
Peter Norvig talks how search is evolving, thanks to AI algorithms - DanielRibeiro
http://www.stonetemple.com/search-algorithms-with-google-director-of-research-peter-norvig/
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blrs
This seems like SIFT ([http://en.wikipedia.org/wiki/Scale-
invariant_feature_transfo...](http://en.wikipedia.org/wiki/Scale-
invariant_feature_transform)) in a nutshell.

 _Here’s how image recognition works in a nutshell. It starts with identifying
points of interest in an image — the points, lines, and patterns that provide
sharp contrasts or really stick out from a bland, featureless background. It’s
similar in some ways to how the human eye picks out edges and points by keying
off the places where there’s sharp contrast.

Then it looks at how these points are related to each other — the geometry of
the whole set of points. You could picture it as looking like a constellation
of stars, even though really it’s a more sophisticated mathematical model of
these points of interest and how they relate.

Now it compares that model to all the other models in a huge database. Those
other models come from images it has already analyzed from around the web. It
looks for a matching model, but it doesn’t have to be a perfect match. In
fact, it’s important that it be a bit flexible, so it doesn’t matter if it’s
turned around, or shrunken, or twisted a bit. The Taj Mahal still has the
basic geometry of the Taj Mahal even if you photograph it from a little bit of
a different angle or photograph it lower in the frame. When Google recognizes
that it matches that model best, it guesses it’s probably the Taj Mahal._

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apu
Norvig's talk at Startup School '08 went into more details on this:
<http://www.youtube.com/watch?v=LNjJTgXujno>

They do use SIFT (or at least a variant thereof) for finding and describing
interest points, but by itself, there is no geometric matching in SIFT. There
are various competing approaches on how do it, although in many cases, you can
get very good results even without it. (It's very slow to do geometric
matching so people often skip that step, or only apply it to the best
matches.)

Landmark detection is a recent "hot topic" in computer vision, and given a
large enough dataset, it essentially works now for the most part.

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bfrs
Thanks for the link.

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heynk
Sorry if this adverts the discussion, but where are we with AI in weather
prediction? It seems to me that we're mostly still using an older model with
scientists analyzing models to find patterns, but machine learning and weather
forecasting seems like a big field for AI improvements.

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sp332
I don't think we need AI to do forecasts, because we can already do a direct
simulation of all the parameters at once. Sure, it takes a supercomputer, but
there's enough money in to to still be profitable. So why take shortcuts?

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tensor
I was under the impression that weather models are at least partially chaotic.
Is this not so?

In such systems, even if we have a perfect model we cannot know the initial
conditions with sufficient accuracy to make reliable long term predictions.
This is not just a limit of our current technology, but a property of chaotic
systems. If weather is a chaotic system, then we will never make serious
progress beyond short term forecasts.

~~~
sp332
You can vary the initial conditions a bit and make several runs. Then you can
see what the most likely outcome is, given your uncertainty.

If you don't know what the initial conditions are, how will AI help?

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vecter
If weather is chaotic, how will sampling initial conditions help?

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queensnake
... you can get 'attractors', basins in the dynamics. If either several
different models, or slightly tweaked starts with the same model give you the
same or similar-enough answers, then you know you've stumbled into a basin by
luck. Small errors aren't going to change much. If your outcomes are different
though, then you know that the weather from that starting state is very
sensitive and unpredictable.

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traveldotto1
The challenge Google faces on how to improve the way users retrieve
information from the web is actually limited by its own search interface.
Google's simple search box interface has successfully trained most users to
abstract complex queries into few keywords, and as result, much context are
lost as we are searching. In some way, Google Instant is a way to mitigate
this problem by previewing results per keywords, but the search experience is
still limited to a keyword index, regardless the interface Google wraps around
with.

What's interesting moving forward how AI can help search is about how NOT to
discard the constraints of human language. In ML, constraints are good, and
for search, it can help the indexer to more intelligently quantify the
n-grams. End of day, human being still converse in sentences, not keywords.

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zeratul
Information retrieval and information extraction is evolving, thanks to
statistical machine learning theories.

Successful applications of statistical pattern recognition models improve user
experience (speech recognition, face tracking, sentiment analysis, and other
countless examples). Other parts of Artificial Intelligence have just benign
effect.

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gradstudent
Information Retrieval is not search!

