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Brain-inspired randomness makes artificial vision more reliable

Humans are usually remarkably good at recognizing objects, even under poor conditions. Artificial vision systems, by contrast, reach their limits more quickly. Researchers at the Hertie Institute for Clinical Brain Research have now investigated what artificial vision systems can learn from the brain. They found that combining two brain-inspired features can make image recognition considerably more reliable. The research findings are published in the journal Patterns.

The study focused on two questions: What makes human vision so robust, and what can artificial vision systems learn from it? Such systems use artificial neural networks, for example, to recognize objects in images. The more reliably they can do so even when visual information is degraded or incomplete, the better suited they are for use under challenging conditions.

One possible explanation for the robustness of biological vision lies in how the brain works. When we see, information does not simply travel from the eyes to the brain. Higher brain areas also send signals back to earlier processing stages, influencing how incoming sensory information is processed. Researchers refer to this as top-down feedback. Such connections are a fundamental feature of biological visual systems but are not incorporated into many artificial vision systems.

A microbiome-bile acid axis mediates protection from chronic liver disease in females

IsoUDCA, a female-enriched bile acid produced by a gut bacterial consortium favored by estrogen, promotes hepatic cholesterol handling and bile acid metabolism through SREBP2-STARD4-CYP7B1 signaling, thereby alleviating chronic liver injury.

New AI tool can predict cancer survival by a single patient selfie, study suggests

It is one of the most comprehensive demonstrations to date of how AI analysis of a selfie could help assess a patient’s health and potentially guide their medical care, and was trained on data from more than 40 million facial photographs across a range of ethnicities and ages.

Details of the breakthrough were published in the Lancet Digital Health journal.

The system can estimate a person’s biological age – the age a person looks, rather than their actual, chronological age – and predict the chances of survival for cancer patients from an ordinary photograph of their face, according to the results of a study that examined the model.

Advancing coding in low‑resource programming languages

COBOL devs are becoming migration auditors.

We’ve heard “this tool kills programmers” since compilers, 4GLs, and offshore outsourcing. What it usually kills is the task, not the people — they move one abstraction layer up. The question worth watching is whether this time the layer they move to is big enough to absorb them.

OpenAI’s new models are getting scarily good at modernizing legacy code — and that’s a big deal for finance.

COBOL, Fortran, and Visual Basic still run enormous swaths of the global financial system, and migrating them has traditionally been slow, expensive, and error-prone. OpenAI’s latest benchmark on COBOL migration shows how quickly the gap is closing: GPT-6.1 Sol hits roughly 97% accuracy at just $0.50 per graded trial, while the previous GPT-6 Sol needed about $2 to reach similar performance — a 4x cost reduction in one generation.

OpenAI’s framing is notable too: rather than treating this as a novelty, they’re positioning it as core enterprise capability — understanding decades of buried business logic while preserving what works. They’re also actively soliciting enterprise input on which languages and workflows to tackle next, which suggests this is a strategic priority, not a side project.

#ArtificialIntelligence #COBOL #LegacySystems #FutureOfWork


Individual atomic collisions can now be detected in the lab, potentially opening up pathways to new physics

Tiny atoms and molecules are constantly bombarding each other all around us. While we regularly measure the large-scale effects of these interactions, measuring the effect of each individual collision is far more difficult. But now, researchers have managed to measure how tiny gas particles affect a single silica particle in the lab. In their new study, published in Physical Review Letters, the team describes how they detected the momentum transfers of individual collisions of gas particles with a nanoparticle levitated in laser light.

On a large scale, the billions of constant collisions in the air create what we experience as pressure. At higher altitudes, the particles are more spread out and the pressure is weaker than at sea level. The same thing happens in liquids, resulting in increasing pressures at increasing depths. Researchers have all kinds of tools (and multiple units) for measuring pressure.

In 1827, Robert Brown noticed that grains of pollen jiggled in water under a microscope. Later work by Einstein showed that this random movement of particles suspended in a fluid was caused by constant collisions with fast-moving atoms or molecules in the surrounding medium, now referred to as “Brownian motion.” Usually, the individual impacts are too small and frequent to distinguish without getting lost in background noise.

US superconducting smart chip analyzes fusion plasma at record speeds

Scientists in the state of New York have created a first-of-a-kind superconducting neural chip that can reportedly process about one million fusion plasma samples per second.

The new Super Intelligence (SI) computer chip was designed by researchers at the US Department of Energy’s (DOE) Brookhaven National Laboratory (BNL). For the project, they partnered with the Princeton Plasma Physics Laboratory (PPPL), the DIII-D National Fusion Facility, and Colorado-based tech company Great Sky.