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Category: robotics/AI

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.

Nearly 4,000 US industrial devices exposed to Iranian cyberattacks

The attack surface targeted by Iranian-linked hackers in cyberattacks against U.S. critical infrastructure networks includes thousands of Internet-exposed programmable logic controllers (PLCs) manufactured by Rockwell Automation.

According to a joint advisory issued by multiple U.S. federal agencies on Tuesday, Iranian state-backed hacking groups have been targeting Rockwell Automation/Allen-Bradley PLC devices since March 2026, causing operational disruptions and financial losses.

“Iranian-affiliated APT targeting campaigns against U.S. organizations have recently escalated, likely in response to hostilities between Iran, and the United States and Israel,” the authoring agencies warned.

We are already gene editing humans

You just haven’t noticed.

George Church, Harvard geneticist and Human Genome Project pioneer, explains why CRISPR wasn’t the real breakthrough, how multiplex gene editing unlocked organ transplants and de-extinction, and why aging will likely require rewriting many genes at once.

Hosted by Mgoes → https://twitter.com/m_goes_distance
Brought to you by SuperHuman Fund → https://superhuman.fund/

0:00 — Gene Editing Mammals → Humans
8:36 — Germline vs Somatic
14:56 — Modified Humans Are Already Here
18:50 — Enhancing Healthy Humans
25:00 — Aging Therapies vs Cognitive Enhancement
30:20 — Embryo Selection
38:10 — Is US Losing To UAE?
42:33 — Biotech Failures
49:31 — Next Dire Wolf Moment
54:21 — AI x Science
1:02:07 — Synthetizing Entire Genomes.

The Accelerate Bio Podcast explores the future of humanity in the age of Artificial Intelligence. Subscribe for deep-dive conversations with founders, scientists, and investors shaping AI, biotechnology, and human progress.

This episode discusses George Church, gene editing, CRISPR, human enhancement, longevity, aging, embryo selection, synthetic biology, multiplex editing, AI biotech.

Continue reading “We are already gene editing humans” | >

The Power and Responsibility of Sam Altman

This week, Laurie Segall sits down exclusively with OpenAI CEO Sam Altman for his first interview since shutting down the Disney-partnered Sora and making the Department of War deal. From power to parenthood, tech addiction and AI acceleration, Laurie interviews Altman about AI’s human impact and the weight of OpenAI’s influence. In a wide ranging interview, Altman describes a near-term future where automated AI researchers could compress a decade of scientific discovery into a single year, fundamentally reshaping society and an era of AI abundance, where solo-founders can build billion-dollar companies with AI agents. But that innovation sits against a complex backdrop with fundamental human questions at stake. Altman addresses concerns over AI-related job loss and reveals what he thinks are AI-proof jobs. Altman, who is also a father, discusses parenting in the age of AI, when he plans to introduce his own product to his child, and how he believes AI could benefit kids in the long run. This is a conversation with Sam Altman you’re not going to hear anywhere else, where the tech titan answers some fundamental questions about control, innovation, consequences, and the world we’ll leave behind for our children.

If you have thoughts or questions for Laurie about this episode or anything Mostly Human, email us at hello@mostlyhuman.com

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