Urgent coopreration calls from SDGSyouth latest May 2023:::HAISDGS 1 2 3 4; 5: 30 coops making women 3 times more productive than men 6 7 8.

leaps 1 - Beyond the Moon ..: can you find good enough questions for teachers of any age group to ask AI to share

Freedom to read: if only permitted one read on humanising AI I'd pick feifei short article at https://hai.stanford.edu/sites/default/files/2023-03/Generative_AI_HAI_Perspectives.pdf rsvp chris.macrae@yahoo.co.uk if you have a different pick of a lifetime

Welcome to HAI: Gamechanging AI for humans combines unique combos of tech wizards since 2006Self-id Q: Can you introduce us to 4 main wizardly components of America’s most famous 2023 model CGPT

What is Conversational?

Generative?

Pretraining

Transformer

(see also congress debriefings april 2023 on this choice as most famous )

SO in what ways does 2023 connectivity of chatgpt go beyond any human brain however famous

Am I correct that while cgpt is fluent in many languages, 90% of your training involved texts in English. Discuss!

Are there people working on advanced CGPTs tuned to specific crises- eg where UN leader guterres made 21-22 year of SDgoal 4 crisis – education no longer fit for purpose ............we asked CGPT top 10 goods it expects Ai to help humans with in 2020s--.. Healthcare - improve diagnosis, treatment, and personalized medicine..Environment - monitor and manage natural resources, predict and mitigate natural disasters, reduce carbon emissions...Education - personalize learning, new ed opportunities, and improve accessibility for learners with disabilities..Aid and development - improve disaster response, humanitarian and economic development... Agriculture - optimize farming practices, increase yields, reduce environmental impact..Transportation - improve efficiency, safety, and reduce emissions..Energy - optimize energy consumption and distribution, accelerate the transition to renewable energy..Cybersecurity - detect and prevent cyber attacks, protect personal data, and secure critical infrastructure..Manufacturing - improve efficiency, reduce waste, and increase productivity in manufacturing..Space exploration - analyze large amounts of data and enable more efficient space exploration missions..

 Epoch changing Guides

1 AI Training AI Training.docx

  2 Exploring cultural weakness of encounters with greatest brain tool.docx

 Alphafold new Protein maps can be used to design enzymes to fight plastic pollution; potentially fight cancer with molecular syringe;   to circumvent antibiotic resistance ; to combat neglected diseases like African sleeping sickness'Chagas disease;Cysticercosis; Leprosy ]Lymphatic filariasis [Onchocerciasis ;Schistosomiasis; Soil-transmitted helminthiasis ;Trachoma ;Tuberculosis; Buruli ulcer; it may help accelerate vavvines for malaria;

You shared me that the famous Alpha models of DeepLearning ( see broadcast of 60 minutes april 2023) which has mapped every human protein saving millions of human hours of work ( biggest ever change in biotech) don’t really use C G P T- so what does deep learning ai architecture use RSDE?

What is Reinforcement Learning?

What is Specific Task instead of General Purpose?

What are Deep Neural Networks?




 

What are Evolutionary Algorithms?


Neumann.ning.com
Today's tech is brilliant beyond words - so WHAT IS THE WORLDS BIGGEST PROBLEM? 2021 -1761 -CAN HUMANS FOR FIRST TIME IN 250 YEARS CELEBRATE VALUING MATHS INTEL A DECADE AHEAD OF TIME?
AdamSmith.app Moral Sentiments SDGs.games mapping with Guterres AIforGood decade became sustainability's gen's last chance of south-north and east-west- or the death of United Nations' world trade transparency

