“It was the best of times, it was the worst of times…”
What a glorious decade for global media distribution. Content consumption is higher than it’s ever been, borders have been stretched, pushed or removed entirely, “foreign” content is captivating “foreign” audiences and the inaccessible is finally becoming accessible to all.
Technology is set to play a crucial role in the fight against climate change by helping us to reduce greenhouse gas emissions, enhance energy efficiency, and promote sustainable practices. Is there potential for AI to also play a part in this? Google DeepMind certainly thinks so and is using the latest AI developments to help fight climate change and build a more sustainable, low-carbon world. But although AI has received a lot of attention since the launch of the large language model, ChatGPT, last year, AI and machine learning (ML) are not new concepts. Content creators, technology vendors, and service providers in the video industry have been using ML for some time. The difference now is that generative AI models have become more advanced, and are now being used by a wider audience. If organizations like Google DeepMind aim to use generative AI to fight climate change, can the video industry also use generative AI to optimize systems, create more sustainable consumption habits, and reduce the industry’s carbon impact?
The media industry is experiencing the transformative impact of AI and ML technologies. These innovations have revolutionised various aspects of content creation, distribution, marketing, and monetization.
AI on entertainment platforms has led to a host of benefits, including data-driven enhanced efficiency, better personalization, and more informed program and content decision-making capabilities. AI in media production and post production has enhanced light ray rendering capabilities and can even edit the production using prescribed user preferences. In sports, AI editing can go as far as making whole game highlight reels. In archive semantic AI can discover scenes with car chases or even romantic scenes. There is no longer a debate about whether AI will happen; it is here, and it is here to stay.
Media and entertainment is a well-established industry, with a heritage to be proud of. But maintaining a pivotal role in the consumer landscape for several decades comes with a unique set of challenges. As media and broadcast has evolved from a handful of linear channels through to a multi-platform ecosystem, more content needs to be reformatted and repurposed to reach an increasingly fragmented audience.
You don’t need to be Nostradamus to work out that linear TV will one day go the way of Monty Python’s parrot: it will cease to be. The timing, however, is less predictable. Because unlike Python’s Norwegian Blue, scheduled TV continues to provide meaningful company in our living rooms. It will inevitably fall from its perch, but with a sizeable audience still feeding it, there’s plenty of life in the old thing yet. As legacy media inches towards a digital-only world, the prolonged squawk of scheduled TV is a major complication. Companies need to deliver for today while planning for a different tomorrow.
The media industry has evolved over the past century, from inventions to disruptions in communication and new-age technologies. In the early 1900s, radio was the crucial link to information, followed by television which by the mid-1900s became the most potent medium for news and entertainment. The late 20th century introduced the internet, and service & media providers entered a new evolution of connectivity. Websites and social media platforms flood the market, providing more choices than ever before. In the 21st century, smartphones are standard, and content consumption requires anytime, to any device, and anywhere access. The traditional television model is disrupted with streaming services like Netflix and Hulu and social media becomes a primary source of news and entertainment with Facebook, Twitter, YouTube, etc.
Red Hat began by providing software to run on Linux about 30 years ago. As the largest open-source company in the world, we believe using an open source development model helps create more stable, secure, and innovative technologies. Our portfolio is broader, including hybrid cloud infrastructure, middleware, agile integration, cloud-native development, and management and automation solutions for service providers.
Jeff Bezos once compared Amazon’s approach to customer experience to hosting a party 24/7. “We see our customers as invited guests to a party, and we are the hosts. It’s our job, every day, to make every important aspect of the customer experience a little bit better.” Bezos’ comments came way back in 2004. But they could just as easily be describing the challenges facing broadcast media today as brands look for growth in the OTT market.
Broadcasters and media companies are implementing technologies powered by Artificial Intelligence (AI) and Machine Learning (ML) across the value chain. We see countless use cases for AI based automation or support and new opportunities keep emerging. So far, the focus has been on the usefulness of AI systems in terms of accuracy and performance in relation to a specific task. This is now changing with a wider uptake of AI, new capabilities for ML and public debates on this technology.
FAST. AVOD. SVOD. MVPD. vMVPD. OTA. These services represent the options available to content owners or aggregators to deliver entertainment, sports and news content from centralized hubs to individual consumers. Their goal is simple – expose and monetize their content libraries to as many consumers as possible. However, that doesn’t mean the consumer is top of mind when it comes to facilitating their journey to their content of choice.