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Category: robotics/AI

Mathematicians find one pi formula to rule them all

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.

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.

Continue reading “The Singularity Needs a Navigator” | >

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.

Why Hollywood Is Facing a Very Unhappy Ending

Layoffs, consolidation, streaming losses, artificial intelligence and the rise of the creator economy are reshaping Hollywood, raising questions about whether the industry is just hitting a rough patch or in terminal decline.

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China’s New DuClaw AI Just Made OpenClaw Instant and Unstoppable

China just released DuClaw, a new platform that lets anyone run OpenClaw AI agents instantly from a web browser without dealing with deployment, servers, or API keys. At the same time, researchers at Stanford introduced OpenJarvis, a framework that allows personal AI assistants to run entirely on your own computer instead of the cloud. Meanwhile Google is using Gemini to build the largest flash flood dataset ever created, mapping millions of disaster events across the planet. And a new toolkit called gstack is turning AI coding into something far more autonomous, allowing AI systems to plan software, test applications, and review code automatically.

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🧠 What You’ll See.
Baidu launches DuClaw to run OpenClaw AI agents directly from a browser.
SOURCE: https://pandaily.com/baidu-ai-cloud-l… introduces OpenJarvis for fully local AI assistants SOURCE: https://www.marktechpost.com/2026/03/.… Google uses Gemini to build the largest flash flood dataset ever created SOURCE: https://www.wsj.com/articles/google-t… gstack toolkit organizes AI into automated software development workflows SOURCE: https://www.producthunt.com/products/.… 🚨 Why It Matters These developments show how quickly artificial intelligence is moving toward more autonomous systems. From browser based AI agents that run instantly, to personal assistants that operate entirely on local machines, the way people interact with AI is changing rapidly. At the same time, large scale AI systems are being used to analyze global disasters and predict floods, while new developer tools are allowing AI to plan, test, and review software almost like an engineering team. #ai #artificialintelligence #ainews.

Stanford introduces OpenJarvis for fully local AI assistants.
SOURCE: https://www.marktechpost.com/2026/03/.…

Google uses Gemini to build the largest flash flood dataset ever created.
SOURCE: https://www.wsj.com/articles/google-t…

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The Rapid Trajectory Of Artificial Intelligence

Please see my latest Forbes article: The Rapid Trajectory of Artificial Intelligence: From Machine Learning Foundations to Generative Creativity, Agentic Autonomy, Human Augmentation, Neuromorphic Intelligence, and the Cyborg Horizon.

Thanks and have a great weekend!

#artificialintelligence #tech #ai #future @forbes

Artificial intelligence continues to evolve at an accelerating pace, transitioning from narrow, data-driven tools to systems capable of reasoning, and autonomous action.

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