A monthly overview of things you need to know as an architect or aspiring architect. Unlock the full InfoQ experience by logging in! Stay updated with your favorite authors and topics, engage with ...

Any sufficiently advanced technology is indistinguishable from magic. In recent years with machine learning taking over new industries and applications, where the number of users far outnumber experts ...

This talk will attempt to demystify, for a non-technical audience, the current state of neural network explainability and interpretability, as well as trace the boundaries of what is in principle ...

One of the major challenges facing businesses using AI is understanding exactly how these models make decisions. Traditionally, AI has been treated like a black box: Inputs go in, outputs come out, ...

Deep neural networks display impressive performance but suffer from limited interpretability. Biology-inspired deep learning, where the architecture of the computational graph is based on biological ...

The field of interpretability investigates what machine learning (ML) models are learning from training datasets, the causes and effects of changes within a model, and the justifications behind its ...

Neural networks are famously incomprehensible — a computer can come up with a good answer, but not be able to explain what led to the conclusion. Been Kim is developing a “translator for humans” so ...

Unlock the full InfoQ experience by logging in! Stay updated with your favorite authors and topics, engage with content, and download exclusive resources. Dany Lepage discusses the architectural ...

An effective fault detection strategy has always been the focus of smart grid system research. Fast and accurate fault detection is the basis for complex systems to maintain reliability and security.

Explore how AI phenotypic screening transforms image-based drug discovery through advanced phenotypic data analysis and ...