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Driverless cars are on the rise and now we may know why they crash

For the first time, new algorithms may be able to automatically explain why some self-driving cars crash—a question crucial to answer as more autonomous vehicles take to the roads. This new approach, developed by researchers at King’s College London, reviews past events to explain why specific instances of failure happened, in the hope that this can be used to make improvements in the future.

The research was presented at the 2026 IEEE International Conference of Robotics and Automation.

Self-driving vehicles are increasingly being rolled out across the globe, in cities like London and San Francisco, but collisions and serious breaches of road safety have put pressure on manufacturers to explain why they make the mistakes they do. This is often hard to do, and current methods only provide limited explanations for these.

Roman Yampolskiy: AI Can’t Be Controlled. We Need to Pause Now!

Roman Yampolskiy spent two decades trying to prove superintelligence can be controlled. He couldn’t — and now says the proof runs the other way: not difficult, mathematically impossible.

If he’s right, we’re building the one machine we can never switch off, and the people building it are racing to do it faster.

Roman is a professor of computer science at the University of Louisville and one of the earliest researchers in AI safety.
We cover: why you’re the squirrel in the human-AI intelligence gap, what the halting problem really says about predicting a smarter mind, why he calls all current AI safety \.

Bio-Computing: Making Computers with Human Neurons (Interview with FinalSpark)

Are human brain cells the computer chips of the future? Three companies are competing to make biocomputers out of human neurons, and using them to play Doom, detect explosive molecules, and more.

This is a conversation with Dr. Fred Jordan and Dr. Ewelina Kurtys of FinalSpark. We talk about the differences between AI’s \.

Brain-inspired phototransistor could cut AI energy use by sensing and storing data

Inspired by the human brain, Oregon State University researchers have developed a new light-sensitive device that combines sensing and memory while controlling how digital memories strengthen or fade over time. The research was published in Advanced Functional Materials.

Technology that functions more like the human brain could enable artificial intelligence systems to work faster while consuming less electricity, said Larry Cheng of the OSU College of Engineering.

The new device integrates light sensing, memory and signal processing in a single phototransistor. Current AI hardware, Cheng explains, typically spreads those functions among different components, requiring information to move between them and increasing energy demands while reducing efficiency.

David Brin: What’s Important Isn’t Me. And It Isn’t You. It’s Us!

David Brin warned us. In 1989.

Global warming. Cyberwarfare. The World Wide Web, named in a novel before most people had ever heard of it.

I recorded this conversation with him 14 years ago. Astrophysicist. Hugo and Nebula winner. The mind behind the Uplift novels and Existence.

We dug into the most powerful form of science fiction. Not the prophecy that comes true. The prophecy that prevents itself. Orwell’s 1984 is the classic case. The warning so loud the future course-corrects.

We also went straight at #transparency. His book asks a question that hits harder now than it did then: will technology force us to choose between #privacy and freedom? Fourteen years on, with AI watching everything, that question is no longer hypothetical.

And then there is the line from David that I have never been able to shake.

Scientists develop wearable robotic system to restore hand function

Researchers at the Medical University of Vienna, in collaboration with ETH Zurich, the Technical University of Munich and Medical Faculty Belgrade, have developed a wearable neurorobotic system that combines electrical neurostimulation with hand exoskeletons. In a clinical trial involving 14 patients with hand impairments caused by neurological injury, the technology supported finger mobility, tactile perception and grip control. The results demonstrate the potential of personalised assistive systems for people living with the consequences of spinal cord or brain injury. The study has recently been published in the journal Science Advances.

Hand movements and the sense of touch are essential for everyday activities such as grasping, eating, dressing or personal hygiene. However, after damage to the central nervous system, motor and sensory impairments of the hand often persist. Conventional rehabilitation can achieve improvements, but does not always lead to sufficient restoration of hand function. There is therefore a great need for assistive technologies suitable for everyday use.

A research team led by study director Stanisa Raspopovic from the Center for Medical Physics and Biomedical Engineering at MedUni Vienna has developed the “SensoExo” system for assisting people with hand sensorimotor impairements. It combines a wearable hand exoskeleton with a custom-fitted neurostimulation sleeve. The sleeve stimulates specific nerves and muscles in the forearm through the skin. Sensors on the fingers detect touch and gripping forces and translate this information into electrical stimulation, providing users with tactile feedback. In addition, functional electrical stimulation can assist users open and close their fingers more easily.

New AI math tool could sharpen image editing, drug discovery and simulations

Clarkson University researchers have developed a new mathematical tool that could make artificial intelligence systems more accurate, controllable and useful across applications ranging from image editing to drug discovery.

Clarkson University postdoctoral researcher Zander Blasingame and Chen Liu, professor of electrical and computer engineering, created a new family of numerical solvers called Rex that improves how generative AI models move between random noise and meaningful data. Their work, “Rex: A Family of Reversible Exponential (Stochastic) Runge-Kutta Solvers,” will be presented this summer at the International Conference on Machine Learning (ICML 2026), and an earlier version of the paper is available on the arXiv preprint server.

Diffusion and flow-matching models are the foundation of many modern generative AI systems, including image generators, molecular design tools and scientific simulators. They work by gradually transforming random noise into useful outputs. While that process is effective for creating new content, many important applications require running it in reverse. Existing methods often introduce errors that make it difficult to accurately recover the original information.

Elon Musk UPDATE Neuralink 4.0 Chip Destroy Entire BCI Industry!

Elon Musk UPDATE Neuralink 4.0 Chip introduces Neuralink’s next-generation O1 brain chip developed with Samsung.
This video explores the latest progress of the Neuralink 4.0 chip, including movement restoration, speech recovery, Blindsight vision technology, and how Neuralink patients are using brain-computer interfaces today.
We also examine Samsung’s 4nm partnership, the new R1 surgical robot, and competition from Synchron, Paradromics, and China’s NEO system to understand how the Neuralink 4.0 chip could shape the future of the BCI industry.
If you’re interested in Elon Musk, AI, neuroscience, and future medical technology, this breakdown explains why many experts view the Neuralink 4.0 chip as one of the most important developments in brain-computer interfaces.

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How to Build a Synthethic Mind: Brain Inspired AI Exists Now

Further Reading.
Thumbnail image credit: Adobe Stock.

Brains and algorithms partially converge in natural language processing.
https://www.nature.com/articles/s4200

Strong Prediction: Language Model Surprisal Explains Multiple N400 Effects.
https://pmc.ncbi.nlm.nih.gov/articles

Foundation model of neural activity predicts response to new stimulus types.
https://www.nature.com/articles/s4158

Dendrites endow artificial neural networks with accurate, robust and parameter-efficient learning.
https://www.nature.com/articles/s4146

A Computational Perspective on NeuroAI and Synthetic Biological Intelligence.

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