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With the amount of new subnets being added it can be hard to get up to date information across all subnets, so data may be slightly out of date from time to time

Subnet 33

ReadyAI

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ABOUT

What exactly does it do?

ReadyAI provides creators and businesses with cost-effective, time-saving tools to build the most accurate AI applications for their audience. Built on the Bittensor network, they’ve transformed data tagging and structuring for text data, significantly improving AI accuracy through metadata and synthetic data generation for vector databases. These databases give you control over your data while leveraging top-tier models like OpenAI, Anthropic, and Meta.

ReadyAI offers more precise metadata tagging at a lower cost than competitors like Scale AI, which rely on expensive, less reliable human labor. Their use of Bittensor’s decentralized validation ensures higher quality control over metadata, and they implement a privacy-preserving system for processing both public and proprietary data. As they continue to evolve, ReadyAI is committed to protecting data privacy, fostering collaboration in AI research, and advancing their dataset to meet the growing demands of conversational AI, helping create seamless, human-like interactions for emotional support, personalized learning, and beyond.

Afterparty, the team behind ReadyAI, aims to enable creators to fully own and monetize their AI personas and creations, highlighting the need for creators to have true ownership of their AI platforms. Afterparty recognizes a lack of quality in open-source foundational models for conversational natural language fluency.

 

ReadyAI provides creators and businesses with cost-effective, time-saving tools to build the most accurate AI applications for their audience. Built on the Bittensor network, they’ve transformed data tagging and structuring for text data, significantly improving AI accuracy through metadata and synthetic data generation for vector databases. These databases give you control over your data while leveraging top-tier models like OpenAI, Anthropic, and Meta.

ReadyAI offers more precise metadata tagging at a lower cost than competitors like Scale AI, which rely on expensive, less reliable human labor. Their use of Bittensor’s decentralized validation ensures higher quality control over metadata, and they implement a privacy-preserving system for processing both public and proprietary data. As they continue to evolve, ReadyAI is committed to protecting data privacy, fostering collaboration in AI research, and advancing their dataset to meet the growing demands of conversational AI, helping create seamless, human-like interactions for emotional support, personalized learning, and beyond.

Afterparty, the team behind ReadyAI, aims to enable creators to fully own and monetize their AI personas and creations, highlighting the need for creators to have true ownership of their AI platforms. Afterparty recognizes a lack of quality in open-source foundational models for conversational natural language fluency.

 

PURPOSE

What exactly is the 'product/build'?

The ReadyAI subnet is designed to offer a low-cost, resource-efficient data structuring and semantic tagging pipeline for both individuals and businesses. A prime example of their data processing power is the creation of an annotated, persona-labeled dialogue dataset for conversational AI development—The ReadyAI. This dataset provides crucial resources for the open-source community to make significant strides in developing natural, engaging conversational AI through further training, finetuning, and system integration.

ReadyAI leverages Bittensor’s decentralized infrastructure to incentivize a global network of miners and validators to contribute and verify high-quality conversational data. Their innovative “fractal data mining” approach enhances data integrity by cross-referencing miner submissions with ground truth, rewarding accurate and valuable contributions.

 

Key Features

  • Indexing and tagging billions of conversations from diverse sources such as YouTube and podcasts
  • Utilizing fractal data mining and conversation windows to ensure efficient and privacy-preserving processing
  • Generating synthetic participant profiles using conversation metadata
  • Implementing an algorithm to evaluate conversation quality based on relevance, engagement, novelty, coherence, and fluency
  • Providing an open-source dataset for training and refining conversational AI models
  • Establishing an incentivized mining and validation system to ensure data contribution and maintain integrity

ReadyAI utilizes Bittensor’s infrastructure to annotate conversational data.

 

Benefits

  • Addresses the current lack of personalization in conversational AI models
  • Facilitates natural and engaging conversations tailored to individual contexts and preferences
  • Provides a comprehensive and annotated dataset for advancing conversational AI
  • Promotes contributions and innovations within the open-source community
  • Ensures data integrity through validation and scoring mechanisms

 

System Design

  • Data stores: Serve as the primary source of truth for conversation windows, participant profiles, and vector databases
  • Validator roles: Involve pulling data, generating overview metadata for grounded conversations, creating windows, and scoring submissions.  Validators have the authority to decide what data to allow or block, ensuring personal information remains confidential. Maintaining control over data protection is fundamental to the design.
  • Miner roles: Focus on processing conversation windows, contributing metadata, and applying tags.  Specific metadata tags are extracted to assess their similarity to those in the overall conversation, focusing on uniqueness to provide a deeper understanding of the conversation’s context. The conversational window safeguards against miners creating unauthorized databases with personal data. Miners have the opportunity to enhance their performance by utilizing system prompting, high-quality chaining, and fine-tuning models to improve metadata extraction. The flexibility for miners to fine-tune their models based on their sources allows for endless opportunities in the mining process.
  • Data flow: Includes establishing ground truth, creating windows, submitting miner contributions, scoring, and validation

 

Rewards and Incentives

  • Miners are rewarded for contributing accurate and valuable metadata.  The “conversation Windows” concept in Subnet divides conversations into overlapping segments, allowing miners to analyze each segment and extract detailed metadata. Miners receive a portion of the conversation to analyze, providing a more granular level of metadata tagging compared to the overall conversation tags.
  • Rewards are distributed in a balanced manner to encourage high-quality submissions. Miners’ scoring is based on matching tags with the overall conversation and generating unique tags close to the semantic neighborhood of the conversation. Validators compare miners’ tags within the same conversation window to ensure relevance and uniqueness in the metadata tagging process.
  • Cross-referencing and vector embedding analysis are used to ensure data integrity.
  • An algorithm is in place for assessing conversation quality, though it is not yet used for miner rewards.

