With NVIDIA's announcement of AI Enterprise 5.0 and NVIDIA Inference Microservices at the GTC conference, CEO Jensen Huang plans to usher in an era of making enterprise AI deployment easier and more applicable than ever, possibly while changing the primary way in which that people interact with computers.
The idea of controlling and programming computers with prompts alone is similar to what Humane has proposed with its prompt-based Ai Pin, but Huang extends it to developers and IT, as well as consumers: “The computer's job is not require C++ to be useful,” Huang said during the NVIDIA GTC press Q&A session held on March 19 in San Jose, California (Figure A).
Figure A
NVIDIA CEO Jensen Huang speaks during a question-and-answer session with the press during the NVIDIA GTC in San Jose, California, on March 19. Image: Megan Crouse/TechRepublic
Huang: Rapid engineering is transforming programming
When asked whether programming will still be a useful skill in the era of generative AI prompts, Huang said, “I think people should learn all kinds of skills” and compared coding to juggling, playing the piano, or learn calculus. However, Huang said, “Programming will not be essential for you to be a successful person.”
SEE: Huang announced a wide range of NVIDIA products for data centers, enterprise AI, cryptography and more during the GTC conference keynote. (Technological Republic)
Generative AI, Huang said, is “closing the technology gap. You don’t have to be a C++ programmer to be successful,” he stated. “You just have to be a quick engineer. And who can't be a punctual engineer? When my wife talks to me, she quickly manipulates me. …We all need to learn how to goad AIs, but that's no different than learning how to goad teammates.”
Huang went on to say, “But if someone wants to learn how to do it (programming), let them do it because we are hiring programmers.”
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Rapid engineering is a rapidly changing skill
Will rapid engineering replace traditional programming when it comes to creating generative AI from generative AI, as Huang suggested?
“I wouldn't quit my day job just yet to become a fast-track engineer,” Gartner analyst Chirag Dekate said in a call to TechRepublic on March 19. “Unfortunately, the market is overcorrecting.”
And the market is overcorrecting to a surge in demand for what used to be rapid engineering. In a rapidly changing industry, optimizing prompts to get an AI to generate the right text may no longer be the way AI prompt engineering is done; instead, indications can be multimodal.
NIMs are notable, Dekate said, because they fit generative AI neatly into the hybrid multicloud context in which many companies operate. “What NVIDIA is building now is a foundation for next-generation AI-native enterprises, where everywhere enterprises go they will experience NIM,” she said.
However, NVIDIA may not be the company to make the transformation a reality. Dekate pointed to Cognition AI, which last week introduced Devin, its “AI software engineer,” as a sign that the way software engineering is done may change in the future.
No matter what name ends up on the most common software, Dekate said the way developers interact with generative AI will change quickly.
“The pace of innovation in generative AI continues to accelerate,” Dekate said. “We most likely will not interact with any of these models using our inherited perceptions. I refer to technology from three or six months ago as legacy. “Generative AI changes that quickly.”
David Nicholson, research director at The Futurum Group, told TechRepublic via email that in a generative AI future “a facility with human language becomes an important computing skill.”
“Your degree in English (or) history or law suddenly helps you become an engineer quickly, but a major in computer science will never hurt,” Nicholson wrote. “It is not an exaggeration from NVIDIA. “It is truly a revolution.”
Disclaimer: NVIDIA paid for my airfare, lodging, and some meals for the NVIDIA GTC event held March 18-21 in San Jose, California.
