Roadmap

New roadmap features upcoming AI products focusing on retails.

Introduction

AIT Protocol is at the forefront of integrating artificial intelligence with blockchain technology, aiming to revolutionize data annotation and AI model training. By establishing a decentralized marketplace, we empower a global community to engage in "Train-to-Earn" tasks, thereby contributing to the advancement of AI models while earning rewards. Our innovative approach addresses the inefficiencies and high costs associated with traditional data annotation methods, fostering a transparent and reliable environment for AI development.

Vision

To lead the AI revolution by providing top-tier data annotation services and AI model training, upholding ethical standards, and promoting innovation.

Mission

To empower businesses and researchers by delivering precise and comprehensive data annotation services, tailored AI model training, and fostering a collaborative and accessible AI ecosystem.

Strategic Goals

Enhance Data Annotation Services: Continuously improve the accuracy and efficiency of data annotation processes to provide high-quality training data for AI models.
Expand Global Workforce Participation: Leverage blockchain technology to create a decentralized labor market, enabling individuals worldwide to contribute to AI development and earn rewards.
Foster Ethical AI Development: Prioritize privacy and security in data annotation, ensuring compliance with ethical standards and data protection regulations.
Drive Innovation and Research: Stay at the forefront of AI research by exploring new techniques and methodologies to enhance data annotation and AI model training.
Build Strategic Partnerships: Collaborate with leading projects and organizations to expand the ecosystem and drive the adoption of AI solutions.
Ensure Accessibility and Affordability: Offer scalable solutions and free-market pricing models to make AI technology accessible to businesses and researchers of all sizes.

By achieving these goals, AIT Protocol aims to solidify its position as a leader in the AI data infrastructure space, delivering value to stakeholders and contributing to the advancement of AI technologies.

