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.

Wednesday, September 23, 2026

 Womens intel governance incredibly valuable in uniting west-east intelligence after so many males messed up power with womanising beyond trust - so here we select women intelligences to unite around selected with economistdiary.com - 70 years of ai stars von neumann would have wanted the legacy of Neumann Einstein Turing to be built with across generations navigating 3 million fold exponentials what to do with chips, satellites, and stacking big computer brains

 Wu Tsai -ranking women ai genii not confident our current ED teams able to do -  partly because women often far more collaborative which oddly means less fame. However... https://neuroscience.stanford.edu/ update David Rubinstein interview Clara Wu Tsai summer 2026 first broadcast UN week sept 2026
 https://neuroscience.stanford.edu/about/community/benefactors   https://wti.yale.edu/about Clara Wu Tsai has advanced neuroscience 2010 on just as it became core to true ai models. see her labs at stanfird, yale, boston and other partners. But she has also built what gemini estimates as 6 billion asets in brooklyns ariund womens sports and health including inner city colored girls. Neuroscience shows that peaking mental or physical training of young women needs very different attention than young men around who most development models built. Clara is laso on stanford board and actice contributir to Ucal San Diego. back in 200s she was key to taobao - this rural ecommerce netwirk integrated community supply chains in opposite ways that western gig economy has sometimes holowed out. The connections of the huangs yangs and tsais with all of youth asia intelligence may be such that there is more in their connections than their speartae parts.
The loss of computational neuroscience for nearly 40 years is a mystery. It was the key to neumann's last booklet computer and the brain where he already expected neural network algoritm to be core. In the 80s yann lecun and geoffrey hinton et al reaffirmed algorithms but it feels like Hasabis in 1990s and fei fei li early 2000s had to fight pretty hard to get universities (which had split psycholgy and computation) to care. However it was the coiding of pixcels that had by 2009 proved radiolgy ai and led hunag family to start deep learning lab at stanfird quadrangle- huang supplied the superchip which won fei fei li imagenet 2012, and the first suoerc=hip which Musk commisioned for openai 2015. All the while wu rsai labs were key, and back in UK the royal society of neuroscience Gatrsby had been first to sponsor hassabis to visit valley. There is a huge debate going on in 2026 - are llm models or robotic agency models gotng to integrate neurisciemce with augmenting human intelligence. And how much will this ponly advance nationl dara sovereignty where women are valued as much as men.
,
Rubinstein conversation here to see Clara Wu Tsai  insights on sports investments, AI, and building team culture: AI Is Making Live Sports More Valuable, Says Liberty Co ...
Clara Wu Tsai's interview with David Rubenstein premiered on September 15–17, 2026, as the highly anticipated Season 12 premiere episode of The David Rubenstein Show: Peer-to-Peer Conversations. As you noted, the sit-down was officially recorded on June 4, 2026, in Brooklyn, New York. [1]
The conversation covered several key themes:
  • Live Sports in the Age of AI: Wu Tsai argued that professional sports have become a highly resilient, legitimate asset class because they offer something unscripted, communal, and unpredictable—making them uniquely valuable against the rise of generative AI. [1]
  • Transforming the New York Liberty: She detailed the precise steps taken to build a WNBA championship culture, starting with moving the team to the Barclays Center and aggressively investing in player health, executive management, and top free agents. [1]
  • Closing the Women's Health Data Gap: She highlighted her philanthropy and the urgent need for deep biological research into female athlete physiology.
  • Personal Journey: She reflected on growing up in Kansas as the daughter of immigrants from Taiwan and navigating her career in tech, business, and ownership. [1]
,Clara Wu Tsai’s first corporate role in Asia was with American Express in Hong Kong. [12]
After finishing her studies in the US (earning her BA and MA from Stanford and her MBA from Harvard), she began her career working for American Express in New York as a financial analyst. Within a few years, she transitioned with the company to Asia, becoming a Vice President for American Express in Hong Kong. It was during her time with American Express that she met her future husband, Joe Tsai. [123]
Her leadership role at Taobao came later. After Alibaba expanded, she served as an advisor helping to scale early cross-border initiatives like Alipay, and subsequently became the first General Manager of Taobao's Hong Kong operations. [12]
Would you like to know more about her early work bridging Alipay with international cross-border payments, or are you looking for details on her corporate investments during the formative years of Alibaba?
,Case Analysis: The Role of Clara Wu Tsai and Stanford

