Artificial Intelligence is shaping the everyday world in which we live. From healthcare advancements in the prevention and treatment of diseases to the personalized recommendations Netflix feeds us, AI has a role in much of the things we do and how we function.

But beneath the umbrella term ‘Artificial Intelligence’ lie many subsets which operate according to their own function and purpose. One of those—Video AI— is applied across many industries but particularly relevant in entertainment and influencer marketing, where content—and most often video content—is king.

To better understand its role and fully utilize its value, let’s take a deeper look at Video AI and where it comes into play. In this article, you’ll:

What is ‘Video AI’?

‘Video AI’ is one of the hottest topics in AI today, but also one of the most challenging. Before getting into the difficulties posed by Video AI, some better understanding of the two words separately can clarify things.

First, what is a video? It’s best described as a sequence of images or frames, that when put together produce movement, motion, or animation. Different from an image, a video has a time component. Because of time, more content (history, information, etc.) can be added to a scene. In many situations, that temporal information makes videos more interesting than isolated images, providing better user experiences. But as a result, video is much more difficult to analyze, track, and store.

Next, AI. We can think of AI as a class of computer algorithms that learns from data or content, such as images and videos. This is key for processing and understanding unstructured data—data that doesn’t easily fit in a pre-defined schema and organize itself into spreadsheet columns. One specific type of algorithm is known as Deep Learning (DL). The inspiration for DL algorithms comes from the human brain: a set of artificial neurons connect, forming a layer, and that layer connects to another layer, creating an Artificial Neural Network (ANN). Like the brain, this advanced set of algorithms can learn from and process vast amounts of data, but with far greater speed than humans. (Read more about how AI and Deep Learning apply to Entertainment here.)

Video AI is the technology that uses artificial intelligence to process and analyze video content and produce understanding. To illustrate this idea, let’s look at classifying something like sports actions in order to identify YouTube channels relevant to soccer brands. The first step is to collect video data containing “soccer actions” (i.e. soccer players doing soccer things) and label the videos. The labeling process is critical because the accuracy of the model will rely on it. Next, we need to define a model architecture. There are two ways you can go about things, the first of which is training a model to process each frame in a video. This model analyzes five or more frames before classifying the video, which increases the overall time it takes to process, but is far more accurate. You could also classify the whole video based on the information of just one frame. This model trains faster, but accuracy is generally compromised because the model ranks the full video based on a single piece of information. Once the model is trained to act on soccer, it is used to autonomously process YouTube videos and predict which channels have soccer actions.

Due to the immense amount of information within a video, Video AI faces some challenges at scale, including substantial computational costs for training models, the ability to analyze long sequences and retain model memory, and an increased difficulty of designing ANN architectures that can learn from end to end (i.e. processing raw data without the need of feature engineering). All these issues combined—without the right resources and human expertise—prove difficult to solve.

When and how is Video AI used?

Video AI can be applied in a variety of problem-solving scenarios, ranging from emotional recognition to self-driving cars. However, deeper challenges—and sometimes even regulation—may exist when human lives are involved.

In the world of autonomous vehicles, for example, the algorithm needs to detect and classify objects that appear in front of the car in a matter of milliseconds, with no room for error. Formulated information is passed to the car’s control system instantaneously in order to make decisions, such as stopping to avoid a pedestrian.

What’s the value in Video AI for marketers and brands?

The use of Video AI extends far beyond the realm of practicality and lifestyle improvements. Nowadays, it’s being used to assist brands, marketers, and creators by giving businesses a better understanding of their customers and their content, along with insights on how to increase metrics that matter most (like ROI).

It plays a very crucial role for brands investing in brand integration (product placement) via influencer marketing. Through analysis of influencer video content, Video AI can determine the best moment to place brand messaging for the biggest impact. This maximizes conversion for brands and ensures their messaging is not only seen but acted on. To help with that, Video AI also examines the specific call to action desired by the brand (i.e. “Click here to download the app and start saving”) and predicts if it will produce the desired KPIs.

This is just the beginning. I believe the next step in Video AI will be the actual creation and synthesization of content given a set of descriptions as inputs. Just think: a brand like Pepsi works with a creator to build a video campaign showing how people in different locations can socialize with the drink. The Video AI algorithm takes this statement as input and produces the best video, with the quality of the video relying heavily on the algorithm’s experience (data). An alternative is the Video AI model providing the first draft which the creator then uses as a foundation to work with. This brings AI and humans together collaborating towards the same goal.

How is BEN shaping the future of Video AI?

Video makes up almost 100% of the content we work with in our integrations, so we’re striving hard to develop technology that better interprets video content and provides impactful, unrivaled services for our clients. Understanding the vast amounts of unstructured data available and devising better data-driven AI models of human behavior will go a long way at helping create seamless advertising opportunities for brands.

BEN is pushing the field of Video AI to the next level. Our work focuses on three main blocks:

1) Algorithms (AI architectures and learning algorithms): We’re building new ANN architectures for video processing. Once we have these models, we’ll use them for understanding content within YouTube videos.

2) Computational Infrastructure: We’re expanding our hardware and network resources, targeting faster data transfers and model training at scale.

3) Data: We’re storing large amounts of benchmark data sets and proprietary data and building new sets that integrate video and human behavior.

We’re excited to continue these innovative projects and usher in the future of Video AI—and more.