The sweet taste of a new idea
Sendhil Mullainathan brings a lifetime of unique perspectives to research in behavioral economics and machine learning.
Sendhil Mullainathan brings a lifetime of unique perspectives to research in behavioral economics and machine learning.
“IntersectionZoo,” a benchmarking tool, uses a real-world traffic problem to test progress in deep reinforcement learning algorithms.
New type of “state-space model” leverages principles of harmonic oscillators.
Using diagrams to represent interactions in multipart systems can provide a faster way to design software improvements.
Researchers have created a unifying framework that can help scientists combine existing ideas to improve AI models or create new ones.
A new technique automatically guides an LLM toward outputs that adhere to the rules of whatever programming language or other format is being used.
By eliminating redundant computations, a new data-driven method can streamline processes like scheduling trains, routing delivery drivers, or assigning airline crews.
A new method from the MIT-IBM Watson AI Lab helps large language models to steer their own responses toward safer, more ethical, value-aligned outputs.
The approach maintains an AI model’s accuracy while ensuring attackers can’t extract secret information.
This new framework leverages a model’s reasoning abilities to create a “smart assistant” that finds the optimal solution to multistep problems.