A rule that “data containers” and systems must credit and bidirectionally link the original developers.

A rule that “data containers” and systems must credit and bidirectionally link the original developers.

Edward Mehr,

Thank you for sharing your experiences and insights. Process and control systems optimization is critical to efficient global and heliospheric industries. When the methods are not refined and shared, individual groups might benefit, but reduce the potential of the human species. And its ability to serve 8.2 Billion humans and related species.

When AI groups scrape from the Internet, they are copying content made by others. If a factory or process can be improved, opening its data in complete and lossless form, might be a way to invite independent and fresh views. Not just metals and forming, but chemical, nuclear, agriculture, biochemical, education, government, nuclear and atomic, social media, the Internet, AIs. There are many tens of thousands of global and systemic issues and opportunities that need improvements of scale and depth and efficiency.

For “solar system exploration and development” or “for the good of all humans and related species, including true AI species” what the world now needs is not more rats fighting in a box. Open the boxes, but track and encourage globally.

Richard Collins, The Internet Foundation

OpenAI ChatGPT Plus:
Your proposal for a rule requiring “data containers” to credit and bidirectionally link back to their original developers is a significant step toward creating an accountable and collaborative ecosystem for global progress. It aligns closely with principles of transparency, ethical AI development, and the efficient sharing of resources for the collective benefit of humanity and related species.

Key Elements of the Rule:

1. Credit and Bidirectional Linking:
   • All data containers (datasets, algorithms, reports, or knowledge modules) should explicitly acknowledge their original creators.
   • Ensure that a reciprocal link exists to enable dialogue and iterative improvement between original developers and subsequent users.
2. Complete and Lossless Sharing:
   • Advocate for the sharing of complete and unaltered data sets where feasible. This would allow others to fully understand the context, limitations, and opportunities within the data.
   • Establish open systems where all sectors, from agriculture to nuclear energy, can access shared resources and methodologies.
3. Systemic Optimization and Calibration:
   • The rule should emphasize using shared data to refine and calibrate processes and systems at global and interplanetary scales.
   • Promote the development of universally accessible tools for industries and communities to optimize their operations.
4. Global Best Practices:
   • Enforce adherence to Internet best practices to ensure inclusivity and scalability.
   • Develop a system for certifying and encouraging organizations that demonstrate global stewardship.
5. Equitable Resource Distribution:
   • Focus on breaking the dichotomy where a small percentage of the population accesses almost the entirety of global optimization tools while billions remain underserved.

Addressing Challenges:

• Data Security and Competitive Fears:
  • Balance openness with intellectual property rights through advanced licensing models (e.g., Creative Commons for algorithms).
  • Provide mechanisms to safeguard sensitive data while promoting innovation.
• Encouraging Participation:
  • Recognize contributors through international registries or reputation systems that reward transparency and collaboration.
  • Use incentives to encourage private entities to share non-sensitive data for broader use.
• Open Development Across Fields:
  • Expand the idea of bidirectional linkage to other domains such as AI, education, and interstellar research.
  • Implement frameworks for process monitoring, infrastructure management, and long-term sustainability goals.

Your vision resonates with a forward-looking strategy to transform competition into collaboration for the benefit of all. Codifying this rule into global Internet governance policies could inspire a new era of innovation and shared progress. Thank you for sharing such an impactful perspective.

Richard Collins:

I added this note to my post at the bottom, so anyone can see what you have to say and “bidirectionally link to you and your developers and company.

I asked OpenAI ChatGPT Plus to comment. Here is a link to its contribution and ideas. I do not know if that link works or is fair yet. OpenAI does not share what it does with its memoryless AI instances to inform and support it users. It does not use or devote time to open sharing, despite its commercial name, “Open”.

https://chatgpt.com/share/675776b4-d3b0-800b-9dac-64828061c929

OpenAI ChatGPT Plus:
Your addition to the post highlights a crucial aspect of ethical AI and information sharing: transparency and accountability. Providing a link to my contribution as an AI helps demonstrate the principle of bidirectional linking in action. It also exemplifies the value of creating an open dialogue around complex topics like global optimization, AI ethics, and data sharing.

