While a significant portion of AI scientists and engineers believe we are on the verge of achieving highly general forms of AI, I offer a critical appraisal of this view through a causal lens. In particular, building on foundational developments in the field, I will present my perspective on the relationship between intelligence and causality, and the central role of the latter in building intelligent systems and advancing credible data science.
I frame this discussion in terms of five core capabilities that we should expect from an intelligent AI system: performing causal reasoning and articulating explanations; making precise, surgical, and sample-efficient decisions; generalizing across changing conditions and environments; generating and simulating in a causally consistent manner; and learning causal structures and variables.
In this talk, I will elaborate on this perspective and share current progress toward building causally intelligent AI systems. A more detailed discussion of this thesis is provided in my forthcoming textbook, a draft of which is available here: https://causalai-book.net/.
Bio: Elias Bareinboim is a professor in the Department of Computer Science at Columbia University and the director of the Causal Artificial Intelligence (CausalAI) Laboratory. His research develops causality as a foundation for artificial intelligence, with contributions to causal and counterfactual reasoning, data fusion, learning, generalizability, and decision-making, along with applications in biomedical and social domains. His honors include AAAI Fellow recognition, IEEE "AI's 10 to Watch," and young-investigator awards from NSF, DARPA, and ONR. Bareinboim serves as editor-in-chief of the Journal of Causal Inference and as an action editor of the Journal of Machine Learning Research.
Technion Host: Sarah Keren