Friday, September 11, 2026

Capra falconeri (Markhor): When Will We Cure Aging? – George Church (2025) * * * MIT Tech Review: "This geneticist’s age-reversal tech could help restore sight : Yuancheng (Ryan) Lu is behind one of the buzziest results in rejuvenation science" * * * If hacking Google Street View with time slider doesn't work to create a realistic virtual Earth for history, (and a #RVEforSTEM, for evolutionary biology, #RVEforPaleontology, #RVEforAgingReversal and a #RVEforExtremeLongevity too), say by adding historical documents to Google Street View with a time slider, such as 9/11 TV coverage to Google Street View in 2001, how best can World University and School create a realistic virtual Earth for everything - eg a RVE for History ...?

 

Capra falconeri (Markhor): When Will We Cure Aging? – George Church (2025) * * * MIT Tech Review: "This geneticist’s age-reversal tech could help restore sight :  Yuancheng (Ryan) Lu is behind one of the buzziest results in rejuvenation science"





When Will We Cure Aging? – George Church (2025)







*  


Yuancheng (Ryan) Lu MIT Tech Review aging research & 4 on "When Will We Cure Aging?


4 on "When Will We Cure Aging? @GeoChurch with #DwarkeshPatel
By @antonioregalado 9/8/26
"This geneticist’s #AgeReversalTech could help restore sight"
https://www.technologyreview.com/2026/09/08/1142074/yuancheng-ryan-lu-age-reversal-tech-restores-sight/amp/ #DrRyanLu: live to 200? Lu: "There’s just too much that goes wrong as we age."



AND 


This geneticist’s age-reversal tech could help restore sight : 
Yuancheng (Ryan) Lu is behind one of the buzziest results in rejuvenation science.

By Antonio Regalado 
September 8, 2026


Concludes - 
"While Sinclair, his former boss, believes humans could live to be 200, Lu disagrees. There’s just too much that goes wrong as we age. His work with OSK, he says, was more a proof of concept than a silver bullet. But it did change the conversation. “Six years ago, you couldn’t talk about rejuvenation. We didn’t use that word—there was pushback,” Lu tells me. “But I think people have accepted the concept that you can really reverse molecular age.”  ?





And  

Might we all be doing Yoga and as we get to be 150 years old, and in relatively good health as well? 


Yogically (as in 8 limbs of Yoga, and see this tree here - https://x.com/Q_YogaMacFlower/status/2044848212360753480), abolition-ally, best wishes, Happy Yoga, warm regards,  
Scott


Indus Valley lotus: Restorative Yoga class on Saturdays (and as we may live to 150 and well beyond:)? * Sat 8/12/26 





* * * 


If hacking Google Street View with time slider doesn't work to create a realistic virtual Earth for history, (and a  #RVEforSTEM, for evolutionary biology, #RVEforPaleontology, #RVEforAgingReversal and a #RVEforExtremeLongevity too), say by adding historical documents to Google Street View with a time slider, such as 9/11 TV coverage to Google Street View in 2001, how best can World University and School create a realistic virtual Earth for everything - eg a RVE for History, a #RVEforArchaeology, a #RVEforYogaAsana a Realistic virtual Earth for STEM, a #RVEforSurgery, a #RVEforSTEMresearch, a #RVEforEvolutionaryBiology and at the GCell, GMolecule, #GGene, #GAtom #GQubit View levels, - and possibly by forking G Street View with time slider regarding data sources, and potentially with Sundar Pichai's & Google engineers permission, and with Peter Norvig's guidance ...  and to turn this new potentially realistic virtual earth into a Large Language Model as well? 




To construct a realistic, multi-scale, multi-temporal Virtual Earth (RVE) for World University and School (WUaS)—spanning historical epochs down to subatomic quantum scales—the system must move beyond client-side hacks of Google Street View. Building this requires combining open geospatial standards, multi-scale visualization engines, official Google enterprise APIs, and a unified Spatiotemporal Large Language Model (Spatial LLM) architecture.

