Most of 1993/12 entries collate snapshots of 33 years in Nvidia rising as arguably the intelligence company benchmark every millennials education needs to include - browse examples to make up your deep mind!
Nvidia's, one of world's largest corporations - nearly 3 trillion dollar annual economy - importace to West Coast USA and to Taiwan -and to future intelligence og millennaisl - cannot be understated.
May 2025 we asked Grok for help in detailing Nvidia's unique to humanity contributions
Jensen Huang’s Computex
2025 Announcement: The Eight Taiwanese Companies
At Computex 2025 (May
20–23, 2025, Taipei), Nvidia CEO Jensen Huang announced a partnership to build
Taiwan’s first AI supercomputer, emphasizing Taiwan’s role in the global AI
ecosystem. Nvidia designed supercomputers are 25 times more energy efficient for deep data analysis than other supercomputers. At previous computex, Jensen announced Tiawan Earth2.0 model to lead world on resolution of weather data alerts. The suptercomputer initiative involves Nvidia, the Taiwanese government, Foxconn,
TSMC, and the National Science and Technology Council (NSTC), among others.
Huang highlighted collaboration with eight Taiwanese companies, describing them
as key partners in Nvidia’s next stage of AI development.
Identified Partners
- Foxconn (Hon Hai
Precision Industry): The world’s largest electronics contract
manufacturer, Foxconn is pivotal in assembling AI servers and other
hardware critical for Nvidia’s AI infrastructure. Its role in the AI
supercomputer project aligns with its diversification into AI and
robotics.
- TSMC: As the leading
semiconductor foundry, TSMC manufactures Nvidia’s advanced AI GPUs (e.g.,
Blackwell GPUs). Huang noted TSMC’s significant workload due to AI demand,
reinforcing its central role.
- National Science and
Technology Council (NSTC): Representing the Taiwanese government, the NSTC
facilitates R&D and infrastructure support for the AI supercomputer,
aligning with Taiwan’s ambition to strengthen its AI ecosystem.
- MediaTek: A fabless
chip design company, MediaTek is adopting TSMC’s 2nm technology for AI
applications, as announced by CEO Rick Tsai at Computex 2025. Its
involvement likely supports edge AI development.
Likely Additional Partners
(Remaining 4 of 8):
Based on Huang’s history
of collaboration and Computex 2025 reports, the other five companies are likely
drawn from Nvidia’s key Taiwanese supply chain partners. While not explicitly
named in the provided sources for this specific announcement, the following are
strong candidates based on their prominence and prior Nvidia partnerships:
- Quanta Computer: A
major manufacturer of AI servers and data center equipment, Quanta is
frequently cited as a key Nvidia partner. Huang’s meetings with Quanta
executives during his Taiwan visits underscore its role in AI
infrastructure.
- Wistron: Another
leading electronics manufacturer, Wistron collaborates with Nvidia on AI
server production and data center solutions. It is often mentioned
alongside Foxconn and Quanta in Nvidia’s supply chain.
- Delta Electronics:
Specializing in power components and data center cooling systems, Delta is
critical for AI supercomputer infrastructure, as highlighted at Computex
2025. Its products support the energy-intensive demands of AI computing.
- Asia Vital Components
(AVC): Known for data center cooling systems, AVC is another key player in
ensuring AI supercomputers operate efficiently. Its presence at Computex
2025 aligns with the focus on AI hardware.
These eight
companies—Foxconn, TSMC, NSTC, MediaTek, Quanta, Wistron, Delta Electronics,
and AVC—represent Taiwan’s comprehensive ecosystem, spanning chip
manufacturing, AI server assembly, power management, and government-backed
R&D. Huang’s announcement underscores their role in building a “world-class
AI infrastructure” to advance education, science, and technology in Taiwan and
globally.
Draft addition chris.macrae@yahoo.co.uk
Nvidia is one of world's biggst companies with valuation close to 3 trillion dollars. It is building Taiwan's supercomouter with 8 Tiawan business partners.
