Kids Run the Darndest Experiments: Causal Learning in Children with Alison Gopnik

800 800 The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)

About This Episode

Today we close out the 2021 NeurIPS series joined by Alison Gopnik, a professor at UC Berkeley and an invited speaker at the Causal Inference & Machine Learning: Why now? Workshop. In our conversation with Alison, we explore the question, “how is it that we can know so much about the world around us from so little information?” and how her background in psychology, philosophy, and epistemology has guided her along the path to finding this answer through the actions of children. We discuss the role of causality as a means to extract representations of the world and how the “theory theory” came about, and how it was demonstrated to have merit. We also explore the complexity of causal relationships that children are able to deal with and what that can tell us about our current ML models, how the training and inference stages of the ML lifecycle are akin to childhood and adulthood, and much more!

Watch on Youtube

Thanks to our Sponsor!

Today’s show is brought to you by our good friends at SigOpt. Building effective models is a scientific process that requires experimentation to get right. With SigOpt, modelers design novel experiments, explore modeling problems and optimize models to meet multiple objective metrics in their iterative workflow. Whether tracking your training runs or running at scale hyperparameter optimization jobs, SigOpt is designed to meet your needs. Learn why teams from PayPal, Two Sigma, OpenAI, Numenta, Accenture and many more rely on SigOpt by signing up to use SigOpt for free forever at sigopt.com/signup.

Connect with Alison!

Resources

Join Forces!

Leave a Reply

Your email address will not be published.