Spencer Schneidenbach, a Microsoft MVP and the president of Aviron Software, dives into the world of agentic AI. He discusses using large language models to improve healthcare customer interactions, from summarizing conversations to identifying sentiments. Spencer highlights the ease of integrating Microsoft's Semantic Kernel with OpenAI models, enhancing API connections. The conversation also explores the challenges of building scalable AI solutions, the importance of effective prompt crafting, and the critical role of user feedback in AI tool development.
Spencer Schneidenbach discusses the use of large language models in healthcare to enhance customer interactions by summarizing conversations to identify sentiments and concerns.
The integration of Microsoft's Semantic Kernel with OpenAI models simplifies the connection to APIs, facilitating the development of reliable AI applications in .NET.
The ongoing challenges in AI, such as model consistency and emergent behaviors, highlight the need for continuous testing and user feedback to enhance system reliability.
Deep dives
End of World War II
1945 marked a pivotal year in history, signifying the conclusion of World War II with the surrender of Nazi Germany in May and Japan in August, following the bombings of Hiroshima and Nagasaki. The war's aftermath notably included significant discussions at the Potsdam Conference, where leaders from the US, UK, and Soviet Union deliberated on post-war Europe and the division of Germany. Essential military operations like the bombing of Dresden and the Battle of Okinawa also defined this tumultuous period, illustrating the complexities and brutalities of war. These events not only signaled the end of conflict but also set the stage for future geopolitical tensions and realignments.
Scientific Innovations of 1945
The year 1945 also witnessed groundbreaking scientific advancements, including the Trinity Test, marking the United States' first successful atomic bomb test. The development of the ENIAC, the first fully programmable computer, revolutionized military operations by enabling complex calculations necessary for artillery. This era also saw visionary proposals like Arthur C. Clarke's concept of geostationary satellites, which predicted the future of communications technology, demonstrating how wartime advancements paved the way for civilian applications in the following decades. Such innovations emphasized the intertwining of scientific progress with military necessity during and post-war.
The Rise of AI Development
As technology evolved, interest in artificial intelligence (AI) surged, exploring its potential applications across various sectors, despite challenges such as high development costs and the predominance of programming languages like Python. The discussion highlighted a specific project where a .NET developer shifted towards AI, showcasing how AI can enhance user interaction through natural language processing. The focus on integrating AI technologies like semantic kernel into .NET frameworks emphasized the feasibility and effectiveness of leveraging existing languages for AI projects. This shift illustrates a growing trend within tech communities to explore AI capabilities beyond traditional programming paradigms.
Chatbot Development for Customer Insights
A notable case involved the development of a chatbot to analyze customer service calls in the healthcare industry, aiming to extract insights from patient interactions. This platform allows users to query customer data efficiently, revealing trends, concerns, and feedback that inform better service practices. The organization structured the AI system to handle complex queries, making it more intuitive for users to interact with large datasets. Part of this involved using semantic kernels to bridge AI capabilities with existing .NET systems, demonstrating a real-world application of AI in improving customer service effectiveness.
Challenges in AI Integration
Throughout the AI integration journey, challenges such as model consistency, cost implications, and emergent behaviors were prevalent, necessitating ongoing testing and adaptation. Developers emphasized the importance of establishing guardrails in AI output to manage the inherent unpredictability of machine learning responses, particularly in natural language processing tasks. Continuous user feedback and a structured testing environment were integral to refining the system, addressing inaccuracies, and ensuring user satisfaction. These experiences revealed the complexity of balancing creativity with reliability in AI functions, underscoring the necessity for both robust engineering practices and responsive adjustment based on real-world user interactions.
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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