Show notes
Data scientists today are under pressure to adopt the latest tools - machine learning, LLMs, generative AI. But in the rush to embrace what's new, many are leaving some of the most powerful analytical tools sitting on the shelf. Tools that handle something modern AI largely can't: uncertainty.In this episode, Prof. Rob Hyndman joins Dr. Genevieve Hayes to make the case for why rigorous statistical thinking remains indispensable in the age of AI, and what data scientists are giving up when they abandon it.In this episode, you'll discover:Why throwing data at an LLM is no substitute for building a model that understands the problem [04:27]How combining classical statistics and machine learning can produce better forecasting results than either approach alone [08:22]What data scientists lose when they stop thinking probabilistically - and why it matters for decision making [12:38]Where to start if you want to strengthen your statistical foundations [25:10]Guest BioProf. Rob Hyndman is one of the world’s most influential applied statisticians and a Professor in the Department of Econometrics and Business Statistics at Monash University. He has maintained an active statistical consulting practice for over 40 years, published over 200 research papers, co-authored more than 65 R packages and written five books on time series forecasting. He is also a Fellow of both the Australian Academy of Science and the Academy of Social Sciences in Australia.LinksRob's websiteOtexts' websiteConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE