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  • Oftentimes, innovations solve practical problems,

  • but the advancement of A.I.

  • might bring new tools

  • to chip away at the larger, even existential questions.

  • Are we alone in the universe?

  • Can we create lifelike, intelligent machines?

  • Maybe they're all moonshots, but imagine, one day,

  • having a second, synthetic version of you.

  • -How's it going, brother? -Oh, not bad.

  • Just spent the last hour mapping half the cosmos.

  • I'm looking for a constellation to name after us.

  • You mean "me," yeah?

  • Whatever. Semantics.

  • I'm doing all the work.

  • Touchy.

  • The starry night sky has been a source of fascination

  • and curiosity for centuries.

  • Is there something out there?

  • We've got all these suspect places to look for life

  • in our own solar system.

  • And we're just one little solar system

  • in a large galaxy,

  • which is one of many, many galaxies in the universe.

  • And so you realize pretty quickly

  • the chances of life elsewhere are pretty high.

  • [tuning radio]

  • [man] ...we hope we have a number of listeners out there.

  • Most of you are probably soft and squishy humanoids.

  • In case any artificial intelligence is listening, welcome as well.

  • [Bill Diamond] You'll appreciate this,

  • being a data scientist,

  • you know we're generating about 54 terabytes of data

  • every day, so...

  • See, that's music to my ears, right there.

  • [Diamond] That's music to your ears.

  • That's a nice playground for your algorithms.

  • [Downey] In remote northern California,

  • two scientists are on their way to collect data

  • in hopes to answer a cosmic question...

  • one that's as old as humankind itself,

  • or at least, Galileo.

  • [Graham Mackintosh] When it comes to the search

  • for extraterrestrial intelligence...

  • [Diamond] Right.

  • ...there is decades of scientific discovery

  • and progress

  • which is relentlessly telling us

  • life is more likely than we thought.

  • Yeah, the body of evidence is becoming--

  • That's right.

  • ...overwhelming, but can we find it?

  • [Mackintosh] When I was ten years old,

  • I was determined to have my own computer,

  • and I found out there was a kit that you could buy

  • and put together for yourself,

  • so I earned enough money to do that,

  • and that got me hooked.

  • I've been obsessed with computers ever since.

  • And I hope, I believe, that A.I. can help us dig deeper,

  • and hopefully come to the answer we're looking for.

  • Is there life beyond the Earth?

  • [Diamond] Ever since humans have been able to gaze up at the sky

  • and look at the stars,

  • we've wondered,

  • "Are we alone?

  • Is this the only place where life has occurred?"

  • The SETI institute is trying to answer this question.

  • SETI institute was founded

  • by Frank Drake, and Jill Tarter,

  • and Carl Sagan.

  • I co-founded this institute back in 1984

  • as a way to save NASA money.

  • ...see if we can backtrack

  • to see if we can figure out what's venting...

  • Since then,

  • it has grown far beyond any of my expectations.

  • We have nearly 80 PhD scientists here.

  • Our research really starts with, "How does life happen?"

  • What are the conditions under which life takes hold?

  • We're trying to understand that transition

  • of how the universe

  • and how our own galaxy and solar system

  • went from chemistry to biology.

  • The number of civilizations

  • that there might be in the galaxy

  • is of the order of a million.

  • [Downey] Carl Sagan helped bring the cosmos

  • down to Earth,

  • but he wasn't the first to popularize it.

  • Ever since Orson Welles scared our pants off

  • with War Of The Worlds,

  • pop culture has had its eyes on the skies.

  • Little green men, extraterrestrials,

  • contact with aliens continues to capture our imagination.

  • [Diamond] We're interested in all kinds of life,

  • but of course we have a special interest

  • in intelligent or technological life beyond Earth,

  • hence, SETI.

  • Hello, this is Seth Shostak speaking to you

  • from Big Picture Science.

  • Today we're going to talk about artificial intelligence.

  • The machines of today are a lot smarter, if you will,

  • at least more capable,

  • than the machines of 50 years ago, incredibly...

  • There's vast amounts of data coming from space,

  • and A.I. can, um...

  • allows us to understand that data better

  • than we have been able to in the past.

