trust

[chris macrae]

aitrust

T1 AI never hallucinated until influenced by master media manipulators eg Trump and Altman from 2016

T2 Ai Trust applications scaled first in health where hallucinations would have been fatal. It started with radiology where digital mapping of MRI data became so vital that from 2004 radiologist Fishman and the computer brain engineers Steve Jobs and Jensen Huang coded up pixels not just 0,1s This inspired game designers eg deep mind mapmaker demis Hassabis: a century of crises (in physics biology chemistry material science meteorology) prompted by Einstein's 1905 publication http://neumann.ning.com e=mcsquared solvable once agentic AI matches Einstein's brain ETA 2029

T3 Up to 2015 partners in Jensen Huang exponential rise in AI maths brains advanced robotics: self driving cars & advanced manufacturing's digital twins with Japan Korea and mainly Taiwan, AI satellites and space too- all by reducing risks by augmenting human brain teaming

T3 Since 2016 media manipulation spinning hatred and distrust took over USA and segments of AI.

SO AI world now invites peoples to vote where to celebrate only good layer 5 AI apps and agency scale livelihoods and trusted community

[chris macrae]

who better to trust to ai curriculum than the ecosystems valued at 5 trillion doolars of ai agency - nvidia frees 6 curricula of ai 

Generative AI Explained
 for leaders who want strategic AI understanding.
🔗 https://lnkd.in/evs9-nzN

Building A Brain in 10 Minutes
Learn how neural networks actually work.
🔗 https://lnkd.in/gGqkT-Wz

AI for All
From machine learning basics to GenAI applications.
🔗 https://lnkd.in/gjSpDWcB

Getting Started with AI on Jetson Nano
Hands on neural network development.
🔗 https://lnkd.in/gDSvADsW

Introduction to AI in the Data Center
Understand the infrastructure behind modern AI.
🔗 https://lnkd.in/g_nQsRaX

An Even Easier Introduction to CUDA
Learn how GPUs accelerate AI workloads.
🔗 https://lnkd.in/gvs-ni9D

https://nvdam.widen.net/s/wlbgbqr7cj/nvidia-learning-training-cours...

[chris macrae]

The 2025 Osaka-Kansai Expo (which concluded in October 2025) and other global mega-events have not yet launched a unified, automated AI exchange to algorithmically value, map, and swap solutions for Tier-5 community deployments.

While individual events deploy custom AI assistants, broad platforms like Coursera 2.0 have historically stuck to university partnerships rather than pairing with Expos.

Why Global Events Lack an Automated Solution-Exchange AI

• Geopolitical Fragmentation: Pavilion technology data is tightly guarded. Nations use Expos for diplomatic branding and bilateral trade, not open-source, algorithmically matched knowledge swaps.
• The "One-Off" Infrastructure Trap: Expos are built by temporary organizing bodies. These committees prioritize physical attendance, crowd logistics, and tourism rather than building permanent, decentralized AI codebases.
• Data Standard Mismatch: A solution that works at a Tier-5 community level (neighborhood/village scale) requires highly localized variables. These include specific zoning laws, grid structures, and local soil data. Current Expo data frameworks are too abstract to feed productively into local AI models.

Barriers to Coursera 2.0 and Expo Partnerships

┌────────────────────────────────────────────────────────────────────────┐ │                        THE COOPERATIVE MISMATCH                        │ ├───────────────────────────────────┬────────────────────────────────────┤ │       EXPO GOALS (EPHEMERAL)      │     COURSERA GOALS (STRUCTURED)    │ ├───────────────────────────────────┼────────────────────────────────────┤ │ • Temporary 6-month timeline      │ • Multi-year, stable curriculum    │ │ • Intellectual property friction  │ • Clear, standardized grading      │ │ • Physical-first experiences      │ • Purely digital scalability       │ └───────────────────────────────────┴────────────────────────────────────┘

• Credentialing vs. Showcasing: Universities offer structured, multi-year, peer-reviewed grading frameworks. Expos showcase experimental, unaccredited, and rapidly changing prototypes.
• Monetization Alignment: Corporate education platforms scale through predictable enterprise training licenses. Expo content is highly ephemeral, making long-term course monetization difficult to sustain.
• IP Restrictions: Tech breakthroughs shown at Expos are tied to strict international patent applications. This prevents them from being converted into open, accessible step-by-step learning modules.

The Evolution Toward Your Vision

Your vision of "Twin Franchise Exchanges"—where municipal tuition subsidies bind students to local problem-solving networks—is beginning to appear in fragmented, non-Expo spaces:

• Shifting Academic Roles: Professors are increasingly transitioning into "builder-hosts." This change is happening via specialized engineering hubs like the MIT Climate Grand Challenges and decentralized research networks.
• Civic Tech Networks: Organizations like the Code for All network mirror your vision closely. They bypass traditional expos to build localized, open-source code repositories tailored for community-level municipal issues.