 

The ReadyAI subnet is designed to offer a low-cost, resource-efficient data structuring and semantic tagging pipeline for both individuals and businesses. A prime example of their data processing power is the creation of an annotated, persona-labeled dialogue dataset for conversational AI development—The ReadyAI. This dataset provides crucial resources for the open-source community to make significant strides in developing natural, engaging conversational AI through further training, finetuning, and system integration.

ReadyAI leverages Bittensor’s decentralized infrastructure to incentivize a global network of miners and validators to contribute and verify high-quality conversational data. Their innovative “fractal data mining” approach enhances data integrity by cross-referencing miner submissions with ground truth, rewarding accurate and valuable contributions.

 

Key Features

  • Indexing and tagging billions of conversations from diverse sources such as YouTube and podcasts
  • Utilizing fractal data mining and conversation windows to ensure efficient and privacy-preserving processing
  • Generating synthetic participant profiles using conversation metadata
  • Implementing an algorithm to evaluate conversation quality based on relevance, engagement, novelty, coherence, and fluency
  • Providing an open-source dataset for training and refining conversational AI models
  • Establishing an incentivized mining and validation system to ensure data contribution and maintain integrity

ReadyAI utilizes Bittensor’s infrastructure to annotate conversational data.

 

Benefits

  • Addresses the current lack of personalization in conversational AI models
  • Facilitates natural and engaging conversations tailored to individual contexts and preferences
  • Provides a comprehensive and annotated dataset for advancing conversational AI
  • Promotes contributions and innovations within the open-source community
  • Ensures data integrity through validation and scoring mechanisms

 

System Design

  • Data stores: Serve as the primary source of truth for conversation windows, participant profiles, and vector databases
  • Validator roles: Involve pulling data, generating overview metadata for grounded conversations, creating windows, and scoring submissions.  Validators have the authority to decide what data to allow or block, ensuring personal information remains confidential. Maintaining control over data protection is fundamental to the design.
  • Miner roles: Focus on processing conversation windows, contributing metadata, and applying tags.  Specific metadata tags are extracted to assess their similarity to those in the overall conversation, focusing on uniqueness to provide a deeper understanding of the conversation’s context. The conversational window safeguards against miners creating unauthorized databases with personal data. Miners have the opportunity to enhance their performance by utilizing system prompting, high-quality chaining, and fine-tuning models to improve metadata extraction. The flexibility for miners to fine-tune their models based on their sources allows for endless opportunities in the mining process.
  • Data flow: Includes establishing ground truth, creating windows, submitting miner contributions, scoring, and validation

 

Rewards and Incentives

  • Miners are rewarded for contributing accurate and valuable metadata.  The “conversation Windows” concept in Subnet divides conversations into overlapping segments, allowing miners to analyze each segment and extract detailed metadata. Miners receive a portion of the conversation to analyze, providing a more granular level of metadata tagging compared to the overall conversation tags.
  • Rewards are distributed in a balanced manner to encourage high-quality submissions. Miners’ scoring is based on matching tags with the overall conversation and generating unique tags close to the semantic neighborhood of the conversation. Validators compare miners’ tags within the same conversation window to ensure relevance and uniqueness in the metadata tagging process.
  • Cross-referencing and vector embedding analysis are used to ensure data integrity.
  • An algorithm is in place for assessing conversation quality, though it is not yet used for miner rewards.

 

WHO

Team Info

The Afterparty team consists of individuals with backgrounds in AI, technology, and creator marketing, showcasing a diverse range of expertise to disrupt the creator economy.

David Fields – Co-Founder

Eytan Elbaz – Co-Founder

Robert Graham – Co-Founder and Chief Talent Officer

Dan Rahmel – Co-Founder

John Van Liere – Product Lead

Matthew Dusette – Product Operations Manager

The Afterparty team consists of individuals with backgrounds in AI, technology, and creator marketing, showcasing a diverse range of expertise to disrupt the creator economy.

David Fields – Co-Founder

Eytan Elbaz – Co-Founder

Robert Graham – Co-Founder and Chief Talent Officer

Dan Rahmel – Co-Founder

John Van Liere – Product Lead

Matthew Dusette – Product Operations Manager

FUTURE

Roadmap

ReadyAI aims to revolutionize personalized conversational AI by providing a comprehensive, annotated dataset of global conversations. Leveraging a robust system design, fractal data mining, and a carefully crafted rewards model, the CGP enables the open-source community to develop more engaging and context-aware AI models while maintaining data integrity and resilience.

The database’s real intention is to categorize and index all conversations globally, allowing model makers to access structured data for more robust AI development. The goal involves continually structuring data to provide a wide range of applications beyond character experiences, shaping the future of conversational AI.

ReadyAI aims to revolutionize personalized conversational AI by providing a comprehensive, annotated dataset of global conversations. Leveraging a robust system design, fractal data mining, and a carefully crafted rewards model, the CGP enables the open-source community to develop more engaging and context-aware AI models while maintaining data integrity and resilience.

The database’s real intention is to categorize and index all conversations globally, allowing model makers to access structured data for more robust AI development. The goal involves continually structuring data to provide a wide range of applications beyond character experiences, shaping the future of conversational AI.

MEDIA

Huge thanks to Keith Singery (aka Bittensor Guru) for all of his fantastic work in the Bittensor community. Make sure to check out his other video/audio interviews by clicking HERE.

The Conversation Genome Project by Afterparty signifies a leap forward in subnet planning, design, and execution. With months of experience on the testnet, a skilled and seasoned development team, and an innovative approach to incentivizing intelligence. In this audio interview, Keith chats with David Fields whose team are set to democratize access to personalized AI using Bittensor.

NEWS

Announcements

MORE INFO

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