VMware by Broadcom Private AI Primer – An Emerging Category

Private AI as an architectural approach that aims to balance the business gains from AI with the practical privacy and compliance needs of the organization. What is most important is that privacy and control requirements are satisfied, regardless of where AI models and data are deployed. This session will walk through the core tenets of Private AI and the common use cases that it addresses.

Chris Wolf, Global Head of AI and Advanced Services at VMware by Broadcom, discusses the evolution of application innovation, highlighting the shift from PC applications to business productivity tools, web applications, and mobile apps, and now the rise of AI applications. He emphasizes that AI is not new, with its use in specialized models for fraud detection being a longstanding practice. Chris notes that financial services with existing AI expertise have quickly adapted to generative AI with large language models, and he cites a range of industry use cases, such as VMware’s use of SaaS-based AI services for marketing content creation.

He mentions McKinsey’s projection of the annual potential economic value for generative AI being around $4.4 trillion, indicating a significant opportunity for industry transformation. Chris discusses the early adoption of AI in various regions, particularly in Japan, where the government invests in AI to compensate for a shrinking population and maintain global competitiveness.

The conversation shifts to privacy concerns in AI, with Chris explaining the concept of Private AI, which is about maintaining business gains from AI while ensuring privacy and compliance needs. He discusses the importance of data sovereignty, control, and not wanting to inadvertently benefit competitors with shared AI services. Chris also highlights the need for access control to prevent unauthorized access to sensitive information through AI models.

He then outlines the importance of choice, cost, performance, and compliance in the AI ecosystem, asserting that organizations should not be locked into a single vertical AI stack. Chris also describes the potential for fine-tuning language models with domain-specific data and the use of technologies like retrieval augmented generation (RAG) for simplifying AI use cases.

Finally, Chris emphasizes the need for adaptability in AI solutions and mentions VMware’s focus on adding value to the ecosystem through partnerships. He briefly touches on technical implementation, including leveraging virtualization support for GPU resources and partnering with companies like IBM Watson for model serving and management. He concludes by providing resources for further information on VMware’s AI initiatives.