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Pearl, Judea
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The seven tools of causal inference, with reflections on machine learning
Pearl J. Communications of the ACM 62(3): 54-60, 2019. Type: Article, Reviews: (3 of 3)
There are three obstacles to meeting the increasing expectations for artificial intelligence (AI), according to this article: the lack of adaptability or robustness; the lack of explainability; and “the lack of understanding ...
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Mar 23 2020
The book of why: the new science of cause and effect
Pearl J., Mackenzie D., Basic Books, Inc., New York, NY, 2018. 432 pp. Type: Book (978-0-465097-60-9), Reviews: (2 of 2)
In fields such as data science, explainable artificial intelligence (AI), and big data analytics, “why?” is the ultimate question that needs to be answered to make sense of real data. “Why?” has ...
...
Aug 2 2019
The seven tools of causal inference, with reflections on machine learning
Pearl J. Communications of the ACM 62(3): 54-60, 2019. Type: Article, Reviews: (2 of 3)
This is one of the most influential and eye-opening articles I’ve read in the last two or three years. The author, an ACM Turing Award recipient, makes clear distinctions between machine learning (ML), artificial intelligence...
...
Jul 2 2019
The seven tools of causal inference, with reflections on machine learning
Pearl J. Communications of the ACM 62(3): 54-60, 2019. Type: Article, Reviews: (1 of 3)
Social media tells us that data science, machine learning (ML), and artificial intelligence (AI) are big. The author of this article, whose title evokes a well-known self-help book [1], has made important contributions to these areas. ...
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Mar 21 2019
The book of why: the new science of cause and effect
Pearl J., Mackenzie D., Basic Books, Inc., New York, NY, 2018. 432 pp. Type: Book (978-0-465097-60-9), Reviews: (1 of 2)
The well-known 20th century scientist J. B. S. Haldane is famouslyquoted as having said, “Teleology is like a mistress to a biologist: he cannot live without her, but he’s unwilling to be seen with her in public.&am...
...
Nov 27 2018
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