This article is part of Demystifying AI, a series of posts that (try to) disambiguate the jargon and myths surrounding AI. Where will humans fit in a world where robots outsmart them? This is the focus of a heated debate between thought leaders and tech billionaires. Some believe we're steadily meandering toward an AI apocalypse, where humans are either obliterated or enslaved by robots, and we must act quickly to prevent it. Others will tell you that artificial intelligence will always be the subservient best friend of mankind, even when it outwits its creators, and we should move ahead with developing AI at full speed.

So what do you think are the key trends that will shape earned media in 2018? Are they likely to be technologically-driven? Or are the most significant changes going to be cultural ones that are a response to the ever evolving world of news media? Gorkana asked a series of experts for their predictions for 2018 and came up with four major trends, two tech and two cultural, that it believes anyone in earned media needs to be following. Among the experts who peered into their virtual crystal balls for Gorkana was our very own MD Sara Collinge.

Basic human error or a lack of understanding of how disclosure works will always remain potential stumbling blocks even if AI was made available to help alleviate the increased volume of data now being gathered to ensure a successful prosecution. A senior UK police chief revealed in a speech in London on Wednesday, February 7, forces should examine using artificial intelligence to help cope with the scale of information involved in investigations and avoid the kind of mistakes that resulted in a string of collapsed rape trials. Sara Thornton, the chair of the National Police Chiefs' Council, said the volume of date held by individuals had massively increased the number of potential lines of enquiry that officers must pursue to understand a case. Her views have, however, been questioned by a leading expert in counter-terrorism and organised crime who insists the police have already had the ability to comb through large volumes of digital data under the Criminal Procedure and Investigations Act 1996. In an exclusive interview, David Vidicette, now the author of several crime thriller books including The Theseus Paradox, said this has always been standard procedure and normal police and detective work.

New York, NY--February 8, 2018-- AdTheorent, Inc., an advertising technology company using data, predictive targeting and machine learning to provide competitive advantages to marketers as measured by real-world business outcomes, today announced it has been named a winner in the 2018 BIG Innovation Awards presented by the Business Intelligence Group. AdTheorent's Machine Learning-Powered Predictive Advertising Platform is being honored with a product innovation award. The immense volume of data available to marketers today provides both an opportunity and a challenge. Despite a 50X growth in the amount of data available, only .5% of data is analyzed and used by advertisers. AdTheorent addresses this issue for marketers through its highly advanced predictive advertising platform, which uses machine learning more effectively, and efficiently, across larger data sets, than any other solution in market.

What comes to your mind when you hear these words. Artificial intelligence is changing the face of technology and business. And it has also changed the face of SEO. As you know, people made billions of searches on Google every day. And Google said that 15% of those queries had never been seen before.

The very first one I remember is Ski Free. It's pretty simplistic; you race down a hill, pursued by a yeti, avoiding obstacles and trying desperately not to crash. In my distant memory, others follow: a blur of pixellated colours and basic sound effects by today's symphonic standards. They've always been there, part of the landscape of this digital world in which I've grown up. There was a Game Boy in the house soon after, and with it, that childhood phenomenon: Pokémon.

The quest to give machines human-level intelligence has been around for decades, and it has captured imaginations for far longer -- think of Mary Shelley's Frankenstein in the 19th century. Artificial intelligence, or AI, was born in the 1950s, with boom cycles leading to busts as scientists failed ...

My guest Robert Draper says one of the greatest threats to our democracy is gerrymandering, in which the party in power in a state redraws the map of election districts to give the advantage to that party's candidates. Since districts are redrawn only every 10 years following the census, gerrymandering can almost guarantee that the majority party will stay in power. There are a couple of gerrymandering cases currently before the Supreme Court. Draper has reported on gerrymandering, and we'll talk about that a little later. First, we're going to talk about his new article "They Are Watching You - And Everything Else On The Planet" published in this month's National Geographic. It's about state-of-the-art surveillance from closed-circuit TV to drones and satellites and the questions these surveillance technologies raise about privacy. As part of his research, he spent time in surveillance control rooms in London. And he went to a tech company in San Francisco whose mission is to image the entire Earth every day. Draper is a contributing writer for National Geographic and a writer at large for The New York Times Magazine. So let's start with surveillance. Why did you choose England as the place to report on surveillance? ROBERT DRAPER: Well, England has become kind of an obvious focal point to talk about surveillance. It's become, in a way, a petri dish for the subject, I suppose, for a couple reasons. First of all, the U.K. is where George Orwell wrote his dystopian classic "1984" back in 1949 when the totalitarianism of Nazi Germany and the USSR were his prime reference points.

In my last article I mentioned that Google and Facebook are the leaders in the artificial intelligence gold rush era we are now living in. I always was a Google guy, my first phone was an Android phone, I got the Chromecast went it first came out, I learned how to code watching YouTube videos and I am a Google Drive paying customer so one should read this article knowing that I have a preference for Alphabet products but in this fake news and paid influencers era please believe me I am not getting paid in any way by Google or any of its subsidiary corporations. I have a preference for its products like any of you reading this article have his or her preference for Facebook as a social media or Apple for its products instead of Android. Nevertheless I always try to be objective in my opinions especially in regard to artificial intelligence, machine learning and data science. In October 2017, Google CEO came out with a pretty strong Ai focused statement and said that "Google is now an Ai first company".

We consider the design of forecasting competitions in which multiple forecasters make predictions about one or more independent events and compete for a single prize. We have two objectives: (1) to award the prize to the most accurate forecaster, and (2) to incentivize forecasters to report truthfully, so that forecasts are informative and forecasters need not spend any cognitive effort strategizing about reports. Proper scoring rules incentivize truthful reporting if all forecasters are paid according to their scores. However, incentives become distorted if only the best-scoring forecaster wins a prize, since forecasters can often increase their probability of having the highest score by reporting extreme beliefs. Even if forecasters do report truthfully, awarding the prize to the forecaster with highest score does not guarantee that high-accuracy forecasters are likely to win; in extreme cases, it can result in a perfect forecaster having zero probability of winning. In this paper, we introduce a truthful forecaster selection mechanism. We lower-bound the probability that our mechanism selects the most accurate forecaster, and give rates for how quickly this bound approaches 1 as the number of events grows. Our techniques can be generalized to the related problems of outputting a ranking over forecasters and hiring a forecaster with high accuracy on future events.