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X-WR-CALDESC:Veranstaltungen für AKTUARVEREINIGUNG ÖSTERREICHS (AVÖ)
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DTSTART;TZID=Europe/Vienna:20261207T090000
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DTSTAMP:20260907T134550Z
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UID:10000687-1796634000-1796644800@avoe.at
SUMMARY:GenAI Beyond the Basics: Advanced Concepts for Actuaries
DESCRIPTION:Generative AI (GenAI) tools such as ChatGPT are rapidly reshaping how actuaries approach problem-solving\, analysis\, and communication. Beyond their familiar chat interfaces\, these tools offer far more powerful capabilities through programmatic access\, allowing users to interact directly with the underlying Large Language Models (LLMs) via APIs (Application Programming Interfaces). Compared with manual\, web-based use\, API access lets actuaries embed GenAI seamlessly into their existing workflows\, process larger volumes of data\, and automate repetitive tasks at scale. In this three-hour web session\, participants will follow live demonstrations of advanced GenAI concepts in a Jupyter notebook that introduces each concept and applies it to a concrete actuarial use case. The notebook will be shared with all attendees to encourage experimentation and support adoption in their own work. \nAfter a foundations-and-best-practices block that covers how to access LLMs both through cloud APIs and locally via Ollama\, together with practical prompt-engineering techniques\, we will explore the following advanced GenAI concepts: \n\nStructured Outputs: Generating responses in structured formats like JSON to support easier and more reliable downstream processing.\nFunction Calling: Enabling LLMs to execute predefined functions\, such as calculations or database queries\, to perform specific operations.\nFine-Tuning: Customizing pretrained LLMs with domain-specific data to improve accuracy and relevance in generating responses.\nRetrieval-Augmented Generation (RAG): Combining LLMs with external data sources to produce contextually enriched outputs.\n\nThe session then closes with a dedicated introduction to Agentic AI\, showing how a single LLM can be turned into an autonomous\, tool-using agent and how several such agents can be orchestrated as a multi-agent system that collaborates to automate complex\, multi-step actuarial workflows.\nFor each concept\, the session will explain its purpose and underlying principles\, demonstrate it through a dedicated actuarial use case\, and point to further applications and resources. It will conclude with a forward-looking outlook on emerging developments\, followed by an open Q&A and discussion.
URL:https://avoe.at/event/genai-beyond-the-basics-advanced-concepts-for-actuaries/
CATEGORIES:European Actuarial Academy (EAA)
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Vienna:20261207T090000
DTEND;TZID=Europe/Vienna:20261207T120000
DTSTAMP:20260909T085508Z
CREATED:20260909T085508Z
LAST-MODIFIED:20260909T085508Z
UID:10000688-1796634000-1796644800@avoe.at
SUMMARY:GenAI Beyond the Basics: Advanced Concepts for Actuaries
DESCRIPTION:Generative AI (GenAI) tools such as ChatGPT are rapidly reshaping how actuaries approach problem-solving\, analysis\, and communication. Beyond their familiar chat interfaces\, these tools offer far more powerful capabilities through programmatic access\, allowing users to interact directly with the underlying Large Language Models (LLMs) via APIs (Application Programming Interfaces). Compared with manual\, web-based use\, API access lets actuaries embed GenAI seamlessly into their existing workflows\, process larger volumes of data\, and automate repetitive tasks at scale. In this three-hour web session\, participants will follow live demonstrations of advanced GenAI concepts in a Jupyter notebook that introduces each concept and applies it to a concrete actuarial use case. The notebook will be shared with all attendees to encourage experimentation and support adoption in their own work. \nAfter a foundations-and-best-practices block that covers how to access LLMs both through cloud APIs and locally via Ollama\, together with practical prompt-engineering techniques\, we will explore the following advanced GenAI concepts: \n\nStructured Outputs: Generating responses in structured formats like JSON to support easier and more reliable downstream processing.\nFunction Calling: Enabling LLMs to execute predefined functions\, such as calculations or database queries\, to perform specific operations.\nFine-Tuning: Customizing pretrained LLMs with domain-specific data to improve accuracy and relevance in generating responses.\nRetrieval-Augmented Generation (RAG): Combining LLMs with external data sources to produce contextually enriched outputs.\n\nThe session then closes with a dedicated introduction to Agentic AI\, showing how a single LLM can be turned into an autonomous\, tool-using agent and how several such agents can be orchestrated as a multi-agent system that collaborates to automate complex\, multi-step actuarial workflows.\nFor each concept\, the session will explain its purpose and underlying principles\, demonstrate it through a dedicated actuarial use case\, and point to further applications and resources. It will conclude with a forward-looking outlook on emerging developments\, followed by an open Q&A and discussion. \nThe aim of this web session is to equip participants with the knowledge and practical skills needed to apply advanced GenAI concepts in actuarial contexts. By the end of the session\, participants will be able to: \n\nUnderstand how to interact with LLMs programmatically through APIs.\nRun open-weights LLMs locally via Ollama and weigh the trade-offs between local and cloud deployment.\nApply Python programming skills to develop and integrate GenAI-powered solutions into actuarial workflows.\nApply prompt-engineering best practices\, such as few-shot prompting\, chain-of-thought reasoning\, and clear role and\noutput-format instructions\, to obtain reliable and reproducible results.\nGenerate structured outputs in JSON format to ensure compatibility of the responses with actuarial processes and systems.\nLeverage Function Calling capabilities to trigger the execution of predefined functions in the underlying programming language\, such as calculations or database queries.\nCustomize LLMs using fine-tuning techniques to address domain-specific requirements.\nCombine external data with LLMs through Retrieval-Augmented Generation to produce contextually relevant outputs.\nBuild agentic AI solutions\, including multi-agent systems in which several specialized agents collaborate to automate complex\, multi-step actuarial tasks.\n\nBy balancing conceptual understanding with hands-on programming\, the session gives participants practical tools to enhance the accuracy\, efficiency\, and creativity of their actuarial work.
URL:https://avoe.at/event/genai-beyond-the-basics-advanced-concepts-for-actuaries-2/
LOCATION:Online/Streaming
CATEGORIES:European Actuarial Academy (EAA)
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