Abstract
Philosophers 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.
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