DataCrowd

Overview

As global demand for artificial intelligence (AI) rapidly increases, the performance and capabilities of AI models have become crucial. Ensuring these models are effectively trained requires high-quality data.

Various companies have talked about the lack of training data, and the importance of high-quality training data.

Researchers from Epoch, an AI research and forecasting organization, claimed that by as early as 2026, the types of data typically used for training language models may be used up.
Thomas Wolf, co-founder and Chief Science Officer of Hugging Face, said at the conference Emtech 2023, "It's not enough to just scrub the internet to train LLLM. Quality data counts – we all are going back to this truth". In the same conference, Hanlin Tang, co-founder and CTO of Mosaic ML also mentioned that "it's still about the data (how clean is your data, how diverse, data operations etc)".
Bhaskar Chakravorti, wrote an article for Harvard Business Review that touched upon the topic. "Besides, AI models also risk running out of new high-quality data to train on and neutralizing biases arising from limited/low-quality datasets", he said.

DataCrowd is a decentralized data annotation platform built on blockchain technology and Web3 concepts, designed to connect AI companies with individuals who provide data annotation services. The platform offers high-quality annotated data covering various data types, such as text and images, via a comprehensive reward system.

DataCrowd's core advantage lies in its decentralized architecture and economic incentive mechanism, incentivizing users to participate in tasks through blockchain smart contracts while ensuring data security and transparency. Leveraging on The Open Network (TON) blockchain, DataCrowd has designed an economic ecosystem that fairly rewards contributors according to a mechanism that evaluates on five metrics, namely: engagement, quality, difficulty, reputation, and comprehensiveness.

Key Features:

Decentralized: Operates on a blockchain framework, eliminating intermediaries and ensuring transaction transparency
Diverse Data Types: Supports multiple data formats to meet different AI model needs
Economic Incentives: Contributors are rewarded with tokens based on their work, forming an ecosystem that values high-quality contributions

NextBackground

Last updated 2 months ago

Overview

Various companies have talked about the lack of training data, and the importance of high-quality training data.

Researchers from Epoch, an AI research and forecasting organization, claimed that by as early as 2026, the types of data typically used for training language models may be used up.

Thomas Wolf, co-founder and Chief Science Officer of Hugging Face, said at the conference Emtech 2023, "It's not enough to just scrub the internet to train LLLM. Quality data counts – we all are going back to this truth". In the same conference, Hanlin Tang, co-founder and CTO of Mosaic ML also mentioned that "it's still about the data (how clean is your data, how diverse, data operations etc)".

Bhaskar Chakravorti, wrote an article for Harvard Business Review that touched upon the topic. "Besides, AI models also risk running out of new high-quality data to train on and neutralizing biases arising from limited/low-quality datasets", he said.

Key Features:

Decentralized: Operates on a blockchain framework, eliminating intermediaries and ensuring transaction transparency

Diverse Data Types: Supports multiple data formats to meet different AI model needs

Economic Incentives: Contributors are rewarded with tokens based on their work, forming an ecosystem that values high-quality contributions