Does this artificial intelligence think like a human?
A new technique compares the reasoning of a machine-learning model to that of a human, so the user can see patterns in the model’s behavior.
A new technique compares the reasoning of a machine-learning model to that of a human, so the user can see patterns in the model’s behavior.
An efficient machine-learning method uses chemical knowledge to create a learnable grammar with production rules to build synthesizable monomers and polymers.
MEng graduate students engage with IBM to develop their research skills and solutions to real-world problems.
A new methodology simulates counterfactual, time-varying, and dynamic treatment strategies, allowing doctors to choose the best course of action.
John Cohn and Franz-Josef Ulm, along with 19 additional MIT alumni, are honored for significant contributions to engineering research, practice, and education.
Twist is an MIT-developed programming language that can describe and verify which pieces of data are entangled to prevent bugs in a quantum program.
Strategy accelerates the best algorithmic solvers for large sets of cities.
Summit features the latest research of women and other underrepresented genders in MIT EECS, along with an opportunity to network, share experiences, and learn.
New technique applied to small computer chips enables efficient vision and detection algorithms without internet connectivity.
A new “common-sense” approach to computer vision enables artificial intelligence that interprets scenes more accurately than other systems do.
MIT-IBM Watson AI Lab researchers aim to design concrete mixtures that use AI to shrink environmental footprint and cost, while recycling byproducts and increasing performance.
Chandrakasan honored for his “contributions to ultralow-power circuits and systems, and leadership in academia and advancing diversity in the profession.”
A new AI-powered, virtual platform uses real-world physics to simulate a rich and interactive audio-visual environment, enabling human and robotic learning, training, and experimental studies.
Reducing the complexity of a powerful machine-learning model may help level the playing field for automatic speech-recognition around the world.
Scientists employ an underused resource — radiology reports that accompany medical images — to improve the interpretive abilities of machine learning algorithms.