Building a new large language model (LLM) from scratch can cost a company millions — or even hundreds of millions. But there are several ways to deploy customized LLMs that are faster, easier, and, most importantly, cheaper.
Businesses are using DLP tools to help secure generative AI and reduce risks of ChatGPT and similar applications.
Data issues are still among the chief reasons why AI projects fall short of expectations, but the advent of generative AI has added a few new twists.
In 2022, the big AI story was the technology emerging from research labs and proofs-of-concept, to it being deployed throughout enterprises to get business value. This year started out about the same, with slightly better ML algorithms and improved data pipelines. But that seems so long ago now. And in the few months left of 2023, change could further offset what we’ve come to expect.
Enterprise software vendors of all stripes are beginning to add generative AI features to their applications. Here’s how Stream Financial, NFP, and Thomson Reuters are (cautiously) taking advantage of the technology.
In addition to controlling the use of ChatGPT and other standalone tools, companies now have to grapple with generative AI being built into the productivity apps their employees use every day
Highlights include a private AI platform developed with Nvidia that lets enterprises run generative AI applications on their proprietary data within VMware’s hybrid cloud infrastructure.
Enterprises—and their technology vendors—continue to fall over themselves to add gen AI capabilities to tools and platforms. But moving too fast can expose companies to present and future compliance risks, as well as potential legal liabilities.
Traditional advanced analytics are not going to disappear with the rise of generative AI. Cultural and quality challenges hinder organizations as attention shifts to new trends.
VMware Private AI Foundation with Nvidia will enable enterprises to customize models and run generative AI applications on their own infrastructure in their data centers, at the edge, and in their clouds.
Beyond the household names for generative AI models, a burst of other options are emerging for the enterprise. Here’s a look at some that might be the right fit where others fall short.
Getting business value out of data requires data that’s accurate, comprehensive, and unbiased. Having a team composed of people with varying demographic, educational, and professional backgrounds can help companies reach that goal.
Rapid growth and development of AI is pushing the limits of cybersecurity and CISOs must take charge now to be ahead of a range of risks including data leak, compliance and prompt injection attacks.
When asked, IT leaders, and the companies they represent, must be able to immediately harness the power of generative AI, and understand, as well as manage, potential vulnerabilities, biases, and other risks to protect sensitive data and maintain customer trust.
Regulators around the world are taking a renewed look at AI and it’s no surprise, what with all the warnings about the potential of an extinction event. But even if AI doesn’t go SkyNet on us, there are still risks — privacy, bias, and misinformation chief among them.
