I am a data scientist at STATWORX, and I enjoy making data make sense.

Who is liable for a car accident when no one's driving the car? That was one of several questions explored by experts in Arizona's autonomous vehicle industry during a special symposium last Thursday as representatives from public and private sectors hashed out what the near future looks like for driverless cars. As more self-driving cars move around the East Valley, stakeholders and policymakers gathered in Chandler to discuss how Arizona will adapt to the rapidly-developing technology. "A lot of work needs to be done," said Jill Sciarappo, senior marketing director for Intel, "and we need to come together to solve a lot of problems to make that happen." A reoccurring theme of the symposium, organized by the Chandler Chamber of Commerce, involved the liability factors involving self-driving cars.

This is an example of how digital assistants/bots have started making a difference in consumers' lives. Recently, the Singapore-based communications company UIB put out this case study on its Website of how it had tied-up with the UAE-based telco provider du Telecom to create'Blu', a – UIB UnificationEngine -powered virtual assistant on WhatsApp & Facebook Messenger. Through this UIB UnificationEngine conversational artificial intelligence (AI) platform-powered chatbot, customers can contact du 24/7 on WhatsApp with queries which receive instant responses, said Ken Herron Chief Marketing Officer, UIB, on the company Website. What's more, even Facebook sat up & took note of this partnership in an official WhatsApp case study/success story. Emirates Integrated Telecommunications Company, also known as du, is a telecom operator in the UAE, having about 9 million customers with its mobile, fixed line & broadband services.

In this special guest feature, Robert Roe from Scientific Computing World looks at the development of deep learning and its impact on scientific applications. Deep learning has seen a huge rise in popularity over the last five years in both enterprise and scientific applications. While the first algorithms were created almost 20 years ago with the development of artificial neural networks in 2000, the technology has come of age due to the massive increases in compute power, development of GPU technologies, and the availability of data to train these systems. Today the use of this technology is widespread across many scientific disciplines, from earthquake prediction, high-energy particle physics and weather and climate modeling, precision medicine and even the development of clean fusion energy. With so many possible applications, it can be difficult for scientists to figure out if artificial intelligence (AI) or deep learning (DL) can fit into workflow.

Machine learning allows computers to function without explicit programming, which means, the algorithms are trained to improve upon the data that is provided to them. Machine learning also gave us the concept of self-driven cars, spam-protected emails, speech recognition, personalized marketing and much more. Today, the technology is used to determine pickup locations, cab arrival times, provide entertainment recommendations, and identifies patterns, finding map routes and much more. Gartner predicts that 10% of all vehicles will possess self-governing driving abilities by 2021. Along with Machine Learning, autonomous driving systems also draw their power from Artificial Intelligence.

Given a swell of dire warnings about the future of Artificial Intelligence over the last few years, the field of AI ethics has become a hive of activity. These warnings come from a variety of experts such as Oxford University's Nick Bostrom, but also from more public figures such as Elon Musk and the late Stephen Hawking. The picture they paint is bleak. In response, many have dreamed up sets of principles to guide AI researchers and help them negotiate the maze of human morality and ethics. Now, a paper in Nature Machine Intelligence throws a spanner in the works by claiming that such high principles, while laudable, will not give us the ethical AI society we need.

At the Common Good in the Digital Age tech conference recently held in Vatican City, Pope Francis urged Facebook executives, venture capitalists, and government regulators to be wary of the impact of AI and other technologies. "If mankind's so-called technological progress were to become an enemy of the common good, this would lead to an unfortunate regression to a form of barbarism dictated by the law of the strongest," he said. In a related but contextually different conversation, this summer Joy Buolamwini testified before Congress with Rep. Alexandria Ocasio-Cortez (D-NY) that multiple audits found facial recognition technology generally works best on white men and worst on women of color. What these two events have in common is their relationship to power dynamics in the AI ethics debate. Arguments about AI ethics can wage without mention of the word "power," but it's often there just under the surface. In fact, it's rarely the direct focus, but it needs to be. Power in AI is like gravity, an invisible force that influences every consideration of ethics in artificial intelligence. Power provides the means to influence which use cases are relevant; which problems are priorities; and who the tools, products, and services are made to serve. It underlies debates about how corporations and countries create policy governing use of the technology.

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations.

I live in a city filled with reminders of epic travelers from the historic journeys of Lewis and Clark to the cross country treks in automobiles along Route 66. There are still many icons around Saint Louis and across Missouri modern travelers can visit today. From Missouri's rolling hills and natural wonders explored by Lewis and Clark to the kitschy man-made attractions that helped make Route 66 a favorite for family road trips, the unique views make a Missouri road trip memorable. For blind travelers, this is a part of the trip they miss. A new prototype smart car window aims to change the experience by enabling blind or partially-sighted people to visualize passing scenery through touch.

As Moore's law runs out of steam, new programming approaches are being pursued with the goal of greater hardware performance with less coding. The Defense Advanced Projects Research Agency is launching a new programming effort aimed at leveraging the benefits of massive distributed parallelism with less sweat.