Lyricists must write concretely enough to be evocative, yet vaguely enough to allow each listener his personal interpretation. The nineteen-sixties and seventies saw an especially rich balance struck between resonant ambiguity and massive popularity -- aided, as many involved parties have admitted, by the use of certain psychoactive substances. Half a century later, the visions induced by those same substances offer the closest comparison to the striking fruits of visual artificial-intelligence projects like Google's Deep Dream a few years ago or DALL-E today. Only natural, perhaps, that these advanced applications would sooner or later be fed psychedelic song lyrics. The video at the top of the post presents the Electric Light Orchestra's 1977 hit "Mr. Blue Sky" illustrated by images generated by artificial intelligence straight from its words.

From the Beatles Let It Be to John Coltrane's A Love Supreme and Radiohead's OK Computer, record labels often remaster classic albums from the greats in many genres of music. These higher fidelity remasters are a welcome treat for aficionados and mainstream fans alike. But what if I told you, just like Led Zeppelin's or Van Halen's greatest hits, semiconductor chips could be "remastered" as well, and these remasters could help bail us out of the current chip shortage? For some folks that would totally rock, pun intended, but let's take a step back and look at the problem and potential solutions at hand first. The process of designing and verifying chips like the modern processors, controllers and sensors in cars, for example, can take years and require millions of dollars of R&D.

During a flight over New Jersey on May 6, 1937, the enormous German airship Hindenburg suddenly engulfed in flames while attempting to dock with its mooring tower. The airship plummeted to the ground in front of terrified onlookers, and in the 32 seconds it took for the zeppelin to be entirely incinerated, 35 people on the airship and one member of the ground crew died. Although a spark of static electricity is assumed to be the cause of the fire, no consensus has been achieved on the subject. Moreover, there should never have been a fire in the first place. The original concept of the infamous disaster included filling the zeppelin with non-flammable helium gas, which the United States had a ban on exporting.

Zeppelin dates back to the 1870s when, as the name suggests, it used to manufacture large airships designed by founder Ferdinand von Zeppelin. After the Second World War, the company changed direction, and is now known mostly for providing construction equipment rentals. Andreas Zientek, systems engineer at the brand, explains how Zeppelin began using Splunk's free license in 2010, and now has a 100GB license as the firm absorbs more data from its various systems including VM Ware, an in-house SAP system and various databases. "All the data these systems produce, like log files or performance data, we index in Splunk," he says. Where previously the firm used Splunk for monitoring, reporting, performance analysis and performance optimisation, it began using it for other business and internet of things (IoT) use cases in 2017.

Apache SystemML is now a Top-Level Project (TLP) and supports many different environments. If you are interested to try out new IBM's Watson Machine Learning service - click here With the recent partnership announcement between IBM and Hortonworks, this post describes how to add Apache SystemML to an existing Hortonworks Data Platform (HDP) 2.6.1 cluster for Apache Spark 2.1. Users interested in Python, Scala, Spark, or Zeppelin can run Apache SystemML as described in the corresponding sections. Apache SystemML provides a Python interface that can be installed using pip. See Using VirtualEnv with PySpark - Hortonworks for details on setting up a Python virtual environment.

Data Discovery: The first phase involves analysis on historical data to build the machine learning model. Analytics Using the Model: The second phase uses the model in production on live events. Data Discovery: The first phase involves analysis on historical data to build the machine learning model. Analytics Using the Model: The second phase uses the model in production on live events. In this first post, I'll help you get started using Apache Spark's machine learning K-means algorithm to cluster Uber data based on location.