Abstract:  The scientific method has been transformative to humankind. However, there are signs that despite great investment in the area, scientific discovery is approaching a state of stagnation. In the context of scientific discovery, a fundamental problem is to explain natural phenomena in a manner consistent with both (noisy) experimental data, and a body of (possibly inexact and incomplete) background knowledge about the laws of the universe. Historically, models were manually derived in a first-principles deductive fashion. The first-principles approach often offers the derivation of interpretable symbolic models of remarkable levels of universality while being substantiated by little data. Nonetheless, derivation of such models is time-consuming and relies heavily upon domain expertise. Conversely, with the rising pervasiveness of statistical AI and data-driven approaches, automated, rapid construction and deployment of models has become a reality. Many data-driven modeling techniques demonstrate remarkable scalability due to their reliance upon predetermined, exploitable model form (functional form) structures. Such structures, entail non-interpretable models, demand Big Data for training, and provide limited predictive power for out-of-set instances. In this lecture, we will delve into the two distinct discovery frameworks, examine recent efforts to bridge their gap, and consider the possibility of achieving a unified understanding of fundamental natural laws at scale.

Talk time in other timezones:
AEST 12:00 AM Fri 12 Apr,
JST  11:00 PM Thu 11 Apr,
CEST  4:00 PM Thu 11 Apr,
BST   3:00 PM Thu 11 Apr,
UTC  14:00    Thu 11 Apr,
EDT  10:00 AM Thu 11 Apr,
CDT   9:00 AM Thu 11 Apr,
MDT   8:00 AM Thu 11 Apr,
PDT   7:00 AM Thu 11 Apr