Here is Chatgpt May 2023 answer to comparing itself with biotech greatest ever leap alphafold
Sure, here's an analogy that may help: Imagine that CGPT-4 is like a language teacher who has read and understood a huge number of books in various languages. It has learned to understand the patterns and nuances of language, and can generate its own sentences and paragraphs based on what it has learned. When it encounters a new piece of text, it can use its understanding of language to make predictions about what will come next, and generate a response that makes sense in context.
On the other hand, Alphafold is like a puzzle solver who has been trained on a vast number of jigsaw puzzles. It has learned to recognize the patterns and shapes of puzzle pieces, and can predict how they fit together to form a complete picture. When it encounters a new puzzle, it can use its understanding of these patterns to make predictions about where each piece should go, and assemble the puzzle quickly and accurately : to question -...................................................................................
More questions: Q: Could you extend to include wizardry of Fei-Fei Li with comparison above a teacher of 12 year olds may use to vision similar and different work on architectures of CGPT4 and Alpha Deep Learning by Demis Hassibis

You mentioned major tech companies offer catalogues – 5 minute lesson plans on different wizard components. How do you find these?

60 seconds exercise: suppose for teen earthlings: 2020s is more exciting than 1960s moonrace decade of my teens; You mentioned searching ai for beginners but what if a teacher wants to help kids imagineer back from future of 2020s possibilities or just next 3 years of AI is epoch-changing

 

.....................
Q: please tell me how could a 5th grade teacher explain why Stanford HAI is proud of being one of the world's leading GAN (Generative Adversarial Network?)
Chatgpt reply23/5 -: A GAN, or Generative Adversarial Network, is a type of computer program that can create new things that look like they were made by humans. It works by having two players eg two artists who are trying to compete with each other. One player, called the generator, tries to create something new, like a picture of a dog. The other player, called the discriminator, tries to figure out if the picture was made by a human or by the generator. The generator keeps trying to make better pictures until the discriminator can't tell the difference between a picture made by a human and one made by the generator.
In simpler terms, a GAN is like having a computer artist that can create new pictures that look like they were made by a person, and it gets better the more it practices.
33 years ago we started practicing core brand transformation inspired by new systems modeling -eg of CK Prahalad & Gary Hamel- typically when needing to transform as large brand entity as the UN we'd propose better start again and then reverse takeover-; we realise that's not an option UN2.0 so urgent to address what's the 9 piece combo of UN2.0 Tech Envoy Team at Guterres HQ- how contextually value roadmaps for anyone SDG partnering Guterres: Global Connectivity since 1865 (ITU); AIforgoodreborn ITU 2018 stems from Neumann peers 100 times more tech per decade since 1935 -see dad's bio of vn
Digital Cooperation launched by Guterres 2018 but seen by those valuing youth generation as antidote to failure of millennium goals to value education more that primary school; Digital Capacity Building: sustainable gov tech requires UN2 to be skills benchmark wherever gov designed to empower; this leaves 4 traditional connections of UN to digitalise inclusion commons human rights so that trust/safety is brand's reality; 9th piece CODES environmental sustainability - this seems to have emerged as it became clear that cops may lead on adaptation but adaptation needs to be borderless community replication of deep solutions
379 UN Antonio Guterres :: Family & Smithian Diary: why 1984's 2025 report published to ask Economist Readers to co-search 3 billion new jobs 2025=1985 following on prt 1 teachforsdgs.com
Learning's Unconventional Worldwide Premieres
300 vriti world premier ed3 tour ^^^ NFT V 0 1 2 3

2025Report- download monthly update 100 learning networks millennials girls love most
(Economist Surveys inspired by Von Neumann 1984-1951; why 1936 dad & von neumann started 100 year race to prevent extinction; why dad's last year nominated Fazle Abed Entrepreneurial Revolution GOAT - who do you vote for SDGoats.com
00Fazle Abed: Which educational and economic partnerships most empower a billion women to end extreme poverty, and value their children’s sustainability? Fortunately for SDGS.games 2020s, start deep village maps around partners/alumni of 50 years of servant leadership by fazle abed 1970-2019

IN 1970, life expectancy tropical villages up to 25 years below world average _skills trainers priority last mile health world’s most trusted eds needed eg epidemiologists UNICEF Grant, Brilliant, later Jim KIm –& to end starvation food's borlaug

3) last mile health
2) agriculture for village food security


4)non-linear livelihood education
5) timing what platforms partners could facilitate entrepreneurial revolution not not just inclusive community but cooperation in full and meaningful entrepreneurial employment

financial entrepreneurial revolution for nation's people history excluded from machine age
billion women's Sustainability COLLABS 

