♪ ♪
MILES O'BRIEN:
Machines that think like humans.
Our dream to create machines
in our own image
that are smart and intelligent
goes back to antiquity.
Well, can it bring it to me?
O'BRIEN: Is it possible that
the dream of artificial intelligence
has become reality?
They're able to do things
that we didn't
think they could do.
MANOLIS KELLIS: Go was thought to be
a game where machines would never win.
The number of choices
for every move is enormous.
O'BRIEN: And now, the
possibilities seem endless.
MUSTAFA SULEYMAN:
And this is going to be
one of the greatest boosts
to productivity in the
history of our species.
That looks like just a hint
of some type of smoke.
O'BRIEN: Identifying
problems before a human can...
LECIA SEQUIST: We
taught the model to recognize
developing lung cancer.
O'BRIEN: and
inventing new drugs.
PETRINA KAMYA: I never
thought that we would be able
to be doing the things
we're doing with A.I..
O'BRIEN: But
along with the hope...
[imitating Obama]:
This is a dangerous time.
O'BRIEN: comes deep concern.
One of the first drops
in the feared flood
of A.I.-created disinformation.
We have lowered barriers
to entry to manipulate reality.
We're going to live in a world
where we don't know what's real.
The risks are uncertain
and potentially enormous.
O'BRIEN: How powerful
is A.I.? How does it work?
And how can we reap
its extraordinary benefits...
Sybil looked here,
and anticipated that
there would be a problem.
O'BRIEN: without
jeopardizing our future?
"A.I. Revolution"
right now, on "NOVA!"
[whirring]
♪ ♪
Tell me the backstory
on inflection A.I..
[voiceover]: Our story begins
with the making of this story.
PI [on computer]: The story of
Inflection A.I. is an exciting one.
O'BRIEN [voiceover]:
I was researching
an interview subject.
Who is Mustafa Suleyman?
[voiceover]: Something I've done
a thousand times
in my 40-year career.
PI [on computer]: Mustafa
Suleyman is a true pioneer
in the field of
artificial intelligence.
[voiceover]: But this
time, it was different:
I wasn't typing
out search terms.
What is machine learning?
O'BRIEN [voiceover]: I was having
a conversation with a computer.
PI: Sounds like an
exciting project, Miles.
[voiceover]: It felt like
something big had changed.
PI: Machine learning, ML, is
a type of artificial intelligence.
O'BRIEN [voiceover]:
And as it happened,
I was focused on one of the
innovators of this revolution.
Okay, so if I do this...
[voiceover]: Mustafa
Suleyman is Co-Founder
of a startup called Inflection.
It makes an artificial
intelligence assistant
called Pi.
So now you can speak...
O'BRIEN [voiceover]: I
met them both in London.
It's fundamentally
different, isn't it?
Yeah, it's a
conversational style.
All of us humans
learn through stories,
and through narrative, and
through interactive dialogue.
And now, the machine
can kind of come alive,
and talk to you
about whatever it is
that's on top of your mind.
O'BRIEN: Tell me about
the PBS program "NOVA."
[voiceover]: Chatbots can offer
up quick answers, write poems,
finish essays, and
translate languages
among many other things.
PI [over phone]: "NOVA" is a
science documentary series...
O'BRIEN [voiceover]:
They aren't perfect,
but they have put artificial
intelligence in our hands,
and into the public
consciousness.
And it seems we're
equal parts leery
and intrigued.
SULEYMAN: A.I. is a tool
for helping us to understand
the world around us,
predict what's likely to
happen, and then invent
solutions that help
improve the world around us.
My motivation was
to try to use A.I. tools
to, uh, you know,
invent the future.
The rise in artificial
intelligence...
REPORTER: A.I.
technology is developing...
O'BRIEN [voiceover]: Lately, it
seems a dark future is already here...
The technology could
replace millions of jobs...
O'BRIEN [voiceover]: if
you listen to the news reporting.
The moment civilization
was transformed.
O'BRIEN [voiceover]: So how
can artificial intelligence help us,
and how might it hurt us?
At the center of the
public handwringing:
how should we put
guardrails around it?
We definitely need more
regulations in place...
O'BRIEN [voiceover]: Artificial
intelligence is moving fast
and changing the world.
Can we keep up?
Non-human minds
smarter than our own.
O'BRIEN [voiceover]: The news
coverage may make it seem like
artificial intelligence
is something new.
At a moment of revolution...
O'BRIEN [voiceover]: But human
beings have been thinking about this
for a very long time.
I have a very fine brain.
Our dream to create
machines in our own image
that are smart and intelligent
goes back to antiquity.
Uh, it's, it's
something that has,
has permeated the evolution
of society and of science.
[mortars firing]
O'BRIEN [voiceover]: The modern
origins of artificial intelligence
can be traced back
to World w*r II,
and the prodigious
human brain of Alan Turing.
The legendary
British mathematician
developed a machine
capable of deciphering coded
messages from the Nazis.
After the w*r, he was among
the first to predict computers
might one day match
the human brain.
There are no surviving
recordings of Turing's voice,
but in 1951, he gave a
short lecture on BBC radio.
We asked an A.I.-generated
voice to read a passage.
TURING A.I. VOICE: I think
it is probable, for instance,
that at the end of the century,
it will be possible to
program a machine
to answer questions
in such a way
that it will be extremely
difficult to guess
whether the answers
are being given by a man
or by the machine.
O'BRIEN [voiceover]: And
so, the Turing test was born.
Could anyone build a machine
that could converse
with a human in a way
that is indistinguishable
from another person?
In 1956,
a group of pioneering
scientists spent the summer
brainstorming at
Dartmouth College.