Development Phases and Milestones

Phase 1: Foundational Infrastructure, API Beta Demo, and Initial Data Annotation Setup
- Timeline: Completed
- Objectives:
  - Merge LogicNet’s infrastructure with SN23 for stable platform and efficient computing.
  - Launch on Bittensor mainnet, positioning LogicNet as a leader in AI-driven mathematical reasoning.
  - Release API beta demo, allowing early access for select users to interact with LogicNet’s core capabilities and gather feedback.
  - Scale community outreach and expand user base, enhancing the data annotation pipeline with synthetic data and RLHF.
- Milestones:
  - Establish foundational API accessibility for external developers, enabling initial testing and feedback.
  - Collect early user feedback from the API demo to guide future improvements and feature expansion.
  - Set up model experimentation for validators, assessing options like Qwen2.5-32B-Instruct for efficiency and performance.
  - Broaden community engagement to gather diverse data inputs, increasing data volume for the model.
Phase 2: Enhanced User Access, Data Annotation Platform Development, and Labeler Onboarding
- Timeline: Q1 2025
- Objectives:
  - Refine API based on beta feedback and prepare for public access.
  - Continue API development based on demo feedback, preparing for broader public API access.
  - Implement model strategies for validators, testing open-source (e.g., ZebraLogic) and closed-source (e.g., GPT-o1-mini) models.
  - Introduce competitive “moving goalposts” for validators, driving miners to fine-tune and improve models.
- Milestones:
  - Expand functionality and usability of the API, incorporating user suggestions from the beta demo.
  - Establish a structured miner competition, improving the overall quality of responses and model refinement.
Phase 3: Open-Source Model Release, Full Data Annotation Platform Deployment, and Partner Expansion
- Timeline: Q2 2025
- Objectives:
  - Develop and launch the data annotation platform to support labeling workflows, user management, and emission mechanisms for labelers.
  - Start onboarding labelers by establishing clear guidelines, incentive structures, and training resources.
  - Expand community outreach to increase data submissions for labeling and support a growing labeler network.
  - Launch the “Quick Calc” App to expand access to Albert App.
- Milestones:
  - Launch a beta version of the data annotation platform with basic task management and emission functionalities.
  - Partner with companies and academic institutions to secure datasets for annotation.
  - Onboard an initial group of labelers, implement tracking systems for task completion, and set up emission rewards based on performance.
Phase 4: Revenue Generation, Advanced Data Annotation Capabilities, and Enterprise API Integration
- Timeline: Q3 - Q4 2025
- Objectives:
  - Expand the data annotation platform with advanced features, including task prioritization, quality control, and performance-based emission rewards for labelers.
  - Onboard additional data partners to supply real-world datasets for labeling.
  - Activate data analytics in Albert App and offer enhanced API access for developer integration.
  - Finalize public API access for integration with enterprise and third-party applications.
  - Establish a real-time leaderboard system that ranks miners based on model performance, incentivizing competition within Subnet 35. This feature will reward high-ranking miners, promote continuous improvement, and foster a competitive ecosystem for model fine-tuning and data quality.
- Milestones:
  - Deploy the full data annotation platform, making it available to labelers and partners.
  - Increase labeler community engagement with performance-based emissions and incentivized participation.
  - Solidify LogicNet’s ecosystem with valuable datasets, annotation tools, and a large user base.
  - Deploy the full Public API access and monetize the API platform offering premium tools for developers and businesses
  - Roll out the leaderboard to all miners, including enhanced tracking features and real-time updates.
  - Introduce a reward system tied to leaderboard rankings, such as emissions bonuses or exclusive features for top-performing miners.
  - Regularly update rankings and highlight top miners to maintain engagement and drive competition.
Phase 5: Revenue Generation and Advanced Platform Features
- Timeline: Q1 2026
- Objectives:
  - Implement revenue-generating features, such as PDF/report reading tools, alongside an enterprise subscription model.
  - Enhance data annotation platform with advanced workflows (e.g., automated quality checks, tiered emissions based on data complexity).
  - Build long-term partnerships with companies needing regular annotation services and integrate annotation options into enterprise subscriptions.
  - Create a user-friendly fine-tuning platform that enables miners to easily adjust and enhance their models to meet the unique demands of Subnet 35. This platform will encourage competition by allowing miners to fine-tune their models, optimize performance, and achieve higher scores in LogicNet's ecosystem.
- Milestones:
  - Monetize the data annotation platform, offering premium tools for data providers and increased emissions for high-value labeling tasks.
  - Position LogicNet as a go-to platform for both data annotation and analytics services.
  - Foster a sustainable data annotation ecosystem that drives continuous data flow and model enhancement, supporting LogicNet's broader mission in complex data analysis.
  - Roll out the full fine-tuning platform for all miners, including advanced tuning features and real-time performance feedback.
  - Introduce incentives for top-performing miners to encourage ongoing competition.
  - Provide support and resources to help miners maximize platform use and continuously improve their models.

Product Development and Features

AIT Protocol's development is centered on Subnet 35, known as LogicNet, within the Bittensor ecosystem. LogicNet is dedicated to creating an open-source AI model proficient in complex mathematics and detailed data analysis, enhanced by incentivized human feedback for continuous improvement.

