AbstractPhilosophers as well as scientists in
psychology, neuroscience, animal cognition research etc. have not found
satisfying answers to what intelligence is with their crude, overly systematic
and reductive approaches. At the same time, both recent findings about the
capabilities of smart animals such as corvids (Nieder et al., 2020) or octopi
(Godfrey-Smith, 2018) and novel types of Artificial Intelligence (AI), from
social robots to cognitive assistants, are provoking the demand for new answers
for meaningful comparison with other kinds of intelligence. In this paper, we
devote ourselves to addressing this need by proposing an open malleable
and loose framework for making sense of intelligence in humans, other animals
and AI, which is ultimately based on causal learning as the central theme of
intelligence. The goal is not just to describe, but mainly to explain queries
like why one kind of intelligence is more intelligent than another, whatsoever
the intelligence.
Keywords: Nietzsche;
pluralism; intelligence; causal learning; AI; radex model
Contents 1. Introduction 2. Prior Work: The Ladder of Causation 3. A Necessary Condition for Being Intelligent 4. The Refined Universal Taxonomy of Causal Learning 5. The Inverted Radex Model of Intelligence 6. Implications for Philosophical Reflections on AI 7. Conclusion |
