Talk by Kenneth D. Forbus on Analogical Learning for AI Systems

Date: 10.01.2023
Time: 16:00 - 17:30
Location: Remote
Venue: CoAI
Target groups: Researchers, Early career researchers, Graduates
Publicity: public event

Title: “Qualitative Representations and Analogical Learning for Human-like AI Systems”

Abstract: While there has been substantial progress in AI, we are still far away from systems that can learn incrementally from small amounts of data while producing results that are understandable by human partners. Our hypothesis is that qualitative representations and analogical learning are central in human cognition, and that these ideas provide the basis for new technologies that will help us create more human-like AI systems. We illustrate using examples from vision, language, and reasoning. These advances should support building software social organisms, that interact with people as collaborators rather than tools, which ultimately could revolutionize how AI systems are built and used.

This talk is part of the thematic lecture series, “Co-Constructing Intelligence,” which is jointly supported by the universities of Bremen, Paderborn and Bielefeld. The series focuses on the human-machine interface, and on questions about how humans and machines can learn together and work together to acquire new knowledge and skills. Our presenter, Kenneth D. Forbus (Northwestern University, Chicago) is an expert in artificial intelligence and education, and he works on human-inspired learning processes in AI and using AI to develop new learning experiences and activities for humans.

Lecture recording

Homepage of Kenneth D. Forbus