Speaker's bio:

For over a decade, Adam Kolář has contributed to a wide array of AI projects across business and academia. His journey includes developing image search and enhancing full-text search at Seznam.cz, researching general artificial intelligence at GoodAI, achieving precise measurements in healthcare with HealthMode, and founding startups—some successful, some not—for VC investors. Beyond his professional roles, Adam is an educator, offering courses and mentoring companies. He is also a founding member of the Brno Machine Learning Meetups.

Presentation outline:

As the hype around large language models (LLMs) has grown, so has the open-source toolbox, offering surprising capabilities even with limited budgets and computational resources. In this case study, we’ll present practical and easily transferable methods for applying small (8b-class) LLMs to a unique dataset from clinical trials for anxiety treatments. Our focus is on minimizing human-factor variability in measuring anxiety using the Hamilton Anxiety Scale (HAM-A). We’ll demonstrate how language models can automate these assessments from HAM-A interview audio recordings, identify inconsistencies, and provide feedback to clinicians.

Zoom Meeting access details:

https://cesnet.zoom.us/j/99658365873?pwd=E0tbAwI1Ov9GUGySlCFjHPtquudUXD.1 

• Meeting ID: 996 5836 5873
• Passcode: 306236