HOW HAS ARTIFICIAL intelligence, associated with hubris and disappointment since its earliest days, suddenly become the hottest field in technology? The term was coined in a research proposal written in 1956 which suggested that significant progress could be made in getting machines to "solve the kinds of problems now reserved for humans…if a carefully selected group of scientists work on it together for a summer". That proved to be wildly overoptimistic, to say the least, and despite occasional bursts of progress, AI became known for promising much more than it could deliver. Researchers mostly ended up avoiding the term, preferring to talk instead about "expert systems" or "neural networks". The rehabilitation of "AI", and the current excitement about the field, can be traced back to 2012 and an online contest called the ImageNet Challenge.

One difficulty faced by artificial intelligence is predicting what humans are going to do next. To help solve that problem, researched have trained an algorithm by making it binge-watch TV. Computer vision experts from MIT's Computer Science and Artificial Intelligence Lab (CSAIL) made an algorithm watch 600 hours of TV shows including Ugly Betty, Scrubs, The Big Bang Theory, The Office (US) and more. In each of the clips, taken from YouTube, humans were performing tasks and interacting with each other. After analysing the videos, the AI was then made to watch a clip it hadn't seen before and predict what would happen.

Facebook is well known for its early and increasing use of artificial intelligence. The social media site uses AI to pinpoint its billion-plus users' individual interests and tailor content accordingly by automatically scanning their newsfeeds, identifying people in photos and targeting them with precision ads. And now behind the scenes the social network's AI researchers are trying to take this technology to the next level--from pure data-crunching logic to a nuanced form of "common sense" rivaling that of humans. AI already lets machines do things like recognize faces and act as virtual assistants that can track down info on the Web for smartphone users. But to perform even these basic tasks the underlying learning algorithms rely on computer programs written by humans to feed them massive amounts of training data, a process known as machine learning.

WHEN the 2016 Wimbledon Championships start on June 27th millions of tennis fans will begin posting on social networks such as Twitter, Facebook and Instagram about everything from the matches to the attire, hairdos and headbands of their favourite players. The contest's organiser, the All England Lawn Tennis Club (AELTC), would quite like to know what the hottest topics are. So it is using a powerful computer to find out. That computer is Watson, an IBM machine which in 2011 famously won the American TV quiz "Jeopardy!" and nowadays resides as a cloud-computing service. The idea, says Alexandra Willis, the AELTC's digital supremo, is to use its machine learning and natural language-processing techniques to discover the most pressing topics of conversation among the vast output from fans. Knowing that, the club's editorial team--which provides content for Wimbledon's mobile app, its website and its video feeds--can respond quickly with relevant articles, posts, tweets, statistics and images.

Romanian startup Grobyk is organizing ready-to-use, trending information from your chosen sources, using Artificial Intelligence algorithms. Their 3 words pitch is: Instant Relevant Research. Content marketers keep up with a lot of sources, from social media, blogs, news site and so on. Search engine trends are misleading. What's relevant are social media shares.

One early morning in March 2011, Albert Chretien and his wife, Rita, loaded their Chevrolet Astro van and drove away from their home in Penticton, British Columbia. Their destination was Las Vegas, where Albert planned to attend a trade show. Rather than stick to the most direct route, they decided to take a scenic road less travelled, Idaho State Highway 51. The Chretiens figured there had to be a turnoff from Idaho 51 that would lead them east to US Route 93 all the way to Vegas. Albert and Rita had known each other since high school. During their 38 years of marriage, they had rarely been apart. They worked together, managing their own small excavation business.

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Would you like a version of yourself to live on after death? A radical new concept called "augmented eternity" could make that fantasy a reality, creating a posthumous impression of our knowledge, opinions and even parts of our personality in digital form. Researchers at the MIT Media Lab and Ryerson University in Toronto believe that by applying artificial intelligence to all the data we produce each day, we may be able to transfer our thoughts to a virtual entity that not only survives our physical demise but continues to learn as new information is plugged into it. Dr Hossein Rahnama, a visiting scholar at the Media Lab and research and innovation director of Ryerson's Digital Media Zone, is planning to publish a paper on augmented eternity later this year. "My ultimate goal is to bridge the gap between life and death by eternalizing our digital identity," says Rahnama, who is also founder of Flybits, a cloud-based service that modifies the behavior of mobile apps based on where and how the customer is using it.

Thinking about how important the funnel of a customer is for a company, we appreciate when new tools make the process easier. This is why we built Marketeer and today we are presenting version 3.0 with Marketeer Intelligent CRM. I have been working with artificial intelligence for a while – since the end of the 90s to be exact meanwhile I was running an advertising company. In 2012 I decided to invest time and money into building a new product that would help me make communication easier and effective for my clients. And today, we have it!

An anti-ship cruise missile (ASCM) developed by the U.S. Defense Advanced Research Projects Agency (DARPA) that "thinks" thanks to its onboard artificial intelligence (AI) will arm U.S. Navy warships and strike aircraft beginning 2019 and U.S. Air Force combat aircraft a year earlier. The Long Range Anti-Ship Missile (LRASM) can find a warship target on its own by using its onboard AI to locate a specific warship from among a fleet of enemy warships. A multi-mode seeker guided by the AI ensures the correct warship is hit in a specific area to maximize the probability of sinking the target. The AI enables autonomous targeting by using on-board targeting systems to independently acquire a target without the need for prior precision intelligence, GPS or data-links. These capabilities allow positive target identification and the precision engagement of moving warships in an enemy fleet heavily defended by surface-to-air missiles (SAMs) and electronic countermeasures. The missile is designed with counter-countermeasures to evade active defense systems on enemy ships.