How A.I. is searching for Aliens | The Age of A.I.

00:36:12
https://www.youtube.com/watch?v=VwtC_4t2g5M

摘要

TLDRThe video examines the potential of artificial intelligence (AI) to address big existential questions like the presence of extraterrestrial life and the creation of lifelike intelligent machines. It follows scientists at the SETI Institute using AI to enhance the search for extraterrestrial intelligence, including a project involving the Allen Telescope Array designed to capture astronomical data. Additionally, the video explores a company named Sanctuary A.I. working to create 'synths,' synthetic humans that mimic human emotion and intelligence, raising questions about what it means to be human and the ethical considerations inherent in such technology. The discussion includes the historical intrigue humans have had with cosmic life and how AI might bring us closer to answers, transforming our understanding of life and humanity itself.

心得

  • 🌌 Exploring AI in cosmic inquiries about life beyond Earth.
  • 🛰 Functions of the Allen Telescope Array in SETI’s mission.
  • 🤖 Sanctuary A.I.'s 'synths' mimicking human traits.
  • 🧠 Creating a physical and cognitive human replica raises philosophical queries.
  • 📊 AI's capability to sift through vast data in search for extraterrestrial life.
  • 🔍 AI as a vital tool in analyzing cosmic data for anomalies.
  • 🌍 The longstanding human curiosity about life beyond our planet.
  • 🤔 Ethical considerations surrounding the advancement of AI.
  • 🔬 Impact of AI advancement on understanding human life.
  • 🚀 The potential for AI to revolutionize space exploration and synthetic life creation.

时间轴

  • 00:00:00 - 00:05:00

    The video begins with a discussion about how AI might be able to address existential questions like the existence of extraterrestrial life and creating lifelike machines. It highlights the vast and complex universe, raising the possibility of life beyond Earth and delves into the contributions of scientists in exploring these ideas, emphasizing the role of AI in processing vast amounts of data generated by space research.

  • 00:05:00 - 00:10:00

    The narrative transitions to a focus on the Allen Telescope Array (ATA). The array is described as a sophisticated tool far superior to past telescopes, designed specifically to search for extraterrestrial life by capturing a wide range of frequencies. The segment highlights a field experiment where scientists employ AI to process signals from the Trappist-1 system, discussing the system’s unique planetary alignments as opportune moments for seeking communications or anomalies.

  • 00:10:00 - 00:15:00

    The video deepens into a demonstration of AI's role in SETI's exploratory missions, utilizing advanced algorithms to filter out noise and potentially detect unnatural signals. The ATA's use in scanning large swathes of sky for signs of extraterrestrial technology is highlighted, showcasing how AI helps in identifying anomalies among vast data, akin to finding a needle in a haystack.

  • 00:15:00 - 00:20:00

    Shifting to AI's potential in recreating human-like life, the video introduces Sanctuary A.I., a startup crafting synthetic humans or "synths." Their mission is to endow robots with human-like physical, cognitive, and emotional capabilities. Suzanne Gildert, a founder, aims to clone herself as a robot, sparking complex discussions on ethics, human consciousness, and identity.

  • 00:20:00 - 00:25:00

    Sanctuary A.I.'s endeavors illustrate the technical and philosophical challenges of constructing human-equivalent intelligence. The synth project touches on the intricacies of replicating human senses and artfully mimicking human emotions, all while grappling with the deeper question of what constitutes humanity. The narrative emphasizes the technical marvel and ethical considerations of such advancements.

  • 00:25:00 - 00:30:00

    The video shifts back to showcasing the reality of AI's integration into understanding life beyond earth and within terrestrial realms, pondering AI's role in bridging technological aspirations with human values. It discusses how AI's development mirrors evolutionary leaps, profoundly impacting future societal norms and ethical frameworks.

  • 00:30:00 - 00:36:12

    Concluding with reflections on AI's dual potential as both a powerful tool and a philosophical challenge, the video ties back to how humanity itself is redefined in the AI era. By posing open-ended questions on human identity and societal evolution, it underscores AI's role as a transformative, yet controllable, aspect of our existence.

显示更多

思维导图

Mind Map

常见问题

  • What is the focus of the video?

    The video focuses on the intersection of AI and the search for extraterrestrial life.

  • Who founded the SETI institute?

    SETI was founded by Frank Drake, Jill Tarter, and Carl Sagan.

  • What is the Allen Telescope Array used for?

    The Allen Telescope Array is used to search for extraterrestrial life.

  • How can AI assist in the search for extraterrestrial life?

    AI can process vast amounts of data to find patterns and anomalies that might indicate the presence of extraterrestrial life.

  • What is the main mission of the Sanctuary A.I. startup?

    Sanctuary A.I. aims to create synthetic humans that are indistinguishable from real humans.

  • What are synths according to the video?

    Synths are synthetic humans created with AI to mimic human appearance and behavior.

  • What role does AI play in understanding outer space data?

    AI helps process and analyze vast data from outer space, finding patterns that humans might miss.

  • Who is Suzanne Gildert?

    Suzanne Gildert is the founder of Sanctuary A.I., working on creating synthetic humans.

  • What ethical challenges does AI development present?

    AI development presents ethical challenges around rights, biases, values, and accountability for AI actions.

  • What future possibility does the video suggest about intelligent life?

    The video suggests we might find intelligent extraterrestrial life or create human-like machines here on Earth.

