Unlocking Creativity and the Global Potential through Shared Contribution Upside

A Blockchain, Today’s Technologies, and WiSE Relational Edge AI© risk/reward system protecting novelty

Abstract: This whitepaper proposes a framework for navigating the complex intersection of artificial intelligence (AI), intellectual property (IP) rights, and global collaboration. It addresses the challenges of AI-generated output, advocates for adaptive legal frameworks leveraging blockchain and today’s LLM, transformers, knowledge graphs, and edge AI, and introduces WiSE Relational Edge AI© as a tool for building a global shared upside network. The paper examines the unique perspectives of the US, Japan, China, and Russia, and concludes by emphasizing the importance of cultural and ethical considerations, workforce adaptation, and international cooperation to unlock the full potential of AI for the benefit of creators, funders/employers, consumers, and society as a whole, while establishing a robust risk/reward system to incentivize and protect continuous innovation.

The Challenge: The rapid rise of AI presents significant challenges to traditional notions of IP ownership, employment structures, and research funding models. AI-generated inventions and creative works blur the lines of authorship, creating uncertainty about who owns the resulting IP. Existing legal frameworks, largely built around human ingenuity, struggle to accommodate AI’s role in the creative process, leading to ambiguity and potential disputes. This necessitates a re-evaluation of legal definitions of inventorship and authorship to clearly delineate the contributions of humans and AI in the creation of new works, particularly in the context of a risk/reward system that incentivizes the continuous invention of novelty.

World Intellectual Property Organization (WIPO) Conversation on Intellectual Property (IP) and Artificial Intelligence (AI)1. WIPO has been actively engaged in discussions and initiatives exploring the challenges and opportunities at the intersection of AI and IP.
The US Copyright Office’s Guidance on Works Containing AI-Generated Material.2 The Copyright Office has issued guidance clarifying that copyright protection applies only to the human-authored aspects of works that contain AI-generated material.
The European Patent Office (EPO)’s Guidelines for Examination in the European Patent Office.3 The EPO has specific guidelines addressing the patentability of AI-related inventions, highlighting the complexities of determining inventorship and ownership in this domain.

Simultaneously, AI is transforming job roles and creating new forms of work, impacting traditional employment structures and necessitating a re-evaluation of employee rights and company ownership of AI-generated output. Clear contractual agreements and company policies are needed to define ownership, residual rights, and compensation related to AI-assisted work within a risk/reward framework. This includes specifying the scope of “work for hire” agreements in the context of AI, ensuring fair compensation for employees who contribute to AI-generated inventions or creative works, and addressing the potential for job displacement and the need for workforce retraining to adapt to the evolving landscape of AI-driven innovation.

Furthermore, funding basic AI research, crucial for long-term innovation, often faces challenges due to its high-risk, long-term nature. Bridging the gap between basic research and applied technologies requires strategic investment and innovative funding models that balance risk and reward. Governments must play a crucial role in supporting theoretical and basic research, fostering public-private partnerships, and promoting technology transfer to ensure that promising discoveries translate into real-world applications, while also incentivizing private investment in high-risk, high-reward AI research.

OECD AI Policy Observatory:4 This platform provides a wealth of information on AI policies and initiatives across OECD member countries, highlighting different approaches and opportunities for international cooperation.

Finally, the global race for AI dominance necessitates international cooperation on IP protection, ethical standards, and workforce development to ensure that the benefits of AI are shared equitably and responsibly. Harmonizing IP laws across borders, establishing shared ethical guidelines, and investing in education and skills development are crucial for fostering a global AI ecosystem that benefits all of humanity, while also creating a level playing field for innovation and promoting a risk/reward system that encourages continuous development and sharing of novel AI technologies.

UNESCO Recommendation on the Ethics of Artificial Intelligence.5 This recommendation provides a global framework for ethical AI development and use, emphasizing the importance of international cooperation in addressing the ethical challenges posed by AI.
Global Partnership on Artificial Intelligence (GPAI):6 This international initiative aims to support the responsible development and use of AI, grounded in human rights, inclusion, diversity, innovation, and economic growth.

Proposed Solution: To address these challenges, we propose a multi-faceted solution that includes adaptive legal frameworks, a global shared upside network, strategic funding initiatives, and international collaboration, all within a risk/reward system that incentivizes and protects the continuous invention of novelty. IP laws must evolve to accommodate AI-generated output, clarifying inventorship, authorship, and ownership, while also providing clear mechanisms for rewarding novelty and protecting the rights of innovators. This can be achieved by:

Integrating blockchain technology: To provide a transparent and immutable record of AI’s involvement in the creative process, enabling accurate attribution and facilitating licensing, while also establishing a secure and verifiable record of novelty.

INTA Bulletin: “Blockchain for Intellectual Property.”7
Harvard Business Review: “How Blockchain Could Help Protect Intellectual Property.”8

Enhancing algorithmic transparency: By recording the code and training data used in AI systems, making it easier to audit for bias and ensure accountability, thereby mitigating risks associated with AI development.
Edge AI and decentralized ownership: Managed through blockchain-based solutions, allowing for a more granular and accurate attribution of ownership based on the specific contributions of both the AI model and the human user at the edge, while also enabling a more distributed and equitable distribution of rewards for innovation.

