Q&A: The climate impact of generative AI
As the use of generative AI continues to grow, Lincoln Laboratory's Vijay Gadepally describes what researchers and consumers can do to help mitigate its environmental impact.
Machine learning
Teaching AI to communicate sounds like humans do
Inspired by the mechanics of the human vocal tract, a new AI model can produce and understand vocal imitations of everyday sounds. The method could help build new sonic interfaces…
Unlocking the hidden power of boiling — for energy, space, and beyond
Associate Professor Matteo Bucci’s research sheds new light on an ancient process, to improve the efficiency of heat transfer in many industrial systems.
Ecologists find computer vision models’ blind spots in retrieving wildlife images
Biodiversity researchers tested vision systems on how well they could retrieve relevant nature images. More advanced models performed well on simple queries but struggled with more research-specific prompts.
Need a research hypothesis? Ask AI.
MIT engineers developed AI frameworks to identify evidence-driven hypotheses that could advance biologically inspired materials.
Using natural language in Amazon Q Business: From searching and creating ServiceNow incidents and knowledge articles to generating insights
In this post, we’ll demonstrate how to configure an Amazon Q Business application and add a custom plugin that gives users the ability to use a natural language interface provided…
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.
Teaching a robot its limits, to complete open-ended tasks safely
The “PRoC3S” method helps an LLM create a viable action plan by testing each step in a simulation. This strategy could eventually aid in-home robots to complete more ambiguous chore…
AI in health should be regulated, but don’t forget about the algorithms, researchers say
In a recent commentary, a team from MIT, Equality AI, and Boston University highlights the gaps in regulation for AI models and non-AI algorithms in health care.
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.