Understand
Despite recent breakthroughs in the field of artificial intelligence (AI) - or more specifically machine learning (ML) algorithms for object recognition and natural language processing - it seems to be the majority view that current AI approaches are still no real match for natural intelligence (NI).
- More importantly, philosophers have collected a long catalogue of features which imply that NI works differently from current AI not only in a gradual sense, but in a more substantial way: NI is closely related to consciousness, intentionality and experiential features like qualia (the subjective contents of mental states) and allows for understanding (e.g., taking insight into causal relationships instead of 'blindly' relying on correlations), as well as aesthetical and ethical judgement beyond what we can put into (explicit or data-induced implicit) rules to program machines with.
- Additionally, Psychologists find NI to range from unconscious psychological processes to focused information processing, and from embodied and implicit cognition to 'true' agency and creativity.
- NI thus seems to transcend any neurobiological functionalism by operating on 'bits of meaning' instead of information in the sense of data, quite unlike both the 'good old fashioned', symbolic AI of the past, as well as the current wave of deep neural network based, 'sub-symbolic' AI, which both share the idea of thinking as (only) information processing.