Deep neural networks are currently the most widespread and successful technology in
artificial intelligence. However, these systems exhibit bewildering new vulnerabilities: most …

Many of the computing systems programmed using Machine Learning are opaque: it is
difficult to know why they do what they do or how they work. Explainable Artificial …

Does the human mind resemble the machines that can behave like it? Biologically inspired
machine-learning systems approach “human-level” accuracy in an astounding variety of …

Simple idealized models seem to provide more understanding than opaque, complex, and
hyper-realistic models. However, an increasing number of scientists are going in the …

Artificial intelligence (AI) has historically been conceptualized in anthropomorphic terms.
Some algorithms deploy biomimetic designs in a deliberate attempt to effect a sort of digital …

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

Situated approaches to cognition maintain that cognition is embodied, embedded, enactive,
and affective (and extended, but that is not relevant here). Situated approaches are often …

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 …

The term robustness is ubiquitous in modern Machine Learning (ML). However, its meaning
varies depending on context and community. Researchers either focus on narrow technical …

The present perspective discusses methods to detect concepts in internal representations
(hidden layers) of deep neural networks (DNNs), such as network dissection, feature …