查看更多视频摘要

即时访问由人工智能支持的免费 YouTube 视频摘要！
字幕
en
自动滚动:
  • 00:00:07
    Oftentimes, innovations solve practical problems,
  • 00:00:11
    but the advancement of A.I.
  • 00:00:12
    might bring new tools
  • 00:00:14
    to chip away at the larger, even existential questions.
  • 00:00:18
    Are we alone in the universe?
  • 00:00:21
    Can we create lifelike, intelligent machines?
  • 00:00:24
    Maybe they're all moonshots, but imagine, one day,
  • 00:00:27
    having a second, synthetic version of you.
  • 00:00:32
    -How's it going, brother? -Oh, not bad.
  • 00:00:34
    Just spent the last hour mapping half the cosmos.
  • 00:00:37
    I'm looking for a constellation to name after us.
  • 00:00:39
    You mean "me," yeah?
  • 00:00:41
    Whatever. Semantics.
  • 00:00:44
    I'm doing all the work.
  • 00:00:46
    Touchy.
  • 00:00:51
    The starry night sky has been a source of fascination
  • 00:00:54
    and curiosity for centuries.
  • 00:00:58
    Is there something out there?
  • 00:01:00
    We've got all these suspect places to look for life
  • 00:01:03
    in our own solar system.
  • 00:01:04
    And we're just one little solar system
  • 00:01:06
    in a large galaxy,
  • 00:01:08
    which is one of many, many galaxies in the universe.
  • 00:01:11
    And so you realize pretty quickly
  • 00:01:13
    the chances of life elsewhere are pretty high.
  • 00:01:25
    [tuning radio]
  • 00:01:27
    [man] ...we hope we have a number of listeners out there.
  • 00:01:29
    Most of you are probably soft and squishy humanoids.
  • 00:01:31
    In case any artificial intelligence is listening, welcome as well.
  • 00:01:35
    [Bill Diamond] You'll appreciate this,
  • 00:01:36
    being a data scientist,
  • 00:01:37
    you know we're generating about 54 terabytes of data
  • 00:01:40
    every day, so...
  • 00:01:42
    See, that's music to my ears, right there.
  • 00:01:43
    [Diamond] That's music to your ears.
  • 00:01:44
    That's a nice playground for your algorithms.
  • 00:01:46
    [Downey] In remote northern California,
  • 00:01:48
    two scientists are on their way to collect data
  • 00:01:50
    in hopes to answer a cosmic question...
  • 00:01:54
    one that's as old as humankind itself,
  • 00:01:57
    or at least, Galileo.
  • 00:01:59
    [Graham Mackintosh] When it comes to the search
  • 00:02:00
    for extraterrestrial intelligence...
  • 00:02:01
    [Diamond] Right.
  • 00:02:03
    ...there is decades of scientific discovery
  • 00:02:06
    and progress
  • 00:02:07
    which is relentlessly telling us
  • 00:02:08
    life is more likely than we thought.
  • 00:02:11
    Yeah, the body of evidence is becoming--
  • 00:02:13
    That's right.
  • 00:02:14
    ...overwhelming, but can we find it?
  • 00:02:16
    [Mackintosh] When I was ten years old,
  • 00:02:17
    I was determined to have my own computer,
  • 00:02:19
    and I found out there was a kit that you could buy
  • 00:02:23
    and put together for yourself,
  • 00:02:24
    so I earned enough money to do that,
  • 00:02:26
    and that got me hooked.
  • 00:02:27
    I've been obsessed with computers ever since.
  • 00:02:32
    And I hope, I believe, that A.I. can help us dig deeper,
  • 00:02:35
    and hopefully come to the answer we're looking for.
  • 00:02:39
    Is there life beyond the Earth?
  • 00:02:46
    [Diamond] Ever since humans have been able to gaze up at the sky
  • 00:02:48
    and look at the stars,
  • 00:02:50
    we've wondered,
  • 00:02:51
    "Are we alone?
  • 00:02:52
    Is this the only place where life has occurred?"
  • 00:02:56
    The SETI institute is trying to answer this question.
  • 00:03:05
    SETI institute was founded
  • 00:03:06
    by Frank Drake, and Jill Tarter,
  • 00:03:09
    and Carl Sagan.
  • 00:03:11
    I co-founded this institute back in 1984
  • 00:03:14
    as a way to save NASA money.
  • 00:03:16
    ...see if we can backtrack
  • 00:03:17
    to see if we can figure out what's venting...
  • 00:03:19
    Since then,
  • 00:03:21
    it has grown far beyond any of my expectations.
  • 00:03:25
    We have nearly 80 PhD scientists here.
  • 00:03:27
    Our research really starts with, "How does life happen?"
  • 00:03:33
    What are the conditions under which life takes hold?
  • 00:03:40
    We're trying to understand that transition
  • 00:03:42
    of how the universe
  • 00:03:43
    and how our own galaxy and solar system
  • 00:03:45
    went from chemistry to biology.
  • 00:03:49
    The number of civilizations
  • 00:03:51
    that there might be in the galaxy
  • 00:03:52
    is of the order of a million.
  • 00:03:54
    [Downey] Carl Sagan helped bring the cosmos
  • 00:03:56
    down to Earth,
  • 00:03:57
    but he wasn't the first to popularize it.
  • 00:03:59
    Ever since Orson Welles scared our pants off
  • 00:04:02
    with War Of The Worlds,
  • 00:04:03
    pop culture has had its eyes on the skies.
  • 00:04:06
    Little green men, extraterrestrials,
  • 00:04:09
    contact with aliens continues to capture our imagination.
  • 00:04:13
    [Diamond] We're interested in all kinds of life,
  • 00:04:16
    but of course we have a special interest
  • 00:04:18
    in intelligent or technological life beyond Earth,
  • 00:04:21
    hence, SETI.
  • 00:04:25
    Hello, this is Seth Shostak speaking to you
  • 00:04:28
    from Big Picture Science.
  • 00:04:29
    Today we're going to talk about artificial intelligence.
  • 00:04:32
    The machines of today are a lot smarter, if you will,
  • 00:04:35
    at least more capable,
  • 00:04:36
    than the machines of 50 years ago, incredibly...
  • 00:04:39
    There's vast amounts of data coming from space,
  • 00:04:41
    and A.I. can, um...
  • 00:04:43
    allows us to understand that data better
  • 00:04:45
    than we have been able to in the past.
  • 00:04:47
    It's this new capacity we have to see patterns in data...
  • 00:04:56
    [Tarter] We are trying to find evidence
  • 00:04:58
    of somebody else's technology out there.
  • 00:05:01
    We can't define intelligence,
  • 00:05:03
    but we're using technology as a proxy,
  • 00:05:06
    so if we find some technology,
  • 00:05:07
    something that's engineered,
  • 00:05:09
    something that nature didn't do,
  • 00:05:10
    then we're going to infer
  • 00:05:12
    that at least at some point in time,
  • 00:05:14
    there were some intelligent technologists
  • 00:05:16
    who were responsible.
  • 00:05:17
    [Diamond] So, Graham, we call it Area 52.
  • 00:05:21
    [chuckling]
  • 00:05:23
    [Mackintosh] We are headed to the Allen Telescope Array,
  • 00:05:25
    and tonight we are going to be doing an observation
  • 00:05:27
    which really is looking for signs of extraterrestrial life,
