Rethinking intelligent behaviour through the lens of accurate prediction: Adaptive control in uncertain environments

While recent cognitive science research shows a renewed interest in understanding intelligence, there is still little consensus on what constitutes intelligent behaviour and how it should be assessed. Here we propose a refined approach to biological intelligence as accurate prediction, according to which intelligent behaviour should be understood as adaptive control driven by the minimisation of uncertainty in dynamic environments with limited information. Central to this view is the concept of accuracy, which we argue is key to determining the success of predictions. We identify tensions in applying this framework to contemporary artificial systems such as large-language models, which, despite their impressive capacities for abstract prediction, show deficits in terms of context-sensitive knowledge transfer.