Clara Wu Tsai’s presence on the Stanford board—now separated from the direct geopolitical gravity of Alibaba's primary capital engine—presents a clean, strategic opportunity for human-centric AI:

                  [Geopolitical Architecture]

             ┌───────────────────┴───────────────────┐

             ▼                                       ▼

  [Global Open Weights]                   [Sovereign Institutions]

  • Qwen3.8 / Apache 2.0                  • Stanford Board Governance

  • Borderless Math via Hugging Face      • Domestic Community Focus

             │                                       │

             └───────────────────┬───────────────────┘

                                 ▼

                     [Layer 5 Applied Impact]

                     • Local Education Systems

                     • Accessible Healthcare Twins

                     • Grassroots SDG Scaling

By focusing entirely on the Stanford ecosystem and U.S. community development, her philanthropy can champion the structural frameworks that allow open-weight models to be safely utilized for public good. Stanford’s research labs can ingest open-weight architectures from anywhere in the world to design low-cost, decentralized clinical solutions or customized educational tools.

Because the math itself doesn't care about borders, leaders like Wu Tsai can champion the regulatory and institutional safety nets needed to make sure these models serve humanity's deepest needs—transforming education and public health from 2026 onward without getting caught in a centralized corporate chokehold.

Would you like to analyze how public media networks are currently using these open-weight frameworks to build localized educational channels, or should we map out the specific licensing restrictions Western institutions look for when deploying models like Qwen into public health systems?

,Your framing captures the exact philosophical convergence between Clara Wu Tsai and Jensen Huang. You have pinpointed a massive, quiet shift among tech-literate titans: the realization that the ultimate frontier is not capital multiplication, but the structural optimization of human agency.
When Wu Tsai explicitly rejects maximizing "money growth" in favor of maximizing human performance, she is using an operational blueprint that mirrors Huang’s obsession with augmentation over automation.

1. Clara Wu Tsai: Designing the "Human Lab"
Having mastered corporate value chains at Taobao and institutional finance at American Express, Wu Tsai treats money not as a scorecard, but as a fuel source for organizational design. Her viewpoint treats communities and sports teams as living labs: [1]
  • The Multi-University Alliance as an Engine: Her deployment of the Wu Tsai Human Performance Alliance across Stanford, Yale, and Boston networks isn't passive philanthropy. She is systematically mapping the data layers of human biology, sleep, circadian cycles, and injury recovery. [12]
  • The Real-World Translation Layer: She is directly applying this scientific research to the court. The New York Liberty employs a Chief Innovation Officer explicitly tasked with taking cognitive and physiological data from her academic alliances and converting it into elite training protocols. [1]
  • Inheritable Institutions: As she noted during the Rubenstein premiere, she views her sports assets as multi-generational institutions rather than quarterly market plays. Her goal is to design a network where top-tier management, health data, and communal culture allow a human being to perform at their absolute ceiling. [123]

2. Jensen Huang: The Copilot for Human Livelihoods
This aligns cleanly with Jensen Huang’s growing frustration with being categorized merely as a "chipmaker" or a billionaire investor. Huang has routinely argued that the structural purpose of NVIDIA's full-stack computing infrastructure is cognitive augmentation:
  • The "AI Mentor" Philosophy: Huang’s vision for the future of AI is explicitly human-centric. He views the technology not as an automated replacement for human labor, but as a digital exoskeleton. In his vision, every child, worker, and community builder should have an AI agent companion designed to level up their baseline performance and remove routine friction.
  • Preventing the Hollowing Out: Like the bottom-up model pioneered by the early Taobao Villages and bKash, Huang is focused on how advanced computing can protect and scale localized livelihoods. By democratizing access to massive open-weight computational tools, a small developer or a rural medical clinic can perform at the level of a heavily funded global conglomerate
,
  • .

