1979 saw japan 20th and last year of being place to live to most increase communal productvity

 As surveyed by the Economist and seceonded by JFKennedy, by 1960 Japan had not only locally rebuilt itself from rubble but was best in world at both borlaug food science on rice and deming qyality scuence - bullet trains, contaiersisation supercitity, undergriounds, microelectronucs, reliable automotives; 

this continued but was mayve ending by 1979 for several reasons

it was actually microelectronucs designed out of japan that were main beneficiary of first 15 years of moores law chip 100 fold advancement per decade (before chips were put into computers)

the property bubble was about to explode

ezra vogel's 1979 publication japan as number 1 is even more complete as he knew over 2000 years the easy leapt firward when chinma and japan exchnaged knowledge; this happened culturally many times over 2 millenial and was hapening now as japan engineers trained chian's at tsinghua

Uts of interest to note where china freed corporations for the first time to do what state enterprise could not

rural village social busienss networks especially those needed by china's barefoot medics and rice farmers

taiwanese inward investurs wherever they brough infrastructuire or capotal

The Content list of Ezra's book read

1 Challenge as A Mirror fir America

2 The Japanese Miracle

3 Successes 1/7  Knowledge: Pursuit & Consensus'

4 2/7 State : Meritocratic Guidance & Private Initiative

5 3/7 Politics: Higher Interests & Fair Shares

6 4/7 The Large Company : Identification & Performance

7 5/7 Basic education: qualuty & equality

8 6/7 Welfare security without entitlement

9 7/7 Crime Control : Enforcement & Public Support

10 American Response - Can a Western Nation Learn from the East

What I am not clear whether Ezra saw was Taiwan would inherit Japan's role as 1000 fold more tech from moores law 1980 expoenetially accelerated to quintillion times more 

moores law*satellites mobilsation of death of disrance data clouds* jensens law

Here's Dibs on Humans Intelligence AI learning starters for kids and mentors: -- under 7, DANCE  under 10 DRAWING under 13 ETHICS ENVIRONMENT 11 and plus - NOFAKEFACES PLEASE - part 2 plus debate over fame vs privacy -eg EconomistSports.net For those who want to change maths at whatever age you can -see 4 N's (Nets) GA NEUral RecurrentNEU ConvolutionalNEU (gwiz and thought english was my mother tongue)   please help us update dibs on age-sensitive AIgood Bard tells me Neural Network was named as maths model for brain in 1943 (see footnote) Fei-Fei Li coined the term "HAI" or Human-centric AI. in a mooc 2017 AI for Everyone: Succeeding in the Age . She likely coined these terms - in her mooc deep learning" use of artificial neural networks to learn from data. Elsewhere: Transfer learning is a technique that allows a neural network trained on one task to be used for another task. Zero-shot learning is a technique that allows a neural network to learn to classify images of objects that it has never seen before.

From The Economist's change of decade70s/80s wishes  - what the world needs now is for the greatest female  mathematician's star to be born Fast forward AIGames 2023 - see worlds of greatest  female intelligence stars orbiting round fei-fei li at www.economistwomen.com  & www.economistenglish.net/2023/08  - rsvp if you feel we have missed ai20s.com female rockstars  of AIforgood Why did Fei-Fei Li leave Google? Where did Fei-Fei Li go to school? Videos 41:43  Ng & Fei-Fei Li Human-Centered Artificial ... YouTube · Stanford Online 41 minutes, 43 seconds May 11, 2023 10 key moments in this video From 00:00 Introduction From 01:23 Fei-Fei Li Background From 08:40 Vision for AI From 11:47 ImageNet From 16:36 CalTech 101 Dataset From 21:06 Fei-Fei's work in healthcare From 27:39 Educating Legislators From 32:10 Advice for people getting started in AI and Machine Learning From 37:17 AI4ALL From 40:55 Final Thoughts 43:31 Scale Transform 2021 - HAI, Fei Fei Li YouTube · Scale AI 43 minutes, 31 seconds Jul 19, 2021 10 key moments in this video 17:48 Fei-Fei Li: How we're teaching computers to understand pictures TED 17 minutes, 48 seconds Mar 23, 2015 27:58 Stanford HAI 2019 - Introduction to Stanford HAI: Fei-Fei Li YouTube · Stanford 27 minutes, 58 seconds Apr 22, 2019 10 key moments in this video From 04:21 The Fourth Industrial Revolution From 05:45 Opportunities of AI From 08:39 Fundamental Principles From 10:00 Fluency in human experience From 12:16 AI in medicine From 16:37 Ethics of AI From 20:07 Goals of Stanford HAI From 22:45 AI's Next Chapter From 24:07 AI is a story of humanity From 27:21 What Stanford HAI was founded on Feedback View all

Recurrent name appeared in 1986, and convolutional 1989 Yann LeCun referring to when maths makes a third funntion from 2 functions

x the complete history of which mathematicians studied brains and which neuroscientists studied maths or computation can be very rewarding to valuing humanity but does not need to be immediately viewed to make sure you play good ai 2020s

for the record bard shares :

• John von Neumann was a Hungarian-American mathematician and physicist who is considered to be one of the founders of computer science. He wrote a paper in 1958 titled "The Computer and the Brain" in which he discussed the similarities between computers and the human brain. In this paper, he proposed that neural networks could be used to create artificial intelligence.

• Alan Turing was an English mathematician and computer scientist who is considered to be the father of theoretical computer science and artificial intelligence. He wrote a paper in 1950 titled "Computing Machinery and Intelligence" in which he proposed the Turing test, which is a test of a machine's ability to exhibit intelligent behavior equivalent to, or indistinguishable from, that of a human. In this paper, he also discussed the possibility of creating artificial neural networks.

However, it is important to note that neither von Neumann nor Turing actually built a neural network. The first neural network was built in 1958 by Frank Rosenblatt, who called his neural network the perceptron.

fast forwsrd to 2014- also year 5 of fei-fei li and stanfird sharing vision coding imagenet with the world --

• Generative adversarial networks (GANs) are a type of neural network that can generate new data. GANs consist of two neural networks: a generator and a discriminator. The generator is responsible for generating new data, and the discriminator is responsible for distinguishing between real and fake data.

The name "generative adversarial" comes from the fact that the generator and discriminator are in a constant battle with each other. The generator is trying to create data that is so realistic that the discriminator cannot tell the difference between it and real data. The discriminator is trying to learn to distinguish between real and fake data.

The names for these three types of neural networks were chosen by different people. The name "convolutional neural network" was coined by Yann LeCun in 1989. The name "recurrent neural network" was coined by David Rumelhart and James McClelland in 1986. The name "generative adversarial network" was coined by Ian Goodfellow, Yoshua Bengio, and Aaron Courville in 2014.