When Google's algorithm AlphaGo beat South Korean Go Grandmaster Lee Se-dol by 4-1 last week, it was a significant event in the world of algorithms and artificial intelligence. This is because it represented a new form of artificial intelligence: intuitive artificial intelligence, something which is remarkably more challenging than standard artificial intelligence. The disruption happening thanks to algorithms is happening all around us. The largest taxi company in the world, Uber, owns no taxis, but uses smart algorithms to connect drivers and passengers. The largest telephone company in the world, WhatsApp, has no telecom infrastructure, but sends over 35 billion message per day.

Some 250 billion billion (250 x1018) transistors were produced in 2014. That means that every second of that year, on average, 8 trillion transistors were produced - about 25 times the number of stars in the Milky Way (this statistic is from 2014, so according to Moore's Law production should now have doubled). The enormous surge in computing power which we are witnessing heralds some other lapel-grabbing metrics: 58% of job activities can be automated; 47% of jobs will be lost to cognitive machines in the next ten years. Taking advantage of this exponential is a wave of machine learning, deep learning and AI specialists. One such company is Seldon, a talented start-up selected to join the Barclays Accelerator powered by Techstars.

I know this because they actually resell these accounts. Once you have purchased 10,000 of these verified Yahoo email accounts for 100 on Bulkaccounts.net, You will need another web robot to log on automatically on all these accounts, compose a message and then get it sent out - but it's easy to find a hacker working remotely overseas and paid via Paypal. I'm assuming that after a few hundred messages per account, these Yahoo accounts will be terminated, but in the meanwhile, they have offered the criminal a possibility to massively deliver his payload - typically spam messages with links to get your computer infected, turned into a bot, or get your personal info stolen.

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.

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.