Brenden M. Lake

Our lab aims to understand the ingredients of intelligence. We use advances in machine intelligence to better understand human intelligence, and use insights from human intelligence to develop more fruitful kinds of machine intelligence. We focus on human cognitive abilities that elude the best AI systems. Our recent projects focus on few-shot learning of new concepts, learning by generating new goals, learning by asking questions, and learning by producing novel combinations of known components. Our technical efforts focus on modern neural network modeling, including meta-learning, fine-tuning LLMs, neuro-symbolic modeling, and learning from child headcam videos — seeing the world through their “eyes and ears”. By exploring what makes human intelligence unique, we aim to advance both psychology and computer science.

[2023] Lake, B. M. and Baroni, M. (2023). Human-like systematic generalization through a meta-learning neural network. Nature, 623, 115-121. [2023] Wang, W., Vong, W. K., Kim, N., and Lake, B. M. (2023). Finding Structure in One Child’s Linguistic Experience. Cognitive Science, 47, e13305. [2023] Lake, B. M. and Murphy, G. L. (2023). Word meaning in minds and machines. Psychological Review, 130, 401-431. [2023] Stojnic, G., Gandhi, K., Yasuda, S., Lake, B. M., and Dillon, M. R. (2023). Commonsense Psychology in Human Infants and Machines. Cognition, 235, 105406.