AIVON-Artifical Intelligene Video Open Network

AIVON is a decentralized, open-source blockchain protocol and ecosystem being built on a consensus network of Artificial Intelligence (AI) computing 10 resources and a community of human experts, working together to generate normalized and enhanced metadata for video content. This enhanced metadata will be the basis for the AIVON protocol’s first decentralized application – an open search engine for video, one that is decentralized, indexed and maintained by our community.  AIVON focuses on decentralizing and democratizing online video, giving power back to Internet users.

Artificial Intelligence

Specialized AI algorithms will be deployed on mining nodes, so that miners’ CPU and GPU resources can be used to scan media files, generate the enhanced metadata including time-coded tags, classification, categories, transcripts and translations, and an index of the video objects.

Content Graph

AIVON platform will use AI to define a confidence score for each of several content safety attributes, such as: nudity, adult, offensive language, hate speech, violence, guns, alcohol etc. These confidence scores will be combined into a vector called a ContentGraphTM which can be visualized as a bar graph.

Human Expert

AIVON uses a network of freelancers with skills in tagging, metadata management, transcribing and translation. AIVON will empower its community with tools which enable individuals to review, verify and correct the AI-derived metadata, including the categorizations, transcriptions and translations.

AIVON is built on a standard Ethereum blockchain as well as Plasma Network. All the video metadata will be anchored to Ethereum blockchain and Decentralized Applications (DApps) can easily access the data and either spend or earn AVO token Attributes of AVO tokens:

• blockchain protocols: Ethereum (ETH) Mainnet & AIVON Plasma Chain

• Token standard: ERC20

• Total supply: capped at 1 billion

• Token issuance at genesis: 40% of total supply

AIVON Ecosystem

There are 5 independent parties in the AIVON ecosystem:

1. Requesters

2. Computing Resource Providers (nodes running the AI)

3. Human Experts (similar to Mechanical Turks)

4. Validators

5. Software developers


1. Requesters submit videos to AIVON to obtain a normalized metadata of videos.

2. Requesters pay for the job in the native token of AIVON.

Computing Resource Providers (CRP)

1. Providers run AIVON decentralized open source AI software that runs inside isolated Docker container and is based on popular Deep Learning frameworks like Caffe and TensorFlow; Convolutional Neural Network will be used as it is the current state-ofthe-art model architecture for image classification tasks.

2. Providers share its computer’s computing resource (CPU/GPU) to run AIVON AI software to generate the enhanced metadata including ContentGraph.

3. Providers gain token in exchange for the provided computing resource and tasks performed.

Human Experts (HE)

1. Human Experts perform simple micro tasks to verify that the output from AI.

2. Human Experts also provide additional metadata that cannot be easily AI

3. Relevant results can be fed into the AI network to teach and improve it. 20

4. Human Experts gain token in exchange for well performed tasks.


1. Validators stake a significant amount of AVO Token and gain the rights to perform the following tasks in accordance to consensus: a. Picking up the chunks of video jobs and distributing them to providers and workers. b. Validation of the results submitted from providers and workers c. Analyzing and combining of the results to produce a single coherent ContentGraph for each video.

2. Validators mint new block on AIVON plasma, gain token for the job and assign rewards to participating CSPs and HEs.

3. Validator may lose a portion of the staked tokens if it is not performing the tasks in accordance to consensus.

4. Validator decides the next validator via psuedo-random algorithm that is independently provable and bias-free.

Software Developers

1. Developers play a role in the ecosystem by developing applications that interact with AIVON or utilize ContentGraph

2. iVideoSmart will be developing at least the following 2 applications: a. Open Source Search engine and relevant API b. Advertisement matching service and relevant API

3. Developers would be able to utilize AIVON to create new applications which they could charge using our tokens

4. Developers gain access to list of non-private videos processed by AIVON, with their corresponding Content Graph

How It Works

1. Requester passes a video to be processed through a client software.

2. The client software cuts the video up into individual chunks of pre-defined duration and submit them into AIVON

3. Using a Proof-of-Stake algorithm, validators are randomly selected with probability proportional to their stake. Selected validators then pick up a set of chunks from the job pool, duplicate them multiple times, and add in random challenger “markers” unique to each duplicate copy at random sequence to create temp clip.

