Transform the Workplace With Video Powered by AI
Artificial Intelligence (AI) refers to a broad set of approaches for allowing computers to mimic human abilities. This is distinct from automation, which is the process of creating hardware or software capable of conducting process-based tasks without human intervention.
Fundamentals of Modern Artificial Intelligence
The most common form of AI today is Machine Learning, where massive amounts of data are "fed" into an algorithm in order to train it. Once trained, the algorithm is able to identify and then categorize items in subsequent data feeds unassisted. Machine Learning algorithms use an iterative process, so as the learning models get exposed to new data, they adapt from what they have "learned." A key shortcoming of machine learning is the reliance on vast amounts of sample data in order to become accurate enough to use. Thus, current applications of Machine Learning are limited depending on sources of high quality input data.
Another AI discipline, and one most relevant to the application of AI to video is Computer Vision. In Computer Vision, the goal is to interpret the visual elements of an image or video using Artificial Intelligence. Computer Vision may use either Machine Learning or Deep Learning techniques to accomplish this goal, and is the foundation of emerging technology applications such as Facial Recognition and automated vehicles. Teaching computers to process visual data just as a human would has proven much harder than simply connecting algorithms to cameras. Much of the challenge is rooted in an only basic understanding of how human vision actually works in order to replicate it. Despite this, Computer Vision is currently one of the most exciting facets of AI for business strategists, with 58% of purchase influencers beginning to plan computer vision investments in their enterprise technology portfolio within the next year according to Forrester.
The Building Blocks of Video AI
Spoken words are a critical component of video and there are a number of ways that AI is helping interpret speech.
Machine Transcription: An example of one of the earliest examples of Artificial Intelligence, where an algorithm is able to interpret voice data into a text transcript. This technology is now commonplace and even cooked into our smartphones, but is also undergoing a renaissance thanks to innovative new deep learning techniques becoming available.
Machine Translation: Once spoken words are digested into text data, it unlocks other abilities like translation into additional languages. One of the key AI pioneers in this field has been Google, who first launched their translation service in 2006, using United Nations & European Parliament transcripts as the foundation linguistic data. As of May 2017, Google supported over 100 languages and was serving 500 million people daily.
Speaker Recognition: This is the ability of an AI to recognize the identity of a speaker based off their voice and speech patterns. A key dependency of this ability is an existing sample of the person’s voice to train the AI on.
Optical Character Recognition (OCR): OCR is the art of recognizing text from within visual content, such as the text on embedded presentation slides. The primary benefit of OCR in the business world is further enabling search engines to offer up visual content to users without over dependence on accurate and comprehensive metadata.
Sentiment Analysis: Another way to enrich text data is via an additional layer of information called sentiment. This algorithm interprets dialogue to both identify and quantify affective states. Affective states are distinct from emotions as affective states are longer lasting mood states (such as anxiety or depression) which are the results of many events.
Text Summarization: One of the newer text applications that will help build the next generation of video Artificial Intelligence is content summarization. This is when an algorithm is able to boil down hours of video into a concise text summary. Summarization algorithms will take into account the placement or emphasis of messages within a video.
To learn more on the foundations of video AI, read the Vbrick Blog "The Foundations of Video Artificial Intelligence."
Beyond spoken words and text found in video, AI promises to identify objects and actions to further enhance the value it can bring to video.
Object Recognition: After a machine learning algorithm has digested a video frame, the Object Recognition process identifies the various subjects within it. Object Recognition for an AI is a collection of related tasks and not the single step human vision perceives it as. The key elements of Object Recognition include image classification, object localization, and finally object detection.
Action Detection: One key advantage of video content is the ability to show instead of tell a story. Computer Vision advances are enabling AIs to decode what is being done and not just who is in it.
Combining Object Recognition with Action Detection will allow the analysis or prediction of why an object is committing an action. The algorithm once more needs extensive training to recognize an action, and this action will need to be visually detectable. The ability to guess an off-screen action has occurred still eludes AI observers.
The application of Artificial Intelligence is becoming much more commonplace and we are seeing the value it can bring to our personal and professional lives. As the use of live streaming and on-demand video continues to grow in the workplace, the addition of AI promises to exponentially increase how video can be used and the value it can bring in transforming how work is done and how workers communicate and collaborate.
To learn more about Video AI and see how Vbrick is implementing Video AI features into our product roadmap, be sure to register for our webinar "How Video AI Is Transforming The Workplace" on September 19th.
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