Financing Movie Making requires convergence of investors, bankers and several financial institutions coming together. The entire movie making process is complex across the lifecycle of pre-production, production, post-production, distribution etc.
As a producer of a movie, the intent is to ensure the success of the content (movie) and make financial profit. The entire moving making process results in a lot of data generation (from scripts, marketing assets, actors, posters, trailers, props, exchange of information, ideas and so many other aspects across the lifecycle).
Can AI or GenAI help with finding patterns through the latitude of data across the movie making lifecycle? Can it help with prediction of success of movies that allows producers, directors, financiers to take informed and wise decisions for moving making? NStarX Data Scientists have been looking at this problem for a while now!
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In recent years, with the rapid development of AI and cloud technology, my company and I have faced the impact of new technologies on the sports broadcasting industry, tried to apply cloud and AI to our business, and achieved good results. I will now share our practical experience in utilizing AI and cloud tech to enhance production efficiency and operational capabilities.
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In the age of 24/7 news coverage, broadcasters face the challenge of ensuring that stories are not only understood and entertaining, but wide reaching. This is no mean feat for any media organization that plans to cover this year’s elections.
According to the World Economic Forum, an estimated 2 billion people will vote this year in 50 countries around the world, and broadcasters are tasked with providing correct information as it unfolds. To ensure accuracy, data needs to come in real-time and be relayed to audiences as fast as possible; to ensure brand consistency and retain viewers, the data needs to be communicated in a visually stunning way.
This is where graphics come in. With the purpose of illustrating real-time data, graphics simplify and entertain viewers at home. In fact, research conducted by Vizrt found that 42% of people surveyed believe on-screen graphics help them understand complex stories – for Gen Z, it’s over half (51%).
To produce the best stories, broadcasters rely heavily on what can be created in the studio and what can be captured in the field.
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We have all seen the consternation in the media about the rising challenge of AI in a wide range of industries and the potential for mass job losses as a result. Should we be concerned that the same could happen to workers in the broadcast graphics sector? The trouble with these kinds of sweeping statements is that they cover such a broad set of roles that it becomes meaningless. To make informed comments we really need to address each niche within ‘broadcast graphics’ separately and look at what AI could do, or is already doing, to see how it affects the users involved. What is true for some areas may be very different for others.
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Virtual production is already a mature technology that not only allows for high-quality results, but also reduces costs and contributes to increase the industry’s sustainability. The introduction of advanced rendering technologies such as game engines or real-time ray tracing significantly boosted the quality of the real-time renderings, allowing content creators of any size and kind to produce photo realistic content, meaning high-end results no longer restricted for high-budget filmmakers, but available to a wider range of creators.
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Originating as a VJ software for one-man shows, now a highly developed tool for complex TV productions, Aximmetry is becoming a household name in the broadcast industry. Over the years, the team purposefully molded the software into a universal, efficient, and affordable tool to fit the needs of broadcast professionals.
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The rise of the mega streamer has brought the broadcast media industry into a period of volatility, uncertainty, complexity, and ambiguity. The acronym VUCA first described the complex and challenging geopolitical situation in 1987 following the Cold War, and now aptly defines the current media landscape. It’s an environment characterized by volatility in that challenges are unexpected and sometimes incomprehensible; by uncertainty in that change may happen, or not; by complexity in that it is influenced by numerous variables; and by ambiguity in that causal relationships can be difficult or impossible to define.
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Judging by the number of trade publication articles and speaking sessions that focus on the topic, you’d think that the entire media and entertainment industry is focussed on cutting carbon costs. But is that really the case? True, broadcasters have set ambitious targets to reach net zero, the streaming giants are following suit, and they’re putting pressure on production companies to reduce their environmental impact and include sustainability messaging in the content they produce. Carbon emissions have even become a critical consideration in planning new studio builds. But not every part of the production chain is putting the environment first.
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In the world of modern content production, the choice between cloud-based solutions and traditional hardware solutions has become increasingly critical. This is a question that Chyron has tackled along with customers and prospective customers, gaining insight into common assumptions, requirements, and opportunities with regard to cloud-based solutions for live production.
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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.
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