Tencent is pushing ahead with efforts to sign up schools to use its all-in-one WeChat app as part of a broader shift by the company to go beyond consumers and serve more institutions. Since WeChat started partnering with public schools and private training centres in 2014, students have been to make purchases at canteens, check test scores and communicate in group chats with their peers, parents and teachers using the app's multiple functions. Tencent revamped its organisation structure on the eve of its 20th anniversary this year to align the company to compete for the industrial internet. In an open letter last month, chairman Pony Ma Huateng laid out the rationale for the restructuring, saying that digitisation of the economy meant the battle for the internet has moved on from consumers to industry. "WeChat Pay has been a tool to connect people, and is now increasingly connecting industries under the company's new direction to embrace the industrial internet."

Applying artificial intelligence to self-driving cars to smooth traffic, reduce fuel consumption, and improve air quality predictions may sound like the stuff of science fiction, but researchers at the Department of Energy's Lawrence Berkeley National Laboratory (Berkeley Lab) have launched two research projects to do just that. In collaboration with UC Berkeley, Berkeley Lab scientists are using deep reinforcement learning, a computational tool for training controllers, to make transportation more sustainable. One project uses deep reinforcement learning to train autonomous vehicles to drive in ways to simultaneously improve traffic flow and reduce energy consumption. A second uses deep learning algorithms to analyze satellite images combined with traffic information from cell phones and data already being collected by environmental sensors to improve air quality predictions. "Thirty percent of energy use in the U.S. is to transport people and goods, and this energy consumption contributes to air pollution, including approximately half of all nitrogen oxide emissions, a precursor to particular matter and ozone – and black carbon (soot) emissions," said Tom Kirchstetter, director of Berkeley Lab's Energy Analysis and Environmental Impacts Division, an adjunct professor at UC Berkeley, and a member of the research team.

AI is not a silver bullet, but it could help tackle some of the world's most challenging social problems. Artificial intelligence (AI) has the potential to help tackle some of the world's most challenging social problems. To analyze potential applications for social good, we compiled a library of about 160 AI social-impact use cases. They suggest that existing capabilities could contribute to tackling cases across all 17 of the UN's sustainable-development goals, potentially helping hundreds of millions of people in both advanced and emerging countries. Real-life examples of AI are already being applied in about one-third of these use cases, albeit in relatively small tests. They range from diagnosing cancer to helping blind people navigate their surroundings, identifying victims of online sexual exploitation, and aiding disaster-relief efforts (such as the flooding that followed Hurricane Harvey in 2017). AI is only part of a much broader tool kit of measures that can be used to tackle societal issues, however. For now, issues such as data accessibility and shortages of AI talent constrain its application for social good. This article is a condensed version of our discussion paper, Notes from the AI frontier: Applying AI for social good (PDF–3MB).

Cutting Edge Deep Learning For Coders, Part 2 from fast.ai

In the past few years, there's been a lot of discussion around Artificial Intelligence and it's implications for everything from industrial applications to smart phone apps--and if you have a smart phone, chances are you've come across TED Talks. If you haven't listened to a TED Talk, you should. Presented by people who are deeply connected to the field they are speaking about, it gives you the opportunity to listen to people with an insider's expertise that most of us would never hear from, but who can provide valuable perspectives. So whether or not you've ever seen a TED Talk, if you've been looking to know more about the current state and future direction of Artificial Intelligence in our world, these 10 TED Talks are essential presentations to help you become fully informed about the state of this revolutionary technology. Presented by Educator and Entrepreneur Sebastian Thrun & TED Conference Curator Chris Anderson, this Ted Talk is an excellent overview of what people talk about when they talk about artificial intelligence, what people are concerned about, and why.

"Machine learning" and "data science" - two terms that might sound mysterious to someone who has never dealt with these processes before. However, if you have heard about machine learning before, you probably know that it is a process that aims to change the very fundaments of how our society is built... Sounds dramatic? Well, perhaps, but I do have a strong case for that - you'll find it in my machine learning course! Why Choose this Machine Learning Course? Machine learning, as a concept, is indeed a very interesting one.

People in the tech world love to say that everybody should learn to code -- and there are a lot of good reasons to pick up the trade: earn a higher salary, understand the implications of A.I. and cybersecurity, protect yourself from automation. While wages have been stagnant, there has been consistent growth of high-paying jobs in software (for now). Despite all the incentives bringing people into programming and other technology fields, we actually need more people willing to break out of it. Don't get me wrong: I teach computer science and computer programming. I believe wholeheartedly in the power of technology to improve lives.

One of the most difficult things about learning a new skill is finding time to study. Being able to complete assignments in between meetings or while traveling can make all the difference in the ability to make regular progress. Unfortunately, none of the online courses in programming, data science and machine learning I've taken over this past year have great mobile solutions. Much of the work still requires a laptop. After a great deal of searching, I finally found a solution in two applications that allow users to run Jupyter notebooks and python terminal commands, both of which are common tools for completing machine learning tasks.

The graph represents a network of 1,652 Twitter users whose tweets in the requested range contained "iiot bigdata", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Friday, 30 November 2018 at 15:53 UTC. The requested start date was Friday, 30 November 2018 at 01:01 UTC and the maximum number of tweets (going backward in time) was 5,000. The tweets in the network were tweeted over the 6-day, 2-hour, 7-minute period from Friday, 23 November 2018 at 22:52 UTC to Friday, 30 November 2018 at 01:00 UTC. Additional tweets that were mentioned in this data set were also collected from prior time periods.

Machine-learning engineer was the fastest growing job category in the five years to 2017, according to LinkedIn. But tech's hottest role isn't a simple field to break into, requiring at least high school math and some programming knowledge, even to get started. Luckily there are an increasing number of options for those wanting to get a grounding in the field, with Amazon Web Services (AWS) being the latest tech giant to release a set of machine-learning courses for free. That's in addition to the existing well-regarded material available online from the likes of fast.ai and Andrew Ng and Coursera. It's worth noting that most of these courses will benefit from having a basic knowledge of Python and high school linear algebra, statistics and calculus.