Exploring the Three Sharing Models of JNI

In this podcast episode, host Darren Pulsipher, Chief Solution Architect of Public Sector at Intel, discusses the operationalization of generative AI with returning guest Dr. Jeffrey Lancaster. They explore the different sharing models of generative AI, including public, private, and community models. The podcast covers topics such as open-source models, infrastructure management, and considerations for deploying and maintaining AI systems. It also delves into the importance of creativity, personalization, and getting started with AI models.

Exploring Different Sharing Models of Generative AI

The podcast highlights the range of sharing models for generative AI. At one end of the spectrum, there are open models where anyone can interact with and contribute to the model’s training. These models employ reinforcement learning, allowing users to input data and receive relevant responses. Conversely, some private models are more locked down and limited in accessibility. These models are suitable for corporate scenarios where control and constraint are crucial.

However, there is a blended approach that combines the linguistic foundation of open models with additional constraints and customization. This approach allows organizations to benefit from pre-trained models while adding their layer of control and tailoring. By adjusting the weights and words used in the model, organizations can customize the responses to meet their specific needs without starting from scratch.

Operationalizing Gen AI in Infrastructure Management

The podcast delves into the operationalization of generative AI in infrastructure management. It highlights the advantages of using open-source models to develop specialized systems that efficiently manage private clouds. For example, one of the mentioned partners implemented generative AI to monitor and optimize their infrastructure's performance in real time, enabling proactive troubleshooting. By leveraging the power of AI, organizations can enhance their operational efficiency and ensure the smooth functioning of their infrastructure.

The hosts emphasize the importance of considering the type and quality of data input into the model and the desired output. It is not always necessary to train a model with billions of indicators; a smaller dataset tailored to specific needs can be more effective. By understanding the nuances of the data and the particular goals of the system, organizations can optimize the training process and improve the overall performance of the AI model.

Managing and Fine-Tuning AI Systems

Managing AI systems requires thoughtful decision-making and ongoing monitoring. The hosts discuss the importance of selecting the proper infrastructure, whether cloud-based, on-premises, or hybrid. Additionally, edge computing is gaining popularity, allowing AI models to run directly on devices reducing data roundtrips.

The podcast emphasizes the need for expertise in setting up and maintaining AI systems. Skilled talent is required to architect and fine-tune AI models to achieve desired outcomes. Depending on the use case, specific functionalities may be necessary, such as empathy in customer service or creativity in brainstorming applications. It is crucial to have a proficient team that understands the intricacies of AI systems and can ensure their optimal functioning.

Furthermore, AI models need constant monitoring and adjustment. Models can exhibit undesirable behavior, and it is essential to intervene when necessary to ensure appropriate outcomes. The podcast differentiates between reinforcement issues, where user feedback can steer the model in potentially harmful directions, and hallucination, which can intentionally be applied for creative purposes.

Getting Started with AI Models

The podcast offers practical advice for getting started with AI models. The hosts suggest playing around with available tools and becoming familiar with their capabilities. Signing up for accounts and exploring how the tools can be used is a great way to gain hands-on experience. They also recommend creating a sandbox environment within companies, allowing employees to test and interact with AI models before implementing them into production.

The podcast highlights the importance of giving AI models enough creativity while maintaining control and setting boundaries. Organizations can strike a balance between creative output and responsible usage by defining guardrails and making decisions about what the model should or shouldn't learn from interactions.

In conclusion, the podcast episode provides valuable insights into the operationalization of generative AI, infrastructure management, and considerations for managing and fine-tuning AI systems. It also offers practical tips for getting started with AI models in personal and professional settings. By understanding the different sharing models, infrastructure needs, and the importance of creativity and boundaries, organizations can leverage the power of AI to support digital transformation.