The Matrix, Terminator, Iron Man – the list of films in which some type of AI suddenly goes crazy and tries to take over the planet is long. The idea has been around since the 50s, but it seems we're getting closer to a world where computers are as smart as humans or even smarter. The narrative of a robot revolution or'AI taking over the world' is so overused that it has become a cliché. The question is: what will that look like? Will the machines be able to rebel against us?

WIRE)--What's New: Today, Intel and the National Science Foundation (NSF) announced award recipients of joint funding for research into the development of future wireless systems. The Machine Learning for Wireless Networking Systems (MLWiNS) program is the latest in a series of joint efforts between the two partners to support research that accelerates innovation with the focus of enabling ultra-dense wireless systems and architectures that meet the throughput, latency and reliability requirements of future applications. In parallel, the program will target research on distributed machine learning computations over wireless edge networks, to enable a broad range of new applications. "Since 2015, Intel and NSF have collectively contributed more than $30 million to support science and engineering research in emerging areas of technology. MLWiNS is the next step in this collaboration and has the promise to enable future wireless systems that serve the world's rising demand for pervasive, intelligent devices."

Health researchers have put artificial intelligence to work in crunching big data, allowing them to develop technology that can predict the future onset of around 20 diseases so people can make preventative lifestyle changes. The model developed at Hirosaki University and Kyoto University calculates one's probability of developing a disease within three years based on data obtained from voluntary health checkups on about 20,000 people in Japan. If a patient agrees to disclose data on some 20 categories collected during checkups, the model can project the potential development of arteriosclerosis, hypertension, chronic kidney disease, osteoporosis, coronary heart disease and obesity, among other conditions. The team set up two groups of people for each disease -- those whose data suggested they could develop the ailment in the future and a control group -- and crunched their health data to predict whether would will actually develop the disease. "We made correct predictions on whether individuals will develop the diseases within three years with high accuracy," said Yasushi Okuno, professor at Kyoto University's Graduate School of Medicine.

Imagine sitting in a circle with a few people where each of you knows only two languages -- one shared with the person on your left, and one shared with the person on your right. If you say something to the person on your right and ask them to pass on the message, it might very well be that, after being passed along all the languages, it comes out sounding very different from the original message. This might seem like a very weird game of Telephone to you, but in the same way that whispering impairs your ability to hear the message, so translation works as an imperfect communication channel. When you try to translate a message into a different language, you can change its intended meaning without being aware of it. Oftentimes messages are subjective, ambiguous, or, in some cases, even impossible to represent without any loss of information. But why is translation such a challenge? And in being so, can we ever achieve such a thing as a perfect translation?

What are the best tools for performing data science tasks? And which tool should you pick up as a newcomer in data science? I'm sure you've asked (or searched for) these questions at some point in your own data science journey. There is no shortage of data science tools in the industry. Picking one for your journey and career can be a tricky decision.

Whether it is Google or Tesla's self-driven cars, AI is ruling all over. Assimilating these AI technologies into marketing can bring in substantial growth in the businesses. Fremont, CA: Artificial Intelligence (AI) is replicating the cognitive behavior of humans today. Its scope is well recognized in the advertising and marketing fields, where the professionals are required to make well-informed decisions and strategies for upscaling their business. Let us take a look at how Artificial intelligence is employed in advertising and marketing.

According to the Cambridge dictionary, the meaning of AI is, "the study of how to produce machines that have some of the qualities that the human mind has, such as the ability to understand language, recognize pictures, solve problems, and learn". When a machine is able to make an intelligent decision, it can be referred to as being intelligent, but artificially. We mostly see people using the terms of machine learning, deep learning, and AI synonymously. However, Deep Learning is a subset of Machine Learning, and Machine Learning is a subset of AI. The seeds of modern AI were planted by classical philosophers who attempted to describe the process of human thinking as the mechanical manipulation of symbols.

Robert Williams went to jail because a computer--and a pair of Detroit police officers--made a mistake. The officers relied on facial recognition software to identify Williams as a suspect in a 15-month-old shoplifting case. They were wrong--making Williams perhaps the first known case of a wrongful arrest resulting from faulty facial recognition. Earlier this month, IBM, Microsoft, and Amazon swore off or paused their sale of facial recognition tools to US police and called on Congress to regulate the technology. It was sold by police contractor DataWorks Plus, and powered by algorithms from Japanese tech firm NEC and Colorado-based Rank One Computing.

Chinese ride-hailing giant Didi Chuxing, backed by SoftBank Group, launched a test robotaxi service in a Shanghai suburb on Saturday after installing V2X (Vehicle to Everything) hardware throughout the area. Under the test, customers within a designated area will be able to hire free on-demand rides using Didi's autonomous vehicles, which currently also have safety drivers, the company said. V2X enables autonomous vehicles (AVs) to communicate with their surroundings and Didi has installed equipment at major junctions within the test area to minimize safety blind spots and coordinate communication among its fleet, it said. The equipment will mainly be used by Didi's own cars, although cars operated by other autonomous driving companies could also link up to the system, said Meng Xing, the chief operating officer of Didi's autonomous driving unit.

Sujal is a Software Quality Professional and Test Automation expert with almost 20 years of combined experience in all software engineering life cycle phases of software development, software testing as well as product deployment. He is highly trained individual with a proven track record of delivery, strong strategic vision and demonstrated ability to inspire/mentor/manage QA teams and its activities in the geographically dispersed companies with his outstanding leadership ability and creative problem solving skills. Extensive experience doing performance testing and load testing of web API/web based application using various testing tools like VSTS, HP Load Runner, JMeter/Blazemeter, Fiddler, SoapUI and Postman. In depth knowledge of manual and automated testing of mobile application including mobile sites, native apps and hybrid apps. Sujal currently resides in Chicago USA.