In the history of the quest for human-level artificial intelligence, a number of rival paradigms
have vied for supremacy. Symbolic artificial intelligence was dominant for much of the 20th …

Contemporary neural networks still fall short of human-level generalization, which extends
far beyond our direct experiences. In this paper, we argue that the underlying cause for this …

Neuro-symbolic artificial intelligence refers to a field of research and applications that
combines machine learning methods based on artificial neural networks, such as deep …

The term “explainable AI” refers to the goal of producing artificially intelligent agents that are
capable of providing explanations for their decisions. Some models (eg, rule-based …

Neuro-symbolic and statistical relational artificial intelligence both integrate frameworks for
learning with logical reasoning. This survey identifies several parallels across seven …

Artificial intelligence (AI) has undergone a renaissance recently, making major progress in
key domains such as vision, language, control, and decision-making. This has been due, in …

Our world can be succinctly and compactly described as structured scenes of objects and
relations. A typical room, for example, contains salient objects such as tables, chairs and …

Current advances in Artificial Intelligence and machine learning in general, and deep
learning in particular have reached unprecedented impact not only across research …

A key aspect of human intelligence is the ability to infer abstract rules directly from high-
dimensional sensory data, and to do so given only a limited amount of training experience …

Neither deep neural networks nor symbolic artificial intelligence (AI) alone has approached
the kind of intelligence expressed in humans. This is mainly because neural networks are …