If Adam Dorr is correct, robots and artificial intelligence will dominate the global economy within a generation and put virtually the entire human race out of a job. The social scientist doubles up as a futurist and has a stark vision of the scale, speed and unstoppability of a technological transformation that he says will replace virtually all human labour within 20 years. The Guardian's journalism is independent. We will earn a commission if you buy something through an affiliate link. Dorr heads a team of researchers who have studied patterns of technological change over millennia and concluded that the current wave will not just convulse but obliterate the labour market by 2045.

When you think of artificial intelligence, the field of botany probably isn't uppermost in your mind. When you picture settings for cutting-edge computational research, century-old museums may not top the list. And yet, a just-published article in the Biodiversity Data Journal shows that some of the most exciting and portentous innovation in machine learning is taking place at none other than the National Herbarium of the National Museum of Natural History in Washington, D.C. The paper, which demonstrates that digital neural networks are capable of distinguishing between two similar families of plants with rates of accuracy well over 90 percent, implies all sorts of mouth-watering possibilities for scientists and academics going forward. The study relies on software grounded in "deep learning" algorithms, which allow computer programs to accrue experience in much the same way human experts do, upping their game each time they run.

We describe a novel system for discovering and characterizing emerging events. We define event emergence to be a developing situation comprised of a series of sub-events. To detect sub-events from a very large, continuous textual input stream, we use two techniques: (1) frequency-based detection of sub-events that are potentially entailed by an emerging event; and (2) anomaly-based detection of other sub-events that are potentially indicative of an emerging event. Identifying emerging events from detected sub-events involves connecting sub-events to each other and to the relevant emerging events within the event models and estimating the likelihood of possible emerging events. Each sub-event can be part of a number of emerging events and supports various event models to varying degrees. We adopt a coherent and compact model that probabilistically identifies emerging events. The innovative aspect of our work is a well-defined framework where statistical Big Data techniques are informed by event semantics and inference techniques (and vice versa). Our work is strongly grounded in semantics and knowledge representation, which enables us to produce more reliable results than would otherwise be possible with a purely statistical approach.