And they told the world
that they have coined
a new academic field of study.
They called it
artificial intelligence
O'BRIEN [voiceover]: For
decades, their aspirations remained
far ahead of the
capabilities of computers.
In 1978,
"NOVA" released its first
film on artificial intelligence.
We have seen the
first crude beginnings
of artificial intelligence...
O'BRIEN [voiceover]: And the
legendary science fiction writer,
Arthur C. Clark was,
as always, prescient.
It doesn't really
exist yet at any level,
because our most complex
computers are still morons,
high-speed morons,
but still morons.
Nevertheless, we have
the possibility of machines
which can outpace
their creators,
and therefore, become
more intelligent than us.
At the time, researchers were
developing "expert systems,"
purpose-built to
perform specific tasks.
So the thing that we need to do
to make machine understand,
um, you know, our world,
is to put all our
knowledge into a machine
and then provide
it with some rules.
♪ ♪
O'BRIEN [voiceover]: Classic A.I.
reached a pivotal moment in 1997
when an artificial intelligence
program devised by IBM,
called "Deep Blue" defeated
world chess champion
and grandmaster Garry Kasparov.
It searched about 200
million positions a second,
navigating through
a tree of possibilities
to determine the best move.
RUS: The program analyzed
the board configuration,
could project forward
millions of moves
to examine millions
of possibilities,
and then picked the best path.
O'BRIEN [voiceover]:
Effective, but brittle,
Deep Blue wasn't
strategizing as a human does.
From the outset, artificial
intelligence researchers
imagined making machines
that think like us.
The human brain, with
more than 80 billion neurons,
learns not by following rules,
but rather by taking in
a steady stream of data,
and looking for patterns.
KELLIS: The way that
learning actually works
in the human brain is
by updating the weights
of the synaptic connections
that are underlying
this neural network.
O'BRIEN [voiceover]: Manolis Kellis
is a Professor of Computer Science
at the Massachusetts
Institute of Technology.
So we have trillions of
parameters in our brain
that we can adjust
based on experience.
I'm getting a reward.
I will update the strength
of the connections
that led to this reward...
I'm getting punished,
I will diminish the
strength of the connections
that led to the punishment.
So this is the original
neural network.
We did not invent it, we,
you know, we inherited it.
O'BRIEN [voiceover]: But
could an artificial neural network
be made in our own
image? Turing imagined it.
But computers were nowhere near
powerful enough
to do it until recently.
It's only with the advent
of extraordinary data sets
that we have, uh,
since the early 2000s,
that we were able to
build up enough images,
enough annotations,
enough text to be
able to finally train
these sufficiently
powerful models.
O'BRIEN [voiceover]: An
artificial neural network is, in fact,
modeled on the human brain.
It uses interconnected
nodes, or neurons,
that communicate
with each other.
Each node receives
inputs from other nodes
and processes those
inputs to produce outputs,
which are then passed
on to still other nodes.
It learns by adjusting the
strength of the connections
between the nodes based
on the data it is exposed to.
This process of
adjusting the connections
is called training,
and it allows an
artificial neural network
to recognize patterns and
learn from its experiences
like humans do.
A child, how is it
learning so fast?
It is learning so fast
because it's constantly
predicting the future
and then seeing what happens
and updating their weights
in their neural network
based on what just happened.
Now you can take this
self-supervised
learning paradigm
and apply it to machines.
O'BRIEN [voiceover]: At first, some
of these artificial neural networks
were trained on vintage
Atari video games
like "Space Invaders"
and "Breakout."
Games reduce the
complexity of the real world
to a very narrow set of
actions that can be taken.
O'BRIEN [voiceover]:
Before he started Inflection,
Mustafa Suleyman
co-founded a company called
DeepMind in 2010.
It was acquired by
Google four years later.
When an A.I. plays a game,
we show it
frame-by-frame, every pixel
in the moving image.
And so the A.I. learns
to associate pixels
with actions that it can take
moving left or right or
pressing the fire button.
O'BRIEN [voiceover]: When it
obliterates blocks or shoots aliens,
the connections between the
nodes that enabled that success
are strengthened.
In other words, it is rewarded.
When it fails, no reward.
Eventually, all those
reinforced connections
overrule the weaker ones.
The program has
learned how to win.
This sort of repeated
allocation of reward
for repetitive behavior is
a great way to train a dog.
It's a great way to teach a kid.
It's a great way for us as
adults to adapt our behavior.
And in fact, it's
actually a good way
to train machine learning
algorithms to get better.
O'BRIEN [voiceover]: In 2014, DeepMind
began work on an artificial neural network
called "AlphaGo"
that could play the ancient,
and deceptively complex,
board game of Go.
KELLIS: Go was thought to be a
game where machines would never win.
The number of choices
for every move is enormous.
O'BRIEN [voiceover]:
But at DeepMind,
they were counting on
the astounding growth
of compute power.
And I think that's the key
concept to try to grasp,
is that we are massively,
exponentially growing
the amount of computation
used, and in some sense,
that computation is a proxy
for how intelligent
the model is.
O'BRIEN [voiceover]:
AlphaGo was trained two ways.
First, it was fed a large
data set of expert Go games
so that it could learn
how to play the game.
This is known as
supervised learning.
Then the software played
against itself many millions of times,
so-called
reinforcement learning.
This gradually improved
its skills and strategies.
In March 2016,
AlphaGo faced Lee Sedol,
one of the world's
top-ranking players
in a five-game match
in Seoul, South Korea.
AlphaGo not only won,
but also made a move so
novel, the Go cognoscenti
thought it was a huge blunder.
That's a very surprising move.
There's no question to
me that these A.I. models
are creative.
