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Forbes contributor Michael T. Nietzel spotlights the newest cohort of Rhodes Scholars, which includes Yiming Chen '24, Wilhem Hector, Anushka Nair, and David Oluigbo from MIT. Nietzel notes that Oluigbo has “published numerous peer-reviewed articles and conducts research on applying artificial intelligence to complex medical problems and systemic healthcare challenges.”
Yiming Chen '24, Wilhem Hector, Anushka Nair, and David Oluigbo have been named 2025 Rhodes Scholars, report Brian P. D. Hannon and John Hanna for the Associated Press. Undergraduate student David Oluigbo, one of the four honorees, has “volunteered at a brain research institute and the National Institutes of Health, researching artificial intelligence in health care while also serving as an emergency medical technician,” write Hannon and Hanna.
Researchers at MIT have developed a new AI model capable of assessing a patient’s risk of pancreatic cancer, reports Erez Meltzer for Forbes. “The model could potentially expand the group of patients who can benefit from early pancreatic cancer screening from 10% to 35%,” explains Meltzer. “These kinds of predictive capabilities open new avenues for preventive care.”
Craft in America visits Prof. Erik Demaine and Martin Demaine of CSAIL to learn more about their work with computational origami. “Computational origami is quite useful for the mathematical problems we are trying to solve,” Prof. Erik Demaine explains. “We try to integrate the math and the art together.”
Neural Magic, an AI optimization startup co-founded by Prof. Nir Shavit and former Research Scientist Alex Matveev, aims to “process AI workloads on processors and GPUs at speeds equivalent to specialized AI chips,” reports Kyle Wiggers for TechCrunch. “By running models on off-the-shelf processors, which usually have more available memory, the company’s software can realize these performance gains,” explains Wiggers.
Researchers at MIT have developed a robot capable of assembling “building blocks called voxels to build an object with almost any shape,” reports Alex Wilkins for New Scientist. “You can get furniture-scale objects really fast in a very sustainable way, because you can reuse these modular components and ask a robot to reassemble them into different large-scale objects,” says graduate student Alexander Htet Kyaw.
Researchers at MIT have developed a new virtual training program for four-legged robots by taking “popular computer simulation software that follows the principles of real-world physics and inserting a generative AI model to produce artificial environments,” reports Jeremy Hsu for New Scientist. “Despite never being able to ‘see’ the real world during training, the robot successfully chased real-world balls and climbed over objects 88 per cent of the time after the AI-enhanced training,” writes Hsu. "When the robot relied solely on training by a human teacher, it only succeeded 15 per cent of the time.”
Researchers at MIT have developed a new model for training robots dubbed Heterogeneous Pretrained Transformers (HPT), reports Brain Heater for TechCrunch. The new model “pulls together information from different sensors and different environments,” explains Heater. “A transformer was then used to pull together the data into training models. The larger the transformer, the better the output. Users then input the robot design, configuration, and the job they want done.”
MIT researchers have developed a new training technique called Heterogeneous Pretrained Transformers (HPT) that could help make general-purpose robots more efficient and adaptable, reports Christopher Isak for TechAcute. “The main advantage of this technique is its ability to integrate data from different sources into a unified system,” explains Isak. “This approach is similar to how large language models are trained, showing proficiency across many tasks due to their extensive and varied training data. HPT enables robots to learn from a wide range of experiences and environments.”
Liquid AI, an MIT startup, is unveiling a new AI model based on a liquid neural network that “has the potential to be more efficient, less power-hungry, and more transparent than the ones that underpin everything from chatbots to image generators to facial recognition systems, reports Will Knight for Wired.
Graduate student Nouran Soliman speaks with NBC Boston about the use of “personhood credentials,” a new technique that can be used to verify online users as human beings to help combat issues such as fraud and misinformation. “We are trying to also think about ways of implementing a system that incorporates personal credentials in a decentralized way,” explains Soliman. “It's also important not to have the power in one place because that compromises democracy.”
Researchers at MIT and elsewhere have developed a 3D printing method that allows “precise control over color, shade, texture, all with just a single material,” reports Vanesa Listek for 3Dprint.com. This technique “promises a faster and more sustainable solution than traditional approaches relying on multiple materials and nozzle changes,” explains Listek.
Researchers at MIT and elsewhere have developed “a new method of 3D printing that uses heat-responsive materials to print multi-color and multi-textured objects in one step,” reports Laura Griffiths for TCT Magazine. “The method has so far been tested using three heat-responsive filaments including a foaming polymer with particles that expand as they are heated, and wood and cork fiber-filled filaments,” explains Griffiths.
Researchers at MIT have developed GenSQL, a new generative AI system that can be used “to ease answering data science questions,” reports Allison Proffitt for Bio-It World. “Look how much better data science could be if it was easier to use,” says Research Scientist Mathieu Huot. “It’s not perfect yet, but we believe it’s quite an improvement over other options.”
Researchers at MIT have found large language models “often struggle to handle more complex problems that require true understanding,” reports Kirimgeray Kirimli for Forbes. “This underscores the need for future versions of LLMs to go beyond just these basic, shared capabilities,” writes Kirimli.