FEED Summer 2024 Web


BRIAN KENWORTHY: I particularly like speech recognition, generation and auto-translation. These tools play a pivotal role in democratising content accessibility, breaking down barriers to information and entertainment for global audiences. Through automatically transcribing, translating and generating content, ML empowers individuals to engage effortlessly with a wide array of media content – regardless of linguistic or cultural differences. This democratisation not only enhances inclusivity, but also fosters cultural exchange and understanding on a global scale. By reducing reliance on manual translation processes, these tools alleviate the workload of human translators, allowing them to concentrate on more intricate and culturally nuanced projects. In essence, the democratising effect of ML in broadcasting transcends linguistic boundaries, promoting a more interconnected and culturally enriched media landscape. TIM JUNG: When this question is directed at someone committed to bridging language barriers, the response is predictable. In 2020, we launched the world’s first live subtitling solution. From the outset, this feature garnered enthusiasm, especially during an online fan meeting between a K-pop idol and their international fanbase. Previously, these fans had watched the shows without fully understanding the discussions. Our real-time translation transformed their experience, allowing them to engage deeply with discussions. The tech has continued evolving massively. We have achieved major advancements in real- time translation. Generative AI has enabled us to automatically incorporate glossaries specific to the topic at hand. MICHAEL CIONI: My favourite application of ML in broadcast is in sports. For years, we have heard this joke that watching sports at home is

better than the stadium. With the advancement in real-time analytics, this becomes even more true. You can know how fast someone ran, how high they jumped or the probability that the play that happened was supposed to happen. It’s extremely engaging, interesting and connects the viewer to the game in an entirely new way.


MICHAEL CIONI: Any new technology will present challenges in any sector; ML will not be immune to this. The new opportunities will not always outweigh the costs until the tech develops to solve the problems it created. One of the biggest challenges will be in economics. We have seen large projects make billions of dollars but cost hundreds of millions to make. If you take it down a scale, it has historically been difficult to spend thousands of dollars and make millions. Essentially, the largest monetary gain was in the big projects and the studios were the only ones who could output the millions to get the big payoff.

These tools alleviate the workload of translators @feedzinesocial

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