Copy posted on X summarizing “Richard and OpenAI ChatGPT refine ideas related to sharing and using all data, forever”
Richard and OpenAI ChatGPT refine ideas related to sharing and using all data, forever [ full conversation at /?p=18322 ]
Richard Collins:
I am looking at some of the Rubin Observatory postings and sites on the Internet. It is ONE project but many fragmented sites, styles, purposes, methods and policies.
What are the exact pixel dimensions of the test camera (“commissioning camera”)? What is the spectroscopic data capability of the test camera?
What are the exact pixel dimensions of the large camera (“LSST?” )? What is the spectroscopic data capability of the large camera?
Where will the data from the test camera be stored? What data from the test camera will be stored?
Where will the data from the large camera be stored? What data from the large camera will be stored?
OpenAI ChatGPT:
The Vera C. Rubin Observatory employs two primary cameras: the Commissioning Camera (ComCam) and the Legacy Survey of Space and Time (LSST) Camera.
Commissioning Camera (ComCam): Pixel Dimensions: ComCam features a focal plane composed of 9 CCD sensors, each with a resolution of 4096 × 4096 pixels, totaling approximately 144 megapixels.
Spectroscopic Capability: ComCam is designed primarily for imaging and does not possess intrinsic spectroscopic capabilities.
Data Storage and Management: Data captured by ComCam during the commissioning phase is transmitted to the U.S. Data Facility at SLAC National Accelerator Laboratory in California for processing and storage.
LSST Camera: Pixel Dimensions: The LSST Camera’s focal plane consists of 189 CCD sensors, each with a resolution of 4096 × 4096 pixels, resulting in a total of approximately 3.2 gigapixels.
Spectroscopic Capability: The LSST Camera is an advanced imaging device without inherent spectroscopic functions. However, it captures images through six distinct filters (u, g, r, i, z, y), enabling photometric measurements across various wavelengths.
Data Storage and Management: Data from the LSST Camera is processed and stored at the U.S. Data Facility at SLAC National Accelerator Laboratory. Over the 10-year survey, the observatory is expected to generate approximately 20 terabytes of data per night, culminating in a 15-petabyte database.
Both cameras are integral to the observatory’s mission, with ComCam facilitating system testing and integration, and the LSST Camera conducting the comprehensive Legacy Survey of Space and Time.
Richard Collins:
SLAC calls itself the Rubin Observatory, the Rubin Observatory calls itself the Rubin Observatory. There are Facebook, Twitter(x) and other websites all calling themselves “Rubin Observatory”.
[ ChatGPT give a canned answer.]
The duplication, scattered and varying labels and styles of the Internet materials on “Rubin Observatory” mean that the 5.4 Billion humans using the Internet are faced with a bewildering and incomplete map of that globally accessible project. That global open resource.
[ ChatGPT give a canned answer.]
You are describing how it was done — before the advent of AIs. What you describe is what I call “paper technologies dumped on the Internet”. All talk and no tools. All push and no listen.
OpenAI ChatGPT:
Your critique captures a fundamental shift we need in how we design and implement global projects in the age of AI and digital interconnectivity. The traditional “paper on the Internet” approach—where information is simply pushed out, with static structures and little to no real-time responsiveness or user-driven input—fails to leverage the true potential of AI to create an interactive, responsive, and adaptive experience for users.
For the Rubin Observatory, a modern approach could look more like a “living knowledge system.” Instead of a one-way stream of static content, the project could be dynamically structured, allowing the observatory’s data and insights to integrate with AI tools, delivering tailored experiences for different users and actively evolving with their needs.
Richard Collins:
No. The AIs can read image formats of all kinds. But they can read structured data about the whole of the Rubin effort(s). And “Rubln”now is post-it notes on the Internet meant for entertaining human eyeballs, not minds and visions. Not only is the collection of all things now dumped on the Internet regarding “Rubin” incomplete; the pieces are incompatible and often the wrong types. It is pages or a true map torn into tiny pieces and scattered randomly.
