SAS outlined its generative AI strategy that revolves around connecting its data science and analytics platform to a choice of large language models, focusing on "last mile" delivery of AI applications and focusing on industries
Generative AI and large language models (LLMs) have received plenty of buzz, but enterprises need to stay focused on how domain-specific models develop. Why? That's where the returns will be.
Why a CTO can become a CFO at Chevron: The company's ability to drive returns and efficiency is enabled by technologies including analytics, robotics, machine learning and high performance computing.
Databricks infused its data platform with generative AI capabilities across its Lakehouse Platform with tools to enable customers to leverage large language models (LLMs), federate and govern data and knowledge engines that learn corporate cultures.
PC executives are hopeful demand will start to improve in part because enterprises will need devices capable of handling generative AI models, data science, analytics and collaboration workloads.
"We are still in the early stages of using large data models to power improved user experiences and efficiencies across our platform, with much more to come," says Uber CEO Dara Khosrowshahi.
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