  • 00:05:31
    and we're gonna be using A.I. models
  • 00:05:33
    in a way that's never been done before.
  • 00:05:35
    [Diamond] All right, we are good to go.
  • 00:05:40
    So I gotta turn my cell phone off, no Bluetooth, nothing?
  • 00:05:43
    Nope, we need to be in a place that is radio quiet,
  • 00:05:46
    so you don't have interference,
  • 00:05:48
    or at least, you minimize interference.
  • 00:05:50
    We're gonna come around another bend a little up ahead,
  • 00:05:53
    and you'll see the dishes.
  • 00:05:57
    [Mackintosh exclaiming] Oh!
  • 00:05:58
    [Diamond] There we are.
  • 00:06:01
    Welcome to the Allen Telescope Array.
  • 00:06:06
    [Downey] The sole mission of the Allen Telescope Array,
  • 00:06:09
    or A.T.A.,
  • 00:06:10
    is to search for extraterrestrial life.
  • 00:06:13
    Past telescopes were basically toy binoculars
  • 00:06:16
    compared to the A.T.A.,
  • 00:06:17
    which was built in 2007
  • 00:06:19
    with support from Microsoft's Paul Allen.
  • 00:06:22
    Part of what makes it light-years ahead
  • 00:06:24
    is its wider field of view,
  • 00:06:26
    and ability to capture a greater range of frequencies.
  • 00:06:30
    It's also an array,
  • 00:06:32
    which basically means
  • 00:06:33
    it's a group of many small dishes
  • 00:06:34
    working together to cover more ground,
  • 00:06:36
    or sky.
  • 00:06:40
    Welcome to the A.T.A.
  • 00:06:42
    Fantastic.
  • 00:06:43
    Okay. Looks like Jon is out there.
  • 00:06:45
    I think he's manually turning those dishes to get 'em lined up.
  • 00:06:48
    [laughing]
  • 00:06:52
    -Hey, Jon. -Jon!
  • 00:06:54
    -Good to see you, man! -Good to see you, yeah!
  • 00:06:57
    My name is Jon Richards,
  • 00:06:58
    and I'm the Senior Software Engineer
  • 00:07:00
    at the Allen Telescope Array.
  • 00:07:02
    Radio astronomy is similar to optical astronomy,
  • 00:07:06
    except the radio wave frequencies
  • 00:07:08
    are much lower than visual,
  • 00:07:10
    so to receive radio waves, you need an antenna.
  • 00:07:14
    Take a look, Graham. Under the bell jar,
  • 00:07:16
    you see the actual antenna
  • 00:07:18
    that's picking up the signals coming from space.
  • 00:07:21
    This is spectacular.
  • 00:07:22
    [Diamond] It's kept
  • 00:07:23
    below the temperature of liquid nitrogen.
  • 00:07:25
    That brings the noise level down,
  • 00:07:27
    exactly what we want for deep space observation.
  • 00:07:29
    Just amazing.
  • 00:07:30
    [Richards] The radio signals from each one of these dishes
  • 00:07:33
    are brought into our control room,
  • 00:07:34
    digitized, made into binary ones and zeroes,
  • 00:07:39
    and combined together
  • 00:07:40
    to create the effect of having one large dish,
  • 00:07:43
    so we can actually map out the sky
  • 00:07:46
    much like you would
  • 00:07:47
    with a regular optical telescope.
  • 00:07:49
    All right, let's head back.
  • 00:07:51
    Let's go.
  • 00:07:54
    The observation we're gonna do tonight
  • 00:07:56
    is with the Trappist-1 system.
  • 00:08:00
    This is a star that has planets circling,
  • 00:08:03
    and at 8:00 tonight,
  • 00:08:04
    two of those planets are gonna align perfectly with Earth,
  • 00:08:07
    which makes it exactly the right moment
  • 00:08:10
    to do an observation.
  • 00:08:11
    We're gonna be listening in
  • 00:08:13
    for signs of any kind of communication
  • 00:08:15
    between these two planets,
  • 00:08:17
    even if that's not communication directed at us.
  • 00:08:21
    [Diamond] We're counting down to 8:01 p.m.,
  • 00:08:23
    which is when the orientation of these planets
  • 00:08:26
    are going to be lined up in our line of sight,
  • 00:08:29
    the so-called conjunction.
  • 00:08:33
    [Downey] It's a little like an intergalactic stake-out.
  • 00:08:35
    The guys are waiting
  • 00:08:36
    till the two planets are closest together,
  • 00:08:38
    and then plan to eavesdrop on their conversation.
  • 00:08:41
    They have no idea what they're listening for,
  • 00:08:44
    or if there's even gonna be a conversation.
  • 00:08:47
    [Richards] So we can take out this board here.
  • 00:08:49
    We're gonna repurpose it.
  • 00:08:51
    -So that's ready to go? -Yeah, let's go put it in.
  • 00:08:52
    All right, let's get it in.
  • 00:08:54
    [Richards] Since the site's getting close to 20 years old now,
  • 00:08:56
    my job is to get all this data coming in cleanly
  • 00:09:00
    and recorded cleanly,
  • 00:09:02
    and that is a challenge.
  • 00:09:03
    Here's the computer which is sending all the data
  • 00:09:06
    that we receive from all of our dishes
  • 00:09:07
    to our 48 terabytes of data storage,
  • 00:09:10
    so we need to replace a card.
  • 00:09:12
    This card will control our data storage.
  • 00:09:15
    [Mackintosh] You know, often
  • 00:09:16
    when people think of the search for extraterrestrial life,
  • 00:09:19
    they're thinking of someone with headphones
  • 00:09:20
    listening in on something that is sent to us,
  • 00:09:23
    something that's obvious.
  • 00:09:24
    It's really not like that.
  • 00:09:26
    It's a lot more subtle,
  • 00:09:27
    and that's why we're going to be collecting
  • 00:09:29
    enormous amounts of data.
  • 00:09:31
    All of the different parameters we might have to explore
  • 00:09:34
    set that volume, that exploration volume,
  • 00:09:37
    set it equal to the volume of all the oceans on the Earth.
  • 00:09:42
    So how much have we done, in 50 years?
  • 00:09:45
    Well, we've searched one glass of water
  • 00:09:49
    from the Earth's oceans.
  • 00:09:50
    The technologies that we've had to use until now
  • 00:09:54
    were not big enough, not adequate to the job.
  • 00:09:57
    Okay.
  • 00:09:58
    [Mackintosh] That's why we need computer systems
  • 00:10:00
    and artificial intelligence systems
  • 00:10:02
    to really turn that search on its head.
  • 00:10:05
    [Parr] When we think about traditional software,
  • 00:10:08
    we think about human beings writing lines of code.
  • 00:10:10
    What's extraordinary about A.I.
  • 00:10:12
    is that we're teaching machines how to learn.
  • 00:10:15
    This is why it's a quantum leap,
  • 00:10:17
    because for the first time,
  • 00:10:18
    instead of human beings writing the software,
  • 00:10:20
    the computer's actually building an understanding itself.
  • 00:10:33
    [Richards] We have to keep in mind
  • 00:10:35
    that the Trappist-1 system is 39.4 light-years...
  • 00:10:39
    39.6.
  • 00:10:39
    39.6 light-years away,
  • 00:10:42