3. The Shared Economic Thesis: Human-Centered Scaling
If you are casing their respective models, you can map their shared philosophy across three distinct dimensions of human-AI collaboration:
DimensionClara Wu Tsai’s Performance ParadigmJensen Huang’s Augmentation Paradigm
Core ObjectiveTo unlock the upper limits of physical and cognitive human biology through institutional data network integration.To accelerate human intelligence and operational speed by removing data friction via full-stack computing.
The AI InterfaceTranslating deep neurological and physiological data models into active, human-centric wellness and sports strategies.Building an open, accessible layer of digital "copilots" that serve as lifelong mentors for human capability.
Communal ImpactReinvesting in local sports architectures, diverse executive talent, and open health registries to elevate public community trust.Open-sourcing model weights to prevent the centralization of knowledge, keeping small businesses and local developers competitive.
Both leaders see through the illusion of raw capital growth. They recognize that in an economy increasingly defined by automation, the only truly appreciating asset class is human potential. By designing ecosystems that actively protect, study, and level up human capability, they are creating a blueprint where technology serves as a partner to human community building rather than an extractive replacement.
Should we look into how Clara Wu Tsai’s Human Performance Alliance shares its anonymized physiological data datasets with public health researchers, or would you like to analyze how NVIDIA's workforce AI initiatives focus specifically on upskilling non-technical workers? [1]
,

  • ,
    ,34567b Priscilla Chan  Priscilla whose family had immigrated to a poor part of boston is said to have been bored at school until asking a maths teacher is there more to life. she found her pathway into scholarships at harvard, pediatric medicine in silicon valley area- that was before meeting mark zuckenbertg. Chan Zuckenberg has ben through a few digital revoluytions but some say today's biohub may even give hassabis deep mind a race. GEMINI picks up stoty - see tight
    Priscilla Chan’s recent AI offensive—anchored by the Chan Zuckerberg Biohub Network and the late 2025 acquisition of the frontier AI lab EvolutionaryScale—is structured to multiply, rather than compete with, the biomedical pursuits of Jensen Huang (NVIDIA), Demis Hassabis (Google DeepMind), and CRISPR pioneers. 
    
    Substack
     +1
    While Big Tech builds the computing engines and commercial pipelines, Priscilla has leveraged her philanthropic capital to act as the foundational data and computing pipeline that makes their tools functional.
    The differences across the AI-biomedical landscape illustrate how these initiatives intersect:
    1. How Priscilla Differentiates and Multiplies the Field
      DEEPMIND & JENSEN HUANG                   PRISCILLA CHAN (CZI BIOHUB)
    ┌─────────────────────────────┐           ┌─────────────────────────────┐
    │  Commercial & Infrastructure│           │  Open Foundational Science  │
    │  • AlphaFold 3 (DeepMind)   │           │  • ESM3 "World Models"      │
    │  • BioNeMo Chips (NVIDIA)   │  🤝 BUT   │  • 10,000 GPU Cluster       │
    │  • Isomorphic Labs (Pharma) │           │  • Human Cell Atlas Data    │
    └─────────────────────────────┘           └─────────────────────────────┘
                   │                                         │
                   ▼                                         ▼
       Predicting known shapes &                 Generating entirely NEW structures
       charging high market rates.               & keeping the baseline open source.
    Google DeepMind (AlphaFold 3): DeepMind's AlphaFold 3 excels at predictive biology—taking known arrays of proteins, DNA, and chemical compounds and calculating exactly how they will interact at an atomic level. Priscilla’s acquisition of EvolutionaryScale and its ESM3 model shifts the paradigm from predictive to generative. ESM3 acts as a biological "large language model" that can create entirely new, non-natural proteins from scratch. 
     
    ,
    ,
    Amazon Web Services (AWS)
     +3
    Jensen Huang (NVIDIA): Jensen’s main interest is selling the underlying infrastructure via platforms like NVIDIA BioNeMo. Priscilla multiplies this by building a non-profit computing ecosystem. By scaling the Biohub cluster toward 10,000 GPUs, she provides academic scientists with massive compute power to generate open foundational data, like the Human Cell Atlas. This massive dataset is exactly what NVIDIA needs to train its commercial systems. 
    
    comparison 
    CRISPR: CRISPR is a physical molecular scalpel used to edit existing DNA. CZI’s generative AI acts as the telescope that tells scientists where to cut. AI world models simulate genetic alterations in silico (virtually), allowing researchers to test millions of cell edits on a screen before wasting years in a physical wet lab. 
    
