Artificial intelligence (AI) is changing the face of business. No longer a futuristic concept, its impact is real. From tech giants like Google, Apple and Amazon to user-centric behemoths like Uber and Starbucks, everyone seems to be using AI technology to transform the customer experience (CX). But, it's not just corporate giants that are deploying AI. Smaller organizations are following suit.

I view the World Wide Web as an information food chain. The maze of pages and hyperlinks that comprise the Web are at the very bottom of the chain. The maze of pages and hyperlinks that comprise the Web are at the very bottom of the chain. Today's Web is populated by a panoply of primitive but popular information services. Is the Web challenge a distraction from our long-term goal of understanding intelligence and building intelligent agents?

Would you like to take a course on Keras and deep learning in Python? Consider taking DataCamp's Deep Learning in Python course! Also, don't miss our Keras cheat sheet, which shows you the six steps that you need to go through to build neural networks in Python with code examples! Before going deeper into Keras and how you can use it to get started with deep learning in Python, you should probably know a thing or two about neural networks. As you briefly read in the previous section, neural networks found their inspiration and biology, where the term "neural network" can also be used for neurons.

With this in mind, how might artificial intelligence (AI) affect the world of retirement accounts, specifically defined contribution (DC) plans? With fee transparency, few surviving providers, and robos directing consumers to the most suitable investment products and strategies, the employer's role in retirement funds will probably diminish. In turn, true personal pension accounts, akin to bank accounts, will likely emerge at the expense of company-run plans. He has over a decade of experience in roles spanning product development, consulting, and strategy across the DC landscape.

Now, she's turned her AI naming capabilities towards beer. While writing about her neural network-generated paint color names, Gizmodo's Ryan Mandelbaum mentioned the issues craft brewers were having coming up with names for their beers -- issues that have at times led to legal action. Not long after that, with the help of a Gizmodo reader who put together a dataset of beer names culled from BeerAdvocate.com, Shane plugged the names into a neural network and out popped a bunch of new names for brewers to use. "For these names I turned the neural network's creativity variable higher and got results that can be described mainly as ... interesting," she said on the blog.

Here's what retailers can get from using recommendation systems: Increased customer loyalty by sending offers based on specific customer needs. The idea is simple: we define a market basket for every customer and calculate the distance between the specific customer and others having similar items in the market basket. Then, we recommend customers buy the goods purchased earlier by those customers with similar market baskets. If a customer feature set coincides with an item feature set, then this customer gets a recommendation for this specific item.

CDC has even identified food safety as one of seven "winnable battles"; however, progress to date has been limited. We show that adaptive inspection process is 64 percent more effective at identifying problematic venues than the current state of the art. If fully deployed, our approach could prevent over 9,000 cases of foodborne illness and 557 hospitalizations annually in Las Vegas alone. Additionally, adaptive inspections result in unexpected benefits, including the identification of venues lacking permits, contagious kitchen staff, and fewer customer complaints filed with the Las Vegas health department.