Artificial Intelligence is one of the signature issues of our time, reshaping both business and societal landscapes. In financial services, both traditional players and fintech companies commonly use AI and machine learning to prospect customers, underwrite risks, flag cases for suspected fraud, and identify new trading strategies.
Though ubiquitous, modern AI is also commonly misunderstood. Today’s AI is less about creating human-like general intelligence than it is about creating tools that do cognitive “spade work” and more generally enhance or extend human intelligence. AI tools based on statistical learning, big data, and pattern recognition can perform a growing number of tasks that are difficult or impossible for humans. At the same time, they are poor at many aspects of cognition that come naturally to humans, such as formulating hypotheses, understanding cause and effect relationships, using commonsense reasoning, picking up on social cues and nonverbal forms of communication, and expressing empathy.
The complementary nature of human and algorithmic intelligence points to the need for an interdisciplinary approach that draws on such fields as computer science, human psychology, behavioral economics, and design thinking to design collaboration systems that better enable forms of human-computer collective intelligence. James Guszcza shares the principles of human-computer collaboration, organizes them into a framework, and offers several real-life examples in which human-computer cognitive collaboration has been crucial to the economic success of a project.
5:00 p.m. Tea, Vincent Hall 120
5:30 - 6:30 p.m. Lecture, Vincent Hall 16