有人工智能 & ML Can Address Emerging Streaming Monetization Challenges
Learn more about AI and streaming at 流媒体东部2022.
Read the complete transcript of this clip:
埃里克·博尔顿: We work very closely with our entire customer base to try to attack the problem sets that are--to use that industry jargon--a lot of pain points. But within that, the thing that you don't trust is the thing we're trying to actually solve for. We're looking to find correlations and patterns that, if I pulled up one of our top engineers, they could look through a set of data and say, “嗯, this means this and this correlates to that, 这就是为什么会发生这种情况."
你如何大规模地做到这一点? And how do you bring those things up so that people can have one extreme data visualization to look at things in multiple dimensions that you would be impossible to look at. We pull a hundred different dataset types that we're trying to just harvest and bring together.
And while we're solving our world in this larger ecosystem, I think you can hear the diatribes. So we want to solve today's problems of, "How do I have broadcast streaming, 和视频, maintain a certain level of quality?但除此之外, how do those things intersect with more future-setting monetization and cost goals of any organization? And I think that's a journey and, predictive versus prescriptive versus autonomous. I completely understand in a sense, but you're going to have the idea that someone's watching a piece of programming and they're gonna do what television doesn't like--change the channel, which today is not how that exactly works. But how does one maintain that audience interaction through the varieties of programming available? You are gonna have data available that tells you what that person is, what their demographic is.
Nielsen ratings ran the television is industry forever. I know that the Weather Channel and other things is look at much more smaller market segments that would break the United States into much tighter areas. How does Discovery or any other client find that I can say, "I know what your weather is. 我知道你喜欢什么运动." And all of that has to come together to make a compelling video experience. And I think that when we talk to our clients, we accept that this is an ongoing worki-in-process. AI和ML不是, as you held up your little speaker, this mythical black box that waves, pixie dust on it and goes away. It's an ongoing evolution of what the technology that was available and is now being applied into the media to entertainment.
相关文章
Tim Siglin of Help Me Stream 研究 Foundation sits down with Ben Ratner, the Director of 新闻 Technology at Boston 25 新闻, 聊一聊人工智能, 社交媒体, and the future of broadcast news.
2023年6月1日
太经常了, the monetization discussion revolves around a simplistic binary that expands only slightly on the old ad-supported broadcast model for home-based media and entertainment: subscription vs. 广告. Savvy publishers, rightsholders, and streaming services see a more nuanced landscape. 游戏化, 交互性, 销售, and direct-to-consumer apps let both publishers and advertisers super-serve fans with content that goes beyond live linear or VOD.
11月23日2022
The uses for artificial intelligence (AI) and machine learning (ML)—from text identification to contextual 广告 to cultural sensitivity—are multiplying. Here's a look at some of the most compelling offerings on the market today.
2022年5月19日
紫溪 Head of Product Tim Baldwin discusses 紫溪's approach to live linear over IP in this clip from a 在线直播 Summit panel at 流媒体 West 2019.
2020年2月7日
Companies and Suppliers Mentioned