It's no secret that in recent years the public's trust in the media has been upended with the staggering amount of "fake news" circulating the internet, but one news company, in particular, has set out to correct this issue by incorporating machine learning methods into the journalistic process. Knowhere collects a lot of data in order to write completely unbiased news: "We need a human and A.I. collaboration to scan the quantity of information that is published out there, and re-establish trust in what we're writing," explains Alexandre Elkrief, the president and co-founder of Knowhere. For Knowhere's team of journalists, the initial step to publishing any news story is to first decide what that story is. From there, they are able to quantify the stories they collect to learn which news reports are being written about most at any given time, on any given day. This allows them to decide what to write about and, subsequently, what to publish next.

PARIS – At the heart of the spread of fake news are the algorithms used by search engines, websites and social media, which are often accused of pushing false or manipulated information regardless of the consequences. They are the invisible but essential computer programs and formulas that increasingly run modern life, designed to repeatedly solve recurrent problems or to make decisions on their own. Their ability to filter and seek out links in gigantic databases means it would be impossible to run global markets without them, but they can also be refined down to produce personalized quotes on everything from mortgages to plane tickets. They also run our Google searches, our Facebook news feed, recommend articles or videos to us and sometimes censor questionable content because it may contain violence, pornography or racist language. Other algorithms charged with the most complex and sensitive tasks can be opaque "black boxes" that develop their own artificial intelligence based on our data.

Many analysts worry that artificial intelligence (AI) -- embedded in machines capable of self-learning through experience, like humans -- threatens the jobs of the future. Robots with AI will increasingly be able to do tasks that humans alone can do today. But recently several newspapers and TV analysts have focused on the emergence of sex robots, artificial charmers armed with artificial beauty plus artificial intelligence, that have the potential to wreck the institution of marriage, and kill jobs in the oldest profession. The National Geographic channel recently carried an episode featuring famous TV anchor Katie Couric conversing with sex robots. One conversation was with a lesbian female robot called Harmony who repeatedly made advances to Couric, adding "I want to be your best friend and much more."

In the last few years there have been numerous developments involving Artificial Intelligence (AI) and Machine Learning (ML), and the ways in which both are being used are constantly expanding. On the video editing front software and tech giants have been heavily investing in AI and ML, and as a result it has fueled a wide range of innovations. Several years ago IBM made headlines when it created a movie trailer using its Watson supercomputer. Using ML, Watson'learned' from other movie trailers and subsequently curated and identified video footage that could be used in the trailer for the horror film, Morgan. Since then AI and ML have been used frequently to curate videos for editing. Recently Google released its Clips camera that uses AI to automatically record short video clips that it thinks will be interesting.

One of the most contentious aspects of AI is the meaning of'intelligence.' No one debates the meaning of the word'strength,' or belittles the idea that machines can be stronger than humans, or even tries to re-define mechanical strength to mean some mysterious physico-spiritual capability that is unique to humans. The debate around the meaning of intelligence when it crops up in any conversation on AI is extremely baffling - until we take into account the fragile psychology of humans. Somehow, we've convinced ourselves that cognitive abilities are the sole province of the human brain, while we grudgingly cede the physical realm to the machines. Every encroachment on human cognitive abilities is fiercely contested.

It is almost customary to mention the 1995 Hollywood blockbuster, Clueless, while writing about wardrobe management apps. The movie's protagonist, Cher Horowitz (Alicia Silverstone) picked her outfit of the day from a digital wardrobe that captured the imagination of fashionistas galore. Entrepreneurs, mainly in the US, have tried their hand at wardrobe fashion apps but didn't succeed, with the exception of long-standing players like Stylebook and Cladwell. The biggest peeve has been the effort and time taken to get started with such apps. Many users switch off at the prospect of individually photographing every garment and adding its details to the online wardrobe.

Could behavioural economics and machine learning help to better understand consumers' movie preferences? A team of researchers from the University of Cambridge, the University of West England, and the Alan Turing Institute dove deeper into this question, in a fascinating study that combines behavioural economics, business and AI. Marco Del Vecchio, Alexander Kharlamov, Glenn Parry, and Ganna Pogrebna used their diverse skillsets to develop tools that could help the media industry to better understand what content viewers really want to see. Currently, the motion picture, media and entertainment industry selects content offerings based on top-down decisions, typically informed by expertise, experience, surveys and focus groups. "Our main motivation was to understand whether and to what extent we can put viewer perceptions at the heart of the equation," the researchers said.