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The following instance is included to illustrate how the AIVON Network can be utilized in the actual world matters to solve actual world requirements. Remember that, these D Apps (Decentralized applications) can be built by AIVON Network, IVS (I Video Smart) and many others.
Superior Content Metadata for enhanced Discoverability and Search
Presently, metadata are delivered by consumers and in a few matters extremely consistent, incomplete and very subjective. For instance, when 2 people are uploading same cat completion video file, one might put tags such as cat, show and the many other might tag it with some more information, 24 such as cat, terrier, fines in show, 2018, West-minster Cat show.
Neither is incorrect but the latter Meta data is a lot more detailed and descriptive. Hence, standardized and normalized mete data will help individuals question better matching outcomes.
Over the top video (OTT) platforms can advantage with completed, consistent, normalized and standardized metadata. This'll let their video file to be simply discoverable and searchable by subscribers. OTT platform givers could be used AIVON Network in many ways to add or improve their meta data. They'd receive detailed sense perfect metadata as well as tags for each and every item, paying AIVON Network in tokens for the Artificial Intelligence Computation and person work.
Scene Level Metadata
Metadata now is done at the video file level, meaning the info that's applied to explain, classify and index content is just descriptive of a complete project or clip. But, there's so much underlying info at the scene level that presently is not tagged. And it's impossible to perform scene level tagging applying human just because it's simply too expensive as well as not scalable.
When scene level Meta data is properly extracted, it'll also enhance suggestions engines by inferring what you like back on underlying meta data of scenes you have watched in the past time. Though improved and enhanced recommendations engine file will become discoverable and searchable if correctly tagged to such that granularity of frame and scene. Viewers could search down to the 2nd or jump right to a particular scene backed smoothly on the video content. Finally, from the engine information, video fine editors could simply find b-roll footage to create and assemble videos in its place of scrubbing via videos file to find footage.
AI Match Correction and Verification
AI is not ideal, and since faces and video objects can look same, there'll forever be misidentifications or bogus positivity. Humans, while restricted by dataset and speed, remain super at comparing and verifying faces as well objects. Therefore, a hybrid approach of joining AI with personal identification and correction is one of the most optimal methods of achieving AI System vision at accuracy and scale.
AIVON Network will provide AI match identification and correction via the AIVON Network open community.
Requesters such as AI tech givers and content holders can use AIVON Network to request AI match correction and verification on their AI produced data base. They'd then pay the AIVON Network open community for this match correction and verification using AVO tokens.
AI Decentralized Based Distributed Computing Cloud
Though the potential of AI is effective and scalable, it remains needs lots of computing power to the procedure, tag content recognition, and match. One rising pain of AI is the absence of computing resources a firm can have and utilized without efficiency, jeopardizing cost and scalability. Moreover, the compute demand might fluctuate backed on the valleys and spikes, of content uploaded. A distributed and decentralized AI computing cloud applying crowd sourced computing cycles is a fine solution to manage the fluctuations in demand while maintaining optimal rates.
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• Whitepaper: https://aivon.io/download-whitepaper/
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