How AI in Video Will Enhance Work in the Modern-Day Work Environment
Shaking off the dust from what could be described as the longest year known to man — remote work is a hot topic in the world of employment. By establishing both its benefits, as well as its challenges, remote work has people talking about its permanence. What is more, employees have become accustomed to remote working, in fact, many of them actually prefer it to the office. According to a FlexJobs survey, 65% of employee respondents reported wanting to be full-time remote post-pandemic, and 31% want a hybrid remote work environment — that’s 96% who desire some form of remote work.
These numbers inevitably mean that the methods in which we worked during the pandemic, primarily via the screen and through video calls, will have some longevity.
In the past year, there has been a daunting amount of “incidental” or unintentional content creation via the many different digital platforms we now operate on. With massive amounts of data, however, there are large sums of insight to be had.
With the right tools, your business can work smarter, not harder, and have this valuable knowledge extracted from content derived from workers’ day-to-day interactions. This acumen can be the competitive edge your business needs as we move forward with our technological workday—with AI in video enhancing many factors of the modern age work world.
With the growing amounts of online meetings and content creation happening in 2021, the key to video streaming in the modern work world is navigating it. Video has the potential to bring content to life, but more importantly, it gives the ability to access what’s in the video in an intuitive and efficient way.
Let’s look at it this way, would you buy a textbook if it had no table of contents, index, or chapters? Of course, you wouldn’t. It would be crazy to have to just find your way through pages of unstructured text, but that’s what we do for video.
By implementing AI into video, you have the ability to customize and easily access all of the contexts that exist in the video’s contents.
Through Machine Learning (ML) and Natural Language Processing (NLP), AI can do all of the hard work of deriving data for you—helping to mitigate your search time and any fatigue that might come along with it. Through audio and visual data, AI takes all of the available understanding from the video and tags content by keywords, concepts, and important and relevant topics.
The ML and NLP then construct a transcript, and from there, the AI creates an intuitive index—creating transcriptions, chapters, and chapter titles, and finally a table of contents. This makes searching for content easier and more efficient for each user.
To date, when it has come to utilizing the power of video, most of the time it has been done in a highly meticulous manner. Rather than manually tagging video media with editor tool applications—crafting tags one-by-one or creating a time-sliced video by tagging minute intervals—AI can do the work for you.
One label or title, or a tag at “minute six” is pretty much meaningless when it comes to searching because the keyword is limited to the interpretation of the publisher.
When you are looking for anything—whether that be in the grocery store or on Google’s search engine—you most often have something specific in mind. AI allows for a new variation of video tagging with the capability to draw relevance to a plethora of topics and keywords. This enhances both the approachability and scope of video organization and use. AI saves companies staff, time, and resources to apply these methods to their existing bank of video content.
An emerging video technology, Optical Character Recognition (OCR), can now read the still-snapshots in your video and determine if any relevant text can be drawn out. This can be used on things such as PowerPoint presentations in the background or words written on a whiteboard behind the speaker in a video.
