Tensorplex Dojo Subnet: Democratizing AI Dataset Creation
The development of open-source AI models is often hindered by the lack of high-quality human-generated datasets. Closed-source AI developers, aiming to reduce data collection costs, have created significant social and economic equity challenges, with workers being paid less than $2 per hour for mentally and emotionally taxing tasks. The benefits of these models have been concentrated among a select few, exacerbating inequalities among contributors.
Enter Tensorplex Dojo Subnet — an open platform designed to crowdsource high-quality human-generated datasets. Powered by Bittensor, the Dojo Subnet addresses these challenges by allowing anyone to earn TAO by labeling data or providing human-preference data. This approach democratizes the collection of human preference data, addressing existing equity issues and paving the way for more inclusive and ethical AI development.
Key Features
To ensure the quality and integrity of the data collected, Dojo introduces several novel features:
- Synthetic Task Generation: Unique tasks are generated by state-of-the-art Large Language Models (LLMs) to collect human feedback data, which can be used to improve open-source models.
- Synthetic Ground Truth Validation Mechanism: Validators can synthetically generate partial ground truths, allowing them to determine the quality of responses provided by individual participants.
- Obfuscation: Techniques to prevent sybil attacks and ensure contributions are genuinely human.
Use Cases
The Dojo Subnet offers multiple use cases:
- Synthetically Generated Tasks: These tasks can bootstrap the human participant pool and can be used for model training or fine-tuning from the outset.
- Cross-subnet Validation: Validators can use responses to rate the quality of outputs across other Bittensor subnets, thereby incentivizing miners to improve their performance.
- External Data Acquisition: Entities outside the Bittensor ecosystem can tap into the subnet to acquire high-quality human-generated data.
By creating an open platform for gathering human-generated datasets, Tensorplex Dojo Subnet aims to solve the challenges of quality control, human verification, and sybil attack prevention while promoting a more equitable distribution of benefits in AI development.
Benefits to participants contributing through Dojo
- Open platform: Anyone capable can contribute, ensuring broad participation and diverse data collection.
- Flexible work environment: Participants enjoy the freedom to work on tasks at their convenience from any location.
- Quick payment: Rewards are streamed consistently to participants, as long as they complete sufficient tasks within a stipulated deadline and have them accepted by the subnet.