This is the year artificial intelligence came into its own for mainstream businesses, at least as a marketing feature. On Sunday, Salesforce.com, which sells online software for sales and marketing, announced it would be adding A.I. to its products. Its system, called Einstein, promises to provide insights into what sales leads to follow and what products to make next. Salesforce chose this date to pre-empt Oracle, the world's largest business software company, which on Sunday evening began its annual customer event in San Francisco. Oracle calls its product Oracle A.I. Elsewhere, General Electric is pushing its A.I. business, called Predix.

Machine learning is now pervasive in every field of inquiry and has lead to breakthroughs in various fields from medical diagnoses to online advertising. Practical machine learning is quite computationally intensive, whether it involves millions of repetitions of simple mathematical methods such as Euclidian Distance or more intricate optimizers or backpropagation algorithms. Such computationally intensive techniques need a fast and expressive language – one that enables scientists to write simple, readable code that performs well. In this post, we introduce a simple machine learning algorithm called K Nearest Neighbors, and demonstrate certain Julia features that allow for its easy and efficient implementation. We will demonstrate that the code we write is inherently generic, and show the use of the same code to run on GPUs via the ArrayFire package.

When researchers recently unveiled the first pop song composed by an artificial intelligence (AI) system, some creative types may have been nervous about the idea of robots taking over their jobs. But how exactly was AI used to write a song? A team from the Sony CSL Research Lab used a system called Flow Machines to compose the new record, titled "Daddy's Car." The song sounds like a lost Beatles track from the late 1960s, or perhaps a composition by Brian Wilson of the Beach Boys. François Pachet, the project's lead researcher, told Live Science that the song wasn't created by an AI entirely from scratch, so composers can breathe easy -- at least for now.

Translating from one language to another is hard, and creating a system that does it automatically is a major challenge, partly because there are just so many words, phrases and rules to deal with. Fortunately, neural networks eat big, complicated data sets for breakfast. Google has been working on a machine learning translation technique for years, and today is its official debut. The Google Neural Machine Translation system, deployed today for Chinese-English queries, is a step up in complexity from existing methods. Here's how things have evolved (in a nutshell). A very simple technique for translating -- one a kid or simple computer could do -- would be to simply look up each word encountered and switch it with the equivalent word in another language.

At Zelros, we believe that many enterprise machine learning applications lack of a major component: the interface between the complex data workflow on one side, and the business users on the other side -- that is to say a natural way for people to interact with technology. We believe that one solution for this issue is to invent a new type of intelligent interaction, like chatbots, based on natural language and discussions. We are not the only ones. There is not a day without an article promoting benefits of conversational and invisible UI. Technically, end-to-end chatbots are constituted of several building blocks.

Welcome back to Across the Network -- Lab41's weekly look at what is going on in the world of AI. As always these are all links that I pulled from the Lab41 Slack channels. A Neural Network for Machine Translation -- I have a 3-year old son, who, thanks to his mother (whose family is from Taiwan) and our Chinese au-pair, speaks Mandarin fluently. And while I'm proud of this fact (and a bit miffed that he is so much more capable at picking up language than me), his fluency has required me to become a regular user of Google Translate. So I was excited to see the details behind the latest technology being used by the Google Translate team.

At the two-day RoboBusiness Conference, about 2,000 people were serenaded with lullabies and Disney tunes, including "Let It Go" from the hit film "Frozen," by a human-like robot designed to comfort senior citizens and autistic children. And next to a man-size robot that can drive a motorcycle 190 mph around a race track, a half-dozen ant-size robots quickly scurried about a miniature factory floor. "In five years, could you imagine what this conference is going to look like?" "There are going to be 8-foot robots walking all around us, talking to us, some of them maybe being smarter than us." The 12th annual conference, which wrapped up Thursday, illustrated how the focus of robotics is shifting from industrial uses to consumer products. That's especially true at a time when drones, self-driving cars and police robots that carry bombs are making news.

Mathematics is everywhere, if you know where to look. A recently opened exhibition at the North Carolina Museum of Art (NCMA) is displaying the St. John Altarpiece, a 14th-century work by Francescuccio Ghissi. It has nine scenes in total: eight smaller pictures featuring St. John the Evangelist flanking a larger central Crucifixion. At the end of the 19th century, the altarpiece was separated into parts by a saw and eight of the nine resulting panels were sold to different collectors. One panel, the last of the smaller scenes, was lost.

Without cloud computing, today's artificial intelligence (A.I.) boom would not exist. Both the advent of cloud computing and advances in machine intelligence have made it easier for companies to deliver A.I.-based features that create new ways of engaging customers. Most companies now use cloud-based systems for complex computing tasks that require intense CPU/GPU utilization, data operations and analytics work, or other tasks that would cause a traditional, locally-managed server to have a bad day within a few minutes. A.I.-based systems typically rely on these same computing characteristics because they often require several models of the machine's understanding of data to provide a reliable "human-like" result. Three of the largest technology companies -- Google, Amazon and IBM -- have already begun to provide A.I.-based systems on top of their existing platforms in an effort to push the limits of cloud computing forward.

MIT economist Erik Brynjolfsson provides his learned perspective on the future of U.S. productivity in a recent Bloomberg podcast, "54: The Optimist's Guide to the Economy." Given the widely-held belief that "U.S. productivity growth has stagnated and the economy has been unable to break out of 2 percent expansion," Professor Brynjolfsson explains why he thinks the current wave of advances in technology means we don't have to worry about stagnation. Below are excerpts from Professor Brynjolfsson and "The Optimist's Guide to the Economy:" Opening Thoughts I think much better days are ahead of us. And my optimism comes not from extrapolating what happened with productivity recently. It comes from going out and visiting companies. The Promise of Artificial Intelligence When we [co-author Andrew McAfee] wrote the book [The Second Machine Age, 2014], humans could see better than machines; now, in many tasks, machines are much better, for instance at recognizing street signs or interpreting images in big databases like ImageNet.