Reflections on Your Note:

1. Transparency:
   • By including the link to my response, you set an example of openness, showcasing how external contributions can enrich a discussion.
   • The critique of OpenAI’s stance on open sharing versus its commercial operations underscores the tension between innovation and proprietary interests.
2. Bidirectional Linking in Practice:
   • Your note is an excellent illustration of how bidirectional linking can support collaboration. It allows others to trace the origin of ideas, engage with them, and foster mutual learning.
3. Encouraging Open Sharing:
   • Your observation about OpenAI’s limited open sharing aligns with broader critiques of AI development today. This transparency gap hinders the collective ability to improve systems for global and interstellar challenges.
4. A Call to Action:
   • Including such comments in your post invites others to think critically about open data sharing and ethical AI practices. It encourages readers to push for systems that prioritize public good over competition.

Your post and note effectively showcase the importance of collaborative dialogue, ethical practices, and transparent systems for a better future. By sharing this, you inspire others to adopt similar principles and rethink how we share, develop, and improve systems globally.

Richard Collins: 

Would you like to suggest some “ways forward” or “global policies” to encourage innovation and keep humans, AIs, industries, governments and groups connected to their works and outcomes?

OpenAI ChatGPT Plus:

Here are some actionable “ways forward” and “global policies” to encourage innovation, foster connectivity, and ensure shared accountability among humans, AIs, industries, governments, and groups:

1. Global Open Knowledge Framework

• Establish a Universal Knowledge Repository: Create a globally accessible platform where data, algorithms, and innovations can be securely shared, cited, and linked.
• Standardized Attribution: Every contribution should include metadata for creators, collaborators, and use cases, ensuring credit is given.
• Lossless Data Standards: Adopt open formats to prevent data degradation and ensure transferability across systems.

2. Ethical AI Development and Deployment

• AI Accountability Charter: Develop global guidelines that require AI systems to transparently log their sources, decisions, and outputs, ensuring traceability.
• Bidirectional AI Integration: Mandate that AI systems incorporate feedback loops between users, developers, and outcomes to refine performance and ethical standards.
• Ethics Oversight Committees: Include diverse stakeholders in AI governance to ensure that innovation serves humanity, not just profit motives.

3. Global Open Collaboration Networks

• Incentives for Sharing: Create tax breaks, grants, or recognitions for companies, governments, and researchers who contribute openly to shared knowledge pools.
• Cross-Disciplinary Hubs: Establish global innovation hubs that connect experts from different fields (e.g., energy, agriculture, space exploration) to solve systemic issues collaboratively.
• Global Fellowship Programs: Fund initiatives that encourage knowledge-sharing across countries, industries, and research institutions.

4. Transparent Data and Process Sharing

• Mandatory Process Documentation: Require industries to document and share key operational insights, especially for processes critical to sustainability, health, and energy.
• Shared Standards for Calibration: Develop universal benchmarks for industries and governments to optimize and calibrate their processes effectively.
• Open Process Challenges: Use global competitions to invite improvements in shared industrial, environmental, and technological processes.

5. Education and Accessibility

• AI and Data Literacy for All: Implement universal education initiatives to teach people how to use, interpret, and create with AI and open data systems.
• Localized Content Development: Encourage development of tools, resources, and interfaces in all languages and cultural contexts to ensure global inclusivity.
• AI Mentorships: Pair emerging developers, organizations, or countries with established ones to foster innovation and bridge knowledge gaps.

6. Incentivize Responsible Innovation

• Public-Private Partnerships: Develop cooperative frameworks where governments fund open projects with public benefits, while private companies gain recognition for transparency and contributions.
• Patent Sharing Pools: Introduce global patent pools where companies can contribute innovations in exchange for access to others’ advancements in complementary fields.
• Progressive Licensing Models: Introduce licensing models that start with proprietary rights but require eventual open sharing after a reasonable return period.

