

Judea Pearl, big brain behind AI, wins Turing Award (Nobel Prize in Computing) - alphadoggs
http://www.networkworld.com/news/2012/031512-turing-award-257288.html

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eric_bullington
Pearl won the Turing Award for his work applying Bayesian analysis to machine
learning, among other accomplishments.

For HNers just out of college, it's important to note that Bayesian analysis
has not always been as popular as it is now. In fact, even as recently as the
1990s, it was regarded with suspicion by many statisticians, who strongly
disliked the idea that prior and posterior distributions are meant to
represent subjective states of belief. I was fortunate enough to have a very
progressive statistics professor in undergrad in the 1990s, who was interested
in Bayesian analysis. It's my understanding that most upper-level statistics
and probability coursework avoided doing much Bayesian analysis until around
the turn of the millennium. (if you went to university in the 1990s or
earlier, was this your experience?).

For those interested in learning more about this topic, an excellent book on
the history of the Bayes theorem controversies was recently published by Yale
Unviersity Press: _The Theory That Would Not Die_ by Sharon Bertsch McGrayne

To be fair to Judea Pearl, I do see that he wrote an essay entitled,
"Bayesianism and causality, or, why I am only half-bayesian". Nonetheless,
much of his work appears to involve what we now know as Bayesian analysis. So
like many (all?) scientists who make major breakthroughs, Judea Pearl was
going against the accepted understanding of probability by pushing Bayesian
analysis in the 70s, 80s, and 90s. It's good to see his daring rewarded.

~~~
_delirium
Part of the reason Bayesian analysis was avoided was because, prior to Pearl's
work, inference was impractical in non-toy examples. A major contribution of
Pearl's work was to make it feasible, by structuring the probability
distributions as Bayesian networks that limited the possible dependencies,
coupled with the belief-propagation algorithm to do approximate updates.

I don't see Pearl as primarily interested in the Bayesian v. frequentist
debate himself, though, but rather in how to efficiently do probabilistic
reasoning in non-trivial problems in general, with a heavy tilt towards
questions of representing causality. Methodologically his work over the years
has used all sorts of things from various camps; for example, he was also an
authority in the early 1980s on heuristic search.

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ajays
It saddens me a little to see the " _(Nobel Price in Computing)_ " in the
headline.

This is HackerNews! People here are expected to know what the Turing Award is;
if they don't, they can find out for themselves.

~~~
k-mcgrady
I don't see what the problem is in helping people who don't know what it is.
There's no harm in saving people from having to do a search to find out.

~~~
wl
The problem is that the headline is wrong and the Turing Award isn't a Nobel
Prize. It may be similar in prestige to a Nobel, but it is awarded by
different people.

Maybe there is no harm in saving people from having to do a search, but there
is harm in being misleading.

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cing
His work on causal inference was featured recently on Michael Nielsen's blog:
[http://www.michaelnielsen.org/ddi/if-correlation-doesnt-
impl...](http://www.michaelnielsen.org/ddi/if-correlation-doesnt-imply-
causation-then-what-does/) [http://www.michaelnielsen.org/ddi/guest-post-
judea-pearl-on-...](http://www.michaelnielsen.org/ddi/guest-post-judea-pearl-
on-correlation-causation-and-the-psychology-of-simpsons-paradox/)

------
andyjohnson0
I'd like to see some comments on his contributions to AI. Wikipedia says his
work is applicable to cognitive modelling. Anyone want to comment?

~~~
zmj
You've probably heard of Hidden Markov Models. They're widely used in many
machine learning applications. An HMM is just a simple, easy-to-compute
Bayesian inference network.

The idea of Markov chains predates Pearl. His work was a demonstration of the
accuracy and power of Bayesian inference networks. He revitalized that idea at
a time where programmers were just beginning to have the data and processing
power to apply machine learning.

The rest is history.

~~~
zmj
PG's old spam filter (<http://www.paulgraham.com/spam.html>) is another
example of applied Bayesian inference. Not directly related to Pearl's work,
but he basically deserves credit for the wider renaissance in Bayesian
techniques.

~~~
psykotic
> Not directly related to Pearl's work, but he basically deserves credit for
> the wider renaissance in Bayesian techniques.

Post hoc ergo propter hoc? The success of naive Bayes classifiers for spam
filtering must have improved popular perceptions of Bayesian techniques, but
let us not go overboard.

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HardyLeung
Congrats to Professor Judea Pearl! Though I did not take any of his classes
when I was at UCLA, in chance encounters he gave me a very good impression -
nice, intelligent, and unassuming. I ended up using a lot of his materials
(Bayesian analysis) after college in my projects... Here's a Tagxedo I made of
today's announcement in the shape of UCLA's logo:
[http://daily.tagxedo.com/march-15-uclas-judea-pearl-
named-20...](http://daily.tagxedo.com/march-15-uclas-judea-pearl-
named-2011-winner)

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jarrodvanda
For those who don't know he is also the father of Daniel Pearl

