Using AI to optimize for rapid neural imaging
MIT CSAIL researchers combine AI and electron microscopy to expedite detailed brain network mapping, aiming to enhance connectomics research and clinical pathology.
Computer Science and Artificial Intelligence Laboratory (CSAIL)
Accelerating AI tasks while preserving data security
The SecureLoop search tool efficiently identifies secure designs for hardware that can boost the performance of complex AI tasks, while requiring less energy.
New techniques efficiently accelerate sparse tensors for massive AI models
Complimentary approaches — “HighLight” and “Tailors and Swiftiles” — could boost the performance of demanding machine-learning tasks.
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.
AI copilot enhances human precision for safer aviation
Designed to ensure safer skies, “Air-Guardian” blends human intuition with machine precision, creating a more symbiotic relationship between pilot and aircraft.
From physics to generative AI: An AI model for advanced pattern generation
Inspired by physics, a new generative model PFGM++ outperforms diffusion models in image generation.
Multi-AI collaboration helps reasoning and factual accuracy in large language models
Researchers use multiple AI models to collaborate, debate, and improve their reasoning abilities to advance the performance of LLMs while increasing accountability and factual accuracy.
AI-driven tool makes it easy to personalize 3D-printable models
With Style2Fab, makers can rapidly customize models of 3D-printable objects, such as assistive devices, without hampering their functionality.
How an archeological approach can help leverage biased data in AI to improve medicine
Although computer scientists may initially treat data bias and error as a nuisance, researchers argue it’s a hidden treasure trove for reflecting societal values.
Helping computer vision and language models understand what they see
Researchers use synthetic data to improve a model’s ability to grasp conceptual information, which could enhance automatic captioning and question-answering systems.