Unlocking the quantum future
At the MIT Quantum Hackathon, a community tackles quantum computing challenges.
At the MIT Quantum Hackathon, a community tackles quantum computing challenges.
MIT CSAIL postdoc Nauman Dawalatabad explores ethical considerations, challenges in spear-phishing defense, and the optimistic future of AI-created voices across various sectors.
A new algorithm reduces travel time by identifying shortcuts a robot could take on the way to its destination.
Faster and more accurate than some alternatives, this approach could be useful for robots that interact with humans or work in tight spaces.
Professor Ernest Fraenkel has decoded fundamental aspects of Huntington’s disease and glioblastoma, and is now using computation to better understand amyotrophic lateral sclerosis.
By breaking an intractable problem into smaller chunks, a deep-learning technique identifies the optimal areas for thinning out traffic in a warehouse.
Alumni-founded Pienso has developed a user-friendly AI builder so domain experts can build solutions without writing any code.
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.
Exploiting the symmetry within datasets, MIT researchers show, can decrease the amount of data needed for training neural networks.
The ambient light sensors responsible for smart devices’ brightness adjustments can capture images of touch interactions like swiping and tapping for hackers.
June Odongo uses free, online MIT courses to train high-quality candidates, making them job-ready.
Atacama Biomaterials, co-founded by Paloma Gonzalez-Rojas SM ’15, PhD ’21, combines architecture, machine learning, and chemical engineering to create eco-friendly materials.
Although artificial intelligence in health has shown great promise, pressure is mounting for regulators around the world to act, as AI tools demonstrate potentially harmful outcomes.
MIT CSAIL researchers develop advanced machine-learning models that outperform current methods in detecting pancreatic ductal adenocarcinoma.