Research

Key Highlights Over 80 new scientific systems powered by NVIDIA accelerated computing platform have been unveiled globally in the last year America’s largest academic supercomputer, the 300-petaflop Horizon system, is set to accelerate breakthroughs in science and engineering NVIDIA-accelerated supercomputers are fueling research in areas such as healthcare, weather and climate modeling, robotics, and materials science The world of research is undergoing a significant transformation, driven by the increasing adoption of accelerated computing. This move reflects broader industry trends, where researchers are leveraging the power of artificial intelligence (AI) and high-performance computing to tackle complex problems. At the forefront of this revolution is NVIDIA, whose accelerated computing platform is being used to power a new wave of supercomputers around the globe. ...

Simply telling someone a claim is “false” rarely changes their mind. Researchers have now built something far more sophisticated: an AI system that doesn’t just label misinformation—it creates full-blown debates to dismantle it. The catch? When this system gets it wrong, it’s persuasive enough to spread the very misinformation it was designed to fight. The Core Concept: AI Agents Duke It Out in Structured Debates The ED2D (Evidence-based Debate Detection) framework operates like a high-stakes debate tournament. Instead of one AI making a judgment call, the system creates two competing teams: ...

When AI agents dive into complex research tasks, they face a challenge remarkably similar to a detective drowning in case files. Traditional AI research methods pile up information like an endless scroll, eventually collapsing under their own weight. Enter IterResearch—a breakthrough approach that’s rewriting the rules of how AI handles deep research. The Core Problem: Why Traditional AI Research Hits a Wall The conventional approach to AI research, known as the mono-contextual paradigm, works like a single, ever-expanding notebook. Every web search result, every piece of data, every thought process gets appended to one continuous context. It’s simple in theory, but catastrophic in practice. ...

Scientific Machine Learning (SciML) combines data-driven learning methods with traditional physics-based modeling to tackle complex problems in science and engineering. Yet designing effective SciML models remains heavily dependent on expert knowledge—a time-intensive, labor-demanding process that creates a significant bottleneck in scientific progress. What if we could automate this expertise-driven process? Enter AgenticSciML. What Is AgenticSciML? AgenticSciML is a collaborative multi-agent system composed of over 10 specialized AI “agents” working together to discover novel solutions for scientific problems. Think of it as a brainstorming team where each member brings different expertise to the table. ...

The quest for effective treatments against infectious diseases has led researchers to explore the potential of antibody therapies. This move reflects broader industry trends towards personalized medicine and targeted interventions. By designing synthetic antibodies that can neutralize specific pathogens, scientists aim to reduce the severity of infections and even cure chronic conditions like HIV. One promising area of research focuses on the H5N1 avian influenza virus, also known as bird flu. Researchers like Runhong Zhou and his team at Hong Kong University have developed innovative antibody therapies that target multiple parts of the virus, increasing their efficacy. For instance, Zhou’s team has created an antibody that targets the stem region of proteins on the virus’s surface and receptors on human cells, demonstrating superior results in cell-based experiments. ...

The intersection of artificial intelligence and mathematics is yielding groundbreaking results, with Google DeepMind’s AI for Math Initiative at the forefront of this revolution. By leveraging AI-powered tools, mathematicians can now tackle complex problems with unprecedented speed and accuracy. This move reflects broader industry trends, where AI is being increasingly used to augment human capabilities in various fields. At the heart of this initiative are five prestigious research institutions: Imperial College London, Institute for Advanced Study, Institut des Hautes Études Scientifiques (IHES), Simons Institute for the Theory of Computing (UC Berkeley), and Tata Institute of Fundamental Research (TIFR). These institutions will work together to identify areas where AI can drive mathematical breakthroughs, develop new tools and infrastructure, and accelerate the pace of discovery. ...

The rapid advancement of DNA sequencing technologies has led to an explosion of genomic data, with over 100 petabytes of information currently stored in central databases such as the American SRA and the European ENA. This move reflects broader industry trends towards big data and precision medicine. However, searching through these vast amounts of data has been a significant challenge for researchers, requiring massive computing power and resources. To address this issue, computer scientists at ETH Zurich have developed a digital tool called “MetaGraph,” which enables efficient and accurate searching of petabase-scale sequence repositories. As Professor Gunnar Rätsch notes, “It’s a kind of Google for DNA.” This innovative search engine allows researchers to quickly identify specific DNA sequences, including those related to antibiotic resistance and unknown pathogens. ...