The media and entertainment industry is at the cusp of rapid transformation, with cutting-edge companies leveraging AI and machine learning to create personalized customer experiences, reduce operating costs and drive more value from delivering content. Last year at EGG, we got the chance to hear first-hand from some of the industry leaders driving the AI disruption in the media landscape.
Following up on our last ranking, here is our take on the best media and entertainment EGG talks from 2019:
5. Panel: Magical Experiences And Greater Well-Being
AI can be used to create scalable personalized experiences that take into account our individual interests and well-being. In this fireside chat, we sat down with data science leaders from Disney Parks & Resorts, Warner Media, and Blue Cross Blue Shield, who are using personalization to create magical and life changing experiences in a responsible way. They shared use cases, how they are making decisions around personalization, and advice for empowering teams to make smart, responsible decisions that foster consumer trust.
4. Individualism, Collectivism, and Training Machines to Humanize Dating
While machine learning recommender algorithms traditionally solve for the personalized preference problem, in online dating we must also consider our user base's collective fulfillment. More generally, balancing individualism and collectivism is central to sustaining a product ecosystem as well as to using machine learning responsibly. In her insightful talk, Shanshan Ding discussed ways this tension manifests on Hinge, some of the existing solutions to address this tension, and the open problems that continue to be explored.
3. Users Floating in Space: A Study in Recommendations
This talk by Tyler Neylon, CEO of Unbox Research, covers several technical approaches that brought a music recommendation service into the AI era. In particular, this is a case study of a collaboration between machine learning experts (Unbox Research) and a music service company (Roon Labs). By implementing new personalization features, their joint work was able to significantly improve product quality and user engagement.
2. Pitfalls of using ML for Fighting Online Abuse
Fighting fake registrations, phishing, spam and other types of abuse on the consumer web appears at first glance to be an application tailor-made for machine learning. However, building machine learning systems to address these problems in practice turns out to be anything but a textbook process. In his talk, David Freeman, research engineer at Facebook, explains how machine learning is typically used to solve abuse problems, discuss these and other challenges that arise, and describe some approaches that can be implemented to produce robust, scalable systems.
1. Ethics, Data Science, and Public Service Media
In this talk, Ben Fields from BBC News looks at the contributions that public service media organizations can play in the emerging understanding of the responsible and ethical practice of data science. He looks at some specific project examples, such as automatic decision-making processes, as they need to come to Public Service Medias (PSM) because they represent significant competitive advantage and competitive potential. But PSM are about making sure that people have a shared understanding of the world around them. How can you balance these two different expectations? Find out in his fascinating talk on ethics, data science and PSM.
