Data
Data

MIT researchers develop AI tool to improve flu vaccine strain selection

VaxSeer uses machine learning to predict virus evolution and antigenicity, aiming to make vaccine selection more accurate and less reliant on guesswork.

Simpler models can outperform deep learning at climate prediction

New research shows the natural variability in climate data can cause AI models to struggle at predicting local temperature and rainfall.

Helping data storage keep up with the AI revolution

Storage systems from Cloudian, co-founded by an MIT alumnus, are helping businesses feed data-hungry AI models and agents at scale.

New algorithms enable efficient machine learning with symmetric data

This new approach could lead to enhanced AI models for drug and materials discovery.

The unique, mathematical shortcuts language models use to predict dynamic scenarios

Language models follow changing situations using clever arithmetic, instead of sequential tracking. By controlling when these approaches are used, engineers could improve the systems’ capabilities.

Study could lead to LLMs that are better at complex reasoning

Researchers developed a way to make large language models more adaptable to challenging tasks like strategic planning or process optimization.

Exploring data and its influence on political behavior

In MIT’s course 17.831 (Data and Politics), students are introduced to the power of analysis, visualization, and research-supported insight into political outcomes.

Merging AI and underwater photography to reveal hidden ocean worlds

The LOBSTgER research initiative at MIT Sea Grant explores how generative AI can expand scientific storytelling by building on field-based photographic data.

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.

Celebrating an academic-industry collaboration to advance vehicle technology

MIT Advanced Vehicle Technology Consortium marks a decade of developing data that improve understanding of how drivers use and respond to increasingly sophisticated automotive features.