    so this actual positioning was 39.6 years ago.
  • 00:10:46
    So not only are we, uh,
  • 00:10:48
    are we doing SETI research tonight,
  • 00:10:50
    we're time-traveling.
  • 00:10:51
    [Downey] That's right.
  • 00:10:52
    Because of how far away these planets are,
  • 00:10:55
    and how long it takes radio waves
  • 00:10:57
    to travel through space,
  • 00:10:58
    the guys are listening to a conversation
  • 00:11:00
    from about 40 years ago.
  • 00:11:03
    Here's some perspective.
  • 00:11:04
    It takes about eight minutes
  • 00:11:05
    for radio waves to get from here to the sun.
  • 00:11:08
    So, these planets?
  • 00:11:10
    Yeah, a little farther away.
  • 00:11:12
    [Diamond] Over your shoulder, Graham,
  • 00:11:14
    there's a NASA illustration of the Trappist system,
  • 00:11:16
    and there's at least three rocky, Earth-like planets
  • 00:11:19
    where liquid water can potentially be maintained--
  • 00:11:22
    Right.
  • 00:11:23
    ...and that gives rise to the possibility
  • 00:11:25
    that biology could have formed in this system.
  • 00:11:27
    What's really interesting
  • 00:11:29
    about this particular planetary system,
  • 00:11:31
    these planets are very close together,
  • 00:11:33
    much closer than, for example, Earth to Mars.
  • 00:11:36
    That means there could be communication happening
  • 00:11:39
    between these planets,
  • 00:11:41
    and what we can potentially do is listen in.
  • 00:11:45
    Not that we can have a conversation
  • 00:11:47
    or understand what they're, uh...
  • 00:11:49
    -[Mackintosh] We don't need to. -We don't need to.
  • 00:11:50
    [Mackintosh] I love this kind of observation
  • 00:11:53
    because it has as its basic principle
  • 00:11:56
    something that's really important.
  • 00:11:58
    It's not all about us.
  • 00:12:00
    No one's sending us a signal,
  • 00:12:02
    no one's trying to get our attention.
  • 00:12:04
    The whole point
  • 00:12:05
    about the search for extraterrestrial intelligence
  • 00:12:07
    is you don't even--
  • 00:12:09
    We don't know what we're looking for.
  • 00:12:10
    Right, right.
  • 00:12:11
    Instead of looking for something specific,
  • 00:12:14
    you have to look for the exceptions
  • 00:12:16
    from what is normal.
  • 00:12:17
    That is where I think
  • 00:12:19
    A.I. is gonna just completely change the game for SETI.
  • 00:12:21
    [Mackintosh] Maybe it's communication,
  • 00:12:23
    maybe it's just a byproduct
  • 00:12:26
    of some technologically advanced civilization
  • 00:12:28
    going about its business.
  • 00:12:30
    All we care about
  • 00:12:31
    is it doesn't look like the rest of nature.
  • 00:12:35
    If it's a needle in a haystack,
  • 00:12:37
    it doesn't look like hay.
  • 00:12:39
    It's like this, each one of these little blips
  • 00:12:41
    is like a point in time of radio power,
  • 00:12:44
    and we take different points in time,
  • 00:12:47
    different windows into the data,
  • 00:12:49
    and we analyze them together
  • 00:12:51
    to see if there's any kind of repetition,
  • 00:12:54
    anything at all
  • 00:12:55
    that might indicate that something isn't random,
  • 00:12:58
    like this, right in the middle here,
  • 00:13:00
    where the random dots aren't random.
  • 00:13:03
    In a computer,
  • 00:13:05
    think of it like a thousand of these sheets,
  • 00:13:07
    and it's moving them a million times a second.
  • 00:13:10
    [Downey] To find order in the randomness,
  • 00:13:12
    the A.I. picks a small area
  • 00:13:14
    and studies its radio frequency data
  • 00:13:16
    to learn what normal sounds like.
  • 00:13:18
    Then, it uses this info to filter out background signals
  • 00:13:21
    from all the data that's been collected.
  • 00:13:23
    What's left is any signal, pattern, or repetition
  • 00:13:27
    that is unnatural.
  • 00:13:39
    They're coming up to perfect alignment.
  • 00:13:41
    Conjunction now!
  • 00:13:43
    [Richards] We're recording.
  • 00:13:45
    [Diamond] Wanna check the audio?
  • 00:13:51
    This is good.
  • 00:13:57
    This is good, nice clean data.
  • 00:13:59
    Crispy clean.
  • 00:14:01
    [Richards] Silence.
  • 00:14:02
    Yeah, that's what we want. I just, well--
  • 00:14:06
    I mean, we've been working up to this for the last month.
  • 00:14:08
    It looks like, it looks like nothing to us,
  • 00:14:09
    but that's the point.
  • 00:14:10
    [Diamond] That's the point.
  • 00:14:11
    That random sound is music to my ears.
  • 00:14:14
    This picture here is just immediate,
  • 00:14:17
    real-time results,
  • 00:14:18
    something that your normal Allen Telescope Array
  • 00:14:21
    would discard as nothing.
  • 00:14:23
    Our point is, not so fast.
  • 00:14:25
    There could well be more in there than we realize.
  • 00:14:31
    We do see some little blip right here...
  • 00:14:35
    That's true, in and around it.
  • 00:14:37
    Yeah. So here, let's press...
  • 00:14:41
    So, now, this all looks similar.
  • 00:14:43
    It's the sort of normal signal,
  • 00:14:45
    but that's interesting.
  • 00:14:47
    It just seems, I don't know--
  • 00:14:48
    It's like it spreads here for some reason.
  • 00:14:50
    Well, I don't know what that means.
  • 00:14:51
    It also is a higher average power.
  • 00:14:53
    It is.
  • 00:14:54
    So, yeah, it's... this is weird, right?
  • 00:14:56
    It is.
  • 00:14:57
    [Diamond] There are a couple of things
  • 00:14:59
    that we are looking at in the data
  • 00:15:01
    that look interesting.
  • 00:15:02
    Now, it's very subtle,
  • 00:15:04
    and this is why we'll need machine-learning to extract
  • 00:15:06
    whether what we're seeing is just something we're seeing,
  • 00:15:08
    or it's real, a real phenomenon.
  • 00:15:11
    All right, so we are done with the Trappist system.
  • 00:15:14
    [Mackintosh] This is great.
  • 00:15:16
    We've clearly grabbed good data.
  • 00:15:17
    It's exactly what we need.
  • 00:15:19
    [Downey] It's gonna take Graham a few days
  • 00:15:21
    to analyze the data,
  • 00:15:22
    nothing compared to what it used to take
  • 00:15:24
    to do manually.
  • 00:15:25
    [Pedro Domingos] Some people think
  • 00:15:27
    that the emergence of artificial intelligence
  • 00:15:28
    is the biggest event on the planet since life,
  • 00:15:32
    because it's going to be a change that is as big
  • 00:15:34
    as the emergence of life.
  • 00:15:36
    It will lead to different kinds of life
  • 00:15:37
    that are very different
  • 00:15:39
    from the entire set of, you know, DNA, carbon-based life
