91 citations · 127 across the 24 of their papers we have counts for
13 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…
Sycophantic Chatbots Cause Delusional Spiraling, Even in Ideal Bayesians
Kartik Chandra, Max Kleiman-Weiner, Jonathan Ragan-Kelley +1
"AI psychosis" or "delusional spiraling" is an emerging phenomenon where AI chatbot users find themselves dangerously confident in outlandish beliefs after extended chatbot convers…
AI Gamestore: Scalable, Open-Ended Evaluation of Machine General Intelligence with Human Games
Lance Ying, Ryan Truong, Prafull Sharma +9
Rigorously evaluating machine intelligence against the broad spectrum of human general intelligence has become increasingly important and challenging in this era of rapid technolog…
Assessing Adaptive World Models in Machines with Novel Games
Lance Ying, Katherine M. Collins, Prafull Sharma +11
Human intelligence exhibits a remarkable capacity for rapid adaptation and effective problem-solving in novel and unfamiliar contexts. We argue that this profound adaptability is f…
What's in the Box? Reasoning about Unseen Objects from Multimodal Cues
Lance Ying, Daniel Xu, Alicia Zhang +3
People regularly make inferences about objects in the world that they cannot see by flexibly integrating information from multiple sources: auditory and visual cues, language, and…
Resource Rational Contractualism Should Guide AI Alignment
Sydney Levine, Matija Franklin, Tan Zhi-Xuan +8
AI systems will soon have to navigate human environments and make decisions that affect people and other AI agents whose goals and values diverge. Contractualist alignment proposes…
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…
Hypothesis-Driven Theory-of-Mind Reasoning for Large Language Models
Hyunwoo Kim, Melanie Sclar, Tan Zhi-Xuan +5
Existing LLM reasoning methods have shown impressive capabilities across various tasks, such as solving math and coding problems. However, applying these methods to scenarios witho…
Infinite Ends from Finite Samples: Open-Ended Goal Inference as Top-Down Bayesian Filtering of Bottom-Up Proposals
Tan Zhi-Xuan, Gloria Kang, Vikash Mansinghka +1
The space of human goals is tremendously vast; and yet, from just a few moments of watching a scene or reading a story, we seem to spontaneously infer a range of plausible motivati…
Towards Guaranteed Safe AI: A Framework for Ensuring Robust and Reliable AI Systems
David "davidad" Dalrymple, Joar Skalse, Yoshua Bengio +14
Ensuring that AI systems reliably and robustly avoid harmful or dangerous behaviours is a crucial challenge, especially for AI systems with a high degree of autonomy and general in…
Pragmatic Instruction Following and Goal Assistance via Cooperative Language-Guided Inverse Planning
Tan Zhi-Xuan, Lance Ying, Vikash Mansinghka +1
People often give instructions whose meaning is ambiguous without further context, expecting that their actions or goals will disambiguate their intentions. How can we build assist…
Grounding Language about Belief in a Bayesian Theory-of-Mind
Lance Ying, Tan Zhi-Xuan, Lionel Wong +2
Despite the fact that beliefs are mental states that cannot be directly observed, humans talk about each others' beliefs on a regular basis, often using rich compositional language…
Modeling human intention inference in continuous 3D domains by inverse planning and body kinematics
Yingdong Qian, Marta Kryven, Tao Gao +2
How to build AI that understands human intentions, and uses this knowledge to collaborate with people? We describe a computational framework for evaluating models of goal inference…