Research from the Data Provenance Initiative, led by MIT researchers, has “found that many web sources used for training AI models have restricted their data, leading to a rapid decline in accessible information,” reports Gary Drenik for Forbes.
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.”
Arago, an AI startup co-founded by alumnus Nicolas Muller, has been named to the Future 40 list by Station F, which selects “the 40 most promising startups,” reports Romain Dillet for TechCrunch. Arago is “working on new AI-focused chips that use optical technology at the chipset level to speed up operations,” explains Dillet.
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.
Michael Truell '21, Sualeh Asif '22, Arvid Lunnemar '22, and Aman Sanger '22 co-founded Anysphere, an AI startup working on developing Cursor, an AI-powered coding assistant, reports Marina Temkin for TechCrunch.
Researchers at MIT have developed a “new type of transistor using semiconductor nanowires made up of gallium antimonide and iridium arsenide,” reports Alex Knapp for Forbes. “The transistors were designed to take advantage of a property called quantum tunneling to move electricity through transistors,” explains Knapp.
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.”
Research Scientist Nick van der Meulen speaks with Financial Times reporter Bethan Staton about how automation could be used to help employers plug the skills gap. “You can give people insight into how their skills stack up . . . you can say this is the level you need to be for a specific role, and this is how you can get there,” says van der Meulen. “You cannot do that over 80 skills through active testing, it would be too costly.”
Graduate student Aruna Sankaranarayanan speaks with Mashable reporter Cecily Mauran the impact of political deepfakes and the importance of AI literacy, noting that the fabrication of important figures who aren’t as well known is one of her biggest concerns. “Fabrication coming from them, distorting certain facts, when you don’t know what they look like or sound like most of the time, that’s really hard to disprove,” says Sankaranarayanan.
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.”
Prof. Jacopo Buongiorno speaks with Nature reporter Davide Castelvecchi about how AI has increased energy demand and the future of nuclear energy.
Researchers at MIT have developed “Clio,” a new technique that “enables robots to make intuitive, task-relevant decisions,” reports Jennifer Kite-Powell for Forbes. The team’s new approach allows “a robot to quickly map a scene and identify the items they need to complete a given set of tasks,” writes Kite-Powell.
MIT researchers have developed a security protocol that utilizes quantum properties to ensure the security of data in cloud servers, reports Andrew Corselli for Tech Briefs. “Our protocol uses the quantum properties of light to secure the communication between a client (who owns confidential data) and a server (that holds a confidential deep learning model),” explains postdoc Sri Krishna Vadlamani.
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.