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) …
IRI Demand Forecasting, to help marketing, finance, and sales teams identify opportunities to drive sales growth. IRI Revenue Management, to help users optimize and track price and promotion strategies. A new Price Recommendation Engine leverages machine learning to evaluate products, competitors, retailers, and geographies and subsequently identify pricing and promotion opportunities. IRI Assortment Optimization now helps users leverage machine learning to proactively look across retailers and products to alert opportunities. Patterns of incremental sales potential are prescriptively evaluated across millions of possible improvements.
Today, recommendation engines are perhaps the biggest threat to societal cohesion on the internet--and, as a result, one of the biggest threats to societal cohesion in the offline world, too. The recommendation engines we engage with are broken in ways that have grave consequences: amplified conspiracy theories, gamified news, nonsense infiltrating mainstream discourse, misinformed voters. Recommendation engines have become The Great Polarizer. Ironically, the conversation about recommendation engines, and the curatorial power of social giants, is also highly polarized. A creator showed up at YouTube's offices with a gun last week, outraged that the platform had demonetized and downranked some of the videos on her channel.
The world of IT can't get enough of machine learning (ML) and its potential to transform the way future generations interact with the world. With this technology, self-driving vehicles have gone from a theory to a reality that is less than a decade away from being widely accepted in open roads. ML has other valuable applications such as advanced fraud detection techniques by studying and countering some of the most prevalent fraud initiatives around, as well as its email-filtering abilities to help keep phishing scams at bay with cybersecurity software. The sky is the limit for this technology, which may soon make life easier for us in the form of more advanced cognitive learning applications and improved personalized capabilities for recommendation engines. WorkFusion offers a slew of intelligent automation softwares that help companies monitor incoming threats, automate menial tasks and save on overhead costs.
The recommendation engine market based on AI, is projected to grow at a CAGR of 40.7% during the forecast period The market for recommendation engine based on AI, is expected to grow from USD 801.1 million in 2017 to USD 4414.8 million by 2022, at a Compound Annual Growth Rate (CAGR) of 40.7% during the forecast period. The growth in focus toward enhancing the customer experience is a major factor driving the growth of the recommendation engine market. Moreover, enhancing customer experience is important to achieve customer engagement and retention, thereby achieving higher sales and Return on Investment (RoI). However, designing of targeted campings, as well as relevant product and content recommendations, could help organizations engage more customers. Hence, analysis of customer data here plays a vital role to understand the customer behavior and preferences.
Machine learning – a piece of the artificial intelligence constellation – holds a lot of promise for enterprises, enabling programs and algorithms to become ever more intelligent. However, there's one problem: even the best-educated humans need more learning before they can understand machine learning. Bob Hayes, a professional data scientist and keen observer of all things data, picked up on a survey by Kaggle that finds that even data scientists still have a grasp on machine learning. The survey "revealed that a limited number of data professionals possess competency in advanced machine learning skills," says Hayes. "About half of data professionals said they were competent in supervised machine learning (49%) and logistic regression (53%). Deep learning techniques were among the ML skills with the lowest competency rates."
CIOs are developing "a sort of tunnel vision about AI," Isaac Sacolick, a former CIO who now advises CIOs on big initiatives like digital transformation, told me recently. Looking to establish accountability across disparate project teams? Trying to automate processes or allow for lean methodology support? Hoping to enable business consequence modeling or real-time reporting? If you answered'yes' to any of these questions, then you need to download this comprehensive, 68-page PDF guide on selecting, managing, and tracking IT projects for superior service delivery.
Perhaps the single most important algorithmic distinction between "born digital" enterprises and legacy companies is not their people, data sets, or computational resources, but a clear real-time commitment to delivering accurate, actionable customer recommendations. Recommendation engines (or recommenders) force organizations to fundamentally rethink how to get greater value from their data while creating greater value for their customers. "Build real recommendation engines fast" is my mission-critical recommendation to companies aspiring -- or struggling -- to creatively cross the digital divide. Use recommenders to make it easier to gain better insight into customers while they're getting better information about you. Recommenders' true genius comes from their opportunity to build virtuous business cycles: The more people use them, the more valuable they become; the more valuable they become, the more people use them.