Three engineers — Jensen Huang, Chris Malachowsky and Curtis Priem — gathered at the diner in what is now the heart of Silicon Valley to discuss building a computer chip that would make graphics for video games faster and more realistic.
Huang added that training AI models is becoming a faster process as they learn to become “multimodal” — able to understand text, speech, images, video and 3-D data — and also “to reason and plan.”
Now Nvidia’s specialized chips are key components that help power different forms of artificial intelligence, including the latest generative AI chatbots such as ChatGPT and Google’s Gemini.
Tech giants are snapping up Nvidia chips as they wade deeper into AI — a movement that’s enabling cars to drive by themselves, and generating stories, art and music.
They have a deep innovation engine that goes all the way back to the early 2000s,” said Chirag Dekate, a VP analyst at Gartner, a tech research and consulting firm.
“What Nvidia did two decades ago is they both identified and they nurtured an adjacent market where they discovered that the same processors, same GPUs that they were using for graphics could be shaped to solve highly parallel tasks.”
The original article contains 769 words, the summary contains 201 words. Saved 74%. I’m a bot and I’m open source!
This is the best summary I could come up with:
Three engineers — Jensen Huang, Chris Malachowsky and Curtis Priem — gathered at the diner in what is now the heart of Silicon Valley to discuss building a computer chip that would make graphics for video games faster and more realistic.
Huang added that training AI models is becoming a faster process as they learn to become “multimodal” — able to understand text, speech, images, video and 3-D data — and also “to reason and plan.”
Now Nvidia’s specialized chips are key components that help power different forms of artificial intelligence, including the latest generative AI chatbots such as ChatGPT and Google’s Gemini.
Tech giants are snapping up Nvidia chips as they wade deeper into AI — a movement that’s enabling cars to drive by themselves, and generating stories, art and music.
They have a deep innovation engine that goes all the way back to the early 2000s,” said Chirag Dekate, a VP analyst at Gartner, a tech research and consulting firm.
“What Nvidia did two decades ago is they both identified and they nurtured an adjacent market where they discovered that the same processors, same GPUs that they were using for graphics could be shaped to solve highly parallel tasks.”
The original article contains 769 words, the summary contains 201 words. Saved 74%. I’m a bot and I’m open source!