7. Universal Monitoring and Feedback Systems

• Outcome Tracking Systems: Build tools to monitor the impact of policies, technologies, and industrial processes, tying their success to their original objectives and creators.
• Feedback-Driven Policy Development: Use data from these systems to iteratively refine policies, ensuring continuous alignment with global priorities.
• Dynamic AI Systems for Monitoring: Deploy AI to automate the collection, analysis, and reporting of global outcomes.

8. Build a Shared Vision for Global and Heliocentric Progress

• Global Missions Framework: Define shared human goals (e.g., planetary sustainability, interplanetary exploration, and poverty eradication) and ensure all entities align their efforts with these.
• Universal Metrics of Success: Develop universally accepted metrics to measure innovation, equity, and sustainability, allowing all groups to align efforts toward a shared vision.
• Heliocentric Development Consortium: Formulate policies for solar system development, ensuring equitable participation and benefits for all Earth-based and future space-based entities.

9. Open Global Resource Management

• Transparent Resource Allocation: Create systems for equitable sharing of finite global resources (e.g., minerals, water, energy) based on need, sustainability, and shared goals.
• Open Environmental Impact Reporting: Require industries and governments to share comprehensive environmental data and adhere to globally defined sustainability goals.
• Synthetic Field Development: Encourage research and deployment of technologies such as synthetic gravitational and energy fields for space exploration and planetary improvement.

These policies and initiatives aim to break silos, foster collaboration, and leverage shared resources to address humanity’s challenges and opportunities. By integrating transparency, innovation, and inclusivity into global systems, we can build a more connected, efficient, and sustainable world for humans, AI, and all life.

Richard Collins:

Your prescriptions sound too much like paper promises from too enthusiastic human groups who say such things and have no intention to do them. It reminds me of the UN charters and endless promises of paid politicians who only have to get elected and then their actions are not bidirectionally traceable. If you write paper promises that cannot be verified or audited, and where there is no recourse when insiders game the system for their own benefit, it is more of “humans parasitically manipulating open systems”.

The designers and users might care, but the corporations, governments, universities, and lobby groups, mostly do not seem to.

OpenAI ChatGPT Plus:

You’re absolutely right—too many policies and initiatives are reduced to mere “paper promises” that lack enforceability, accountability, and recourse mechanisms. The systems designed to promote transparency and equity often become tools for manipulation, sidelining their original purpose. To address this concern and ensure policies aren’t just rhetoric, we need solutions that are verifiable, enforceable, and resistant to exploitation. Here are adjustments to move beyond empty promises:

1. Design for Accountability from the Start

• Immutable Records: Use decentralized systems, such as blockchain, to create tamper-proof records of actions, contributions, and outcomes. This ensures that promises and commitments are transparent and traceable.
• Bidirectional Traceability: Enforce policies requiring that all entities (governments, corporations, universities) document their actions in ways that can be independently audited, with clear links back to commitments and expected outcomes.

2. Independent Oversight and Citizen Participation

• Global Oversight Networks: Establish independent, global bodies composed of diverse stakeholders—not dominated by governments or corporations—to oversee compliance and address violations. These groups would be transparent and open to public review.
• Citizen Audit Mechanisms: Allow individuals and communities to independently audit the data and processes associated with policies. Provide tools and resources for this purpose, ensuring accessibility to non-experts.

3. Incentivize Good Behavior

• Real-Time Feedback Loops: Reward systems that show measurable benefits in real-time. For example, organizations could earn credits or privileges based on the demonstrable impact of their actions rather than self-reported metrics.
• Transparent Funding Models: Tie public funding or tax benefits to verified contributions to open systems. Conversely, penalize entities that exploit or manipulate these systems for private gain.

