MIT’s VLASH helps robots plan ahead, move faster and react smoothly, boosting performance in dynamic tasks and real-world missions.
Category: robotics/AI – Page 33
Inspired by how children learn, new AI framework learns to theorize the world from observations
A KAIST research team has developed a next-generation world model, an internal model an AI builds to understand and predict the world, that learns executable theories from observation alone.
The team led by Professor Sungjin Ahn from the School of Computing proposed a new learning paradigm called Learning-to-Theorize (L2T), which trains AI to theorize how the world works using only observed information. The team also built the Neural Theorizer (NEO), a neural network-based model that implements this paradigm.
The research was presented at the 43rd International Conference on Machine Learning (ICML 2026), held in Seoul from July 6–11. The paper, published on the arXiv preprint server, was also selected for the Best Paper Award at the Compositional Learning Workshop.
James Martin: We Can Control Accelerating Technology
In February 2011, I spent an hour on Skype asking one of the most influential computer scientists alive whether we could still steer the technologies we were building.
James Martin said yes.
He had earned the right to that answer. Computerworld ranked him fourth among the 25 people who most shaped computer science. The Sunday Times called him Britain’s leading futurist. He wrote 104 textbooks, picked up a Pulitzer nomination, collected honorary doctorates from six continents, then gave away more than $100 million to found the Oxford Martin School so 30 institutes could work on the hardest problems of the century.
So when he told me accelerating technology is controllable, he was not being naive. He was being deliberate. Control, in his telling, was never a technical property of the machines. It was a civilizational choice, and he thought this century was the narrow window in which we get to make it.
We talked about exponential growth in genetics, robotics, nanotech and #AI. We talked about The Meaning of the 21st Century and the project he was working on then, the Transformation of Humankind. He was not selling optimism. He was assigning homework.
Fifteen years later, the claim in the title is a lot harder to defend than it was when he made it. Or maybe that is precisely his point, and we are the ones who failed the assignment.
Thin films ‘dance’ with substrates that are no longer inert, opening path toward 3D chips
Many of today’s electronic devices—from the semiconductors in your cell phone to the photovoltaic cells in your solar panels—are built on thin-film substrates. The thin film is an electrically conductive material, while the substrate is an inert material. Or is it?
Physicists and materials scientists have long assumed substrates do not react to electrical stimuli, but new research from the University of California San Diego and a team of collaborators has shown that substrates are not inert after all. The discovery has the potential to help engineers build the dense, three-dimensional, brain-inspired computer chips needed for more energy-efficient computing. This work appears in Science.
The research began four years ago in UC San Diego Associate Professor of Physics Alex Frañó’s lab. Frañó is a principal investigator and assistant director at the Quantum Materials for Energy-Efficient Neuromorphic Computing (Q-MEEN-C), one of the U.S. Department of Energy’s Energy Frontier Research Centers. One of the goals of Q-MEEN-C is to develop quantum materials that can be used in neuromorphic, or “brain-like,” computing.
Quantum neural networks get their first hardware test
Neural networks have transformed how machines find patterns in data, from recognizing faces in photos to predicting the shapes of proteins. So far, all of this progress has been made on ordinary classical computers, but with quantum computers now edging into practical use, there is a real possibility that neural networks could tap into distinctly quantum effects and operate in ways that classical machines never could. So far, however, neural networks have proven far more difficult to run on quantum hardware.
Through new research published in Physical Review Letters, Djamil Lakhdar-Hamina and colleagues at the University of Maryland, College Park, have built a neural network that runs on two different types of quantum computer, allowing them to test directly whether these systems can live up to their theoretical promise.
Highly tunable electro-optic isolator achieves one-way light flow on photonic chips
Integrated photonic devices—tiny circuits that use light instead of electrons—are becoming increasingly important for scalable photonics technologies and high-bandwidth communications. They are particularly valuable for managing low-power, light-based data transfer inside data centers, which are needed for artificial intelligence, cloud computing and high-performance signal processing.
A major challenge for the field is developing approaches that force light to propagate in only one direction within a photonic circuit, since this can improve robustness to manufacturing defects, protect laser sources and impart greater stability to optical signals within the system.
Researchers at the University of Illinois Urbana-Champaign’s Grainger College of Engineering have developed a photonic integrated circuit that functions as a linear optical isolator, allowing light to pass in only one direction with extremely low loss while blocking almost all light propagating in the opposite direction. The results are published in Nature Communications.
Microsoft Says New Cybersecurity AI Model Helps MDASH Hit 95.95% at Half the Cost
Microsoft has launched its first cybersecurity-specific model inside MDASH, its multi-model vulnerability identification and remediation harness.
The company says MDASH, using MAI-Cyber-1-Flash and GPT-5.4, scored 95.95% on CyberGym. It also claims the configuration costs 50% less than its current best MDASH combination of GPT-5.4, GPT-5.4 mini, and GPT-5.3 Codex. Access is limited to approved MDASH customers through an Azure AI Foundry private preview.
MAI-Cyber-1-Flash is designed to handle up to 90% of MDASH tasks, with GPT-5.4 reserved for the hardest 10%. It is available only inside MDASH, not as a standalone public model or general-purpose application programming interface.