They're incredibly creative.
O'BRIEN [voiceover]: It turns out
the move was a stroke of brilliance.
And this emergent
creative behavior
was a hint of what was to come:
generative A.I.
Meanwhile,
a company called
OpenA.I. was creating
a generative A.I. model
that would become ChatGPT.
It allows users to
engage in a dialogue
with a machine that
seems uncannily human.
It was first released in 2018,
but it was a subsequent version
that became a global sensation
in late 2022.
This promises to
be the viral sensation
that could completely
reset how we do things.
Cranking out entire essays
in a matter of seconds.
O'BRIEN [voiceover]: Not only
did it wow the public, it also caught
artificial intelligence
innovators off guard.
YOSHUA BENGIO:
It surprised me a lot
that they're able
to do things that
we didn't think they
could do simply by
learning to imitate
how humans respond.
And I thought this kind
of abilities would take
many more years or decades.
O'BRIEN [voiceover]: ChatGPT
is a large language model.
LLMs start by consuming
massive amounts of text:
books, articles and websites,
which are publicly
available on the internet.
By recognizing patterns
in billions of words,
they can make guesses at
the next word in a sentence.
That's how ChatGPT
generates unique answers
to your questions.
If I ask for a haiku
about the blue sky
it writes something that
seems completely original.
KELLIS: If you're
good at predicting
this next word,
it means you're understanding
something about the sentence.
What the style
of the sentence is,
what the feeling
of the sentence is.
And you can't tell whether
this was a human or a machine.
That's basically the
definition of the Turing test.
O'BRIEN [voiceover]: So,
how is this changing our world?
Well, It might change my
world... as an arm amputee.
Ready for my
casting call, right?
MONROE [chuckling]: Yes.
Let's do it. All right.
O'BRIEN [voiceover]: That's
Brian Monroe of the Hanger Clinic.
He's been my prosthetist
since an injury took
my arm above the elbow
ten years ago.
So what we're going to do today
is take a mold of your arm. Uh-huh.
Kind of is like a cast
for a broken bone.
O'BRIEN [voiceover]: Up until now, I
have used a body-powered prosthetic.
Harness and a cable
allow me to move it
by shrugging my shoulders.
The technology is
more than a century old.
But artificial intelligence,
coupled with small
electric motors,
is finally pushing prosthetics
into the 21st century.
Which brings me to Chicago
and the offices of a small
company called Coapt.
I met the C.E.O., Blair Locke,
a pioneer in the push to
apply artificial intelligence
to artificial limbs.
So, what do we have here?
What are we going to do?
This allows us to very easily
test how your control would be
using a pretty simple cuff;
this has electrodes in it,
and we'll let the
power of the electronics
that are doing the
machine learning
see what you're capable
of. All right, let's give it a try.
[voiceover]: Like most amputees,
I feel my missing hand
almost as if it was still there...
a phantom.
Everything will
touch. Is that okay?
Yeah. Not too tight?
No. All good. Okay.
O'BRIEN [voiceover]: It's almost
entirely immobile, stuck in molasses.
Make a fist, not too hard.
O'BRIEN [voiceover]: But I am able
to imagine moving it ever so slightly.
And I'm gonna have you
squeeze into that a little bit harder.
Very good, and I see
the pattern on the screen
change a little bit.
O'BRIEN [voiceover]:
And when I do,
I generate an array of faint
electrical signals in my stump.
That's your muscle information.
It feels, it feels
like I'm overcoming
something that's really stuck.
I don't know, is
that enough signal?
Should be. Oh, okay.
We don't need a lot of signal,
we're going for information
in the signal,
not how loud it is.
O'BRIEN [voiceover]: And this is
where artificial intelligence comes in.
Using a virtual prosthetic
depicted on a screen,
I trained a machine learning
algorithm to become fluent
in the language of my
nerves and muscles.
We see eight different
signals on the screen.
All eight of those sensor sites
are going to feed in together
and let the algorithm
sort out the data.
What you are experiencing
is your ability to
teach the system
what is hand-closed to you.
And that's different than
what it would be to me.
O'BRIEN [voiceover]: I told the
software what motion I desired,
open, close, or rotate,
then imagined moving my
phantom limb accordingly.
This generates an array
of electromyographic,
or EMG, signals in
my remaining muscles.
I was training the A.I.
to connect the pattern
of these electrical signals
with a specific movement.
LOCK: The system adapts,
and as you add more
data and use it over time,
it becomes more robust,
and it learns to
improve upon use.
O'BRIEN: Is it me that's learning,
or the algorithm that's learning?
Or are we learning together?
LOCK: You're learning together.
Okay.
O'BRIEN [voiceover]: So, how does the
Coapt pattern recognition system work?
It's called a Bayesian
classification model.
As I train the software,
it labels my various
EMG patterns
into corresponding
classes of movement...
hand open, hand closed,
wrist rotation, for example.
As I use the arm,
it compares the electrical
signals I'm transmitting
to the existing library of
classifications I taught it.
It relies on
statistical probability
to choose the best match.
And this is just one
way machine learning
is quietly
revolutionizing medicine.
Computer scientist
Regina Barzilay
first started working
on artificial intelligence
in the 1990s, just as
rule-based A.I. like Deep Blue
was giving way
to neural networks.
She used the techniques
to decipher dead languages.
You might call it a
small language model.
Something that is fun and
intellectually very challenging,
but it's not like it's
going to change our life.
O'BRIEN [voiceover]: And then
her life changed in an instant.
CONSTANCE LEHMAN:
We see a spot there.
O'BRIEN [voiceover]: In 2014, she
was diagnosed with breast cancer.
