Workshop: Imagination-Based AI: Envisioning the Future Beyond Deep Learning
In this workshop, we explore an emerging frontier of AI and machine learning research that goes beyond correlational data mining approaches, such as deep learning. In a large variety of challenging business and societal problems, from combating traffic congestion in urban metropolises, and delivering better quality healthcare and education, to improving the success rate of digital marketing and online campaigns, and modeling interventions that combat climate change, what is fundamentally necessary is integrating three types of reasoning by combining correlational, causal, and imagination-based models. Participants in this workshop will be introduced to a powerful class of modeling languages that extend deep learning through a three layer architecture that combines correlational (layer one), causal and interventional decisions (layer two), as well as counterfactual and imagination-based reasoning (layer three).
Most of deep learning is currently restricted to producing a statistical summarization of data, that is, they answer “What is” queries. For example, generative adversarial networks (GANs) can be trained to produce new samples from a given fixed distribution (e.g, new face images given a dataset of face images). We introduce novel and powerful new methods that can also answer “What-if” queries, as well as “Why” queries. Integrating the three fundamental modes of reasoning, from “What is” to “What if” and “Why”, will enable the next generation of AI systems to be far more powerful than current deep learning architectures, like GANs. The workshop presentation will include a detailed overview of the underlying modeling languages, as well as hands-on demonstrations of how to use these new emerging tools in a diverse range of practical real-world problems.
Sridhar Mahadevan is currently Director of Data Science at Adobe Research in San Jose, and holds faculty appointments at Stanford Univeristy (visiting professor in the School of Engineering) and a Full Professor at the College of Information and Computer Sciences at the University of Massachusetts, Amherst. He is a Fellow of the Association for the Advancement of Artificial Intelligence (AAAI), in recognition of his distinguished contributions to machine learning. He has published over 150 peer-reviewed articles in AI and machine learning over a 30 year career. His most recent paper on Imagination Machines received the Blue Sky Best Paper Award at the AAAI Conference in New Orleans in February 2018. He has lectured widely on AI and machine learning in over three dozen countries and many leading industrial research laboratories, including Google Brain and Deep Mind, IBM Watson Research (Almaden Labs and Yorktown Heights), and Microsoft Research (Cambridge, Boston, and New York).