Opportunities and challenges of large language models
First talk by Micael Gustafsson & Petronella Melén
Experiences from the demand side using digital health care as an example
Using LLM and AI in digital health is one of the ways to solve the huge demand for health care that we can see in front of us. But is it the solutions we have waited for? Micael and Petronella will elaborate on todays trends within digital health, how LLM can play a role for better health care and how to use AI and LLM for solving an existing problem - the lack of resources within mental health treatment.
Second talk by Kobra (Farzaneh) Etminani
Opportunities and challenges of Large Language Models within healthcare
Large Language models (LLMs) have emerged enormously recently and everyone is talking about threats come with them, for example ChatGPT. In this talk, I will shortly explain how LLMs are trained. Many have addressed the challenges with them, and I will mention them as well as the opportunities they can bring within healthcare domain.
Third talk by Behruz Bozorg
GPT by GPT / Fine Tuning a pre-trained Language Model
Nowadays LLMs are very trendy. Most of the successful language models are implemented and trained by giants in the field, e.g Google, OpenAI, Microsoft. Is there any room for small companies to efficiently train and use LLM's for their very specific tasks? I showcase such an effort and the challenges you can expect when training your very own LLM.
Fourth talk by Olof Bengtsson
Classifying Topics in Swedish Podcasts using LLMs
The podcast industry is constantly growing and many people listen to podcasts on a weekly and even a daily basis. Despite the popularity of the field, producers and content creators have access to far fewer tools for analyzing their work compared to traditional broadcast mediums, such as television and radio. One such tool could be to collect episode metadata and compare to listener behavior. In this presentation I will describe how GPT-3 was used to classify the topics covered as well as determine the sentiment in podcast episodes.
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