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https://leon.bottou.org/
2018-09-23T12:51:55-0400leon.bottou.org
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https://leon.bottou.org/_media/favicon.icotext/html2018-09-19T11:52:19-0400leonb (leonb@undisclosed.example.com)feuilleton:confounding-simpson - [Kidney stones]
https://leon.bottou.org/feuilleton/confounding-simpson?rev=1537372339
Confounding
This post presents and comments a classic example of confounding variable in real life.
The intricacies of the phenomenon and their relation to Bayes'rule
are discussed in a second post).
Kidney stones
Kidney stones are hard mineral deposits that form inside the kidneys,
can lodge themselves in annoying locations of the urinary tract,
become very painful, and may have to be removed. A 1986 studytext/html2018-09-19T11:51:13-0400leonb (leonb@undisclosed.example.com)feuilleton:turing - [4. Looking for first principles]
https://leon.bottou.org/feuilleton/turing?rev=1537372273
AI for the open world
We propose that a 2 month, 10 man study of artificial intelligence be carried out during the summer of 1956 at Dartmouth College in Hanover, New Hampshire. The study is to proceed on the basis of the conjecture that every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it. An attempt will be made to find how to make machines use language, form abstractions and concepts, solve kinds of…text/html2018-09-19T11:34:02-0400leonb (leonb@undisclosed.example.com)feuilleton:sidebar
https://leon.bottou.org/feuilleton/sidebar?rev=1537371242
Part I
The limits of empiricism.
* AI for the open world.
* ...
Part II
The gap
* ...
Part III
Causes and effects
* Confounding
* Probabilistic vs causal inference
* ...
Part IV
Causal intuitions
* ...text/html2018-09-12T13:25:34-0400leonb (leonb@undisclosed.example.com)feuilleton:causation_and_decisions - [Causation without decisions]
https://leon.bottou.org/feuilleton/causation_and_decisions?rev=1536773134
Causation without decisions
The foundational texts on statistical causationGuido W. Imbens, Donald B. Rubin, 2015. Causal Inference in Statistics, Social, and Biomedical Sciences. Cambridge University Press. frequently remind us that causal inference is necessary to estimate the outcomes of decisions. Suppose for instance that we have access to a trove of medical reports describing the outcomes $Z$$Y$$X$$P(Z|X,Y)$text/html2018-09-12T13:05:53-0400leonb (leonb@undisclosed.example.com)feuilleton:confounding-bayes
https://leon.bottou.org/feuilleton/confounding-bayes?rev=1536771953
<- Confounding
Probabilistic vs. Causal inference
This post revisits the confounding phenomenon under the
angle of probabilistic reasoning with conditional probabilities
and Bayes' theorem. This analysis reveals what
sets probabilistic and causal inference apart. It also
shows how probabiistic models and causal models
formulate assumptions of a fundamentally different nature.$Y$$X$$Z$$Z$$Y$$(Z,X)$$P(Z,X,Y)$$P(Y|Z,X)$$Z$$X$$P(Y|Z,X)$$(Z,X)$$Z$\begin{equation}
\label{eq:xyz}
P(Z,X,Y) =…text/html2018-09-11T12:01:47-0400leonb (leonb@undisclosed.example.com)feuilleton:start - [Recent entries]
https://leon.bottou.org/feuilleton/start?rev=1536681707
From Machine Learning to Artificial Intelligence
ML2AI Feed
There is a big gap between our Machine Learning techniques
and our ambition to make a dent in Artificial Intelligence.
The purpose of these pages is to explain the nature of the gap,
that is, clarify some of the problem we must solve, and describe
conceptual tools to reason about them and maybe lead
to solutions.text/html2018-09-11T11:57:02-0400leonb (leonb@undisclosed.example.com)feuilleton - created
https://leon.bottou.org/feuilleton?rev=1536681422
start