Synthetic imagery sets new bar in AI training efficiency
MIT CSAIL researchers innovate with synthetic imagery to train AI, paving the way for more efficient and bias-reduced machine learning.
Computer science and technology
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.
Explained: Generative AI
How do powerful generative AI systems like ChatGPT work, and what makes them different from other types of artificial intelligence?
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.
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.
To excel at engineering design, generative AI must learn to innovate, study finds
AI models that prioritize similarity falter when asked to design something completely new.
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 method to interpret AI might not be so interpretable after all
Some researchers see formal specifications as a way for autonomous systems to “explain themselves” to humans. But a new study finds that we aren’t understanding.
New tools are available to help reduce the energy that AI models devour
Amid the race to make AI bigger and better, Lincoln Laboratory is developing ways to reduce power, train efficiently, and make energy use transparent.