This week, banker's fears were confirmed and the robot revolution has begun. The Royal Bank of Scotland (RBS) will sack hundreds of face-to-face advisers and replace them with a robo-advisory online service, after the Financial Conduct Authority (FCA) approved this technology. "Our customers increasingly want to bank with us using digital technology. As a result, we are scaling back our face-to-face advisers and significantly investing in an online investing platform that enables us to help a new group of customers with as little as 500 to invest," a RBS and NatWest spokesperson said in a statement according to the BBC. More and more banks are searching for digital options because they are cheaper and easier to use, and this attitude is advocated by the FCA.

Atom Bank is offering customer support via machine learning software on its mobile app. The bank, which gained its licence last year, delivers its products and services through an app for mobile devices and desktop computers. It is integrating artificial intelligence (AI) into its mobile app, which it said will provide near-human customer care. The machine learning technology from WDS – part of Xerox – will use analytics to capture the context of each customer inquiry and respond appropriately. It will also learn from experience.

Daniel Faggella is founder of TechEmergence, a news and advice website for entrepreneurs and investors interested in the intersection of technology and the mind. "Science has not yet mastered prophecy. We predict too much for the next year and yet far too little for the next ten." These words, articulated by Neil Armstrong at a speech to a joint session of Congress in 1969, fit squarely into most every decade since the turn of the century, and it seems to safe to posit that the rate of change in technology has accelerated to an exponential degree in the last two decades, especially in the areas of artificial intelligence and machine learning. Artificial intelligence is making an extreme entrance into almost every facet of society in predicted and unforeseen ways, causing both excitement and trepidation.

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.

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.

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.

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.