Another big part of the food supply comes from ranches and farms that raise and slaughter various livestock. While ranching is sometimes bundled with agriculture, I discussed farming in Big Data in Agriculture, so we'll focus on ranching this time around. Somewhat surprising is that big data usage in ranching appears more limited than in farming. That said, there are a number of novel uses of technology and data in animal husbandry. At a high level, the goals of ranching and farming are the same as any business: increase yields and lower costs. Production maximization has long played a role in large operations.

In fact, unit shipments of artificial intelligence (AI) systems used in infotainment and ADAS systems are expected to rise from just 7 million in 2015 to 122 million by 2025, according to IHS Inc. (NYSE: IHS), the leading global source of critical information and insight. The attach rate of AI-based systems in new vehicles was 8 percent in 2015, and the vast majority were focused on speech recognition. However, that number is forecast to rise to 109 percent in 2025, as there will be multiple AI systems of various types installed in many cars. "An artificial-intelligence system continuously learns from experience and by its ability to discern and recognize its surroundings," said Luca De Ambroggi, principal analyst-automotive semiconductors, IHS Technology. "It learns, as human beings do, from real sounds, images, and other sensory inputs. The system recognizes the car's environment and evaluates the contextual implications for the moving car." Specifically in ADAS, deep learning -- which mimics human neural networks -- presents several advantages over traditional algorithms; it is also a key milestone on the road to fully autonomous vehicles.

Innovation is among the key factors driving a country's economic and social growth. But what are the factors that make a country innovative? How do they differ across different parts of the world and different stages of development? In this work done in collaboration with the World Economic Forum (WEF), we analyze the scores obtained through executive opinion surveys that constitute the WEF's Global Competitiveness Index in conjunction with other country-level metrics and indicators to identify actionable levers of innovation. The findings can help country leaders and organizations shape the policies to drive developmental activities and increase the capacity of innovation.

Hindsait's AI platform identifies and extracts actionable data from information trapped in clinical notes and other electronic records for improved patient outcomes at lower costs Based on its recent analysis of the market for artificial intelligence (AI) systems for the healthcare industry, Frost & Sullivan recognizes Hindsait, Inc. with the 2016 Global Frost & Sullivan Award for Visionary Innovation Leadership. Hindsait has demonstrated a strong commitment to breaking down communication barriers and enhancing healthcare delivery using AI and cognitive computing technology. With its novel software-as-a-service (SaaS) platform, Hindsait helps payers reduce costs and improve the quality of services while simultaneously enabling healthcare providers, such as accountable care organizations (ACOs) and hospitals, to reduce risks and offer superior, more affordable care. Although there are organizations that apply AI and cognitive computing in healthcare, many of them simply adapt approaches applied in other industries to healthcare. Hindsait, on the other hand, is dedicated to the needs of the healthcare industry.

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.