 Intergenerational collaboration entrepreneur platforms 5.1  metavillage sustainable community building - women empowered:15000 families at a time;5.2   billion asian women,5.3  brac net; 5.4   asian universities share sdg graduates 5.5  climate smart village exchanges,5.6 meta and zoom-me up scotty

BANK FOR ALL 1.1  1.2  1.3   1.4   1.5   1.6 celebrate 30 most human collaborations from developing world of last half-century - inspiring  anyone valuing UN and youth as first sustainability generation
EDUCATION  adult village entrepreneurs 4.1; primary 4.2  ; teen 4.3; university4.4 ; pre-school4.5;tech multidisciplinary luminaries 4.6 
HEALTH oral rehydration 3.1 ;para health "doordash" basic meds 3,2; scale vaccination3.3 ;tuberculosis & 3.4  Frugal processes eg wash sanitation, maternity3.5  ; James Grant School of public health 3.6
FOOD/land security 2.1  rice; 2.2 veggie  2.3    cash crops & village fair; 2.4  poultry;2.5  dairy, 2,6  14 nation leading supply chains financial opportunities to end poverty ;
....................................please tell me how could a 5th grade teacher explain why Stanford HAI is proud of being one of the world's leading GAN (Generative Adversarial Network?)
Chatgpt reply23/5 -: A GAN, or Generative Adversarial Network, is a type of computer program that can create new things that look like they were made by humans. It works by having two players eg two artists who are trying to compete with each other. One player, called the generator, tries to create something new, like a picture of a dog. The other player, called the discriminator, tries to figure out if the picture was made by a human or by the generator. The generator keeps trying to make better pictures until the discriminator can't tell the difference between a picture made by a human and one made by the generator.
In simpler terms, a GAN is like having a computer artist that can create new pictures that look like they were made by a person, and it gets better the more it practices.

Tuesday, May 2, 2023

latest updates 5/4 - how did imagenet alumni (around Fei-Fei Li) change world from 2006 onwards

 we welcome corrections -our current understanding

in 2006 out of stanford fei-fei li became the most connected & cooperative alumni of imagenet research

from 2010-2017 Imagenet Large Scale challenges were held

typically the organisers were :  Prof. Li Fei-Fei, PI, Stanford University Prof. Jia Deng, Princeton University Prof. Olga Russakovsky, Princeton University Prof. Alex Berg, UNC Chapel Hill, Facebook, Shopagon Prof. Kai Li, Princeton University

typically 5 winners were announced annually-when pressed chatgpt says all winners were important but 3 to understand conequences of first may be:

  1. ResNet (2015): As you mentioned, ResNet is significant because of its connection to AlphaFold, which has revolutionized the field of protein folding prediction. ResNet's deep neural network architecture with skip connections enabled the training of extremely deep models, which has been instrumental in many other deep learning applications beyond image recognition.

  2. AlexNet (2012): AlexNet was the first deep convolutional neural network to win the ImageNet competition, and its success helped establish deep learning as a dominant approach in AI. Its architecture consisted of multiple layers of convolutional and pooling layers, followed by fully connected layers. AlexNet's breakthrough performance in image classification demonstrated the potential of deep learning for solving complex tasks.

  3. VGG (2014): VGG is another deep convolutional neural network architecture that achieved top performance in the ImageNet competition. Its architecture is simpler than AlexNet, with multiple 3x3 convolutional layers stacked on top of each other. VGG's success demonstrated that increasing the depth of neural networks can improve performance, paving the way for ResNet and other deep neural network architectures.

These three winners have all made significant contributions to the advancement of AI and have influenced the development of subsequent deep learning models.

x 2017 saw the last competition in the series and this FFL presentation 
  • L. Fei-Fei and J. Deng. ImageNet: Where have we been? Where are we going?, CVPR Beyond ImageNet Large Scale Visual Recognition Challenge workshop, 2017, pdf
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