1 code implementation • 25 Jul 2024 • Eunice Yiu, Maan Qraitem, Charlie Wong, Anisa Noor Majhi, Yutong Bai, Shiry Ginosar, Alison Gopnik, Kate Saenko
This paper investigates visual analogical reasoning in large multimodal models (LMMs) compared to human adults and children.
no code implementations • 21 May 2024 • Eliza Kosoy, Soojin Jeong, Anoop Sinha, Alison Gopnik, Tanya Kraljic
Those haven't been studied before and it is also the case that the children's models are dynamic as they use the tools, even with just very short usage.
no code implementations • 18 May 2023 • Eliza Kosoy, Emily Rose Reagan, Leslie Lai, Alison Gopnik, Danielle Krettek Cobb
We propose that using classical experiments from child development is a particularly effective way to probe the computational abilities of AI models, in general, and LLMs in particular.
no code implementations • 8 May 2023 • Eunice Yiu, Eliza Kosoy, Alison Gopnik
Much discussion about large language models and language-and-vision models has focused on whether these models are intelligent agents.
1 code implementation • 16 Jun 2022 • Eliza Kosoy, David M. Chan, Adrian Liu, Jasmine Collins, Bryanna Kaufmann, Sandy Han Huang, Jessica B. Hamrick, John Canny, Nan Rosemary Ke, Alison Gopnik
Recent work in machine learning and cognitive science has suggested that understanding causal information is essential to the development of intelligence.
no code implementations • 21 Feb 2022 • Eliza Kosoy, Adrian Liu, Jasmine Collins, David M Chan, Jessica B Hamrick, Nan Rosemary Ke, Sandy H Huang, Bryanna Kaufmann, John Canny, Alison Gopnik
Despite recent progress in reinforcement learning (RL), RL algorithms for exploration still remain an active area of research.
1 code implementation • 6 May 2020 • Eliza Kosoy, Jasmine Collins, David M. Chan, Sandy Huang, Deepak Pathak, Pulkit Agrawal, John Canny, Alison Gopnik, Jessica B. Hamrick
Research in developmental psychology consistently shows that children explore the world thoroughly and efficiently and that this exploration allows them to learn.
no code implementations • WS 2018 • Kaylee Burns, Aida Nematzadeh, Erin Grant, Alison Gopnik, Tom Griffiths
The decision making processes of deep networks are difficult to understand and while their accuracy often improves with increased architectural complexity, so too does their opacity.
3 code implementations • EMNLP 2018 • Aida Nematzadeh, Kaylee Burns, Erin Grant, Alison Gopnik, Thomas L. Griffiths
We propose a new dataset for evaluating question answering models with respect to their capacity to reason about beliefs.