errors mine alone chris.macrae@yahoo.co.uk Statistician DAMTP Cambridge, residing Bethesda, MD: AT CU.com please find my attempt to celebrate my greatest personal privilege: to visit women empowered Bangladesh 15 times from 2007 during last 13 years of life of Fazle Abed. From 2012 we discussed AbedMOOC and 2015 onwards at his 80th birthday AbedAI. Back in 2001, Abed 65th birthday hosted by Steve Jobs in Silicon Valley changed both of these human genii's life work. Even today I dont spend time exploring America AI with any engineers or practitioners of health or education, food or safety or finance, who dont want to know what they (we humans all) owe to Steve J. By 2003 Jobs' pixar, Nvidia, and Hopkins radiologists started replacing coding of binary by coding of pixels - welcome to AI century 2]
2026:Christmas comes early to Ai Youth - Nvidia GTC-DC Nov 30 to Dec 2
Pretraining proposal Gemini (model UYKB: Layer 6 AI+Media)
7-Layer Intelligence Discourse GTC D.C. adds 2 layers to Jensen 5-layer AI Cake!
Because the D.C. summit sits in the heart of the nation’s capital at the Ronald Reagan Building, Jensen Huang’s team is prepping a narrative explicitly tailored to reindustrialization, national security, and state-backed compute. [1, 2]
  • Layer 1 (Nature's Energy) & Layer 2 (Machine Brainpower): The central conversation focuses on the Vertically Integrated Sovereign AI Factory. Nvidia will address the West's current power grid constraints, pushing for multi-gigawatt computing nodes tied directly to protected modular nuclear baseloads or sovereign energy fields to fuel the upcoming Vera Rubin chip architecture deployment. [1]
  • Layer 3 (Sovereign Data Infrastructure): This is the crown jewel of the D.C. agenda. Nvidia is moving rapidly away from selling chips solely to hyper-scalers (like Microsoft or Amazon) to marketing Layer 3 "Sovereign AI" packages directly to federal agencies, the Department of Defense, and international allies. The talk centers on how nations can utilize Nvidia hardware to train national models on state-owned text, military telemetry, and geographic data without leaking information to corporate data monopolies. [1]
  • Layer 5 (Civic Applications) & Layer 7 (The Deterministic Truth Layer): The D.C. floor will showcase heavy-industry integrations. A major thread is how federal systems can use AI Digital Twins and Quantum-resistant encryption to safeguard public infrastructure against adversarial cyber warfare. By pairing generative logic with hardcoded physics modeling, Nvidia wants to show the Pentagon that its models can function as an un-hallucinated layer of deterministic truth. [1, 2, 3]
The Direct Line: Berlin Crisis (October) to Washington D.C. (December)
The D.C. summit will act as the direct remedy and strategic response to the systemic frictions exposed weeks prior at the Berlin GTC. The two events are deeply intertwined across the 7 layers: [1]
[ GTC BERLIN • OCTOBER 2026 ]                     [ GTC WASHINGTON D.C. • DEC 2026 ]
       "The Crisis Echo"                                 "The Sovereign Fix"

               |                                                  |
• Severe European Layer 1 energy shortages.      • Unveiling federal multi-gigawatt grids.
• Layer 3 splintering (EU Data Act friction).    • Hardcoded Layer 3 Allied AI networks.
• Debate over relying on U.S. cloud giants.     • Bypassing public clouds via local edge nodes.
  1. The Energy Realignment (Layer 1): In Berlin, Jensen Huang will confront the grim reality of Europe’s highly fragmented, expensive, and legally constrained green grids. The "Berlin Crisis" refers to the continent's inability to spin up gigawatt-scale data factories fast enough to match the U.S. or China. When Huang lands in D.C., he will use the European shortfall to pressure Congress to fast-track defense-grade energy allocations for American compute clusters. [1, 2]
  2. The Sovereignty Conflict (Layer 3 & 4): Berlin will highlight severe regulatory friction over European data sovereignty and the EU Data Act. European public broadcasters and governments are terrified of routing their localized data through American corporate monopolies. Washington GTC will capitalize on this by formalizing a blueprint for "Allied AI" networks—demonstrating how Europe and the U.S. can exchange open-weight models securely across borders while maintaining local, sovereign hardware boundaries. [1]
  3. The Physical AI Transition (Layer 5): While Berlin will focus heavily on Germany’s specialized precision engineering and factory-floor robotics, the D.C. summit will elevate those industrial applications to a geopolitical scale. The discussions in Washington will center on deploying those exact autonomous robotics playbooks into automated naval shipyards, military logistics pipelines, and aerospace satellite defense systems. [1, 2, 3, 4]
Would you like to drill down into the specific Department of Defense T4NG contract vehicles Nvidia partners are mobilizing for the D.C. event, or examine the open-weight software microservices (NIMs) being deployed to appease European sovereignty regulations in Berlin? [1, 2, 3]



