When Tesla Motors CEO Elon Musk proclaims that artificial intelligence is "our biggest existential threat," it makes headlines worldwide. But what goes unreported is that the very search engines people used to find Musk's comments are themselves an example of how AI has subtly but forcefully become a part of everyday, real-world life. When it comes to a discussion of AI, it helps to have a sense of history--as well as a sense of humor. Thanks to premonitory proclamations by Musk, Microsoft's Bill Gates, Cambridge's Stephen Hawking and other prominent technologists, AI has become a popular topic again, after a 20-year cooling-off period. It's tempting to assume that the "dire warnings" about AI being a threat to mankind were mostly tongue-in-cheek, but the end result is that just as it happened in the 1980s and '90s, the hype over AI is again outpacing the reality (virtual and otherwise). The first question that needs to be answered though is: Whatever happened to AI and why did it go underground for so many years?

In February this year, hackers managed to steal 81 million from the central bank of Bangladesh after exploiting vulnerabilities with a sophisticated malware. In January this year, press reports highlighted how highly destructive malware infected three regional power utility service providers in Ukraine, which led to a power failure. In June last year, CNN reported how hackers successfully managed to ground 1400 passengers, as Poland's national carrier was forced to cancel 20 flights due to an attack on its IT systems. In an age of connected machines, these incidents show how hackers can cause irreparable damage. Despite putting in place the best IT infrastructure, some of the biggest firms have got hacked, as vulnerabilities exist in every enterprise and hackers only need one loophole to sneak in an enterprise and steal data.

Code for machine learning is done in algorithms. Ideally, it should be elegant, beautifully efficient and, if it's doing its job well enough, you shouldn't even notice it's there while the machine is doing its thing. The only trace of the code that should be present is what the code is there for; to enable the machine to learn and think. A certain class of historical warriors were quite similar; they were brutally efficient, worked elegantly, and kept their presence hidden. One could only venture a guess that they had been there, if they did their job well, and a nebulous guess at that.

In this tutorial, we will give you a taste of the powerful Machine Learning libraries in Apache Spark via a hands-on lab. We will also introduce the necessary steps to get you up and running with Apache Zeppelin on a Hortonworks Data Platform (HDP) Sandbox. This tutorial is a part of series of hands-on tutorials to get you started with HDP using Hortonworks Sandbox. Please ensure you complete the prerequisites before proceeding with this tutorial. Note: if you are attending a Meetup/Crash Course your speaker/instructor may have additional instructions regarding the Sandbox VM image.

Tony Jebara is a Professor of Computer Science at Columbia University and Director of Machine Learning Research at Netflix. His research intersects computer science and statistics to develop new frameworks for learning from data with applications in social networks, spatio-temporal data, vision and text. At the Deep Learning Summit in Boston, on April 2016, Tony presented'Double-Cover Inference in Deep Belief Networks'. I caught up with him to hear more about his work at Netflix and his thoughts on the recent advancements in deep learning. Tell us more about your work as Director of Machine Learning Research at Netflix.

I am excited to announce that the upcoming Apache Spark 1.4 release will include SparkR, an R package that allows data scientists to analyze large datasets and interactively run jobs on them from the R shell. R is a popular statistical programming language with a number of extensions that support data processing and machine learning tasks. However, interactive data analysis in R is usually limited as the runtime is single-threaded and can only process data sets that fit in a single machine's memory. SparkR, an R package initially developed at the AMPLab, provides an R frontend to Apache Spark and using Spark's distributed computation engine allows us to run large scale data analysis from the R shell. The SparkR project was initially started in the AMPLab as an effort to explore different techniques to integrate the usability of R with the scalability of Spark.

Join us at Spark Summit to hear more about new functionalities of Apache Spark. Use the code Databricks20 to receive a 20% discount! As many data scientists and engineers can attest, the majority of the time is spent not on the models themselves but on the supporting infrastructure. Key issues include on the ability to easily visualize, share, deploy, and schedule jobs. More disconcerting is the need for data engineers to re-implement the models developed by data scientists for production.

Creating and training machine-learning models is more complex on distributed systems, but there are lots of frameworks for abstracting that complexity. There are more options now than ever from proven open source projects for doing distributed analytics, with Python and R become increasingly popular. In this post, you'll learn the options for setting up a simple read-eval-print (REPL) environment with Python and R within the Cloudera QuickStart VM using APIs for two of the most popular cluster computing frameworks: Apache Spark (with MLlib) and H2O (from the company with the same name). To compare these approaches, you'll train a linear regression against a data set with known coefficients. Spark includes PySpark (supported by Cloudera), the Python API for Spark.

Google has made no secret of its A.I. ambitions, and on Thursday it announced the next step in its bold plans to realize them: a brand-new research group in Europe focused squarely on machine learning. Based in Google Research offices in Zurich, Switzerland, the new group will focus on three key areas of artificial intelligence: machine intelligence, machine perception, and natural language processing and understanding, according to a blog post by Emmanuel Mogenet, head of Google Research for Europe. It will research ways to improve machine-learning infrastructure and enable the technology for practical use, for instance. Researchers will also work closely with linguists to advance natural language understanding, Mogenet said. Zurich, meanwhile, is home to Google's largest engineering office outside the U.S. Researchers there developed the engine that powers Knowledge Graph as well as the conversation engine that powers the Google Assistant in its Allo messaging app. Google's presence in Europe hasn't been entirely smooth, however: It's facing ongoing scrutiny over antitrust concerns and tax issues.

Genpact (NYSE: G), a global leader in digitally-powered business process management and services, announces that it has entered into a definitive agreement to acquire PNMsoft, a Gartner Magic Quadrant-rated dynamic workflow, case management and work optimization solutions provider based around Tel Aviv, Israel. PNMsoft complements and easily integrates pre-existing systems of records that typically host manual process work, and will act as a core component in Genpact's digital portfolio whose roadmap comprises close to 100 digital solution components ("digital assets"). Terms of the transaction were not disclosed. Closing is subject to satisfaction of certain customary conditions and expected in the third quarter. The transaction is not expected to be material to current year financial performance.