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Historically speaking, AI-researchers have made two assumptions. The first is that intelligent processes can be described by algorithms ('effective procedures'). The second (based on the Church-Turing Thesis) is that algorithms can be implemented on some general-purpose computer. Both 'classical AI' and 'Connectionism' share these assumptions though they are otherwise different and not philosophically unchallenged. Moreover, classical and connectionist AI share the idea of a commitment to internal representation as integral to knowledge. They posit identifiable structures 'in the head' that are distinguishable from the system’s processing and that stand for things in the world. This presupposition is not shared by 'evolutionary AI' (including 'situated robotics' and 'artificial life') which represents an anti-representationalist approach thus (partly) implementing early philosophical criticisms of classical AI and its inherent Cartesianism. The course investigates philosophical problems central to the AI-paradigm including questions such as: can classical or connectionist AI explain thinking or conceptualization and is evolutionary AI better of? Is computationalism sufficient? Can meaning be explained by AI? Can computers have beliefs or desires and could AI explain consciousness? Might intelligence be better explained by less intellectualistic approaches based on the model of skills and know-how rather than explicit representation?