Jordan Hart
CTO & co-founder - Causa
Crossing the chasm between data and actions with causal AI
45 min talk 🌶️ Intermediate 🤖 Data + AIThe data “explosion” of the past two decades has been hailed as a revolution, yet many organisations find themselves sitting on vast piles of data without really knowing what to do with it. This session explores the gap between the abundance of data and the availability of tools to make the most of it.
We’ll examine why common machine learning methods often fall short when applied to decision-making processes, how machine learning validation can fail, and why throwing data into an LLM won’t help either!. Drawing on real-world examples from our work at Causa, we’ll introduce the concepts of causal AI and decision theory, and show how they can improve decision-making and actions in the real world.
By the end of this session, you’ll (hopefully) have a clearer understanding of the challenges in translating data into actions and the potential of causal AI to address these challenges.
Key Takeaways
- some real-world applications of causal AI to interesting problems
- an understanding of the limitations of many AI approaches to decision-making
- some fun anecdotes of building an early stage startup in this space
LinkedInJordan is CTO & co-founder of Causa, a causal AI startup building next generation causal AI technologies to help organisations go from data to actions. His academic background is in mathematics and he holds a PhD in applied data science.
Jordan has previously applied his skills in software engineering and ML roles working on diverse problems from biological ecosystem analysis to nuclear fusion reactor simulations.