Nasa has announced that it has found evidence of flowing water on Mars. Scientists have long speculated that Recurring Slope Lineae -- or dark patches -- on Mars were made up of briny water but the new findings prove that those patches are caused by liquid water, which it has established by finding hydrated salts. Several hundred camped outside the London store in Covent Garden. The 6s will have new features like a vastly improved camera and a pressure-sensitive "3D Touch" display

This post basically takes the tutorial on Classifying MNIST digits using Logistic Regression which is primarily written for Theano and attempts to port it to Keras. So, what better way to put that claim to the test than to write some code! Keras comes with great documentation. One can really get up and running in a matter of minutes. Everything needed to accomplish the goal can be found on the Guide to Sequential Model page (assuming of course the initial setup and configuration is all taken care of).

"The elimination of poverty worldwide is the first of 17 UN Sustainable Development Goals for the year 2030. To track progress towards this goal, we require more frequent and more reliable data on the distribution of poverty than traditional data collection methods can provide." Those are the opening words on the website of Stanford University's Sustainability and Artificial Intelligence Lab, and its researchers have come up with an unusual -- and effective -- way to map and predict the distribution of poverty; their method combines high-resolution satellite imagery with machine learning. The researchers explain their methodology, which they call "cheap and scalable," in a video. The study, titled "Combining satellite imagery and machine learning to predict poverty," was published in the journal Science.

Artificial intelligence can find, map poverty, researchers ... Ai Weiwei exhibit extended until Sept. 11 at Warhol

Until we open our mouths to speak, it is possible for most of us to keep our thoughts to ourselves. But computers could soon be able to predict what you are thinking by looking for distinct patterns of activity in your brain that relate to sentences. Researchers have developed a computer program that is able to search for the brain activity related to certain words and then use this to predict a sentence being thought even it hasn't seen it before. Scientists have created a computer model that can predict unspoken sentences by looking at the neural activity in the brain. They say the system is able to get the predictions right around 70 per cent of the time.

By combining satellite data and sophisticated machine learning, researchers have developed a technique to estimate household consumption and income. Such data is particularly difficult to obtain in poorer countries, yet it is critical for informing research and policy, and for efforts including resource allocation and targeted intervention in these developing nations. The African continent provides a particularly striking example of limited insights into economic wellbeing. According to World Bank data from 2000 to 2010, 39 out of 59 African countries conducted less than two surveys substantial enough to result in poverty measures. Surveys are costly, infrequent, and cannot always reach countries or regions within countries, for instance, due to armed conflict.

Scientists at Stanford University have found a new method in predicting poverty through the use of machine learning and satellite images. The technique could make it easier for organizations to know where across the world their aid is needed most. Also, this could help governments develop a better policy to prevent or fight poverty. Using three data sources namely daytime images, night light images, and survey data, scientists built an algorithm to predict how wealthy or poor an area is. The results of the study have been published in the journal Science. "The idea is that if we train our models right, they help us predict poverty in areas where we don't have the surveys, which will help out aid orgs that are working on this issue," explained Neal Jean, co-author of the study and a doctoral candidate at Stanford.

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.

Welcome to the Applied Materials Earnings Conference Call. During the presentation, all participants will be in a listen-only mode. Afterwards you will be invited to participate in a question-and-answer session. As a reminder, this conference is being recorded. I'd now like to turn the conference over to Michael Sullivan, Vice President of Investor Relations. In a moment, we'll discuss the results for our third quarter which ended on July 31. Joining me are Gary Dickerson, our President and CEO; and Bob Halliday, our Chief Financial Officer. Before we begin, let me remind you that today's call contains forward-looking statements including Applied's current view of its industries, performance, products, share positions, profitability and business outlook. These statements are subject to risks and uncertainties that could cause actual results to differ materially from those expressed or implied by such statements, and are not guarantees of future performance.

LONDON – 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 recognizes signs of poverty through a process called machine learning, a type of artificial intelligence, he said.