Francesca Rossi (IBM) - Thinking fast and slow in AI (14/01/2021)

Francesca Rossi
IBM fellow and AI Ethics Global Leader
Thinking fast and slow in AI
AI systems have seen dramatic advancement in recent years, bringing many
successful applications that are pervading our everyday life. However, we are
still mostly seeing instances of narrow AI. Also, they are tightly linked to the
availability of huge datasets and computational power. State-of-the-art AI still
lacks many capabilities that would naturally be included in a notion of
intelligence, for example, if we compare these AI technologies to what human
beings are able to do: generalizability, robustness, explainability, causal
analysis, abstraction, common sense reasoning, ethics reasoning, as well as a
complex and seamless integration of learning and reasoning supported by both
implicit and explicit knowledge. We argue that a better comprehension
regarding of how humans have, and have evolved to obtain, these advanced
capabilities can inspire innovative ways to imbue AI systems with these
competencies. To this end, we propose to study and exploit cognitive theories of
human reasoning and decision making (with special focus on Kahneman’s
theory of thinking fast and slow) as a source of inspiration for the causal source
of these capabilities, that help us raise the fundamental research questions to be
considered when trying to provide AI with desired dimensions of human
intelligence that are currently lacking.