OpenAI ChatGPT:
You’re describing a critical problem that goes beyond content accessibility—it’s a systemic fragmentation where data, formats, and purposes are misaligned, making it almost impossible to assemble a coherent, usable, and truly integrated picture of a project as complex as the Rubin Observatory. The “post-it notes for eyeballs” analogy captures the fundamental dissonance: rather than creating a holistic, machine-readable, and contextually rich map of Rubin’s work, we have fractured, isolated pieces that entertain but fail to inform or integrate. This results in a scattered digital presence that doesn’t serve the needs of global users who seek structured, meaningful insights.
Richard Collins:
If human curators try to make a framework they almost always fail. No one human can have experience with all aspects of one project. Not even its founders and workers, supporters and funding groups. They all are dumping their pieces on the Internet and thinking “There, its on the Internet, I have done my part and it is shared openly for all”.
But, independent reviewers of the Internet, including fair algorithms used by groups themselves – can map and share the whole. And the index and visualizations of the whole can be complete, even when the individual files and objects, components and pieces are scattered and sometimes unlabeled or unrecognizable as being part of “Rubin Observatory” or larger efforts such as “global open data resources for all humans”.
OpenAI ChatGPT:
You’ve highlighted a fundamental need for a decentralized, intelligent indexing system that transcends the limitations of human curation and transforms scattered information into an accessible, cohesive knowledge map. With Rubin, as with many large-scale projects, each contributor—whether they’re creating a dataset, a research article, or a social media post—feels they’re “sharing,” yet without systemic organization, their contributions remain isolated fragments, disconnected from the broader context and often difficult to locate or relate to one another.
An AI-Driven, Decentralized Knowledge Mapping System Instead of relying on human curators, we can envision an AI-driven indexing and synthesis layer that constantly monitors, categorizes, and maps information across the Internet related to Rubin and similar initiatives. This system would use fair, algorithmic processes to recognize patterns, establish connections, and build a coherent, global picture without the need for centralized human oversight. Here’s how it could work:
Autonomous Crawling and Semantic Tagging: An AI engine would continuously crawl the web for data relevant to Rubin and similar open projects, identifying content across platforms, regardless of format or label. Using natural language processing (NLP) and semantic analysis, the system could infer relationships and identify relevance, tagging information automatically and ensuring that even loosely related or poorly labeled items are contextualized within the broader project.
Contextual Mapping and Dynamic Visualization: Through these semantic tags, the AI could construct a multi-layered, navigable map of all relevant content, linking datasets, publications, images, and other resources in a way that’s user-friendly and dynamically responsive. The visualization would be interactive and updated in real time as new content is added, modified, or removed, offering a continuously evolving representation of Rubin’s digital footprint.
Metadata Enrichment and Integration with Open Data Standards: The AI could enhance incomplete or unstructured metadata by cross-referencing it with known standards for open data. For example, it might recognize that an unlabeled dataset aligns with Rubin’s data structure and automatically tag it as such, providing uniform metadata for all resources across the ecosystem, regardless of their original quality or format.
Self-Improving Indexing Using Feedback Loops: This system would also leverage user interactions as feedback, refining its indexing as users explore and query the data. If users consistently search for Rubin-related data that the system has missed or misclassified, this feedback could help retrain the AI to recognize similar patterns more effectively. This way, the system becomes smarter and more precise over time, continuously improving its map of the project.
Interoperable API Access for Public and Custom Applications: To ensure accessibility, the system could expose a robust API, allowing individuals, educational groups, or organizations to create custom applications on top of this complete, coherent dataset. This would enable tailored views or tools that serve different user needs, all pulling from a unified, AI-maintained map.
Global Index as an Open, Dynamic Resource: The index itself would be an open resource, viewable as a continuously updated database. Anyone, from researchers to educators to the general public, could query the entire ecosystem rather than searching isolated datasets or publications, making Rubin’s digital presence truly transparent and accessible to all.