4. Validator assign random AI nodes to process each clip. Upon result submission by AI nodes, Validator will use the random challenger marker to verify the result as a Proofof-Work.

5. Results are submitted to human experts to perform a binary verification.

6. Results from human experts are gathered and processed by Validator.

7. Requester monitors the network, once all tasks for chunks that make up a complete video are completed, Requester would be able to make a claim by providing the order of the chunks and publish an aggregated score of the original video, also known as ContentGraph.

8. Claim by Requester will be evaluated based on consensus. If it passes, Validator will publish the result as ContentGraph onto a new block on AIVON Plasma Chain.



1.Better Content Metadata for Better Search and Discoverability

OTT (Over The Top Video) Platforms can benefit with consistent, completed, normalized and standardized metadata. This will allow their content to be easily discoverable and searchable by subscribers. OTT Platform providers could use AIVON in multiple ways to add or enhance their metadata. They would receive detailed scene-accurate metadata and tags for each item, paying AIVON in tokens for the AI computation and human work.

2.Scene Level Metadata

Once scene level metadata is extracted, it will also improve recommendation engines by inferring what you like based on underlying metadata of scenes you’ve watched in the past. Through enhanced and improved recommendation engine content will become discoverable and searchable if tagged to such that granularity of scene and frame. Viewers could search down to the second or jump right to a particular scene based simply on the content.

3.AI Match Verification and Correction

AIVON will provide AI match verification and correction through the AIVON open community. Requesters such as AI technology providers and content owners, can use AIVON to request AI match verification and correction on their AI generated datasets. They would then pay the AIVON open community for this match verification and correction using AVO Tokens.

4.AI Decentralized Distributed Computing Cloud

A decentralized and distributed AI computing cloud using crowd-sourced computing cycles is a good solution to handle the fluctuations in demand while maintaining optimal costs.

5.Content Safety

Through AIVON, videos like above can be easily detected and flagged as inappropriate by the power of AI and human experts. AIVON’s ContentGraph score would label rejected and with a low score of safety. The scene level detection and metadata, will clearly define the inappropriate scenes for children.

6.Brand Safety for Advertisers

AIVON can ensure brand safety for advertisers with the ContentGraph scoring that is applied to each video. This ContentGraph is based on a score as determined by AIVON’s decentralized AI compute network and verified by AIVON’s crowdsourced open community, which would be compensated with tokens

7.Globalization and Localization of Content

To obtain accuracy in transcription and translation, AIVON empowers the ability of both human experts and AI. If a human round of verification and correction are added behind the machine transcription and then again after machine translation, accuracy can be much higher.


Incentivizing individuals who make available their computing resources for AI processing, or who apply their expertise to tagging, verification, transcription and translation is the primary purpose of the token. AIVON is preparing to launch a token sale in November 2018 to fund the development, operations and marketing of the AIVON protocol and ecosystem. The adoption and use of the AVO Token will benefit from the existing user base of over 500 million addressable users globally that already use an IVS-powered online video platform. Upon launch, IVS platform users will all be able to offer their computing resources or human expertise and be able to use AVO Tokens on IVS-powered platforms.

The token (symbol: AVO) is a token developed using the Ethereum ERC20 token standard. This token serves the following functions:

• As a micro-accounting tool and payment solution

• A medium of exchange within the platform

• As a rewards and incentive token for platform contributors

Token Sale – Key Facts

Token Symbol : AVO (TBC)

Private Sales Start Date : 1 August 2018

Whitelisting Date : 1 October 2018

Public Sales Date : November 2018 (TBC)

Initial issue size : 400,000,000 (400 million)

Maximum issue size : 1,000,000,000 (1 billion, after 10 years)

Token Price : 6.25 AIVON to 1 USD (token price in Ether will be fixed 48 hours before start of the sale)

Hardcap Target : USD18,000,000


#AIVON #videosearchengine #tokensale #AI #videoadvertising #metadata

For more information visit


Whitepaper file:///C:/Users/HP/Downloads/AIVON-WhitepaperEN-1-1.pdf



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