  • 00:15:42
    that we've had so far.
  • 00:15:43
    [Downey] While some are ramping up the search
  • 00:15:46
    in outer space,
  • 00:15:47
    others are using A.I.
  • 00:15:49
    to further explore inner life.
  • 00:15:59
    [Suzanne Gildert] In 20 to 30 years' time,
  • 00:16:01
    you might see a street like this,
  • 00:16:03
    with humans walking up and down it,
  • 00:16:04
    but there might also be a new thing,
  • 00:16:07
    which is human-like robots
  • 00:16:08
    might be walking up and down, too, with us.
  • 00:16:12
    Humans and robots
  • 00:16:13
    are really gonna be doing the same kinds of things,
  • 00:16:15
    and some of the things they'll be doing
  • 00:16:17
    will be maybe superior to humans.
  • 00:16:20
    [Downey] Suzanne is one of the founders
  • 00:16:21
    of Sanctuary A.I.,
  • 00:16:23
    a tech startup that's building what they call "synths,"
  • 00:16:26
    or synthetic humans.
  • 00:16:28
    That's right.
  • 00:16:29
    Artificial intelligence wrapped in a body.
  • 00:16:32
    [Gildert] Our mission is to create machines
  • 00:16:34
    that are indistinguishable from humans
  • 00:16:36
    physically, cognitively, and emotionally.
  • 00:16:39
    [Downey] Doing so
  • 00:16:40
    involves solving problems of engineering,
  • 00:16:42
    computer science, neuroscience,
  • 00:16:44
    biology, even art and design.
  • 00:16:47
    But for her,
  • 00:16:49
    the problem of artificially replicating a person
  • 00:16:51
    boils down to a deeper question...
  • 00:16:53
    What does it mean to be human?
  • 00:16:55
    [Gildert] Understanding what it is to be human
  • 00:16:57
    is a question that we've been asking ourselves
  • 00:16:59
    for many thousands of years,
  • 00:17:01
    so I'd like to turn science and technology to that question
  • 00:17:05
    to try and figure out who we are.
  • 00:17:06
    [Downey] We love stories and films about clones
  • 00:17:09
    and replicants and humanoid robots.
  • 00:17:12
    Why are we so obsessed
  • 00:17:14
    with the idea of recreating ourselves?
  • 00:17:17
    Is it biological?
  • 00:17:18
    Existential?
  • 00:17:21
    [Gildert] To try and understand something fully,
  • 00:17:23
    you have to reverse-engineer it,
  • 00:17:25
    you have to put it back together.
  • 00:17:28
    [Downey] The human that Suzanne knows best
  • 00:17:30
    is... Suzanne,
  • 00:17:32
    so one of her projects
  • 00:17:33
    is to build a synthetic replica of herself.
  • 00:17:36
    [Gildert] There's this thing called the Turing test,
  • 00:17:38
    which is trying to have an A.I.
  • 00:17:40
    that you can't tell is not a human.
  • 00:17:43
    So I wanna try and create a physical Turing test,
  • 00:17:46
    where you can't tell whether or not
  • 00:17:48
    the system you're actually physically interacting with
  • 00:17:51
    is a person, or whether it's a robot.
  • 00:17:53
    So here we have 132 cameras...
  • 00:17:57
    which are all pointed at me,
  • 00:17:59
    and they all take a photograph simultaneously.
  • 00:18:02
    This data is used to create
  • 00:18:05
    a full three-dimensional body scan of me
  • 00:18:07
    that we can then use to create a robot version of me.
  • 00:18:11
    [Downey] Suzanne believes
  • 00:18:12
    that we experience life through the senses,
  • 00:18:14
    so she's putting as much work into making the body lifelike
  • 00:18:17
    as she is the mind.
  • 00:18:19
    [Gildert] We broke down this very ambitious project
  • 00:18:21
    into several different categories.
  • 00:18:23
    The first category is physical.
  • 00:18:25
    Can you build a robotic system that looks like a person?
  • 00:18:34
    So the synth has bones and muscles
  • 00:18:36
    that are roughly analogous to the human body,
  • 00:18:40
    but not quite as complex.
  • 00:18:43
    These hands are 3D-printed as an entire piece
  • 00:18:46
    on our printers.
  • 00:18:48
    [Gildert] We can actually print in carbon fiber
  • 00:18:50
    and Kevlar,
  • 00:18:51
    and we can create robot bones
  • 00:18:53
    that are stronger than aluminum machined parts,
  • 00:18:57
    with these beautiful organic biological shapes.
  • 00:19:00
    So I'm adding in a finger sensor.
  • 00:19:04
    This, uh, current generation
  • 00:19:05
    has a single sensor on the fingertip.
  • 00:19:09
    [Gildert] We build a machine that perceives like a human
  • 00:19:11
    by trying to copy the human sensorium very accurately.
  • 00:19:17
    The most complicated part of the perception system
  • 00:19:21
    is actually the sense of touch.
  • 00:19:23
    Are you monitoring the touch?
  • 00:19:24
    Yes. Touch received.
  • 00:19:28
    [Holly Marie Peck] We've actually embedded
  • 00:19:30
    capacitive touch sensors in the synth's hand,
  • 00:19:32
    essentially pressure sensors
  • 00:19:34
    allowing it to feel, uh, its environment,
  • 00:19:36
    and interact and manipulate objects.
  • 00:19:38
    Let's just test the pressure.
  • 00:19:40
    -Okay. -This should max it out.
  • 00:19:42
    Yep, yep. Maxed out.
  • 00:19:43
    Just stretch out her hand. Okay, go.
  • 00:19:45
    [Gildert] The reason the hand and the arm
  • 00:19:47
    is able to move so fluidly
  • 00:19:49
    is because of pneumatic actuators.
  • 00:19:51
    They work using compressed air.
  • 00:19:55
    You actuate one of these devices,
  • 00:19:57
    and it kind of contracts
  • 00:19:59
    and pulls on a tendon,
  • 00:20:01
    so the actuation mechanism is very similar to a human muscle.
  • 00:20:05
    It's just not yet quite as efficient.
  • 00:20:09
    [Shannon] I'm adding the camera into the eyeball.
  • 00:20:12
    Now I'm adding the cosmetic front of the eye.
  • 00:20:15
    [Gildert] The eyes are super important to get right.
  • 00:20:18
    Similar to our own vision system,
  • 00:20:20
    they can see similar color spectrum,
  • 00:20:22
    and they can also, because there's two cameras,
  • 00:20:25
    they can have depth perception too.
  • 00:20:26
    [Peck] Restarting facial detection.
  • 00:20:30
    [Gildert] That actually looks pretty good.
  • 00:20:32
    [Peck] Mm-hmm. Do you wanna come forward a little bit?
  • 00:20:34
    Yeah.
  • 00:20:35
    -I'm gonna restart her headboard. -[Gildert] Okay.
  • 00:20:37
    That information
  • 00:20:38
    is fed through a series of different A.I. algorithms.
  • 00:20:42
    One algorithm is a facial detection system.
  • 00:20:45
    She's definitely seeing me.
  • 00:20:47
    [Peck] Yes, she is.
  • 00:20:49
    I can tell she's looking at me,
  • 00:20:50
    'cause she looked straight at me.
  • 00:20:52
    Yeah, gaze tracking is working.
