New research explains why so many biological networks, including the human brain (a network of neurons), exhibit a hierarchical structure, and will improve attempts to create artificial intelligence. The study, published in PLOS Computational Biology, demonstrates this by showing that the evolution of hierarchy - a simple system of ranking - in biological networks may arise because of the costs associated with network connections. Like large businesses, many biological networks are hierarchically organised, such as gene, protein, neural, and metabolic networks. This means they have separate units that can each be repeatedly divided into smaller and smaller subunits. For example, the human brain has separate areas for motor control and tactile processing, and each of these areas consist of sub-regions that govern different parts of the body.

The sports fields are empty, the science labs closed. No babies have been born for years. Cut to a split screen of human and robots kissing passionately. "They're trapped!" says the narrator, voice like gravel. Words slam against the screen, a warning. Except Futurama's 2001 episode "I Dated a Robot", with its post-apocalyptic world of silvers and blues, wildly overestimated how long it would take before this fear became flesh. It's November 2015, and in Malaysia, where humidity is at 89% and it is almost certainly still raining, David Levy, a founder of the second annual Congress on Love and Sex with Robots, is free to talk on the phone – he is less busy than planned.

The diagram is actually a computer program, so once the user designs the workflow, they can simply click a button to conduct the analysis. WAIVS includes not just discrete tonal analysis but other image-analysis algorithms, including the latest computer vision and artistic style algorithms. Recent work by Leon Gatys and others at the University of Tübingen, Germany, has demonstrated the use of deep learning techniques and technology to create images in the style of the great masters like Van Gogh and Picasso. The specific deep learning approach, called convolutional neural networks, learns to separate the content of a painting from its style. The content of a painting consists of objects, shapes and their arrangements but usually does not depend upon the use of colors, textures and other aspects of artistic style.

BMW is hiring experts in machine learning and artificial intelligence as it develops the Vision Next 100 into a flagship production model due to launch in 2021. According to BMW CEO Harald Krueger says the high-speed of technological advances is giving BMW new rivals such as Tesla but also new opportunities in areas such as artificial intelligence. "Until very recently our competitors were Mercedes, Audi and Porsche," Krueger said. "Now we are looking at newcomers such as Tesla or Faraday Future -- who knew these companies 10 years ago? "It shows how our industry is changing," the CEO said Wednesday in a keynote speech in the BMW World exhibition center near the company's Munich headquarters, held on the sidelines of the Automotive News Europe Congress. Earlier this year, the German automaker announced its new strategy focusing on electric vehicles, automated driving and new services in the field of premium individual mobility. To highlight the importance of machine learning, the BMW boss gave two powerful examples. Furthermore, Krueger says BMW's Vision Next 100 self-driving concept already showcases such advances. "The companion in the Vision Next 100 car is just that: a real companion that gets to know you as a person and responds to you as an individual," Krueger said. "I firmly believe this is not a threat to us, it's much more a huge opportunity to all of us -- mobility become more versatile.

For the second time in months, Syrian President Bashar al-Assad has said "we will fight on to liberate every inch of our land". The last time Assad made a similar statement, he was scolded by the Russian ambassador to the UN who said this was not in line with the Kremlin's policies. At the time, it wasn't - Russia was pushing for a political settlement and was involved in efforts with the United States to bring about a cessation of hostilities to create a conducive atmosphere for peace talks. This time around, however, Assad has so far not been told off. Instead, Russia sent its defence minister to Iran's capital Tehran to take part in talks with his Syrian and Iranian counterparts.

We look at the latest developments in robotics, including'cobots': intelligent devices that may soon be appearing at a care home near you. The BBC's Rory Cellan-Jones has been meeting some of them at Europe's leading annual robotics exhibition. Also, we talk to Hillary Clinton's former senior advisor for innovation, Alec Ross, who's written a book called The Industries of the Future about evolutions in the workplace. And we talk to Chetan Dube, president and CEO of the company Ipsoft, who's just announced that his company is building an entire business practice around a virtual agent called Amelia. Is all of this innovation a burden off our backs?