NVIDIA: “What is Edge AI?”9
State of the Edge Report.10

Furthermore, WiSE Relational Edge AI©, with its ability to analyze vast amounts of data and identify valuable innovations, can be leveraged to build a global shared upside network that incentivizes and rewards the continuous invention of novelty. This network would connect creators with those who need their solutions, foster collaboration, and personalize the innovation process to ensure that AI benefits everyone, while also providing a platform for recognizing and rewarding novel contributions. WiSE Relational Edge AI© can also play a crucial role in facilitating international cooperation by identifying cross-border innovation opportunities, harmonizing IP frameworks, and promoting cultural understanding, thereby creating a global ecosystem that supports and rewards the continuous development of novel AI technologies.

It’s important to highlight that WiSE Relational Edge AI© is built upon the foundation of the Thurston Geometrization Conjecture and 35 years of applied mathematics research by Nathaniel Thurston, the son of renowned mathematician William Thurston. This deep connection to advanced mathematical concepts provides WiSE Relational Edge AI© with a unique capability to understand and analyze complex relationships within data, enabling it to identify truly novel and impactful innovations. By incorporating this technology into the proposed framework, we can tap into its potential to drive significant advancements in various fields, from drug discovery to personalized healthcare, while ensuring that the benefits of these innovations are shared widely and equitably.

IPVIVE | WiSE Edge Relational AI©11
Nathaniel Thurston PhD Thesis 12+ Code13
William Thurston14
Geometry and Imagination15
eFPGA16, potential WiSE Applied Tech Partner©
UP ELECTROMODS17, a WiSE Solution Partner© focused on first fundamentals and matching necessity/competitive triggers and novelty/creative triggers to talent and mutually profitable ecosystem customers and partners.

Use Cases and Value: This framework offers significant value to various stakeholders within a risk/reward system that promotes continuous innovation. Creators can benefit from clear IP ownership, access to a global marketplace for their innovations, and opportunities for collaboration, while also being incentivized to continuously develop and share novel AI technologies. Funders and employers can leverage AI to drive innovation, increase efficiency, and access a global pool of talent, while also benefiting from a system that rewards and protects their investments in AI research and development. Consumers and society can benefit from access to AI-powered solutions that address societal challenges, improve quality of life, and promote economic growth, while also enjoying the fruits of a continuous stream of novel AI technologies. Specific use cases include:

AI-assisted drug discovery: Accelerate the development of new drugs and therapies by leveraging AI to analyze vast datasets and identify promising candidates, while also incentivizing researchers to explore novel approaches and share their discoveries.
AI-generated news articles: Automate the creation of news content, freeing up journalists to focus on investigative reporting and in-depth analysis, while also enabling the development of new forms of AI-powered journalism that can provide more personalized and engaging news experiences.
AI-driven automation in manufacturing: Increase efficiency, reduce costs, and improve quality in manufacturing processes through AI-powered automation, while also incentivizing the development of novel AI-driven manufacturing technologies that can create new jobs and industries.
National AI strategies for defense and security: Develop AI-powered tools for national security purposes, such as threat detection, intelligence analysis, and cybersecurity, while also promoting international cooperation and ensuring that AI is used responsibly and ethically.

The EU’s Artificial Intelligence Act18.
The White House’s Blueprint for an AI Bill of Rights19.

AI-powered personalized healthcare: Provide personalized treatment plans and improve patient outcomes by leveraging AI to analyze individual health data and tailor treatments accordingly, while also incentivizing the development of novel AI-powered healthcare technologies that can improve access to care and reduce healthcare costs.

Potential Roadmap and its Risks/Opportunities: Implementing this framework requires a phased approach with a focus on developing adaptive legal frameworks, fostering a culture of collaboration, and investing in research and development, all within a risk/reward system that encourages continuous innovation and protects novelty. A potential roadmap includes:

Phase 1: Legal and Ethical Foundations (2025-2030): Develop clear legal frameworks for AI-generated IP that incorporate a risk/reward system for protecting novelty, establish ethical guidelines, and foster international cooperation on AI governance. This includes addressing issues such as data privacy, algorithmic bias, and the responsible use of AI in sensitive areas like healthcare and defense.
Phase 2: Building the Global Network (2030-2035): Develop and deploy WiSE Relational AI© to create a global shared upside network that incentivizes and rewards the continuous invention of novelty, connecting creators, funders, and consumers. This involves building a robust and secure platform for sharing innovations, facilitating collaboration, and promoting knowledge transfer, while also establishing mechanisms for recognizing and rewarding novel contributions.
Phase 3: Scaling and Integration (2035-2040): Scale the network, integrate AI solutions into various sectors, and address societal impacts through workforce adaptation and education initiatives. This includes investing in education and training programs to prepare the workforce for the changing nature of work in the age of AI and addressing potential job displacement through reskilling and upskilling initiatives, while also promoting the development and adoption of novel AI technologies that can create new jobs and industries.

This roadmap presents both risks and opportunities. Risks include potential misuse of AI, exacerbation of existing inequalities, and challenges in achieving international consensus. Opportunities include accelerated innovation, economic growth, improved quality of life, and solutions to global challenges.

Conclusion: The AI revolution presents a unique opportunity to unlock creativity and drive global progress. By embracing adaptive legal frameworks that incorporate a risk/reward system for protecting novelty, fostering collaboration, and investing strategically, we can build a future where the benefits of AI are shared by all. This requires a commitment to ethical development, international cooperation, and a recognition that the full potential of AI can only be realized through a shared commitment to innovation, responsibility, and a vision of a more equitable and prosperous future for all.

Footnotes:

Unlocking Creativity and the Global Potential through Shared Contribution Upside

Unlocking Creativity and the Global Potential through Shared Contribution Upside

A Blockchain, Today’s Technologies, and WiSE Relational Edge AI© risk/reward system protecting novelty

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