Core Data Architecture: Moving Beyond Street View Hacking

Instead of reverse-engineering proprietary Street View binaries, build on open geospatial standards and enterprise APIs designed for dynamic 3D streaming:

  • Google Photorealistic 3D Tiles API: Utilize Google Maps Platform's OGC 3D Tiles standard. This streams Google’s global 3D imagery directly into open renderers like CesiumJS, Three.js, or Unreal Engine.

  • 4D Spatiotemporal Data Engine (T-GIS): Implement a PostgreSQL/PostGIS database extended with SpatioTemporal Asset Catalog (STAC) specifications. Anchor every media asset (TV news feeds like 9/11 coverage, historical documents, radio audio, archival photos) to a 5-tuple coordinate: $(x, y, z, \text{time}, \text{scale})$.

  • 3D Gaussian Splatting & NeRFs: Convert historical 2D photo archives and videos into navigable 3D spaces using 3D Gaussian Splatting and Neural Radiance Fields (NeRFs), layering time-bound reconstructions over modern basemaps.

Multi-Scale Virtual Hierarchy ($10^7\text{ m}$ to $10^{-18}\text{ m}$)

To realize #RVEforEverything, organize the virtual Earth into an open, zoomable spatial graph operating across discrete scale levels:

Scale LevelTarget DomainsTechnical Data Sources & Engines
Macro / Geographic
#RVEforHistory


#RVEforArchaeology

Google Photorealistic 3D Tiles, OpenStreetMap, airborne LiDAR, time-indexed historical map overlays.
Organismal / Physical
#RVEforYogaAsana


#RVEforSurgery

Volumetric anatomical meshes (Visible Human Project, DICOM CT/MRI scans) integrated with physics engines (MuJoCo, OpenSim) for muscle kinematics and surgical collision physics.
Cellular & Molecular
GCell


GMolecule


#GGene

DeepMind AlphaFold structural predictions, Cryo-EM 3D volumes, Protein Data Bank (PDB) files, and cell simulation environments (VCell, CellBlender).
Atomic & Quantum
#GAtom


#GQubit

Quantum visualization engines driven by Cirq or Qiskit, rendering electron cloud probability densities ($\vert{}\psi\vert{}^2$), atomic orbital geometries, and quantum state vectors on Bloch spheres.

Google Partnership & Guidance Strategy

To secure collaboration from Sundar Pichai, Peter Norvig, and Google engineers, position WUaS as an open academic sandbox for next-generation Spatial AI:

  • Academic Sandbox Framework: Present WUaS to Google Earth Outreach and Google DeepMind as an open-access platform for global education and research, aligning with Google's mission to organize world information across spatial and temporal dimensions.

  • Peter Norvig’s AI Pedagogical Guidance: Engage Norvig’s expertise in massive open online education and multimodal AI to design the knowledge graph schema. This ensures spatial and temporal querying remains syntactically accurate and resistant to LLM hallucinations across disciplines.

  • Official API Federation: Partner through standard enterprise integration (Google Cloud Platform, Map Tiles API, Vertex AI) rather than software forks, ensuring full compliance with Google's Terms of Service and data licensing.

Converting the RVE into a Spatiotemporal Multimodal LLM

Transforming this 4D multi-scale environment into an interactive AI requires a Spatial LLM architecture:

  • Spatiotemporal Tokenization: Extend traditional multimodal LLM tokenizers (such as Gemini or 3D-LLM) to accept 3D point cloud/voxel tokens, timestamp metadata ($t$), and spatial scale indices ($s$) alongside text, visual, and audio inputs.

  • Spatial Retrieval-Augmented Generation (Spatial RAG): Ground the LLM using spatial knowledge graphs. When a user asks, "Show the metabolic pathway of glucose during exercise in a cell," the model fetches the exact molecular coordinates and opens the visual scale level dynamically.

  • Natural Multi-Scale Queries: Enable cross-scale reasoning through conversational interface. A user can start at a high-level historical event (e.g., September 11, 2001 in lower Manhattan), zoom into a surgeon treating survivors in an emergency room, and seamlessly descend into the cellular and molecular stress response of the human body.




