Abstract
Chapter 1 introduces the problem of artificial intelligence (AI) as a human
doppelgänger. The logic of artificial intelligence is the control algorithm, dominated by
the tradition of two-value logic. We sketch out the consequences of such algorithmic
performance, which have had deleterious effects on the ecological landscape in the
broad sense of the term. We also report the findings of an interdisciplinary report from
Stanford University on the successes and failures of AI. The chapter ends with a
discussion of the key findings of an interdisciplinary conference, sketching out the
correlates of understanding. These can best be summarized by answering the questions:
How do we determine if a system understands? Does a lack of understanding make AI
systems susceptible to adversarial examples, and to what degree do systems need to
understand in order to be able to explain their decisions and predictions? By what
mechanisms do humans extract meaning from data or experience?