Computers read 1.8 billion words of fiction to learn how to anticipate human behaviour

The quest to create virtual assistants that can understand and anticipate human behaviour and needs is one of the current lodestars of artificial intelligence research, but is challenged by the diversity and limitations of available datasets, as well as the expense and complexity involved in generating new proprietary ones. Researchers at Stanford University decided to approach the problem by using descriptions of everyday human activities found in online fiction, namely 600,000 stories from 500,000 writers at online writing community WattPad – input totalling 1.8 billion words – to inform a new knowledge base called Augur, designed to power vector machines in making predictions about what an individual user might be about to do, or want to do next. As the researchers’ new paper notes, ‘While we tend to think about…


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