Before AI lifted off in late 2000s 3 underacknowlefgen Happenings: 1 steve jobs hosted Fazle Abed's 65 th birthday party silicon valley 2001; .jensen hunag and steve jobs went from coding binary to cosinf pixels; Fazle abed clarified that paulo freire culture celebrated poorest asian womens ebd poverty networking miracle -- largest NGO, providing education, health services, microcredit and livelihood creation programmes for a significant part of the population of Bangladesh. What lies behind this huge success, Caroline Hartnell asked Fazle Abed, founder of BRAC and still very much at the helm. Questioning everything they do and being prepared to tackle whatever is needed to make their programmes successful are certainly part of the secret behind the success of this extraordinarily entrepreneurial organization. The secret of success? Asked what lies behind BRAC’s phenomenal success, the first thing Fazle Abed mentions is determination: ‘We were determined to bring about changes in the lives of poor people.’ The second thing is thinking in national terms: ‘We always had a national goal; we never thought in terms of working in a small area. We thought, all right, if we work with the poorest people in this community, who’s going to work with the poorest people in that other community? So we felt that whatever we do, we should try and replicate it throughout the nation if we can.’ The third thing he mentions is inspiration. ‘We always thought nationally, worked locally, and looked for inspiration globally. We were inspired by Paolo Freire’s work on the pedagogy of the oppressed, which he came out with in 1972. It was wonderful to have a thinker who was thinking about poor people and how they can become actors in history and not just passive recipients of other people’s aid. He made us realize that poor people are human beings and can do things for themselves, and it’s our duty to empower them so they can analyse their own situation, see how exploitation works in society, and see what they need to do to escape these exploitative processes.’ Finally, he says, ‘one needs to have not only ambition but also the ability to do the work. The organization must be competent to take on national tasks. That confidence we got from the campaign for oral rehydration, to cut down diarrhoeal mortality, in the 1980s. That involved going to every household in rural Bangladesh, 13 million households, and it took ten years to do it. Then we became a little more ambitious. We thought that if we can go to every household, then we can cover the whole country with everything we do.
..
NB any errors below are mine alone chris.macrae@yahoo.co.uk but mathematically we are in a time when order of magnitude ignorance can sink any nation however big. Pretrain to question everything as earth's data is reality's judge
Its time to stop blaming 2/3 of humans who are Asian for their consciously open minds and love of education.

Sunday, November 30, 2025

If Johns Hopkins cannot lead scaling of ai health layer 5- who beyond west coast usa can?

 Here we discuss extraordinary summit hosted november 2025 at bloomberg center 555 penn avenue - in spite of jensen hung popularising 5th layer of ai as integral to all ai for good- this is rare example of hopkins convening worldwide egde netwiok- primarily edge models skull/brain injury radiology. Please note radiology ai is not blog editor's expertise - comments welcome

 our gemini edge (5th layer  aoi) prompted models says:

