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The Computer Scientist Peering Inside AI’s Black Boxes
Cynthia Rudin wants machine learning models, responsible for increasingly important decisions, to show their work.
A New Approach to Computation Reimagines Artificial Intelligence
By imbuing enormous vectors with semantic meaning, we can get machines to reason more abstractly — and efficiently — than before.
The Unpredictable Abilities Emerging From Large AI Models
Large language models like ChatGPT are now big enough that they’ve started to display startling, unpredictable behaviors.
The Researcher Who Would Teach Machines to Be Fair
Arvind Narayanan uses quantitative methods to expose and correct the misuse of quantitative methods.
In Neural Networks, Unbreakable Locks Can Hide Invisible Doors
Cryptographers have shown how perfect security can undermine machine learning models.
An Applied Mathematician With an Unexpected Toolbox
Lek-Heng Lim uses tools from algebra, geometry and topology to answer questions in machine learning.
To Teach Computers Math, Researchers Merge AI Approaches
Large language models still struggle with basic reasoning tasks. Two new papers that apply machine learning to math provide a blueprint for how that could change.
Researchers Discover a More Flexible Approach to Machine Learning
“Liquid” neural nets, based on a worm’s nervous system, can transform their underlying algorithms on the fly, giving them unprecedented speed and adaptability.
Machines Learn Better if We Teach Them the Basics
A wave of research improves reinforcement learning algorithms by pre-training them as if they were human.