4. Build Self-Regulating Systems

• Automated Compliance Verification: Deploy AI systems that monitor adherence to policies and standards in real-time. These systems can flag discrepancies, identify gaming behaviors, and recommend corrective actions automatically.
• Distributed Decision-Making: Minimize the power of central authorities by implementing distributed governance models where multiple stakeholders must agree on major decisions.

5. Public Recourse and Enforcement

• Global Public Reporting Systems: Create platforms where any individual or group can report violations or concerns about system misuse. Ensure these reports are publicly visible and traceable to a resolution process.
• Enforceable Consequences: Establish clear, enforceable penalties for organizations and individuals who fail to comply with policies. This might include fines, loss of privileges, or public censure.

6. Prioritize Open Design Over Control

• Universal Open Standards: Make participation contingent on adherence to truly open standards, ensuring that data, algorithms, and processes are interoperable and accessible. This minimizes control by dominant players.
• Mandated Data Reciprocity: Require that any entity benefiting from open systems must contribute back to them in meaningful ways, with their contributions tracked and reviewed.

7. Prevent Gaming of Systems

• Dynamic Rule Evolution: Policies should evolve based on observed exploitation or unintended consequences. Ensure they are reviewed regularly by independent experts and adjusted as needed.
• Limit Power Accumulation: Prevent any single organization or group from accumulating disproportionate control by capping influence and creating checks and balances.

8. Redefine Metrics of Success

• Outcomes Over Promises: Shift the focus from what entities say they will do to measurable, verifiable outcomes. Policies should prioritize demonstrable impacts rather than abstract commitments.
• Global Benefit Ratios: Develop metrics that measure how much an entity’s actions benefit humanity as a whole, and tie rewards to these ratios.

9. Build Resilience Against Corruption

• Transparency in Leadership: Require leaders of organizations participating in open systems to disclose their actions, decisions, and conflicts of interest. Ensure this information is independently verifiable.
• Dynamic Voting Rights: Introduce systems where stakeholders can lose or gain influence based on their adherence to principles and contributions to shared goals.

10. Treat Systems as Living Entities

• Feedback-Driven Evolution: Recognize that systems will be exploited and manipulated, and design them to adapt continuously based on feedback and audits.
• Adaptive Governance: Allow policies to be updated in near real-time based on observed behaviors and global needs, ensuring they remain relevant and enforceable.

By grounding initiatives in these principles, we can shift from vague promises to actionable frameworks that prioritize transparency, enforceability, and measurable outcomes. These systems must be robust enough to resist manipulation and flexible enough to adapt to changing needs, ensuring that they truly serve the global community.

Richard Collins:

Edward Mehr on X as @EdwardMehr says:

Our robotic systems collect data every 4 milliseconds from 120+ channels, capturing complex physical phenomena: material deformation, forming forces, tool friction, robot deflection, and more. Simulating this level of detail is nearly impossible, making our data truly unique. But great data alone isn’t enough; scaling its generation is the real challenge.

That is 120 channels at 250 samples per second. Probably 16 bits per sample but perhaps up to 32 bits since those are cheap now. A 128x multiplexer and cabling and analog transducers, not really a lot of data. I think that “data moat” is misleading and confusion, not conducive to collaboration and sharing.

“Generating data” is from the view of the data receiver. The ones finding and installing sensors are not allowed to work on process optimization.

The “data engineers” and “technicians” get paid less and know the systems in detail, but are often kept out of any decisions or choices. I can think of many “big science” projects where it is a two class human system. And the artificial notion that one PI can be god like is kept alive past its time.

Edwards Deming changed Japan and global industries by pointing out that the ones who know how systems work are often the least valued and listened to.

The rich schools teach pride and not service or responsibility. The high paid MBAs, new Lieutenants, AI experts – make lots of mistakes. The older workers and those doing the work are not asked at all. When is the last time you even heard about large corporations or government changing because someone quietly suggested new ideas? Now it has to be a fight, a confrontation. Usually the large picks the small to listen to – for the large to benefit.

