ML offers a promise for a creator to spend thousands and potentially make millions. While millions is not billions, the initial investment isn’t required and the profit margin can be much higher. This will cause many challenges to the media-tech space, as it will have to adapt to the changing market conditions. Teams will work differently, be of different sizes, have different requirements and demand innovation from the technology they use. HARRY BLOXHAM: While there is often fear and trepidation surrounding the adoption of AI and ML in all industries, not just media – generally around jobs being replaced – the better way to view it is as the introduction of a whole new suite of tools to help make your tasks simpler and more efficient. There is one major challenge to the adoption of
greatest opportunity. Taking virtual production as an example (an area which has grown massively in recent times), there used to be significant demands on time and resource to work together in various locations. It’s been well covered that virtual production brings the location to the studio in almost real time, offering huge benefits. Yet behind the scenes – as post-production is being brought earlier in the pipeline to create the environments on a virtual production set – the advancements in ML are only making it easier for flexible contributions. The ability to create and work together in digital spaces, accelerated and enhanced by AI, may well revolutionise media production as we know it today. BRIAN KENWORTHY: The future for ML in broadcast holds an explosion of content creation and innovation driven by a more democratised creative community. Automated editing, captioning and metadata generation will streamline workflows, allowing creators to focus on the creative and produce amazing, high-quality stories for broadcast. This streamlined process enables higher-quantity creation of high-quality stories for broadcast, expanding opportunities for creators across diverse backgrounds. Additionally, ML technologies will optimise audience reach by tailoring content to individual preferences, fostering deeper engagement and loyalty across the board. MICHAEL CIONI: There will be extensive usage of ML in the future. Technology companies will need to focus on the latency and stability demands of broadcast – and that will take some time. The future of the broadcast landscape will live or die by personalisation. these tools in the media space: the models are often based on open-source data. The issue comes with the data; to amass the quantity required, it is often simply collected from the internet and will include copyrighted data, which leads to ethical and legal issues in its commercial use. Initial investment isn΄t required and profit margins can be much higher
WHAT OTHER OPPORTUNITIES LIE AHEAD FOR ML IN BROADCAST?
TIM JUNG: As more processing power is added and more ML models are optimised for real- time use cases, we can expect broader adoption of ML algorithms in the broadcasting industry. These applications can range from validation and analysis to production and post-production processes. Real-time broadcasting monitoring offers significant opportunities for censorship compliance, copyright management and delivering personalised ads. Expanding viewership through real-time transcription and translation – along with the integration of sign languages – will greatly enhance accessibility. In the near future, content might be entirely or partially created by ML models in real time, such as generating video streams from radio broadcasts. HARRY BLOXHAM: Although we’ve mentioned the automation, efficiency and collaboration delivered by ML-powered applications and tools, it may be the combination of these that holds the
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