AI tool generates high-quality images faster than state-of-the-art approaches
Researchers fuse the best of two popular methods to create an image generator that uses less energy and can run locally on a laptop or smartphone.
National Science Foundation (NSF)
Validation technique could help scientists make more accurate forecasts
MIT researchers developed a new approach for assessing predictions with a spatial dimension, like forecasting weather or mapping air pollution.
User-friendly system can help developers build more efficient simulations and AI models
By automatically generating code that leverages two types of data redundancy, the system saves bandwidth, memory, and computation.
Toward video generative models of the molecular world
Starting with a single frame in a simulation, a new system uses generative AI to emulate the dynamics of molecules, connecting static molecular structures and developing blurry pictures into videos.
MIT researchers introduce Boltz-1, a fully open-source model for predicting biomolecular structures
With models like AlphaFold3 limited to academic research, the team built an equivalent alternative, to encourage innovation more broadly.
Researchers reduce bias in AI models while preserving or improving accuracy
A new technique identifies and removes the training examples that contribute most to a machine-learning model’s failures.
A new way to create realistic 3D shapes using generative AI
Researchers propose a simple fix to an existing technique that could help artists, designers, and engineers create better 3D models.
Photonic processor could enable ultrafast AI computations with extreme energy efficiency
This new device uses light to perform the key operations of a deep neural network on a chip, opening the door to high-speed processors that can learn in real-time.
MIT researchers develop an efficient way to train more reliable AI agents
The technique could make AI systems better at complex tasks that involve variability.
Despite its impressive output, generative AI doesn’t have a coherent understanding of the world
Researchers show that even the best-performing large language models don’t form a true model of the world and its rules, and can thus fail unexpectedly on similar tasks.