  • 00:20:53
    Okay, cool. Now, do you wanna just smile?
  • 00:20:54
    I'll see if she's actually capturing your emotion?
  • 00:20:58
    [Gildert] If you're smiling,
  • 00:20:59
    the corners of your mouth come up,
  • 00:21:01
    your eyes open a little bit,
  • 00:21:02
    and the A.I. system can actually detect
  • 00:21:04
    how those landmarks have moved relative to one another.
  • 00:21:08
    [Rana el Kaliouby] I think the moment in time
  • 00:21:09
    we're at right now
  • 00:21:10
    is very exciting
  • 00:21:12
    because there's this field that's concerned
  • 00:21:14
    about building human-like generalized intelligence,
  • 00:21:17
    and sometimes even kind of surpassing human intelligence.
  • 00:21:22
    [Daphne Koller] There's people out there
  • 00:21:23
    who believe that this is on our immediate horizon.
  • 00:21:27
    I don't.
  • 00:21:28
    I think we're a long ways away
  • 00:21:31
    from machines that are truly conscious
  • 00:21:35
    and think on their own.
  • 00:21:36
    She's responding. I can see her face changing.
  • 00:21:39
    [synth] You look happy.
  • 00:21:41
    -Good. -Mm-hmm.
  • 00:21:42
    I'm gonna look sad.
  • 00:21:45
    You look sad.
  • 00:21:47
    Okay, good.
  • 00:21:48
    [Peck] We have actually configured
  • 00:21:50
    a lot of A.I. algorithms on the back end
  • 00:21:52
    that give the robot
  • 00:21:53
    the capabilities of recognizing people,
  • 00:21:55
    detecting emotion,
  • 00:21:56
    recognizing gestures and poses that people are making.
  • 00:22:00
    It then responds in various ways
  • 00:22:02
    with its environment.
  • 00:22:04
    [Gildert] Bring up her node graph
  • 00:22:05
    so you can see what's running in her brain.
  • 00:22:08
    Yeah, let's see all the online modules.
  • 00:22:10
    The chatbot, emotion detection,
  • 00:22:12
    object detection...
  • 00:22:14
    Wonderful. Gaze tracking...
  • 00:22:15
    [Gildert] The body, in a way, is the easy part.
  • 00:22:19
    Creating the mind is a lot harder.
  • 00:22:21
    [Downey] Creating the mind is more than hard.
  • 00:22:24
    It's basically impossible,
  • 00:22:25
    at least for now,
  • 00:22:27
    and maybe forever,
  • 00:22:28
    because a mind is not just knowledge,
  • 00:22:30
    or skill, or even language,
  • 00:22:32
    all of which a machine can learn.
  • 00:22:34
    The part that makes us really human is consciousness;
  • 00:22:37
    an awareness, a sense of being,
  • 00:22:39
    of who we are
  • 00:22:40
    and how we fit in time and space around us.
  • 00:22:43
    A human mind has that...
  • 00:22:45
    and memory.
  • 00:22:47
    "I remember the experience of buying a new pencil case
  • 00:22:50
    and the supplies to go in it,
  • 00:22:51
    getting all those new little things
  • 00:22:53
    that smelled nice,
  • 00:22:54
    and were all clean and colorful."
  • 00:22:56
    If you think about how people work,
  • 00:22:59
    it's very unusual for you to meet a person
  • 00:23:01
    that doesn't have a backstory.
  • 00:23:02
    I can use all the data that I have about myself
  • 00:23:05
    to try and craft something that has my memories,
  • 00:23:09
    it has my same mannerisms,
  • 00:23:10
    and it thinks and feels the way I do.
  • 00:23:18
    I would like them to become their own beings,
  • 00:23:20
    and to me,
  • 00:23:22
    creating the copy is a way of pushing the A.I. further
  • 00:23:26
    towards making it a realistic human
  • 00:23:28
    by having it be a copy of a specific human.
  • 00:23:31
    I remember going to Bolton Town Center
  • 00:23:35
    quite often.
  • 00:23:36
    We just called it "Town."
  • 00:23:39
    [Gildert] The basic idea
  • 00:23:41
    is you send in a large amount of text data,
  • 00:23:43
    and the system learns correlations between words,
  • 00:23:47
    and the idea
  • 00:23:48
    is that the synth could use one of these models
  • 00:23:50
    to kind of blend together an idea of a memory
  • 00:23:53
    that may have happened or may not have happened,
  • 00:23:55
    so it's a little bit of an artistic way
  • 00:23:58
    of recreating memories.
  • 00:23:59
    I remember going into WH Smith.
  • 00:24:02
    It had a very distinct smell that I can still recall.
  • 00:24:06
    [Gildert] So by giving them these backstories now,
  • 00:24:10
    we believe that we will be able to learn in the future
  • 00:24:13
    how they can create their own memories
  • 00:24:15
    from their experiences.
  • 00:24:18
    [Bran Ferren] I love the idea
  • 00:24:19
    that there are passionate people
  • 00:24:20
    who are dedicating their time and energy
  • 00:24:24
    to making these things happen.
  • 00:24:25
    Why?
  • 00:24:26
    Because if and when it does happen,
  • 00:24:28
    it's going to be because of those passionate people.
  • 00:24:31
    We talk about the computer revolution
  • 00:24:33
    like it's done.
  • 00:24:34
    It's barely begun.
  • 00:24:37
    We don't understand
  • 00:24:38
    where the impact of these technologies will be
  • 00:24:41
    over the next five, ten,
  • 00:24:42
    20, 30, 50, 100 years.
  • 00:24:44
    If you think it's exciting and confusing now,
  • 00:24:48
    fasten your seatbelts,
  • 00:24:50
    because it hasn't begun.
  • 00:24:51
    What is your name?
  • 00:24:53
    My name is Holly.
  • 00:24:55
    What is your name?
  • 00:25:00
    Hmm.
  • 00:25:01
    [Gildert] Of course there's that unknown,
  • 00:25:02
    like are we gonna run into a problem
  • 00:25:04
    with trying to recreate a mind
  • 00:25:06
    that no one's thought of yet?
  • 00:25:07
    My name is Nadine.
  • 00:25:10
    Interesting.
  • 00:25:11
    I am glad to see you.
  • 00:25:13
    [Downey] Even if we do one day figure out
  • 00:25:15
    how to create a virtual mind,
  • 00:25:17
    it's not just the science.
  • 00:25:18
    There's also the ethics.
  • 00:25:20
    What kind of rights will the robots have?
  • 00:25:22
    Can we imbue it with good values,
  • 00:25:24
    make sure it's unbiased?
  • 00:25:26
    What if breaks the law or commits a crime?
  • 00:25:28
    Are we responsible for our synths?
  • 00:25:31
    [el Kaliouby] There are big ethical challenges
  • 00:25:33
    in the field of A.I.
  • 00:25:35
    I believe that as a community of A.I. innovators
  • 00:25:38
    and thought leaders,
  • 00:25:39
    we have to really be at the forefront
  • 00:25:41
    of enforcing and designing
  • 00:25:45
    these best practices and guidelines
  • 00:25:47
    around how we build and deploy ethical A.I.
  • 00:25:50
    I like to say that artificial intelligence
  • 00:25:52
    should not be about the artificial,
