MIT Associate Professor Phillip Isola studies the ways in which intelligent machines “think” and perceive the world, in an effort to ensure AI is safely and effectively integrated into human society.

Goldman Sachs Group Inc. is co-leading financing for a Texas project to build private power campuses for artificial intelligence.

MagicBot, China’s first humanoid robot achieving multi-robot collaboration, enabling flexible production and efficiently handling complex industrial scenarios.

These days, large language models can handle increasingly complex tasks, writing complex code and engaging in sophisticated reasoning. But when it comes to four-digit multiplication, a task taught in elementary school, even state-of-the-art systems fail. Why?

A new paper posted to the arXiv preprint server by University of Chicago computer science Ph.D. student Xiaoyan Bai and faculty co-director of the Data Science Institute’s Novel Intelligence Research Initiative Chenhao Tan finds answers by reverse-engineering failure and success.

They worked with collaborators from MIT, Harvard University, University of Waterloo and Google DeepMind to probe AI’s “jagged frontier”—a term for its capacity to excel at complex reasoning yet stumble on seemingly simple tasks.

A biologically grounded computational model built to mimic real neural circuits, not trained on animal data, learned a visual categorization task just as actual lab animals do, matching their accuracy, variability, and underlying neural rhythms.