Dear friends in causality research,
Three months ago, I sent you a special greeting, announcing the forthcoming publication of The Book of Why (Basic Books, co-authored with Dana MacKenzie). Below please find an update. The Book came out on May 15, 2018, and has since been featured by the Wall Street Journal, Quanta Magazine, and The Times of London. You can view these articles here: http://bayes.cs.ucla.edu/WHY/ Eager to allay public fears of the dangers of artificial intelligence, these three articles interpreted my critics of model-blind learning as general impediments to AI and machine learning. This has probably helped put the Book on Amazon's #1 bestseller lists in several categories. However, the limitations of current machine learning techniques are only part of the message conveyed in the Book of Why. The second, and more important part of the Book describes how these limitations are circumvented through the use of causal models, however qualitative or incomplete. The impacts that causal modeling has had on the social and health sciences make it only natural that a similar 'revolution' will soon be sweeping machine learning research, and liberate it from its current predicaments of opaqueness, forgetfulness and lack of explainability. (See, for example, http://www.sciencemag.org/news/2018/05/ai-researchers-allege-machine-learning-alchemy and https://arxiv.org/pdf/1801.00631.pdf) I was happy therefore to see that this positive message was understood by many readers who wrote to me about the Book, especially readers coming from traditional machine learning background (See, for example, www.inference.vc/untitled) It was also recognized by a more recent review in the New York Times https://www.nytimes.com/2018/06/01/business/dealbook/review-the-book-of-why-examines-the-science-of-cause-and-effect.html which better reflects my optimism about what artificial intelligence can achieve. I am hoping that you and your students will find inspiration in the optimistic message of the Book of Why, and that you take active part in the on-going development of "model-assisted machine learning." Sincerely, Judea _______________________________________________ uai mailing list uai@ENGR.ORST.EDU https://secure.engr.oregonstate.edu/mailman/listinfo/uai
