A new technique using artificial intelligence to read satellite images could aid efforts to eradicate global poverty by indicating where help is needed most, a team of U.S. researchers said on Thursday. The method would assist governments and charities trying to fight poverty but lacking precise and reliable information on where poor people are living and what they need, the researchers based at Stanford University in California said. Eradicating extreme poverty, measured as people living on less than $1.25 U.S. a day, by 2030 is among the sustainable development goals adopted by United Nations member states last year. A team of computer scientists and satellite experts created a self-updating world map to locate poverty, said Marshall Burke, assistant professor in Stanford's Department of Earth System Science. It uses a computer algorithm that recognises signs of poverty through a process called machine learning, a type of artificial intelligence, he said.

The global market of artificial intelligence is anticipated to grow at a CAGR of over 60% during 2017-2022. On the basis of application, global artificial intelligence market has been broadly segmented into image recognition, natural language processing, speech recognition, gesture control & others. Among these categories, image recognition dominated global artificial intelligence market in 2016, and the segment is expected to maintain its dominance over the next five years as well. These machines are also capable of taking decisions by self-learning from the nearby environment. Global artificial intelligence market is expected to grow at a robust pace over the next five years, owing to its widespread implementation in numerous industries, such as automobile, finance, healthcare, consumer electronics, etc.

Researchers have combined satellite imagery with AI to predict areas of poverty across the world. There's little reliable data on local incomes in developing countries, which hampers efforts to tackle the problem. A team from Stanford University were able to train a computer system to identify impoverished areas from satellite and survey data in five African countries. Neal Jean, Marshall Burke and colleagues say the technique could transform efforts to track and target poverty in developing countries. "The World Bank, which keeps the poverty data, has for a long time considered anyone who is poor to be someone who lives on below $1 a day," Dr Burke, assistant professor of Earth system science at Stanford, told the BBC's Science in Action programme.

Databases are widespread, yet extracting relevant data can be difficult. Without substantial domain knowledge, multivariate search queries often return sparse or uninformative results. This paper introduces an approach for searching structured data based on probabilistic programming and nonparametric Bayes. Users specify queries in a probabilistic language that combines standard SQL database search operators with an information theoretic ranking function called predictive relevance. Predictive relevance can be calculated by a fast sparse matrix algorithm based on posterior samples from CrossCat, a nonparametric Bayesian model for high-dimensional, heterogeneously-typed data tables. The result is a flexible search technique that applies to a broad class of information retrieval problems, which we integrate into BayesDB, a probabilistic programming platform for probabilistic data analysis. This paper demonstrates applications to databases of US colleges, global macroeconomic indicators of public health, and classic cars. We found that human evaluators often prefer the results from probabilistic search to results from a standard baseline.

Amazon just made news when its Prime Air delivery drone did its first public demo, landing on a lawn in California to drop off a few bottles of sunscreen for attendees of an Amazon conference on automation. But the cofounder and CTO of the drone delivery startup Zipline doesn't think much of Amazon's experiments in this area, or Google's either. Have you been near one of those quadrocopters when it sets down, Zipline's Keenan Wyrobek asks? "They're huge things with the power and blade size of four lawnmowers," he says. While Amazon dropped off sunscreen for Silicon Valley insiders, and Google has talked with Domino's about pizza delivery, Zipline is delivering life-saving blood to hospitals in Rwanda.

On this last day of the quarter, the stock market is so tickled with its Q1 spoils that it's about to give a bit back. That is how it usually goes when the S&P 500 is up nicely for a three-month stretch, note Bespoke Investment Group's number crunchers. They checked out how the last trading day has gone when the S&P is up at least 5% for a quarter. That final session is typically a bust, with the index retreating 0.4% on average, according to Bespoke's look at the last eight years. But who knows exactly how the S&P will close today?

Many people wish they could predict what will happen next in the world. Many predictions are assigned to the waste bin of time very quickly. With hindsight, unforeseen factors come into play that changed their'models'. It is because there were so many factors involved to predict. The ability of models to analyse and interpret means technology was not able to process, analyse and predict with a high degree of success.

Last month, Mark Clattenburg, who is generally regarded as one of the finest referees in professional soccer, left England's Premier League for a better-paying position in Saudi Arabia. Plenty of famous players have chosen riches over prestige and joined less established leagues in Asia and the Middle East. But this was one of the first times that a referee of Clattenburg's stature and prominence had left in his prime. Just last year, he was selected to referee two of the most important matches on Earth: the finals of the Champions League, in May, and then the European Championship, two months later--plum gigs that testified to his skill. To commemorate the occasion, he had the feat tattooed on his arm--a testament to his knack for preening.

Elon Musk, the genius founder of Tesla and SpaceX, recently predicted computers and robots are likely to take over the human workforce, and that the government will need to provide a salary--also known as universal basic income (UBI)--to keep society afloat. While I'm a tech guy and won't proclaim to know the economic rationale behind UBI, I do know this: When Elon Musk speaks, I listen. In fact, Musk is not alone. A recent study from Oxford University estimates that 47 percent of U.S. jobs could be made obsolete in the next two decades as automation and deep learning continue to advance. Even highly technical jobs are in danger of being overtaken by AI.

NEW YORK, LONDON & HONG KONG--(BUSINESS WIRE)--In the next stage of artificial intelligence adoption, banks will use AI to help understand the intentions and emotions of customers and enable better interactions, according to a new report from Accenture (NYSE:ACN). The report, Accenture Banking Technology Vision 2017, draws on the analysis of an advisory board of more than two dozen individuals, interviews with technology luminaries and industry experts, and results of a survey of more than 600 bankers. According to the report, more than three-quarters (78 percent) of bankers believe that AI will enable simpler user interfaces that will help banks create a more human-like customer experience. In addition, four out of five respondents (79 percent) believe that AI will revolutionize the way banks gather information and interact with customers, and three-quarters (76 percent) believe that within three years, banks will deploy AI as their primary method for interacting with customers. "Consumers' diverse needs and priorities are forcing financial services firms to redefine how they interact with them to determine the best products and services to meet individuals' needs," said Alan McIntyre, a senior managing director at Accenture and head of the company's Banking practice.