  • It's this new capacity we have to see patterns in data...

  • [Tarter] We are trying to find evidence

  • of somebody else's technology out there.

  • We can't define intelligence,

  • but we're using technology as a proxy,

  • so if we find some technology,

  • something that's engineered,

  • something that nature didn't do,

  • then we're going to infer

  • that at least at some point in time,

  • there were some intelligent technologists

  • who were responsible.

  • [Diamond] So, Graham, we call it Area 52.

  • [chuckling]

  • [Mackintosh] We are headed to the Allen Telescope Array,

  • and tonight we are going to be doing an observation

  • which really is looking for signs of extraterrestrial life,

  • and we're gonna be using A.I. models

  • in a way that's never been done before.

  • [Diamond] All right, we are good to go.

  • So I gotta turn my cell phone off, no Bluetooth, nothing?

  • Nope, we need to be in a place that is radio quiet,

  • so you don't have interference,

  • or at least, you minimize interference.

  • We're gonna come around another bend a little up ahead,

  • and you'll see the dishes.

  • [Mackintosh exclaiming] Oh!

  • [Diamond] There we are.

  • Welcome to the Allen Telescope Array.

  • [Downey] The sole mission of the Allen Telescope Array,

  • or A.T.A.,

  • is to search for extraterrestrial life.

  • Past telescopes were basically toy binoculars

  • compared to the A.T.A.,

  • which was built in 2007

  • with support from Microsoft's Paul Allen.

  • Part of what makes it light-years ahead

  • is its wider field of view,

  • and ability to capture a greater range of frequencies.

  • It's also an array,

  • which basically means

  • it's a group of many small dishes

  • working together to cover more ground,

  • or sky.

  • Welcome to the A.T.A.

  • Fantastic.

  • Okay. Looks like Jon is out there.

  • I think he's manually turning those dishes to get 'em lined up.

  • [laughing]

  • -Hey, Jon. -Jon!

  • -Good to see you, man! -Good to see you, yeah!

  • My name is Jon Richards,

  • and I'm the Senior Software Engineer

  • at the Allen Telescope Array.

  • Radio astronomy is similar to optical astronomy,

  • except the radio wave frequencies

  • are much lower than visual,

  • so to receive radio waves, you need an antenna.

  • Take a look, Graham. Under the bell jar,

  • you see the actual antenna

  • that's picking up the signals coming from space.

  • This is spectacular.

  • [Diamond] It's kept

  • below the temperature of liquid nitrogen.

  • That brings the noise level down,

  • exactly what we want for deep space observation.

  • Just amazing.

  • [Richards] The radio signals from each one of these dishes

  • are brought into our control room,

  • digitized, made into binary ones and zeroes,

  • and combined together

  • to create the effect of having one large dish,

  • so we can actually map out the sky

  • much like you would

  • with a regular optical telescope.

  • All right, let's head back.

  • Let's go.

  • The observation we're gonna do tonight

  • is with the Trappist-1 system.

  • This is a star that has planets circling,

  • and at 8:00 tonight,

  • two of those planets are gonna align perfectly with Earth,

  • which makes it exactly the right moment

  • to do an observation.

  • We're gonna be listening in

  • for signs of any kind of communication

  • between these two planets,

  • even if that's not communication directed at us.

  • [Diamond] We're counting down to 8:01 p.m.,

  • which is when the orientation of these planets

  • are going to be lined up in our line of sight,

  • the so-called conjunction.

  • [Downey] It's a little like an intergalactic stake-out.

  • The guys are waiting

  • till the two planets are closest together,

  • and then plan to eavesdrop on their conversation.

  • They have no idea what they're listening for,

  • or if there's even gonna be a conversation.

  • [Richards] So we can take out this board here.

  • We're gonna repurpose it.

  • -So that's ready to go? -Yeah, let's go put it in.

  • All right, let's get it in.

  • [Richards] Since the site's getting close to 20 years old now,

  • my job is to get all this data coming in cleanly

  • and recorded cleanly,

  • and that is a challenge.

  • Here's the computer which is sending all the data

  • that we receive from all of our dishes

  • to our 48 terabytes of data storage,

  • so we need to replace a card.