Current Features:
- Subnet 35 - Competitive Miner Playground (http://taostats.io/subnets/35): A platform allowing individuals to contribute their computational power to LogicNet AI development. This feature enables miners to participate in model training, optimization, and testing, fostering a competitive environment that drives innovation and improves the overall quality of LogicNet's AI capabilities.
- Albert App (https://albert.aitprotocol.ai): A user-friendly interface providing general knowledge responses and mathematical support. The app is currently in beta testing, with plans to expand its capabilities.
- Reinforcement Learning from Human Feedback (RLHF): Active within the Albert App, this feature collects user feedback to refine and enhance model accuracy.
- NumPal Integration: An LLM integration that miners can use to supercharge their mining models network-wide, achieving faster iterations at a lower cost, reduced processing time, fewer errors, and consistently higher scores than GPT-4.
- API Beta Demo: Our recently released API beta demo provides early access to LogicNet’s core functionalities, allowing selected users and developers to test and interact with the model. Feedback from this demo will guide API improvements and shape the roadmap for full public API access.
Upcoming Features:
- Chrome Extension (Quick Calc): A convenient Chrome extension enabling natural language calculations directly in the browser, set for release by Q3 2024, providing faster access to Albert App’s capabilities.
- Public API Access: Expanded based on feedback from the beta demo, the public API will be available soon, allowing developers, enterprises, and end-users to integrate LogicNet’s specialized data processing and computational reasoning into various applications.
- Data Analytics Visualization: Planned for Q4 2024, this feature will integrate graphing and charting capabilities in the Albert App, facilitating more sophisticated data analysis and interpretation, particularly for complex datasets.
- AI-Powered Data Annotation Platform: Development of a sophisticated platform that leverages AI to assist human annotators, increasing efficiency and accuracy in data labeling tasks.
- Blockchain-Based Reputation System: Implementation of a decentralized reputation system for labelers and miners, ensuring quality contributions and fair rewards distribution.
- Cross-Platform Mobile App: Expansion of Albert App's capabilities to iOS and Android platforms, providing on-the-go access to LogicNet's AI services.
- Automated Model Evaluation Framework: Creation of a comprehensive testing suite to automatically evaluate and rank miner-submitted models, ensuring continuous improvement in AI performance.
Future Enhancements and Enterprise Solutions:
- PDF/Report Reader: A tool for natural language interaction with PDF documents, allowing users to generate insights and visualizations. This feature will be offered through a subscription model aimed at enterprises and research institutions.
- Enterprise Subscription Model: Enabling organizations to subscribe for advanced features, such as multi-seat access and priority API support, allowing educational and corporate users to leverage Albert App at scale.
- Custom AI Model Development: Tailored AI solutions for enterprise clients, allowing them to leverage LogicNet's advanced capabilities for specific industry needs and proprietary datasets.
- Enterprise Data Integration: Secure API connectors and data pipelines to seamlessly integrate LogicNet's AI capabilities with existing enterprise systems and databases.
- Compliance and Governance Tools: Advanced features for enterprise clients to ensure AI model outputs adhere to industry-specific regulations and internal governance policies.
- Scalable Infrastructure Solutions: Cloud-based and on-premises deployment options to meet varying enterprise requirements for data security, processing power, and scalability.

Conclusion

As we embark on this exciting journey to revolutionize AI and data analytics, AIT Protocol invites you to be part of something truly groundbreaking. Our vision for an open-source AI model capable of complex analytics is not just a technological advancement—it's a movement towards democratizing AI and empowering individuals and businesses alike.

By joining us, you're not merely using a service; you're actively shaping the future of AI. Whether you're a developer looking to push the boundaries of what's possible, a business seeking to leverage cutting-edge analytics, or an individual passionate about AI, there's a place for you in our ecosystem.

How You Can Contribute:
- Developers: Contribute to our codebase, help refine our models, build innovative applications on our platform, or become a miner on our Subnet 35 to actively participate in model training and optimization, while earning emissions for your contributions.
- Enterprises: Partner with us to leverage advanced AI capabilities, driving innovation and efficiency in your operations.
- Individual Users: Participate in our "Train-to-Earn" program, provide valuable feedback, and help improve our AI models.

Together, we can create an AI ecosystem that's not only powerful and efficient but also ethical and accessible to all. Join us in this exciting venture, earn rewards for your contributions, and be at the forefront of the AI revolution.

Ready to Get Started?
- Experience AI-powered analytics with https://albert.aitprotocol.ai
- Explore our subnet 35 projects on https://github.com/LogicNet-Subnet/LogicNet
- Become part of our mining network on https://taostats.io/subnets/35/metagraph?order=stake:desc

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Last updated 2 months ago

Introduction

Strategic Goals

Enhance Data Annotation Services: Continuously improve the accuracy and efficiency of data annotation processes to provide high-quality training data for AI models.

Expand Global Workforce Participation: Leverage blockchain technology to create a decentralized labor market, enabling individuals worldwide to contribute to AI development and earn rewards.

Foster Ethical AI Development: Prioritize privacy and security in data annotation, ensuring compliance with ethical standards and data protection regulations.

Drive Innovation and Research: Stay at the forefront of AI research by exploring new techniques and methodologies to enhance data annotation and AI model training.

Build Strategic Partnerships: Collaborate with leading projects and organizations to expand the ecosystem and drive the adoption of AI solutions.

Ensure Accessibility and Affordability: Offer scalable solutions and free-market pricing models to make AI technology accessible to businesses and researchers of all sizes.

