Recommendation systems are algorithms with an objective to suggest the most relevant information to users, whether that be similar products on Amazon, similar TV shows on Netflix, or similar songs on Spotify. There are two main types of recommendation systems: collaborative filtering and content-based filtering. Recommendation systems are one of the most widely used and most practical data science applications. Not only that, but it also has one of the highest ROIs when it comes to data products. It's estimated that Amazon increased its sales by 29% in 2019, specifically due to its recommendation system. As well, Netflix claimed that its recommendation system was worth a staggering $1 billion in 2016! But what makes it so profitable? As I alluded to earlier, it's about one thing: relevancy. By providing users with more relevant products, shows, or songs, you're ultimately increasing their likelihood to purchase more and/or stay engaged longer.

Learn how AI can solve real business problems such as reducing costs, increasing efficiency, eliminating a lot of manual processing, increasing the speed of transactions and increasing business transparency. Understanding AI from a leader's perspective and the practical, data-driven methods to identify and quantify opportunities that leverage artificial intelligence to create competitive advantage and radically change how your business operates. This workshop is designed for senior strategists, data analysts, financial services professionals, Chief marketing Officers, CIOs, senior strategists and entrepreneurs and anyone who wishes to fully understand the practical applications of artificial intelligence to solve real business problems such as reducing costs, increasing efficiencies and eliminating manual processing. This full-day, in-person course is designed to be highly interactive and hands-on, giving your teams the practical experience they need to get the most out of the program.

In the technology industry, it's not unusual for vendors to want to latch on to the latest trend and claim to have a product or service that fits the category. Artificial intelligence is no exception, which means customers need to be vigilant about querying vendors to ensure their technology can really be classified as AI at all, if not intelligent process automation (IPA) specifically. The London-based venture capital firm MMC found that of 2,830 startups in Europe that were classified as AI companies, only 1,580 – about 56% – actually offered AI technology. "We looked at every company, their materials, their product, the website and product documents," David Kelnar, head of research for MMC, told Forbes. "In 40% of cases we could find no mention of evidence of AI."

Gartner and McKinsey Global Institute forecast trillions of dollars in annual impact from artificial intelligence, yet most marketers still struggle to understand what AI is and how to pilot it in their organizations. A recent research project from MIT Sloan Management Review and The Boston Consulting Group (BCG) analyzed AI adoption based on a global survey of 3,076 business executives. Now, keep in mind, this study is not specific to marketing, so the Pioneers percentage would be significantly less if only applied to our industry. The vast majority of brands we talk to at Marketing Artificial Intelligence Institute (MAII) fall into the Passives group, with some easing into the Experimenters category. No matter how you look at it, we are in the infancy of AI adoption, meaning you and your organization have the opportunity now to be proactive in advancing knowledge and capabilities before your competitors beat you to it.

The premise behind Google's Cloud Machine Learning Engine and TensorFlow technologies is to democratize access to machine learning tools and technologies. Additionally, these products are able to be implemented without the help of a PhD-educated data scientist. At the 2017 Google Cloud Next conference in San Francisco, a breakout session explained how a host of companies in various industries are using machine learning tools. Here are seven companies that implemented Google's machine learning tools to solve problems in their business. AXA is an international insurance firm.