Can artificial intelligence help visually impaired people recognize objects around them and improve their quality of life? EyeSense, an iPad app developed in Egypt, has the ability to "learn" objects in its environment, having been trained by its users. A visually impaired person can point their device in the direction of where they think something might be--say, a coffee cup--and a voice will say that the app recognizes that object. "The key strength of the app is that it also recognizes basic facial expressions, like winks or smiles. This enhances human interaction," says Joanna Marczak, a spokesperson for its developer, ID Labs.

Once a year, when Slava Epstein was growing up in Moscow, his mother took him to the Exhibition of the Achievements of the National Economy, a showcase for the wonders of Soviet life. The expo featured many things--from industrial harvesters to Uzbek wine--but Epstein, who began going in the nineteen-sixties, when he was eight or nine, was interested primarily in one: the Cosmos Pavilion, a building the size of a hangar, with a ceiling shaped like a giant inverted parabola. Space fever was running high in the city. Since 1961, when Yuri Gagarin orbited the globe, unmanned vessels had been launched toward Mars and Venus. Beside the expo's entrance, the towering Monument to the Conquerors of Space depicted a probe swooping up to the heavens. The Pavilion displayed futuristic technology--Vostok rockets and Soyuz orbiters--but Epstein was less interested in the glories of advanced thruster design than in the glories of space. He wanted to devote himself to astronomy. When a textbook that he found on the topic began with algebraic formulas, he prodded his older brother to explain them. During high school, he enrolled in classes in physics and math at Moscow State University. His parents disapproved of his desired career: because he is half Jewish, Epstein would face harsh Soviet quotas limiting Jews in the study of physics, a field deemed relevant to national security. But after his first lecture the professor invited him for a walk, and affirmed what they had been saying all along. "Don't do it," he warned. Soviet Russia may have been a fatalist's paradise, but from a young age Epstein felt that he was hardwired for optimism. He convinced himself that what is truly important in science is the ability to connect ideas, no matter the field, and so he took up biology. Rather than telescopes, he would use microscopes, which he began taking with him on trips to the White Sea, near the Arctic Circle, to study protozoa along the shore--research that could be conducted with minimal state interference. Over time, he grew interested in even smaller, more ancient forms of life: bacteria. Studying microbes inevitably causes a reordering of one's perceptions: for more than two billion years, they were the only life on this planet, and they remain in many ways its dominant life form. To a remarkable extent, the microbial cosmos was less explored than the actual cosmos: precisely how the organisms evolve, replicate, fight, and communicate remains unclear. Nearly all of microbiology, Epstein eventually learned, was built on the study of a tiny fraction of microbial life, perhaps less than one per cent, because most bacteria could not be grown in a laboratory culture, the primary means of analyzing them. By the time he matured as a scientist, many researchers had given up trying to cultivate new species, writing off the majority as "dark matter"--a term used in astronomy for an inscrutable substance that may make up most of the universe but cannot be seen.

Artificial intelligence is one of the most popular topics in IT world. Famous inventors and celebrities Elon Musk and Steve Wozniak are concerned about AI researches and assume that its creation is a real threat to humanity. In fact, science fiction and Hollywood films created many misconceptions about AI. Technology blog Gizmodo decided to penetrate into the topic and find out if AI really poses a danger to us. So what makes us imagine Skynet destroying our planet and can AI trigger unemployment? On the other hand, can it be our way to prosperity?

In this work, we consider compressed sensing reconstruction from $M$ measurements of $K$-sparse structured signals which do not possess a writable correlation model. Assuming that a generative statistical model, such as a Boltzmann machine, can be trained in an unsupervised manner on example signals, we demonstrate how this signal model can be used within a Bayesian framework of signal reconstruction. By deriving a message-passing inference for general distribution restricted Boltzmann machines, we are able to integrate these inferred signal models into approximate message passing for compressed sensing reconstruction. Finally, we show for the MNIST dataset that this approach can be very effective, even for $M < K$.

This tutorial will describe how a surprisingly small amount of code can be used to build a recommendation engine using the MapR Sandbox for Hadoop with Apache Mahout and Elasticsearch. This tutorial will run on the MapR Sandbox. The tutorial also requires Elasticsearch and Mahout to be installed on the sandbox. Step 1: Indexing the movie meta data in Elasticsearch In Elasticsearch, documents contain fields which are, by default, all indexed. Typically documents are written as a single-level JSON structure.

Recently a drone filmed a pair of whales feeding for perhaps the first time. Bryde's whales (named after the Norwegian merchant who built the first whaling stations in South Africa) reach a maximum length of over 54 feet. That's humongous, and should make it easy to observe them. The vastness of the ocean disagrees, and happily hides the whales in their vast tropical water range. Until recently, Bryde's whales had rarely been observed feeding in the wild.

It was an historic week as Hillary Clinton secured enough delegates to be the first woman to become the presumptive nominee of a major political party for the highest office of the most powerful country on Earth. Meanwhile, we're reviewing another debate about where Seattle's startup ecosystem ranks nationally; new data on urban startup clusters; a 15 million funding round for BitTitan; Bill Gates' poultry program; news of artificial intelligence watching and writing sci-fi films; and a good read on how "Silicon Valley" delivers such an accurate satire of the real Silicon Valley. It doesn't rank as high as NYC, LA, or Boston in the number of startups funded or capital invested. So on a dollars in/dollars [out], Seattle outperforms. The perennial debate about who's No. 2 (always behind Silicon Valley) is tiresome, but look, here I am writing about it. One really good thing to come out of this is Tren Griffin's essay at GeekWire on what it takes for a city to develop a top-tier tech and startup ecosystem.

Sending packages by airplane is nothing new, but the task could soon be taken over by drones. DHL recently completed of a three-month-long test of its automated drone delivery system, the Parcelcopter. It works with a combination helipad and mailbox dubbed Skyport, which can automatically load and unload the drone's payload when it lands and store it in one of the station's lockers. Testing took place between January and March of 2016 in Bavaria, Germany. The idea was to see if the drone could be used to deliver packages to areas that are remote and where standard delivery takes a long 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]

Last week, Elon Musk, the billionaire founder of Tesla Motors, SpaceX, and other cutting-edge companies, took a surprising question at the Code Conference, a technology event in California. What, a man in the audience asked, did Musk make of the idea that we are living not in the real world, but in an elaborate computer simulation? Musk exhibited a surprising familiarity with this concept. "I've had so many simulation discussions it's crazy," Musk said. Citing the speed with which video games are improving, he suggested that the development of simulations "indistinguishable from reality" was inevitable.