Interview

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3 Questions: On the future of AI and the mathematical and physical sciences

ai, AI (Artificial Intelligence), Algorithms, Artificial Intelligence, Astronomy and astrophysics, Chemistry, Computer science and technology, data, Faculty, IDSS, Interview, Laboratory for Nuclear Science, Machine Learning, Materials science and engineering, Mathematics, MIT Schwarzman College of Computing, National Science Foundation (NSF), Physics, Research, School of Science

Curiosity-driven research has long sparked technological transformations. A century ago, curiosity about atoms led to quantum mechanics, and eventually the transistor at the heart of modern computing. Conversely, the steam engine was a practical breakthrough, but it took fundamental research in thermodynamics to fully harness its power. Today, artificial intelligence and science find themselves at a […]

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3 Questions: Building predictive models to characterize tumor progression

ai, AI (Artificial Intelligence), Artificial Intelligence, Biology, Cancer, Computational biology, DNA, Evolution, Faculty, Genetics, Health sciences and technology, Institute for Medical Engineering and Science (IMES), Interview, Koch Institute, Machine Learning, Medicine, School of Science

Just as Darwin’s finches evolved in response to natural selection in order to endure, the cells that make up a cancerous tumor similarly counter selective pressures in order to survive, evolve, and spread. Tumors are, in fact, complex sets of cells with their own unique structure and ability to change. Today, artificial Intelligence and machine learning

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3 Questions: Using AI to help Olympic skaters land a quint

ai, AI (Artificial Intelligence), Alumni/ae, Artificial Intelligence, data, IDSS, Interview, Literature, Mechanical engineering, MIT Sloan School of Management, Research, School of Engineering, Sports and fitness

Olympic figure skating looks effortless. Athletes sail across the ice, then soar into the air, spinning like a top, before landing on a single blade just 4-5 millimeters wide. To help figure skaters land quadruple axels, Salchows, Lutzes, and maybe even the elusive quintuple without looking the least bit stressed, Jerry Lu MFin ’24 developed

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3 Questions: Using AI to accelerate the discovery and design of therapeutic drugs

ai, AI (Artificial Intelligence), Antibiotics, Artificial Intelligence, Bacteria, Biological engineering, Broad Institute, Collaboration, Computer science and technology, Disease, Drug development, Faculty, Harvard-MIT Health Sciences and Technology, Health sciences and technology, Institute for Medical Engineering and Science (IMES), Interview, Jameel Clinic, Microbes, School of Engineering, Synthetic biology

In the pursuit of solutions to complex global challenges including disease, energy demands, and climate change, scientific researchers, including at MIT, have turned to artificial intelligence, and to quantitative analysis and modeling, to design and construct engineered cells with novel properties. The engineered cells can be programmed to become new therapeutics — battling, and perhaps

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3 Questions: How AI could optimize the power grid

ai, AI (Artificial Intelligence), Algorithms, Alternative energy, Artificial Intelligence, Batteries, Climate change, Computer science and technology, Electrical engineering and computer science (EECS), Energy, Energy storage, Faculty, Interview, Laboratory for Information and Decision Systems (LIDS), Machine Learning, MIT Schwarzman College of Computing, Pollution, Research, School of Engineering, Sustainability, Sustainable computing

Artificial intelligence has captured headlines recently for its rapidly growing energy demands, and particularly the surging electricity usage of data centers that enable the training and deployment of the latest generative AI models. But it’s not all bad news — some AI tools have the potential to reduce some forms of energy consumption and enable cleaner grids.One

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3 Questions: Using computation to study the world’s best single-celled chemists

Artificial Intelligence, Bacteria, Biology, Electrical engineering and computer science (EECS), Evolution, Faculty, Genetics, Interview, Machine Learning, Microbes, MIT Schwarzman College of Computing, School of Engineering, School of Science

Today, out of an estimated 1 trillion species on Earth, 99.999 percent are considered microbial — bacteria, archaea, viruses, and single-celled eukaryotes. For much of our planet’s history, microbes ruled the Earth, able to live and thrive in the most extreme of environments. Researchers have only just begun in the last few decades to contend

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Interview: From CUDA to Tile-Based Programming: NVIDIA’s Stephen Jones on Building the Future of AI

Artificial Intelligence, Editors Pick, Interview, Machine Learning, Staff, Technology

As AI models grow in complexity and hardware evolves to meet the demand, the software layer connecting the two must also adapt. We recently sat down with Stephen Jones, a Distinguished Engineer at NVIDIA and one of the original architects of CUDA. Jones, whose background spans from fluid mechanics to aerospace engineering, offered deep insights

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3 Questions: How AI is helping us monitor and support vulnerable ecosystems

Abdul Latif Jameel Water and Food Systems Lab (J-WAFS), Algorithms, Animals, Artificial Intelligence, Computer Science and Artificial Intelligence Laboratory (CSAIL), Computer science and technology, Computer vision, data, Ecology, Electrical engineering and computer science (EECS), Environment, Graduate, postdoctoral, Interview, Machine Learning, MIT Schwarzman College of Computing, National Science Foundation (NSF), Pollution, Research, School of Engineering, Students, Sustainability

A recent study from Oregon State University estimated that more than 3,500 animal species are at risk of extinction because of factors including habitat alterations, natural resources being overexploited, and climate change.To better understand these changes and protect vulnerable wildlife, conservationists like MIT PhD student and Computer Science and Artificial Intelligence Laboratory (CSAIL) researcher Justin Kay

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3 Questions: The pros and cons of synthetic data in AI

Algorithms, Artificial Intelligence, Computer science and technology, data, Electrical engineering and computer science (EECS), Interview, Laboratory for Information and Decision Systems (LIDS), Machine Learning, MIT Schwarzman College of Computing, Research, School of Engineering

Synthetic data are artificially generated by algorithms to mimic the statistical properties of actual data, without containing any information from real-world sources. While concrete numbers are hard to pin down, some estimates suggest that more than 60 percent of data used for AI applications in 2024 was synthetic, and this figure is expected to grow

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