By Vinayak Shrivastav
It is no secret that technology adoption rates have improved exponentially, and one of the greatest successes of this drastically improved adoption are mobile phones. Coupled with the faster transfusion of technology and the increased internet penetration we are truly in the digital age of communication, and in today’s age video is the preferred mode of communication. Backed by socio-economic impulses, the advent of new platforms, and the democratisation of means of production, video content has truly exploded. It is established that users view 1 billion hours of video every day through YouTube alone and on average 300 hours of videos are uploaded on YouTube every minute. By 2022, the number of videos crossing the internet per second will be close to 1 million and 82% of all consumer internet traffic will come from videos.
To make sense of this mountain of video data, metadata becomes incredibly important. Meta-tagging and machine learning are critical tools to navigate this digital age of communication. By providing catalogable information about every aspect of a video asset these technologies are redefining how we access, interpret, and use these assets while maximising their offtake across the disparate audience sets.
Meta-tagging and Video
Adopting new strategies to reach out to the audience through video content has been at the top of every content creator’s priority list in recent years. But the success of these strategies lies in the ability of models to understand the asset and map them to the right audiences. Metadata tagging has traditionally been a manual and time-consuming operation focussed on a small set of high importance video assets. However, with the arrival of artificial intelligence (AI) and machine learning, metadata tags may be developed much faster and with higher precision; completely automating the process results in shorter turn-around times, less resource reliance, more coverage and tremendous cost savings.
Meta-tagging not only assists in improving the use of data and compensating for inconsistencies caused by human error but also supports and empowers brands to fully explore their potential in data-led transformations.
Technology-centric companies are leveraging cutting-edge technologies to understand their audiences and to produce and curate the right content. For this to succeed the setup requires strong metadata foundations. This environment is highly dynamic and as systems continuously change and evolve the foundational layer of metadata needs to transform as well. Self-learning AI-based metadata processes makes this evolution easier and ‘instantaneous’ putting less strain on the system.
Machine learning is a multifaceted approach to problem-solving. This type of computer process essentially learns what produces a significant outcome and what does not, and it improves itself based on the data it collects. These smart-systems are more efficient at handling meta-data tasks and at servicing more personalised and customised requests.
Machine learning is fast being implemented in many industries — the market is expected to grow at US$8.81 billion by 2022 at a compound annual growth rate of 44.1%, according to Research and Markets. One of the key reasons attributing to this is that companies collect large data, from which they need valuable insights to analyse how their content is performing. Video-based companies need to take the initiative in adopting machine learning as it seamlessly integrates with the value chain of content production and delivery based on a data-first approach.
While machine learning is still a new component in many industries, it can be applied to automate any process – including live video delivery (broadcasting). Machine learning can also be used to simplify the process of post-production by natively adding digital elements (or removing elements) based on visual information captured as metadata, making the videos more customisable.
With the help of AI and machine learning technologies content creators can chart the final frontier of developing automated short-form content from long-form assets driving five times more traction in comparison. Incorporation of machine learning can simultaneously reduce time to produce more content which on an average took 2-3 hours on the editing table to a few minutes. Reduction of manual labour hours and the linked costs is a tangible advantage that is being realised by companies that employ these technologies when dealing with video content. The industry has seen an 80% cost reduction on video production of additional assets.
As we live in a rapidly growing video-dominated society the creation, management, and distribution of video content across different platforms requires a synergy of technologies. The combination of meta-tags and machine learning technology offers endless opportunities to automate, streamline and tailor video assets to enhance the video experience for the end consumer.
The author is co-founder and CEO of Toch.ai. Views expressed are personal.