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Category: robotics/AI

Toward a policy for machine-learning tools in kernel development

The first topic of discussion at the 2025 Maintainers Summit has been in the air for a while: what role — if any — should machine-learning-based tools have in the kernel development process? While there has been a fair amount of controversy around these tools, and concerns remain, it seems that the kernel community, or at least its high-level maintainership, is comfortable with these tools becoming a significant part of the development process.

Sasha Levin began the discussion by pointing to a summary he had sent to the mailing lists a few days before. There is some consensus, he said, that human accountability for patches is critical, and that use of a large language model in the creation of a patch does not change that. Purely machine-generated patches, without human involvement, are not welcome. Maintainers must retain the authority to accept or reject machine-generated contributions as they see fit. And, he said, there is agreement that the use of tools should be disclosed in some manner.

But, he asked the group: is there agreement in general that these tools are, in the end, just more tools? Steve Rostedt said that LLM-generated code may bring legal concerns that other tools do not raise, but Greg Kroah-Hartman answered that the current developers certificate of origin (“Signed-off-by”) process should cover the legal side of things. Rostedt agreed that the submitter is ultimately on the hook for the code they contribute, but he wondered about the possibility of some court ruling that a given model violates copyright years after the kernel had accepted code it generated. That would create the need for a significant cleanup effort.

AI Decoder Could Cut Quantum Errors by Up to 17×, Study Finds

Don’t listen to TLC. When it comes to error correction, in fact, do go chasing waterfalls.

A new study shows that artificial intelligence can unlock a “waterfall” effect in error correction, sharply reducing error rates and processing time.

Researchers from Harvard University reported in the pre-print server arXiv that they developed a neural-network-based decoder that outperforms existing methods by wide margins, while revealing a previously hidden regime of error suppression that challenges long-standing assumptions about how quantum systems scale.

Dr David Sinclair: Can Aging Be Reversed? After 8 Weeks, Cells Appeared 75% Younger In Tests!

Progress is accelerating but clarity isn’t always keeping up.

Check out our new sponsor, NADclinic at nadclinic.com. They are the one-stop-shop marketplace for longevity, and pioneers in NAD+ solutions.
From longevity and AI to the future of healthcare, innovation is moving fast but understanding is still catching up. The result is a growing tension between what’s being promised and what’s actually proven.

Today, David Ewing Duncan brings a grounded, big-picture perspective on these shifts. Drawing from his work at the intersection of science, technology, and human behavior, he explores why skepticism is rising, how hype can distort progress, and what it really means to live in an era of rapid innovation.

The conversation goes beyond longevity touching on self-awareness, the limits of current science, the role of AI, and how we can think more critically about the future we’re building.

Are we asking better questions or just chasing better tools?
David Ewing Duncan is an award-winning science journalist, bestselling author, and speaker known for exploring the intersection of health, technology, and the future of human life.

What You’ll Learn

Continue reading “Dr David Sinclair: Can Aging Be Reversed? After 8 Weeks, Cells Appeared 75% Younger In Tests!” | >

A nanoscale robotic cleaner can hunt, capture and remove bacteria

Tiny robots—around 50 times smaller than the diameter of a human hair—open up fascinating possibilities: they enable the controlled manipulation of objects far too small for human hands. This brings us closer to a long-standing dream—the direct interaction with the microscopic world.

Particularly relevant are biological objects in aqueous environments, such as single cells or bacteria. Handling such objects in a controlled and targeted way has remained a major challenge.

A team of researchers have demonstrated how such microscopic cleaners can be employed and precisely controlled. The study is published in the journal Nature Communications. The nanorobots presented demonstrate that controlled manipulation, including collection and relocation of bacteria, is already achievable.

Physics-Informed LSTM for Fatigue Life Prediction of Rubber Isolators under Thermo-Mechanical Coupling

【】 Full article: (Authored by Shen Liu and Fei Meng, from University of Shanghai for Science and Technology, China.)

Rubber supports are essential in automotive, heavy machinery, and aerospace engineering. They offer excellent hyper elasticity, viscoelastic dissipation, and noise reduction. However, their fatigue evolution under coupled thermo-mechanical loading is exceptionally complex. This study develops an LSTM-Physics-Informed Neural Network (PINN) framework that integrates prior physical knowledge transfer with Partial Differential Equation (PDE) constraints, to address the challenge of predicting the fatigue life of rubber_isolators under thermo-mechanical-damage coupling.

