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How Nvidia got here and where it’s going next sheds light on how the company has achieved that valuation, a story that owes a lot to the rising importance of specialty chips in business—and accelerating interest in the promise of generativeAI.
As the generativeAI bandwagon gathers pace , Nvidia is promising tools to accelerate it still further. On March 21, CEO Jensen Huang (pictured) told attendees at the company’s online-only developer conference, GTC 2023, about a string of new services Nvidia hopes enterprises will use to train and run their own generativeAI models.
GenerativeAI in healthcare is a transformative technology that utilizes advanced algorithms to synthesize and analyze medical data, facilitating personalized and efficient patient care. GenerativeAI models now play a role, in drug discovery and development by reducing time and costs associated with bringing new medications to market.
GenerativeAI in healthcare is a transformative technology that utilizes advanced algorithms to synthesize and analyze medical data, facilitating personalized and efficient patient care. GenerativeAI models now play a role, in drug discovery and development by reducing time and costs associated with bringing new medications to market.
GenerativeAI is the wild card: Will it help developers to manage complexity? It’s tempting to look at AI as a quick fix. Whether it will be able to do high-level design is an open question—but as always, that question has two sides: “Will AI do our design work?” Did generativeAI play a role?
Conversational AI has come a long way in recent years thanks to the rapid developments in generativeAI, especially the performance improvements of large language models (LLMs) introduced by training techniques such as instruction fine-tuning and reinforcement learning from human feedback.
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