Today, inside the towering glass and steel Four Seasons Hotel in downtown Seoul, South Korea, Google will put the future of artificial intelligence to the test. At one o'clock in the afternoon local time, a digital Google creation will challenge one of the world's top players at the game of Go, the ancient Eastern pastime that's often compared to chess--though it's exponentially more complex. This Google machine is called AlphaGo, and to win, it must mimic not just the analytical skills of a human, but at least a bit of human intuition. Over the years, machines have topped the best humans at checkers, chess, Othello, Scrabble, Jeopardy!, and so many other contests of human intellect. But they haven't beat the very best at Go.

Note: This is the 8th and last part of a short essay series aiming to condense knowledge on the Artificial Intelligence Revolution. Feel free to start reading here or navigate to Part 1, previous essay or table of contents. The project is based on the two-part essay AI Revolution by Tim Urban of Wait But Why. I recreated all images, shortened it x3 and tweaked it a bit. Read more on why/how I wrote it here.

Artificial intelligence is nothing new at Alphabet/Google, but today we learned just how big of a role top boss Sundar Pichai sees it playing in defining our future. Answering an analyst query on Google-parent company Alphabet's Q1 2016 earnings call about how the company is leading innovation, rather than simply adapting to changes in technology, Pichai talked about his role in projecting where Alphabet is going in the next 10 years. He gave a shout out to VR as the hot new platform, and then wrapped up his comments by saying: "In the long run I think we will evolve in computing from a mobile-first world to an AI-first world." Earlier in the call he cited Google's DeepMind AlphaGo super computer defeating a human champion as an extraordinary AI achievement. He also said the company is investing in AI and machine learning, and both areas are hitting their stride.

Editor's Note: This article was originally printed in the 2008 Scientific American Special Report on Robots. It is being published on the Web as part of ScientificAmerican.com's In recent years the mushrooming power, functionality and ubiquity of computers and the Internet have outstripped early forecasts about technology's rate of advancement and usefulness in everyday life. Alert pundits now foresee a world saturated with powerful computer chips, which will increasingly insinuate themselves into our gadgets, dwellings, apparel and even our bodies. Yet a closely related goal has remained stubbornly elusive. In stark contrast to the largely unanticipated explosion of computers into the mainstream, the entire endeavor of robotics has failed rather completely to live up to the predictions of the 1950s. In those days experts who were dazzled by the seemingly miraculous calculational ability of computers thought that if only the right software were written, computers could become the articial brains of sophisticated autonomous robots. Within a decade or two, they believed, such robots would be cleaning our oors, mowing our lawns and, in general, eliminating drudgery from our lives.

There can only be one winner in the race towards what is referred to as "super artificial intelligence." The country that launches the first truly super intelligent a.i that is both self aware and capable of learning at an exponential rate will become the world's most powerful nation and they will stay that way.

The new Google logo is displayed at the Google headquarters on September 2, 2015 in Mountain View, California. Technology investors who bought shares of Alphabet last year on optimism that new CFO Ruth Porat would increase fiscal discipline were disappointed Friday. They pushed the Google parent's GOOGL shares down 5% to 737.77 on heavy trading volume, a day after learning that the company's investments in what it calls Other bets remain a drag on short-term profits. The category posted a first-quarter operating loss of 802 million, while generating sales of just 166 million, just a tiny fraction of overall revenue of 20 billion. Alphabet's money-losing moonshots take shine off Google's ad business Other bets include expensive, speculative projects such as building a self-driving car or bringing fiber-optic Internet cables into neighborhoods.

This post is made up of a collection of 10 Github repositories consisting in part, or in whole, of IPython (Jupyter) Notebooks, focused on transferring data science and machine learning concepts. This warmup notebook is from postdoctoral researcher Randal Olson, who uses the common Python ecosystem data analysis/machine learning/data science stack to work with the Iris dataset. Aaron Masino has shared a series of very detailed, very technical machine learning IPython Notebook learning resources. From UC Boulder's Research Computing group, this older collection of notebooks (it's from way back in Fall 2013) covers a wide range of material, with an apparent focus on Linux command line-powered data management.

This is a great project undertaken by Jordi Warmenhoven to implement the concepts from the book An Introduction to Statistical Learning with Applications in R by James, Witten, Hastie, Tibshirani (2013) in Python (the book has practical exercises in R, as you may have guessed). The book is freely available in as a PDF, which makes this repo even more attractive to those looking to learn.

We have moved from the age where organizations had to jump through hoops to get relevant data about their customers, business segments, market, etc. With the onset of massive digitization across the business world, getting and collecting information is no longer a challenge, but processing them into meaningful insights is. Enterprises in this competitive business landscape are focusing towards an integrated data driven approach to enable rapid and accurate decision making. Machine learning, no longer lying in the realms of sci-fi, with its multi-faceted applications and benefits is emerging as a front-runner to overcome this challenge. Enterprises implementing machine learning can easily get new market insights, predict customer behavior or preferences, and reduce operating costs by improving the effectiveness and efficiency of the business processes.

Swarms of drones flying in terrifyingly perfect formation could be one step closer, thanks to a control algorithm being developed at MIT. The complexities involved in controlling teams of moving robots so they don't crash into each other, or indeed wipe out other objects/entities that cross their path, is a hard problem that continues to keep roboticists busy. But the team of researchers at MIT reckon they have made a breakthrough that could make perfect complex drone formations easier to pull off. They say their decentralized planning algorithm can handle both stationary and moving obstacles, and do so with reduced computational overheads. Why are decentralized control algorithms better than centralized control algorithms?