Serial killer David Berkowitz, also known as the "Son of Sam," famously claimed that he heard voices in the form of a dog telling him to commit murder. In fact, according to the authors of a recent study published in the journal Brain, enhanced attention-related nerual pathways might cause these illusory sounds. People hear them because their brains may be especially primed to pick up speech. "It's true that lots of people who hear voices have serious mental health issues," Ben Alderson-Day, a psychological research at Durham University and lead author on the study told Popular Science. "But roughly 5 to 15 percent of the general population will have some experience of hearing unusual voices at some point in their lives. We think potentially up to one percent might have pretty frequent experiences and just don't really tell anyone and get on with their everyday lives."

The field of artificial intelligence has made great strides recently, as in AlphaGo's victories in the game of Go over world champion South Korean Lee Sedol in March 2016 and top-ranked Chinese Go player Ke Jie in May 2017, leading to great optimism for the field. But are we really moving toward smarter machines, or are these successes restricted to certain classes of problems, leaving others untouched? In 2015, the Allen Institute for Artificial Intelligence (AI2) ran its first Allen AI Science Challenge, a competition to test machines on an ostensibly difficult task--answering eighth-grade science questions. Our motivations were to encourage the field to set its sights more broadly by exploring a problem that appears to require modeling, reasoning, language understanding, and commonsense knowledge in order to probe the state of the art while sowing the seeds for possible future breakthroughs. Challenge problems have historically played an important role in motivating and driving progress in research.

When the crew of an $80-million super-yacht in the Ionian Sea checked its computer, they realized they were drifting slightly off course, likely as a result of strong currents buffeting their ship. The crew made adjustments and went back to work--without realizing they were now taking directions from a hacker. In the bowels of the ship, Todd Humphreys, an associate professor in the Department of Aerospace Engineering and Engineering Mechanics at the University of Texas at Austin, worked with his team to feed the super-yacht's crew false navigation data using a few thousand dollars worth of hardware and software. The crew was completely unaware they were now piloting in a direction of Humphreys' choosing. Thankfully, it was all an experiment that took place with the yacht owner's blessing.

I applaud Peter J. Denning's Viewpoint "Remaining Trouble Spots with Computational Thinking" (June 2017), especially for pointing out the subject itself is often characterized by "vague definitions and unsubstantiated claims"; "computational thinking primarily benefits people who design computations and . . . Moreover, the accompanying table outlined various historic definitions of "computational thinking," including a comparison of what Denning called the "new" and the "traditional" view of the subject. However, my own interest in computational thinking differs somewhat from Denning's. First, I question the legitimacy of the term "computational" itself. Why say it, when the very subject is "computers" and the chief academic approach to their study is "computer science"? If one looks at how computers are actually used, it may come as a surprise to learn that few such uses actually involve computing. For example, applications that deal with scientific and engineering problems are of ...

Despite the fact that he does not see very well, Alexei Efros, recipient of the 2016 ACM Prize in Computing and a professor at the University of California at Berkeley, has spent most of his career trying to understand, model, and recreate the visual world. Drawing on the massive collection of images on the Internet, he has used machine learning algorithms to manipulate objects in photographs, translate black-and-white images into color, and identify architecturally revealing details about cities. Here, he talks about harnessing the power of visual complexity. You were born in St. Petersburg (Russia), and were 14 when you came to the U.S. What drew you to computer science?

Algorithms are replacing or augmenting human decision making in crucial ways. People have become accustomed to algorithms making all manner of recommendations, from products to buy, to songs to listen to, to social network connections. However, algorithms are not just recommending, they are also being used to make big decisions about people's lives, such as who gets loans, whose résumés are reviewed by humans for possible employment, and the length of prison terms. While algorithmic decision making can offer benefits in terms of speed, efficiency, and even fairness, there is a common misconception that algorithms automatically result in unbiased decisions. In reality, inscrutable algorithms can also unfairly limit opportunities, restrict services, and even improperly curtail liberty.

The recent Efma-Accenture Innovation in Insurance Awards 2017 offered a valuable opportunity to learn how global insurers are leveraging digital technologies to transform themselves into everyday insurers. The innovations we saw are built on exponentially expanding digital technologies such as Artificial Intelligence (AI), Internet of Things (IoT), big data, analytics, and blockchain, and are being applied across the entire insurance value chain, from product development to claims. As customers come to expect increasingly personalized, on-demand service, insurers must evolve to meet their expectations. To that end, leading insurers are embracing new technologies to shape innovative business models and client value proposition. I strongly believe that those who will innovate at scale will be in a position to weather disruption and transform into everyday insurers.

Amazon Rekognition is a service that makes it easy to add image analysis to your applications. It's based on the same proven, highly scalable, deep learning technology developed by Amazon's computer vision scientists to analyze billions of images daily for Amazon Prime Photos. Facial recognition enables you to find similar faces in a large collection of images. In this post, I'll show you how to build your own face recognition service by combining the capabilities of Amazon Rekognition and other AWS services, like Amazon DynamoDB and AWS Lambda. This enables you to build a solution to create, maintain, and query your own collections of faces, be it for the automated detection of people within an image library, building access control, or any other use case you can think of.

This post is part of a continuing series, "Digital: from A to Z," that explores what it means to be "digital" from A to Z, broken down into individual blog posts diving deeper into various subjects. Check back here regularly to see continuing posts as I work my way through the alphabet and let me know: What's in your A to Z of digital? You can find me on twitter @Max_Hemingway or leave a comment below. Machine Learning (ML) allows a computer to learn and act without being explicitly programmed with that knowledge. For example, if you get a computer to recognise a picture of a car and show it some examples of a car, it will then be able to recognise cars going forward and apply what it has learnt against new pictures shown.

A "robot priest" adorned with Buddhist robes and a hi-resolution tablet is now programmed to perform funeral rites for less cost than a human. Japanese plastic molding maker Nissei Eco Co. created software for Pepper The Robot enabling the humanoid to chant Buddhist mantras and recite sutras typically performed by monks at funerals. First introduced in 2014 by Japanese telecommunications company, SoftBank Group Corp., Pepper is a human-shaped robot that welcomes customers to more than 140 SoftBank Mobile stores. The robot boasts several cameras and microphones in addition to its sophisticated AI ability to perceive and respond to human emotions. Michio Inamura, a Nissei executive adviser, tells Reuters that Japan's aging and declining population has led to Buddhist priests receiving less financial backing in communities and working outside of their temple duties to make ends meet.