WorkML.ai is developing a revolutionary platform that will harness the potential of hundreds of thousands of annotators from around the globe. Through comprehensive training programs, WorkML.ai aims to qualify annotators to produce high-quality Metadata, essential for enhancing AI models.
Annotators will be compensated in WML tokens for their contributions, creating a dynamic ecosystem. This platform not only democratizes participation in the development of AI but also ensures a seamless and efficient flow of services and rewards across borders, marking a transformative step forward in the industry.
Through their deep engagement with the development of AI models, they unearthed several critical bottlenecks.
The initial challenge emerged from handling vast data sets, a conundrum adeptly resolved by Nvidia.
However, a less apparent but equally significant bottleneck persists, discernible only to those intimately involved in the AI training processes.
This hidden challenge lies in the meticulous preparation of Metadata, an essential component that accompanies the primary data fed into the models, shaping the landscape of AI development with its complexity.
Business Value of WorkML.ai project
- Essential Data Needs: Business projects and startups creating AI products require data and Metadata to train their neural networks. The accuracy of these predictions hinges on the quality and precision of this information. Business projects require millions of Metadata units for training AI models. This represents an extremely complex and costly task.
- Challenge of Metadata Complexity: The process of obtaining high-quality Metadata presents a significant bottleneck, illustrating the complexity of the task.
WorkML.ai conducted an analysis (research link) and discovered that to obtain 35 million units of high-quality Metadata, it would take one person 12,345 years to process such an amount.
- Cost Implications: Gathering and processing high-quality Metadata can be prohibitively expensive, adding a substantial cost to AI development.
The cost of creating the same 35 million units of Metadata could exceed 20 million dollars.
- Global Annotation Hub: WorkML.ai introduces a revolutionary global annotation hub, facilitating the creation of Metadata by mobilizing a vast network of annotators worldwide. This hub significantly reduces the time and cost associated with data annotation.
- Optimized Approach: Our approach is to optimize the entire Metadata generation process. At the first stage, we try to get them automatically using trained neural networks – this data is necessarily checked by a human validator. At the second stage, if automatic Metadata creation is not possible, annotators are connected to the work, which perform markup, and then their work is checked by a combination of AI and human validators.
- Efficient Solution: WorkML.ai offers a solution that is ten times cheaper and twenty-five times faster than traditional methods, addressing both cost and speed barriers effectively. For instance, a task that would traditionally take over a millennium with a single person can be completed in just a week with thousands of annotators working around the clock.
- Cryptocurrency Integration: The project incorporates cryptocurrency, utilizing the WML token for internal payments and annotator remunerations. This integration not only streamlines transactions but also provides additional incentives through systems like Proof of Stake (PoS), Humans Proof of Stake (H-PoS) and Humans Proof of Work (H-PoW).
- Revenue and Cost Reduction: The platform’s innovative use of a large, trained annotator workforce and cryptocurrency payments provides a low-risk, highly profitable model for both the company and its stakeholders, ensuring a steady revenue stream and reducing costs by about tenfold.
Crypto Value of WorkML.ai project
- WML Token Launch: The introduction of the WML token is crucial for facilitating internal payments and rewarding participants within the ecosystem.
- Proof of Stake (PoS): Provides variable payouts, rewarding token holders based on the amount of their stake, incentivizing long-term holding and investment.
- Human’s Proof of Work (H-PoW): Rewards annotators based on the quality and quantity of their work, directly influencing their compensation. This mechanism aligns the incentives of annotators with the quality of data annotation.
- Human’s Proof of Stake (H-PoS): An innovative feature that offers double payouts for those who reinvest their earnings obtained through Human’s Proof of Work (H-PoW), significantly increasing rewards for active participants.
- Perpetual Discounts: Payments made with WML tokens for services on the platform receive perpetual discounts, enhancing the token’s liquidity and appealing to users to transact using WML.
- Three-Level Referral System: A multi-tiered referral program rewards users who help expand the community by inviting new annotators and customers, fostering a growing and engaged network.
- Token Growth Potential: Given the high business value and innovative features of the project, there is a potential for the WML token to increase in value by more than ten times.
- Airdrops: The budget includes 2% of all tokens allocated for airdrops, providing an opportunity to earn free tokens and engage a wider audience in the project’s ecosystem.
About WorkML.ai
WorkML.ai is a revolutionary project developed by Advanced AI Solutions Inc., founded by Michael Bogachev and Denis Davydov. This initiative aims to transform the field of artificial intelligence with its global annotation hub, empowering hundreds of thousands of annotators worldwide.
Through extensive training, WorkML.ai provides a diverse global workforce with the tools to produce high-quality Metadata crucial for the development of more intelligent and accurate AI models. The platform utilizes the WML token to facilitate seamless internal payments and rewards for annotators, ensuring a fair and equitable system for all participants. The integration of cryptocurrency into the platform streamlines economic interactions, increases liquidity, and promotes a more inclusive global community.
WorkML.ai significantly reduces the time and costs associated with data annotation through its scalable workforce that efficiently manages large datasets. Innovations like Human’s Proof of Stake (H-PoS) and Human Proof of Work (H-PoW) encourage quality and productivity among annotators, enhancing the platform’s efficiency.
By lowering barriers to high-quality AI training data, WorkML.ai facilitates the wider adoption of AI technologies in various industries, improving neural network training and system performance. Under the leadership of its visionary founders, WorkML is not just a technological solution but a crucial step towards a more innovative and fair future in AI development.
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