How to build AI scaling laws for efficient LLM training and budget maximization
MIT-IBM Watson AI Lab researchers have developed a universal guide for estimating how large language models will perform based on smaller models in the same family.
MIT-IBM Watson AI Lab researchers have developed a universal guide for estimating how large language models will perform based on smaller models in the same family.
MIT CSAIL researchers developed a tool that can model the shape and movements of fetuses in 3D, potentially assisting doctors in finding abnormalities and making diagnoses.
The FabObscura system helps users design and print barrier-grid animations without electronics, and can help produce dynamic household, workplace, and artistic objects.
Study of 3.5 million cells from more than 100 human brains finds Alzheimer’s progression — and resilience to disease — depends on preserving epigenomic stability.
Balancing automation and agency, Associate Professor Arvind Satyanarayan develops interactive data visualizations that amplify human creativity and cognition.
MIT CSAIL researchers developed SustainaPrint, a system that reinforces only the weakest zones of eco-friendly 3D prints, achieving strong results with less plastic.
New findings could help manufacturers design gels, lotions, or even paving materials that last longer and perform more predictably.
VaxSeer uses machine learning to predict virus evolution and antigenicity, aiming to make vaccine selection more accurate and less reliant on guesswork.
A new approach can reveal the features AI models use to predict proteins that might make good drug or vaccine targets.
You can adjust the frequency range of this durable, inexpensive antenna by squeezing or stretching its structure.
Ianacare, co-founded by Steven Lee ’97, MEng ’98, equips caregivers with the resources, networks, and tools they need to support loved ones.
By visualizing Escher-like optical illusions in 2.5 dimensions, the “Meschers” tool could help scientists understand physics-defying shapes and spark new designs.
This new approach could lead to enhanced AI models for drug and materials discovery.
Neural Jacobian Fields, developed by MIT CSAIL researchers, can learn to control any robot from a single camera, without any other sensors.
MIT researchers found that special kinds of neural networks, called encoders or “tokenizers,” can do much more than previously realized.