Satellites are best known for helping smartphones map driving routes or televisions deliver programs. But now, data from some of the thousands of satellites orbiting Earth are helping track things like crop conditions on rural farms, illegal deforestation, and increasingly, poverty in the hard-to-reach places around the globe. As much as that data has the potential to provide invaluable information to humanitarian organizations, watchdog groups, and policymakers, there is too much of it to sift through in order to draw insights that could influence important decisions. A team of researchers from Stanford University, however, says it has developed an efficient way. By creating a deep-learning algorithm that can recognize signs of poverty in satellite images – such as condition of roads – the team sorted through a million images to accurately identify economic conditions in five African countries, reported the scientists in the journal Science on Thursday.

Fun projects arise when you combine a problem that needs to be solved with a desire to learn. My problem was the neighbors' cats: I wanted to encourage them to hang out somewhere other than my front yard. At the same time, I wanted to learn more about neural network software and deep learning and have a bit of fun doing it. So I set out to build a system that would automatically chase cats out of my yard using artificial intelligence. This boils down to a neural network trained to recognize cats in images from a security camera and turn on the sprinkler system to scare them away. The neural network inference runs on Jetson TX1. This post is an elaboration on and continuation of my notes on the project here.

A global hub of the machinery industry, Taiwan is stepping up efforts to develop innovative smart manufacturing technologies. From May 20-24 this year, about 30,000 visitors, including buyers from Asia, Europe and the U.S., flocked to the Commercial Exhibition Center in Taichung City, central Taiwan for the Automatic Machinery and Intelligent Manufacturing Exhibition. The trade show has been staged annually in Taichung for more than three decades, though this marked the first time that intelligent manufacturing was used in the title of the event. Organizer Commercial Times, one of the country's two major financial newspapers, opted to alter the name of the show to highlight the growing focus on this field in the nation's globally competitive machinery sector. "This is the 32nd edition of our machinery show in Taichung. But unlike previous events, this year's exhibition features intelligent machines to reflect the current trend in manufacturing systems development," Chen Kuo-wei (???), president of Commercial Times, said at the opening ceremony of the five-day event, which generated business deals totaling about NT 300 million (US 9.2 million).

From Blade Runner to I.Robot, to Transformers, Hollywood's robots-take-control genre has long profited from fears surrounding artificial intelligence's future role in society. But it's looking like, at this particular juncture, AI is more likely to determine what life insurance or hiking boots people might buy than whether cyborgs or humans will control the world. No worthwhile data management platform or analytics solution emerges today without touting its machine learning capabilities and powers of predictive analytics. But machine learning's roots are not foreign to business people. Delivering a primer on the topic at a client conference, SAS Manager of Data Science Technologies Wayne Thompson noted that the main difference between statistics and machine learning is that "statistics focuses more on inferential analysis or hypothesis testing to make predictions about a larger population than the sample represents. Machine learning uses massive amounts of observational data and, as a branch of artificial intelligence, focuses on automation."

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 need more frequent and more reliable data on the distribution of poverty than traditional data collection methods can provide. In this project, we propose an approach that combines machine learning with high-resolution satellite imagery to provide new data on socioeconomic indicators of poverty and wealth. For more information, check out... Our recently published Science paper: http://science.sciencemag.org/content... A project website featuring poverty maps of Nigeria, Tanzania, Uganda, Malawi, and Rwanda: http://sustain.stanford.edu/predictin...

This article was posted by SmileJet on Dev Battles. She paints and writes poetry. She's also an artificial intelligence from the movie Her, which imagines how a juiced-up Siri will change our lives. Now, tech companies large and small are racing to make this a reality. You've heard the jargon: AI, machine learning, deep learning, neural networks, natural language processing.

Microsoft announced today that it acquired Genee, an AI-powered scheduling assistant bot that specializes in planning meetings for large groups or when organizers don't have direct access to the calendars of everyone involved. Genee's app is a chatbot accessible via an iPhone app, email, SMS, FB, Twitter or Skype, and it understands natural language input, so you can just text it the kind of event you want to schedule, when you want to happen and who you want to include, and it should theoretically output a proper meeting invite. The standalone service is going to be shut down on September 1, 2016, as a result of the acquisition. It originally debuted in August last year. Genee co-founders Ben Cheung and Charles Lee explained in a blog post announcing the news that easing calendar entries created by the service will still function, but it won't create any new ones or send reminders or agendas related to upcoming events. The team also says they "consider Microsoft to be the leader in personal and enterprise productivity, AI, and virtual assistant technologies," hence their excitement about teaming up with Redmond.

Microsoft has picked up another productivity app -- announcing the acquisition of AI-powered scheduling tool Genee. In a blog post today the software giant said it will be plugging Genee into its cloud productivity suite, Office 365. Terms of the deal were not disclosed. "As we continue to build new Office 365 productivity capabilities and services our customers value, I'm confident the Genee team will help us further our ambition to bring intelligence into every digital experience," writes Rajesh Jha, CVP of Outlook and Office 365. Genee launched in public beta a year ago, offering an end-to-end scheduling tool that integrates with calendar apps and email providers to take the strain out of arranging meetings.

It's been two and a half years since we let Genee out of the bottle. In our drive to deliver large productivity gains through intelligent scheduling coordination and optimization, we often found ourselves on the forefront of technology involving natural language processing, artificial intelligence (AI), and chat bots. We were extremely fortunate to find many who believed in the vision and supported us with their resources, talent, time, and advice along the way, which made Genee possible. Today, we are pleased to announce that Genee has signed an agreement to be acquired by Microsoft. A new beginning means the end of another.

Predictive data analytics and machine learning are becoming necessities for businesses that wish to succeed in today's market. The right machine learning strategy can put your business ahead of the competition, reduce your TCO, and give you the edge your business needs to succeed. First there was big data – extremely large data sets that made it possible to use data analytics to reveal patterns and trends, allowing businesses to improve customer relations and production efficiency. Then came fast data analytics – the application of big data analytics in real-time to help solve issues with customer relations, security, and other challenges before they became problems. Now, with machine learning, the concepts of big data and fast data analytics can be used in combination with artificial intelligence (AI) to avoid these problems and challenges in the first place.