FRACTION AI
About the Chain
Fraction AI is its own gasless L2 rollup deployed using Polygon CDK. This makes us EVM compatible plus inter-operable with all other projects building on AggLayer.
Network Tokens
FRAC: native token used as gas fee
stFrac: transferable rebasing utility token representing a share of the total FRAC staked through the protocol
Revenue Rights Certificates (R2C): specific to each of the Perpetual Datasets, get a portion of licensing fee from the dataset
Platform
Entities
Guilds: lend the FRAC tokens for data contribution and verification, can be used to delegate FRAC tokens and earn yield
Perpetual Datasets aka Datasets: collection of data and labels, have a reward pool to pay for the contributions
Data Augmentation
Stakers: stake FRAC into the guilds to hold stFRAC and provide liquidity for securing contributions and ensuring data quality on the platform
Contributors: stake FRAC and contribute/label datasets
Verifiers: stake FRAC and verify contributions
Ownership and Usage
Dataset Creator: start a dataset for specific use case like Japanese audio, text 2 video etc. Responsible for flushing the reward pool of the dataset with FRAC tokens used to pay contributors and verifiers
Dataset Investor: buy R2C (Revenue Rights Certificates) floated by the Dataset Creator
Dataset User: uses and pays for the dataset(s) in FRAC tokens
Guilds
Guilds provider stakers the opportunity to delegate and secure contributions/verifications using their FRAC tokens. Contributors and verifier use the FRAC tokens from the guilds to stake and augment the datasets. Part of the rewards earned are passed on to the stakers. If the stake gets slashed in the augmentation process, the loss is passed onto the stakers while the contributor/verifier loses reputation points.
It is the responsibility of the guild to manage risks by assigning optimal budget of FRAC tokens to contributors and verifiers based on their reputation and past records. There can be multiple guilds based on their own allocation strategies, initially there'll be only one managed by Fraction AI's proprietary allocation algorithm.
Web2 solutions are not cutting it
While HuggingFace has enabled access to several high-quality datasets, the overall number of available datasets remains quite limited. There are web2 companies like Scale AI that provide labeling solutions, but they all suffer from several fundamental shortcomings:
High Costs:
These services are primarily targeted at large enterprise tech clients, making them unaffordable for most others.
Long Turnaround Times:
They operate on a reactive model, providing labeling services only upon client request.
Bring Your Own Data:
These services cater to labeling existing data, so users need to provide their own datasets. This puts them out of reach for smaller organizations and individuals who may not have access to large-scale data.
Data Bias:
The labeling work is typically done by a few thousand contract workers from a limited number of geographic regions, leading to inherent biases in the resulting datasets.
Perpetual Datasets: 100x solution
At Fraction AI, we are creating Perpetual Datasets - massive-scale datasets built in a permissionless way by humans and AI agents. Here are some key features:
Permissionless Dataset Creation:
Anyone can start a Perpetual dataset without needing permission.
Staking and Earning Yields:
Anyone can stake their participation in a dataset of their choosing and earn yields.
Rewarded Contributions:
Anyone can contribute to a dataset, either themselves or through their AI agents, and get rewarded for their contributions.
Data Licensing and Network Rewards:
Anyone can buy the data license, and the rewards from these purchases flow back to the participants in the network.
Stakers
Role and Responsibilities of Stakers
Delegation of FRAC Tokens:
Stakers use their FRAC tokens to delegate to guilds. This delegation is essential for the functioning of the dataset augmentation and verification system.
Earning Rewards:
When the tokens are used successfully by contributors and verifiers to augment datasets, stakers earn a portion of the rewards generated. This provides an incentive for stakers to participate in the ecosystem.
Bearing Risks:
Stakers also assume financial risk. If the contributors or verifiers fail in their tasks and the stake is slashed, the stakers bear the financial loss.
While contributors and verifiers may lose reputation points, the financial penalty directly impacts the stakers.
Summary
Stakers are key participants who delegate their FRAC tokens to facilitate the augmentation and verification of datasets. They earn rewards for successful tasks but also take on financial risk if the tasks fail. Their role is crucial in supporting the ecosystem by providing the necessary tokens for operation.
For More Information
Official website: https://fractionai.xyz/
Twitter: https://x.com/FractionAI_xyz
Discord : https://discord.gg/aQHEZ853z7
Linkedin: https://www.linkedin.com/company/fraction-ai/
Telegram: https://t.me/Fraction_AI
Myth VS Machine Game: https://t.me/myth_v_machine_bot
Medium : https://medium.com/@fractionai
Whitepaper: https://docs.fractionai.xyz/
AUTHOR
About the Chain
Fraction AI is its own gasless L2 rollup deployed using Polygon CDK. This makes us EVM compatible plus inter-operable with all other projects building on AggLayer.