Around world its cooperatively stages connect at least 50 big cooperation platforms as well as at least 100 engineers who have been doing their lifetime works with team jensen since start of 21st C
let's try and iteratively list types of platforms
there is advanced biotech ai which nvidia calls clara ; rtecursion is one of its key partnerships; it was hassabis deep mind story in 2009 which redoubled jensen's interest in betteing the company on deep learning ai
as eraly as 2002 pixel codeing with steve jobs pixar seems to have been first expliocity leap away from 0.1 coding - soon rendering for games and eg hollywood inagintaions like spielnerg had also become radiliogy ai and then AI for any surgeon needing an operation map
consequently jensen and demis hassabis (and google who b acked hassabis since 2012) have always been close - deep minds in silico lab company is isomorphics; the open source data leap from 2021 has been deepminds alphafold 3 - 250 million proteins
nvidia leads platforms self driving car services (and electric cars) interconnecxt with video vision being integral to safely of the car which can be thought of as first mass market for mobile autonomous machines- in 2025 gtu jensen announced reuniting of noividia , deep mind and pixar now owne by disney- to develop newton advancing robotys sense of tocuch and physical ai- this connects with omniverse being nvidia and probably the world's number 1 lad for experimenting with robots (synthetic data is big in robot development - after humans have trained robots in some specific movements - extended parallel moves can be deduced without the human needing to demonstrate every variation)
Robit ai is also connected with digital twinning of factiries or inded any space where safe interaction olf humana and rib ot scaling production can be digitally twinned; in other words the living architecture of a production soace with robos and humans is worth digitally simulating from the gbetgo; this is the only way to build in cost-effectice design changes over time; but it also has the benefit that the digital blueprint can be replicated elsewhere-- homg kong's leading medical coleges are advancing medica center digital twinning which may look for worldwide partners - its quite likely that the operating room will be one critiical sub-design
from 2009 coding 5 human senses of ai becamme a chalenge nbvidi has monitired and once scaled celebrated - initially fei-fei li's imagenet comoetition became the global demonstration soace and alexnet using nvidia's mist advanced chip of the time and neural network algorithms proved machine coding of 5 human senses would be doable from 2012
in part this has led to llm chats and then llms with deep reasoning models - when you add in olhysical ai to this you return to humanoid ai regarding autonomous machines and plotenetially agentic ai for personal orr team experiential learning
ai is good at any data which is intended to conform to systemic structure - which includes big corporation buisness process reenegineering - it would seem major partners of nvidias in this are are servicenow, datbrcks snowflake(data warehousing), salesforce;
most trillion dolar sectors are now in a race for lead ai reenginered sectior leader - ironically as recemtly as 2018 ka fu li's book on ai superpowers was mainly on how ai had beomee digital inside of corporations; in some cases china ai startups had taken a lead on this in secturs such as big box retailing where china had never really had bricks version of big rtetail and so had leapfrogged into digital algorithms
llms turn out to be good at translating langiages (human and potentially coding) as well as form filling; hiwever the idea that only big llms work seems to be 1 shown to be wrong as soon as you demand llm reasoning where the reasoning may be a plugged in algorithm; there is need to understand what ai at edge is through models smaller enough for ai in pcs (and full stach=k up); there is a supicion that the really big foundation llm models may allo have been minly based on teh same data; at one stage during colvid most publishers permiited a massive search beyind copuyright to go beyond thoujsands of part similar literaure viewiews; this may or may not have been what started big llm models.
ai poentially intersects with all emerging technologies as well as drones nd space which all demand smart coordination even if these are already tools without direct human piloting
of course ai should be optimal for data mapping earth in every way which also brings us to waether and climate contexts as well as optimising complex grid flows/designs
the fact that nvidia inception startup program has over 2000 members would appear to counter the view that ai is only a b ig players game; put another way the mobile phone has hundreds of different components and so may many tools which ultimately need to understand all 5 human senses the way humans do as well as at the nano-level that humans cannot see and which human brains alone cannot analyse
---- we asked grok june 2025 to offer a 15 year olds view of nvidia
NVIDIA’s Intelligence Reach: Explained for 15-Year-Olds and Teachers
NVIDIA is a tech giant worth $3 trillion (like 3 million million dollars!), making AI (artificial intelligence) power everything from video games to robot cars and life-saving medicines. Imagine NVIDIA as a super-smart toolbox using Core Maths (like algebra and statistics) to build AI brains for the 2035 world. Here’s what 15-year-olds and teachers should know, with a fun, iterative list of NVIDIA’s platforms and how they connect to AI, digital twinning, photonics, and more.
1. What Makes NVIDIA So Big?
Who They Are: NVIDIA, led by Jensen Huang, makes chips (like H100 GPUs) that are the brains of AI, games, and robots. They’re building Taiwan’s supercomputer with 8 partners (e.g., TSMC,) to run mega-AI for science and business.
Global Reach: NVIDIA works with 50+ big companies (e.g., Google, Disney) and 100+ engineers who’ve been with Jensen since the 2000s, creating AI platforms worldwide.
Why It’s Cool: Their chips make your phone, VR headset, or self-driving car smarter, faster, and greener!
Core Maths: Linear algebra (for neural networks) and statistics (for data analysis) power NVIDIA’s AI chips.
For Teens: NVIDIA’s like the chef cooking AI recipes with math spices—yummy tech for 2035!
2. Iterative List of NVIDIA’s AI Platforms
Let’s list NVIDIA’s key platforms (like apps for AI) that use Core Maths to solve real-world problems. Each is interconnected with AI, digital twinning, and photonics (light-powered tech from CIOE 2025,).
Clara (Biotech AI):
What It Is: NVIDIA’s Clara uses AI to help doctors and scientists, like mapping surgeries or designing drugs.
