Collaborating Authors


Artificial Intelligence at Netflix - Two Current Use-Cases


Netflix launched in 1997 as a mail-based DVD rental business. Alongside the growing US DVD market in the late 1990s and early 2000s, Netflix's business grew and the company went public in 2002. Netflix posted its first profit a year later. By 2007, Netflix introduced its streaming service, and by 2013, the company began producing original content. Today, Netflix is one of the world's largest entertainment services with over 200 million paid memberships spanning 190 countries, according to the company's 2020 Annual Report.

Challenges of Artificial Intelligence -- From Machine Learning and Computer Vision to Emotional Intelligence Artificial Intelligence

Artificial intelligence (AI) has become a part of everyday conversation and our lives. It is considered as the new electricity that is revolutionizing the world. AI is heavily invested in both industry and academy. However, there is also a lot of hype in the current AI debate. AI based on so-called deep learning has achieved impressive results in many problems, but its limits are already visible. AI has been under research since the 1940s, and the industry has seen many ups and downs due to over-expectations and related disappointments that have followed. The purpose of this book is to give a realistic picture of AI, its history, its potential and limitations. We believe that AI is a helper, not a ruler of humans. We begin by describing what AI is and how it has evolved over the decades. After fundamentals, we explain the importance of massive data for the current mainstream of artificial intelligence. The most common representations for AI, methods, and machine learning are covered. In addition, the main application areas are introduced. Computer vision has been central to the development of AI. The book provides a general introduction to computer vision, and includes an exposure to the results and applications of our own research. Emotions are central to human intelligence, but little use has been made in AI. We present the basics of emotional intelligence and our own research on the topic. We discuss super-intelligence that transcends human understanding, explaining why such achievement seems impossible on the basis of present knowledge,and how AI could be improved. Finally, a summary is made of the current state of AI and what to do in the future. In the appendix, we look at the development of AI education, especially from the perspective of contents at our own university.

Is Disney World the New Netflix?


Netflix leans on machine learning to power its recommendation algorithms and shape its future … How deep will Disney go in catering its suggestions?

The Role of Social Movements, Coalitions, and Workers in Resisting Harmful Artificial Intelligence and Contributing to the Development of Responsible AI Artificial Intelligence

There is mounting public concern over the influence that AI based systems has in our society. Coalitions in all sectors are acting worldwide to resist hamful applications of AI. From indigenous people addressing the lack of reliable data, to smart city stakeholders, to students protesting the academic relationships with sex trafficker and MIT donor Jeffery Epstein, the questionable ethics and values of those heavily investing in and profiting from AI are under global scrutiny. There are biased, wrongful, and disturbing assumptions embedded in AI algorithms that could get locked in without intervention. Our best human judgment is needed to contain AI's harmful impact. Perhaps one of the greatest contributions of AI will be to make us ultimately understand how important human wisdom truly is in life on earth.

Hulu's 'False Positive' is a vexing horror puzzle you'll be dying to solve


False Positive is an imperfect movie worth watching. Co-written by star Ilana Glazer and director John Lee, the newest horror title from A24 begins with a familiar premise. When hopeful parents-to-be Lucy (Glazer) and Adrian (Justin Theroux) seek the help of charming fertility specialist Dr. John Hindle (Pierce Brosnan), the couple experiences near-instant success -- conceiving not one, but three children on their first attempt. For Lucy, however, the joy of expecting soon gives way to an eerie sense that something isn't right, and a thorny psychodrama between her, her doctor, and her husband starts to unfold. Speaking with a friend (Sophia Bush), Lucy confides, "They're trying to make me think that I'm crazy" -- but crazy about what, she isn't so sure.

Smart Recommendation System For OTT platforms


The recommendation engine has become quite popular across diverse industries in recent years. The recommendation engine is gaining rapid traction from OTT (Over the Top) platforms to e-commerce stores. Whether you have just started your OTT platform or plan to scale it up, recommendation engines can significantly improve your profitability. A Recommendation engine or recommendation system is an information filtering tool that provides the most relevant suggestions regarding products or services to various customers. A recommendation engine uses machine learning algorithms to collect and analyze user activities such as their preferences, search history, and others.

How Netflix uses AI to find your next series binge


Wait, how did Netflix know I wanted to watch that? Through the use of Machine Learning, Collaborative Filtering, NLP and more, Netflix undertake a 5 step process to not only enhance UX, but to create a tailored and personalised platform to maximise engagement, retention and enjoyment. In the last decade, learning algorithms and models at Netflix have evolved with multiple layers, multiple stages and nonlinearities. This has developed to the stage at which they now use machine learning and deep variants to rank large catalogues of content by determining the relevance of each of their titles to each user, creating a personalized content strategy. Not only is the content customized, it is then also ranked from most to least likely to be watched.

Leveraging Data Science for OTT Content Personalization


Why is content personalization important? OTT (Over the Top) platforms are transforming the global entertainment scene. The critical players, like Hulu, Netflix, and Disney, are competing in terms of viewership and revenues. With the increasing overlap of content across all these platforms, it is crucial for these services to improve the consumer experience by delivering relevant and engaging content to prevent audience churn. Content personalization is, therefore, vital to acquire more viewing time and improve market share.

Python at Netflix


As many of us prepare to go to PyCon, we wanted to share a sampling of how Python is used at Netflix. We use Python through the full content lifecycle, from deciding which content to fund all the way to operating the CDN that serves the final video to 148 million members. We use and contribute to many open-source Python packages, some of which are mentioned below. If any of this interests you, check out the jobs site or find us at PyCon. We have donated a few Netflix Originals posters to the PyLadies Auction and look forward to seeing you all there.

Three Ways Big Data and Machine Learning Reinvent Online Video Experience 7wData


With traditional TV viewing on the decline, we discuss several ways Big data and Machine Learning can assist with online video, including redefining user recommendations, improving video buffering and leveraging MAM orchestration. Let's face it: traditional TV is fading. Viewing habits have totally changed, with spectators now favoring online video. In this competitive market where big players like Netflix and Hulu are racing for most eyeballs it might be rather difficult to encourage audiences to stay tuned to your video content. According to NewVantage Venture Partners, Big Data and Machine Learning (ML) deliver true value to enterprises.