If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
Netflix's long list of suggested movies and TV shows is a fantastic example of a personalized user experience. In fact, about 70 percent of everything users watch is a personalized recommendation, according to the company. Getting to that point hasn't been easy, and improving on its recommendation system is an ongoing process. Netflix has spent well over a decade developing and refining its recommendations. In 2006, it launched the Netflix Prize to search for machine learning experts who could improve its previous algorithm.
What we are looking at is a recommendation engine problem. Given the personal data you want to make the diet recommendation that are best suited for the person based on the data inserted. For creating this recommendation engine, you'll need data for personal details and appropriate recommendations. Based on this training data, you'll create the recommendation engine which will map the personal information to appropriate diet recommendations.
Personalisation today is a must, not longer a maybe. Research from BCG suggests that brands which create personalised experiences by integrating advanced digital technologies and proprietary data for customers are seeing revenues increase by 6% to 10% – two to three times faster than those that don't. The need for personalisation has infiltrated a wide variety of industries. Why do you think you can personalise your Coke with your own name, or get the perfect song recommended on Spotify? The travel industry has slowly started to go in the same direction.
This article is part of CMO.com's December series about 2018 trends, predictions, and new opportunities. Despite a vibrant economy, individual companies will confront unceasing changes in technology and inflated consumer expectations. That's why Forrester Research is calling 2018 a "year of reckoning." It sees both as an existential threat that makes the fate of individual companies uncertain. This environment has prompted a radical shift in what is traditionally meant by marketing; some even view the traditional role of chief marketing officer as outmoded.
Artificial Intelligence's (AI) role in marketing is wide-reaching. Marketers use AI to automate campaigns on social media, through email, and in paid advertising. It can also provide real-time analytics and analysis of data gathered across multiple platforms. AI identifies potential customers based on purchasing history and online behavior. This technology has been assisting marketers for some time now, but its role is expanding as the technology grows more sophisticated.
Marketers evaluate recommender systems not on their algorithms but on how well the vendor's expertise and interfaces will support achieving business goals. Driven by a business model that pays based on recommendation success, vendors guide clients through continuous optimization of recommendations. While recommender technology is mature, the solutions and market are still young. As a result, solutions are not fully integrated with other business systems and technology platforms. While the market is retail-focused today, interest and vendor offerings are rapidly expanding to other areas.
This approach to predictive analytics applications can be illustrated by an example. Let's consider an e-commerce company that wants to boost its profits by growing sales to existing customers. The objectives might be to increase both the number of items bought by individual customers and the average amount they spend overall in purchase transactions. A typical strategy to accomplish those goals involves using a recommendation engine to try to influence customers to add items to their online cart as they shop. There are a variety of different analytics methods that the online retailer can incorporate into its recommendation engine to assign similar customers to groups so the engine can suggest products that they might be inclined to buy.
Content recommendation engines are everywhere these days, used by publishers in the hopes of solving the ever growing content discovery problem. With what seems like an infinite amount of content being published every day, how do consumers know where to look first? Search engines have changed the world by enabling people to find any and all kinds of information. But how can someone discover something new if they don't already know to look for it? The words recommendation and personalization may be used interchangeably, but they're not at all the same thing.
Ever wondered how Santa knows what you wanted for Christmas? It ain't magic, it's cold-hard data science! Now, you can also experience the hardships of Santa Claus's job using our hyper realistic simulation software. Have you ever thought about how gifts end up under your Christmas tree? That new laptop, that fancy smartphone, selfie stick, quadcopter drone, capacitive gloves, Halo 5 Collectors Ed., Starbucks gift card, Forbes subscription, just what you wanted, right?