Abstract

Rubber supports are ubiquitous in modern vibration isolation systems. Their fatigue evolution under coupled thermo-mechanical loading is exceptionally complex. Traditional life prediction methods rely heavily on empirical formulas. These methods often lack accuracy and extrapolation capabilities under varying temperatures. To address this, we propose a novel LSTM-PINN architecture. This framework integrates physical constitutive relations and temperature effects into a neural network. We used transfer learning to extract baseline physical data across wide temperature ranges. Long Short-Term Memory (LSTM) layers capture sequential loading features. We embedded partial differential equations (PDEs) into the loss function. These PDEs are based on strain energy density (SED) and Arrhenius thermodynamics. This approach ensures strict adherence to physical laws. Results demonstrate that LSTM-PINN achieves high precision even with small datasets. It also exhibits superior out-of-distribution (OOD) generalization. This framework provides a new paradigm for evaluating the reliability of rubber components.

Rubber Isolator, Fatigue Life, PINN, LSTM, Thermo–Mechanical Coupling

Artificial intelligence in cardiovascular imaging: risks, mitigations and the path to safe implementation

Artificial intelligence (AI) is rapidly transforming cardiovascular imaging by automating tasks such as image segmentation, feature extraction, and risk prediction — leading to significant improvements in diagnostic precision and efficiency. However, the integration of AI into clinical workflows comes with critical risks that must be addressed to ensure safe and reliable patient care.

This review explores the technical, clinical, and ethical challenges of AI in cardiovascular imaging, particularly highlighting the risks of model errors, data drift and inappropriate usage. We also examine concerns about explainability, the potential for deskilling of healthcare professionals, generalisability across diverse populations, and accountability in AI implementation.

We present real-world examples of where these risks have been realised, along with attempts at mitigations, including the adoption of explainable AI techniques, rigorous validation frameworks to ensure fairness and broad applicability, continuous performance monitoring, and transparency at every stage of model development and deployment.

When AI meets muscle: Context-aware electrical stimulation guides humans through new movements

Imagine traveling in a foreign country, reaching for a window you’ve never seen before, and instead of struggling to open it, you feel your own muscles gently guide you through the motion, as if an invisible teacher was there, lending their know-how. Now picture that same sensation helping you twist open a child-proof pill bottle, operate a camera, or perform tasks you’ve never practiced before.

This is not science fiction. It’s the vision realized by Ph.D. students Yun Ho and Romain Nith, under the supervision of associate professor Pedro Lopes in the Department of Computer Science at the University of Chicago. Their work, recently honored with the Best Paper Award at the ACM CHI 2026 conference, is turning heads across the human-computer interaction community.

The study is also published on the arXiv preprint server.

After Anthropic’s Mythos AI uncovers thousands of zero-day bugs, top US officials huddle with bank CEOs

The heads of America’s biggest banks met this week with Federal Reserve Chairman Jerome Powell and Treasury Secretary Scott Bessent to weigh the security implications of a new artificial intelligence system developed by Anthropic, according to reports Friday.

The gathering was convened on the sidelines of an event in Washington, with officials calling the extra session to address Anthropic’s newly unveiled Claude Mythos model, Bloomberg and the Financial Times reported.

The US Treasury Department did not immediately respond to a request for comment. The Federal Reserve had no comment.

AI chips could get faster with 30-nanometer embedded memory that cuts data shuttling

When we watch videos or ask AI questions, enormous amounts of data are constantly moving inside computers. In particular, data centers that support AI must process and transfer vast amounts of data at very high speeds. However, current computers have a fundamental limitation: the place where calculations are performed and the place where data is stored are physically separated.

Because of this, data has to travel back and forth many times within a chip. This repeated movement takes time and consumes energy, creating a bottleneck that limits both speed and efficiency.

Skydio secures USAFCENT contract for drone security in Middle East

The US Air Forces Central (USAFCENT), a component of US Central Command (CENTCOM), has placed an order worth over $9m with US-based drone manufacturer Skydio for the supply of Skydio Dock and X10 systems.

The drones and infrastructure will be used to secure US airbases in the Middle East as part of one of the largest deployments of autonomous drone security systems by the US Air Force (USAF) for international base protection.

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