BARZILAY [voiceover]: When
you go through the treatment,
there are a lot of
people who are suffering.
I was interested in
what I can do about it, and
clearly it was not continuing
deciphering dead languages,
and it was quite a journey.
O'BRIEN [voiceover]: Not surprisingly,
she began that journey with mammograms.
LEHMAN: It's a little
bit more prominent.
O'BRIEN [voiceover]: She
and Constance Lehman,
a radiologist at Massachusetts
General Hospital,
realized the Achilles heel
in the diagnostic system
is the human eye.
BARZILAY [voiceover]: So
the question that we ask is,
what is the likelihood
of these patients
to develop cancer
within the next five years?
We, with our human eyes,
cannot really make
these assertions
because the
patterns are so subtle.
LEHMAN: Now, is that different
from the surrounding tissue?
O'BRIEN [voiceover]: It's a perfect
use case for pattern recognition
using what is known as a
convolutional neural network.
♪ ♪
Here's an example of
how CNNs get smart:
they comb through a picture with
many virtual magnifying glasses.
Each one is looking for a
specific kind of puzzle piece,
like an edge, a
shape, or a texture.
Then it makes
simplified versions,
repeating the process on
larger and larger sections.
Eventually the puzzle
can be assembled.
And it's time to make a guess.
Is it a cat? A dog? A tree?
Sometimes the guess is
right, but sometimes it's wrong.
And here's the learning part:
with a process called
backpropagation,
labeled images are sent back
to correct the previous operation.
So the next time it
plays the guessing game,
it will be even better.
To validate the model, Regina
and her team gathered up
more than 128,000 mammograms
collected at seven
sites in four countries.
More than 3,800 of them
led to a cancer diagnosis
within five years.
You just give to it the image,
and then the five
years of outcomes,
and it can learn the likelihood
of getting a cancer diagnosis.
O'BRIEN [voiceover]: The
software, called Mirai, was a success.
In fact, it is between
in predicting future
cancer diagnoses.
Then, a friend of Regina's
developed lung cancer.
SEQUIST: In lung
cancer, it's actually
sort of mind boggling
how much has changed.
O'BRIEN [voiceover]: Her friend
saw oncologist Lecia Sequist.
She and Regina wondered
if artificial intelligence
could be applied
to CAT scans of patients' lungs.
SEQUIST: We taught the model
to recognize the patterns
of developing lung cancer
by using thousands of CAT scans
from patients who
were participating
in a clinical trial.
From the new study?
Oh, interesting. Correct.
SEQUIST [voiceover]: We had
a lot of information about them.
We had demographic information,
we had health information,
and we had outcomes information.
O'BRIEN [voiceover]:
They call the model Sibyl.
In the retrospective
study, right,
so the retrospective data...
O'BRIEN [voiceover]:
Radiologist Florian Fintelmann
showed me what it can do.
FINTELMANN: This is
earlier, and this is later.
There is nothing
that I can perceive,
pick up, or describe.
There's no, what we
call, a precursor lesion
on this CT scan.
Sibyl looked here
and then anticipated that
there would be a problem
based on the baseline
scan. What is it seeing?
That's the million
dollar question.
And, and maybe not
the million dollar question.
Does it really matter? Does it?
O'BRIEN [voiceover]: When
they compared the predictions
to actual outcomes from
previous cases, Sybil fared well.
It correctly forecast cancer
between 80% and 95% of the time,
depending on the
population it studied.
The technique is
still in the trial phase.
But once it is deployed,
it could provide a
potent tool for prevention.
The hope is that if you
can predict very early on
that the patient is
in the wrong way,
you can do clinical trials,
you can develop the drugs
that are doing the prevention,
rather than treatment
of very advanced disease
that we are doing today.
O'BRIEN [voiceover]: Which takes
us back to DeepMind and AlphaGo.
The fun and games
were just the beginning,
a means to an end.
We have always
set out at DeepMind
to, um, use our technologies
to make the world a better place.
O'BRIEN [voiceover]: In 2021,
the company released AlphaFold.
It is pattern
recognition software
designed to make it
easier for researchers
to understand proteins,
long chains of amino acids
involved in nearly every
function in our bodies.
How a protein folds
into a specific,
three-dimensional shape
determines how it interacts
with other molecules.
SULEYMAN: There's
this correlation between
what the protein does
and how it's structured.
So if we can predict
how the protein folds,
then say something
about their function.
O'BRIEN: If we know how a
disease's protein is shaped, or folded,
we can sometimes
create a drug to disable it.
But the shape of millions of
proteins remained a mystery.
DeepMind trained AlphaFold
on thousands of
known protein structures.
It leveraged this
knowledge to predict
nearly all the proteins
known to science.
SULEYMAN: You take
some high-quality known data,
and you use that to, you know,
make a prediction about how
a similar piece of information
is likely to unfold
over some time series,
and the structure
of proteins is,
you know, in that
sense, no different to
making a prediction in
the game of Go or in Atari
or in a mammography scan,
or indeed, in a large
language model.
KAMYA: These thin sticks here?
Yeah? They represent
the amino acids
that make up a protein.
O'BRIEN [voiceover]:
Theoretical chemist
Petrina Kamya works
for a company called
Insilico Medicine.
It uses AlphaFold
and its own deep-learning models
to make accurate predictions
about protein structures.
What we're doing in drug design
is we're designing a molecule
that is analogous to
the natural molecule
that binds to the protein,
but instead it will
lock it, if this molecule
is involved in a disease
where it's hyperactive.
O'BRIEN [voiceover]:
If the molecule fits well,
it can inhibit the
disease-causing proteins.
