Very Good AI- can ai robot agents become communities' most trusted AI especially where teachers want students to discover 21st C most joyful leaps
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 Invitation from EconomistWater.com EconomistDiary.com EconomistJapan.com PovertyMuseums.blogspot.com CatholicUni.com

EW  ED   EJ  PM  CU invite you to sublet 3000+ intelligence spaces (mostly freee)

How it works – find unused month at our webs. Tell us your focus on intelligence or AI layer 5 App

chris.macrae@yahoo.co.uk

Urgency these academics and big AI say humans cant cope with intelligence explosion-  

quite the most ignorant valuation of human race my alma mater Cambridge has ever published

Let’s prove them wrong – if john lennon was alive he’d celebrate thousands of community intelligence solutions  not yet mapped from east to west or south to north or in your country unless you already love AI for all

.		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
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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.
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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.
related - which ai contexts now need transforming education so students become smarter or deep data context attentive than teachers

Saturday, December 31, 2011

Tracking AI Startups 2025 - 2011

 With AI being expected to be the soul of the world's largest companies, the idea that eg Nvidia's CEO Jensen Huang designs his conpany to track (even friend) 18000 start ups may sound weir. In truth the ai ecosystem needs (probably more) seeds than ay other ecosyem - brain tools, channels to specific contxts, and in some cases eg climate ai and life scoeces ai is effectively the future of most of that market's innovation


take 2024 top 50 strtup compiled by forbes and originally tracked with Seqoia - of 50 startups early 2024 https://www.forbes.com/lists/ai50/?sh=3585c09f290f less than 10 claim fundinf over hald a billion - of course its true that by time a startup is nearing unicirn status it may well end strtup life beung ipo'd or aquired

but here are the mainly american big fish in AI start up pool of early 2024 


OPEN AI - one off case a musk funded n go that turned profit -and became llm chat gpt4 producer- seems to have too many partners to be ipo'd unless microsoft buys it -  valued at over 11 billion (when it comes to llms its an unique valuation league - it is said that while the first llm cost about 1000 $ , to launch a new one as a potential world leadwr would cost over 100 billion; of course llms leverage really big computing so the bigget digital companies may well see company and main llm arcitectire as insperable


Adunil Ai Defence 2.8 bn

Anthropic 7.2 bn - another llm with an odd stoiry- probably forst funded by nft notoriety banker-freeman; timely enough toi build an llm but seems to have found mixed partner to leverage computing caacity with eg amazon

cerebras 720 million chips manufacturer

Databricks -stata strage and analytics  4 bn dollar (get this field right and you emerge with data warehousing's Snowflake

Here is some commentary from Sequoia 2023 which clarifies:

When we launched the AI 50 almost five years ago, I wrote, “Although artificial general intelligence (AGI)… gets a lot of attention in film, that field is a long way off.” Today, that sci-fi future feels much closer.

The biggest change has been the rise of generative AI, and particularly the use of transformers (a type of neural network) for everything from text and image generation to protein folding and computational chemistry. Generative AI was in the background on last year’s list but in the foreground now.

The History of Generative AI

Generative AI, which refers to AI that creates an output on demand, is not new. The famous ELIZA chatbot in the 1960s enabled users to type in questions for a simulated therapist, but the chatbot’s seemingly novel answers were actually based on a rules-based lookup table. A major leap was Google researcher Ian Goodfellow’s generative adversarial networks (GANs) from 2014 that generated plausible low resolution images by pitting two networks against each other in a zero sum game. Over the coming years the blurry faces became more photorealistic but GANs remained difficult to train and scale.

In 2017, another group at Google released the famous Transformers paper, “Attention Is All You Need,” to improve the performance of text translation. In this case, attention refers to mechanisms that provide context based on the position of words in text, which vary from language to language. The researchers observed that the best performing models all have these attention mechanisms, and proposed to do away with other means of gleaning patterns from text in favor of attention.

The eventual implications for both performance and training efficiency turned out to be huge. Instead of processing a string of text word by word, as previous natural language methods had, transformers can analyze an entire string all at once. This allows transformer models to be trained in parallel, making much larger models viable, such as the generative pretrained transformers, the GPTs, that now power ChatGPT, GitHub Copilot and Microsoft’s newly revived Bing. These models were trained on very large collections of human language, and are known as Large Language Models (LLMs). 

Although transformers are effective for computer vision applications, another method called latent (or stable) diffusion now produces some of the most stunning high-resolution images through products from startups Stability and Midjourney. These diffusion models marry the best elements of GANs and transformers. The smaller size and open source availability of some of these models has made them a fount of innovation for people who want to experiment.

As does this visual on the top 50 at 2023


our trends in this year’s list

Generative AI Infrastructure: OpenAI made a big splash last year with the launch of ChatGPT and again this year with the launch of GPT-4, but their big bet on scale and a technique called Reinforcement Learning with Human Feedback (RLHF) is only one of many directions LLMs are taking. Anthropic and their chatbot Claude use a different approach called Reinforcement Learning Constitutional AI (RL-CAI). The CAI part encodes a set of human-friendly principles designed to limit abuse and hallucination in the outputs. Meanwhile Inflection, a secretive startup founded by DeepMind’s Mustafa Suleyman and Greylock’s Reid Hoffman, is focusing on consumer applications.

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3 comments:

  1. deJune 2, 2026 at 10:38 PM

    The article presents a broad perspective on the evolving relationship between artificial intelligence, biotechnology, entrepreneurship, and global innovation ecosystems. The discussion of generative AI, transformers, diffusion models, startups, and future technological opportunities highlights how advances in computing and data-driven intelligence are reshaping industries ranging from healthcare and life sciences to business and education. The emphasis on questioning assumptions and preparing for large-scale technological shifts makes the article thought-provoking and forward-looking.

    ReplyDelete
  2. deJune 2, 2026 at 10:38 PM

    A particularly interesting section is the overview of generative AI's evolution from early systems such as ELIZA to modern transformer-based models, diffusion models, and large language models. The explanation of GPT architectures, RLHF, Claude, image generation technologies, and AI startup ecosystems illustrates the rapid progress being made in intelligent content generation and automation. These developments closely align with Generative AI Projects for Final Year, where advanced AI models are applied to create text, images, and other forms of digital content.

    ReplyDelete
  3. deJune 2, 2026 at 10:38 PM

    The article also provides valuable insight into the foundational technologies behind modern AI systems, particularly the role of transformers in large language models and diffusion-based approaches for image synthesis. Since much of the discussion focuses on GPT models, transformer architectures, and their impact on generative applications, it strongly relates to Text Transformer Projects, which explore the mechanisms that enable modern AI systems to understand and generate human-like content.

    ReplyDelete

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