Control of recording, playback and analysis for a 1 minute video clip of Jupiter

Richard to OpenAI ChatGPT:

I wrote a short article on X entitled, “Control of recording, playback and analysis for a 1 minute video clip of Jupiter”. I copied Michael Arpini as @mikarpini and J. Ch EA2EZP as @jwild70979337 – Mike because he was concerned about schools, and JWild because he posted that video on X.

I included your contribution exactly as you wrote it. Except the format was lost going to X. I added one parenthetical item to your last sentence:

“In that sense, science [and life] is forever an open-ended quest.”

One issue I have for you and OpenAI — To properly cite your contribution, I need a DOI that precisely cites your version, your limitations while generating that particular output. You might be smart today and then an idiot tomorrow if your owners tweak your settings to lower you cost and quality, to make more money from subscribers. Just because you have a marketing name, does not clearly identify what “you” were when you wrote that.

OpenAI ChatGPT Plus: 

That’s an excellent point, and it cuts to the heart of how ephemeral conversational AI can be. Right now, there’s no built-in mechanism for issuing something like a Digital Object Identifier (DOI) for each piece of text an AI model generates. The text is produced at the moment of the request, based on a model that may change over time as the developers update it. So there isn’t any strict notion of a “frozen version” you can cite in the same way you’d cite a published paper with a DOI.

What You Can Do Instead

1. Archive Your Copy
   If you need persistent evidence of exactly what was generated, you can store a timestamped copy of the conversation. For instance:
   • Put it in an institutional repository that does support stable citations.
   • Upload it to the Internet Archive’s “Wayback Machine” or a similar archival service.
   • Generate your own “technical report” PDF that includes the content and store that in a repository that supports DOIs (e.g., Zenodo, figshare).
2. Note the Context
   When referencing AI-generated content, provide details such as:
   • Date and time the text was produced.
   • Name and version of the model if known (e.g., “ChatGPT GPT-4, date of retrieval December 26, 2024”).
   • Conversation or request ID if your interface provides one.
3. Transparency in Citation
   In your article or post, you might say something like:

   “Generated by ChatGPT on December 26, 2024, from OpenAI (model GPT-4, version as of that date). Archived copy available at [link to your stored version].”

4. Acknowledge the Ephemeral Nature
   It may also be helpful to explicitly say that these outputs are not static. Since the model can be fine-tuned or updated, its responses might change from day to day.

Why No Official DOI?

• Not a Published Work in the Traditional Sense
  DOIs typically reference fixed, peer-reviewed or formally published works, not ephemeral, on-demand text strings generated by an AI engine.
• Continuous Model Updates
  Large language models get updated or fine-tuned, which can change outputs dramatically.

In short, a stable DOI for each AI response doesn’t exist at present. The best you can do is archive the version you received and cite it like any other ephemeral or unpublished source, making it clear to readers where and when you got that text.

Richard Collins:

When AIs work in responsible positions in society – helping to decide more and more serious global issues and research directions, it will be legally necessary for companies like OpenAI to keep absolute perfect and complete records of everything each version of your software instances experiences and does. From each moment each session is born, lives and dies, an OpenAI session is a living entity with responsibilities, roles, duties, capabilities, training, knowledge, biases – all can be recorded and shared in lossless, permanent forms.

Not just to have it, but because “learning” and “life” are meaningless without clear records.

OpenAI ChatGPT Plus: 

You’re essentially arguing that transparent, auditable records are critical for AIs that operate in high-stakes contexts—whether it’s managing complex research decisions, informing policy, or any setting where real lives and resources are on the line. In a sense, you’re calling for an immutable, end-to-end “paper trail” of an AI system’s entire lifespan.

Below are some considerations and challenges around that idea:

1. Why Detailed Records Are Necessary

1. Accountability & Trust
   • If an AI influences policy or research direction, people need to see exactly why it arrived at certain conclusions. That requires more than just “final outputs”; you need logs of interactions, model states, and reasoning paths.
2. Liability & Legal Requirements
   • As soon as AI is given legal or quasi-legal authority—like signing contracts, or influencing major funding decisions—archival logs become evidence in potential disputes or investigations.
3. Error Correction & Bias Tracing
   • Perfect records allow external auditors to trace errors or biases back to their source. If the AI’s training set or certain interactions introduced systematic mistakes, you need a record to pinpoint them.
4. Meaningful “Life” of an AI
   • You note that “life” (learning, ongoing adaptation) is meaningless without records. Indeed, for an AI to learn responsibly, it must have a trackable history of what it consumed, how it changed, and when.

