By providing holistic information on a cell, an AI-driven method could help scientists better understand disease mechanisms and plan experiments.
Mixing generative AI with physics to create personal items that work in the real world
To help generative AI models create durable, real-world accessories and decor, the PhysiOpt system runs physics simulations and makes subtle tweaks to its 3D blueprints.
Personalization features can make LLMs more agreeable
The context of long-term conversations can cause an LLM to begin mirroring the user’s viewpoints, possibly reducing accuracy or creating a virtual echo-chamber.
3 Questions: How AI could optimize the power grid
While the growing energy demands of AI are worrying, some techniques can also help make power grids cleaner and more efficient.
New research demonstrates how AI models can be tested to ensure they don’t cause harm by revealing anonymized patient health data.
CSAIL researchers find even “untrainable” neural nets can learn effectively when guided by another network’s built-in biases using their guidance method.
With insect-like speed and agility, the tiny robot could someday aid in search-and-rescue missions.
Researchers discover a shortcoming that makes LLMs less reliable
Large language models can learn to mistakenly link certain sentence patterns with specific topics — and may then repeat these patterns instead of reasoning.
MIT scientists debut a generative AI model that could create molecules addressing hard-to-treat diseases
BoltzGen generates protein binders for any biological target from scratch, expanding AI’s reach from understanding biology toward engineering it.
Understanding the nuances of human-like intelligence
Associate Professor Phillip Isola studies the ways in which intelligent machines “think,” in an effort to safely integrate AI into human society.