Who Invented Deep Residual Learning?

New release of eScience Center's explainable AI tool DIANNA   - eScience Center

DIANNA is an explainable AI (XAI) tool that helps researchers get insights into the predictions of Deep Neural Networks. New version released!

Bayes in the age of intelligent machines

The success of methods based on artificial neural networks in creating intelligent machines seems like it might pose a challenge to explanations of human cognition in terms of Bayesian inference. We argue that this is not the case, and that in fact these systems offer new opportunities for Bayesian modeling. Specifically, we argue that Bayesian models of cognition and artificial neural networks lie at different levels of analysis and are complementary modeling approaches, together offering a way to understand human cognition that spans these levels. We also argue that the same perspective can be applied to intelligent machines, where a Bayesian approach may be uniquely valuable in understanding the behavior of large, opaque artificial neural networks that are trained on proprietary data.

Ein Philosoph und ein Neurologe wetteten um die Natur des Bewusstseins

Vor 25 Jahren schlossen Christof Koch und David Chalmers eine Wette ab, ob sich das Bewusstsein wissenschaftlich erklären lässt. Nun wurde der Sieger gekürt

An architecture that combines deep neural networks and vector-symbolic models

Researchers at IBM Research Zürich and ETH Zürich have recently created a new architecture that combines two of the most renowned artificial intelligence approaches, namely deep neural networks and vector-symbolic models. Their architecture, presented in Nature Machine Intelligence, could overcome the limitations of both these approaches, solving progressive matrices and other reasoning tasks more effectively.