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Researchers at MIT have developed PlasmoSniff, a breath test that uses nanoparticles and enhanced Raman spectroscopy to detect pneumonia and other lung conditions in minutes, potentially enabling rapid at-home or clinical diagnosis.
MIT engineers discovered that rice seeds germinate 30-40% faster when exposed to the sound vibrations of falling rain, providing the first direct evidence that plant seeds can sense sound as a cue for optimal growth depth.
MIT researchers developed a wristband with ultrasound stickers that images muscles and tendons, using AI to translate those images into hand movements to wirelessly control a robotic hand with high dexterity.
MIT alumni and friends are urged to support the institute's mission of advancing scientific leadership, merit-based admissions, and affordable education to boost US health, security, and prosperity.
MIT has launched the MIT4America Calculus Project to provide remote calculus tutoring to students in underresourced US high schools, addressing the lack of calculus access in nearly half of American high schools.
Research from the MIT Hardness Group proves that Super Mario levels can be undecidable, meaning no computer program can always determine if Mario can reach the castle, placing Super Mario in the hardest complexity class.
The article describes how semi-automated offside technology (SAOT), developed with the MIT Sports Lab, was used in the 2022 World Cup final to make a critical offside call, highlighting the role of AI in sports officiating.
The MIT AI and Society Forum convened experts to discuss AI's societal impacts on labor, work, civil discourse, and arts, featuring keynotes from economists and computer scientists, panel discussions, and a musical performance exploring generative AI in the arts.
Turing Award winner Richard Sutton gives a 71-minute talk at MIT criticizing current AI development and advocating for self-taught, play-based, abstract learning as a path to superintelligence.
MIT researchers have developed a new system-on-a-chip that enables tiny robots to create detailed 3D maps of their environments in real-time using only about 6 milliwatts of power, potentially enabling long-duration autonomous navigation in complex spaces.
MIT open-sourced the RLM (Recursive Language Models) inference library, which handles ultra-long contexts by having the model recursively call itself programmatically, solving the limited context window problem of traditional models.
MIT researchers have developed a machine-learning-based approach to accurately model the behavior of metal alloys, regardless of chemical complexity, enabling faster and cheaper materials innovation.
The article details the investigation into the origin of the name 'Zork' for the classic text adventure game, tracing Wikipedia edits and uncovering conflicting sources about whether 'zork' was a jargon word for unfinished programs or simply a nonsense word.
MIT affiliates are highlighted in the Boston Globe's 2026 Tech Power Players list, with President Kornbluth emphasizing Massachusetts' potential to lead in AI and other tech fields. The article covers MIT's AI initiatives, entrepreneurship support, and role in the region's innovation ecosystem.
MIT researchers co-authored a paper showing that general-purpose policy gradient algorithms can outperform specialized game-theoretic algorithms in imperfect-information games, challenging long-held assumptions in the field.
A tweet recommends a lecture by an OpenAI researcher on how LLMs are built, claiming it taught an MIT CS grad more than his entire degree.
MIT researchers developed a long-term memory framework called DAAAM that enables robots to form and recall spatiotemporal memories of environments using language, allowing them to answer queries like 'Where did I leave my wallet?' The method combines advanced map representations with rich descriptions and outperforms state-of-the-art approaches.
This paper proposes Supervised Memory Training (SMT), which uses Transformer as a super teacher to distill memory states in parallel, then trains RNN with one-step supervised learning, achieving fully parallel training and reducing gradient path from O(T) to O(1), significantly improving long-range dependency learning.
MIT's Initiative for New Manufacturing (INM) celebrated its first anniversary with Manufacturing Week, attracting over 800 participants to discuss AI on factory floors, workforce solutions, and startup innovation, while launching programs to commercialize manufacturing technologies.
Chaosnet is a local network developed in 1975 by MIT's AI Lab for the Lisp Machine system, designed for high-speed, reliable communication within a small area without centralized control.