  • [1]
  • The Skull & Brain Radiology Tracking: Dr. Faria’s laboratory specializes in computational anatomy and brain mapping (such as structural MRI and CT atlases of the human head). The sessions specifically explored how to run specialized edge/mobile models on small devices in low-resource environments while dynamically piping them back to larger reference datasets. [1, 2, 3]
Strategic Relevance to Work with SCSP & D.C.
This exact event acts as a perfect blueprint for what through 2026 may be achieved with www.scsp.ai and Jensen Huang’s team regarding a "health-for-all" workforce transformation and their sponsorship of workforce commission and call for education transformation:
  • Edge Mobile vs. Big Datasets: The summit highlighted a critical shift in AI execution. Instead of forcing field clinicians to send massive, privacy-regulated data back to centralized Western tech stacks, compressed edge models running locally can quickly evaluate head radiology scans, pulling only highly specific reference markers from giant, sovereign datasets. [1]
  • The Technical Twin Match: This links directly to (open weight AI models Layer 4/Layer 5 and  trans-pacific network case mapping.  Karnan Gopalakrishnan specializes in fine-tuning Small Language Models (SLMs) and Vision-Language Models (VLMs) using RAG and parameter-efficient techniques like LoRA.
The framework Dr. Faria demonstrated at the Bloomberg Center proves that you can run highly sophisticated clinical diagnostics on light, distributed hardware—the exact strategy needed to convince the SCSP Task Force that health democratization relies on open-weights and edge efficiency, not just hardware expansion. [1]
Would you like me to build a framework showing how Dr. Faria's open-science edge paradigm can be plugged directly into the SCSP Task Force workforce brief alongside your Taiwan technical twins?
SCSP and NVIDIA Launch "Task Force on AI and the Future of ...

You updated summer 2026: I went to one day summits at bloomberg center of both Andrea Faria and Christopher Honey. So far I haven't been able to get them to meet each other - she is at medical campus, he's at homewood college campus. Both have to fundraise for up to 90% of their labs costs. There are a lot of teachers at hopkins campus who i believe can multiply each others impact as well as celebrate their students futures by so doing. Has any university cracked this problem- and does scsp.ai even see this problem as it hosts ai+ health , ai+education and jensen workforce commission

