Have you ever wanted to mess with a video by making its cast say things they never would on camera? You might get that chance. Researchers have built a face detection system that lets you impose your facial expressions on people in videos. The software uses an off-the-shelf webcam to create a 3D model of your face in real time, and distorts it to fit the facial details in the target footage. The result, as you'll see below, is eerily authentic-looking: you can have a dead-serious Vladimir Putin make funny faces, or Donald Trump blab when he'd otherwise stay silent.

A year ago today, I published my original attempt at mapping the machine intelligence ecosystem. So much has happened since. I spent the last 12 months geeking out on every company and nibble of information I can find, chatting with hundreds of academics, entrepreneurs, and investors about machine intelligence. This year, given the explosion of activity, my focus is on highlighting areas of innovation, rather than on trying to be comprehensive. Despite the noisy hype, which sometimes distracts, machine intelligence is already being used in several valuable ways.

Genpact believes that the next phase of its growth will continue to be driven by Analytics and is seeking an Assistant Vice President (AVP) – Machine Learning. This is a pre-sales analytics Subject Matter Expert (SME) and platform solution role across all vertical and horizontal business groups within Genpact. This person will lead bid/win process and pre-sales technical support for information science engagements specific to machine learning and other technology tools and techniques to maximize automation and process improvement. This person will liaise with service lines and delivery to ensure proper solution design and fit. This person will be the SME for Machine Learning to the salesforce.

Nineteen years after Big Blue defeated chess master Garry Gasparov by winning 3 ½ – 2 ½ (2 wins, 1 loss and 3 draws), history is repeating itself. This time, it involves the game of Go and AlphaGo, and a team of researchers from Alphabet taking on the South Korean master Lee Sedol. The AlphaGo team has claimed victory in 4 out of 5 games and has accomplished the unthinkable one more time--the machine is better than the human at playing a game with billions of possibilities. Beside the obvious excitement of pitting man vs. machine, why should you care about the game of Go and AlphaGo? The AlphaGo algorithm is powered by a combination of techniques including machine learning that allow him to learn millions of moves and how to play the game.

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Last week, Google's in house Artificial Intelligence department announced a breakthrough. Their software has learnt to play a game. Not just any game, but Go. It is thousands of years old, and considered by many to be the hardest board game in the world with more potential move combinations than you can shake a stick at and a requirement to see patterns and combinations and to play by'feel'. The complexity of the game is one thing, but the real point here is that Google's AI engine has learnt to play the game.

To be in sync with Airbnb's vision that enabling humans to partner with a machine in a symbiotic way to exceed the capabilities of humans and machines, its project AeroSolve focused on improving the understanding of data sets by assisting people in interpreting complex data with easy to understand models. Instead of hiding meaning beneath many layers of model complexity, Aerosolve models expose data to the light of understanding.

There is no meaningful sense in which there is an abstract notion of purpose. That is, purpose is something that comes from history. One of the things that might be true about computation, might be true about our world, that would be disappointing, is maybe we go through all this history and biology and civilization and so on, and at the end of the day, the answer is 42 or something. That's the end, so to speak. We got to the answer.

IT WAS not quite a whitewash, but it was close. When DeepMind, a London-based artificial intelligence (AI) company bought by Google for 400m in 2014, challenged Lee Sedol to a five-game Go match, Mr Lee--one of the best human players of that ancient and notoriously taxing board game--confidently predicted that he would win 5-0, or maybe 4-1. He was right about the score, but wrong about the winner. The match, played in Seoul to crowds on the edges of their seats and streamed to millions online, was won by the computer, four games to one. Ever since Garry Kasparov, a chess grandmaster, lost to a computer in 1997, Go--which is far harder for machines--has been an unconquered frontier.

Now that we've clarified what Artificial Intelligence actually is, the question is how to use it to improve Twitter Ads Campaigns. Using AI gives us a big advantage over most online marketing agencies that carry out their campaigns manually, programming and combining texts and images ad by ad. The network itself does not offer any way to combine texts and images automatically, nor to simultaneously publish ads with multiple segmentations.