Hi, my name's John.

I lead the search and machine
learning teams at Google.

I think it's amazingly inspiring

that people all over the world

turn to search engines to
ask trivial questions

and incredibly important questions.

So it's a huge
responsibility to give them

the best answers that we can.

Hi, my name's Akshaya and 
I work on the Bing search team.

There are many times where
we will start looking

into artificial intelligence
and machine learning,

but we have to address how are
the users going to use this,

because at the end of the day,
we want to make an impact to society.

Let's ask a simple question.

How long does it take to travel to Mars?

Where did these results come from

and why was this listed
before the other one?

Okay, let's dive in and
see how the search engine

turned your request into a result.

The first thing you need to
know is when you do a search,

the search engine isn't actually
going out to the World Wide Web

to run your search in real time.

And that's because there's
over a billion websites

on the internet and hundreds more are
being created every single minute.

So if the search engine
had to look through

every single site to
find the one you wanted,

it would just take forever.

So to make your search faster,

search engines are constantly
scanning the web in advance

to record the information that might
help with your search later.

That way, when you search
about travel to Mars,

the search engine
already has what it needs

to give you an answer in real time.

Here's how it works.

The internet is a web of pages
connected to each other by hyperlinks.

Search engines are
constantly running a program

called a Spider that cross
through these web pages

to collect information about them.

Each time it finds a hyperlink,

it follows it until it
has visited every page

it can find on the entire
internet.

For each page the spider visits,

it records any information
it might need for a search

by adding it to a special
database called a search index.

Now, let's go back to
that search from earlier

and see if we can figure
out how the search engine

came up with the results.

When you ask how long does
it take to travel to Mars,

the search engine looks
in each of those words

in the search index to
immediately get a list

of all the pages on the
internet containing those words.

But just looking for these search terms

could return millions of pages,

so the search engine needs
to be able to determine

the best matches to show you first.

This is where it gets tricky
because the search engine

may need to guess what
you're looking for.

Each search engine
uses its own algorithm

to rank the pages based on
what it thinks you want.

The search engine's ranking
algorithm might check

if your search term shows
up in the page title,

it might check if all of the
words show up next to each other,

or any number of other calculations

that help it better determine

which pages you'll want
to see and which you won't.

Google invented the most 
famous algorithm

for choosing the most relevant results
for a search by taking into account

how many other Web pages
linked to a given page.

The idea is that if
lots of websites think

that a web page is interesting,

then it's probably the one 
you're looking for.

This algorithm is called page rank,

not because it ranks web pages,

but because it was named after
its inventor, Larry Page,

who's one of the founders of Google.

Because a website often makes
money when you visit it,

spammers are constantly
trying to find ways

to game the search algorithm 
so that their pages

are listed higher in the results.

Search engines regularly
update their algorithms

to prevent fake or untrustworthy
sites from reaching the top.

Ultimately, it's up to you 
to keep an eye out

for these pages that are untrustworthy

by looking at the web address and
making sure it's a reliable source.

Search programs are always evolving

to improve the algorithms 
wo they return better results,

faster results than their competitors.

Today's search engines
even use information

that you haven't explicitly provided
to help you narrow down your search.

So, for example,
if you did a search for dog parks,

many search engines
would give you results

for all the dog parks nearby,

even though you didn't
type in your location.

Modern search engines
also understand more

than just the words on a page,

but what they actually mean
in order to find the best one

that matches what you're looking for.

For example, if you search 
for fast pitcher,

it will know you're
looking for an athlete.

But if you search for large pitcher,

it will find you options
for your kitchen.

To understand the words better, 
we use something called machine learning,

a type of artificial intelligence.

It enables search
algorithms to search out

not just individual letters
or words in the page,

but understand the underlying
meaning of the words.

The internet is growing exponentially,

but if the teams that design
search engines do our jobs right,

the information you want should
always be just a few keystrokes away.