Computer Science and Artificial Intelligence Laboratory (CSAIL)
Computer Science and Artificial Intelligence Laboratory (CSAIL)

Computer Science and Artificial Intelligence Laboratory (CSAIL)

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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.

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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.

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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.

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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.

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Reasoning and reliability in AI

PhD students interning with the MIT-IBM Watson AI Lab look to improve natural language usage.

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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.

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AI agents help explain other AI systems

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

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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.

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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.

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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.