Professor Pascal Poupart is a senior researcher at Borealis AI, an RBC research institute, and a professor of computing science at the University of Waterloo. My parents thought I was crazy when I announced my intention to study artificial intelligence. Back then, in 1998, AI was considered a fringe group within the academic community, even though we knew the field had the potential to transform lives. Today, AI is among the hottest fields in academia, attracting the focus of every major university and the courtship of the world's biggest companies as they try to apply cognitive machine learning to just about every challenge in the marketplace. Thanks to this commercial interest and the academic pursuit of AI, we now have personal assistants in our smartphones, intelligent cybersecurity to protect our credit cards, and even recommendation engines in our favourite movie and music streaming services.

Imagine it is the spring of 2019. A bottom-feeding website, perhaps tied to Russia, "surfaces" video of a sex scene starring an 18-year-old Kirsten Gillibrand. It is soon debunked as a fake, the product of a user-friendly video application that employs generative adversarial network technology to convincingly swap out one face for another. It is the summer of 2019, and the story, predictably, has stuck around -- part talk-show joke, part right-wing talking point. "It's news," political journalists say in their own defense.

FOR the moment at least, automation is occurring all too slowly. The displacement of workers by machines ought to be bringing on a consistent rise in productivity and a faster growing economy. This is not happening in the US, Europe and Japan as well as in most parts of the world, including Malaysia – all being persistently disappointed with the outcomes. Experts warn that rapid advances in robotics and artificial intelligence (AI) could destroy millions of jobs and pose a "Terminator" style threat to human life as we know it. However unnerving the prospect, present day limitations of AI appear all too evident.

MTV's "TRL" recently welcomed Poppy, a rising star with hologram-perfect skin, an avant-garde Japanese schoolgirl wardrobe and a voice like Betty Boop's on benzos. For much of the show, she perched silently on the couch and methodically stacked candies on a glass table. A longhaired handler called Titanic Sinclair accompanied her, explaining, "I'm just making sure she doesn't malfunction." Poppy proved a tough interview. Asked what she thought of the Grammys, which had aired the night before, she chirped: "Um, I don't really remember them."

The trial opening Monday in San Francisco federal court comes nearly a year after Google spin-off Waymo sued Uber, accusing it of ripping off key pieces of its self-driving car technology in 2016. Uber paid $680 million for a startup run by Anthony Levandowski, one of the top engineers in a robotic vehicle project that Google began in 2009 and later spun out into Waymo.

Last September, Stanford professor Michal Kosinski unleashed a torrent of controversy when he used artificial intelligence to attempt to predict peoples' sexual orientation from their faces. Now he has set himself the challenge of deciphering his subjects' political beliefs with similar software.

Rex Sham and his WALL-E-inspired robots are improving fire detection to slash carbon emissions and make the world better. If he were in a movie, Rex Sham would be the bad guy, or at the very least the well-meaning scientist who unwittingly wipes out the human race. In reality, Sham, the co-founder and chief science officer of Insight Robotics, is using his ingenious, WALL-E-like fire-detecting robots to save the planet. What Sham has developed is an automated early warning system that combines a high-precision, pan-tilt robot with thermal imaging sensors and advanced artificial intelligence (AI) vision technology. In its tests of the Computer Vision Wildfire Detection System, the Guangdong Academy of Forestry (Insight Robotics' research partner since 2010) has recorded a 100 per cent detection rate in multiple field trials and deployments.

While Netflix dropped a day-and-date trailer for its Cloverfield flick, HBO has a more traditional reveal for season two of Westworld. Its Super Bowl spot (the first one HBO has ever aired for a specific series) showed off the network's "most-watched freshman series ever" with some new scenes that were interesting without revealing too much about where the show will go. We won't go too deep into the specifics for those who haven't checked out the first season yet, but at least now the vision of robots running amok includes massive mechanical bulls. Richard's been tech-obsessed since first laying hands on an Atari joystick.

I was looking forward a high-level explanation of these technologies. "we want to use the surrounding words to represent the target words with a Neural Network" This could use some rewording I think? I get what you mean, but it seems to mean we're representing words with a neural network, as opposed to use a neural network to compute a vector representation of a word. "For CBOW, all the examples with the target word as target are fed into the networks, and taking the average of the extracted hidden layer." I don't think I got this... in CBOW the vector(?) representing a word is the average (a scalar?) of the middle layer of the NN?