arXiv:2510. 02660v2 Announce Type: replace-cross Abstract: When researchers claim AI systems possess ToM or mental models, they are fundamentally discussing behavioral predictions and bias corrections rather than genuine mental states.
By Xiaoyun Yin, Elmira Zahmat Doost, Shiwen Zhou, Garima Arya Yadav, Jamie C. Gorman
arXiv:2608. 14407v1 Announce Type: new Abstract: We present a survey of the past and future of AI Scientists: machines capable of automating science.
By Ross D. King
The article discusses how artificial intelligence is beginning to automate scientific discovery, specifically in the realm of cognitive science. It outlines four key challenges for developing an automated science of the mind: representing experiments, generating synthetic behavior, synthesizing models, and closing the loop to discover psychological theories. The authors argue that addressing these challenges will enable AI to systematically advance our understanding of the mind.
By Akshay K. Jagadish, Milena Rmus, Kristin Witte, Marvin Mathony, Marcel Binz, Eric Schulz
The article titled "The AI That Learned to Understand Long After It Stopped Trying" discusses a small, strange discovery in machine learning known as grokking. It highlights how this phenomenon involves an AI developing understanding after ceasing to actively try. The piece was originally published on Towards Data Science.
By Utkarsh Mangal
They aren’t designed, you can’t help perceiving one anyway, and that makes them an engineering problem almost no one is solving. The post Where Does an AI’s Personality Actually Come From?
By Slava Polonski, PhD
A new method for surgically removing training examples from a model reveals that as datasets grow, the link between what a model learns and what it produces dissolves.
By Rachel Gordon | MIT CSAIL