So you're filtering it down
like you're choosing an
Airbnb or something to,
you know, number of bedrooms,
whatever. To suit your needs.
[laughs] Exactly, right.
Right, yeah. That's
a very good analogy.
It's sort of like Airbnb.
So you are putting
in your criteria,
and then Airbnb will filter out
all the different properties
based on your criteria.
So you can be
very, very restrictive
or you can be very,
very free... Right.
In terms of guiding the
generative algorithms
and telling them what
types of molecules
you want them to generate.
O'BRIEN [voiceover]: It will take
to identify the best
candidates ranked in order.
How long would it have taken you
to figure that out as a
computational chemist?
I would have thought
of some of these,
but not all of them. Okay.
O'BRIEN [voiceover]: While there
are no shortcuts for human trials,
nor should we hope for that,
this could greatly speed up
the drug development pipeline.
There will not be the
need to invest so heavily
in preclinical discovery,
and so, drugs can
therefore be cheaper.
And you can go
after those diseases
that are otherwise neglected,
because you don't
have to invest so heavily
in order for you to
come up with a drug,
a viable drug.
O'BRIEN [voiceover]: But
medicine isn't the only place
where A.I. is
breaking new frontiers.
It's conducting
financial analysis,
helps with fraud detection.
[mechanical whirring]
It's now being deployed
to discover novel materials
and could help us build
clean energy technology.
And It is even
helping to save lives
as the climate
crisis boils over.
[indistinct radio chatter]
In St. Helena, California,
dispatchers at the
CAL FIRE Sonoma-Lake-Napa
Command Center
caught a break in 2023.
Wildfires blackened nearly
We were at 400,000
acres in 2020.
Something like that would
generate a response from us...
O'BRIEN [voiceover]: Chief Mike
Marcucci has been fighting fires
for more than 30 years.
MARCUCCI [voiceover]: Once we
started having these devastating fires,
we needed more intel.
The need for intelligence
is just overwhelming
in today's fire service.
O'BRIEN [voiceover]:
Over the past 20 years,
California has
installed a network
of more than 1,000
remotely operated
pan, tilt, zoom surveillance
cameras on mountaintops.
PETE AVANSINO: Vegetation
fire, Highway 29 at Doton Road.
O'BRIEN [voiceover]: All those
cameras generate petabytes of video.
CAL FIRE partnered with
scientists at U.C. San Diego
to train a neural network
to spot the early
signs of trouble.
It's called ALERT California.
SeLEGUE: So here's
one that just popped up.
Here's an anomaly.
O'BRIEN [voiceover]: CAL FIRE Staff Chief
of Fire and Intelligence Philip SeLegue
showed me how it works
while it was in action,
detecting nascent fires,
micro fires.
That looks like
just a little hint
of some type of
smoke that was there...
O'BRIEN [voiceover]: Based on
this, dispatchers can orchestrate
a fast response.
A.I. has given us the
ability to detect and to see
where those fires are starting.
AVANSINO: Transport
O'BRIEN [voiceover]:
For all they know,
they have nipped some
megafires in the bud.
The success are the fires
that you don't hear
about in the news.
O'BRIEN [voiceover]:
Artificial intelligence
can't put out
wildfires just yet.
Human firefighters
still need to do that job.
But researchers are pushing hard
to combine neural networks
with mobility and dexterity.
This is where
people get nervous.
Will they take our jobs?
Or could they turn against us?
But at M.I.T.,
they're exploring
ideas to make robots
good human partners.
We are interested
in making machines
that help people with
physical and cognitive tasks.
So this is really great,
it has the stiffness
that we wanted...
O'BRIEN [voiceover]: Daniela Rus is
director of M.I.T.'s Computer Science
and Artificial Intelligence Lab.
Oh, can you bring it to me?
O'BRIEN [voiceover]: CSAIL.
They are different,
like, kind of like muscles
or actuators.
RUS [voiceover]: We
can do so much more
when we get people and
machines working together.
We can get better reach.
We can get lift,
precision, strength, vision.
All of these are
physical superpowers
we can get through machines.
O'BRIEN [voiceover]:
So, they're focusing
on making it safe for humans
to work in close
proximity to machines.
They're using some of
the technology that's inside
my prosthetic arm.
Electrodes that can read
the faint EMG signals generated
as our nerves command
our muscles to move.
They have the capability
to interact with a human,
to understand the human,
to step in and help
the human as needed.
I am at your disposal
with 187 other languages,
along with their various
dialects and sub tongues.
O'BRIEN [voiceover]:
But making robots as useful
as they are in the
movies is a big challenge.
♪ ♪
Most neural networks run
on powerful supercomputers...
thousands of processors
occupying entire buildings.
RUS: We have brains that require
massive computation,
which you cannot include
on a self-contained body.
We address the size challenge by
making liquid networks.
O'BRIEN [voiceover]:
Liquid networks.
So it looks like an
autonomous vehicle
like I've seen before,
but it is a little
different, right?
ALEXANDER AMINI: Very different.
This is an autonomous vehicle
that can drive in
brand-new environments
that it has never seen
before for the first time.
O'BRIEN [voiceover]: Most
self-driving cars today rely,
to some extent, on
detailed databases
that help them recognize
their immediate environment.
Those robot cars get
lost in unfamiliar terrain.
O'BRIEN: In this case,
you're not relying on
a huge, expansive
neural network.
You're running on
Correct.
O'BRIEN [voiceover]: Computer
scientist Alexander Amini
took me on a ride in
an autonomous vehicle
with a liquid neural
network brain.
AMINI: We've become very
accustomed to relying on
big, giant data centers
and cloud compute.
But in an autonomous vehicle,
you cannot make such
assumptions, right?
