Jill Watson has been a teaching assistant (TA) at the Georgia Institute of Technology for five years now, helping students day and night with all manner of course-related inquiries. But for all the hard work she has done, she still can't qualify for outstanding TA of the year. That's because Jill Watson, contrary to many students' belief, is not actually human. This ebook, based on the latest ZDNet / TechRepublic special feature, advises CXOs on how to approach AI and ML initiatives, figure out where the data science team fits in, and what algorithms to buy versus build. Created back in 2015 by Ashok Goel, professor of computer science and cognitive science at the Institute, Jill Watson is an artificial system based on IBM's Watson artificial intelligence software.

How often do you receive a message and wonder, "Did a person really write this?" When receiving a message online, it can often be difficult to discern whether it came from a human or a chatbot. Given the rapid rise in automated forms of communication, it would be foolish to assume that words attached to a name and face are in fact human. On the same token, our emails and messages on social platforms are filled with general platitudes, formulaic personalizations, and responses that seem reminiscent of an if this, then that (IFTTT) equation. We typically chalk this up as a win for technologists, who have been busy adding linguistic nuances and flourishes to how chatbots interact with humans.

The other day I had to log in to a service I hadn't used before. Since I was a new user, the website decided that it needed to check that I wasn't a robot and so set me a Captcha (Completely Automated Public Turing test to tell Computers and Humans Apart). This is a challenge-response test to enable a computer to determine whether the user is a person rather than a machine. I was presented with an image of a roadside scene over which was overlaid a grid. My "challenge" was to click on each cell in the grid that contained a traffic sign, or part thereof.

"We become what we behold. We shape our tools and then our tools shape us." This truism--by the media-scholar John Culkin about the work of Marshall McLuhan--is more potent than ever in the age of data and algorithms. The technology is having a profound effect on how people live and think. Some of those changes are documented in "Re-Engineering Humanity" by two technology thinkers from different academic backgrounds: Brett Frischmann is a law professor at Villanova University in Pennsylvania and Evan Selinger teaches philosophy at Rochester Institute of Technology in New York. Upgrade your inbox and get our Daily Dispatch and Editor's Picks.

Kaltheuner, F. and Bietti, E. Data is power: Towards additional guidance on profiling and automated decision-making in the GDPR.

For more than 65 years, computer scientists have studied whether robots' behavior could become indistinguishable from human intelligence. But while we've focused on machines, have we ignored changes to our own capabilities? In a book due to be published next year, Being Human in the 21st Century, a law professor and a philosopher argue that we've overlooked the equally important, inverse question: Are humans becoming more like robots? In 1950, computer scientist Alan Turing put forward what's now known as the "Turing Test." Essentially, Turing proposed that a key test of machine thinking is whether someone asking the same questions to both a human and a robot could tell which is which. This has since become an important method to evaluate artificial intelligence, with regular Turing Test competitions to determine the extent of robots' growing ability to mimic human behavior.

Everyone knows about the Turing Test. It was first proposed by Alan Turing in his famous 1950 paper'On Computing Machinery and Intelligence'. The paper started with the question'Can a machine think?'. Turing noted that philosophers would be inclined to answer that question by hunting for a definition. They would identify the necessary and sufficient conditions for thinking and then they would try to see whether machines met those conditions. They would probably do this by closely investigating the ordinary language uses of the term'thinking' and engaging in a series of rational reflections on those uses. At least, Oxbridge philosophers in the 1950s would have been inclined to do it this way. Turing thought this approach was unsatisfactory.