Otto Dobretsberger
About
Dr. Otto Dobretsberger is a Senior Software Developer at CODE Consulting (part of the CODE Group). He holds a Bachelor's degree in Biomedical Informatics from UMIT, an institution in Austria, as well as a Master's and Ph.D. in Computer Science from the University of Houston. His professional expertise centers on high-performance computational analysis of genetic and genomic data, with extensive experience in C and C++. He specializes in WPF, C#, and C++ development, while maintaining proficiency in multiple additional programming languages. His project portfolio spans biomedical image processing, genetic and genomic data analysis, analytical software solutions for the oil and gas industry, and the development of custom controls. Dr. Dobretsberger has authored scientific papers and magazine articles and is a regular speaker at industry conferences. Since 2012, he has served as an Adjunct Professor at the University of Houston, where he has taught a range of programming courses, including C++, Python, JavaScript, and Java. In addition to his technical and academic roles, Dr. Dobretsberger organizes the Global AI Houston chapter, leading a local community of AI professionals and enthusiasts focused on knowledge sharing and advancements in artificial intelligence. Outside of his professional pursuits, he is an accomplished powerlifter, actively training and competing. He holds multiple USPA National Records and IPL World Records.
Sessions at .NET Conf 2026
2.30 PM – 3.00 PM
Kamis, November 12
2.30 PM - 3 PM PST
How Do AI Systems Like ChatGPT Work? A Clear Explanation for Everyone!
Otto Dobretsberger
This session offers an accessible exploration of the inner workings of AI systems like ChatGPT, demystifying how these advanced language models generate human-like responses. We will break down the process of how large language models (LLMs) construct answers token by token, illustrating the step-by-step mechanics of language generation. Attendees will learn about the key components that drive these systems, including the role of training data, neural network architectures, and the probabilistic nature of response generation. We will also discuss how external factors, such as input prompts and fine-tuning, influence the model's outputs, and explore the dependencies that shape their performance. Designed for a general audience, this talk avoids technical jargon while providing a clear, engaging look at the fascinating technology behind AI language systems, offering insights into their capabilities and limitations.
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