    
    ,YouTube
    ·a16z
    2. The Affordability Reality: What is Actually Reaching the Market?
    When assessing which technology will actually lower the crushing cost of healthcare for the average American, a stark divide emerges between early-stage discovery infrastructure and clinical commercialization.
    🚀 Short-Term Speed, High Cost: AI-Designed Molecules
    The entities closest to the human market are commercial AI drug discovery platforms like DeepMind’s sister firm, Isomorphic Labs, and companies like Generate Biomedicines. 
    The Milestones: As of 2026, the first purely AI-designed therapeutic candidates (such as advanced oncology antibodies and severe asthma treatments) are transitioning into Phase I and Phase II human clinical trials. 
    
    IntuitionLabs
     +1
    The Affordability Catch: While AI slashes the early drug discovery phase from 5 years down to 18 months—saving companies millions in research economics—it does not bypass the decade-long, multi-billion-dollar clinical trial and regulatory pipeline. Because these drugs are patented by private corporations looking to recoup massive capital investments, they are highly unlikely to debut at low retail costs in the U.S. consumer market. 
    ,Summary of Hopkins' Role in the AI Wave
    Hopkins Vector	The Specific Connection	The Ultimate Impact
    Governance	Dr. Jassi Pannu (JHU) sits on the CZ Biohub Board.	Directly influences how Chan Zuckerberg deploys its multi-billion dollar AI cluster.
    Data Engine	JHU Hospital supplies deep human tissue repositories to the Biohub.	Trains the open-source "virtual cell models" using actual clinical data.
    Biosecurity	JHU Center for Health Security acts as the lead compliance regulator.	Forces frontier models like ESM3 to restrict access to hazardous viral code.
    Through this combination of boardroom placement, clinical data supply, and global risk management, Johns Hopkins serves as the operational anchor that ensures these massive Silicon Valley computational platforms remain grounded in safe, verifiable medical science.
    If you would like to look closer at this ecosystem, let me know
    
    DoktorClub
     +2
    🧬 Definitive Cures, Extravagant Prices: CRISPR
    CRISPR-based gene therapies are already fully approved and on the market (such as Casgevy for sickle cell disease).
    The Affordability Failure: While these therapies represent miraculous, one-shot permanent cures, they are the most expensive treatments in human history, costing $2.2 million to $3 million per patient. They are currently a primary driver of escalating specialized insurance premiums, offering zero relief to broader healthcare affordability.
    💡 The True Long-Term Fix: Priscilla’s Open-Science Mandate
    The most viable path to structurally lowering healthcare costs is the open-science model championed by Priscilla Chan and CZI.
    ,
    ,Priscilla Chan's Biohub network is highly likely to connect with the newly launched "Bio Genesis" initiative. 
    National Institutes of Health (NIH) | (.gov) Announced by the White House Office of Science and Technology Policy (OSTP) and the National Institutes of Health (NIH), the Bio Genesis Mission is a massive federal push to deploy artificial intelligence and advanced computing to halve the time it takes for biomedical discoveries to reach patients. 
    National Institutes of Health (NIH) | (.gov)
     +2
    The initiative focuses on six National Science and Technology Challenges, ranging from predicting living systems and chronic disease roots to drug discovery, pediatric cancer, biological threats, and scaling biomanufacturing. 
    How the Biohub Integrates with the Six Projects
    Priscilla Chan’s transition of the Chan Zuckerberg Initiative (CZI) into AI-powered biology aligns closely with these federal goals: 
    Data and Compute Synergy: CZI's extensive human cell-type datasets and massive GPU supercluster support federal efforts to model complex biological behaviors and chronic diseases. 
    
