Students pitch transformative ideas in generative AI at MIT Ignite competition
Twelve teams of students and postdocs across the MIT community presented innovative startup ideas with potential for real-world impact.
MIT-IBM Watson AI Lab
Technique enables AI on edge devices to keep learning over time
With the PockEngine training method, machine-learning models can efficiently and continuously learn from user data on edge devices like smartphones.
New technique helps robots pack objects into a tight space
Researchers coaxed a family of generative AI models to work together to solve multistep robot manipulation problems.
A more effective experimental design for engineering a cell into a new state
By focusing on causal relationships in genome regulation, a new AI method could help scientists identify new immunotherapy techniques or regenerative therapies.
AI models are powerful, but are they biologically plausible?
A new study bridging neuroscience and machine learning offers insights into the potential role of astrocytes in the human brain.
Learning the language of molecules to predict their properties
This AI system only needs a small amount of data to predict molecular properties, which could speed up drug discovery and material development.
When computer vision works more like a brain, it sees more like people do
Training artificial neural networks with data from real brains can make computer vision more robust.
Computer vision system marries image recognition and generation
MAGE merges the two key tasks of image generation and recognition, typically trained separately, into a single system.
Scaling audio-visual learning without labels
A new multimodal technique blends major self-supervised learning methods to learn more similarly to humans.
New tool helps people choose the right method for evaluating AI models
Selecting the right method gives users a more accurate picture of how their model is behaving, so they are better equipped to correctly interpret its predictions.