  • 00:25:54
    it should be about the humans.
  • 00:25:56
    You look angry.
  • 00:25:57
    Landmarks are registering.
  • 00:25:59
    [Ferren] I think it's perfectly reasonable
  • 00:26:02
    to have a set of rules that govern ethical behavior
  • 00:26:05
    when you are dealing with technologies
  • 00:26:08
    that can have direct impact into people's lives
  • 00:26:11
    and their families and the future.
  • 00:26:13
    [Gildert] The vision's very ambitious for this.
  • 00:26:15
    We'd like to think that that is a 10- to 20-year mission.
  • 00:26:19
    You might say we're somewhere like
  • 00:26:20
    five to 10% of the way along.
  • 00:26:23
    Why is her arm doing that?
  • 00:26:25
    It's almost like it's not clearing the buffer.
  • 00:26:28
    Yeah... interesting.
  • 00:26:31
    Let's just restart you so your arm goes--
  • 00:26:33
    Oh, wait, it's going back down again.
  • 00:26:36
    Okay, that's good.
  • 00:26:37
    Okay.
  • 00:26:38
    How do you feel today, Nadine?
  • 00:26:40
    It feels good to be a synth.
  • 00:26:43
    Nice.
  • 00:26:43
    "It feels good to be a synth."
  • 00:26:45
    [Gildert] The synths are not mobile at the moment,
  • 00:26:47
    they can't move around,
  • 00:26:48
    they can't walk yet.
  • 00:26:49
    That's something we're going to be adding in
  • 00:26:51
    within the next couple of years.
  • 00:26:53
    The grand goal
  • 00:26:54
    is to make these into their own beings
  • 00:26:56
    with their own volition and their own rights.
  • 00:27:01
    There are these moments you can have
  • 00:27:03
    where you really feel something that's unusual.
  • 00:27:06
    It's surprising.
  • 00:27:08
    I was adjusting the synth's hair,
  • 00:27:11
    and then she suddenly, like, smiled,
  • 00:27:13
    and opened her mouth a little bit,
  • 00:27:15
    like, you know, like I'd just tickled her or something.
  • 00:27:18
    It was just, like, synchronous with what I was doing.
  • 00:27:21
    [Downey] In some ways,
  • 00:27:23
    Suzanne's vision is already coming alive.
  • 00:27:25
    She's making a connection, albeit small, with a machine.
  • 00:27:28
    Isn't that something?
  • 00:27:30
    [Domingos] I think A.I. is part of evolution.
  • 00:27:32
    The same evolution
  • 00:27:33
    that led from bacteria to animals,
  • 00:27:34
    and has led people to create technology,
  • 00:27:37
    has led them to create A.I.
  • 00:27:38
    In some ways, we're still in the very early infancy
  • 00:27:41
    of this new age.
  • 00:27:43
    [Downey] Will we ever create intelligent life
  • 00:27:45
    here on Earth...
  • 00:27:47
    or maybe we'll find it out there first?
  • 00:27:57
    So I'm on my way
  • 00:27:58
    to the SETI Institute headquarters
  • 00:28:00
    in Mountain View,
  • 00:28:01
    and, and I'm gonna show, uh, what the A.I. system found
  • 00:28:06
    in the data that we collected.
  • 00:28:07
    I'm excited. I'm a little nervous too.
  • 00:28:18
    [Tarter] We need to be able to follow up in real time...
  • 00:28:20
    [Diamond] Mm-hmm.
  • 00:28:21
    [Tarter] ...as closely as we can,
  • 00:28:22
    so that a signal that's there
  • 00:28:24
    is still gonna be there when we go back to look for it,
  • 00:28:27
    and we can then classify it.
  • 00:28:28
    Jill Tarter is really a legend
  • 00:28:30
    in this whole field of SETI research.
  • 00:28:34
    Also really a pioneer as a woman astronomer.
  • 00:28:39
    The character played by Jodie Foster in Contact,
  • 00:28:42
    is based, at least in the first half of that movie,
  • 00:28:44
    on Jill Tarter.
  • 00:28:45
    [Tarter] People often talk
  • 00:28:47
    about finding a needle in a haystack
  • 00:28:49
    as being a difficult task,
  • 00:28:51
    but the SETI task is far harder.
  • 00:28:54
    If I got out of bed every morning
  • 00:28:57
    thinking, "This is the day we're gonna find the signal,"
  • 00:28:59
    I have pretty good odds
  • 00:29:01
    I'm gonna go to bed that night disappointed.
  • 00:29:05
    I don't get up in the morning thinking that.
  • 00:29:08
    What I do get up in the morning thinking
  • 00:29:10
    is that today, I'm going to figure out
  • 00:29:12
    how to do this search better,
  • 00:29:14
    do new things,
  • 00:29:16
    do things you could not do in the past.
  • 00:29:19
    Early on, the technology just wasn't there...
  • 00:29:21
    Mm-hmm.
  • 00:29:22
    ...and now we're doing something
  • 00:29:23
    that we've never been able to do.
  • 00:29:25
    I'm excited.
  • 00:29:30
    -Hello? -Oh, hey!
  • 00:29:31
    -Look who's here! -How are ya?
  • 00:29:33
    -Good to see you! -Hi, Graham.
  • 00:29:34
    -Nice to see you. -Nice to see you.
  • 00:29:36
    Likewise. Good to see you too.
  • 00:29:37
    -Hey, Bill. -It's been a couple of whole days?
  • 00:29:39
    -I know! [laughs] -Thanks for coming down.
  • 00:29:41
    -My pleasure, I'm excited. -Yeah.
  • 00:29:42
    We're thinking maybe you've got some news.
  • 00:29:44
    Well, I wanna step you through it.
  • 00:29:47
    Here you can kinda see
  • 00:29:49
    the system is initially very active.
  • 00:29:50
    It's all lit up,
  • 00:29:52
    and very quickly,
  • 00:29:53
    it starts to get a handle on what the shape,
  • 00:29:56
    you know, what a signal from the Trappist-1 system should look like.
  • 00:29:58
    Over on the far right is its areas of interest...
  • 00:30:02
    What I'm showing here
  • 00:30:03
    is a time-compressed video of the A.I. system
  • 00:30:06
    looking at the signal we gathered.
  • 00:30:09
    ...and if you focus in on that,
  • 00:30:11
    the A.I. system did indeed flag this one area,
  • 00:30:14
    at that point, saying,
  • 00:30:16
    -"Whoa, back up. Something just happened." -[Tarter] Ooh, wow.
  • 00:30:18
    "That's not right,"
  • 00:30:20
    and if you zoom in on the actual data,
  • 00:30:22
    sure enough, there's that spike,
  • 00:30:24
    so that is not from the Trappist system.
  • 00:30:26
    That was generated by the Allen Telescope Array,
  • 00:30:29
    but, you know, beyond that,
  • 00:30:30
    this is an area that the A.I. system is saying,
  • 00:30:32
    "This isn't quite what I would have expected."
  • 00:30:36
    This is a little more interesting
  • 00:30:37
    'cause there's more structure to it,
  • 00:30:39
    and we should take its hints,
  • 00:30:40
    and have a deeper analysis done of this part of the observation.
  • 00:30:45
    We didn't write any code.
  • 00:30:46
    We didn't tell it to... to look for spikes of power
  • 00:30:50
    or anything else.
  • 00:30:51
    We just said, "You know what, you figure out what's normal,
  • 00:30:53
