SQL is referred to as a declarative language as opposed to an imperative language like the 3GL's. You give it a high-level goal, and it figures out which machine learning algorithm to use, and tunes the hyperparameters for you. Are there other declarative machine learning systems out there? Their purpose is to allow non-Spark developers to write machine learning programs in languages they are comfortable in (like Python), yet be able to compile down to Spark Scala when the time comes to deploy to production.

A "new" Rembrandt has been unveiled in Amsterdam, but it's not a long lost dusty relic that was found in someone's loft--it was created by data analysts and computers. The portrait of a man in a black hat is the result of 18 months work by art historians, data scientists, developers, 3-D print technicians, and organizations like Microsoft, Delft University of Technology, the Mauritshuis in The Hague, and Amsterdam's Rembrandt House Museum. It consists of more than 148 million pixels, based on 168,263 painting fragments from Rembrandt's output. The initiative is the brainchild of Bas Korsten, executive creative director at ad agency JWT Amsterdam, and was created for Dutch financial services giant ING. Korsten says ING approached the agency with a brief to "find a way to bring their innovative spirit to their sponsorship of Dutch art and culture in a way that would get people thinking."

This afternoon, while I was discussing with Montserrat (aka @mguillen_estany) we were wondering how long it might take to run a regression model. More specifically, how long it might take if we use a Bayesian approach. My guess was that the time should probably be linear in, the number of observations. But I thought I would be good to check. Here the regression is a subset of smaller size.

SQL is commonly referred to as a 4GL, or fourth-generation programming language, as opposed to all of the 3GL's like Java, C, Python, Scala, etc. SQL is referred to as a declarative language as opposed to an imperative language like the 3GL's. You tell SQL what to do, not how to do it. Well, TuPAQ is the SQL for machine learning. You give it a high-level goal, and it figures out which machine learning algorithm to use, and tunes the hyperparameters for you. When will you be able to use this in production?

In a major breakthrough for artificial intelligence, a computing system developed by Google researchers in Great Britain has beaten a top human player at the game of Go, the ancient Eastern contest of strategy and intuition that has bedeviled AI experts for decades. Machines have topped the best humans at most games held up as measures of human intellect, including chess, Scrabble, Othello, even Jeopardy!. But with Go--a 2,500-year-old game that's exponentially more complex than chess--human grandmasters have maintained an edge over even the most agile computing systems. Earlier this month, top AI experts outside of Google questioned whether a breakthrough could occur anytime soon, and as recently as last year, many believed another decade would pass before a machine could beat the top humans. But Google has done just that.

I do machine learning at work. For a long time (the whole time I've had this job, 2 years), I've struggled with a bunch of questions about complicated machine learning models. Are black box models good? Should we be using them? What can I do about it?

On March 15, DeepMind's AlphaGo, a computer powered by a self-learning artificial intelligence computer program, defeated Go grandmaster Lee Sedol. As the AI community celebrates this major milestone in making machines smart, the debate of "man vs. machine" is heating up. Over the past 25 years -- especially the last five years -- the AI community has transformed theoretical machine learning constructs to solve useful problems. AI techniques such as self-learning, reinforcement learning, and deep neural networks were developed to recognize traffic signs and classify images. The recent rapid progress in AI was powered by the dramatic increase in financial investments in AI.

Next time you schedule a meeting and an assistant named Amy or Andrew Ingram sets up the logistics, here's a pro tip: You may be chatting with a robot. And if it's one of x.ai's bots, you might never know the difference. That was my experience when I exchanged emails with "Andrew" to set up an interview with x.ai's CEO. After I emailed x.ai's press contact, she referred me to Andrew to hammer out the details. Andrew proposed a time, thanked me when I accepted and sent out a calendar invitation.

"People are our best assets" is one of most common sentences we hear from corporations. It implies that recruiting the best people should be a fundamental step to generate future outstanding performances. Recruitment techniques have gone through several eras and fads, for example mentoring, military recruitment, traditional intuitive recruitment, large-scale administrative recruitment, followed by analytical, predictive and scientific recruitment. The problem, again, is that if every company adopts the "state of the art" tool of the moment, it will be able to recruit more or less with the same quality of any other. The results of some researches somehow confirm the above assumption: according to a survey conducted by Arlington-based Corporate Executive Board, nearly a quarter of all new hires leave within a year, while Gallup reports that half of those who do stay reported being "not engaged.

Russian entrepreneur and venture capitalist Yuri Milner, founder of investment firm Digital Sky Technologies Ltd (DST Global), has made a new investment in a tech company Horizon Robotics, a China-based startup focused on artificial intelligence (AI). The size of the investment venture was not disclosed. Horizon Robotics will use the latest proceeds on research and development, including team expansion, reported the China Money Network. The startup was established in July 2015 by Dr. Kai YU, the founder and former head of Institute of Deep Learning (IDL) at Baidu. Horizon Robotics previously received seed funding from Hillhouse Capital, Morningside Ventures, GSR Ventures, Sequoia Capital, among others.