CACM Mar. 2019 - The Seven Tools of Causal Inference

The dramatic success in machine learning has led to an explosion of artificial intelligence (AI) applications and increasing expectations for autonomous systems that exhibit human-level intelligence. These expectations have, however, met with fundamental obstacles that cut across many application areas. One such obstacle is adaptability, or robustness. Machine learning researchers have noted current systems lack the ability to recognize or react to new circumstances they have not been specifically programmed or trained for. Intensive theoretical and experimental efforts toward "transfer learning," "domain adaptation," and "lifelong learning"4 are reflective of this obstacle.
In this video, Judea Pearl discusses "The Seven Tools of Causal Inference with Reflections on Machine Learning," a Contributed Article in the March 2019 Communications of the ACM.

Read the full article here:

https://cacm.acm.org/magazines/2019/3/234929-the-seven-tools-of-causal-inference-with-reflections-on-machine-learning/fulltext

Видео CACM Mar. 2019 - The Seven Tools of Causal Inference канала Association for Computing Machinery (ACM)