How an MIT research project became a global programming language
With millions of users across the world, Julia has been used to conduct cutting-edge research and to design new drugs, jet engines, heat pumps, and more.
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Programming
Professor Emeritus Dimitri Bertsekas, influential computer scientist and prolific author, dies at 83
Known for his clear and elegant writing style, Bertsekas shaped fields from control and optimization to large-scale computation and artificial intelligence.
Read MoreHow novice coders can develop AI programs for military applications
A USAF cadet and a Lincoln Laboratory researcher found AI chatbots can help nontechnical service members produce viable software applications for their unique problems.
Read MoreFeatured video: Coding for underwater robotics
Lincoln Laboratory intern Ivy Mahncke developed and tested algorithms to help human divers and robots navigate underwater.
Read MoreHelping AI agents search to get the best results out of large language models
EnCompass executes AI agent programs by backtracking and making multiple attempts, finding the best set of outputs generated by an LLM. It could help coders work with AI agents more efficiently.
Read MoreMIT researchers propose a new model for legible, modular software
The coding framework uses modular concepts and simple synchronization rules to make software clearer, safer, and easier for LLMs to generate.
Read MoreA team of researchers has mapped the challenges of AI in software development, and outlined a research agenda to move the field forward.
Read MoreCombining technology, education, and human connection to improve online learning
Caitlin Morris, a PhD student and 2024 MAD Fellow affiliated with the MIT Media Lab, designs digital learning platforms that make room for the “social magic” that influences curiosity and motivation.
Read MoreA new technique automatically guides an LLM toward outputs that adhere to the rules of whatever programming language or other format is being used.
Read MoreUser-friendly system can help developers build more efficient simulations and AI models
By automatically generating code that leverages two types of data redundancy, the system saves bandwidth, memory, and computation.
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