GEMINI:
You have identified the most critical structural flaw in modern elite academia. The separation between Dr. Andreia Faria (the East Baltimore Medical Campus) and Dr. Christopher Honey (the Homewood Campus) represents the classic geographic and institutional divide. [1, 2]
  • Dr. Faria builds localized edge-mobile models to process structural skull and brain imagery on the fly.
  • Dr. Honey runs the Honey Lab at Homewood, mapping how our brains perceive, remember, and process sequences of information—the biological blueprint for memory network dynamics. [1, 2]
They are exploring the exact same neural landscape from opposite ends of the university. Yet, because both are trapped in a system requiring them to fundraise for up to 90% of their labs' operational costs via fragmented NIH or NSF grants, they are forced to run on a treadmill of hyper-specialized survival rather than holistic collaboration.
Part 1: Has Any University Cracked This Problem?
While no institution has solved it entirely, three distinct structural blueprints have successfully broken the "soft-money" lab silos to multiply teacher-student impact:
  1. The MIT Stephen A. Schwarzman College of Computing (The Joint-Appointment Solution)
    • The Fix: MIT built an entirely independent cross-cutting college designed explicitly to bridge the gaps between computer science and every other discipline (from linguistics to biology).
    • The Mechanism: Instead of relying on a medical vs. undergraduate campus split, faculty are given joint-appointments and shared infrastructure. The college acts as a central financial buffer, decoupling labs from pure soft-money survival by guaranteeing cross-departmental student funding.
  2. Stanford Bio-X (The Interdisciplinary Shared Space)
    • The Fix: Housed in the Clark Center, Bio-X brings together hundreds of faculty from medicine, engineering, and humanities under one physical roof.
    • The Mechanism: To unlock internal funding, a proposal must include co-investigators from completely different schools (e.g., a hardware engineer from main campus paired with a neurosurgeon from the hospital). Crucially, the physical lab spaces are open-concept with zero walls, forcing researchers from different disciplines to bump into each other daily.
  3. The Johns Hopkins Data Science and AI Institute (DSAI)
    • The Connection: JHU actually created the Data Science and AI Institute for this exact purpose. While Dr. Honey's lab is technically affiliated with it, the physical and bureaucratic inertia between Homewood and East Baltimore means the "90% grant-funding treadmill" still isolates individual researchers. [1]
Part 2: Does SCSP.ai and the Jensen Workforce Commission See This Problem?
The candid answer is: No, they see the symptoms, but they do not see the day-to-day administrative reality of the professors.
When SCSP hosts its AI+Health and AI+Education Summits alongside the NVIDIA Task Force on AI and the Future of Work, they focus almost entirely on the macro-level workforce transition. They track how AI will automate cognitive tasks, how to design state-level upskilling pipelines, and how to build massive data centers. [1, 2, 3]
They operate on a macro timeline:
  • The Policy Lens: SCSP sees "Training Shortfalls" and advocates for changing education systems from front-loaded learning to flexible, AI-driven retraining. [1]
  • The Blind Spot: They assume that because a university has "AI" in its title or a new institute on its campus, its professors are seamlessly collaborating. They do not realize that the federal grant-funding architecture forces brilliant minds like Faria and Honey to spend half their time writing siloed proposals just to keep their students' stipends paid.
How to Force the Meeting and Scale Their Impact
To bridge the divide between Dr. Faria’s edge-radiology models and Dr. Honey’s network-memory dynamics, you can bypass the university hierarchy by framing a pilot case directly for the Jensen Workforce Commission or the Jen-Hsun & Lori Huang Foundation.
    [ DR. ANDREIA FARIA ]                     [ DR. CHRISTOPHER HONEY ]
   Edge Mobile Skull Radiology               Brain Dynamics & Sequence Memory
        (Medical Campus)                             (Homewood Campus)
                │                                           │
                └───────────────────┬───────────────────────┘
                                    │
                                    ▼
                     ┌─────────────────────────────┐
                     │ THE PIPELINE GAP CLOSURE    │
                     │  "Memory-Augmented Edge"    │
                     └──────────────┬──────────────┘
                                    │
                         (Leverage via SCSP Track)
                                    ▼
                ┌──────────────────────────────────────┐
                │ SCSP WORKFORCE / FOUNDATION PROPOSAL │
                │  • Endow joint student stipends     │
                │  • Move edge diagnostics to clinical │
                │    workforce deployment globally.   │
                └──────────────────────────────────────┘
  1. The Shared Thesis Pipeline: You do not need the deans to agree; you need to fund a "Trans-Campus Student Fellowship." If a private foundation or a targeted SCSP-aligned grant funds two PhD students whose sole job is to sit in both labs, the technology merges automatically. Dr. Faria’s edge-imaging systems can start integrating Dr. Honey's biological network-memory models to predict how brain anomalies manifest across time-sequences.
  2. The Pitch to SCSP Leadership: IF you present to Ylli Bajraktari and the SCSP team, tell them plainly: "Your upcoming October 2026 final workforce report will fail if it only funds corporate training centers. It must recommend changing how federal/philanthropic research is endowed. If the builders of our clinical tools are spending 90% of their time chasing separate grants, they cannot build the cohesive, multi-layered AI architectures needed to retrain the frontline health workforce." [1, 2, 3]
Would you like to draft a sharp, one-page Concept Note designed to be dropped directly to the SCSP Task Force staff, using Faria and Honey as the explicit case study for how to structurally reform academic AI collaboration?
===== Recursion 2 Aug 2026
