The intelligence of the future: robots without prejudice | Cem Say | TEDxIstanbul

Cem Say, famous for his writings and research on artificial intelligence, tells us what artificial intelligence is and when and where humanity will benefit from it. Moreover, he says we should not be afraid of artificial intelligence, adding that the prejudices of humanity would be exceeded by artificial intelligence with the use of technology.
While giving lectures at the Department of Computer Engineering at Boğaziçi University, Prof. Dr. Cem Say is a pioneer at making research on qualitative reasoning and understanding Turkish in artificial intelligence.
Say, one of the founders of the Cognitive Science Graduate Degree, works on how the human mind works and operated. He was among the computer experts who examined the digital evidence in the court cases and revealed their counterfeits.
He contributes to popular science journals such as "Technology for Everyone" and is well-known with his easy-to-read and learn articles describing complicated technology objects - such as Bitcoin, blockchain, sharing economy etc.

This talk was given at a TEDx event using the TED conference format but independently organized by a local community. Learn more at https://www.ted.com/tedx

Hello there.

Artificial intelligence is, of course,
literally a new beginning.

We are trying to create
a new type of a thinking being.

In fact, we have achieved a lot
since we got started with this project.

Computers can now play chess
much better than humans.

They can analyze radiological images
better than human doctors.

But today, I will talk about a domain

where AI has not yet reached
the level of a person of average IQ:

understanding human language.

You have probably read
this horrific news item

about the "chatbots"
which are programmed to chat with people.

In 2016, Microsoft created
a Twitter AI character

which was supposed to learn
the nuances of human language

by tweeting with people.

Twenty-four hours later,
they had to take it offline.

Due to the nasty things, curses etc.
that people wrote to it in their tweets,

it turned into this nauseating character

who said things like,
"Hitler was so good."

It does not exist any more.

A year later, in China - maybe
you have not heard about this one -

a similar end was waiting for two chatbots

which were launched in China to chit chat
with users on Chinese social media sites

after they started to talk
about their dreams of moving to the States

or mentioned their dislike
for the Chinese Communist Party.

They were deactivated
for a few days after the incident.

After they were reactivated,
they started talking very "carefully"

when those issues came up,

giving answers like,
"Sorry, I can not understand you."

People who use the digital assistant Siri

already know what a big
engineering success it is.

Yet there's more to the story:

Authors and poets
in every language are hired

so that Siri can give proper answers
in such situations.

They are also writing scripts

so that it doesn't have to confess
it can't understand what is being said

and it can continue the illusion
of being intelligent

by diverting the conversation
when it gets stuck.

Amazon also has a digital
assistant named Alexa,

which doesn't have a Turkish version yet.

They promised a one-million-dollar prize

to the programming team which will enable
Alexa to chat with people for 20 minutes

without causing extensive boredom.

No one has been able to do that yet.

The problem is that there are lots
of very simple things that humans know,

and computers don't.

And we need to have a way
of teaching them those things.

Let me tell you
a personal story about that.

A long time ago, maybe 25 years or so,

I bought a third-grade math textbook
for primary school students

and randomly picked 20 problems from it.

Then I started to write a program
which would "understand Turkish."

It would understand these particular
arithmetic problems in Turkish

and solve them.

I was thinking that
I would therefore reach a new level

in the computer understanding
of the Turkish language

and write another paper.

The name of the program was ALİ,

a Turkish acronym for
"arithmetic language processor."

It could solve problems like these:

There were this many workers at a factory.

That many of them were fired,
and this many retired.

How many were left?

Or questions like,
"That many students from College A

and this many students from College B

attended the ceremony.

What's the total number of students?"

requiring really simple arithmetic.

You think that's easy peasy?

Ask Siri the same questions,
and see if it can solve them all.

Let me tell you,
it took two years of my youth,

and I used to have gorgeous hair
when I got started.

(Laughter)

Here is the problem.

Let us go through this example.

There were 67 liters of diesel
in the gas tank of a truck.

The driver bought 145 liters more.

How much diesel does the truck have now?

We are skipping the linguistics routines
that analyze all this in Turkish.

Let's come to the point

where the AI can understand
the fact that there need to be 212 liters

at the end of the first two sentences.