Consider: Facebook snapped up shopping search engine TheFind this spring and virtual reality startups Pebbles Interfaces and Surreal Vision last year, building on the social networking leader's 2014 purchase of Oculus VR. E-commerce giant Amazon nabbed cloud computing startups Amiato, 2lemetry, Elemental Technologies and Italy's Nice, as well as chipmaker Annapurna Labs, which brought it new data center technology. Google and its parent Alphabet acquired at least 15 small companies last year, many with mobile technologies. By the 1980s, many were dismantled. Some tech pundits fret that Google, Amazon and Facebook could stray too far from their core businesses as well.

Machine Learning is one of the most exciting fields in the world. Every week we discover something new, something amazing, something revolutionary. It's incredible, but it can also be overwhelming. That's why we created This Week in Machine Learning! Each week we publish a curated list of Machine Learning stories as a resource to help you keep pace with all these exciting developments.

"Big data" is a very 21st-century kind of buzzword, which ambiguously invokes the idea of using large sets of data to draw computer-assisted conclusions about trends, patterns and correlations, often about people and their behavior. But if you wanted to trace the origin of using big data for health research, you'd have to go back -- way back, to 17th-century England. There, you'll find a haberdasher by the name of John Graunt, who undertook a peculiar project: He began to study so-called bills of mortality, death records kept during the plague-riddled times, and compiled death details into tables, noting age, gender, cause, location and time. This vital statistics research later turned into a 1662 tome. It marked a seminal moment in demography, the statistical study of populations, but also in epidemiology, the study of what causes diseases and how they spread among different groups of people. "It was totally groundbreaking for its time.

Sleep is a naturally recurring state of mind characterized by altered consciousness, relatively inhibited sensory activity, inhibition of nearly all voluntary muscles, and reduced interactions with surroundings.[1] It is distinguished from wakefulness by a decreased ability to react to stimuli, but is more easily reversed than the state of hibernation or of being comatose. Mammalian sleep occurs in repeating periods, in which the body alternates between two highly distinct modes known as non-REM and REM sleep. REM stands for "rapid eye movement" but involves many other aspects including virtual paralysis of the body. During sleep, most systems in an animal are in an anabolic state, building up the immune, nervous, skeletal, and muscular systems. Sleep in non-human animals is observed in mammals, birds, reptiles, amphibians, and some fish, and, in some form, in insects and even in simpler animals such as nematodes. The internal circadian clock promotes sleep daily at night in diurnal species (such as humans) and in the day in nocturnal organisms (such as rodents). However, sleep patterns vary widely among animals and among different individual humans. Industrialization and artificial light have substantially altered human sleep habits in the last 100 years.[2] The diverse purposes and mechanisms of sleep are the subject of substantial ongoing research.[3] Sleep seems to assist animals with improvements in the body and mind. A well-known feature of sleep in humans is the dream, an experience typically recounted in narrative form, which resembles waking life while in progress, but which usually can later be distinguished as fantasy. Sleep is sometimes confused with unconsciousness, but is quite different in terms of thought process. Humans may suffer from a number of sleep disorders. These include dyssomnias (such as insomnia, hypersomnia, and sleep apnea), parasomnias (such as sleepwalking and REM behavior disorder), bruxism, and the circadian rhythm sleep disorders. In mammals and birds, sleep is divided into two broad types: rapid eye movement (REM sleep) and non-rapid eye movement (NREM or non-REM sleep). Each type has a distinct set of physiological and neurological features associated with it. REM sleep is associated with dreaming, desynchronized and faster brain waves, loss of muscle tone,[4] and suspension of homeostasis[citation needed]. REM and non-REM sleep are so different that physiologists classify them as distinct behavioral states. In this view, REM, non-REM, and waking represent the three major modes of consciousness, neural activity, and physiological regulation.[5] According to the Hobson & McCarley activation-synthesis hypothesis, proposed in 1975–1977, the alternation between REM and non-REM can be explained in terms of cycling, reciprocally influential neurotransmitter systems.[6]