You need to be able to operate,
even if you lose
internet connectivity
and you cannot talk
to the cloud anymore,
your entire neural network,
the brain of the car,
needs to live on the car,
and that imposes a lot
of interesting constraints.
O'BRIEN [voiceover]: To
build a brain smart enough
and small enough to do this job,
they took some
inspiration from nature,
a lowly worm called C. elegans.
Its brain contains
all of 300 neurons,
but it's a very
different kind of neuron.
It can capture more
complex behaviors
in every single
piece of that puzzle.
And also the wiring,
how a neuron talks
to another neuron
is completely different
than what we see
in today's neural networks.
O'BRIEN [voiceover]: Autonomous
cars that tap into today's neural networks
require huge amounts of
compute power in the cloud.
But this car is using
just 19 liquid neurons.
A worm at the wheel... sort of.
AMINI [voiceover]:
Today's A.I. models
are really pushing
the boundaries
of the scale of
compute that we have.
They're also pushing
the boundaries
of the data sets that we have.
And that's not sustainable,
because ultimately,
we need to deploy A.I.
onto the device itself, right?
Onto the cars, onto
the surgical robots.
All of these edge devices
that actually makes
the decisions.
O'BRIEN [voiceover]:
The A.I. worm may, in fact,
turn.
The portability of
artificial intelligence
was on my mind when it came time
to pick up my new
myoelectric arm...
equipped with Coapt
A.I. pattern recognition.
All right, let's just check this
real quick...
O'BRIEN [voiceover]:
A few weeks after
my trip to Chicago,
I met Brian Monroe
at his home office
outside Washington, D.C.
Are you happy with the
way it came out? Yeah.
Would you tell me otherwise?
[laughing]: Yeah,
I would, yeah...
O'BRIEN [voiceover]: As usual,
he did a great job
making a tight socket.
How's the socket
feel? Does it feel like
it's sliding down or
falling out... No,
it fits like a glove.
O'BRIEN [voiceover]: It's
really important in this case,
because the electrodes
designed to read the signals
from my muscles...
have to stay in place snugly
in order to generate
accurate, reliable commands
to the actuators in my new hand.
Wait, is that you? That's me.
[voiceover]: He
also provided me with
a human-like bionic hand.
But getting it
to work just right
took some time.
That's open and it's closing.
It's backwards?
Yeah. Now try.
If it's reversed,
I can swap the
electrodes. There we go.
That's got it. Is it
the right direction?
Yeah. Uh-huh. Okay.
O'BRIEN [voiceover]: It's
a long way from the movies,
and I'm no Luke Skywalker.
But my new arm and
I are now together.
And I'm heartened to know
that I have the freedom
and independence
to teach and tweak it
on my own.
That's kind of cool. Yeah.
[voiceover]: Hopefully
we will listen to each other.
It's pretty awesome.
O'BRIEN [voiceover]:
But we might want to listen
with a skeptical ear.
JORDAN PEELE [imitating Obama]:
You see, I would never say these things,
at least not in a
public address,
but someone else would.
Someone like Jordan Peele.
This is a dangerous time.
O'BRIEN [voiceover]: It's even more
dangerous now than it was in 2018
when comedian Jordan Peele
combined his pitch-perfect
Obama impression
with A.I. software to make
this convincing fake video.
or whether we become
some kind of [bleep] up dystopia.
♪ ♪
O'BRIEN [voiceover]:
Fakes are about as old as
photography itself.
Mussolini, Hitler, and Stalin
all ordered that pictures
be doctored or redacted,
erasing those who
fell out of favor,
consolidating power,
manipulating their
followers through images.
HANY FARID: They've
always been manipulated,
throughout history, but...
there was literally, you
can count on one hand,
the number of people in
the world who could do this.
But now, you need
almost no skill.
And we said, "Give us an image
"of a middle-aged
woman, newscaster,
sitting at her desk,
reading the news."
O'BRIEN [voiceover]: Hany Farid
is a professor of computer science
at U.C. Berkeley.
[on computer]: And this is
your daily dose of future flash.
O'BRIEN [voiceover]:
He and his team
are trying to navigate
the house of mirrors
that is the world of
A.I.-enabled deepfake imagery.
Not perfect.
She's not blinking,
but it's pretty good.
And by the way, he did
this in a day and a half.
FARID [voiceover]: It's
the classic automation story.
We have lowered
barriers to entry
to manipulate reality.
And when you do that,
more and more people will do it.
Some good people will do it,
but lots of bad
people will do it.
There'll be some
interesting use cases,
and there'll be a lot
of nefarious use cases.
Okay, so, um...
Glasses off. How's the framing?
Everything okay?
[voiceover]: About a week before
I got on a plane to
see him... Hold on.
O'BRIEN [voiceover]: He
asked me to meet him on Zoom
so he could get a good recording
of my voice and mannerisms.
And I assume you're
recording, Miles.
O'BRIEN [voiceover]: And he
turned the table on me a little bit,
asking me a lot of questions
to get a good sampling.
FARID [on computer]:
How are you feeling about
the role of A.I. as it
enters into our world
on a daily basis?
I think it's very
important, first of all,
to calibrate the concern level.
Let's take it away from
the "Terminator" scenario...
[voiceover]: The
"Terminator" scenario.
Come with me if
you want to live.
O'BRIEN [voiceover]: You
know, a malevolent neural network
hellbent on
exterminating humanity.
You're really real.
O'BRIEN [voiceover]:
In the film series,
the cyborg assassin
is memorably played by
Arnold Schwarzenegger.
Hany thought it would be fun
to use A.I. to turn
Arnold into me.
Okay.