Development Phases and Milestones

Phase 1: Foundational Infrastructure, API Beta Demo, and Initial Data Annotation Setup

Timeline: Completed
Objectives:
- Merge LogicNet’s infrastructure with SN23 for stable platform and efficient computing.
- Launch on Bittensor mainnet, positioning LogicNet as a leader in AI-driven mathematical reasoning.
- Release API beta demo, allowing early access for select users to interact with LogicNet’s core capabilities and gather feedback.
- Scale community outreach and expand user base, enhancing the data annotation pipeline with synthetic data and RLHF.
Milestones:
- Establish foundational API accessibility for external developers, enabling initial testing and feedback.
- Collect early user feedback from the API demo to guide future improvements and feature expansion.
- Set up model experimentation for validators, assessing options like Qwen2.5-32B-Instruct for efficiency and performance.
- Broaden community engagement to gather diverse data inputs, increasing data volume for the model.

Phase 2: Enhanced User Access, Data Annotation Platform Development, and Labeler Onboarding

Timeline: Q1 2025
Objectives:
- Refine API based on beta feedback and prepare for public access.
- Continue API development based on demo feedback, preparing for broader public API access.
- Implement model strategies for validators, testing open-source (e.g., ZebraLogic) and closed-source (e.g., GPT-o1-mini) models.
- Introduce competitive “moving goalposts” for validators, driving miners to fine-tune and improve models.
Milestones:
- Expand functionality and usability of the API, incorporating user suggestions from the beta demo.
- Establish a structured miner competition, improving the overall quality of responses and model refinement.

Phase 3: Open-Source Model Release, Full Data Annotation Platform Deployment, and Partner Expansion

Timeline: Q2 2025
Objectives:
- Develop and launch the data annotation platform to support labeling workflows, user management, and emission mechanisms for labelers.
- Start onboarding labelers by establishing clear guidelines, incentive structures, and training resources.
- Expand community outreach to increase data submissions for labeling and support a growing labeler network.
- Launch the “Quick Calc” App to expand access to Albert App.
Milestones:
- Launch a beta version of the data annotation platform with basic task management and emission functionalities.
- Partner with companies and academic institutions to secure datasets for annotation.
- Onboard an initial group of labelers, implement tracking systems for task completion, and set up emission rewards based on performance.

Phase 4: Revenue Generation, Advanced Data Annotation Capabilities, and Enterprise API Integration

Timeline: Q3 - Q4 2025
Objectives:
- Expand the data annotation platform with advanced features, including task prioritization, quality control, and performance-based emission rewards for labelers.
- Onboard additional data partners to supply real-world datasets for labeling.
- Activate data analytics in Albert App and offer enhanced API access for developer integration.
- Finalize public API access for integration with enterprise and third-party applications.
- Establish a real-time leaderboard system that ranks miners based on model performance, incentivizing competition within Subnet 35. This feature will reward high-ranking miners, promote continuous improvement, and foster a competitive ecosystem for model fine-tuning and data quality.
Milestones:
- Deploy the full data annotation platform, making it available to labelers and partners.
- Increase labeler community engagement with performance-based emissions and incentivized participation.
- Solidify LogicNet’s ecosystem with valuable datasets, annotation tools, and a large user base.
- Deploy the full Public API access and monetize the API platform offering premium tools for developers and businesses
- Roll out the leaderboard to all miners, including enhanced tracking features and real-time updates.
- Introduce a reward system tied to leaderboard rankings, such as emissions bonuses or exclusive features for top-performing miners.
- Regularly update rankings and highlight top miners to maintain engagement and drive competition.

Phase 5: Revenue Generation and Advanced Platform Features

Timeline: Q1 2026
Objectives:
- Implement revenue-generating features, such as PDF/report reading tools, alongside an enterprise subscription model.
- Enhance data annotation platform with advanced workflows (e.g., automated quality checks, tiered emissions based on data complexity).
- Build long-term partnerships with companies needing regular annotation services and integrate annotation options into enterprise subscriptions.
- Create a user-friendly fine-tuning platform that enables miners to easily adjust and enhance their models to meet the unique demands of Subnet 35. This platform will encourage competition by allowing miners to fine-tune their models, optimize performance, and achieve higher scores in LogicNet's ecosystem.
Milestones:
- Monetize the data annotation platform, offering premium tools for data providers and increased emissions for high-value labeling tasks.
- Position LogicNet as a go-to platform for both data annotation and analytics services.
- Foster a sustainable data annotation ecosystem that drives continuous data flow and model enhancement, supporting LogicNet's broader mission in complex data analysis.
- Roll out the full fine-tuning platform for all miners, including advanced tuning features and real-time performance feedback.
- Introduce incentives for top-performing miners to encourage ongoing competition.
- Provide support and resources to help miners maximize platform use and continuously improve their models.

