content delivery enhances engagement and satisfaction. Plus, ML-powered content creation tools introduce remarkable efficiency gains. Auto rotoscoping, editing and commercial break placement streamline production workflows, while flagging censorship scenes ensures compliance with regulations. Moreover, ML-driven restoration techniques breathe new life into older material, like The Wizard of Oz from 1939. By removing noise from audio and cleaning up imperfections, restoration ML enhances the visual and auditory quality, providing audiences with a pristine viewing experience in the modern era. MICHAEL CIONI: The most exciting innovation in the ML space has to be around globalisation of content. Connecting with people across the world is something the internet has fostered. We can exchange information and interact with each other at the speed of light – but still have a huge problem: we don’t all speak the same language. ML now offers the ability to both translate text as well as media content such as audio and video. People across the world have dramatically different experiences, inspirations and cultures. This type of ML technology enables us to enjoy stories, viewing the content just like it was made in our own language – but in reality, the content was made in a language we may not know a word of. TIM JUNG: While the media industry seems closely aligned with technology, its adoption of tech has ironically been slower compared to sectors like e-commerce, logistics or home appliances. This cautious approach resembles industries where accuracy and safety are crucial, such as healthcare and automotive. Traditionally conservative, the media industry has gradually adopted technologies like cloud-based storage and software systems. This trend extends to ML as well. I categorise ML systems by their level of autonomy: firstly, ML as an alarm, where ML alerts humans who then take action; secondly, ML as a tool, where humans utilise and refine ML-generated suggestions; and lastly, ML as an agent, where ML independently performs tasks. Until 2018, ML in the media was primarily used as an alarm, for functions including broadcast monitoring and detecting censorship violations. However, the landscape is rapidly changing; we now see ML utilised as a tool for creating computer graphics, transcriptions and subtitling, where human experts refine the drafts produced. Some ML algorithms have greatly enhanced user experience on media platforms through ranking, recommendations and personalisation. More recently, ML has moved into real-time applications where post-editing by humans isn’t feasible.
ML has moved into real-time applications where post-editing isn΄t feasible
required but the timescales and complexity are both reduced. Over the years, as these tools have evolved for almost every stage of the creative process, efficiency has been impacted significantly in completing projects faster while using less resources at the same time. Jumping to the last couple of years, this has only accelerated as we’ve seen generative AI tools integrated into applications. For example, the ability to input prompts with tools like Adobe Firefly allows users to automatically add frames and remove objects. Outside of this integration, platforms like Nvidia Omniverse have brought the ML backbones of many individual tools into a collaborative real- time environment, providing a significant boost to efficiency.
WHAT ARE SOME OF THE MOST EXCITING INNOVATIONS YOU΄VE SEEN IN THE ML SPACE?
BRIAN KENWORTHY: One of the most thrilling innovations in the ML space is personalisation. It revolutionises user experiences by tailoring content recommendations and advertisements based on individual preferences and behaviour patterns. Whether it’s suggesting movies aligned with viewing history or presenting targeted ads matching personal interests, personalised
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