National Science Foundation (NSF)
National Science Foundation (NSF)

Generative AI improves a wireless vision system that sees through obstructions

With this new technique, a robot could more accurately detect hidden objects or understand an indoor scene using reflected Wi-Fi signals.

3 Questions: On the future of AI and the mathematical and physical sciences

Professor Jesse Thaler describes a vision for a two-way bridge between artificial intelligence and the mathematical and physical sciences — one that promises to advance both.

New method could increase LLM training efficiency

By leveraging idle computing time, researchers can double the speed of model training while preserving accuracy.

Parking-aware navigation system could prevent frustration and emissions

By minimizing the need to drive around looking for a parking spot, this technique can save drivers up to 35 minutes — and give them a realistic estimate of total travel time.

Study: Platforms that rank the latest LLMs can be unreliable

Removing just a tiny fraction of the crowdsourced data that informs online ranking platforms can significantly change the results.

How generative AI can help scientists synthesize complex materials

MIT researchers’ DiffSyn model offers recipes for synthesizing new materials, enabling faster experimentation and a shorter journey from hypothesis to use.

MIT scientists investigate memorization risk in the age of clinical AI

New research demonstrates how AI models can be tested to ensure they don’t cause harm by revealing anonymized patient health data.

Guided learning lets “untrainable” neural networks realize their potential

CSAIL researchers find even “untrainable” neural nets can learn effectively when guided by another network’s built-in biases using their guidance method.

Enabling small language models to solve complex reasoning tasks

The “self-steering” DisCIPL system directs small models to work together on tasks with constraints, like itinerary planning and budgeting.

New method improves the reliability of statistical estimations

The technique can help scientists in economics, public health, and other fields understand whether to trust the results of their experiments.