The original goal of the AI field was the construction of "thinking machines" -- that is, computer systems with human-like general intelligence. As this task turned out to be way more difficult than initially expected, the majority of AI researchers have spent the last decades focusing on the less ambitious goal referred to as "narrow AI" -- the production of AI systems exhibiting intelligence only with respect to specific, highly constrained tasks. In recent years, however, more and more researchers have recognized the necessity -- and feasibility -- of returning to the original goals of the field. Reasons for the new optimism in attempting to tackle the mentioned old goals are based on new developments in computer science, engineering, and insights in disciplines trying to understand cognition. Examples of such developments are the dramatic increase in computing resources, the digital availability of huge amounts of knowledge, new machine learning paradigms, the possibility to build highly sophisticated robotic applications, and new findings and inspiration from neuroscience and cognitive science. Increasingly, there is a call for a transition back to facing the more difficult issues of "human-level intelligence" and more broadly "artificial general intelligence (AGI)."