    and you let us know
  • 00:30:54
    when something catches your attention,"
  • 00:30:56
    which is exactly what it's doing there.
  • 00:30:58
    It's encouraging,
  • 00:30:59
    because already with just this one observation,
  • 00:31:02
    we started to see some real progress
  • 00:31:04
    in what the A.I. system can do compared to our own eyes,
  • 00:31:07
    and that's just one observation.
  • 00:31:09
    What about the next, and the next,
  • 00:31:11
    and as it gets better
  • 00:31:12
    with each new round of data that we collect?
  • 00:31:14
    This is after two hours.
  • 00:31:16
    I wonder how good it's gonna get after a hundred hours.
  • 00:31:20
    Yeah.
  • 00:31:21
    If we just routinely keep feeding the data from the A.T.A.
  • 00:31:25
    into this model,
  • 00:31:26
    it's gonna get better and better and better.
  • 00:31:28
    We can just scale this out.
  • 00:31:29
    -Right. -Absolutely.
  • 00:31:30
    We just got smarter. Thank you, machine.
  • 00:31:32
    Yes, exactly.
  • 00:31:33
    [Tarter] I'm absolutely so excited.
  • 00:31:35
    I'm really blown away.
  • 00:31:36
    I can see the tools that are being built
  • 00:31:41
    give us a new way of looking for things
  • 00:31:43
    that we hadn't thought of,
  • 00:31:44
    and things that we don't have to define up front,
  • 00:31:47
    anomalies that the machines will find
  • 00:31:51
    simply because they've looked at so much data.
  • 00:31:55
    [Mackintosh] I do think we're going to find ET.
  • 00:31:58
    I do think we are gonna find signs of civilization
  • 00:32:02
    beyond Earth,
  • 00:32:04
    and I do think that it's going to be A.I. that finds it.
  • 00:32:12
    [Downey] Is there intelligent life out there?
  • 00:32:14
    Can we create human-like machines?
  • 00:32:18
    [Domingos] The odds are overwhelming
  • 00:32:20
    that we will eventually be able to build an artificial brain
  • 00:32:24
    that is at the level of the human brain.
  • 00:32:26
    The big question is how long will it take?
  • 00:32:30
    [Downey] Outer space,
  • 00:32:32
    inner life...
  • 00:32:34
    Age-old mysteries now seem more solvable.
  • 00:32:37
    [Chris Botham] If we wanna go to Mars,
  • 00:32:38
    if we wanna populate other planets,
  • 00:32:40
    these types of things require these advanced technologies.
  • 00:32:42
    [Downey] Moonshots, yeah,
  • 00:32:45
    but also other pressing problems,
  • 00:32:47
    like...
  • 00:32:48
    -[gasps of shock] -All five! Whoa!
  • 00:32:51
    [Downey] ...the mind and body.
  • 00:32:52
    [Tim Shaw] Are you working today?
  • 00:32:55
    [beeping]
  • 00:32:56
    It's wonderful.
  • 00:32:59
    [Downey] Adaptation...
  • 00:33:00
    [Jim Ewing] I'm thinking and doing
  • 00:33:01
    and getting instant response.
  • 00:33:03
    It makes it feel like it's part of me.
  • 00:33:05
    [Downey] Work...
  • 00:33:06
    Action!
  • 00:33:07
    [Downey] ...and creativity...
  • 00:33:09
    These types of technologies can help us do our tasks better.
  • 00:33:11
    Three, two, one.
  • 00:33:13
    [computer voice] Autonomous driving started.
  • 00:33:16
    [el Kaliouby] I believe if we do this right,
  • 00:33:18
    these A.I. systems can truly, truly compliment
  • 00:33:21
    what we do as humans.
  • 00:33:22
    [Eric Warren] We use the A.I. tools
  • 00:33:24
    to predict what the future not only is,
  • 00:33:27
    but what it should be.
  • 00:33:28
    Yo, what's up? This is will.i.am.
  • 00:33:30
    [laughing]
  • 00:33:31
    [Mark Sagar] This is the new version of you.
  • 00:33:32
    The way it's looking so far is mind-blowing.
  • 00:33:36
    [firefighter] Stay close, I'll lead.
  • 00:33:38
    [Downey] Survival...
  • 00:33:39
    [firefighter] Over here, I see him! Three yards at 2:00!
  • 00:33:41
    [Martin Ford] I believe that artificial intelligence
  • 00:33:43
    is really going to be
  • 00:33:45
    the most important tool in our toolbox
  • 00:33:47
    for solving the big problems that we face.
  • 00:33:49
    [firefighter] I got him!
  • 00:33:51
    [crowd chanting]
  • 00:33:53
    [Downey] Conservation...
  • 00:33:54
    The fact that we can look across the world
  • 00:33:56
    and find where famine might happen
  • 00:33:58
    four months from now,
  • 00:34:00
    it's mind-blowing.
  • 00:34:01
    [Downey] All out of the realm of sci-fi and magic,
  • 00:34:05
    and now just science.
  • 00:34:07
    Still hard problems, but now possible,
  • 00:34:11
    with innovation,
  • 00:34:13
    computing power, will, and passion...
  • 00:34:15
    -[cheering] Yay! -Yes!
  • 00:34:17
    There it is.
  • 00:34:18
    [Downey] ...and yet, despite all that,
  • 00:34:20
    a vestige of unknown endures.
  • 00:34:23
    Who are we?
  • 00:34:25
    What are we becoming?
  • 00:34:27
    Every major technological change
  • 00:34:30
    leads to a new kind of society,
  • 00:34:32
    with new moral principles,
  • 00:34:34
    and the same thing will happen with A.I.
  • 00:34:36
    [Downey] Technology's changing us, for sure.
  • 00:34:40
    The whole idea of what it means to be human
  • 00:34:42
    is getting rewired.
  • 00:34:44
    A.I. might be humanity's most valuable tool...
  • 00:34:49
    ...but it's also just that.
  • 00:34:51
    A tool.
  • 00:34:52
    [clattering]
  • 00:34:55
    [Downey] What we choose to do with it...
  • 00:34:59
    that's up to you and I.
  • 00:35:08
    [Seth Shostak] If you could project yourself
  • 00:35:10
    into the next millennium,
  • 00:35:11
    a thousand years from now,
  • 00:35:13
    would we look back on this generation and say,
  • 00:35:15
    "Well, they were the last generation of Homo sapiens
  • 00:35:18
    that actually ran the planet"?
  • 00:35:20
    [James Parr] There's a lot of paranoia.
  • 00:35:22
    The media's done a really good job
  • 00:35:24
    of making people frightened,
  • 00:35:25
    but A.I. is just a portrait of reality,
  • 00:35:29
    a very close portrait, but it isn't reality.
  • 00:35:31
    It's just a bucket of probabilities.
  • 00:35:33
    Where I think human beings will always have the edge
  • 00:35:37
    are understanding other humans.
  • 00:35:39
    It's going to take a long time
  • 00:35:41
    before we have an A.I.
  • 00:35:42
    that can understand all of the nuances
  • 00:35:46
    and various layers of the human experience
  • 00:35:48
    at a societal level.
  • 00:35:50
    [Shostak] James Parr, thanks so very much for being with us.
  • 00:35:52
    Great, thank you.
标签
  • AI
  • SETI
  • Extraterrestrial Life
  • Innovation
  • Cosmology
  • Synthetic Intelligence
  • Ethics
  • Data Analysis
  • Telescope Array
  • Space Exploration