Browsing by Subject "classical computationalism"

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    Epistemic opacity: a feature not a bug: an exploration into the relationship between brains and ANNs
    (2025) Schoeman, Keldt; Nefdt, Ryan
    AI in the 21st century has come to be dominated by one school in particular, connectionism. And its successes are all around us – in the media we consume, in the music we listen to, in the cold calls we receive, etc. While this school was founded by psychologists, logicians, and philosophers with the goal of replicating human-level intelligence, the field has undergone a drastic transformation in recent years, entering a paradigm which is now dominated by engineering goals. Within this new paradigm, connectionism is no longer characterized as a field modelling the brain, but rather a mere engineering tool with incredible powers of pattern recognition. However, while the move to employ connectionist AI as a tool has led to remarkable successes in a variety of fields, it has also come with issues such as the black box problem, or epistemic opacity. Within a strictly engineering paradigm, attempts to explain the internal reasoning of these networks remain unsatisfying. Therefore, I propose recoupling connectionist networks with their roots in brain modelling, which would in turn open rich, new explanations for problems like epistemic opacity. Simply put, when we place the problem of opacity within the context of brain modelling, it appears that it may not be a problem at all, but an emergent feature of a complex system. In other words, we are beginning to have difficulty understanding modern connectionist networks in much the same manner we struggle to understand brains. Hence, it might well be feature, not a bug, that these systems should disappear into the mists of complexity.