In this discussion, Spencer Schneidenbach, a Microsoft MVP and CTO of Aviron Software, shares his expertise on integrating large language models in healthcare applications. He elaborates on using AI to improve customer interactions by summarizing conversations and analyzing sentiments. The chat touches on Microsoft's Semantic Kernel, which simplifies connecting AI to APIs. Additionally, Spencer highlights the balance between creativity and consistency in AI responses, emphasizing the necessity of effective prompting and rigorous testing for successful AI implementations.
Spencer Schneidenbach discusses leveraging large language models to enhance customer interactions and identify key sentiment areas in healthcare.
The integration of Microsoft's Semantic Kernel enables easy connectivity between OpenAI models and APIs, streamlining AI application development.
The podcast highlights the balance between creativity and consistency in AI outputs, emphasizing the importance of testing and refining these systems.
Deep dives
The End of a Global Conflict
The year 1945 marked significant global changes with the conclusion of World War II, highlighted by the surrender of Nazi Germany in May and Japan in August. The bombings of Hiroshima and Nagasaki were pivotal moments that ultimately led to Japan's defeat. Additionally, the Potsdam Conference played a crucial role in reshaping post-war Europe as leaders from the US, UK, and Soviet Union convened to determine the division of Germany. This period also witnessed notable military engagements, such as the Battle of Okinawa and the liberation of concentration camps, illustrating the complex end to a devastating war.
Technological Breakthroughs of 1945
A remarkable technological advancement in 1945 was the Trinity test, marking humanity's first encounter with nuclear power. The construction of ENIAC, the first fully programmable computer in the United States, was also initiated during this time, primarily aimed at military applications like calculating artillery tables. Prominent science fiction author Arthur C. Clarke introduced the concept of geostationary satellites, proposing that a satellite could orbit Earth at the same speed as its rotation. These innovations set the stage for future technological progress in computing and space exploration.
The Emergence of AI Technology
The discussion delved into the integration of AI in business processes, highlighting its growing importance in enhancing efficiency and decision-making. The implementation of AI chatbots to analyze customer service calls in sectors like healthcare exemplified practical applications of generative AI. These chatbots can extract insights from conversations, allowing companies to identify trends and address key concerns such as HIPAA compliance. This transition illustrates how AI enables businesses to harness data more effectively and expand their service offerings.
Challenges and Innovations in AI Development
The complexities of AI development were explored, especially the challenges in achieving a balance between creativity and consistency in AI outputs. The conversation emphasized the importance of testing and refining AI systems to maintain performance while managing costs associated with model usage, particularly when leveraging platforms like OpenAI. Strategies like prompt engineering and setting appropriate parameters, such as temperature, were discussed as methods to enhance AI performance. These insights reveal that while AI offers innovative solutions, careful management and a thorough understanding of its limitations are essential for successful implementation.
The Future of AI in Software Development
Looking ahead, the narrative underscored the potential of .NET in the AI landscape, demonstrating that developers can effectively build AI-driven applications using this framework. This perspective champions the idea that with the right tools and understanding, traditional software engineering practices can adapt to embrace AI technologies. As new models and tools continue to emerge, including the growing capabilities of local models, there will be opportunities to experiment and innovate within the field. The overarching message supported the notion that software development and AI can coexist and thrive together, fostering advancements in both areas.
Ready to build an agentic AI in .NET? Carl and Richard talk to Spencer Schneidenbach about his work using large language models to enhance customer interactions in healthcare. Spencer discusses using the LLMs to summarize customer conversations to identify topic areas, sentiment, and other concerns. He digs into how Microsoft's Semantic Kernel makes connecting an OpenAI model to your APIs easy, fetching information and creating a context for testing reliability and consistency with these models. Check out the links for some great tools to help make your AI apps with .NET!
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