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Okay, let's step back and reflect on what
we've learned so far.

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We've got a basic idea of how documents
are stored on the web and how they can be

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retrieved using text indexes.
But, we still haven't really understood

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how big the problem is.
We don't know, for example, how many

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documents there are on the web.
We don't know how to best order the

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documents when they are returned from a
search.

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And we don't really understand what any of
this has to do with intelligence, memory,

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and the kind of things that we're trying
to understand about predictive

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intelligence.
But bear with me for a while, and we'll

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get to these subjects very soon.
So, now that we know what an index is, and

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how to create it, how many web pages do
you think are actually indexed by a search

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engine like Google or Bing for, for that
matter?

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Is it two to five billion?
Is it 30 to 40 billion?

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200 to 300 billion or in the trillions?
Now, the number of possible URL's of web

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pages are probably in the trillions.
So, I mean, that's not really the answer.

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So, we can rule one out immediately.
Well, what about the rest?

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Remember, this is, you're not asking how
many web pages there are.

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How many webpages are indexed by a search
engine.

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Think about it.
You can actually find this out by doing a

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few experiments with your browser.
So how did you do?

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The correct answer according to me is
between 30 and 40 billion.

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And this is how I reason.
If you search for a common word such as,

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a, in, the, or, on Google, you get around
20 to 25 billion results.

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Assuming that, that not all pages are
English, my guess is between 30 and 40

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billion.
Now, let's return to our problem of

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arranging the results of a search query.
As we have seen, the web has a lot of

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pages, billions and billions.
What if the result set is very large.

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For example, when we searched for a or the
in Google, we got 20 to 25 billion

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results.
If you have q-terms in your query, such as

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the quick brown fox , you'll still get
many hundreds of thousands of results and

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assembling these results in the way we had
discussed earlier in terms of which

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results match best is still pretty costly
if the number of results is very large.

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Then, another problem.
Suppose we search for something like

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Clinton plays India cards?
Well, when I did this a little while back

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I got results of two types.
One about Hillary Clinton visiting India

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but Islamabad was not on the cards or more
recently Mitt Romney playing the Hllary

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Clinton card or something like that, about
American politics.

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And the another set of results about a
company called Clinton Cards which was

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acquired, which shut down, which closed
shop, and, and anything to do with that

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topic.
So, similarity from search index is quite

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different from importance of a webpage.
Clinton cards India may actually have

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many, many results, which talk about the
second topic.

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But clearly the first topic is more
popular and more important.

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How does Google figure this out?
