♪ ♪
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 War 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 war, 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 75% and 84% accurate
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
200 million protein structures,
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 48 to 72 hours of computing time
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 700 acres of their territory.
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 1447 responding via MDC.
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 19 neurons, right?
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 terrorist 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.
(gunfire 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 war."
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 terrorists 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 cocktail 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)
♪ ♪