We offer 3 Diary Viewpoints of 2025Report and welcome AI20s.com open co-eds notably female neuroscientists & tech wizards aligned to King Charles AI World Series. - Neumann-Einstein-Turing first suggested to Economist Journalists to map (exponential maths audit Trillion Dollar Markets) future back from 2025 in 1951; from 1984 forward see EconomistDiary.com/1984; for earlier stuff see also Neumann Ning; for some current stuff continue at this web - eg is water the artificial and human intelligence that can help millennilals be first renewable generation? Economistwater.com (EWa) and ED invite you to help us connect our futures alphabet A B D F G H J M S Wo U Y. At Neumann.ning.com -... we are in trouble - good trouble. Its as if everything we published from 73 years of Economist surveys with von neumann whom dad met in 1951 is history. That's good news- thanks 8 billion to AI Angel visiting Washington DC on 1 March 2023 :: ...Golden Oldie flashback: EconomistDiary.com schools brief 14 (1964):

Saturday, April 13, 2024

Nvidia ^ the World from 1993 to 2025

Some dates approximate - we welcome rvisions & if use happy to link to your profile somewhere
WE began eporting brainworkers words adter briefings drom Von Neumann & his peer NET (Neumann-Enstein-Turing) applied over 40 years by dad norman macrae at The Economist ; I was bien in 1951 so growing up with computers was quie curious - in london thery were'nt seen in high schools (Slide ruler was best tool maths people before i started my gap year in 1968; i worked at ministroy f transport; there a director was getting cards ounched in binary - there was a shared mainframe t the central electrcty generating biard; somrimes I was odered to carry the binary cars to and fro; apparently if one card goy t out f rder the whole verninght prgrm broke down; 

anyhow it is sort f amazibg that even when personal comouters arrived eg 1984 beng famous year of apple mac and rical ibm pc getting microsoft software the central processing unt dir sequentail comouting 

Enter 1993 Nvidia and jensen huang - building acceerated comoutng fir comouters sequetial one dimensonal comouting cant do
2 years later 1995 microsoft windws arrved ad a secnd taiwanese amer=ican started up - Jerry Yang Yahoo; 
yu can see nvsia's stry at
blog eg featured jensen stories includeexecutive officer and a member of the board of directors.

article 2015/6 explaims world summits piloted from 2009  includes:

GPU Deep Learning “Big Bang”

 As I wrote in an earlier post (“Accelerating AI with GPUs: A New Computing Model”), 2012 was a landmark year for AI. Alex Krizhevsky of the University of Toronto created a deep neural network that automatically learned to recognize images from 1 million examples. With just several days of training on two NVIDIA GTX 580 GPUs, “AlexNet” won that year’s ImageNet competition (annual world championships hosted by Stanford's Fei-Fei i; , beating all the human expert algorithms that had been honed for decades. That same year, recognizing that the larger the network, or the bigger the brain, the more it can learn, Stanford’s Andrew Ng and NVIDIA Research teamed up to develop a method for training networks using large-scale GPU-computing systems.

The world took notice. AI researchers everywhere turned to GPU deep learning. Baidu, Google, Facebook and Microsoft were the first companies to adopt it for pattern recognition. By 2015, they started to achieve “superhuman” results — a computer can now recognize images better than we can. In the area of speech recognition, Microsoft Research used GPU deep learning to achieve a historic milestone by reaching “human parity” in conversational speech.

Image recognition and speech recognition — GPU deep learning has provided the foundation for machines to learn, perceive, reason and solve problems. The GPU started out as the engine for simulating human imagination, conjuring up the amazing virtual worlds of video games and Hollywood films. Now, NVIDIA’s GPU runs deep learning algorithms, simulating human intelligence, and acts as the brain of computers, robots and self-driving cars that can perceive and understand the world. Just as human imagination and intelligence are linked, computer graphics and artificial intelligence come together in our architecture. Two modes of the human brain, two modes of the GPU. This may explain why NVIDIA GPUs are used broadly for deep learning, and NVIDIA is increasingly known as “the AI computing company.”

on-demand parner videos using new accelerated comouting
or this inm travck of major nvidia summits since 2015
2015 Wahington DC - opening headline Deep Learning

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