134 citations · 242 across the 39 of their papers we have counts for
10 papers · 1 filter
CogGym: Towards Large-Scale Comparative Evaluation of Human and Machine Cognition
Lance Ying, Jinzhou Wu, Yingshan Susan Wang +53
Understanding and modeling human intelligence are parallel goals shared by artificial intelligence (AI) and cognitive science. As AI systems grow increasingly capable, in what ways…
Human agency in initial human-AI proof formalization workflows
Katherine M. Collins, Simon Frieder, Jonas Bayer +14
For centuries, human mathematicians have written proofs to substantiate their mathematical arguments; yet, the ability to automatically verify the validity of proofs has long been…
Medical Model Synthesis Architectures: A Case Study
Katherine M. Collins, Marlene Berke, Ilia Sucholutsky +6
Medicine is rife with high-stakes uncertainty. Doctors routinely make clinical judgments and decisions that juggle many fundamental unknowns, like predictions about what might be c…
Do Large Language Models Mentalize When They Teach?
Sevan K. Harootonian, Mark K. Ho, Thomas L. Griffiths +2
How do LLMs decide what to teach next: by reasoning about a learner's knowledge, or by using simpler rules of thumb? We test this in a controlled task previously used to study huma…
Identifying, Evaluating, and Mitigating Risks of AI Thought Partnerships
Kerem Oktar, Katherine M. Collins, Jose Hernandez-Orallo +4
Artificial Intelligence (AI) systems have historically been used as tools that execute narrowly defined tasks. Yet recent advances in AI have unlocked possibilities for a new class…
On Benchmarking Human-Like Intelligence in Machines
Lance Ying, Katherine M. Collins, Lionel Wong +6
Recent advances in Artificial Intelligence (AI) have yielded powerful computational models that, by learning from vast amounts of human-generated data, are increasingly posited as…
Revisiting Rogers' Paradox in the Context of Human-AI Interaction
Katherine M. Collins, Umang Bhatt, Ilia Sucholutsky
Humans learn about the world, and how to act in the world, in many ways: from individually conducting experiments to observing and reproducing others' behavior. Different learning…
Multilevel Interpretability Of Artificial Neural Networks: Leveraging Framework And Methods From Neuroscience
Zhonghao He, Jascha Achterberg, Katie Collins +13
As deep learning systems are scaled up to many billions of parameters, relating their internal structure to external behaviors becomes very challenging. Although daunting, this pro…
Learning Human-like Representations to Enable Learning Human Values
Andrea Wynn, Ilia Sucholutsky, Thomas L. Griffiths
How can we build AI systems that can learn any set of individual human values both quickly and safely, avoiding causing harm or violating societal standards for acceptable behavior…
Concept Alignment as a Prerequisite for Value Alignment
Sunayana Rane, Mark Ho, Ilia Sucholutsky +1
Value alignment is essential for building AI systems that can safely and reliably interact with people. However, what a person values -- and is even capable of valuing -- depends o…