Dealing with the limitations of our noisy world
Tamara Broderick uses statistical approaches to understand and quantify the uncertainty that can affect study results.
Tamara Broderick uses statistical approaches to understand and quantify the uncertainty that can affect study results.
MIT.nano Immersion Lab works with AR/VR startup to create transcontinental medical instruction.
Daron Acemoglu, David Autor, and Simon Johnson, faculty co-directors of the new MIT Shaping the Future of Work Initiative, describe why the work matters and what they hope to achieve.
The MIT seniors will pursue graduate studies at Cambridge University.
By breaking an intractable problem into smaller chunks, a deep-learning technique identifies the optimal areas for thinning out traffic in a warehouse.
After acquiring data science and AI skills from MIT, Jospin Hassan shared them with his community in the Dzaleka Refugee Camp in Malawi and built pathways for talented learners.
MIT LIDS awarded funding from the Appalachian Regional Commission as part of a multi-state collaborative project to model and test new smart grid technologies for use in rural areas.
Alumni-founded Pienso has developed a user-friendly AI builder so domain experts can build solutions without writing any code.
Using a machine-learning algorithm, researchers can predict interactions that could interfere with a drug’s effectiveness.
MIT engineers developed a tag that can reveal with near-perfect accuracy whether an item is real or fake. The key is in the glue on the back of the tag.
Innovative AI system from MIT CSAIL melds simulations and physical testing to forge materials with newfound durability and flexibility for diverse engineering uses.
Researchers developed a simple yet effective solution for a puzzling problem that can worsen the performance of large language models such as ChatGPT.
Autonomous helicopters made by Rotor Technologies, a startup led by MIT alumni, take the human out of risky commercial missions.
The graduate students will aim to commercialize innovations in AI, machine learning, and data science.
Exploiting the symmetry within datasets, MIT researchers show, can decrease the amount of data needed for training neural networks.