When people think of the automotive Factory of the Future, the first word that comes to mind is automation. They think of the "lights-out" factory that General Motors Chief Executive Roger Smith fantasized about in 1982 and Elon Musk talks about building today--plants so dominated by robots and machines that they don't need lights to work. There's no doubt that the auto industry will continue to vigorously pursue automation solutions to lower the cost of producing cars. But the reality is that any major leap forward on cost and efficiency will no longer be possible through automation alone, since most of the tasks that can be automated in an automotive factory have already been tackled. When a real Factory of the Future arrives, it will not look different because we have automated the processes we use today.

Less than a year after he got his high school diploma and left Shenandoah Junction, W.Va., for Silicon Valley, Robbie Barrat began teaching computers to paint. He fed a few thousand examples of paintings into his artificial intelligence software until it learned how to create landscapes like the one on this issue's cover. By computer standards, these works of art took a long time to produce: a little more than two weeks. "AI is going to be one of the larger art movements of this century," says Barrat, a Stanford researcher who goes by @DrBeef_ on Twitter. "It just has really great untapped potential."

Artificial intelligence has been used to detect the most common causes of dementia and stroke -- small vessel damage, according to a study. Scientists at Imperial College London and the University of Edinburgh in Britain have created machine-learning software to identify and measure the severity of small vessel disease more accurately than some current methods. Their findings were published in the journal Radiology. The researchers said the tests at Charing Cross Hospital, part of Imperial College Healthcare National Health Service Trust, could pave the way for more personalized medicine and quicker diagnosis in an emergency setting. "This is the first time that machine learning methods have been able to accurately measure a marker of small vessel disease in patients presenting with stroke or memory impairment who undergo CT scanning," lead author Dr. Paul Bentley, a clinical lecturer at Imperial College London, said.

University College London Hospitals NHS Foundation Trust, one of Britain's biggest health trusts, has partnered The Alan Turing Institute, a body that collects AI expertise of British universities, to automate tasks ranging from reading CT scans for cancer to prioritising patients at the emergency department, The Guardian reported. NHS is England's National Health Service. It is a health service that everybody in the UK can use. "Machines will never replace doctors, but the use of data, expertise and technology can radically change how we manage our services – for the better," Professor Marcel Levi, chief executive of University College London Hospitals, was quoted as saying. According to the report, the two bodies have signed an artificial-intelligence agreement for three years.

English is spoken by hundreds of millions of people worldwide, but do the development of translation technology and "hybrid" languages threaten its status? Which country boasts the most English speakers, or people learning to speak English? According to a study published by Cambridge University Press, up to 350 million people there have at least some knowledge of English - and at least another 100 million in India. There are probably more people in China who speak English as a second language than there are Americans who speak it as their first. But for how much longer will English qualify as the "world's favourite language"?

They call Amazon the everything store--and Tuesday, the world learned about one of its lesser-known but provocative products. Police departments pay the company to use facial-recognition technology Amazon says can "identify persons of interest against a collection of millions of faces in real-time." More than two dozen nonprofits wrote to Amazon CEO Jeff Bezos to ask that he stop selling the technology to police, after the ACLU of Northern California revealed documents to shine light on the sales. The letter argues that the technology will inevitably be misused, accusing the company of providing "a powerful surveillance system readily available to violate rights and target communities of color." The revelation highlights a key question: What laws or regulations govern police use of the facial-recognition technology?

Despite her rising profile internationally as a strong and hugely popular female voice in the technology sector, Dr Sue Black OBE is very modest for someone who has achieved a great deal of personal triumph while exploring what is important to herself. "It's a lot easier doing stuff you love than working in a job where you are not in control, yeah I work hard but at the same time it's a lot easier being charge of myself," she explains when asked about the numerous projects Black has wholeheartedly embraced, not least her campaign to Saving Bletchley Park, the birthplace of Alan Turing's Bombe machine, which led to her writing her debut book that has become the fasted crowdfunded book when published in 2016. Dr Black is a polite and enthusiastic person, and for the UK tech sector she is every bit the rock star it needs. Having received her OBE from The Queen last May, she has since been recognised by being inducted into Bima's Hall of Fame alongside the likes of Stephen Fry (co-incidentally one the highest profile champions of @savingbletchley), Dame Stephanie Shirley, Sir Jony Ive and Baroness Joanna Shields. "It's really nice to get recognition from organisations outside of academia who realise the potential of the work that I'm trying to do. I'm absolutely honoured and delighted to be recognised in this way because it means that the things that I really care about – other people really care about them as well and that is heart-warming for me that the change I am trying to make in the world is being recognised."

The paper introduces concentric Echo State Network, an approach to design reservoir topologies that tries to bridge the gap between deterministically constructed simple cycle models and deep reservoir computing approaches. We show how to modularize the reservoir into simple unidirectional and concentric cycles with pairwise bidirectional jump connections between adjacent loops. We provide a preliminary experimental assessment showing how concentric reservoirs yield to superior predictive accuracy and memory capacity with respect to single cycle reservoirs and deep reservoir models.

A central problem to understanding intelligence is the concept of generalisation. This allows previously learnt structure to be exploited to solve tasks in novel situations differing in their particularities. We take inspiration from neuroscience, specifically the Hippocampal-Entorhinal system (containing place and grid cells), known to be important for generalisation. We propose that to generalise structural knowledge, the representations of the structure of the world, i.e. how entities in the world relate to each other, need to be separated from representations of the entities themselves. We show, under these principles, artificial neural networks embedded with hierarchy and fast Hebbian memory, can learn the statistics of memories, generalise structural knowledge, and also exhibit neuronal representations mirroring those found in the brain. We experimentally support model assumptions, showing a preserved relationship between grid and place cells across environments.

We present an extension of Monte Carlo Tree Search (MCTS) that strongly increases its efficiency for trees with asymmetry and/or loops. Asymmetric termination of search trees introduces a type of uncertainty for which the standard upper confidence bound (UCB) formula does not account. Our first algorithm (MCTS-T), which assumes a non-stochastic environment, backs-up tree structure uncertainty and leverages it for exploration in a modified UCB formula. Results show vastly improved efficiency in a well-known asymmetric domain in which MCTS performs arbitrarily bad. Next, we connect the ideas about asymmetric termination to the presence of loops in the tree, where the same state appears multiple times in a single trace. An extension to our algorithm (MCTS-T+), which in addition to non-stochasticity assumes full state observability, further increases search efficiency for domains with loops as well. Benchmark testing on a set of OpenAI Gym and Atari 2600 games indicates that our algorithms always perform better than or at least equivalent to standard MCTS, and could be first-choice tree search algorithms for non-stochastic, fully-observable environments.