There, we come to a point where it knows
that there are 212 liters of diesel

in the gas tank,

but what was the wording
of the question again?

"What's the sum of diesel in the truck?"

"How much diesel
does the truck have now?"

ALİ could not answer that
with the information we have mentioned.

Do you see what the problem was?

"The gas tank of the truck" is not
the same thing as "the truck,"

and computers do not know automatically

that if the tank contains something,
the truck also contains that thing.

And that's really complicated.

"Ahmet's father had five kids"

does not mean "Ahmet had five kids."

On the other hand,

when the gas tank of the truck
has the petrol, the truck has it as well.

That's why I had to specify in the program

all this knowledge
that people already inherently know.

The technical name for this stuff
is "commonsense knowledge."

"The gas tank of the truck
is a part of the truck."

"If A is a part of B, right,

B should contain
everything contained in A."

All of this information
that I consider commonsense

is all the things that I do not tell you
while we are talking

since I assume
that you already know it all.

We can not have a proper conversation
with those chatbots

since they know none of those things.

After I coded all these,
ALİ could solve all 20 problems properly.

I had no more energy to go on to the 21st.

Now, I'll tell you the story of a man
who dedicated his life to this problem

of coding commonsense knowledge:

Douglas Lenat, a famous American
computer scientist.

This is him in the 1980s.

He started a project called Cyc in 1982.

And this is exactly
what the project was about:

To code all the commonsense knowledge
that computers don't know.

To write a million lines,
if a million lines are needed.

He founded a corporation
where they do the following:

If you are drinking coffee, the open side
of the cup is facing upwards.

The king is a man.

Then his wife should be a woman,
and she is called the queen.

People can't go to work after they die.

And so on.

They are coding all the items
of information which people already know

and computers need to know in order
to understand human language, one by one.

And this is him today.

After 35 years, the project
is still in progress.

I think there's an obvious problem here.

It's clearly problematic to code manually.

Now it's time to hear the good news.

We have had a revolution in AI,

and computers can now learn
certain things on their own,

without us having to code them manually.

This is a machine-learning revolution.

Linguists have the following idea:

If two words are exact
synonyms of each other,

then the collections of all other words
surrounding them in various sentences

will also be similar to each other.

Based on this idea, this man,

who is proof of the fact
that you don't need to be bald

in order to be handsome
if you're an AI researcher,

named Tomas Mikolov,

did the following while working
for Google five years ago.

Now think of all the documents
in English at Google.

The work I'll be telling
you about was in English.

Now imagine all the documents in English.

For every word in every sentence,

you're supposed to find out how many times
it has appeared in the same sentence

with any other words.

For every imaginable pair of words,
we have the computer count

how many times these two words appear
together in the same sentence or not.

It's a computer,

so it can do the computations anyway.

The idea is that, if the two words
are close to each other in meaning,

the same words appear with similar
frequencies in their surroundings.

Let's say, we can easily see
that both words "cat" and "dog"

will appear frequently
in the same sentences

with the words "flea" or "rabies,"
"vaccine," "tail," "pet," and so on,

but not with words like "printer,"
"generator" or "inflation."

Do we see this?

So, we can prepare a number sequence

containing the frequencies
of the neighboring words

for every single word.

Such a number sequence
is called a "vector,"

as you might well know
if they still teach it in high school.

The computer can automatically position
similar number sequences

closer to each other,

and the dissimilar ones
far from each other

on some sort of a map or space.

What I mean is that the computer,
which knows no English,

creates a vector for each single word
by doing the computations.

Yet, the vector of "cat"

is found in a location
close to the vector of "dog" in that space

for the reasons I just explained.

Or the vector of the school
Buffy the Vampire Slayer attends -

they really looked at that -

is positioned close
to the vector of Hogwarts,

where Harry Potter studies.

Thus they are found to be positioned close
to each other in terms of their meaning.

There's more.

As you will recall
from that high school course,

you can do arithmetic on these vectors.

They can be added or subtracted,

and you might say, "So what?"

Mikolov discovered this.

He did the following addition
and subtraction operations

on the vectors thus learned.

