Marketing agencies, movie studios, broadcasters, and all kinds of media companies that deal with video content face the same problem.
Video takes too much time to make, organize, and manage.
So, they look into ways to automate some tasks inside the whole video generation process to save time.
Today, we will discuss Artificial Intelligence and Cognitive Computing as primary drivers of video content analysis automation — and which one is better at it.
What is Artificial Intelligence and Cognitive Computing?
Before we can jump into the applications of both technologies to video content analysis, we first need to understand the difference between those two.
That means going back to the basics — defining each technology and establishing their core principles.
Artificial intelligence
AI is an umbrella term for a set of algorithms that figure out the problem and come up with a solution to it. AI imitates human intelligence at learning from the environment and uses the extracted data to power its decisions. It acts within the boundaries of familiar problems.
Cognitive Computing
Cognitive Computing, on the other hand, supports decision-making with more insightful data analysis. Such technology chews through large volumes of data, analyzes it to get deeper insights, and even works with unfamiliar problems, relying on previously acquired data and pulling more if needed.
Cognitive Computing can also deal with concepts and symbols, just like humans, utilizing contextual learning.
So, summing up: AI uses the data and makes decisions, Cognitive Computing supports decision making with thorough data-analysis.
Major Differences Between AI and Cognitive Computing
Now, onto the features that set the two technologies apart.
Human input
Cognitive Computing algorithms analyze the data provided and come up with insights that help humans make decisions.
On the contrary, AI possesses a more focused and autonomous approach to things: it tries to solve the problem on its own.
Contextual solutions
Artificial Intelligence can only work with predictable and straightforward situations, since it requires training to perform data analysis.
Then we have Cognitive Computing. The difference here is that it can deal with unpredictable and uncommon situations, since it can pull information from a variety of sources and makes sense of the context of the problem.
That results in better informed decisions that suit a certain situation. Whereas AI comes up with straightforward and general answers with no regard to specifics.
In case of video content analysis, for example, an AI would easily detect the ball going into the goal area, and Cognitive Computing will be able to say if that’s a regular goal or an own goal.
AI vs. Cognitive Computing: Video Content Analysis
All the things that are different between AI and Cognitive Computing dictate the way they approach any tasks, including video content analysis.
Let’s take a simple task of autogenerating a trailer for a movie and see how those two handle it.
To do that, each technology has to analyze the footage and identify the most important scenes that could be used in a trailer. They have to provide some story exposition and contain memorable moments from a film.
Artificial Intelligence, before the whole analysis begins, has to be trained to catch the required content.
Since it cannot analyze the context and understand what’s going on the footage story-wise, we will have to work around it, making the algorithm look for specific things. Those things could be a specific person or object appearing on the screen, like the main characters or a car.
Obviously, the AI won’t know if that person is the main character or if that car is taking part in the chase scene. But the approach still works, even though it might require some moderation from a human editor.
On the other side of things, Cognitive Computing is able to analyze the film more thoroughly and actually makes sense of whatever’s happening in there.
It can tell main characters apart from secondary ones, identify tense dialogue, and high-intensity action scenes.
With all of that information, it is able to gather the best scenes of the movie and compile them into a comprehensive trailer completely automatically.
Bottom Line
With video content being the center of many companies’ media strategies, it becomes increasingly important to analyze, store, and manage it effectively.
But for large-scale production, with companies pumping out hundreds of hours of video, having a couple of people tagging each video doesn’t work as well.
That’s why it is essential to have some automation system in place that can understand the video content and use that data to automatically manage the existing content and even create more of it.
In that regard, we can apply either AI or Cognitive Computing algorithms. While AI is straightforward and autonomous, it lacks the ability to understand the context of the analyzed content, and that can hinder its effectiveness with some tasks.
Cognitive Computing, on the contrary, is built to use as much data and get as deep of an analysis as possible to provide the best results. It can make sense of the content it works with just like the humans do, and that results in more comprehensive outcomes.
Author’s bio
“Pavel Saskovec, Technical Writer at AIHunters – a cognitive computing company providing tech solutions for the media and entertainment industry.
Has extensive experience covering tech-related topics, the majority of which center around technology helping optimize the workflow of the media industry.”
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