Network Tokens
FRAC: native token used as gas fee
stFrac: transferable rebasing utility token representing a share of the total FRAC staked through the protocol
Revenue Rights Certificates (R2C): specific to each of the Perpetual Datasets, get a portion of licensing fee from the dataset
Platform
Entities
Guilds: lend the FRAC tokens for data contribution and verification, can be used to delegate FRAC tokens and earn yield
Perpetual Datasets aka Datasets: collection of data and labels, have a reward pool to pay for the contributions
Data Augmentation
Stakers: stake FRAC into the guilds to hold stFRAC and provide liquidity for securing contributions and ensuring data quality on the platform
Contributors: stake FRAC and contribute/label datasets
Verifiers: stake FRAC and verify contributions
Ownership and Usage
Dataset Creator: start a dataset for specific use case like Japanese audio, text 2 video etc. Responsible for flushing the reward pool of the dataset with FRAC tokens used to pay contributors and verifiers
Dataset Investor: buy R2C (Revenue Rights Certificates) floated by the Dataset Creator
Dataset User: uses and pays for the dataset(s) in FRAC tokens
Guilds
Guilds provider stakers the opportunity to delegate and secure contributions/verifications using their FRAC tokens. Contributors and verifier use the FRAC tokens from the guilds to stake and augment the datasets. Part of the rewards earned are passed on to the stakers. If the stake gets slashed in the augmentation process, the loss is passed onto the stakers while the contributor/verifier loses reputation points.
It is the responsibility of the guild to manage risks by assigning optimal budget of FRAC tokens to contributors and verifiers based on their reputation and past records. There can be multiple guilds based on their own allocation strategies, initially there'll be only one managed by Fraction AI's proprietary allocation algorithm.
Web2 solutions are not cutting it
While HuggingFace has enabled access to several high-quality datasets, the overall number of available datasets remains quite limited. There are web2 companies like Scale AI that provide labeling solutions, but they all suffer from several fundamental shortcomings:
High Costs:
These services are primarily targeted at large enterprise tech clients, making them unaffordable for most others.
Long Turnaround Times:
They operate on a reactive model, providing labeling services only upon client request.
Bring Your Own Data:
These services cater to labeling existing data, so users need to provide their own datasets. This puts them out of reach for smaller organizations and individuals who may not have access to large-scale data.
Data Bias:
The labeling work is typically done by a few thousand contract workers from a limited number of geographic regions, leading to inherent biases in the resulting datasets.
Perpetual Datasets: 100x solution
At Fraction AI, we are creating Perpetual Datasets - massive-scale datasets built in a permissionless way by humans and AI agents. Here are some key features:
Permissionless Dataset Creation:
Anyone can start a Perpetual dataset without needing permission.
Staking and Earning Yields:
Anyone can stake their participation in a dataset of their choosing and earn yields.
Rewarded Contributions:
Anyone can contribute to a dataset, either themselves or through their AI agents, and get rewarded for their contributions.
Data Licensing and Network Rewards:
Anyone can buy the data license, and the rewards from these purchases flow back to the participants in the network.
Stakers
Role and Responsibilities of Stakers
Delegation of FRAC Tokens:
Stakers use their FRAC tokens to delegate to guilds. This delegation is essential for the functioning of the dataset augmentation and verification system.
Earning Rewards:
When the tokens are used successfully by contributors and verifiers to augment datasets, stakers earn a portion of the rewards generated. This provides an incentive for stakers to participate in the ecosystem.
Bearing Risks:
Stakers also assume financial risk. If the contributors or verifiers fail in their tasks and the stake is slashed, the stakers bear the financial loss.
While contributors and verifiers may lose reputation points, the financial penalty directly impacts the stakers.
Summary
Stakers are key participants who delegate their FRAC tokens to facilitate the augmentation and verification of datasets. They earn rewards for successful tasks but also take on financial risk if the tasks fail. Their role is crucial in supporting the ecosystem by providing the necessary tokens for operation.
For More Information
Official website: https://fractionai.xyz/
Twitter: https://x.com/FractionAI_xyz
Discord : https://discord.gg/aQHEZ853z7
Linkedin: https://www.linkedin.com/company/fraction-ai/
Telegram: https://t.me/Fraction_AI
Myth VS Machine Game: https://t.me/myth_v_machine_bot
Medium : https://medium.com/@fractionai
Whitepaper: https://docs.fractionai.xyz/
AUTHOR
Bitcointalk Username: Dwi Aprilani
Bitcointalk profile link: https://bitcointalk.org/index.php?action=profile;u=3241566
Wallet Address: 0x5DeA7D2B033CD377CE8410855900c342e65B2842
