3 Questions: The pros and cons of synthetic data in AI
Artificially created data offer benefits from cost savings to privacy preservation, but their limitations require careful planning and evaluation, Kalyan Veeramachaneni says.
Laboratory for Information and Decision Systems (LIDS)
A new way to test how well AI systems classify text
As large language models increasingly dominate our everyday lives, new systems for checking their reliability are more important than ever.
Eco-driving measures could significantly reduce vehicle emissions
New research shows automatically controlling vehicle speeds to mitigate traffic at intersections can cut carbon emissions between 11 and 22 percent.
New algorithms enable efficient machine learning with symmetric data
This new approach could lead to enhanced AI models for drug and materials discovery.
A new way to edit or generate images
MIT researchers found that special kinds of neural networks, called encoders or “tokenizers,” can do much more than previously realized.
This “smart coach” helps LLMs switch between text and code
The CodeSteer system could boost large language models’ accuracy when solving complex problems, such as scheduling shipments in a supply chain.
How to more efficiently study complex treatment interactions
A new approach for testing multiple treatment combinations at once could help scientists develop drugs for cancer or genetic disorders.
Confronting the AI/energy conundrum
The MIT Energy Initiative’s annual research symposium explores artificial intelligence as both a problem and a solution for the clean energy transition.
LLMs factor in unrelated information when recommending medical treatments
Researchers find nonclinical information in patient messages — like typos, extra white space, and colorful language — reduces the accuracy of an AI model.
Unpacking the bias of large language models
In a new study, researchers discover the root cause of a type of bias in LLMs, paving the way for more accurate and reliable AI systems.