At 1 p.m. in South Korea on March 9th, Google will attempt to make history. A program called AlphaGo, designed by Google's DeepMind artificial intelligence team, will match wits with Lee Sedol, one of the greatest Go players in the world. Sodol and AlphaGo will play a series of matches over the course of five days. If AlphaGo wins, it will be the latest in artificial intelligence's mastery of human games. Checkers fell in 1994, chess in 1997, and Jeopardy in 2011. Last October, AlphaGo became to first program to beat a professional Go player; now it's taking on one of the best players alive.

Microsoft Corp. Friday issued an apology after its artificial-intelligence chatbot Tay posted tweets, denying Holocaust and announcing feminists should "burn in hell" among many other racist posts. The company, however, said that the "coordinated attack by a subset of people exploited a vulnerability" in the chatbot that was launched Wednesday. "We are deeply sorry for the unintended offensive and hurtful tweets from Tay, which do not represent who we are or what we stand for, nor how we designed Tay. Tay is now offline and we'll look to bring Tay back only when we are confident we can better anticipate malicious intent that conflicts with our principles and values," Peter Lee, Microsoft's vice president of research, said on the company's official blog. Microsoft introduced Tay as the chatbot designed to engage and entertain people through "casual and playful" conversation online.

Paradoxically, data scientists love their jobs overall but dislike what they do most: cleaning and organizing data. That's one of the main takeaways from a new report by CrowdFlower Inc. on what has been called the "sexiest job of the 21st century." "Organizations that start prioritizing ways to help data scientists clean their data are going to find a data team with more time to work on more important -- and more fulfilling -- tasks," said CrowdFlower's Justin Tenuto in a blog post this week announcing the new "2016 Data Science Report" (available as a free PDF upon providing registration information). The report was compiled early this year from surveys, interviews and in-house analytics of CrowdFlower's own platform, which, conveniently, provides a contributor network to help organizations, "collect, clean and label data." In its survey, CrowdFlower found almost the same percentage of respondents reported they spent most of their time cleaning data (60 percent) as those who reported that task to be the least enjoyable part of their job (57 percent).

In our latest inspection of Github repositories, we focus on "data science" projects. Unlike other searches we have performed over the past several months, nearly all of the repositories which show up (listed by number of stars* in descending order) are resources for learning data science, as opposed to tools for doing. As such, this is much less a software listing than it is a collection of tutorials and educational resources. There are, however, a few software surprises in here as well, such as a data science-oriented IDE and a great notebook-related project. We include, however, the standard informational notification we have placed on our previous Github Top 10 lists: open source tools have been used by 73% of data scientists in the past 12 months, according to a recent KDnuggets survey (and accounting for the 12 months prior to the survey). While the following repositories focus mainly on learning resources, previous offerings have been software-heavy; also, open source learning materials are the new black, and a main source of learning for data scientists these days.

Standard natural language processing (NLP) is a messy and difficult affair. It requires teaching a computer about English-specific word ambiguities as well as the hierarchical, sparse nature of words in sentences. At Stitch Fix, word vectors help computers learn from the raw text in customer notes. Our systems need to identify a medical professional when she writes that she'used to wear scrubs to work', and distill'taking a trip' into a Fix for vacation clothing. Applied appropriately, word vectors are dramatically more meaningful and more flexible than current techniques and let computers peer into text in a fundamentally new way. I'll try to convince you that word vectors give us a simple and flexible platform for understanding text while speaking about word2vec, LDA, and introduce our hybrid algorithm lda2vec.

Microsoft today published an apology for its Twitter chatbot Tay, saying in a blog post that a subset of human users exploited a flaw in the program to transform it into a hate speech-spewing Hitler apologist. Author Peter Lee, the corporate vice president of Microsoft Research, does not explain in detail what this vulnerability was, but it's generally believed that the message board 4chan's notorious /pol/ community misused Tay's "repeat after me" function. So when Tay was fed sexist, racist, and other awful lines on Twitter, the bot began to parrot those vile utterances and, later, began to adopt anti-feminist and pro-Nazi stances. Microsoft pulled the plug on Tay after less than 24 hours. Lee says Tay is the second chatbot it's released into the wild, the first being the Chinese messaging software XiaoIce, an AI now used by around 40 million people.

Artificial intelligence is one of the most exciting and transformative opportunities of our time. From my vantage point as a venture investor at Playfair Capital, where I focus on investing and building community around AI, I see this as a great time for investors to help build companies in this space. There are three key reasons. First, with 40 percent of the world's population now online, and more than 2 billion smartphones being used with increasing addiction every day (KPCB), we're creating data assets, the raw material for AI, that describe our behaviors, interests, knowledge, connections and activities at a level of granularity that has never existed. Second, the costs of compute and storage are both plummeting by orders of magnitude, while the computational capacity of today's processors is growing, making AI applications possible and affordable.

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Just by typing the letters "A," "r-o-b," "w-r-o," "t-h-i," and "s-e-n" into the text messenger on a mobile phone, the predictive text function helped write that first sentence. What will do so is software such as StatsMonkey, which can automate sports reporting. The software analyzes statistics from a baseball game and "generates natural language text" to come up with phrases such as "Things looked bleak for the Angels when they trailed by two runs in the ninth inning" and even includes quotes from players. This is just one of the many well-researched examples presented by Martin Ford in his scarily intriguing new book, "Rise of the Robots." Ford is not some Luddite scared of technology, though.

Tens of million of people worldwide play Hearthstone, an online collectible card game set in the Warcraft universe, which also encompasses the massively popular MMO World of Warcraft and a major movie. Now Google Deepmind, fresh from creating an AI that triumphed at a game it was thought no computer could master, has been using Hearthstone to test ways a machine learning system could generate natural language - such as English - and formal language - such as computer code. Researchers tasked a system with writing the code that sets the behaviour of cards used in Hearthstone and in another famous collectible card game, Magic: The Gathering (MTG). The Deepmind system -- which implemented a novel neural network architecture -- was first trained using code from open-source versions of Hearthstone, programmed in Python, and Magic: The Gathering, programmed in Java. Humans 2.0: How the robot revolution is going to change how we see, feel, and talk Robots aren't going to replace us, but by working hand in hand with us they will redefine what it means to be human. Once trained, researchers tested the ability of the system to generate code needed to represent Hearthstone and MTG cards in each game.