O'BRIEN [voiceover]: A
week later, I showed up at
Berkeley's School
of Information,
ironically located in the
oldest building on campus.
So you had me do this
strange thing on Zoom.
Here I am. What
did you do with me?
Yeah, well, it's gonna teach you
to let me record your
Zoom call, isn't it?
I did this with
some trepidation.
[voiceover]: I was
excited to see what tricks
were up his sleeve.
FARID [voiceover]: I
uploaded 90 seconds of audio,
and I clicked a box saying
"Miles has given me
permission to use his voice,"
which I don't actually
think you did. [chuckles]
Um, and, I waited about,
eh, maybe 20 seconds,
and it said, "Okay, what
would you like for Miles to say?"
And I started typing,
and I generated an
audio of you saying
whatever I wanted you to say.
We are synthesizing,
at much, much lower resolution.
O'BRIEN [voiceover]: You
could have knocked me over
with a feather
when I watched this.
A.I. O'BRIEN: Terminators
were science fiction back then,
but if you follow the
recent A.I. media coverage,
you might think that Terminators
are just around the corner.
The reality is...
O'BRIEN [voiceover]: The eyes
and the mouth need some work,
but it sure does sound like me.
And consider what
happened in May of 2023.
Someone posted this
A.I.-generated image
of what appeared to
be a t*rror1st bombing
at the Pentagon.
NEWS ANCHOR: Today
we may have witnessed
one of the first drops
in the feared flood
of A.I.-created disinformation.
O'BRIEN [voiceover]:
It was shared on Twitter
via what seemed to be
a verified account
from Bloomberg News.
NEWS ANCHOR: It only
took seconds to spread fast.
The Dow now down
about 200 points...
Two minutes later, the
stock market dropped
a half a trillion dollars
from a single fake image.
Anybody could've
made that image,
whether it was intentionally
manipulating the market
or unintentionally,
in some ways, it
doesn't really matter.
O'BRIEN [voiceover]: So
what are the technological
innovations that make
this tool widely available?
One technique is called
the generative
adversarial network,
or GAN.
Two algorithms
in a dizzying student-teacher
back and forth.
Let's say it's learning
how to generate a cat.
FARID: And it starts
by just splatting down
a bunch of pixels onto a canvas.
And it sends it over
to a discriminator.
And the discriminator has access
to millions and
millions of images
of the category that you want.
And it says,
"Nope, that doesn't look
like all these other things."
So it goes back to the
generator and says, "Try again."
Modifies some pixels,
sends it back to
the discriminator,
and they do this
in what's called
an adversarial loop.
O'BRIEN [voiceover]:
And eventually,
after many thousands of volleys,
the generator finally
serves up a cat.
And the discriminator says,
"Do more like that."
Today, we have a whole
new way of doing these things.
They're called diffusion-based.
What diffusion does
is it has vacuumed
up billions of images
with captions that
are descriptive.
O'BRIEN [voiceover]: It starts
by making those labeled images
visually noisy on purpose.
FARID: And then it corrupts
it more, and it goes backwards
and corrupts it more,
and goes backwards
and corrupts it more
and goes backwards...
and it does that
six billion times.
O'BRIEN [voiceover]:
Eventually it corrupts it
so it's unrecognizable
from the original image.
Now that it knows how to
turn an image into nothing,
it can reverse the process,
turning seemingly nothing,
into a beautiful image.
FARID: What it's
learned is how to take
a completely indescript
image, just pure noise,
and go back to a coherent
image, conditioned on a text prompt.
You're basically reverse
engineering an image
down to the pixel.
Yeah, exactly, yeah.
And it's... and by the
way... if you had asked me,
"Will this work?"
I would have said,
"No, there's no way
this system works."
It just, it just doesn't
seem like it should work.
And that's sort of the magic
of when you get this much data
and very powerful algorithms
and very powerful computing
to be able to crunch
these massive data sets.
I mean, we're not
going to contain it.
That's done.
[voiceover]: I sat
down with Hany
and two of his grad students:
Justin Norman and
Sarah Barrington.
We looked at some
the A.I. trickery
they have seen and made.
Somebody else
wrote some base code
and they got grew on to
and grow on to and grow
on to and eventually...
O'BRIEN [voiceover]:
In a world where anything
can be manipulated
with such ease
and seeming authenticity,
how are we to know
what's real anymore?
How you look at the world,
how you interact
with people in it,
and where you look for
your threats of that change.
O'BRIEN [voiceover]: Generative
A.I. is now part of a larger ecosystem
that is built on mistrust.
We're going to live
in a world where
we don't know what's real.
FARID [voiceover]: There
is distrust of governments,
there is distrust of media,
there is distrust of academics.
And now throw on top
of that video evidence.
So-called video evidence.
I think this is the
very definition
of throwing jet fuel
onto a dumpster fire.
And it's already happening,
and I imagine we
will see more of it.
[Arnold's voice]: Come
with me if you want to live.
O'BRIEN [voiceover]:
But it also can be
kind of fun.
As Hany promised,
here's my face
on the Terminator's body.
[g*nf*re blasting]
Long before A.I. might take
an existential turn
against humanity,
we will need to
reckon with the likes...
Go! Now! O'BRIEN
[voiceover]: Of the Milesinator.
TRAILER NARRATOR:
This time, he's back.
[booming]
O'BRIEN [voiceover]:
Who will no doubt, be back.
Trust me.
O'BRIEN [voiceover]: Trust,
but always verify.
So, what kind of A.I. magic
is readily available online?
It's pretty simple
to make it look
like you're fluent in
another language.
[speaking Mandarin]:
It was pretty easy to do,
I just had to upload
a video and wait.
