"Godfather of AI" Geoffrey Hinton: The 60 Minutes Interview

Whether you think
artificial intelligence

will save the world or end it, you

have Geoffrey Hinton to thank.

Hinton has been called
"the godfather of AI,"

a British computer scientist
whose controversial ideas

help make advanced
artificial intelligence possible,

and so change the world.

Hinton believes that AI
will do enormous good.

But tonight he has a warning.

He says that A.I. systems
may be more intelligent

than we know, and there's
a chance the machines could take over,

which made us ask the question.

[ticking](Unknown Speaker) The story
will continue in a moment.

(Host) Does humanity
know what it's doing?

-No. Um.

I think we're moving into a period when,

for the first time ever,
we may have things

more intelligent than us.

-You believe they can understand?
-Yes.

-You believe they are intelligent?
-Yes.

-You believe these systems
have experiences of their own,

and can make decisions
based on those experiences.

-In the same sense as people do, yes.

-Are they conscious?

-I think they probably don't
have much self-awareness at present.

So in that sense, I don't
think they're conscious.

Will they have self-awareness,
consciousness?

-Oh yes, I think they will in time.

-And so human beings
will be the second-most

intelligent beings on the planet.
-Yeah.

-Geoffrey Hinton told us,
the artificial intelligence he

set in motion was an accident,
born of a failure.

In the 1970s, at the University
of Edinburgh, he dreamed

of simulating a neural network
on a computer, simply as a tool

for what he was really studying:
the human brain.

But back then, almost no one

thought software could mimic the brain.

His PhD advisor told him to drop it,

before it ruined his career.

Hinton says he failed
to figure out the human mind,

but the long pursuit
led to an artificial version.

-It took much, much longer
than I expected.

It took 50 years before it worked well,

but in the end it did work well.

-At what point did you realize that you

were right about neural networks,

and most everyone else was wrong?

-I always thought I was right.

-In 2019, Hinton
and collaborators Yann LeCun

on the left, and Yoshua Bengio,
won the Turing Award.

The Nobel Prize of computing.

To understand how their work
on artificial neural networks

helped machines learn to learn,

let us take you to a game.

Look at that. Oh my goodness.

This is Google's AI lab in London,

which we first showed you
this past April.

Geoffrey Hinton wasn't involved
in this soccer project,

but these robots are a great example
of machine learning.

The thing to understand
is that the robots

were not programed to play soccer.

They were told to score.

They had to learn how on their own.

(Unknown speaker) Wup, goal!

-In general, here's how A.I. does it.

Hinton and his collaborators
created software in layers,

with each layer handling
part of the problem.

That's the so-called neural network.

But, this is the key, when,

for example, the robot scores,

a message is sent back down
through all of the layers

that says that pathway was right.

Likewise, when an answer
is wrong, that message

goes down through the network.

So correct connections get stronger,

wrong connections get weaker,

and by trial and error,
the machine teaches itself.

You think these AI systems are better

at learning than the human mind?

-I think they may be, yes.

And at present, they're
quite a lot smaller.

So, even the biggest chatbots only

have about a trillion connections
in them.

The human brain has about 100 trillion.

And yet, in the trillion connections
in a chatbot,

it knows far more than you do
in your 100 trillion connections,

which suggests it's
got a much better way

of getting knowledge
into those connections.

-A much better way of getting knowledge

that isn't fully understood.

-We have a very good idea of,
sort of, roughly what it's doing,

but as soon as it
gets really complicated,

we don't actually know what's going on,

any more than we know
what's going on in your brain.

-What do you mean, we don't
know exactly how it works?

It was designed by people.

-No it wasn't.

What we did was, we
designed the learning algorithm.

That's a bit like
designing the principle of evolution.

But when this learning algorithm
then interacts with data,

it produces complicated neural networks

that are good at doing things,
but we don't really

understand how they do those things.

-What are the implications
of these systems

autonomously writing
their own computer code,

and executing their own computer code?

-That's a serious worry, right?

So one of the ways in which
these systems might escape control

is by writing their own computer code
to modify themselves,

and that's something we
need to seriously worry about.

-What do you say to someone who
might argue,

if the systems become malevolent,
just turn them off.

-They will be able to
manipulate people, right?

And these will be very good
at convincing people,

because they'll have learned
from all the novels

that were ever written,
all the books by Machiavelli,

all the political connivances.

They'll know that stuff,
they'll know how to do it.

-Know-how, of the human kind,
runs in Geoffrey Hinton's family.

His ancestors
include mathematician George Boole,

who invented the basis of computing.

And George Everest, who surveyed India

and got that mountain named after him.

But as a boy, Hinton himself could never

climb the peak of expectations
raised by a domineering father.

-Every morning when I went to school,

he'd actually say to me,
as I walked down the driveway,

"Get in there pitching,
and maybe when you're

twice as old as me,
you'll be half as good."

-Dad was an authority on beetles.

-He knew a lot more about beetles
than he knew about people.

-Did you feel that as a child?

-A bit. Yes.

When he died, we
went to his study at the university,

and the walls
were lined with boxes of papers

on different kinds of beetle.

And just near the door there
was a slightly smaller box

that simply said, "Not insects."

And that's where he
had all the things about the family.

Today, at 75, Hinton recently retired

after what he calls,
ten happy years at Google.

Now he's professor emeritus
at the University of Toronto,

and he happened to mention, he

has more academic citations
than his father.

Some of his research
led to chatbots like Google's Bard,

which we met last spring.

Confounding. Absolutely confounding.

We asked Bard
to write a story from six words.

For sale. Baby shoes. Never worn.

Holy cow!

The shoes were a gift from my wife,
but we never had a baby.

Bard created a deeply human tale
of a man

whose wife could not conceive,
and a stranger

who accepted the shoes to heal the pain
after her miscarriage.

I am rarely speechless.

I don't know what to make of this.

Chatbots are said to be language models

that just predict the next
most likely word based on probability.

-You'll hear people saying things like,

they're just doing auto-complete,
they're just trying to

predict the next word,
and they're just using statistics.

Well, it's true, they're
just trying to predict the next word.

But if you think about it,
to predict the next word,

you have to understand the sentences.

So, the idea they're
predicting the next word,

so they're not intelligent, is crazy.

You have to be really intelligent,

to predict the next word
really accurately.

-To prove it, Hinton showed us a test he

devised for ChatGPT-4, the chatbot

from a company called OpenAI.

It was sort of reassuring
to see a Turing Award winner

mistype and blame the computer.

-Oh, damn this thing,
we're going to go back and start again.

-That's okay.

Hinton's test
was a riddle about house painting.

An answer would demand
reasoning and planning.

This is what he typed into chat GPT-4.