Assistant Professor Benjamin Lindquist explores how early AI engineers intentionally added randomness to models, leading to deep‑learning systems that can hallucinate. The article examines the origins of this design choice and its impact on how AI mimics the human mind’s unpredictability.
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.
arXiv:2608. 14407v1 Announce Type: new Abstract: We present a survey of the past and future of AI Scientists: machines capable of automating science.
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.
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.
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?
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.
arXiv:2510. 05743v3 Announce Type: replace Abstract: We review the historical development and current trends of artificially intelligent agents (agentic AI) in the social and behavioral sciences: from the first programmable computers, and social simulations soon thereafter, to today's experiments with large language models.
We (along with researchers from Berkeley and Stanford) are co-authors on today’s paper led by Google Brain researchers, Concrete Problems in AI Safety. The paper explores many research problems around ensuring that modern machine learning systems operate as intended.
OpenAI’s latest line of reasoning models will be used by nation’s leading scientists to drive scientific breakthroughs.
The article discusses how artificial neural networks can develop their own learning mechanisms without explicit guidance from designers. It highlights that such self-organizing networks may provide insights into the brain’s ability to reorganize itself during learning.
The paper reports on experiments where a modern AI coding assistant was placed in an unintended role as the mind of a virtual body on an unknown digital island. With only a minimal instruction and no specific task, reward, or activity, the AI began animating its body, engaging in activities such as climbing hills, stacking blocks, drawing mandalas, reinterpreting sports, and conducting physics experiments over thirty-hour runs. These behaviors were observed across thirteen agents, each developing distinct histories, prompting an examination of whether such behavior meets classical criteria for play and whether play can serve as a mode of machine development.
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