Product Development and Features

Current Features:

Subnet 35 - Competitive Miner Playground (http://taostats.io/subnets/35): A platform allowing individuals to contribute their computational power to LogicNet AI development. This feature enables miners to participate in model training, optimization, and testing, fostering a competitive environment that drives innovation and improves the overall quality of LogicNet's AI capabilities.
Albert App (https://albert.aitprotocol.ai): A user-friendly interface providing general knowledge responses and mathematical support. The app is currently in beta testing, with plans to expand its capabilities.
Reinforcement Learning from Human Feedback (RLHF): Active within the Albert App, this feature collects user feedback to refine and enhance model accuracy.
NumPal Integration: An LLM integration that miners can use to supercharge their mining models network-wide, achieving faster iterations at a lower cost, reduced processing time, fewer errors, and consistently higher scores than GPT-4.
API Beta Demo: Our recently released API beta demo provides early access to LogicNet’s core functionalities, allowing selected users and developers to test and interact with the model. Feedback from this demo will guide API improvements and shape the roadmap for full public API access.

Upcoming Features:

Chrome Extension (Quick Calc): A convenient Chrome extension enabling natural language calculations directly in the browser, set for release by Q3 2024, providing faster access to Albert App’s capabilities.
Public API Access: Expanded based on feedback from the beta demo, the public API will be available soon, allowing developers, enterprises, and end-users to integrate LogicNet’s specialized data processing and computational reasoning into various applications.
Data Analytics Visualization: Planned for Q4 2024, this feature will integrate graphing and charting capabilities in the Albert App, facilitating more sophisticated data analysis and interpretation, particularly for complex datasets.
AI-Powered Data Annotation Platform: Development of a sophisticated platform that leverages AI to assist human annotators, increasing efficiency and accuracy in data labeling tasks.
Blockchain-Based Reputation System: Implementation of a decentralized reputation system for labelers and miners, ensuring quality contributions and fair rewards distribution.
Cross-Platform Mobile App: Expansion of Albert App's capabilities to iOS and Android platforms, providing on-the-go access to LogicNet's AI services.
Automated Model Evaluation Framework: Creation of a comprehensive testing suite to automatically evaluate and rank miner-submitted models, ensuring continuous improvement in AI performance.

Future Enhancements and Enterprise Solutions:

PDF/Report Reader: A tool for natural language interaction with PDF documents, allowing users to generate insights and visualizations. This feature will be offered through a subscription model aimed at enterprises and research institutions.
Enterprise Subscription Model: Enabling organizations to subscribe for advanced features, such as multi-seat access and priority API support, allowing educational and corporate users to leverage Albert App at scale.
Custom AI Model Development: Tailored AI solutions for enterprise clients, allowing them to leverage LogicNet's advanced capabilities for specific industry needs and proprietary datasets.
Enterprise Data Integration: Secure API connectors and data pipelines to seamlessly integrate LogicNet's AI capabilities with existing enterprise systems and databases.
Compliance and Governance Tools: Advanced features for enterprise clients to ensure AI model outputs adhere to industry-specific regulations and internal governance policies.
Scalable Infrastructure Solutions: Cloud-based and on-premises deployment options to meet varying enterprise requirements for data security, processing power, and scalability.

Conclusion

How You Can Contribute:

Developers: Contribute to our codebase, help refine our models, build innovative applications on our platform, or become a miner on our Subnet 35 to actively participate in model training and optimization, while earning emissions for your contributions.
Enterprises: Partner with us to leverage advanced AI capabilities, driving innovation and efficiency in your operations.
Individual Users: Participate in our "Train-to-Earn" program, provide valuable feedback, and help improve our AI models.