How to Build a Human Page #5

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Those computers were programmed to

work out all the possible

outcomes of each move,

and then weighed up how each move

would contribute to

a winning strategy.

But we're now entering an age when

computers aren't just

programmed, but

can learn for themselves.

Computers like IBM's Watson.

This is Jeopardy -

The IBM Challenge.

APPLAUSE:

In 2011, Watson was put up to one of

the toughest challenges ever

a general knowledge quiz that

requires logic and quick thinking.

This is Jeopardy. It's

a bit of an American institution.

It's a general knowledge quiz

programme,

and they ask questions in

a really strange way.

They give you the answer and you

have to work out what the

question is.

'Duncan Anderson is IBM's European

chief technology officer for Watson.'

So, we've got the two best

players here.

We've got the person who won the

most amount of money on the show

and the person who has

the longest winning streak.

'Two of the most brilliant brains

had won $5 million between them.

'This game was worth another

million.'

Watson itself is not connected

to the internet,

so it's not out there searching.

It's there, stand-alone, playing

against these champion players.

It was a big risk.

The category is 19th-century

novelists, and here is the clue.

APPLAUSE AND CHEERING

'And this is the point

where Watson's won.'

We've beat the best human

players at Jeopardy.

So, how exactly do you programme

a machine to do something that

it's never done before?

Well, the first thing is you

don't programme it.

Trying to guess every single

question that might come up

and then programme the computer with

the right answer for that question,

we would be here forever.

So, we use this thing called machine

learning, which is an approach

to solving problems whereby the

machine can learn from experiences.

So, we took Watson and we taught it,

we fed it lots of information.

For example, back issues of Time

Magazine, Wikipedia, encyclopaedias.

So, Watson was learning in a way?

Mmm.

And then we go through

a teaching process.

Just like you would teach a child,

we're teaching Watson and we're

testing it, and we're giving it

feedback when it gets it right, and

feedback when it gets it

wrong and then it adjusts its

approach to making decisions.

You could think of it

a bit like trying to find

a pathway through a field.

So, you have a very faint,

distinct path that maybe only one

person has trodden through.

And what you're trying to do is

to feed information

so that that pathway becomes

more defined.

As more people go down that path,

the path gets more trodden through

and becomes more obvious.

So, the more data that you feed into

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