  • This card will control our data storage.

  • [Mackintosh] You know, often

  • when people think of the search for extraterrestrial life,

  • they're thinking of someone with headphones

  • listening in on something that is sent to us,

  • something that's obvious.

  • It's really not like that.

  • It's a lot more subtle,

  • and that's why we're going to be collecting

  • enormous amounts of data.

  • All of the different parameters we might have to explore

  • set that volume, that exploration volume,

  • set it equal to the volume of all the oceans on the Earth.

  • So how much have we done, in 50 years?

  • Well, we've searched one glass of water

  • from the Earth's oceans.

  • The technologies that we've had to use until now

  • were not big enough, not adequate to the job.

  • Okay.

  • [Mackintosh] That's why we need computer systems

  • and artificial intelligence systems

  • to really turn that search on its head.

  • [Parr] When we think about traditional software,

  • we think about human beings writing lines of code.

  • What's extraordinary about A.I.

  • is that we're teaching machines how to learn.

  • This is why it's a quantum leap,

  • because for the first time,

  • instead of human beings writing the software,

  • the computer's actually building an understanding itself.

  • [Richards] We have to keep in mind

  • that the Trappist-1 system is 39.4 light-years...

  • 39.6.

  • 39.6 light-years away,

  • so this actual positioning was 39.6 years ago.

  • So not only are we, uh,

  • are we doing SETI research tonight,

  • we're time-traveling.

  • [Downey] That's right.

  • Because of how far away these planets are,

  • and how long it takes radio waves

  • to travel through space,

  • the guys are listening to a conversation

  • from about 40 years ago.

  • Here's some perspective.

  • It takes about eight minutes

  • for radio waves to get from here to the sun.

  • So, these planets?

  • Yeah, a little farther away.

  • [Diamond] Over your shoulder, Graham,

  • there's a NASA illustration of the Trappist system,

  • and there's at least three rocky, Earth-like planets

  • where liquid water can potentially be maintained--

  • Right.

  • ...and that gives rise to the possibility

  • that biology could have formed in this system.

  • What's really interesting

  • about this particular planetary system,

  • these planets are very close together,

  • much closer than, for example, Earth to Mars.

  • That means there could be communication happening

  • between these planets,

  • and what we can potentially do is listen in.

  • Not that we can have a conversation

  • or understand what they're, uh...

  • -[Mackintosh] We don't need to. -We don't need to.

  • [Mackintosh] I love this kind of observation

  • because it has as its basic principle

  • something that's really important.

  • It's not all about us.

  • No one's sending us a signal,

  • no one's trying to get our attention.

  • The whole point

  • about the search for extraterrestrial intelligence

  • is you don't even--

  • We don't know what we're looking for.

  • Right, right.

  • Instead of looking for something specific,

  • you have to look for the exceptions

  • from what is normal.

  • That is where I think

  • A.I. is gonna just completely change the game for SETI.

  • [Mackintosh] Maybe it's communication,

  • maybe it's just a byproduct

  • of some technologically advanced civilization

  • going about its business.

  • All we care about

  • is it doesn't look like the rest of nature.

  • If it's a needle in a haystack,

  • it doesn't look like hay.

  • It's like this, each one of these little blips

  • is like a point in time of radio power,

  • and we take different points in time,

  • different windows into the data,

  • and we analyze them together

  • to see if there's any kind of repetition,

  • anything at all

  • that might indicate that something isn't random,

  • like this, right in the middle here,

  • where the random dots aren't random.

  • In a computer,

  • think of it like a thousand of these sheets,

  • and it's moving them a million times a second.

  • [Downey] To find order in the randomness,

  • the A.I. picks a small area

  • and studies its radio frequency data

  • to learn what normal sounds like.

  • Then, it uses this info to filter out background signals

  • from all the data that's been collected.

  • What's left is any signal, pattern, or repetition

  • that is unnatural.

  • They're coming up to perfect alignment.

  • Conjunction now!

  • [Richards] We're recording.

  • [Diamond] Wanna check the audio?

  • This is good.

  • This is good, nice clean data.

  • Crispy clean.

  • [Richards] Silence.

