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.

NVIDIA to Build Earth-2 Supercomputer to See Our Future

The earth is warming. The past seven years are on track to be the seven warmest on record. The emissions of greenhouse gases from human activities are responsible for approximately…Read Article

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.”

podcast

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