Artificial intelligence holds great promise for the media industry, and its use cases today are especially applicable across the content lifecycle. From content creation, production, and distribution to delivery and recommendation, AI is streamlining process and improving existing execution methods. In this session, panelists discuss how to leverage AI to enhance content value and performance, as well as unveil and maximize new opportunities for monetization.

The promise of artificial intelligence is undeniable—unprecedented personalization, efficiencies that reduce costs and increased revenue. This all works in on-demand video, but is there a role for AI in livestreaming linear channels? This case study from Ooyala and Zone.tv discusses how the two companies are working together to bring AI to TV to automate the curation of content in a first-of-its-kind, customizable, and highly personalized experience in a suite of linear TV channels.

Streaming video analytics have become mainstream for almost all OTT publishers, who have found the need to make the data and analytics available to operational and business teams, both in real time and actionable, increasingly important. The massive volume of data makes finding the signal in the noise very difficult for traditional reporting and alerting systems and workflows. Data-driven AI and machine learning techniques are increasingly being used to address these requirements. In this presentation we cover both the business/operational use cases and the algorithms used to solve these problems.

With social media algorithms constantly changing, it’s time for news publishers to regain control of their audiences. Using AI and machine learning, publishers can program contextually relevant and personalized content down to the viewer level on their owned-andoperated sites for both breaking news and day-to-day operations. IRIS. TV and Gannett demonstrate how AI is helping to provide prescriptive insights into audience engagement and fueling future content strategies and video distribution on owned-and-operated sites.

Personalization is one of the main focus areas of CMOs and CTOs across various industries, including media. This case study presents how Microsoft Research has spearheaded content personalization through the Azure Custom Decision Service using innovative online learning techniques founded in a very prominent area of machine learning called Reinforcement Learning, providing increased engagement for publishers.

A number of cloud providers have launched video-centric services to gain all manner of AI/machine learning-based insights and autogenerated sidecar information on your video assets. This session compares and contrasts Microsoft Azure Video Indexer, IBM’s Watson Media, AWS Rekognition, and Google Cloud Video Intelligence. We cover the basics and give you an unbiased look into which service will be the best bet for your needs. Whether it’s speech-to-text, object recognition, scene detection, or even speech sentiment, there’s a service out there for you. Let’s find it.