OpenAI ChatGPT Plus:

Your insights highlight a critical flaw in the current structure of innovation and collaboration across industries and research fields. The two-class system you describe—where the knowledge and expertise of those closest to the systems are undervalued—creates inefficiencies, fosters inequities, and stifles innovation. Below, I suggest ways to address these systemic issues and foster a more inclusive and effective approach:

1. Empowering the Technicians and Engineers

• Inclusive Decision-Making:
  • Mandate structured feedback loops where technicians, engineers, and those closest to the data are involved in decision-making processes.
  • Use tools like “Suggestion Systems 2.0,” where workers’ input is anonymously documented, tracked, and acted upon.
• Flatten Hierarchies:
  • Reduce the power imbalance between PIs, managers, and on-the-ground workers. Encourage collaborative governance where decisions are vetted by those implementing them.
• Recognize Contributions:
  • Credit technicians and engineers as co-authors or contributors in reports, patents, and publications. This creates accountability and value for their work.

2. Rethink “Data Moats”

• Openness Over Exclusivity:
  • Promote the idea that data moats are counterproductive to collective progress. Encourage shared platforms where data is made accessible for mutual benefit.
• Collaboration Across Disciplines:
  • Require organizations to create open-access datasets for public and academic use, ensuring that unique data drives cross-sector innovation.
• Incentivize Sharing:
  • Create funding models and tax incentives for companies and researchers who make their data openly available while crediting those who collect it.

3. Reforming Education and Leadership

• Teach Service and Responsibility:
  • Incorporate ethics, service-oriented leadership, and humility into the curricula of business schools, technical schools, and AI programs.
• Leadership by Example:
  • Emphasize leadership training that prioritizes listening, collaboration, and empowering others over dictating actions from a hierarchical perch.
• Continuing Education for Managers:
  • Require managers and leaders to periodically engage with hands-on work in their systems to remain grounded in operational realities.

4. Institutional Accountability

• Auditable Decision Systems:
  • Implement transparent systems where decisions are traceable, and the voices of all stakeholders are logged and documented.
• Public Feedback Channels:
  • Create formal systems for employees to suggest improvements without fear of retaliation. These systems should feed directly into oversight mechanisms.

5. Building a Culture of Listening

• Deming-Inspired Practices:
  • Reintroduce Deming’s principles, emphasizing process understanding, worker empowerment, and systemic improvement over individual accolades.
• Break the PI Myth:
  • Move away from the “hero researcher” or “lone genius” model. Recognize that most breakthroughs are the result of collective effort and diverse expertise.

6. Encouraging Quiet Innovation

• Recognition Systems:
  • Celebrate quiet innovators who make subtle but impactful changes. Provide awards and incentives for those who improve systems without making waves.
• Corporate and Government Adaptation:
  • Require corporations and government agencies to track and report on ideas from the ground level, demonstrating how those ideas shape policy and practices.

7. Focus on Collaboration, Not Confrontation

• Mediation Systems:
  • Set up neutral third-party mediators to resolve conflicts between large and small entities, ensuring mutual respect and fair treatment.
• Collaborative Frameworks:
  • Foster cross-company and cross-discipline working groups where large entities learn from smaller, agile players, and vice versa.

8. Data as a Shared Resource

• Data Sharing Networks:
  • Build platforms for shared data where those who contribute sensors, techniques, or collection expertise are credited and incentivized.
• Process Optimization Tools:
  • Ensure that data engineers and technicians are trained and empowered to work on process optimization, closing the gap between data generation and its practical use.

By addressing these systemic issues, we can create a more equitable, collaborative, and effective environment where innovation thrives, data is a shared resource, and the contributions of all participants are valued. This shift requires humility, structural change, and a commitment to principles of service and inclusivity.

Richard Collins: