Research
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This tiny, tamper-proof ID tag can authenticate almost anything

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.

Using AI to discover stiff and tough microstructures

Innovative AI system from MIT CSAIL melds simulations and physical testing to forge materials with newfound durability and flexibility for diverse engineering uses.

A new way to let AI chatbots converse all day without crashing

Researchers developed a simple yet effective solution for a puzzling problem that can worsen the performance of large language models such as ChatGPT.

How symmetry can come to the aid of machine learning

Exploiting the symmetry within datasets, MIT researchers show, can decrease the amount of data needed for training neural networks.

Doctors have more difficulty diagnosing disease when looking at images of darker skin

Dermatologists and general practitioners are somewhat less accurate in diagnosing disease in darker skin, a new study finds. Used correctly, AI may be able to help.

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.

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.

Technique could efficiently solve partial differential equations for numerous applications

MIT researchers propose “PEDS” method for developing models of complex physical systems in mechanics, optics, thermal transport, fluid dynamics, physical chemistry, climate, and more.

AI agents help explain other AI systems

MIT researchers introduce a method that uses artificial intelligence to automate the explanation of complex neural networks.