What to do about AI in health?
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.
Computer Science and Artificial Intelligence Laboratory (CSAIL)
New hope for early pancreatic cancer intervention via AI-based risk prediction
MIT CSAIL researchers develop advanced machine-learning models that outperform current methods in detecting pancreatic ductal adenocarcinoma.
Reasoning and reliability in AI
PhD students interning with the MIT-IBM Watson AI Lab look to improve natural language usage.
Stratospheric safety standards: How aviation could steer regulation of AI in health
An interdisciplinary team of researchers thinks health AI could benefit from some of the aviation industry’s long history of hard-won lessons that have created one of the safest activities today.
Multiple AI models help robots execute complex plans more transparently
A multimodal system uses models trained on language, vision, and action data to help robots develop and execute plans for household, construction, and manufacturing tasks.
AI agents help explain other AI systems
MIT researchers introduce a method that uses artificial intelligence to automate the explanation of complex neural networks.
A flexible solution to help artists improve animation
This new method draws on 200-year-old geometric foundations to give artists control over the appearance of animated characters.
Image recognition accuracy: An unseen challenge confounding today’s AI
“Minimum viewing time” benchmark gauges image recognition complexity for AI systems by measuring the time needed for accurate human identification.
A computer scientist pushes the boundaries of geometry
Justin Solomon applies modern geometric techniques to solve problems in computer vision, machine learning, statistics, and beyond.
Boosting faith in the authenticity of open source software
Speranza system brings hope to users that the package they download is functional software, not malware.
MIT Generative AI Week fosters dialogue across disciplines
During the last week of November, MIT hosted symposia and events aimed at examining the implications and possibilities of generative AI.
Automated system teaches users when to collaborate with an AI assistant
MIT researchers develop a customized onboarding process that helps a human learn when a model’s advice is trustworthy.
A new optimization framework for robot motion planning
MIT CSAIL researchers established new connections between combinatorial and continuous optimization, which can find global solutions for complex motion-planning puzzles.
What does the future hold for generative AI?
Rodney Brooks, co-founder of iRobot, kicks off an MIT symposium on the promise and potential pitfalls of increasingly powerful AI tools like ChatGPT.
New method uses crowdsourced feedback to help train robots
Human Guided Exploration (HuGE) enables AI agents to learn quickly with some help from humans, even if the humans make mistakes.