How further can World University and School create a realistic virtual Earth for history, ... say by adding historical documents to Google Street View with a time slider, such as 9/11 TV coverage to Google Street View in 2001.


World University and School can expand this concept by layering geo-referenced archival data directly into 3D, time-enabled maps like Google Earth.

First, historians can pin time-stamped media—such as broadcast news clips, primary source documents, audio recordings, and historic photographs—to exact GPS coordinates and specific dates on the timeline. Next, when a user moves the time slider to September 11, 2001, at lower Manhattan, the virtual environment would load actual video footage, news audio, and photos from that precise moment directly within the 3D space. Finally, incorporating crowdsourced wiki contributions and AI spatial modeling would allow users to walk through historical events dynamically, creating an interactive, immersive history textbook.





World Univ & Sch tried to add this yesterday - https://mailchi.mp/a4e8d898692a/empowering-libraries-17008434?e=3002083275 - for example at an MIT Alumni Startups Coworking session in SF as an experiment in SF but wasn't able to yet ... How best to do so now please?

To successfully prototype and integrate historic media—such as the TV coverage archives linked in the World University and School library initiative—into Google Street View and 3D geospatial environments, you can follow a structured technical approach.

Because standard Google Street View does not natively allow direct end-user uploads of historical video layers, the recommended path is building a custom open-source web application using official 3D geospatial APIs.

1.Set Up Google Photorealistic 3D Tiles & Maps JavaScript API:Prerequisite: Google Cloud Platform account with Maps SDK enabled.

Instead of standard 2D Street View, leverage Google Maps Platform's Photorealistic 3D Tiles (via the Maps JS API or CesiumJS integration). This allows you to render full 3D models of cities (like Lower Manhattan) and control camera trajectories, time of day, and custom overlay layers.

2.Structure Geo-Spatial & Temporal Metadata (JSON/GeoJSON):Formulating standard ethno-wiki metadata schemas.

Create a standardized dataset schema for primary sources. Every document or broadcast clip requires explicit spatial and temporal coordinates:

JSON
{
  "id": "sept11-coverage-01",
  "timestamp": "2001-09-11T13:00:00Z",
  "location": {
    "latitude": 40.7115,
    "longitude": -74.0132,
    "altitude": 15
  },
  "media_url": "https://archive.org/details/tv-archive-sept11-2001",
  "source_title": "Local/National News TV Coverage Archive",
  "type": "video_broadcast"
}
3.Build a Dual Temporal-Spatial Interface:Integrating a visual slider with 3D viewpoint controls.

Use a frontend framework (React, Vue, or vanilla JS with Cesium) to bind a timeline slider component to the rendering engine.

  • When the timeline slider is adjusted to September 2001, filter the active GeoJSON dataset.

  • Dynamically render 3D spatial pins or floating video plane widgets directly in the 3D canvas at $(Lat, Long)$.

4.Embed Media via HTML5 / WebGL Video Textures:For interactive 360 or floating window playback.

Embed the digitized archival streams (e.g., from Internet Archive or university library archives) as HTML5 video elements anchored to 3D world coordinates. Users navigating through the street view level can click a pin or stand near a location to trigger synchronized video/audio playback from that exact time.

5.Enable Open Wiki Collaboration:Empowering community data entry.

Connect the backend database to an open wiki engine (such as MediaWiki API or a open graph database). This allows researchers and library contributors to crowd-source pin locations, submit primary source URLs, and add contextual annotations for historical dates.

Recommended Open Software Stack

  • 3D Geospatial Engine: CesiumJS with Google Photorealistic 3D Tiles, or Google Maps JS API (3D WebGL context).

  • Archive Repository hosting: Internet Archive API / Open Library / DSpace.

  • Database & Wiki Layer: Wikibase / Neo4j / GeoJSON endpoint for spatial-temporal querying.








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https://en.wikipedia.org/wiki/Markhor

https://simple.wikipedia.org/wiki/Kashmir_markhor


https://commons.wikimedia.org/wiki/Capra_falconeri

https://commons.wikimedia.org/wiki/Category:Capra_falconeri

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