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Category: robotics/AI – Page 2

A foundation model of vision, audition, and language for in-silico neuroscience

‘The present results strengthen the possibility of a paradigm shift in neuroscience… moving from the fragmented mapping of isolated cognitive tasks toward the use of unified, predictive foundation models of brain and cognitive functions By aligning the representations of Al systems to those of the human brain, we demonstrate that a single architecture can integrate a vast range of fMRI responses across hundreds of individuals, extending the framework that led the 2025 Algonauts competition. The observed log-linear scaling of encoding accuracy mirroring power laws in both artificial intelligence and neuroscience suggests that the ceiling for predicting human brain activity is yet to be reached.’

Cognitive neuroscience is fragmented into specialized models, each tailored to specific experimental paradigms, hence preventing a unified model of cognition in the human brain. Here, we introduce TRIBE v2, a tri-modal (video, audio and language) foundation model capable of predicting human brain activity in a variety of naturalistic and experimental conditions. Leveraging a unified dataset of over 1,000 hours of fMRI across 720 subjects, we demonstrate that our model accurately predicts high-resolution brain responses for novel stimuli, tasks and subjects, superseding traditional linear encoding models, delivering several-fold improvements in accuracy. Critically, TRIBE v2 enables in silico experimentation: tested on seminal visual and neuro-linguistic paradigms, it recovers a variety of results established by decades of empirical research.

EBV Dysregulation Is Associated With Immune Imbalance in Multiple SclerosisEvidence From Integrated Viral and Host Analyses

EBV dysregulation is associated with immune imbalance in multiple sclerosis: evidence from integrated viral and host analyses.

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Adversarial AI reveals mechanisms and treatments for disorders of consciousness

Researchers led by UCLA have developed an adversarial AI framework that may help explain how consciousness breaks down after brain injury — and how it might one day be restored. Published in Nature Neuroscience, the study used deep neural networks trained on more than 680,000 neuroelectrophysiology samples and validated findings across 565 patients, healthy volunteers, and animals. The model identified specific circuit-level disruptions linked to disorders of consciousness, including the basal ganglia indirect pathway and altered inhibitory cortical wiring.

What makes this so important is that it pushes consciousness research closer to mechanism. Instead of only asking what consciousness is, this kind of work asks: what specific brain circuitry fails when consciousness is lost, and can that failure be targeted? The study also identified high-frequency stimulation of the subthalamic nucleus as a promising intervention, supported by human electrophysiological data. This is the kind of neuroscience that makes consciousness feel less like pure philosophy — and more like something we may eventually model, test, and repair.

Abstract: Nature Neuroscience Adversarial AI reveals mechanisms and treatments for disorders of consciousness.

Toker et al. present an AI framework that identifies mechanisms of consciousness. The model predicts new drivers of unconsciousness and identifies subthalamic nucleus stimulation as a potential therapy for disorders of consciousness.

Asking AI to act like an expert can make it less reliable

To get the best out of AI, some users tell it to provide answers as if it were an expert. Others ask it to adopt a persona, such as a safety monitor, to guide its responses. However, this approach can sometimes hurt performance, according to a study available on the arXiv preprint server.

To see how well large language models (LLMs) behave when they are told to be someone else, researchers from the University of California ran a huge test using 12 different personas across six language models. These included experts in fields like math, coding and STEM (science, technology, engineering and mathematics) as well as general roles such as creative writer or safety monitor.

The team found that adopting a persona was something of a double-edged sword. While it makes AI sound more professional and keeps it safer (more likely to follow rules and less likely to generate harmful content), it sometimes performs worse at recalling facts.

GitHub adds AI-powered bug detection to expand security coverage

GitHub is adopting AI-based scanning for its Code Security tool to expand vulnerability detections beyond the CodeQL static analysis and cover more languages and frameworks.

The developer collaboration platform says that the move is meant to uncover security issues “in areas that are difficult to support with traditional static analysis alone.”

CodeQL will continue to provide deep semantic analysis for supported languages, while AI detections will provide broader coverage for Shell/Bash, Dockerfiles, Terraform, PHP, and other ecosystems.

‘Gray-box’ AI reveals why catalysts work while speeding discovery

Self-driving laboratories (SDLs) powered by artificial intelligence (AI) are rapidly accelerating materials discovery, but can they also explain their results? Researchers from the Theory Department of the Fritz Haber Institute, in collaboration with BASF, and BasCat—UniCat BASF JointLab, show that they can.

Their new AI-driven strategy works hand-in-hand with SDLs to identify better catalysts while revealing the chemistry behind their performance. The approach was validated on the industrially crucial conversion of propane into propylene.

An SDL integrates an AI doing the experiment planning with lab automation and robotics. In the race to develop better materials, AI and SDLs are often celebrated for one main reason: speed.

Fish gill-inspired panels reveal path to efficient thermal mixing

A fascination with fish gills has led researchers at Cornell to develop a bio-inspired approach to mixing heat and molecules in fluids—findings that could inform future biomedical devices, heat exchangers and soft robotics.

Moving heat and mass efficiently through flowing liquids is central to technologies ranging from dialysis machines to industrial cooling systems, yet many of those technologies rely on rigid components to get the job done.

Looking for an alternative, Yicong Fu, a mechanical engineering doctoral student, turned to fish gills—soft, porous tissue that constantly stirs water to keep gases and ions flowing. Working with Sunghwan “Sunny” Jung, professor of biological and environmental engineering in the College of Agriculture and Life Sciences, Fu designed a gill-like thermal dispenser that is providing new insights into fluid-structure interactions.

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