Our Solution
AIVON believes that Internet and services on the Internet must be decentralized or else there is too much power in too few powerful hands. AIVON focuses on decentralizing and democratizing online video, giving power back to Internet users. The problem of having the power of the Internet in just a few hands is that there is little or no transparency and even if these few companies mean well, they are now so big that there are many ways to destabilize them. In 2017, both Google and Facebook faced major scandals of bad actors taking advantage of loopholes in the system. Google’s YouTube was compromised multiple times with users posting content that was malevolent creating brand and content safety controversies. Facebook faced both a fake news problem and a data breach by Cambridge Analytica. This is why AIVON is applying blockchain to content and the underlying metadata that powers content cataloging, indexing, search, discovery, trade and monetization and AIVON believes that this will be the backbone of a decentralized video open network and search engine that could act as a check and balance on FAANG and BAT
Each video object, such as a complete program or extracted clip, will be assigned a unique identifier using the Entertainment Identifier Registry (EIDR). The EIDR provides global unique identifiers for the entire range of audiovisual object types that are relevant to entertainment commerce.
AIVON nodes will record their metadata in JSON format anchored to the Ethereum blockchain. Most of the metadata will not be stored directly in the blockchain, but linked to it using the EIDR identifier. This is because the size of the data can be quite large, especially when you include transcripts and translations.
A ContentGraph can be visualized as a bar graph where content attributes are plotted along the horizontal axis and confidence scores plotted on the vertical axis. There will be attributes where the confidence is zero and the feature is not detected. The left-to-right ordering is reserved based on the schema, so that two or more ContentGraph patterns can be compared. Accessing attributes involves a single subscript which represents an array index (e.g. graph [6] returns the value of the 6th element — guns). The number of positions corresponding to specific attributes being recorded can increase over time, without affecting other uses of the graph.
ContentGraph has many interesting features, such as extensibility to add new content safety attributes, addressing the need to stay relevant as social norms change over time. If the AI can be trained to recognize new safety attribute in video content, the attribute can be added to ContentGraph by adding a single digit, without affecting the other attributes or earlier uses of the graph.
Online video is growing making it hard to find video content
AIVON believes the need for OVSE will be in high demand as video becomes harder to find and discover. While YouTube is viewed as a video search engine, it is really a video hosting platform and one can only find videos that users upload to YouTube, which is mostly usergenerated and long tail content. Most premium content publishers typically do not upload their premium video content to YouTube, preferring to upload to their own video site or app.
The Internet and Online Video Need Decentralization
Today, everything we do online including online video as well as web traffic, users, user data, online advertising, search and social networking is dominated by FAANG (Facebook, Apple, Amazon, Netflix, Google) or BAT (Baidu, Alibaba, Tencent) in China. Over 1.1 billion video views are watched on Google and Facebook per day. In premium online video and subscription services, Amazon and Netflix control over 69% of the 113.8 million subscribers for subscription video on demand (SVOD) services in the US and almost 60% of the $14.9 billion US SVOD revenues in 2017. If you want web traffic for your website or videos, over 70% of all Internet traffic comes from either Google or Facebook. If you want to monetize pages and videos, Google and Facebook represent 77% of all online advertising and 99.7% of all new online advertising growth. 99% of all mobile smartphones and app ecosystems are either powered by Android (Google) or iOS (Apple). FAANG and BAT have also developed and control a majority of most AI technologies and applications. It is clear that FAANG and BAT dominate our digital lives on the Internet, which means the power of the Internet is centralized in the hands of these dominant and centralized tech giants. FAANG’s combined market capitalization is over US$3.01 trillion or 27% of the total market capitalization of all companies listed on NASDAQ.
AI Node
A Neural Network is a set of algorithms with interconnecting nodes, modeled loosely after the human brain, that is designed to recognize patterns. Similar to how a child learn by mimicking the behavior of familiar persons (like parents) and applying the learning outcomes to modify its own behavior, a Neural Network “learns” to perform task by optimizing using large labeled training data sets to “minimize” the error between the output and the desired outcome.
Unfortunately, there is no “one size fits all” generalized AI algorithm or Neural Network that can perform all the tasks required. For example, the CNN optimized and trained to identify objects will be different from the CNN configured for identifying faces. Hence, we’ll need to combine various best of breed AI techniques to develop a package of Neural Network based AI software with the specific trained models to perform the following tasks:
• Visual recognition of faces, object scenes at frame level
• Visual recognition of ContentGraph attributes at frame level
• Auto speech-to-text transcription
• Auto text-to-text translation
• Visual recognition of faces, object scenes at frame level
• Visual recognition of ContentGraph attributes at frame level
• Auto speech-to-text transcription
• Auto text-to-text translation
Metadata Extraction
Source metadata is whatever information is implicit in the file. This would include file type, file size, duration, encoding method, resolution, aspect ratio, data rate, number of audio tracks, closed captions, timecodes, etc. Uploader may supply data such as titles, descriptions and locale that can provide a lot of information for indexing purposes.
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