Key Partner: Recursion uses Clara to find new medicines with AI ().
History: Inspired by Demis Hassabis (DeepMind, 2009), Jensen bet NVIDIA on deep learning. DeepMind’s AlphaFold 3 (2021) mapped 250M proteins ().
Core Maths: Statistics analyze genomic data, optimization speeds drug discovery.
Photonics Link: Lasers (from CIOE 2025,) scan cells for AI analysis.
For Teens: Clara’s like a super-smart doctor using light and math to save lives!
Pixel Coding and Omniverse (Graphics and Digital Twinning):
What It Is: Started with Pixar (2002, with Steve Jobs) for movie graphics, now Omniverse creates digital twins (virtual models) of factories, cities, or hospitals.
Examples: Hollywood (e.g., Spielberg), radiology AI, and Hong Kong medical digital twins ().
Core Maths: Geometry renders 3D visuals, linear algebra builds virtual worlds.
Photonics Link: AR/VR displays () visualize digital twins.
For Teens: Omniverse is like Minecraft for real factories, using light screens!
Drive (Self-Driving Cars):
What It Is: NVIDIA’s Drive powers self-driving cars (e.g., Tesla, Baidu) with AI vision.
Why It’s Big: Cars are mobile robots, using video for safety.
Core Maths: Computer vision (using matrices) detects roads, statistics predicts moves.
Photonics Link: LiDAR sensors () map 3D roads for AI.
For Teens: Drive makes cars see like superheroes, using light and math!
Newton (Robotics and Physical AI):
What It Is: Announced in 2025, Newton (with DeepMind, Pixar/Disney) builds robots with touch and physical AI.
Why It’s Cool: Robots learn movements via synthetic data (AI-generated examples,).
Core Maths: Calculus models motion, neural networks train robots.
Photonics Link: Infrared sensors () guide robot senses.
For Teens: Newton’s like teaching robots to dance, using light eyes!
Human Senses AI (Vision, LLMs, Reasoning):
What It Is: NVIDIA’s chips power AI mimicking 5 human senses (e.g., vision, hearing), starting with Fei-Fei Li’s ImageNet (2012, AlexNet).
Examples: LLMs (e.g., ChatGPT), deep reasoning (e.g., Grok 3), and language translation.
Core Maths: Linear algebra for neural nets, probability for reasoning.
Photonics Link: Optical chips () speed AI training.
For Teens: It’s like AI learning to see and talk, powered by light chips!
Business Process AI (Enterprise):
What It Is: NVIDIA partners with ServiceNow, Databricks, Snowflake, and Salesforce to reengineer businesses with AI (e.g., data warehousing, customer service).
Why It’s Big: Trillion-dollar sectors (e.g., retail) use AI to optimize processes.
Core Maths: Optimization streamlines workflows, statistics analyzes data.
Photonics Link: Fiber optics () connect AI data centers.
For Teens: It’s like AI making stores or offices super-efficient, using light cables!
Earth Mapping and Climate AI:
What It Is: NVIDIA’s AI maps Earth (e.g., weather, climate) and optimizes energy grids.
Examples: Climate models, smart grids ().
Core Maths: Differential equations predict weather, graph theory optimizes grids.
Photonics Link: Satellites with optics () collect climate data.
For Teens: AI’s like a weather wizard, using light from space!
Inception Startups (Innovation Ecosystem):
What It Is: NVIDIA’s Inception program supports 2,000+ startups in AI, biotech, drones, and space.
Why It’s Cool: Shows AI isn’t just for big players—small teams innovate too!
Core Maths: Statistics for startup AI, numerical analysis for tools.
Photonics Link: Sensors, optics () in startup drones.
For Teens: It’s like a science fair where kids build AI gadgets with light!
and much more
===============also
see Macraes' Economistdiary 1984
this year family turned to launches of von neumann biography
new Vikings published in Sweden 1993
English manuscript 55 pages avaialable at cost of photocopying - please note norman macrae's final proof cvopy is all that exists in English - to see sample page reproduction free - rsvp chris.macrae@yahoo.co.uk Bethsda +1 240 316 8157
Extracts:
Introduction
The Swedish Employers' Confederation asked me to write this naughty little book in 1992-3 because in 1982-3 my son and I wrote a wider and naughtier one. In Britain this came out intime to offer an alternative endgame to Orwell's Big Brother and was titled The 2024 Report - a concise future history of 1974-2024. The main American version was called 2025 Report as it came out a year later. A French version was called Rapport 2026.
In these previous future histories there was one main event of the 1980s before scaling of digital webs of 1990s. The 1980s positive scenario was Communism Disappears. According to out future histories the Berlin Wall came down on Helige Nacht Chrismas eve, 1989. Since in real life it came down about 6 weeks earlier, I hope this cheerful future history of Sweden is also about 6 weeks too pessimistic.
