Cisco is just wrapping up its annual CiscoLive customer event. This year's proceedings took over Las Vegas, occupying the Bellagio, Luxor, Mandalay Bay, and MGM Grand hotel. At least for this week, Cisco was bigger in Vegas than Wayne Newton, Steve Wynn and even Carrot Top. While digital transformation served as the main theme at CiscoLive, cybersecurity had a strong supporting role throughout the event. For example, of all of the technology and business initiatives at Cisco, CEO Chuck Robbins highlighted cybersecurity in his keynote presentation by bringing the GM of Cisco's cybersecurity business unit (David Goeckeler) on stage to describe his division's progress.

Data scientists have been called "unicorns" because finding the right person with the right set of skills -- including coding, statistics, machine learning, database management, visualization techniques, and industry-specific knowledge -- could be practically impossible. But machine learning and big data itself may be making those unicorns as obsolete as they are mythical. New machine learning algorithms can autonomously analyze data and identify patterns, even interpret the data and produce reports and data visualizations. While most people can see how certain information would be useful and what sort of insights might be derived from it, most lack the technical skills to perform the analytics. They might not have the computers that are able to carry out the large volume of calculations quickly enough to take action, but more often they lack the analytical skills to tell that computer what to do. Natural Language Processing (NLP) technologies can help to break down the barriers to widespread use of data analytics by making complex analytics possible to just about anyone, regardless of their technical ability.

"Everybody uses pinch and zoom, because it's easy to remember," Sang Won Lee, CEO of a gesture control company called Qeexo told Digital Trends. "The reason we don't use three- or four-finger gestures is because we can't remember what they do -- but you can probably hold a computer mouse with your eyes closed, right?" Try making that shape with your hand right now, and we're pretty sure you'll succeed. Now imagine using that same gesture to summon up a virtual mouse on the screen of your tablet; or in the future, an interactive tabletop, complete with left and right buttons, and that always-helpful scroll wheel. Such natural movements could transform the way we interact with touchscreens, especially large ones.

The PokemonGo Bot helps you Catch'Em All. For now, say'Hi' and it will help you fight gym battles. You will soon be able to find locations of awesome Pokemons, Gyms and Pokestops around you. There will also be special tips and tricks to CatchEmAll. Like the Facebook page PokemonGoBot to get updated on release of new features.

In the last 15 years, eBay grew from a simple website for online auctions to a full-scale e-commerce enterprise that processes petabytes of data to create a better shopping experience. When the user searches for a product, how do we find the best results for the user? One factor used in product ranking is user click-through rates or Product sell-through rate. In addition, user behavioural data gives the link from a query, to a product page view, and all the way to the purchase event. Through large-scale data analysis of query logs, we can create graphs between queries and products, and between different products.

That was a few more than 10 papers, but the field is moving fast.

Machine learning underpins Sidecar's optimization technology for product listing ads, and we're not shy about making that fact known. But we understand that the term might sound a little bit sci-fi to some. However, since it's so central to what we do and who we are, we thought a simple explanation of machine learning was in order -- no computer science degree required. At its simplest, machine learning means giving computers the power to teach themselves to make decisions using historical examples, rather than explicitly programming them to perform a task. To illustrate this concept, I'm going to borrow an example from The Data Skeptic, a popular -- and highly recommended -- podcast by Kyle Polich.

In a recent scientific study highlighted by The Atlantic, researchers from the University of Vermont and the University of Adelaide determined the core emotional trajectories of stories by taking advantage of advances in computing power and natural language processing to analyze the emotional arcs of 1,737 fictional works in English available in the online library Project Gutenberg. Their research, published online, involved assigning happiness scores to 10,000 frequently used words determined by a crowdsourcing project, then breaking up blocks of text to analyze the happiness of each block and mapping those scores on an emotional trajectory. The researchers recognize that emotional arcs and plot structures may be similar, but are not necessarily the same. They also point out multiple well-known theories that all narratives can be boiled down three plots, seven plots, a different seven plots, 20 plots, or even 36 plots depending on who you read and believe. Furthermore, the researchers realize that multiple, connected emotional arcs appear in longer, more complex works of fiction, so they limited their study sample to works of fiction between 10,000 words and 200,000 words.

Although artificial intelligence has become commonplace--most smartphones contain some version of AI, such as speech recognition--the public still has a poor understanding of the technology. As a result, a diverse cast of critics, driven by fear of technology, opportunism, or ignorance, has jumped into the intellectual vacuum to warn policymakers that, sooner than we think, AI will produce a parade of horrible outcomes. Unfortunately, their voices have grown so loud that we are nearing a tipping point where their narratives may be accepted as truth, which would create a real risk that policymakers will decide to ratchet back the pace of progress. With the White House convening a discussion later this week on the social and economic implications of artificial intelligence technologies, to be followed just days later by the 25th International Joint Conference on Artificial Intelligence, Rob Atkinson rebuts these pervasive and pernicious myths in the Huffington Post.

Chat bots have ballooned in popularity in recent months, and now we're seeing some interesting examples of how that technology, where computers interact and respond to human requests, is being used to solve other problems. Today, Zendesk is taking the wraps off "Automatic Answers", a service for businesses to reply to emails from customers without ever having a human employee get involved. Automatic Answers is not your average, run-of-the mill email autoresponder. The service was built using a machine learning platform that Zendesk's in-house teams of data scientists and engineers, which are based out of Melbourne, Australia, have been developing on for a while now. That machine learning platform was first announced last year and it also powers a service Zendesk announced last October, Satisfaction Prediction, which is able to monitor customer-company interactions to -- as its name implies -- determine whether the customer is getting what she or he needs. The machine learning/AI element means that the responses in Automatic Answers are not only reading and responding specifically to what you the customer is asking, but it is technically getting smarter with each response (and presumably using a bit of Satisfaction Prediction to figure out if it's getting it right).