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Scientist Connected Light And Matter a Century Before Quantum Physics
The Irish mathematician and physicist William Rowan Hamilton, who was born 220 years ago last month, is famous for carving some mathematical graffiti into Dublin’s Broome Bridge in 1843.
But in his lifetime, Hamilton’s reputation rested on work done in the 1820s and early 1830s, when he was still in his twenties. He developed new mathematical tools for studying light rays (or “geometric optics”) and the motion of objects (“mechanics”).
Intriguingly, Hamilton developed his mechanics using an analogy between the path of a light ray and that of a material particle.
4D printing with smart materials is changing product design
With shape memory polymers, hydrogels, and alloys, 4D printing is turning prototypes into programmable, industry-ready components.
AI system learns from many types of scientific information and runs experiments to discover new materials
Materials science experiments can also face reproducibility challenges. To address the problem, CRESt monitors its experiments with cameras, looking for potential problems and suggesting solutions via text and voice to human researchers.
The researchers used CRESt to develop an electrode material for an advanced type of high-density fuel cell known as a direct formate fuel cell. After exploring more than 900 chemistries over three months, CRESt discovered a catalyst material made from eight elements that achieved a 9.3-fold improvement in power density per dollar over pure palladium, an expensive precious metal. In further tests, CRESTs material was used to deliver a record power density to a working direct formate fuel cell even though the cell contained just one-fourth of the precious metals of previous devices.
The results show the potential for CRESt to find solutions to real-world energy problems that have plagued the materials science and engineering community for decades.
AI can now pass the hardest level of the CFA exam in a matter of minutes
Several artificial intelligence models are now advanced enough to pass the three-part chartered financial analyst exam, even the most difficult Level III test.
Previous research, particularly from two years ago, had found AI could clear Levels I and II of the exam, but it struggled with Level III, due to the essay questions.
The new study was developed by researchers from New York University Stern School of Business and Goodfin, an AI-powered wealth-management platform.
For humans to pass the prestigious, three-part chartered financial analyst exam, it typically takes around 1,000 hours of studying over the course of several years. New research found that the technology underpinning a slew of artificial intelligence models is now advanced enough to pass even the most difficult – Level III – mock exams in a matter of minutes.
The new study – developed by researchers from New York University Stern School of Business and GoodFin, an AI-powered wealth management platform – evaluated 23 large language models on their ability to answer multiple choice and essay questions on mock CFA Level III exams. They found frontier reasoning models, including o4-mini, Gemini 2.5 Pro and Claude Opus, were able to use “chain-of-thought prompting” to successfully pass.
Previous research, particularly from two years ago, had found artificial intelligence could clear Levels I and II of the exam, but it struggled with Level III, due to the essay questions. However, the technology has evolved so rapidly that the researchers wanted to know whether the models could handle, “specialized, high-stakes analytical reasoning required for professional financial decision-making.” The third CFA exam is primarily focused on portfolio management and wealth planning.
Once-a-week pill for schizophrenia shows promise in clinical trials
MIT researchers developed a pill that can be taken once a week instead of daily, gradually releasing medication from within the stomach. In a phase 3 clinical trial, the treatment maintained consistent levels of the drug risperidone in patients with schizophrenia, and it controlled their symptoms just as well as daily doses of the drug.
The algorithm will see you now
Radiology combines digital images, clear benchmarks, and repeatable tasks. But replacing humans with AI is harder than it seems.
AI system for rapid annotation of medical images could accelerate clinical research
Annotating regions of interest in medical images, a process known as segmentation, is often one of the first steps clinical researchers take when running a new study involving biomedical images.
For instance, to determine how the size of the brain’s hippocampus changes as patients age, the scientist first outlines each hippocampus in a series of brain scans. For many structures and image types, this is often a manual process that can be extremely time-consuming, especially if the regions being studied are challenging to delineate.
To streamline the process, MIT researchers developed an artificial intelligence-based system that enables a researcher to rapidly segment new biomedical imaging datasets by clicking, scribbling, and drawing boxes on the images. This new AI model uses these interactions to predict the segmentation.