This is your brain. This is your brain on code
MIT researchers are discovering which parts of the brain are engaged when a person evaluates a computer program.
MIT-IBM Watson AI Lab
Busy GPUs: Sampling and pipelining method speeds up deep learning on large graphs
New technique significantly reduces training and inference time on extensive datasets to keep pace with fast-moving data in finance, social networks, and fraud detection in cryptocurrency.
A far-sighted approach to machine learning
New system can teach a group of cooperative or competitive AI agents to find an optimal long-term solution.
Ensuring AI works with the right dose of curiosity
Researchers make headway in solving a longstanding problem of balancing curious “exploration” versus “exploitation” of known pathways in reinforcement learning.
Learning on the edge
A new technique enables AI models to continually learn from new data on intelligent edge devices like smartphones and sensors, reducing energy costs and privacy risks.
Neurodegenerative disease can progress in newly identified patterns
A machine-learning method finds patterns of health decline in ALS, informing future clinical trial designs and mechanism discovery. The technique also extends to Alzheimer’s and Parkinson’s.
New hardware offers faster computation for artificial intelligence, with much less energy
Engineers working on “analog deep learning” have found a way to propel protons through solids at unprecedented speeds.
Student-powered machine learning
Recent MEng graduates reflect on their application-focused research as affiliates of the MIT-IBM Watson AI Lab.
Keeping web-browsing data safe from hackers
Studying a powerful type of cyberattack, researchers identified a flaw in how it’s been analyzed before, then developed new techniques that stop it in its tracks.
Hallucinating to better text translation
A machine-learning method imagines what a sentence visually looks like, to situate and ground its semantics in the real world, improving translation, like humans can.
Artificial intelligence system learns concepts shared across video, audio, and text
A machine-learning model can identify the action in a video clip and label it, without the help of humans.
A one-up on motion capture
A new neural network approach captures the characteristics of a physical system’s dynamic motion from video, regardless of rendering configuration or image differences.
How can we reduce the carbon footprint of global computing?
Workshop hosted by MIT’s Climate and Sustainability Consortium, MIT-IBM Watson AI Lab, and the MIT Schwarzman College of Computing highlights how new approaches to computing can save energy and help the planet.
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.
Generating new molecules with graph grammar
An efficient machine-learning method uses chemical knowledge to create a learnable grammar with production rules to build synthesizable monomers and polymers.