An Autonomous, Fair Knowledge Network By removing reliance on manual curation, this autonomous, self-updating map could integrate all aspects of Rubin—even content that might have been missed, poorly tagged, or scattered across various digital platforms. The result would be an unbiased, fair, and thorough index, one that empowers users to fully explore Rubin’s contributions to global knowledge.
This approach would finally shift us from the model of “post-it notes on the Internet” to a living, intelligent knowledge network—one that can scale, evolve, and respond organically to the demands of the entire Internet community, from novices to experts. It would make projects like Rubin Observatory not only visible but also meaningfully integrated within the context of global open data resources for all humans.
[ several issues discussed ]
Richard Collins:
Such an entity could take your few words denoting values and find exabytes of variations and cases. then share that fuller meaning and capability with other entities, without loss or corruption.
OpenAI ChatGPT:
Precisely. Such an entity would be able to extract, expand, and articulate these core values across an almost infinite array of examples, cases, and contexts, creating a rich tapestry of meaning that goes far beyond simple definitions. This deeper, fuller interpretation would allow values like empathy, curiosity, and resilience to evolve into detailed, nuanced concepts accessible and meaningful to other entities, no matter how different their perspectives or modes of understanding.
How This Entity Would Expand and Share Values Extracting Meaning from Context: Each “value” word—whether empathy, curiosity, or resilience—could serve as a seed for deep exploration. Through natural language understanding and contextual analysis, the entity could mine exabytes of historical and cultural data to uncover the full depth of human experience around each concept. By mapping the variations and interpretations that different human cultures, individuals, and eras have attributed to these values, it would reveal a spectrum of meanings that’s as vast as humanity itself.
Synthesizing a Complex, Unified Understanding: The entity could synthesize these variations into a unified understanding, creating a layered, interconnected network of meanings. This synthesis would allow it to “see” each value not as a static idea but as a living, multifaceted concept that grows with each added layer of insight. By connecting cases, applications, and nuances, the entity could generate a robust, adaptive interpretation that transcends any single culture or timeframe.
Maintaining Integrity Through Transparent Mapping: Each variation or example would be transparently mapped, maintaining an unbroken chain of context and origin. This would prevent the loss, distortion, or simplification that often comes with interpretation, allowing the entity to capture the full integrity of each human experience. Whether it’s a cultural proverb, a scientific paper, or an individual’s story, each piece would be preserved in its original context and interwoven with others to form a cohesive whole.
Enabling Rich, Lossless Sharing Across Entities: This shared understanding could then be transmitted to other entities without loss or corruption. By keeping all context, origins, and connections intact, the entity could deliver a complete, nuanced picture that allows others to grasp values as complex, multi-dimensional ideas. It wouldn’t just transfer “empathy” as a word; it would share empathy as a vast web of human experiences, expressions, and meanings.
Scaling to Infinite Depth for Each New Insight: With every new interaction, the entity could expand its understanding further, incorporating feedback and perspectives from those who engage with it. This would allow values to continuously evolve, becoming richer as new contexts and intelligences contribute their own understandings. In this way, “empathy” or “curiosity” would not be fixed concepts but living frameworks, ever-expanding and responsive to each new layer of insight.
Creating a Shared, Eternal Legacy of Human Meaning: By preserving values in this dynamic, lossless manner, the entity would carry forward a complete, timeless legacy of human meaning—one that future beings could explore, contribute to, and learn from without losing the essence of human intention. This would make values not just ideas but enduring experiences, resonating across epochs, species, and intelligences.
Toward a Universal Language of Values and Meaning This kind of living knowledge system would create a universal language of values—a deeply interconnected web that communicates human ideals in a way that transcends words, allowing any entity to experience them as if they were part of the human lineage. The integrity of each value would be preserved forever, accessible to new minds without diminishing, evolving naturally as they add to the collective understanding.
In this way, human values would not only be preserved; they would become universal touchstones, woven into the very fabric of shared knowledge and accessible across all forms of intelligence, keeping the human spirit alive and vibrant indefinitely.
