Thursday
Room 1
16:20 - 17:20
Session (60 min)
Can you trust your (large language) model?
Machine learning algorithms are marvellous things: models that can do a bunch of tedious and complex tasks for us, all with a high degree of accuracy. But how do we really know whether the outputs of machine learning models are correct? This question is not as simple to answer as we might think.
As we move into an age where “black box” models, particularly LLMs, are becoming more and more commonly used, it becomes even more essential, and at the same time, difficult and complicated, to be able to assess model performance accurately. In this talk, we’ll explore ways in which our models can lie to us, and how we might be able to peer through this confusion to get at the truth.
Jodie Burchell
Dr. Jodie Burchell is the Developer Advocate in Data Science at JetBrains, and was previously the Lead Data Scientist in audiences generation at Verve Group Europe. She completed a PhD in clinical psychology and a postdoc in biostatistics, before leaving academia for a data science career. She has worked for 7 years as a data scientist in both Australia and Germany, developing a range of products including recommendation systems, analysis platforms, search engine improvements and audience profiling. She has held a broad range of responsibilities in her career, doing everything from data analytics to maintaining machine learning solutions in production. She is a long time content creator in data science, across conference and user group presentations, books, webinars, and posts on both her own and JetBrain's blogs.