Artificial Intelligence is often characterized in terms of “solving intelligence” or building machines that outperform humans at most economically meaningful work. But for scientific, economic, and societal reasons, this paradigm is likely to give way to a more design-focused paradigm of creating human-machine hybrid intelligence systems. This talk will sketch the Hybrid Intelligence paradigm, and discuss various ways in which the needed field of Hybrid Intelligence Systems Design must adopt traits characteristic of the actuarial profession: a respect for data limitations, an acknowledgment of the need to integrate algorithmic design and algorithmic indications with local knowledge and informed judgment; and an ethos of ethical behavior and public service. The discussion will be motivated by examples from insurance and other domains.
Learning Objectives:
Articulate an alternate paradigm to "AI" for technologies and systems that harness big data and machine learning.
Think more systematially about how to integrate predictive algorithm and machine learning insights with local knowledge and expert judgment.
Think critically about claims of the imminence and existential risks posted by "Artificial General Intelligence",
Articulate an alternate paradigm to "AI" for technologies and systems that harness big data and machine learning.
