Elements and Activities
As a two-day event (see detailed timeline in the program tab), our bridge offers a varied set of activities that support education and discussion at various levels of expertise. Educational activities involve two traditional tutorials, as an entry to the respective fields of continual learning and causality, for both newcomers and experts that are only familiar with either of them. Similarly, two software labs will provide respective hands-on experience on the practical level, derived off the popular emerging software tools Avalanche and DoWhy.
Following these educational activities, a set of vision talks on each day will provide positions on how the fields of continual learning and causality can be brought further together, why a necessity for the latter exists, and what respective challenges may need to be faced in the imminent future. The first of these sets intends to leverage the expertise of researchers with an increased level of seniority, who will exploit their broad expertise towards first attempts at a more integrated vision. The second set will include contributed talks, based on the bridge’s call for contributions of two-page position papers by the community, which will be accepted on the grounds of a peer-review process. Apart from a general poster session, a select set of papers with exceptional level of clarity will be invited to share their views.
Finally, the invited vision talks and contributed position works will serve as a basis for interactive breakout sessions towards the end of the bridge. Participants of the bridge will get the chance to network, discuss presented ideas, and delve into a deeper exchange in more detailed conversation. The various outcomes of this session will ultimately be collected and disseminated beyond the bridge attendees.
Our Continual Causality Bridge aims at bringing together researchers, students, and practitioners interested in causality and continual learning, providing a unique opportunity to discuss ideas, challenges, resources, and opportunities in bridging these two fields. Our initial ideas of research items are intended to facilitate discussion among participants about how continual learning and causality may link together. Ideas are intentionally not yet exhaustive and are open for interpretation by the community. The various proposed interactive bridge elements and discussion activities are planned as a catalyst to emerge with a set of even more concise ideas to lay out the future of this exciting field as a direct take-away for the audience. The primary goal is thus to target a broad audience and build a long-lasting inclusive community.
Why Is It the Right Time?
Causality and continual learning are rising sub-fields within AI and are essential and unavoidable components for moving towards the goal of building a fully autonomous or `human-like’ system (Lake 2017). The latter should be capable of learning continuously in the presence of different types of data and perform cause-and-effect reasoning about its surrounding world. As these approaches complement each other, we believe that the time is right to bridge these otherwise separately studied areas.