Agentic AI and quantum technologies are creating new cyber risks. This requires a shift from a reactive to an adaptable, resilient posture for CISO and Boards.
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Dreams drain energy: The REM sleep paradox
The brain demands a lot of energy compared with other organs. However, it can also make do when energy supplies are scarce, flexibly processing information using what is available. How the brain resourcefully allocates this limited energy across internal states remains a key question in neuroscience.
Sleep provides a useful window into answering this question. Although sleep is associated with rest, the brain remains highly active. This is especially true during rapid eye movement (REM) sleep, the stage closely linked to dreaming and memory processing. REM sleep is sometimes called “paradoxical sleep” because the body is largely still while the brain shows wake-like activity. Researchers at Tohoku University have now uncovered another paradox within REM sleep: While energy supply to the dreaming brain appears to rise, the energy molecule used directly by neurons falls. The findings are published in Communications Biology.
“Ever felt exhausted after a vivid dream?” asks Professor Ko Matsui of Tohoku University. “Sleep may appear peaceful, but the brain is highly active—especially when dreaming. We were intrigued by this paradox and wanted to look into the scientific basis behind why dreaming is somehow tiring.”
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.
Scientists decipher how T cells sense enemies—such as cancer—at point of contact
Every encounter between a T cell and a potential target—especially when that target is a developing tumor—begins with a rapid series of molecular decisions. Within seconds, the immune cell must determine whether to launch an attack or stand down. T cells are so potent, so potentially devastating, that misreading the situation can cause serious tissue injury.
But cancer cells come equipped with a bag of tricks that allows them to disarm these powerful warriors of the immune system. All of these activities, whether mediated by T cells or their targets, occur at split-second speed and unfold at the point of cell-to-cell contact.
Now, scientists have identified tiny nanoscale contact points where those decisions are made, revealing how activation and inhibitory signals are integrated at the first moments of a cell-to-cell encounter.
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.