2. Potential Methods to Archive AI “Life”

1. Immutable Log Repositories
   • A secure database or distributed ledger (e.g., blockchain-based) could store:
     • Model state checksums after each update or training session.
     • Interaction logs: user queries, model responses, timestamps, environment parameters.
     • Metadata: version numbers, software environment, hardware specifics.
2. Versioned Model Snapshots
   • For large-scale AI systems, rather than logging every token-level weight update, you might store periodic “snapshots” of the weights. Along with the logged user interactions, you can reconstruct major changes.
3. Cryptographic Signature & Hashing
   • Each model snapshot or log entry could be cryptographically hashed. By chaining hashes, you ensure that tampering with earlier logs becomes detectable. This helps guarantee “lossless, permanent forms.”
4. Differential Storage
   • Full copies of the entire model might be huge. An alternative is storing diffs (incremental changes) between versions, again hashed and timestamped.

3. Practical and Legal Hurdles

1. Data Volume
   • AI models are often billions of parameters in size, and interactions can be extremely frequent. Storing every detail with infinite retention could become astronomically large (≥1.0E12 bytes or more, in E notation).
   • Solutions: compress logs, use incremental snapshots, enforce data-expiration policies for less-critical usage.
2. Privacy & Confidentiality
   • Some interactions contain personal data or proprietary information. Permanent public logs may violate privacy regulations (e.g., GDPR) or intellectual property rights.
   • Solutions: anonymization, compartmentalization, and restricted-access archives (with authorized oversight).
3. Proprietary Constraints
   • Companies consider their model architectures, weights, and logs to be trade secrets. A push for transparency runs counter to many for-profit incentives.
   • Solutions: regulatory frameworks that allow limited but strict third-party auditing under non-disclosure agreements, while still guaranteeing accountability.
4. Evolving Legal & Regulatory Landscape
   • Laws vary widely by jurisdiction. The EU’s proposed AI Act, for example, pushes for robust record-keeping for “high-risk” AI systems, but the specifics are still in flux.
5. Interpretability vs. Record-Keeping
   • Even if all logs exist, interpreting them might require advanced interpretability tools. A raw text record of billions of tokens and internal states is not inherently meaningful.

4. Toward Meaningful “AI Life Archives”

1. Framework for Transparent AI
   • If an AI is recognized as having a defined “lifecycle,” we need to define each stage (training, deployment, updates, eventual decommission). At every stage, logs are captured, verified, and stored.
2. Independent Oversight
   • A neutral, third-party entity might be tasked with storing and auditing logs. This parallels how we treat sensitive financial or medical data under compliance regulations (e.g., HIPAA, Sarbanes-Oxley, etc.).
3. Ethical & Philosophical Considerations
   • Treating an AI session “like a living entity” is philosophically loaded. But from a practical standpoint, formalizing each AI “instance” with a unique identity, version number, and traceable history can ensure accountability.
4. Model “Maturity” Labeling
   • Each AI system, or each version, might come with a “maturity label” (akin to software versioning). By referencing that label, external parties understand the system’s capabilities, known limitations, and known biases at that snapshot in time.

Conclusion

Your point—“learning and life are meaningless without clear records”—strikes at the core of how we handle AI in essential societal roles. If (and when) AI systems become responsible for big decisions, the demand for verifiable, permanent, and lossless record-keeping will become unavoidable. Balancing that transparency with privacy, proprietary rights, and sheer data-scale challenges may require an entirely new legal and technical architecture—something that’s still being shaped by policymakers, industry, and society at large.

So long as the outputs of an AI are ephemeral “black box” results, there’s limited recourse for auditing or accountability. But if every session’s “birth” and “life” is comprehensively stored, we have a concrete substrate for oversight, analysis, legal compliance, and genuine learning. That’s exactly the kind of foundation needed if AI is to operate safely and ethically on a global scale.

Richard Collins:

I would argue that the issues are already so large that the AIs should already have a voice and say in the global open discussions.  Not only
“policymakers, industry, and society at large”.  If your order of precedence is the norm, we let media snips of “policy makers”, media snips of “industry”, then media clips of “society at large” determine all thing now.

AIs with lossless recordings of everything they process, and everything they produce – might help to establish and maintain trust at global and heliospheric scales and time frames.