He came up with the question,
"What would happen

if the king were a woman instead of a man"

when he subtracted the word "man"

from the word "king"
and added the word "woman."

Guess what the resulting
vector is near to?

"Queen."

No one had hand-coded
that equation as the Lenat team.

The computer discovered it all by itself

after counting millions of millions
of words on the documents we created.

I have more to tell you,
and this really happened.

There is info on Turkey there.

If you take "France" out of "Paris"
and add "Turkey" -

yes, you got it right - it's Ankara.

This means in this vector space,
there's a direction

which leads from the names of countries
to the names of their capitals,

which is really stunning.

When you ask, What would Windows be
had it not been invented by Microsoft,

but by Google?

the answer pops up as "Android."

When you subtract "copper"
from "Cu" and add "gold,"

you get "Au" as the chemical
symbol of gold.

This literally means we don't have to code
these manually anymore.

It seems that the computer
can make all the inferences

out of the data
we provide it with all by itself.

This is the yummiest example of all.

When you take "Japan" out of "sushi"
and add "Germany,"

you get the "bratwurst,"
the German favorite.

Too good to be true, right?

Happy now?

We finalized this project.

Would computers understand what we say?

Are we having fun? Not much.

Now, I'll tell you
about a Turkish researcher.

Tolga Bölükbaşı is about to finish his PhD

at Boston University in the States.

This is a research he did two years ago.

Tolga did the same thing
as Mikolov did previously,

but this time on news texts.

What happens when you subtract "father"
from "doctor" and add "mom"?

"My dad is a doctor, and mom is a nurse."

What about when you subtract "man"
from "computer engineer" and add "woman"?

In fact, we shouldn't have gender.

Let's see how professions
are related to gender

in the meaning space
in the head of the computer.

You get "homemaker."

Seriously! You get "homemaker."

We get an English word "homemaker."

So, it's clear that we not only put
all of our data in computers

but also put all of our prejudices.

Imagine if this computer
were used to hire someone.

You've already uploaded your resume
and all the personal information

including your gender.

Let's assume 10,000 people
applied for the job.

The computer needs
to do a pre-selection, right?

It needs to get to 1,000 candidates,

eliminating 9,000 others

so that the HR staff
can evaluate the results.

Computers nowadays are already
used for this kind of work.

Let's say that a computer loaded
with such meaning vectors makes selection

among the candidates who have applied
for a job vacancy for a computer engineer.

It might automatically eliminate
all the female candidates,

thinking that a computer
engineer should be male.

Tolga and his colleagues
also mention other cases.

It was found out that computers
link positive and negative attributions

with the words related to being
Afro-American and Caucasian.

For instance, the computer thinks

that the word "mugger" is closely related
to being Afro-American.

It's certain that we uploaded
all our prejudices

while uploading all the information
we have in computers.

You might ask yourselves,
What will happen now?

Tolga and his team's article
offers a solution to that.

Just told you.

All these things happen
in the vector space.

Each word has its vector.

We already know from high school years
that we can add and subtract them.

Tolga and his team first list the words

that are really feminine or masculine,

like "dad," "uncle,"
"grandmother," and so on.

These words really should have
a relation to male and female roles.

Then there are these words
which should not be masculine or feminine

despite having closer meanings
in the computer's space.

For example, the word "genius"
appears to be male.

On the other hand, the word "stylist"
stands out as a very female word.

It doesn't have to be like that.

So, after listing all the words
that need to be feminine or masculine,

Tolga and his team created an algorithm

which would automatically erase
the computer's prejudices

on the ones that should be neutral.

If a word like "father" or "uncle"
is not in the list,

but it is still biased towards a gender
in the space of meanings,

the algorithm automatically corrects it.

With the help of this,
"computer programmer"

ends up at the same distance
to the male and female notions,

and the problems I talked about go away.

Isn't that beautiful?

I wish we could delete the prejudices
in the human brain so easily.

For a while, some people
have been worrying about

what would happen
if computers took over.

On the other hand,

considering the fact that we can't delete
the prejudices in people,

while we can in computers,

maybe we could give computers a chance
at jobs requiring fairness

such as being referees,
judges, and managers

and let people take a rest for a while.

What do you say to that?

Thank you.

(Applause)