“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
Introducing the MIT Generative AI Impact Consortium The MIT Generative AI Impact Consortium is a collaboration between MIT, founding member companies, and researchers across disciplines who aim to develop open-source generative AI solutions, accelerating innovations in education, research, and industry
How we really judge AI - MIT News A new study finds people are more likely to approve of the use of AI in situations where its abilities are perceived as superior to humans’ and where personalization isn’t necessary
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
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