Massachusetts Institute of Technology - MIT News An AI pipeline developed by CSAIL researchers enables unique hydrodynamic designs for bodyboard-sized vehicles that glide underwater and could help scientists gather marine data
MIT researchers introduce generative AI for databases Researchers from MIT and elsewhere developed an easy-to-use tool that enables someone to perform complicated statistical analyses on tabular data using just a few keystrokes Their method combines probabilistic AI models with the programming language SQL to provide faster and more accurate results than other methods
GitHub Copilot: Sorry, the response matched public code so it was . . . Thanks for explaining This has got to be the worst UX ever Who would want an AI to actively refuse answering a question unless you tell it that it's Ok to answer it via a convoluted and not directly explained config setting? The actual setting is currently called: Suggestions matching public code (duplication detection filter) - This does not sound like a security or licensing issue that
MIT researchers develop an efficient way to train more reliable AI . . . MIT researchers developed an efficient approach for training more reliable reinforcement learning models, focusing on complex tasks that involve variability This could enable the leverage of reinforcement learning across a wide range of applications
Accelerating scientific discovery with AI - MIT News FutureHouse, co-founded by Sam Rodriques PhD ’19, has developed AI agents to automate some of the most critical steps on the path toward scientific progress
“Periodic table of machine learning” could fuel AI discovery After uncovering a unifying algorithm that links more than 20 common machine-learning approaches, MIT researchers organized them into a “periodic table of machine learning” that can help scientists combine elements of different methods to improve algorithms or create new ones