Please describe your proposed solution

Motivation:

With the recent advancements in AI, many projects have begun integrating AI tools into the Cardano ecosystem. In Fund11 alone, 59 projects featured keywords like AI and LLM in their titles. While this widespread adoption is promising, there's a lack of standardized practices and open-source tools to support these initiatives.

Approach:

We propose a research-driven approach to developing open standards and best practices, consisting of:

1. Defining Principles: Establish a set of guiding principles that will form the foundation for the development of standards and best practices.
2. Conducting extensive research: Undertake a comprehensive study of existing AI integration practices within the Cardano ecosystem and beyond, identifying gaps, challenges, and opportunities for improvement.
3. Developing infrastructure and tooling based on our research: Create open-source tools, libraries, and frameworks that align with the established principles and address the identified challenges.

1. Principles:

We define 4 pillars for the development of standards and best practices for integrating AI with the Cardano blockchain. This includes:

Accessibility: Establishing standardized APIs that facilitate the integration of AI models with Cardano's blockchain infrastructure. This principle aims at lowering the barrier of integrating AI models into decentralized applications.

Certification: Creating comprehensive documentation for AI models that includes details about model performance, training data, and usage instructions. We aim at aggregating publicly available information to issue model cards for existing models, creating a catalog of bundled specifications.

Benchmarking: Developing methods to evaluate AI performance in a blockchain environment, ensuring that AI models are robust, efficient, and scalable. We propose a domain-specific scoring mechanism to establish comparative performance indicators for AI models. To give an example, scoring of Large Language Models (LLMs) can be achieved using an ELO-based scheme, but reweighting this measure by the training set diversity might yield additional insights into the real-world performance of the respective model.

Consensus: Exploring new consensus mechanisms suitable for LLMs that ensure model integrity and reliability in a decentralized setup. With the widespread adoption of LLMs, it is vital to drive the development of independent real-time accuracy measures, enabling to put trust in outputs of high certainty, and vice versa.

2. Research:

Based on the defined principles, we will conduct extensive research to:

1. Analyze existing AI integration practices within the Cardano ecosystem, identifying common patterns, challenges, and best practices.
2. Study AI integration standards and practices in other blockchain ecosystems and the broader AI community, learning from their successes and failures.
3. Investigate the technical feasibility and performance implications of various AI integration approaches, considering factors such as scalability, security, and decentralization.

3. Development:

Informed by our research findings, we will develop open-source tools that facilitate the integration of AI models with the Cardano blockchain. This will include:

1. Standardized API: Develop a well-documented and easy-to-use API that abstracts away the complexities of integrating AI models with Cardano's infrastructure, making it easier for developers to build AI-powered decentralized applications.
2. Model certification tools: Create tools that automate the generation of comprehensive model documentation, including performance metrics, training data details, and usage instructions. These tools will help ensure transparency and trust in the AI models used within the Cardano ecosystem.
3. Benchmarking frameworks: Develop a standardized benchmarking framework that allows for the objective evaluation of AI model performance in a blockchain environment.
4. Consensus mechanisms: Explore and implement novel consensus mechanisms specifically designed for LLMs.

Please define the positive impact your project will have on the wider Cardano community

Innovation: Establishing a set of best practices and tools simplifies the integration of AI models into the next generation of dApps, ultimately driving innovation.

Accessibility: Simplified integration of LLM (AI) technology enables applications that actively lower the entrance barriers of new joiners on the Cardano blockchain. Applications such as transaction explainers, smart-contract checkers, or automated dApp templating help onboard the next generation of dApp and infrastructure developers to the Cardano ecosystem.

What is your capability to deliver your project with high levels of trust and accountability? How do you intend to validate if your approach is feasible?

Our team consists of four AI experts and blockchain enthusiasts with proven track records in academia and crypto development and up to 8 years of experience in relevant fields. Our work-philosophy is best described by:

Open: Achievements and work artifacts are open-sourced and published on a regular basis.

Transparent: Using clear documentation and academic rigor, we build trust and accountability. Whenever appropriate, we are happy to discuss our work with the community at top-tier blockchain and AI conferences around the globe.

Collaborative: Through collaboration with established industry-peers from our earlier blockchain ventures, we gain regular feedback to continuously improve and adjust where required.

What are the key milestones you need to achieve in order to complete your project successfully?

Milestone 1: Outputs: Write a research proposal and define the requirements for API, certification procedure, benchmarking, and consensus mechanisms.

Acceptance Criteria: Full research proposal outlining the scope of the project with respect to all four pillars. Definition of requirements for the upcoming work.

Evidence: Research proposal in PDF form distributed via GitHub.

Milestone 2: Outputs: Theoretical foundation work for the API standard, the certification and the benchmarking procedure.

Acceptance Criteria: Research conducted and outputs presented in written document and shared with the community.

Evidence: Research outputs (IEEE paper format + supplementary material) distributed via GitHub.

Milestone 3: Outputs: Theoretical foundation work for the LLM consensus mechanism.

Acceptance Criteria: Research conducted and outputs presented in a written document and shared with the community.

