Mental representations remain the central posits of psychology after many decades of
scrutiny. However, there is no consensus about the representational format (s) of biological …

Scientists depend on complex computational systems that are often ineliminably opaque, to
the detriment of our ability to give scientific explanations and detect artifacts. Some …

Could a machine think? Could the mind itself be a thinking machine? The computer
revolution transformed discussion of these questions, offering our best prospects yet for …

We argue that artificial networks are explainable and offer a novel theory of interpretability.
Two sets of conceptual questions are prominent in theoretical engagements with artificial …

Deep neural networks (DNNs) have become increasingly successful in applications from
biology to cosmology to social science. Trained DNNs, moreover, correspond to models that …

The remarkable performance of large language models (LLMs) on complex linguistic tasks
has sparked a lively debate on the nature of their capabilities. Unlike humans, these models …

This chapter critically examines the potential contributions of modern language models to
theoretical linguistics. Despite their focus on engineering goals, these models' ability to …

In computer science, there are efforts to make machine learning more interpretable or
explainable, and thus to better understand the underlying models, algorithms, and their …

Deep learning has enabled major advances across most areas of artificial intelligence
research. This remarkable progress extends beyond mere engineering achievements and …

Can humans and artificial intelligences share concepts and communicate? One aim of
Making AI Intelligible is to show that philosophical work on the metaphysics of meaning can …