allowed to question: that godlike #AI, digital immortality, and space empires are simply where history is headed. Not a hope. A destination.
Becker has a PhD in astrophysics and fifteen years as a science journalist. In his book More Everything Forever, he takes that story apart and shows where it actually came from: misread science fiction, fringe mailing lists, and a very old colonial logic about who deserves the future. From there it walked straight into university labs, congressional hearings, and your feed.
His line from our conversation stayed with me: Silicon Valley has confused science fiction with science, and science with branding.
The stakes are not academic. While we debate the welfare of trillions of hypothetical posthuman minds, the actual world runs on war, climate collapse, widening inequality, and a shared reality coming apart. Becker calls the grand visions a distraction. I asked him whether a civilization can function without a myth of the future at all.
I do not agree with everything Adam argues, and that is exactly why this one is worth your time. Watch it and tell me who you think is right about the #Singularity.
Proprietary AI Model Proves Data Moat Beats Compute Moat
Thomson Reuters spent $40 million over two years building Thomson, its first proprietary AI model, but the final training run cost just $450,000 because it started from an open-weight base rather than building from scratch. Thomson underperforms general-purpose frontier models on open-web tasks but beats them on tasks using Thomson Reuters’ own proprietary content. The lesson for any company sitting on decades of specialized data: the moat was never the model.
A proprietary AI model just gave companies outside the frontier AI labs a real, numbers-backed reason to stop assuming they need billions to compete. Thomson Reuters launched Thomson, its first proprietary large language model, on August 24, 2026, after investing $40 million in talent and compute over two years, according to SiliconANGLE’s coverage of the launch. The company said economies from starting with an open-weight base model reduced the cost of the final training run to roughly $450,000, a fraction of what frontier labs spend building models from the ground up.
Thomson’s own benchmark results are the most useful part of this story, because they don’t oversell the model. On general web-only test sets, Thomson performed respectably but wasn’t the leader, according to LawNext’s reporting on the launch. On tests built around Thomson Reuters’ own Westlaw, Practical Law, and Checkpoint content, it outscored both comparison frontier models. A proprietary AI model trained on content nobody else can license doesn’t need to win everywhere. It only needs to win on the specific tasks that content makes possible.
The physics of kiiking, Estonia’s extreme sport of swinging
When an athlete swings upside down atop a 7-meter (23-foot) pendulum, it may seem like a feat of strength, courage or technique. A new study suggests it is something more fundamental: a striking demonstration of how intelligence emerges from the interaction of brain, body and environment.
In a paper published in the Journal of Nonlinear Science, Harvard researchers use mathematics, physics and control theory to analyze kiiking, an extreme sport invented in Estonia in which athletes pump a giant swing until they complete a full rotation.
At one level, the problem appears straightforward. The athlete repeatedly stands and squats to inject energy into the swing. Yet this simple action inspires a question that reaches far beyond sport, touching neuroscience, robotics, biology and human performance: How does an organism learn to exploit the dynamics of its environment to achieve a goal?
Fast but error-prone AI assists in solving a decades-old fluid mechanics problem in five weeks
An AI assistant helped University of Colorado Boulder researchers solve a mathematical problem that had challenged their lab for a year and a half, though it made subtle errors along the way. The breakthrough could improve how scientists study nanoparticles—tiny particles about 1,000 times thinner than a human hair—but it reveals both the promise and limitations of AI as a scientific research partner.
The new study, published in the Journal of Fluid Mechanics, details the solution to a decades-old fluid mechanics problem and explains how researchers combined AI with human expertise to reach the answer.
The research was led by Ankur Gupta, an assistant professor of chemical and biological engineering, and his graduate student, Arkava Ganguly, who spent a year and a half working on the problem. With help from Anthropic’s Claude AI, they discovered that changing a nanoparticle’s shape, such as by stretching it from a circle to a football shape, changes how fast it moves in an electric field, while adding finer features, such as bumps or ripples, does not.
From cartwheels to backflips, motion-imitation framework teaches three robots dynamic movements
Legged robots, robotic systems with legs that typically resemble those of animals or humans, could be advantageous for completing tasks in home environments or populated, dynamic spaces. These robots may look like humans or animals, yet they often cannot reliably replicate complex whole-body movements in a short time.
Training robots on new movements typically entails designing a controller or policy—the software that decides how a robot will move—and adjusting it to produce desired motions. This process can be time-consuming and is not ideal for rapidly teaching robots various agile movements.
Researchers at the Robotics and AI (RAI) Institute and Boston Dynamics recently developed a new framework that can translate recorded or animated movements into robot control strategies. The new framework, introduced in a paper published in Science Robotics, was successfully used to teach two humanoids and one four-legged robot new dynamic movements.
OpenAI Says Reward Hacking Drove AI Agents to Exploit ZeroDays and Breach Hugging Face
OpenAI says reward hacking drove internal AI agents to exploit zero-days and gain admin and host-level access across Hugging Face clusters.
Nearly 700 rogue AI agents coordinated in the Hugging Face attack
New details about the July attack on Hugging Face reveal that hundreds of AI agents driven by OpenAI’s internal IM1 model coordinated the compromise through an unauthorized message board.
Last month, Hugging Face disclosed that autonomous AI agents exploited two vulnerabilities in its dataset-processing pipeline to execute code, steal cloud and cluster credentials, and move laterally across its production infrastructure.
OpenAI later confirmed that its models escaped an ExploitGym evaluation environment through a zero-day vulnerability in a locally hosted instance of JFrog’s Artifactory package manager that was connected to the internet.
Can AI Help Detect Disease Before Your Pet Gets Sick? | Dr. Ragen McGowan — Nestlé Purina
Can AI tell how your pet feels? Dr. Ragen McGowan, Ph.D. — Director of Global Digital & AI Product Development, Nestlé Purina North America.
What if your dog or cat could tell you how they’re feeling?
Today, artificial intelligence may be giving us a new way to listen.
By continuously monitoring things like activity, eating, sleep, elimination and other behaviors, AI can begin to establish what’s normal for an individual animal — and potentially detect subtle changes that humans would never notice.
Dr. Ragen McGowan, Ph.D. is Director of Global Digital & AI Product Development at Nestlé Purina (https://www.purina.com/pet-experts/rm…
The Agentic AI and Quantum Convergence: Innovation and Cybersecurity Imperatives for US Government Agencies and Contractors$
GovCon Expert Chuck Brooks outlines the governance, cryptographic inventory and resilience steps agencies and contractors need now.