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.

Dataiku, the maker of the all-in-one predictive analytics software platform Dataiku Data Science Studio (DSS), has today announced the release of Dataiku DSS 3.1, which now enables transformations in Apache Spark's Scala, adds additional external integrations, an improved UX interface, and includes 5 machine learning engines in its visual analysis section. Dataiku DSS 3.1 introduces new visual machine learning engines that allow users to create incredibly powerful predictive applications within a code-free interface. Users of all skill levels can now leverage HPE Vertica machine learning, H2O Sparkling Water, MLlib, Scikit-Learn, and XGBoost directly from within the visual analysis section of Dataiku DSS 3.1 to apply powerful machine learning algorithms to their data science projects without having to write a single line of code. The blending of visual code-free and free-form code-based transformations is one of the main strengths of Dataiku DSS for the prototyping and production of data applications. In addition to Python, R, SQL, Hive, Impala, and Pig, Dataiku DSS 3.1 now enables Apache Spark users to write transformations and interactive notebooks in Scala, bringing the power of Spark's native and most performant language to the data teams using Dataiku DSS.

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.

The World Economic Forum recently published its 2016 list of the Top Ten Emerging Technologies that will likely have the greatest impact on the world in the years to come. The list is compiled by the WEF Meta-Council on Emerging Technologies, a panel of global experts led by Dr. Bernard Meyerson, IBM Fellow and Chief Innovation Officer. "Horizon scanning for emerging technologies is crucial to staying abreast of developments that can radically transform our world, enabling timely expert analysis in preparation for these disruptors," said Dr. Meyerson. "The global community needs to come together and agree on common principles if our society is to reap the benefits and hedge the risks of these technologies." The technologies on the list are not new.

On Friday, one of China's leading tech companies, Cheetah Mobile, announced the 57 million acquisition of New Republic--a move that signals its "journey of a transformation," according to CTO Charles Fan, highlighting its investment in AI and transition into a mobile content company. Founded in 2010, Cheetah Mobile began as a mobile tools provider. Over the last six years, it has become a major player in China's tech scene--and made a big impact, globally. Fan told TechRepublic that the company has over 650,000,000 monthly active users, internationally, in Q1. Fan said he sees the company as a "bridge between China and the world." What makes Cheetah Mobile different, he said, is that 80% of their mobile users are outside of China.

Silicon Valley is full of chatter about artificial intelligence, deep learning neural networks, and machine learning. And Intel, the world's biggest chip maker, is becoming a lot more conversant in that chatter today. Intel executive Diane Bryant announced today that the company is working on a next-generation version of its high-end server chip, the Xeon Phi, for A.I. applications. Baidu will use the upcoming Xeon Phi chips in the data centers it is building for its Deep Speech platform, where its networks will be able to parse natural language speech as quickly and accurately as possible. By 2020, there will be more servers handling data analytics than any other workload, Bryant said.

On the first day of "camp," two dozen rising high school sophomores arrive at the Stanford Artificial Intelligence Laboratory's Outreach Summer program (SAILORS) giddy and ready to get started. The rigorous, two-week program is designed to encourage young women from underrepresented populations to get more involved in the field of science. High school students Ishla Zareef-Mustafa and Genaro Pamatz participate in an anatomy lab as part of the Stanford Medical Youth Science Program. On the last day of the summer residential Stanford Medical Youth Science Program (SMYSP), 24 high school students, surrounded by family members, friends and mentors, present the research they have been working on during the five-week summer program.These programs, which fall under the umbrella of Stanford Pre-Collegiate Studies, are designed to provide teenagers from underrepresented populations with an opportunity to explore careers in science, but also to build new relationships, while taking what they've learned back to their home communities. The SAILORS curriculum includes lectures, hands-on research projects and mentoring activities that are intended to educate and excite young women about artificial intelligence.

Slate and Future Tense are discussing Mr. Robot and the technological world it portrays throughout the show's second season. You can follow this conversation on Future Tense, and Slate Plus members can also listen to Hacking Mr. Robot, a members-only podcast series featuring Lily Newman and Fred Kaplan. In this episode of Hacking Mr. Robot, Fred and Lily discuss Episode 7. Fred Kaplan is the author of Dark Territory: The Secret History of Cyber War.

TAIPEI Taiwan has asked Uber Technologies to pay a sales tax bill estimated by local media to be up to about 6.4 million, the government said on Friday, as a decision looms on whether the global ride-hailing service may be ordered to leave the island.

Facial recognition makes sense as a method for your computer to recognize you. After all, humans already use a powerful version of it to tell each other apart. But people can be fooled (disguises! Now researchers have demonstrated a particularly disturbing new method of stealing a face: one that's based on 3-D rendering and some light Internet stalking. Earlier this month at the Usenix security conference, security and computer vision specialists from the University of North Carolina presented a system that uses digital 3-D facial models based on publicly available photos and displayed with mobile virtual reality technology to defeat facial recognition systems.