Conversation is the new frontier for user interfaces, seamlessly connecting people to brands, their products, and services. There's nothing a customer needs to download, no new interface to learn or complex menus to navigate. The experience for the user is natural and it simply flows, like any conversation should. So what does this mean for businesses? According to Gartner, by 2020 customers will manage 85% of their relationships with the enterprise without interacting with a human.

If you've ever wondered how two or more things relate to each other, or if you've ever had your boss ask you to create a forecast or analyze relationships between variables, then learning regression would be worth your time. In this article, you'll learn the basics of simple linear regression - a tool commonly used in forecasting and financial analysis. We will begin by learning the core principles of regression, first learning about covariance and correlation, and then moving on to building and interpreting a regression output. A lot of software such as Microsoft Excel can do all the regression calculations and outputs for you, but it is still important to learn the underlying mechanics. At the center of regression is the relationship between two variables called the dependent and independent variables.

From helping you take care of email to creating personalized online shopping experiences, AI promises to transform the way we live and work. But with all the hype out there, how do we know which benefits we'll actually see? We're inviting 250 to exhibit at TNW Conference and pitch on stage! What is the top benefit you predict emerging from AI, and do you think the overall benefits will live up to the hype? The greatest benefit of AI -- which is already emerging -- is the elimination of repetitive tasks.

For most of us, building a chatbot serves a real business purpose and isn't just an amusing project. Since a chatbot is meant to serve or interact with customers in some way, it should be given as much information and intelligence as possible to properly interface with customers. The sooner this data is learned by the chatbot, the better -- and it's best when it is done before the chatbot even goes live. A customer-facing chatbot naturally lends itself to using information from customer support tickets. Support tickets are a treasure trove illuminating the types of concerns and issues that customers already have, and they can inform your decisions on what features to build into the chatbot and anticipate what kind of questions your chatbot should be able to answer. Since these are actual, concrete data points, you don't run the risk of imagining problems and solutions unrelated to your customers.

VentureBeat's Bots Channel tracks the most important news and analysis from the exploding field of bots and messaging. Each week, we select the top stories and present them in our free weekly newsletter, BotBeat. We include news stories by VentureBeat staff, guest articles from leading figures in the bots community, and a good number of posts from a wide variety of other outlets. You can subscribe to our BotBeat newsletter to receive this information in your inbox every Thursday. Instead of "Hey, Siri," or "OK, Google," what if you could personalize your device and wake it up accordingly?

That's why CRM is so critical to enterprises, but between incomplete data and clunky workflows, sales and marketing operations at most companies are less than optimal. At the same time, companies that aren't Google or Facebook don't have the billion-dollar R&D budgets to build out AI teams to take away our human efficiencies. Even companies with the right technical talent don't have the petabytes of data that the tech titans use to train cutting-edge neural network models. Salesforce hopes to plug this AI knowledge gap with Einstein. According to chief scientist Richard Socher, Einstein is an "AI layer, not a standalone product, that infuses AI features and capabilities across all the Salesforce Clouds."

Tune in April 7 and find out how to provide stellar customer care with social media in our free webinar. Chatbots are the new rage as more top brands are advancing the technology and integrating it into their chat systems. Big names such as Facebook and Telegram have already made moves in this arena by creating their own chatbots and chatbot platforms. Over the past couple months, I've been trying to implement chatbots into my company Due. For many, chatbot marketing can sound overwhelmingly complicated, but a chatbot, not so much.

While Artificial Intelligence systems continue to improve, one AI is doing that in a unique way. Microsoft and the University of Cambridge partnered on DeepCoder, a deep learning AI meant to mimic the learning patterns found in the human brain. DeepCoder pieces together code from other programs to problem solve. Developers said it's not to mechanize human programmers but to help people create who don't have extensive coding knowledge. " Building an IPS system requires solving two problems. First, the search problem: to find consistent programs we need to search over a suitable set of possible programs. We need to define the set (i.e., the program space) and search procedure. Second, the ranking problem: if there are multiple programs consistent with the input-output examples, which one do we return? Both of these problems are dependent on the specifics of the problem formulation. Thus, the first important decision in formulating an approach to program synthesis is the choice of a Domain Specific Language."

Imagine you're an evil genius in the style of a James Bond villain. You've got a hundred million dollars or so burning a hole in your pocket, and you're looking to cause some destruction. You want to know your options. Greg Allen (@Gregory_C_Allen) is a George Leadership Fellow at Harvard Kennedy School and Harvard Business School. He previously worked on space and robotics issues at the White House Office of Science and Technology Policy.

Economists may be underestimating the impact on labor markets of increasing automation and the rise of artificial intelligence, according to a post published on the Bank of England's staff blog on March 1. RELATED: Starsky Robotics sees'last mile' solution for driverless trucks "The potential for simultaneous and rapid disruption, coupled with the breadth of human functions that AI might replicate, may have profound implications for labor markets," BOE regional agents Mauricio Armellini and Tim Pike wrote in the Bank Underground post. "Economists should seriously consider the possibility that millions of people may be at risk of unemployment, should these technologies be widely adopted." RELATED: Robots could replace 1.7 million American truckers in the next decade Robots and intelligent machines threaten to replace workers in industries from finance to retail to haulage, with BOE Chief Economist Andrew Haldane estimating in 2015 that 15 million British jobs and 80 million in the U.S. could be lost to automation. Past periods of technological upheaval, such as the industrial revolution, may not be a useful guide as the pace of change was slower, giving society longer to mitigate the potential consequences of increasing job displacement and inequality, according to Armellini and Pike.