<span class="vcard">Rachel Gordon | MIT CSAIL</span>
Rachel Gordon | MIT CSAIL
Artificial Intelligence Computer Science and Artificial Intelligence Laboratory (CSAIL) Computer science and technology computer-vision Electrical engineering and computer science (EECS) machine learning Mechanical engineering MIT Schwarzman College of Computing Research Robotics School of Engineering

Robot, know thyself: New vision-based system teaches machines to understand their bodies

Neural Jacobian Fields, developed by MIT CSAIL researchers, can learn to control any robot from a single camera, without any other sensors.

Algorithms Artificial Intelligence Computer Science and Artificial Intelligence Laboratory (CSAIL) Computer science and technology Electrical engineering and computer science (EECS) Human-computer interaction machine learning MIT Schwarzman College of Computing National Science Foundation (NSF) Programming Programming languages Research School of Engineering Software

Can AI really code? Study maps the roadblocks to autonomous software engineering

A team of researchers has mapped the challenges of AI in software development, and outlined a research agenda to move the field forward.

Algorithms Artificial Intelligence Autonomous vehicles Awards, honors and fellowships Computer Science and Artificial Intelligence Laboratory (CSAIL) Computer science and technology Electrical engineering and computer science (EECS) Faculty Human-computer interaction MIT Schwarzman College of Computing Robotics School of Engineering

Daniela Rus wins John Scott Award

MIT CSAIL director and EECS professor named a co-recipient of the honor for her robotics research, which has expanded our understanding of what a robot can be.

Algorithms 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 Research School of Engineering

Citation tool offers a new approach to trustworthy AI-generated content

Researchers develop “ContextCite,” an innovative method to track AI’s source attribution and detect potential misinformation.

Algorithms Artificial Intelligence Climate change Computer modeling Computer Science and Artificial Intelligence Laboratory (CSAIL) Computer science and technology Electrical engineering and computer science (EECS) Environment MIT Schwarzman College of Computing Plants Research School of Engineering Sustainability Urban studies and planning

Advancing urban tree monitoring with AI-powered digital twins

The Tree-D Fusion system integrates generative AI and genus-conditioned algorithms to create precise simulation-ready models of 600,000 existing urban trees across North America.

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

Can robots learn from machine dreams?

MIT CSAIL researchers used AI-generated images to train a robot dog in parkour, without real-world data. Their LucidSim system demonstrates generative AI’s potential for creating robotics training data.

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.

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.

Algorithms Artificial Intelligence Computer Science and Artificial Intelligence Laboratory (CSAIL) computer-vision Electrical Engineering & Computer Science (eecs) machine learning MIT Schwarzman College of Computing MIT-IBM Watson AI Lab Personal robotics Robotics Robots School of Engineering

Precision home robots learn with real-to-sim-to-real

CSAIL researchers introduce a novel approach allowing robots to be trained in simulations of scanned home environments, paving the way for customized household automation accessible to anyone.

Algorithms Artificial Intelligence Computer Science and Artificial Intelligence Laboratory (CSAIL) Computer science and technology computer-vision Electrical Engineering & Computer Science (eecs) Language machine learning MIT Schwarzman College of Computing MIT-IBM Watson AI Lab National Science Foundation (NSF) Research School of Engineering

MIT researchers advance automated interpretability in AI models

MAIA is a multimodal agent that can iteratively design experiments to better understand various components of AI systems.