Emre Yumlu 
Decentralized AI infrastructure provider OORT has achieved notable success on Google’s data science platform Kaggle with its image dataset. Published in early April, the dataset titled “Diverse Tools” quickly climbed to the first page in various categories. This achievement highlights the growing demand for high-quality, community-based training data.
Training Data Produced with Decentralized Infrastructure Gains Attention
Kaggle is a platform owned by Google where data science and machine learning professionals compete, learn, and share projects. The dataset released by OORT attracted significant interest from AI communities and ranked at the top in several engineering, retail, and manufacturing categories.
OORT’s CEO Max Li stated that the interaction metrics they observed confirmed the early-stage demand and relevance of training data created through decentralized methods. Li said:
“The organic interest from the community, including active usage and contributions — clearly shows how decentralized, community-supported data pipelines can achieve rapid distribution.”
New Datasets on the Way
The OORT team plans to release new datasets in the coming months, focusing on different themes. These include in-car voice commands, audio data for smart home technologies, and deepfake videos aimed at improving media verification systems.
Front Page Success Updated
The image dataset in question had been listed on the first page of Kaggle’s General AI, Retail, Manufacturing, and Engineering categories for a period of time. However, updates made on May 6 and May 14 led to changes in those rankings.
Despite this, experts evaluate OORT’s success not just by ranking but also by its transparent and incentive-based model. Unlike centralized solutions, projects like OORT can offer traceability and community oversight thanks to their token-based incentive systems. This suggests that decentralized projects may offer long-term reliability for data-driven AI systems.
High-Quality Visual Data Becoming Scarce
According to data published by AI research firm Epoch AI, human-generated text data may be depleted by 2028. When it comes to visual data, the situation is even more complex. Many artists are using deliberate sabotage techniques to prevent their works from being used in AI training without permission. For example, a tool called Nightshade is used to “poison” images in a way that degrades model performance.
In light of these developments, experts emphasize that high-quality and reliable visual data is becoming increasingly rare, making community-sourced, verifiable datasets more valuable than ever. Such projects may not only serve as alternatives but also become foundational pillars for the ethical development of artificial intelligence.
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