The Book Of Why: The New Science of Cause and Effect
Photo Credit Impression I just finished The Book of Why by Judea Pearl. This book is one of those that I wish I had picked it up a lot earlier. It makes a convincing case on what is missing in traditional probabilistic thinking and why the causal models can help to fill in the gap. Although reading this book probably won’t help you in finding a job as a data scientist or AI/ML engineer, but I genuinely think that every data scientist should read it to better understand the limitation of the current statistical learning methods. The model-free approaches to AI are unlikely to bring us Artificial General Intelligence(AGI). Blindingly throwing data at machine learning algorithms can only get us this far. (There already seems to be some research in reinforcement learning that shows world models that imitate how humans perceive the world can help build more intelligent agents. However, I’m not yet an expert in reinforcement learning, so my interpretation can be wrong.) ...