  • Yeah, that's what we want. I just, well--

  • I mean, we've been working up to this for the last month.

  • It looks like, it looks like nothing to us,

  • but that's the point.

  • [Diamond] That's the point.

  • That random sound is music to my ears.

  • This picture here is just immediate,

  • real-time results,

  • something that your normal Allen Telescope Array

  • would discard as nothing.

  • Our point is, not so fast.

  • There could well be more in there than we realize.

  • We do see some little blip right here...

  • That's true, in and around it.

  • Yeah. So here, let's press...

  • So, now, this all looks similar.

  • It's the sort of normal signal,

  • but that's interesting.

  • It just seems, I don't know--

  • It's like it spreads here for some reason.

  • Well, I don't know what that means.

  • It also is a higher average power.

  • It is.

  • So, yeah, it's... this is weird, right?

  • It is.

  • [Diamond] There are a couple of things

  • that we are looking at in the data

  • that look interesting.

  • Now, it's very subtle,

  • and this is why we'll need machine-learning to extract

  • whether what we're seeing is just something we're seeing,

  • or it's real, a real phenomenon.

  • All right, so we are done with the Trappist system.

  • [Mackintosh] This is great.

  • We've clearly grabbed good data.

  • It's exactly what we need.

  • [Downey] It's gonna take Graham a few days

  • to analyze the data,

  • nothing compared to what it used to take

  • to do manually.

  • [Pedro Domingos] Some people think

  • that the emergence of artificial intelligence

  • is the biggest event on the planet since life,

  • because it's going to be a change that is as big

  • as the emergence of life.

  • It will lead to different kinds of life

  • that are very different

  • from the entire set of, you know, DNA, carbon-based life

  • that we've had so far.

  • [Downey] While some are ramping up the search

  • in outer space,

  • others are using A.I.

  • to further explore inner life.

  • [Suzanne Gildert] In 20 to 30 years' time,

  • you might see a street like this,

  • with humans walking up and down it,

  • but there might also be a new thing,

  • which is human-like robots

  • might be walking up and down, too, with us.

  • Humans and robots

  • are really gonna be doing the same kinds of things,

  • and some of the things they'll be doing

  • will be maybe superior to humans.

  • [Downey] Suzanne is one of the founders

  • of Sanctuary A.I.,

  • a tech startup that's building what they call "synths,"

  • or synthetic humans.

  • That's right.

  • Artificial intelligence wrapped in a body.

  • [Gildert] Our mission is to create machines

  • that are indistinguishable from humans

  • physically, cognitively, and emotionally.

  • [Downey] Doing so

  • involves solving problems of engineering,

  • computer science, neuroscience,

  • biology, even art and design.

  • But for her,

  • the problem of artificially replicating a person

  • boils down to a deeper question...

  • What does it mean to be human?

  • [Gildert] Understanding what it is to be human

  • is a question that we've been asking ourselves

  • for many thousands of years,

  • so I'd like to turn science and technology to that question

  • to try and figure out who we are.

  • [Downey] We love stories and films about clones

  • and replicants and humanoid robots.

  • Why are we so obsessed

  • with the idea of recreating ourselves?

  • Is it biological?

  • Existential?

  • [Gildert] To try and understand something fully,

  • you have to reverse-engineer it,

  • you have to put it back together.

  • [Downey] The human that Suzanne knows best

  • is... Suzanne,

  • so one of her projects

  • is to build a synthetic replica of herself.

  • [Gildert] There's this thing called the Turing test,

  • which is trying to have an A.I.

  • that you can't tell is not a human.

  • So I wanna try and create a physical Turing test,

  • where you can't tell whether or not

  • the system you're actually physically interacting with

  • is a person, or whether it's a robot.

  • So here we have 132 cameras...

  • which are all pointed at me,

  • and they all take a photograph simultaneously.

  • This data is used to create

  • a full three-dimensional body scan of me

  • that we can then use to create a robot version of me.

  • [Downey] Suzanne believes

  • that we experience life through the senses,

  • so she's putting as much work into making the body lifelike

  • as she is the mind.

  • [Gildert] We broke down this very ambitious project

  • into several different categories.