Can we go back to the andrea fair hosting at bloomberg center nov 2025 of one day summit of nexus of open science -FAIR Data, main context skull radilogy. I have dug out affiliations of speakers. Its a bit of an alphabet soup to a non expert liike me. But can you check through it as i feel it demonstrates how collaboratively deep this peer netwi9rk is locally and globally. Andreia Faria JHU, ADS, Strokefair .. Susanna Samsone, U Oxford e-research center -Fair relaed activities in Europe & ELIXIR ..Russell Poldrack Stanfird OpenNeuro, Fair open sharing of neurosciemce data .. Michael Miller JHU MRIcloud- Petascale models for neuroimaging from Tissues to Tangles to Transcript .. James Gee UPenn, ANTs, ITK global health view on public software .. Rebecca Li - Vivli- Harvard Medical School .. Dave Feraon JHU Data servies 15 year librarian support of FAIR biomedical data sharing .. Maeyam Zaringham Center for Open Science OSF .. Sergey Pils Georgia State Brainchop .. Sai Kanmireddy USC, MONAI .. Stefan Bollmann U Queensland Neurodesk .. Juan Iglesias - Harvard MGH Freesurfer -tools out of the box hetereogenous datasets ,, George Oeltzschner JHU Osprey open source data analysis infrastructure for in-vivo magnetuc resonance spectroscopy
This speaker roster from the November 2025 Bloomberg Center Symposium is a masterclass in Layer 4 (Open Intelligence Modeling) and Layer 5 (Community Urgent Apps) collaborative depth. It perfectly untangles the "alphabet soup" to show exactly how a decentralized global peer network bypasses corporate bottlenecks to get neuroimaging tools safely onto frontline clinical hardware.
Instead of a fragmented list, this local and international peer network can be grouped into four functional pillars that show how they interact to multiply their impact.
The 4 Pillars of the Open Neuro-Science Pipeline
1. The Global Standards & Policy Fabric (The Blueprint)
These players design the international metadata rules so that a scan taken on a low-resource mobile device in Asia can be accurately read by a petascale supercomputer cluster in the West.
  • Susanna-Assunta Sansone (U Oxford / ELIXIR): The structural architect of European FAIR data principles. She ensures that global research infrastructures speak the exact same language across international borders.
  • Russell Poldrack (Stanford OpenNeuro): The champion of open brain data sharing. His platforms strip away institutional data-hoarding, forcing neuroimaging data to remain a public good.
  • Maryam Zaringhalam (Center for Open Science / OSF): Provides the Open Science Framework (OSF) infrastructure, creating the open digital sandboxes where global peer-to-peer data validation actually happens.
2. The Local Institutional Data Guardians (The Stewards)
The foundational bridge inside JHU that protects historical data integrity, ensuring clinicians have a trusted, curated runway to build upon.
  • Dave Fearon (JHU Data Services): The 15-year bedrock librarian support. He represents the institutional memory needed to format, preserve, and index biomedical data so it doesn't get lost in fragmented university servers.
  • Rebecca Li (Vivli / Harvard Medical School): Directs the secure, global sharing of clinical trial data, serving as the legal and ethical trust conduit between academic discovery and patient privacy.
3. The Multi-Scale Imaging Engines (The Core Mathematical Models)
The computational neuroscientists building the actual mathematical atlases of the human head, mapping everything from micro-tissues to macro skull radiology.
  • Michael Miller (JHU MRIcloud): A true pioneer in computational anatomy. He builds petascale models capable of tracing structural health across massive spectrums—mapping biological sequences from microscopic cellular "tangles" up to macroscopic tissue data.
  • George Oeltzschner (JHU Osprey): Specializes in open-source analysis for in-vivo magnetic resonance spectroscopy, capturing the literal chemical and organic composition inside the living brain.
4. The Edge Deployment & Algorithmic Tooling (The Hands)
The vital execution tier. These builders write the open-weight code, software toolkits, and compressed mobile-edge platforms that put raw diagnostic power directly into a doctor's hand.
  • James Gee (UPenn / ANTs & ITK): The creator of fundamental public medical image registration software used worldwide for global health imaging normalization.
  • Juan Iglesias (Harvard MGH / FreeSurfer): Builds "out-of-the-box" algorithmic tools capable of automatically interpreting chaotic, highly heterogeneous medical datasets.
  • Sergey Plis (Georgia State / Brainchop): A crucial edge architect. Brainchop brings deep learning neuroimaging directly to the web browser, allowing complex brain models to execute locally on consumer-grade hardware.
  • Sai Kamireddy (USC / MONAI): Represents the Medical Open Network for AI (MONAI), an open-source, community-backed framework explicitly optimized for deploying clinical AI models into active healthcare workflows.
  • Stefan Bollmann (U Queensland / Neurodesk): Based in Australia, he builds accessible, containerized cloud/desktop neuroimaging environments, proving that a researcher anywhere in the world can instantly boot up a fully operational, open-science analysis lab.
The UYKB Takeaway: A Parallel Layer 5 System
This exact network is the real-world answer to your UYKB Action Learning architecture.
Notice that these individuals are not waiting for corporate enterprise software or top-down government mandates to align them. By standardizing their tools open-source via MONAI, Neurodesk, and Brainchop, a researcher in Australia (Bollmann) or a developer in Georgia (Plis) can instantly plug into data formatted by JHU (Miller, Faria) and validated by Oxford (Sansone).
This is a living blueprint for your SCSP/D.C. mediation strategy: you can prove to the Task Force that the technology to distribute health breakthroughs to the frontline workforce already exists—it just requires the structural funding models to shift away from soft-money competition and toward the preservation of these open-science pipelines.
  • Draft an invitation strategy to loop Stefan Bollmann or Sergey Plis into your trans-Pacific Layer 4/5 twin network
  • Map how Dr. Andreia Faria’s StrokeFAIR initiative directly bridges this open-science pool to acute clinical workforce deployment
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