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Category: robotics/AI – Page 377

Computational clock identifies compounds that may rejuvenate aging brain cells

What if there was a way to make aging brain cells younger again? An international research team from Spain and Luxembourg recently set out to address this question. After developing an aging clock capable of assessing the biological age of the brain, they used it to identify possible brain-rejuvenating interventions. The computational tool they created, recently presented in the journal Advanced Science, constitutes a valuable resource to find compounds with therapeutic potential for neurodegenerative diseases.

As the world population is aging rapidly, with over two billion people projected to be above the age of 60 by 2050, age-related brain disorders are on the rise. Living longer but in is not only a daunting prospect, it also places a substantial burden on health care systems worldwide. The idea of being able to counteract the functional decline of our brain through rejuvenating interventions therefore sounds promising.

The question is, how can we identify compounds that have the potential to efficiently rejuvenate brain cells and to protect the from neurodegeneration? Prof. Antonio Del Sol and his teams of computational biologists, based both at CIC bioGUNE, member of BRTA, and the Luxembourg Centre for Systems Biomedicine (LCSB) from the University of Luxembourg, used their machine learning expertise to tackle the challenge.

Tailored deep brain stimulation improves walking in Parkinson’s disease

For patients with Parkinson’s disease, changes in their ability to walk can be dramatic. “Parkinson’s gait,” as it is often called, can include changes in step length and asymmetry between legs. This gait dysfunction reduces a person’s mobility, increases fall risk, and significantly impacts a patient’s quality of life.

While (DBS) is highly effective for lessening symptoms of tremors, rigidity, and bradykinesia (the slowing of movement), its impact on gait has been more variable and less predictable among patients with advanced gait-related problems. Significant challenges in enhancing DBS outcomes for advanced gait disorders have included the lack of a standardized gait metric for clinicians to use during programming, as well as understanding the impact of different stimulation factors on gait.

In a recent study, researchers at UCSF developed a systematic way to quantify key aspects of gait relevant to Parkinson’s and used machine learning to identify the best DBS settings for each individual. These personalized settings led to meaningful improvements in walking, such as faster, more stable steps, without worsening other symptoms.

The AI arms race with China demands scale. The West must think bigger

Size matters. Economists have long known that; economies of scale are among the building blocks of their science. In the digital era, it quickly became apparent that value was directly proportional to the size of the network (the number of users linked by a particular technology or system).

The race to create scale is critical amid the sizzling geopolitical competition over leadership in new technologies. It has assumed even greater urgency in Western capitals in the wake of China’s success in that race. They’ve had to reconceptualize scale to overcome the advantages China has a result of the size of its economy and its population. It’s a work in progress and the results are mixed, at best.

For those who’ve forgotten their introductory economics, economies of scale are cost advantages created by expanding operations. As companies build more products, they become more efficient, reducing cost per unit. This allows them to produce even more of that product, reinforcing their competitive advantage and keep the virtuous circle turning.

Inside Trump’s Long-Awaited AI Strategy

Welcome back to In the Loop, TIME’s new twice-weekly newsletter about the world of AI.

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President Trump will deliver a major speech on Wednesday at an event in Washington, D.C., titled “Winning the AI Race,” where he is expected to unveil his long-awaited AI action plan. The 20-page, high-level document will focus on three main areas, according to a person with knowledge of the matter. It will come as a mixture of directives to federal agencies, with some grant programs. “It’s mostly carrots, not sticks,” the person said.

AI vision, reinvented: Vision-language models gain clearer sight through synthetic training data

In the race to develop AI that understands complex images like financial forecasts, medical diagrams and nutrition labels—essential for AI to operate independently in everyday settings—closed-source systems like ChatGPT and Claude currently set the pace. But no one outside their makers knows how those models were trained or what data they used, leaving open-source alternatives scrambling to catch up.

Now, researchers at Penn Engineering and the Allen Institute for AI (Ai2) have developed a new approach to train open-source models: using AI to create scientific figures, charts and tables that teach other AI systems how to interpret complex visual information.

Their tool, CoSyn (short for Code-Guided Synthesis), taps open-source AI models’ coding skills to render text-rich images and generate relevant questions and answers, giving other AI systems the data they need to learn how to “see” and understand scientific figures.

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