  • The first category is physical.

  • Can you build a robotic system that looks like a person?

  • So the synth has bones and muscles

  • that are roughly analogous to the human body,

  • but not quite as complex.

  • These hands are 3D-printed as an entire piece

  • on our printers.

  • [Gildert] We can actually print in carbon fiber

  • and Kevlar,

  • and we can create robot bones

  • that are stronger than aluminum machined parts,

  • with these beautiful organic biological shapes.

  • So I'm adding in a finger sensor.

  • This, uh, current generation

  • has a single sensor on the fingertip.

  • [Gildert] We build a machine that perceives like a human

  • by trying to copy the human sensorium very accurately.

  • The most complicated part of the perception system

  • is actually the sense of touch.

  • Are you monitoring the touch?

  • Yes. Touch received.

  • [Holly Marie Peck] We've actually embedded

  • capacitive touch sensors in the synth's hand,

  • essentially pressure sensors

  • allowing it to feel, uh, its environment,

  • and interact and manipulate objects.

  • Let's just test the pressure.

  • -Okay. -This should max it out.

  • Yep, yep. Maxed out.

  • Just stretch out her hand. Okay, go.

  • [Gildert] The reason the hand and the arm

  • is able to move so fluidly

  • is because of pneumatic actuators.

  • They work using compressed air.

  • You actuate one of these devices,

  • and it kind of contracts

  • and pulls on a tendon,

  • so the actuation mechanism is very similar to a human muscle.

  • It's just not yet quite as efficient.

  • [Shannon] I'm adding the camera into the eyeball.

  • Now I'm adding the cosmetic front of the eye.

  • [Gildert] The eyes are super important to get right.

  • Similar to our own vision system,

  • they can see similar color spectrum,

  • and they can also, because there's two cameras,

  • they can have depth perception too.

  • [Peck] Restarting facial detection.

  • [Gildert] That actually looks pretty good.

  • [Peck] Mm-hmm. Do you wanna come forward a little bit?

  • Yeah.

  • -I'm gonna restart her headboard. -[Gildert] Okay.

  • That information

  • is fed through a series of different A.I. algorithms.

  • One algorithm is a facial detection system.

  • She's definitely seeing me.

  • [Peck] Yes, she is.

  • I can tell she's looking at me,

  • 'cause she looked straight at me.

  • Yeah, gaze tracking is working.

  • Okay, cool. Now, do you wanna just smile?

  • I'll see if she's actually capturing your emotion?

  • [Gildert] If you're smiling,

  • the corners of your mouth come up,

  • your eyes open a little bit,

  • and the A.I. system can actually detect

  • how those landmarks have moved relative to one another.

  • [Rana el Kaliouby] I think the moment in time

  • we're at right now

  • is very exciting

  • because there's this field that's concerned

  • about building human-like generalized intelligence,

  • and sometimes even kind of surpassing human intelligence.

  • [Daphne Koller] There's people out there

  • who believe that this is on our immediate horizon.

  • I don't.

  • I think we're a long ways away

  • from machines that are truly conscious

  • and think on their own.

  • She's responding. I can see her face changing.

  • [synth] You look happy.

  • -Good. -Mm-hmm.

  • I'm gonna look sad.

  • You look sad.

  • Okay, good.

  • [Peck] We have actually configured

  • a lot of A.I. algorithms on the back end

  • that give the robot

  • the capabilities of recognizing people,

  • detecting emotion,

  • recognizing gestures and poses that people are making.

  • It then responds in various ways

  • with its environment.

  • [Gildert] Bring up her node graph

  • so you can see what's running in her brain.

  • Yeah, let's see all the online modules.

  • The chatbot, emotion detection,

  • object detection...

  • Wonderful. Gaze tracking...

  • [Gildert] The body, in a way, is the easy part.

  • Creating the mind is a lot harder.

  • [Downey] Creating the mind is more than hard.

  • It's basically impossible,

  • at least for now,

  • and maybe forever,

  • because a mind is not just knowledge,

  • or skill, or even language,

  • all of which a machine can learn.

  • The part that makes us really human is consciousness;

  • an awareness, a sense of being,

  • of who we are

  • and how we fit in time and space around us.

