Jutting out into the sea, the industrial port area of Nordhavn in Denmark’s capital, Copenhagen, is currently being transformed into a futuristic waterfront city district made up of small islets. It’s billed as Scandinavia’s largest metropolitan development project and, when complete, will have living space for 40,000 people and workspace for another 40,000.
At the moment, Nordhavn is only served by a nearby S-train station and bus stops located near the station. There are no buses or trains running within the development area, although there are plans for an elevated metro line, and parking will be discouraged in the new neighbourhood. This is a great opportunity for autonomous vehicles (AVs) to operate as a new public transport solution
All living organisms carve out environmental niches within which they can maintain relative predictability amidst the ever-increasing entropy around them (1), (2). Humans, for example, go to great lengths to shield themselves from surprise — we band together in millions to build cities with homes, supplying water, food, gas, and electricity to control the deterioration of our bodies and living spaces amidst heat and cold, wind and storm. The need to discover and maintain such surprise-free equilibria has driven great resourcefulness and skill in organisms across very diverse natural habitats. Motivated by this, we ask: could the motive of preserving order amidst chaos guide the automatic acquisition of useful behaviors in artificial agents?
Canadian artificial intelligence firm BlueDot has been in the news in recent weeks for warning about the new coronavirus days ahead of the official alerts from the Centers for Disease Control and Prevention and the World Health Organization. The company was able to do this by tapping different sources of information beyond official statistics about the number of cases reported.
An artificial neural network can reveal patterns in huge amounts of gene expression data, and discover groups of disease-related genes. This has been shown by a new study led by researchers at Linköping University. The scientists hope that the method can eventually be applied within precision medicine and individualised treatment.
Training machines using unbiased data and methodology is something that should be considered when designing artificial intelligence (AI) systems. Machine decisions can affect our rights, and we need to ensure that AI does not absorb biases by being trained on biased data. Researchers at the University of Bristol have investigated potential biases and looked at ways in which they could be removed.
People give massive amounts of their personal data to companies every day and these data are used to generate tremendous business values. Some economists and politicians argue that people should be paid for their contributions—but the million-dollar question is: by how much?
Modern farming has evolved by adopting technical advances such as machines for ploughing and harvesting, controlled irrigation, fertilisers, pesticides, crop breeding and genetics research. These have helped farmers to produce large crops of a good quality in a fairly predictable way.
But there’s still progress to be made in getting the best possible yields from different kinds of soils. And big losses still occur – especially during and after harvest – where monitoring and handling of produce isn’t done well. The industry needs smart and precise solutions and these are becoming available through new technology.