XGBoost is an optimized distributed gradient boosting system designed to be highly efficient, flexible and portable. It implements machine learning algorithms under the Gradient Boosting framework. XGBoost provides a parallel tree boosting(also known as GBDT, GBM) that solve many data science problems in a fast and accurate way. The same code runs on major distributed environment(Hadoop, SGE, MPI) and can solve problems beyond billions of examples. XGBoost open source project is actively developed by amazing contributors from DMLC/XGBoost community. This work was supported in part by ONR (PECASE) N000141010672, NSF IIS 1258741 and the TerraSwarm Research Center sponsored by MARCO and DARPA.

A project manager once told me that "any job worth doing is worth doing poorly." I understood exactly what she meant, and she knew that I would understand, especially when she preceded our conversation with these words: "I wouldn't say this to everyone, but I know you will understand what I mean." The message was clear to me because I was a perfectionist (and hopefully I have learned over the years to be less of a perfectionist thanks to my project manager's wise counsel). As a perfectionist, I would strive for 100% completion and perfection on every project, every analysis, and every report. It would take me longer than most people to finish the analysis and report, and my manager understood why.

This year marks the 60th anniversary of the first Dartmouth Conference, the research project that is credited with introducing'Artificial Intelligence' (AI) as we understand it today to the world. AI often seems like the stuff of science-fiction, but it did hit the headlines recently when Facebook announced it will use ChatBots to help businesses deliver automated customer support, online shopping guidance, and more'interactive' engagement. Facebook seems to have got its timing right, at least in the consumer market. This technology appears to growing in acceptance amongst the general public, who probably don't even really think of ChatBots as a form of AI. Perhaps because they're getting used to'personal assistants' like Apple's Siri.

GoButler, the startup that originally offered a virtual assistant that let you request anything on-demand, appears to be pivoting for a second (and maybe final) time. The New York-headquartered company is shuttering the GoButler consumer-facing service altogether in favour of offering natural language processing technology to third-parties. Angel.ai is based on the same NLP technology that previously powered GoButler's "fully-automated" flight search and discovery, and will enable commerce companies to build'conversational' user interfaces, such as Facebook Messenger chat bots, or other kinds of natural language search. However, instead of targeting a single vertical, Angel.ai The idea is to let consumers express intent in a more natural way, and by translating natural language expressions into structured, actionable requests, commerce companies can capture that intent and make their products easier to find and purchase.

The widespread conversations about AI took a new turn in March 2016 when Microsoft launched, then quickly unplugged, Tay, its artificial intelligence chat robot.

A machine using artificial intelligence (AI) has replicated a complex Nobel prize-winning experiment -- a stunning advance that could push some physicists into looking for other employment. "I didn't expect the machine could learn to do the experiment itself, from scratch, in under an hour," said co-author Paul B. Wigley from the ANU Research School of Physics and Engineering, in a statement. "A simple computer program would have taken longer than the age of the Universe to run through all the combinations and work this out." The research is detailed in the journal Scientific Reports. The experiment involves creating an unusual state of matter, known as a Bose-Einstein condensate, by trapping super-cold gas in a laser beam.

According to SciFi canon, AI and Big Data might not be a good idea. In recent years, big data and artificial intelligence (AI) have received overwhelming attention, however the interesting – even obvious – connection between the two hasn't often been explored. It is the combination of big data and AI working together that is now enabling business leaders to deliver new insights, efficiencies and even new functions that haven't been possible before. This is evident in the increasingly useful role big data and AI are playing in a broad spectrum of traditionally outsourced functions such as recruitment, HR, finance and supply chain, through to security and IT. The combination has already had a major impact on how the stock market works, synthesising more and more data to the point at which some people believe it will eventually be able to accurately predict both market trends and human influences on the market.

HANNOVER, GERMANY - MARCH 02: Robots play football in a demonstration of artificial intelligence at the stand of the German Research Center for Artificial Intelligence (Deutsches Forschungszentrum fuer Kuenstliche Intelligenz GmbH) at the CeBIT Technology Fair on March 2, 2010 in Hannover, Germany. CeBIT will be open to the public from March 2 through March 6. (Photo: Sean Gallup/Getty Images) Artificial intelligence or AI is among the most favorite subjects today. With AI's versatility and flexibility, you can find them anywhere. There are already companies run by AI and factories with robots operating the machines. But did you know that AI robots also suffer stereotypical sexism in the workplace?

If robots are going to take over the world, they could at least have the courtesy not to bump into us while they're at it. That's not as easy as it sounds, though, especially when a robot is trying to make its way through a bustling space like a mall, hospital, or crowded city street. Thankfully, researchers have developed an algorithm that could give robots the ability to deftly maneuver through spaces packed with unpredictable humans. Robots are gradually leaving controlled spaces like labs and factories and edging into more settings in which they will inevitably encounter human beings (see "Are You Ready for a Robot Colleague?"). We navigate hectic spaces by reading other people's movements and planning our paths accordingly.

IBM wants its Watson computer system to learn how to fight cyber crime and it's asking eight leading universities, including three in Canada, for help. Watson -- IBM's question answering computer system -- was originally designed to compete (and win) on the television quiz show Jeopardy, but the technology has since been used on other problem-solving projects. Now IBM is launching Watson for Cyber Security -- a cloud-based version of their cognitive technology -- that will be trained over the next year to examine threats of cyber crime. Caleb Barlow, vice-president of IBM Security, said it is becoming increasingly difficult for security staff to deal with the growing number of cyber threats. "Your average enterprise is dealing with 200,000 incidents a day that they've got to dig through. Human beings simply cannot look at all of that data," he said.