Revolutionizing AI and Crypto with Global Annotation Hub

Watcher.Guru redefines the crypto market’s landscape by offering tangible value to businesses, investors, and a wide array of users, from clients to data annotators. Moving beyond the speculative wave of token offerings, it establishes a solid revenue model through service commissions. This approach ensures a steady financial stream while grounding the project’s value in the real-world benefits it provides.

Addressing the critical need for detailed datasets in the tech industry, essential for training AI systems, reduces the cost and time involved in AI development. It facilitates the broader adoption of AI technologies in various sectors, contributing high-quality data sets that enhance neural network training and efficiency.

The Story of

The story of the project begins with the meeting of Michael Bogachev and Denis Davydov in 2020 while working at a successful Ukrainian startup, which was acquired by the largest logistics company in the UAE. Later in 2023, as a result of traveling across Europe, they crossed paths in Budapest, where the core concept of the project was discovered.

In their search for an idea, they focused particularly on the existing trends in the fields of AI and cryptocurrencies. Denis already had substantial experience in cryptocurrencies, having worked in American crypto companies between 2022-2023 and participated in AI and crypto startups from 2016 to 2019. Michael also utilized AI in the development of logistics systems from 2016 to 2022.

Based on their experience, they identified some bottlenecks in preparing large AI models. The first bottleneck was processing large datasets, a problem that was successfully solved by Nvidia, whose stocks more than doubled in 2023 after releasing their accelerators.

The second bottleneck is not as obvious, since it can only be identified by those who are directly involved in training models. This bottleneck is the preparation of metadata, which is fed into the model along with the data.

Metadata is a key element that allows the neural network to interpret what is visualized, voiced, or written, and how it relates to other objects.

You can learn more about this information in the project Whitepaper.

Preparation of metadata though, could be a difficult task.

For example, to handle 5 million images and around 30-40 million metadata units are needed since each image features several objects, each requiring distinct marking. More precise annotation methods like polygons improve the performance of neural networks compared to rectangles. Generating this metadata is not only labor-intensive but also demands significant financial resources. For instance, an individual annotator can create 135 units daily, amounting to 2,835 monthly. For 35 million units of metadata, one person would need over a millennium, a team of 100 about a decade, and 1,000 people roughly a year.

The cost of creating such a large amount of metadata can exceed $20 million!

WorkML.AI’s Solution

The solution involves setting up an employment hub on the WorkML platform, where individuals from around the world can take onboarding courses, becoming part of the annotator and data validator workforce. This approach could mobilize tens and hundreds of thousands of annotators for annotation tasks.

Additionally, companies can establish their annotation departments through the WorkML platform, incorporating outsourced annotators into their teams. This strategy is set to increase the quality and speed of annotation by orders of magnitude, while also reducing annotation costs by approximately tenfold.

The WorkML.AI Token: WML

Moreover, to optimize expenses and fees, the project enables the use of cryptocurrencies for transactions. Importantly, the project introduces its token – WML, which will be used for internal payments and annotator remunerations.

The token features rewarding systems such as:

  • Proof of Stake (PoS) with payouts ranging from 0.5% per month (guaranteed) to up to 5% per month (from project profits),
  • Human’s Proof of Stake (H-PoS) offers double profit for annotators who perform the actual work,
  • The annotation mechanism is considered as mining, or human Proof of Work (H-PoW), meaning the more and better work done, the higher the reward.
  • Multilevel referral rewards is reshaping the cryptocurrency market by providing genuine benefits to a broad spectrum of participants, from businesses and investors to clients and data annotators. Instead of relying on speculative token offerings, it has developed a dependable revenue model based on service fees. This approach not only secures a continuous financial inflow but also bases the project’s value on the actual benefits it delivers.Follow across its social network channels:

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