[speaking German]:
And, suddenly, I
look pretty darn smart.
[speaking Greek]:
Sure, it's fun, but
I think you can see
where it leads to mischief
and possibly even mayhem.
[voiceover]: Yoshua Bengio is
an artificial intelligence pioneer.
He says he didn't
spend much time
thinking about
science fiction dystopia
as he was creating
the technology.
But as his brilliant
ideas became reality,
reality set in.
BENGIO: And the more I read,
the more I thought about it...
the more concerned I got.
If we are not honest
with ourselves,
we're gonna fool ourselves.
We're gonna... lose.
O'BRIEN [voiceover]:
Avoiding that outcome
is now his main priority.
He has signed
several public warnings
issued by A.I. thought leaders,
including this stark
single-sentence statement
in May of 2023.
"Mitigating the risk
of extinction from A.I.
"should be a global priority
"alongside other
societal scale risks,
"such as pandemics
and nuclear w*r."
As we approach more and
more capable A.I. systems
that might even become stronger
than humans in many areas,
they become more
and more dangerous.
Can't we just pull
the plug on the thing?
Oh, that's the
safest thing to do,
pull the plug.
Before it gets so powerful that
it prevents us from
pulling the plug.
DAVE: Open the
pod bay doors, Hal.
HAL: I'm sorry, Dave,
I'm afraid I can't do that.
O'BRIEN [voiceover]:
It may be some time
before computers are able
to act like movie
supervillains...
HAL: Goodbye.
O'BRIEN [voiceover]: But there are
near-term dangers already emerging.
Besides deepfakes
and misinformation,
A.I. can also supercharge
bias and hate content,
replace human jobs...
This is why we're striking,
everybody. [crowd exclaiming]
O'BRIEN [voiceover]:
And make it easier
for t*rrorists to
create bioweapons.
And A.I. systems are so complex
that they are difficult
to comprehend,
all but impossible to audit.
RUS [voiceover]:
Nobody really understands
how those systems
reach their decisions.
So we have to be
much more thoughtful
about how we test
and evaluate them
before releasing them.
They're concerned
whether machine will be able
to begin to think for itself.
O'BRIEN [voiceover]: The U.S. and
Europe have begun charting a strategy
to try to ensure safe, secure,
and trustworthy
artificial intelligence.
RISHI SUNAK: in a way that
will be safe for our communities...
O'BRIEN [voiceover]:
But how to do that
in the midst of a frenetic race
to dominate a technology
with a predicted economic impact
of 13 trillion dollars by 2030.
There is such a strong
commercial incentive
to develop this and
win the competition
against the other companies,
not to mention
the other countries,
that it's hard to
stop that train.
But that's what
governments should be doing.
NEWS ANCHOR: The
titans of social media
didn't want to
come to Capitol Hill.
O'BRIEN [voiceover]:
Historically, the tech industry
has bridled against regulation.
You have an army of
lawyers and lobbyists
that have fought us on this...
SULEYMAN [voiceover]:
There's no question that
guardrails will
slow things down,
But, the risks are uncertain
and potentially enormous.
So, it makes sense for us
to start having the
conversation right now.
O'BRIEN [voiceover]:
For me, the conversation
about A.I. is personal.
Okay, no network detected.
Okay, um...
Oh, here we go. Okay.
And now I'm going to open,
open, open, open, open...
[voiceover]: I
used the Coapt app
to train the A.I. inside
my new prosthetic.
♪ ♪
It says all of my
training data is good,
it's four of five stars.
And now let's try to close.
[whirring]
All right.
Seems to be doing
what it was told.
[voiceover]: Was
my new arm listening?
Maybe.
I decided to make
things simpler.
I took off the hand and
attached a myoelectric hook.
[quietly]: All right.
[voiceover]: Function over form.
Not a conversation piece
necessarily at a c*ck party
like this thing is.
This looks more like
Luke Skywalker, I suppose.
But this thing has a tremendous
amount of function to it.
Although, right now,
it wants to stay open.
[voiceover]: And that
problem persisted.
Find a tripod plate...
[voiceover]:
When I tried using it
to set up my basement studio
for a live broadcast.
Come on, close.
[voiceover]: I was
quickly frustrated.
[item drops, audio beep]
Really annoying.
Not useful.
[voiceover]: The hook
continuously opened on its own.
[clattering] Damn it!
[voiceover]: So I
completely reset
and retrained the arm.
And... reset, there we go.
Add data...
[voiceover]: But
the software was
artificially unhappy.
"Electrodes are not
making good skin contact."
Maybe that is my
problem, ultimately.
[voiceover]: My
problem really is
I haven't given
this enough time.
Amputees tell me it can take
many months to really learn
how to use an arm like this one.
The choke point isn't
artificial intelligence.
Dead as a doornail.
[voiceover]: But rather,
what is the best way
to communicate
my intentions to it?
Little reboot there, I guess.
All right.
Close.
Open, close.
[voiceover]: It turns
out machine learning
isn't smart enough to
give me a replacement arm
like Luke Skywalker got.
Nor is it capable of
creating the Terminator.
Right now, it seems
many hopes and fears
for artificial intelligence...
Oh!
[voiceover]: are
rooted in science fiction.
But we are walking down
a road to the unknown.
The door is opening
to a revolution.
[door closes]
♪ ♪
♪ ♪
♪ ♪
♪ ♪
♪ ♪
♪ ♪
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50x08 - Hidden Volcano Abyss
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Nova often includes interviews with scientists doing research in the subject areas covered and occasionally includes footage of a particular discovery.
Nova often includes interviews with scientists doing research in the subject areas covered and occasionally includes footage of a particular discovery.