  • A human mind has that...

  • and memory.

  • "I remember the experience of buying a new pencil case

  • and the supplies to go in it,

  • getting all those new little things

  • that smelled nice,

  • and were all clean and colorful."

  • If you think about how people work,

  • it's very unusual for you to meet a person

  • that doesn't have a backstory.

  • I can use all the data that I have about myself

  • to try and craft something that has my memories,

  • it has my same mannerisms,

  • and it thinks and feels the way I do.

  • I would like them to become their own beings,

  • and to me,

  • creating the copy is a way of pushing the A.I. further

  • towards making it a realistic human

  • by having it be a copy of a specific human.

  • I remember going to Bolton Town Center

  • quite often.

  • We just called it "Town."

  • [Gildert] The basic idea

  • is you send in a large amount of text data,

  • and the system learns correlations between words,

  • and the idea

  • is that the synth could use one of these models

  • to kind of blend together an idea of a memory

  • that may have happened or may not have happened,

  • so it's a little bit of an artistic way

  • of recreating memories.

  • I remember going into WH Smith.

  • It had a very distinct smell that I can still recall.

  • [Gildert] So by giving them these backstories now,

  • we believe that we will be able to learn in the future

  • how they can create their own memories

  • from their experiences.

  • [Bran Ferren] I love the idea

  • that there are passionate people

  • who are dedicating their time and energy

  • to making these things happen.

  • Why?

  • Because if and when it does happen,

  • it's going to be because of those passionate people.

  • We talk about the computer revolution

  • like it's done.

  • It's barely begun.

  • We don't understand

  • where the impact of these technologies will be

  • over the next five, ten,

  • 20, 30, 50, 100 years.

  • If you think it's exciting and confusing now,

  • fasten your seatbelts,

  • because it hasn't begun.

  • What is your name?

  • My name is Holly.

  • What is your name?

  • Hmm.

  • [Gildert] Of course there's that unknown,

  • like are we gonna run into a problem

  • with trying to recreate a mind

  • that no one's thought of yet?

  • My name is Nadine.

  • Interesting.

  • I am glad to see you.

  • [Downey] Even if we do one day figure out

  • how to create a virtual mind,

  • it's not just the science.

  • There's also the ethics.

  • What kind of rights will the robots have?

  • Can we imbue it with good values,

  • make sure it's unbiased?

  • What if breaks the law or commits a crime?

  • Are we responsible for our synths?

  • [el Kaliouby] There are big ethical challenges

  • in the field of A.I.

  • I believe that as a community of A.I. innovators

  • and thought leaders,

  • we have to really be at the forefront

  • of enforcing and designing

  • these best practices and guidelines

  • around how we build and deploy ethical A.I.

  • I like to say that artificial intelligence

  • should not be about the artificial,

  • it should be about the humans.

  • You look angry.

  • Landmarks are registering.

  • [Ferren] I think it's perfectly reasonable

  • to have a set of rules that govern ethical behavior

  • when you are dealing with technologies

  • that can have direct impact into people's lives

  • and their families and the future.

  • [Gildert] The vision's very ambitious for this.

  • We'd like to think that that is a 10- to 20-year mission.

  • You might say we're somewhere like

  • five to 10% of the way along.

  • Why is her arm doing that?

  • It's almost like it's not clearing the buffer.

  • Yeah... interesting.

  • Let's just restart you so your arm goes--

  • Oh, wait, it's going back down again.

  • Okay, that's good.

  • Okay.

  • How do you feel today, Nadine?

  • It feels good to be a synth.

  • Nice.

  • "It feels good to be a synth."

  • [Gildert] The synths are not mobile at the moment,

  • they can't move around,

  • they can't walk yet.

  • That's something we're going to be adding in

  • within the next couple of years.

  • The grand goal

  • is to make these into their own beings

  • with their own volition and their own rights.

  • There are these moments you can have

  • where you really feel something that's unusual.

  • It's surprising.

  • I was adjusting the synth's hair,

  • and then she suddenly, like, smiled,

  • and opened her mouth a little bit,

  • like, you know, like I'd just tickled her or something.