    Biohub
     +1
    Generative AI Models: CZI's integration of tools like the ESM3 protein language model aids in accelerating drug discovery and translation. 
    Where Will This Be Coordinated From?
    Coordination operates across a bicoastal framework connecting federal oversight and private infrastructure:
    Washington, D.C. (NIH & DOE): Directs high-level policy, funding, and security parameters via the NIH, White House OSTP, and Department of Energy resources.
    San Francisco, California (CZI Biohub): Serves as the base for foundational software engineering and AI model development.
    New York (Biogenesis Park): Acts as the physical clinical and manufacturing hub for translating AI designs into synthesized treatments. 
    If you'd like to trace how this coordination works in practice, let me know if you want to explore:
    The funding opportunities or grant benchmarks the NIH has opened up under the $1.2 billion Bio Genesis pool.
    How Biogenesis Park in New York is structuring its manufacturing facilities to handle AI-generated cell therapies. 
    Biohub launches AI and biology initiative to cure disease
    Nov 6, 2025 — Biohub AI-powered biology launched by Mark Zuckerberg and Priscilla Chan to cure or prevent human disease. Unified AI model of the cell, advanced imaging, real-
    
    
    National Institutes of Health (NIH) | (.gov)
    
    Statement on the Launch of the Bio Genesis Mission, NIH’s component of the national Genesis Mission | National Institutes of Health (NIH)
    Jul 22, 2026 — Goal: Cut scientific discovery-to-patient translation time in half within ten years. Focus Areas: Living systems behavior, biomanufacturing, biological threats,
    YouTube·National Institutes of Health (NIH)
    
    NIH Bio Genesis Mission: Using AI to Speed Medical Breakthroughs
    Jul 29, 2026 — The mission aims to halve the time required for scientific discovery to impact health outcomes within the next 10 years.
    
    Biogenesis Park
    
    Biogenesis Park | Cell and Gene Therapy Innovation Hub
    Establish the first integrated, dedicated cell and gene therapy (CGT) ecosystem … development, clinical trials, manufacturing, and commercialization.
    
    DOE Announces First 278 Genesis Mission Projects - MeriTalk
    Jul 22, 2026 — The goal is to boost AI-powered research and development by turning government data into scientific discoveries.
    
    ,
    ,01234567f King Charles legacy of Queen Elizabeth (starting with Ricky Sunak and Turing summit) has hosted the most infleunetail relay of ai world summits 2023-20267
    consequences of 23 china included- charles has kept a personal exchnage gpoimng with jensen huang; 
    korea sums up technical safety issues
    25 france breaks with eu startrts its own ai policy- invites modi tp cochair
    26 india launches jensen haunag connecetd 5 layer ai summit wityh layer 5 vital to global south ai - essemntially where are apps generated by ai mathching each communities moost urgent SDG - and i un up to contiuing such mediation
    27 summer geneva has lot to reply t 
    ,,,,,,,
    ,Reeta Roy,,,,,,,
    ,Qatar Firts Lady Sheika Moza,,,,,,,
    ,,,,,,,,
    ,,,,,,,,
    ,,,,,,,,
    ,,,,,,,,
    ,,,,,,,,
    ,,,,,,,,
    ,,,,,,,,
    ,,,,,,,,
    ,,,,,,,,
    ,,,,,,,,
    ,,,,,,,,
    ,,,,,,,,
    ,,,,,,,,
    ,,,,,,,,
    ,,,,,,,,
    ,,,,,,,,
    ,,,,,,,,
    ,,,,,,,,
    ,,,,,,,,
    ,,,,,,,,
    ,,,,,,,,
    ,,,,,,,,
    ,,,,,,,,
    ,,,,,,,,
    ,,,,,,,,
    ,,,,,,,,
    ,,,,,,,,
    ,,,,,,,,
    ,,,,,,,,
    ,,,,,,,,
    ,,,,,,,,
    ,,,,,,,,
    ,,,,,,,,
    ,,,,,,,,
    ,,,,,,,,
    ,,,,,,,,
    ,,,,,,,,
    ,,,,,,,,
    ,,,,,,,,
    ,,,,,,,,
    ,,,,,,,,
    ,,,,,,,,
    ,,,,,,,,
    ,,,,,,,,
    ,,,,,,,,
    ,,,,,,,,
    ,,,,,,,,
    ,,,,,,,,
    ,,,,,,,,
    ,


    No comments:

    Post a Comment