The story of the WorkML.ai 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.
What is Metadata?
Metadata is a key element that allows the neural network to make an interpretation of what is visualized, voiced, or written, and how it relates to other objects.
You can learn more about this information in the WorkML.ai project Whitepaper.
Why is Metadata preparation considered a challenging task?
It turns out that to create a new neural network, it needs to be trained entirely from scratch with a colossal amount of data (pretrained networks are not suitable for this; it’s mandatory to train anew each time). For this, a developer needs both the data and the metadata describing this data. Furthermore, the more accurate the metadata and the more data used in training the neural network, the smarter and more accurate its predictions will be.
Today, to effectively train a neural network for animal image recognition or picture generation, tens of millions of images (Data) need to be fed into the neural network, accompanied by Metadata (in this case, additional information specifying where exactly on each picture a particular animal is depicted, which can be a rectangle, polygon, fill, or skeleton).
A look at the complexity of annotation
For example, for 10 million images, around 30-40 million units of metadata are needed, as one image can depict from 1 to 10 or more objects, each of which needs to be marked. There’s also a difference in how the objects are highlighted. For instance, if objects are marked with rectangles, the trained neural network will not be as accurate in detection and generation as a network trained on objects outlined with polygons (the shape is traced more precisely with points and lines).
Indeed, it becomes evident that the necessity for metadata exceeds that of the data itself. While the data can be readily obtained in its unadulterated state, crafting the requisite metadata entails a deliberate and thoughtful process.
With an average output of one annotation every two minutes during a continuous 4.5-hour work session, an individual can generate 135 units of high-quality metadata per workday.
In one month, accounting for 21 workdays, this adds up to 2,835 units of metadata.
To prepare 35 million units of metadata, it would take one person 12,345 months, or 1,028 years!
A team of 100 would need 10 years and 3 months to complete the task, while a group of 1,000 could accomplish it in just 1 year.
You can find an approximation of the office setup for annotation in a use-case for clients, where the average costs amount to roughly $1,800 per month per annotator.
In the case of 100 annotators, the costs rise to around $180,000 per month over 10 years!
Or, $1,800,000 per month for 1 year with 1,000 annotators.
This amounts to approximately $21,600,000 for annotating 10 million images with 35 million metadata units.
As you can see, the process of creating metadata is resource-intensive, both in terms of time and financial investment.
However, the founders of WorkML have devised a solution to this challenge!
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 own 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.
Such innovation is as crucial for the AI industry as Nvidia’s accelerators.
The annotation task workflow is described in the diagram above, see the white paper for more details.
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) offering double profit for annotators who perform the actual work,
- The annotation mechanism is considered as mining, or Humans Proof of Work (H-PoW), meaning the more and better work done, the higher the reward.
- Multilevel referral rewards
The project also offers perpetual discounts to customers paying with the WML token for WorkML products, thereby creating additional liquidity.
WorkML.ai — highly profitable and low-risk feature-rich employment hub for investors, customers and annotators.
WorkML.ai 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, WorkML.ai 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.
Investing in WorkML.ai transcends a mere financial venture; it signifies a forward-thinking partnership at the forefront of AI innovation. It offers investors a chance to be part of a pivotal movement, yielding substantial returns and influencing the future technological framework.
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