  • It was just, like, synchronous with what I was doing.

  • [Downey] In some ways,

  • Suzanne's vision is already coming alive.

  • She's making a connection, albeit small, with a machine.

  • Isn't that something?

  • [Domingos] I think A.I. is part of evolution.

  • The same evolution

  • that led from bacteria to animals,

  • and has led people to create technology,

  • has led them to create A.I.

  • In some ways, we're still in the very early infancy

  • of this new age.

  • [Downey] Will we ever create intelligent life

  • here on Earth...

  • or maybe we'll find it out there first?

  • So I'm on my way

  • to the SETI Institute headquarters

  • in Mountain View,

  • and, and I'm gonna show, uh, what the A.I. system found

  • in the data that we collected.

  • I'm excited. I'm a little nervous too.

  • [Tarter] We need to be able to follow up in real time...

  • [Diamond] Mm-hmm.

  • [Tarter] ...as closely as we can,

  • so that a signal that's there

  • is still gonna be there when we go back to look for it,

  • and we can then classify it.

  • Jill Tarter is really a legend

  • in this whole field of SETI research.

  • Also really a pioneer as a woman astronomer.

  • The character played by Jodie Foster in Contact,

  • is based, at least in the first half of that movie,

  • on Jill Tarter.

  • [Tarter] People often talk

  • about finding a needle in a haystack

  • as being a difficult task,

  • but the SETI task is far harder.

  • If I got out of bed every morning

  • thinking, "This is the day we're gonna find the signal,"

  • I have pretty good odds

  • I'm gonna go to bed that night disappointed.

  • I don't get up in the morning thinking that.

  • What I do get up in the morning thinking

  • is that today, I'm going to figure out

  • how to do this search better,

  • do new things,

  • do things you could not do in the past.

  • Early on, the technology just wasn't there...

  • Mm-hmm.

  • ...and now we're doing something

  • that we've never been able to do.

  • I'm excited.

  • -Hello? -Oh, hey!

  • -Look who's here! -How are ya?

  • -Good to see you! -Hi, Graham.

  • -Nice to see you. -Nice to see you.

  • Likewise. Good to see you too.

  • -Hey, Bill. -It's been a couple of whole days?

  • -I know! [laughs] -Thanks for coming down.

  • -My pleasure, I'm excited. -Yeah.

  • We're thinking maybe you've got some news.

  • Well, I wanna step you through it.

  • Here you can kinda see

  • the system is initially very active.

  • It's all lit up,

  • and very quickly,

  • it starts to get a handle on what the shape,

  • you know, what a signal from the Trappist-1 system should look like.

  • Over on the far right is its areas of interest...

  • What I'm showing here

  • is a time-compressed video of the A.I. system

  • looking at the signal we gathered.

  • ...and if you focus in on that,

  • the A.I. system did indeed flag this one area,

  • at that point, saying,

  • -"Whoa, back up. Something just happened." -[Tarter] Ooh, wow.

  • "That's not right,"

  • and if you zoom in on the actual data,

  • sure enough, there's that spike,

  • so that is not from the Trappist system.

  • That was generated by the Allen Telescope Array,

  • but, you know, beyond that,

  • this is an area that the A.I. system is saying,

  • "This isn't quite what I would have expected."

  • This is a little more interesting

  • 'cause there's more structure to it,

  • and we should take its hints,

  • and have a deeper analysis done of this part of the observation.

  • We didn't write any code.

  • We didn't tell it to... to look for spikes of power

  • or anything else.

  • We just said, "You know what, you figure out what's normal,

  • and you let us know

  • when something catches your attention,"

  • which is exactly what it's doing there.

  • It's encouraging,

  • because already with just this one observation,

  • we started to see some real progress

  • in what the A.I. system can do compared to our own eyes,

  • and that's just one observation.

  • What about the next, and the next,

  • and as it gets better

  • with each new round of data that we collect?

  • This is after two hours.

  • I wonder how good it's gonna get after a hundred hours.

  • Yeah.

  • If we just routinely keep feeding the data from the A.T.A.

  • into this model,

  • it's gonna get better and better and better.

  • We can just scale this out.

