The event is sold out. If you would like to pitch or attend the next one, please contact us. Looking for an artificial intelligence that can write all your email messages so you don†t have to? Current law does not really define the FDA†s powers to regulate devices that depend on advances in artificial intelligence and machine learning, as applied to health care. The pace of development of AI is going to get faster.

When it comes to the intersection of smartphones and AI, Motorola had the most surprising news at the show. In case you missed it, Motorola is working with Amazon (and Harman Kardon, most likely) to build a Moto Mod that will make use of Alexa. Even to me, someone who cooled on the Mods concept after an initial wave of interesting accessories slowed to a trickle, this seems like a slam dunk. Even better, Motorola product chief Dan Dery described what the company ultimately wanted to achieve: a way to get assistants like Alexa to integrate more closely with the personal data we keep on our smartphones. In his mind, for instance, it would be ideal to ask an AI make a reservation at a restaurant mentioned in an email a day earlier.

In science fiction, the promise or threat of artificial intelligence is tied to humans' relationship to conscious machines. Whether it's Terminators or Cylons or servants like the "Star Trek" computer or the Star Wars droids, machines warrant the name AI when they become sentient--or at least self-aware enough to act with expertise, not to mention volition and surprise. What to make, then, of the explosion of supposed-AI in media, industry, and technology? In some cases, the AI designation might be warranted, even if with some aspiration. Autonomous vehicles, for example, don't quite measure up to R2D2 (or Hal), but they do deploy a combination of sensors, data, and computation to perform the complex work of driving.

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?

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.

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."