Deep learning is currently the most prominent and widely successful method in artificial
intelligence. Despite having played an active role in earlier artificial intelligence and neural …

In artificial intelligence, recent research has demonstrated the remarkable potential of Deep
Convolutional Neural Networks (DCNNs), which seem to exceed state-of-the-art …

Much of the controversy evoked by the use of deep neural networks as models of biological
neural systems amount to debates over what constitutes scientific progress in neuroscience …

Neuroscientists apply a range of analysis tools to recorded neural activity in order to glean
insights into how neural circuits drive behavior in organisms. Despite the fact that these tools …

" This book provides a framework for thinking about foundational philosophical questions
surrounding machine learning as an approach to artificial intelligence. Specifically, it links …

Developments in the philosophy of mind over the last 20 years have dramatically changed
the nature of the subject. In this major new introduction, Tim Bayne presents an outstanding …

There is a vast literature within philosophy of mind that focuses on artificial intelligence but
hardly mentions methodological questions. There is also a growing body of work in …

Few of the many theories devised to account for consciousness are explicit about the role
they ascribe to evolution, and a significant fraction, by their silence on the subject, treat …

Given the extent of using deep convolutional neural networks to model the mechanism of
object recognition, it becomes important to analyse the evidence of their similarity and the …

Abstract Machine learning is used more and more in scientific contexts, from the recent
breakthroughs with AlphaFold2 in protein fold prediction to the use of ML in parametrization …