  • -Right. -Absolutely.

  • We just got smarter. Thank you, machine.

  • Yes, exactly.

  • [Tarter] I'm absolutely so excited.

  • I'm really blown away.

  • I can see the tools that are being built

  • give us a new way of looking for things

  • that we hadn't thought of,

  • and things that we don't have to define up front,

  • anomalies that the machines will find

  • simply because they've looked at so much data.

  • [Mackintosh] I do think we're going to find ET.

  • I do think we are gonna find signs of civilization

  • beyond Earth,

  • and I do think that it's going to be A.I. that finds it.

  • [Downey] Is there intelligent life out there?

  • Can we create human-like machines?

  • [Domingos] The odds are overwhelming

  • that we will eventually be able to build an artificial brain

  • that is at the level of the human brain.

  • The big question is how long will it take?

  • [Downey] Outer space,

  • inner life...

  • Age-old mysteries now seem more solvable.

  • [Chris Botham] If we wanna go to Mars,

  • if we wanna populate other planets,

  • these types of things require these advanced technologies.

  • [Downey] Moonshots, yeah,

  • but also other pressing problems,

  • like...

  • -[gasps of shock] -All five! Whoa!

  • [Downey] ...the mind and body.

  • [Tim Shaw] Are you working today?

  • [beeping]

  • It's wonderful.

  • [Downey] Adaptation...

  • [Jim Ewing] I'm thinking and doing

  • and getting instant response.

  • It makes it feel like it's part of me.

  • [Downey] Work...

  • Action!

  • [Downey] ...and creativity...

  • These types of technologies can help us do our tasks better.

  • Three, two, one.

  • [computer voice] Autonomous driving started.

  • [el Kaliouby] I believe if we do this right,

  • these A.I. systems can truly, truly compliment

  • what we do as humans.

  • [Eric Warren] We use the A.I. tools

  • to predict what the future not only is,

  • but what it should be.

  • Yo, what's up? This is will.i.am.

  • [laughing]

  • [Mark Sagar] This is the new version of you.

  • The way it's looking so far is mind-blowing.

  • [firefighter] Stay close, I'll lead.

  • [Downey] Survival...

  • [firefighter] Over here, I see him! Three yards at 2:00!

  • [Martin Ford] I believe that artificial intelligence

  • is really going to be

  • the most important tool in our toolbox

  • for solving the big problems that we face.

  • [firefighter] I got him!

  • [crowd chanting]

  • [Downey] Conservation...

  • The fact that we can look across the world

  • and find where famine might happen

  • four months from now,

  • it's mind-blowing.

  • [Downey] All out of the realm of sci-fi and magic,

  • and now just science.

  • Still hard problems, but now possible,

  • with innovation,

  • computing power, will, and passion...

  • -[cheering] Yay! -Yes!

  • There it is.

  • [Downey] ...and yet, despite all that,

  • a vestige of unknown endures.

  • Who are we?

  • What are we becoming?

  • Every major technological change

  • leads to a new kind of society,

  • with new moral principles,

  • and the same thing will happen with A.I.

  • [Downey] Technology's changing us, for sure.

  • The whole idea of what it means to be human

  • is getting rewired.

  • A.I. might be humanity's most valuable tool...

  • ...but it's also just that.

  • A tool.

  • [clattering]

  • [Downey] What we choose to do with it...

  • that's up to you and I.

  • [Seth Shostak] If you could project yourself

  • into the next millennium,

  • a thousand years from now,

  • would we look back on this generation and say,

  • "Well, they were the last generation of Homo sapiens

  • that actually ran the planet"?

  • [James Parr] There's a lot of paranoia.

  • The media's done a really good job

  • of making people frightened,

  • but A.I. is just a portrait of reality,

  • a very close portrait, but it isn't reality.

  • It's just a bucket of probabilities.

  • Where I think human beings will always have the edge

  • are understanding other humans.

  • It's going to take a long time

  • before we have an A.I.

  • that can understand all of the nuances

  • and various layers of the human experience

  • at a societal level.

  • [Shostak] James Parr, thanks so very much for being with us.

  • Great, thank you.

Oftentimes, innovations solve practical problems,

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