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Our Vision

AIWORK is a decentralized, open-source blockchain platform and ecosystem built on
a consensus network of Artificial Intelligence (AI) computing resources and a community of human experts,
used to generate normalized and enhanced metadata for video content.

Why will you
choose AIWORK?

How AIWORK Works

The AIWORK community will provide an open, distributed and crowd-sourced community for transcription and translation. Content owners and distributors in need of transcription and translation of their content will come into the open marketplace to transcribe and translate their content through AIWORK’s AI machine transcription and translation with distributed crowd-sourced computing resources combined with a crowd-sourced open community of transcribers and translators, all incentivized using the AWO (AIWORK Token).

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Architecture

AIWORKAIWORK's Architecture

Architecture

AIWORK is built on a standard Ethereum blockchain. All the video metadata will be anchored to Ethereum blockchain and Decentralized Applications (DApps) can easily access the data and either spend or earn AWO (AIWORK Token).

Attributes of AIWORK tokens:
blockchain protocols: Ethereum (ETH) & AIWORK
Token standard: ERC20
Total supply: capped at 10 billion
Token issuance at genesis: 60% of total supply
AIWORK's challenge

Challenge for AIWORK

The challenge for AIWORK is to decentralize operation of the AI and human expert community on top of a consensus protocol, so that all sorts of Decentralized Applications (DApps), whether free or commercial, can reap the benefits of the AIWORK with much better metadata and ContentGraph.

AI JOB REQUESTORS

AI JOB REQUESTORS

AI Job Requesters submit AI data jobs to the AIWORK Human Expert Network and pay AWO (AIWORK Token) in exchange for the AI data work performed by AI Human Experts and validated by AI Validators. Therefore, one can think of AI Job Requestors as the Buyers in the AIWORK artificial intelligence ecosystem providing jobs and tasks that are fulfilled by AI Human Experts and validated by AI Validators.

AI HUMAN EXPERTS

AI HUMAN EXPERTS

AI Human Experts perform simple micro tasks to verify that the output from AI. AI Human Experts also provide additional metadata that cannot be easily generated by AI. Relevant results can be fed into the AI network to teach and improve it. AI Human Experts gain AWO (AIWORK Token) in exchange for well performed tasks, which must first be verified and validated by AI Validators.

AI VALIDATORS

AI VALIDATORS

AI Validators validate the work of AI Human Experts, staking AWO (AIWORK Token) for the right to perform the validation tasks in accordance to consensus. AI Validator review the results submitted from AI Human Experts. An AI Validator may lose a portion of the staked tokens if they do not perform the tasks in accordance to consensus. AI Validator decides the next validator via psuedo-random algorithm.

Our own Special

Why AIWORK?

AIWORK will open this blockchain platform and ecosystem for use by third-parties, including content distributors, publishers and advertisers. This will help AIWORK achieve broad adoption and network effects benefiting all participants.

GLOBAL NETWORK OF AI HUMAN EXPERTS
AIWORK brings together a global network of crowdsourced AI Human Experts to help generate, verify and validate AI data sets. You can think of AIWORK as the “Uber” of AI.
AI MATCH VERIFICATION AND CORRECTION
AI is not perfect, and since faces and objects can look similar, there will always be misidentifications or “false positives.” Humans, while limited by speed and dataset, are still better at comparing and identifying faces and objects. Therefore, a hybrid approach of combining AI with human verification and correction is the most optimal way of achieving AI computer vision at scale and accuracy. AIWORK will provide AI match verification and correction through the AIWORK open community. Requesters such as AI technology providers and content owners, can use AIWORK to request AI match verification and correction on their AI generated datasets. They would then pay the AIWORK open community for this match verification and correction using AWO (AIWORK Token).
AI DECENTRALIZED DISTRIBUTED COMPUTING CLOUD
Although the power of AI is efficient and scalable, it still requires lots of computing power to process, recognition, match and tag content. One growing pain of AI is the lack of computing resources a company can have and utilize without jeopardizing cost, efficiency and scalability. In addition, the compute demand may fluctuate based on the “spikes and valleys” of videos uploaded. 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. This decentralized AI Compute Network has the added benefit of lower environmental impact by utilizing pre-existing and under-utilized computers around the world.
BETTER CONTENT METADATA FOR BETTER SEARCH AND DISCOVERABILITY
The growth of online video has grown exponentially over the last few years. Yet, currently, video metadata is very inconsistent, incomplete and subjective. Normalized and standardized metadata generated by AIWORK will help individuals query better matching results which will benefit online video platforms providing them with more consistent, completed, normalized and standardized metadata. This will allow their content to be easily discoverable and searchable by subscribers. Online video platforms could use AIWORK in multiple ways to add or enhance their metadata. They would receive detailed scene-accurate metadata and tags for each item, paying AIWORK in AWO (AIWORK Token) for the AI computation and human work.
CONTENT GRAPH
The consumption of video content has increased dramatically over the last few years due to new platforms like YouTube, Facebook, and Twitch. However, it is difficult for these platforms to manage, review and validate all the videos uploaded therefore, leading to underlying issues of content safety. Through AIWORK, videos like above can be easily detected and flagged as inappropriate by the power of AI and human experts. AIWORK’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. Meanwhile, Service Providers could use AIWORK and ContentGraph to offer content safety filters which viewers could use to search for safe and appropriate content. Contributors to the AIWORK platform, those who are providing computing resources or expertise, would be rewarded with tokens.

Road Map

Our itinerary is as follows.

Road Map Road Map

Documentation of AIWORK

Find out more in our white paper.