How AI Will Change Everything We Know About Video
AI is booming. According to Gartner, one in five workers engaged in mostly non-routine tasks will rely on AI to do a job by 2022. Meanwhile, in the media, AI seems to dominate the headlines, be it with items about AI algorithms by Google or Facebook or with breaking news about the American AI Initiative. With so much effort put into the growth of emerging technologies, it seems that our working life will look significantly different in the very near future. Considering the proliferation of generated video content, it’s no surprise that some AI features target video workflows and their staggering amounts of audio-visual data. In the following blog post, we will present a few existing AI features that can significantly help you optimize your video processes. We will divide these features into three video categories that correspond with the three main stages of a video: creation, management, and distribution.
Capture & Creation
The creation stage of a video refers to all processes leading up to its ingestion and encoding. A few AI features that can be used for this stage are:
Auto-Editing is a feature that automates certain tedious editing tasks and can consequently reduce the time it takes an editor to curate video footage from hours to mere minutes. The feature employs a set of algorithms that analyze a video in order to spot so-called “empty” scenes: scenes that seem to contain irrelevant footage, like shots of crew members setting the stage, mic testing, etc. These scenes can then be easily cut out to create a leaner, more engaging edited version.
- Perfect Thumbnail Feature
A video thumbnail is a picture that is supposed to capture the essence of a video. It also has a favorable effect on search engines since users are often drawn to images more than they are to text. A perfect thumbnail feature is an AI feature that is triggered by emotion/ scene detection. It helps users create a thumbnail that best represents their video and that is more likely to result in positive interaction. Once a user chooses a sentiment (for example, anger,happiness, surprise, etc.), the feature will pair the selected sentiment with a matching thumbnail while ensuring that the frame doesn’t contain rapid movements, a person blinking, etc. Watch our video and learn how to choose a thumbnail for your video yourself.
The management stage of a video refers to the classification process, which allows users to easily find video content. A few AI features that can be used for this stage are:
- Enhanced Search
Enhanced search describes a search engine with enhanced abilities that result from automatically-created metadata. Enhanced search allows users to find desired content intuitively and effortlessly. AI-powered enhanced search is mainly a result of a combination between visual metadata generation and audio metadata generation. Visual metadata generation utilizes computer vision—the way digital pictures are processed and interpreted by computers—to generate data that corresponds with the imagery. It allows users to easily find specific objects, actions, and faces within the video, including exact time stamps when they appear on the screen! Audio metadata generation detects context and makes an estimation of whether a
word might be useful as descriptive metadata. It complements computer vision and helps users find relevant verbal content much quicker and easier. Generated metadata includes not only spoken topics, but also named entities, and general themes.
- Video Summary
Decreased attention span has a direct impact on content and video marketing. According to Hubspot, the recommended length of a video is up to two minutes. Video summary attempts to resolve this pain point by reducing a long, archived video into a short video summary composed of snippets. Much like the auto-editing feature, this feature edits out redundant footage, allowing reviews of archived video in a fraction of the original video time.
- Video Description
Recent advances in “deep” machine learning have led to the creation of new, elaborated models that can extract data from videos and automatically generate text descriptions for various events featured in them, like “A man is playing the guitar” or “Eight man are running a race on a track”.
The distribution phase refers to the final phase of the video cycle. By this point, the video has been created, ingested, and is ready to be shared with the world. A few AI features that can be used for this stage are:
- Auto-Generated Subtitles
Subtitles have come a long way since they were first introduced to the public during the silent-film era. Though their primary goal has remained communicating verbal content in writing, both their functionality and use cases have changed tremendously over time. In the last few years, following the growth in video consumption and mobile data consumption, captions have become a standard that is promoted by some of the most influential media experts. Auto-generated subtitles is a feature that aims to further promote this standardization by recognizing speech parts in audio or video files and transcribing them into text. The resulting transcription includes word-level time stamping, accuracy level, and punctuation. movingimage has managed to take this feature one step further by offering a 2-in-1 service: auto-generated subtitles via AI-powered transcription and auto-translation into 54 different languages. Users can use the feature to create captions in a fraction of the time and capital it takes to manually transcribe and translate a video. Since the process is automatic, it requires no technical skills or special training whatsoever. Read more about it here.
- Video Recommendations
The recommendation engine was initially developed for e-commerce purposes and was meant to help shoppers minimize browsing time while helping websites maximize their conversion rate. Today, this technology is being used by everyone from Facebook to Netflix to LinkedIn. Video recommendation systems operate in a similar way and rely on algorithms that aggregate data like consumer’s behaviors and activities. They then use this data to predict and highlight similar videos that may appeal to the viewer.
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