Computer science and technology
Computer science and technology

Computer science and technology

Artificial Intelligence Computer Science and Artificial Intelligence Laboratory (CSAIL) Computer science and technology Data Electrical engineering and computer science (EECS) machine learning MIT Schwarzman College of Computing Research Robotics School of Engineering

A faster, better way to train general-purpose robots

Inspired by large language models, researchers develop a training technique that pools diverse data to teach robots new skills.

Artificial Intelligence Computer Science and Artificial Intelligence Laboratory (CSAIL) Computer science and technology Data Electrical engineering and computer science (EECS) Human-computer interaction machine learning MIT Schwarzman College of Computing Quest for Intelligence Research School of Engineering

Making it easier to verify an AI model’s responses

By allowing users to clearly see data referenced by a large language model, this tool speeds manual validation to help users spot AI errors.

Artificial Intelligence Computer Science and Artificial Intelligence Laboratory (CSAIL) Computer science and technology computer-vision Electrical engineering and computer science (EECS) machine learning MIT Schwarzman College of Computing Research Robotics School of Engineering Video

Combining next-token prediction and video diffusion in computer vision and robotics

A new method can train a neural network to sort corrupted data while anticipating next steps. It can make flexible plans for robots, generate high-quality video, and help AI agents navigate digital environments.

Algorithms Artificial Intelligence Computer modeling Computer Science and Artificial Intelligence Laboratory (CSAIL) Computer science and technology Data Electrical engineering and computer science (EECS) Faculty machine learning MIT Schwarzman College of Computing Profile School of Engineering Social networks

Modeling relationships to solve complex problems efficiently

Associate Professor Julian Shun develops high-performance algorithms and frameworks for large-scale graph processing.

Algorithms Artificial Intelligence Computer Science and Artificial Intelligence Laboratory (CSAIL) Computer science and technology Data Electrical engineering and computer science (EECS) machine learning MIT Schwarzman College of Computing Research Robotics School of Engineering

How AI is improving simulations with smarter sampling techniques

MIT CSAIL researchers created an AI-powered method for low-discrepancy sampling, which uniformly distributes data points to boost simulation accuracy.

Artificial Intelligence Behavior Computer science and technology Human-computer interaction machine learning Media Lab Mental health Research School of Architecture and Planning Social sciences

AI simulation gives people a glimpse of their potential future self

By enabling users to chat with an older version of themselves, Future You is aimed at reducing anxiety and guiding young people to make better choices.

Algorithms Artificial Intelligence Brain and cognitive sciences Computer Science and Artificial Intelligence Laboratory (CSAIL) Computer science and technology computer-vision Electrical engineering and computer science (EECS) Evolution Imaging Lincoln Laboratory machine learning MIT Schwarzman College of Computing Psychology Research School of Engineering School of Science

AI pareidolia: Can machines spot faces in inanimate objects?

New dataset of “illusory” faces reveals differences between human and algorithmic face detection, links to animal face recognition, and a formula predicting where people most often perceive faces.

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MIT launches new Music Technology and Computation Graduate Program

The program will invite students to investigate new vistas at the intersection of music, computing, and technology.

Algorithms Artificial Intelligence Computer science and technology Electrical engineering and computer science (EECS) health Health Care Human-computer interaction Interview Laboratory for Information and Decision Systems (LIDS) machine learning Medicine MIT Schwarzman College of Computing School of Engineering

3 Questions: Should we label AI systems like we do prescription drugs?

Researchers argue that in health care settings, “responsible use” labels could ensure AI systems are deployed appropriately.

Algorithms Artificial Intelligence Computer science and technology Data Ethics Human-computer interaction IDSS Laboratory for Information and Decision Systems (LIDS) machine learning MIT Schwarzman College of Computing Research School of Engineering Video

Study: AI could lead to inconsistent outcomes in home surveillance

Researchers find large language models make inconsistent decisions about whether to call the police when analyzing surveillance videos.