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Three years into our hype-heavy Gemini journey, it's time to ask some tough questions.
Google's Gemini AI assistant is supposed to make our lives easier. And yet...
While Nvidia, AMD and others have benefited the most from AI's rapid adoption, the future will belong to companies that develop energy-efficient chips that can power smaller language models in corporate data centers and edge devices.
Generative AI extensions like Claude for Sheets let you extract emails and phone numbers, categorize text, determine sentiment, and perform other tasks on your text right in Google Sheets — all without writing code.
ChatGPT’s Windows app is now available to everyone — and even if you're a devoted dweller of Microsoft's ecosystem, it's worth your while to try it out.
Forget searching the web: Copilot wants to know how your day went.
One of the Chromebook's most meaningful advantages is about to take a back seat to something silly.
Georgia Tech partnered with Nvidia to roll out its first supercomputer so students can experiment with AI and machine learning to better prepare for a job market where those skills are now critical to success.
The LLM will be developed with the collaboration of the Barcelona Supercomputing Center and the Royal Spanish Academy of Language, Spain’s Prime Minister said.
Nvidia's unprecedented leap in revenue from higher chip sales for AI and cloud use speaks volumes about the future of the technology and its impact on the economy.
Retrieval augmented generation, or 'RAG' for short, creates a more customized and accurate generative AI model that can greatly reduce anomalies such as hallucinations.
Only a week after releasing Gemini 1.0, Google has pushed out for testing its latest multimodal AI model; it offers long-context understanding that can accept more than one million tokens.
Thomson Reuters spent years building an AI platform to cull through massive troves of data and documents for its legal, global trade and compliance clients. But when generative AI came along, the company was forced to up its game.
With a more than 10x explosion in the number of available large language models (LLMs) for companies looking to deploy a generative AI projects, you might assume that all of the models “are basically the same.” Vikram Chatterji, co-founder and CEO at Galileo, joins the show to discuss the major differences between LLMs and what parameters companies need to explore before choosing one for their project.