Bahadur Memorial Lectures: John Lafferty (Day 1)
11:30 am–12:30 pm Jones 303
Title: Abstraction in Artificial and Natural Intelligence: Part I: Relational and Sequential Reasoning
Abstract: Two broad types of natural intelligence are used by humans (and other animals). One type is used to acquire semantic and procedural knowledge about the world. Another type is used to identify novel associations and relations. This second type of intelligence often requires very little data, but significant time to “think” and search for solutions; recent AI models mimic this type of intelligence using “chain of thought.” We present a framework for modeling relational learning and abstraction, using an inductive bias called the relational bottleneck. To assess the flexibility of the relational bottleneck, a universal approximation theory is developed. To analyze the advantages of sequential reasoning, an extension of statistical learning theory for autoregressive models is proposed. This offers insight into how chain of thought sequential supervision can improve learning efficiency.
Bahadur Memorial Lectures: John Lafferty (Day 2)
3:30–4:30 pm Jones 303
Title: Abstraction in Artificial and Natural Intelligence: Part II: Models, Mechanisms, and Experiments
Abstract: Reasoning in terms of relations, analogies, and abstraction is a hallmark of human intelligence. How can abstract symbols emerge from distributed, neural representations? One general approach uses an inductive bias for learning called the “relational bottleneck” that is motivated from principles of cognitive neuroscience. We present a framework that builds this inductive bias into machine learning models that transform distributed symbols to implement a form of abstraction. Computational experiments are presented on a broad range of problems. Biologically plausible mechanisms for these models are proposed to shed light on how abstraction may be implemented in the human brain.