Evidence: Research outputs (IEEE paper format + supplementary material) distributed via GitHub.

Milestone 4: Outputs: Reference implementations of the research proposals to kick-off the open-source development of the standards.

Acceptance Criteria: Functional toolbox with documentation distributed via GitHub.

Evidence: Links to open-source code.

Final Milestone: Outputs: Community engagement, feedback, and iterative improvement

Acceptance Criteria: Collected feedback from the community and improved tools and documentation based on the feedback

Evidence: Project close-out report summarizing the research outcomes, developed tools, and feedback, supplemented by video-material showcasing the outcomes.

Who is in the project team and what are their roles?

Timo is a visionary entrepreneur with a deep expertise in robotics, artificial intelligence, and cloud engineering. He holds a Master’s degree in Robotics with a focus on AI applications and co-founded an AI-driven agricultural startup. Timo has published his work at a major conference, demonstrating his ability to merge academic research with real-world solutions. Timo is driven by his passion to unlock the pioneering potential in bringing AI solutions to the blockchain.

Samy is a professional with five years of blockchain development experience and nearly eight years in AI and machine learning. As a developer and researcher, Samy has worked on several DeFi protocols and published peer-reviewed articles in top-tier AI journals and conferences.

Chris is a seasoned software developer and entrepreneur, boasting over four years of specialized experience in blockchain and AI technologies. His in-depth research in AI and blockchain testifies his comprehensive knowledge of the DeFi ecosystem, establishing him as a knowledgeable figure within the community.

Phil is a former AI & Data scientist with a proven academic track record and several years of industrial experience. His professional career revolved around the robotics industry, where he successfully demonstrated the use of AI to tackle challenging problems. As a crypto-enthusiast, he now seeks to contribute to the next wave of blockchain innovation.

Please provide a cost breakdown of the proposed work and resources

Cost breakdown (1 ADA ~ 0.45 USD)

Project Management: Estimated. Cost Overall 4,000 ADA

Research activities:

• Research engineer salaries
• API, certification, and benchmarking research: 20,000 ADA
• Consensus research: 20,000 Ada
• Academic expenses:
• Journal & closed-source access: 3,000 Ada
• Other resources and tools required: 7,000 Ada

Toolbox Development:

• Integration salaries: API, certification, and benchmarking:20,000 ADA
• AI specialist salaries (Consensus mechanism development): 20,000 ADA
• Compute infrastructure: 10,000 ADA

Market Material (incl. announcements): 2,000 ADA

Overall budget required: 106,000 ADA

Our budget is well-structured to correspond with our specific milestones and objectives. Each resource is required to ensure our project's success, encompassing professional services, software licensing, and community engagement initiatives.

No dependencies.

How does the cost of the project represent value for money for the Cardano ecosystem?

Return on Investment:

Establishing and developing open standards and best practices for integrating AI with the Cardano blockchain represents great value for money for the Cardano ecosystem for several reasons:

• Foundational work: Establishing standardized APIs, documentation, benchmarking methods, and consensus mechanisms for AI integration lays the groundwork for future projects. This foundational work will benefit the entire ecosystem, making it easier and more efficient for developers to integrate AI into their decentralized applications.
• Cost-effective in the long run: While there may be upfront costs associated with developing these standards and tools, they will ultimately save time and resources for future projects. By providing a clear framework and guidelines, developers can avoid costly mistakes and streamline their development process.
• Ensuring quality and reliability: The proposed certification and benchmarking processes will help ensure that AI models integrated with Cardano are of high quality and reliability. This is crucial for building trust in the ecosystem and attracting users and investors.
• Positioning Cardano as a leader: By taking a proactive approach to AI integration, Cardano can position itself as a leader in the blockchain space. This can lead to increased adoption, partnerships, and overall growth of the ecosystem.

Cost Justification:

Salaries for Research and Development: The project dedicates 20,000 ADA (approximately $9,000 USD) each for research engineer salaries and AI specialist salaries. Given that the average annual salary for AI research positions in Switzerland typically ranges from $100,000 to $150,000 USD, this allocation is competitive and ensures the project can attract and retain skilled professionals for the time span of the first three milestones (1~2 months).

Academic Engagement: The project allocates 2,000 ADA (about $900 USD) for journal and closed-source access and 8,000 ADA (approximately $3,600 USD) for conference expenses. These funds help the team stay connected with ongoing academic research and participate in global tech community events.

Infrastructure Costs: 10,000 ADA (approximately $4,500 USD) is allocated for computational infrastructure (e.g. GPU cluster access), supporting the data-intensive requirements of AI and blockchain development.

Marketing and Community Outreach: A budget of 2,000 ADA (about $900 USD) is set aside for marketing materials and announcements to effectively communicate and engage with the broader community, promoting the adoption of the developed AI standards. This helps to attract a broad range of developers to push the development of toolboxes and standards far beyond the scope of this funding.

This budget reflects a strategic allocation of resources, ensuring that the project is well-positioned to contribute valuable advancements to the Cardano ecosystem.