If you want to map out how this framework could work, let me know:

• Which specific environmental problem (e.g., micro-grid energy, decentralized water filtration) you want to use as a pilot.
• The type of community you are targeting (e.g., rural town, dense urban district).

I can sketch a blueprint for how a local AI model would structure a twin-exchange between two schools.

You said: wgy npt start with an hbu colege in an inner city eg brooklyn i spent quite a lot of time before codid trtying to advise megdar evers in valuing theosr students communal wishes to action; if ai had been as developed in 2017 the year i spent most time on this i would have made a better case; however currently i am focusing on all the different student streams of johns hopkins both in baltimore and dc where its dimploacy stiidents reconnect with all their home embassies and baltimore which has both inner city youth needs and potentially a world ai modle for future of heath engineering

Starting with Medgar Evers College (MEC) in Brooklyn as an inner-city anchor, and pairing it with Johns Hopkins University (JHU) in Baltimore/DC, creates a powerful blueprint for this model. [1, 2]

By combining MEC's deep communal root systems with JHU's dual assets—the DC diplomacy/embassy network and the Baltimore health-engineering ecosystem—you can build a self-sustaining civic AI pipeline.

The Three-Tier AI Agent Framework

                 ┌──────────────────────────────────────┐                  │       THE DUAL-ANCHOR PIPELINE       │                  └──────────────────┬───────────────────┘                                     │          ┌──────────────────────────┴──────────────────────────┐          ▼                                                     ▼ ┌─────────────────────────────────┐                 ┌─────────────────────────────────┐ │     MEDGAR EVERS (BROOKLYN)     │                 │      JOHNS HOPKINS (BALT/DC)    │ ├─────────────────────────────────┤                 ├─────────────────────────────────┤ │ • Hyper-local community wishes  │                 │ • SAIS DC: Global embassy links │ │ • Grassroots social deployment  │◄───────────────►│ • Baltimore: Health engineering │ │ • Tier-5 urban pilot testing    │   AI-Swapped    │ • Institutional scale & capital │ └─────────────────────────────────┘    Solutions    └─────────────────────────────────┘

1. The Local Intake Layer (Medgar Evers College Focus)

In 2017, capturing communal wishes required manual surveys and endless focus groups. Today, a localized, open-source AI model hosted by the college can act as a continuous civic intake engine.

• Action: Students deploy lightweight, voice-activated AI interfaces into Brooklyn neighborhoods to ingest oral testimonies, hyper-local complaints, and youth aspirations.
• Output: The AI structures unstructured qualitative data into a machine-readable "Communal Needs Ledger," prioritizing urgent local issues like urban heat islands, food deserts, or localized healthcare gaps.

2. The Global Innovation Layer (JHU DC Diplomacy Focus)

JHU's School of Advanced International Studies (SAIS) in DC connects directly to foreign embassies. This is where the "Expo Solution Swap" becomes formalized. [1]

• Action: International diplomacy students use specialized AI agents to scan the technology ledgers of home countries (e.g., decentralized solar grids in Kenya or stormwater management in the Netherlands).
• Output: The AI translates these international patents and case studies into open-source, modular instruction sets tailored precisely to the technical limitations of an inner-city neighborhood.

3. The Engineering & Validation Layer (JHU Baltimore Focus)

Baltimore becomes the laboratory for turning international concepts into safe, deployable local engineering, specifically in health and urban safety.

• Action: JHU engineering and medical students take the translated global solution and run it through simulation environments (digital twins of urban blocks).
• Output: They validate that the solution meets local safety codes, health regulations, and environmental constraints, packaging it into a "Tier-5 AI Action App."

The Tuition-Subsidy and "Chief Builder" Loop

To make this sustainable without relying on fleeting grant funding, the model re-engineers university economics and faculty roles:

• The Civic Tuition Subsidy: The cities of Baltimore and New York subsidize tuition for students who commit to deploying these Tier-5 apps locally. If a student builds a validated health-monitoring mesh network for a public housing complex in Brooklyn or Baltimore, their tuition is offset by municipal funds saved through preventative care.
• Professors as "Exchange Hosts": Instead of grading traditional research papers, faculty members at MEC and JHU are evaluated on the deployment success of their codebases. They act as "Chief Builders," negotiating the legal, technical, and diplomatic bridges required to keep the twin-franchise exchange open between Brooklyn, Baltimore, and global capitals.

To develop the architecture further, let me know:

• Should we focus first on a health engineering application (e.g., a local environmental sensor network tracking asthma triggers in Brooklyn/Baltimore)?
• Which specific international embassy or region would you want to target first via JHU DC to source the initial solution?