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What are the risks of artificial intelligence, and what should we be doing
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What are the risks of artificial intelligence and what should we be doing

What are the risks of artificial intelligence, and what should we be doing

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  • 00:00

    What are the risks of artificial intelligence, and what should we be doing
    What are the risks of artificial intelligence, and what should we be doing

  • 00:04

    about them? The idea of AI has been around for decades. But recent advances
    about them? The idea of AI has been around for decades. But recent advances

  • 00:11

    have completely transformed what we can achieve with the technology. And as a
    have completely transformed what we can achieve with the technology. And as a

  • 00:15

    result, many experts believe we're teetering on the edge of an AI
    result, many experts believe we're teetering on the edge of an AI

  • 00:19

    revolution. This is fantastic news if you're struggling with really knotty
    revolution. This is fantastic news if you're struggling with really knotty

  • 00:24

    problems like how to cure incurable diseases, or solve world hunger, or even
    problems like how to cure incurable diseases, or solve world hunger, or even

  • 00:30

    design safer and faster ways of getting from A to B. Here, smart machines are
    design safer and faster ways of getting from A to B. Here, smart machines are

  • 00:35

    already paving the way to doing things which would have been impossible without
    already paving the way to doing things which would have been impossible without

  • 00:39

    them. But there are downsides to creating machines that can think and act way
    them. But there are downsides to creating machines that can think and act way

  • 00:44

    faster than humans; and often in ways that we don't fully understand. Because
    faster than humans; and often in ways that we don't fully understand. Because

  • 00:49

    of this, we need to be thinking now about what might possibly go wrong, so we can
    of this, we need to be thinking now about what might possibly go wrong, so we can

  • 00:54

    avoid potentially disastrous mistakes in the future. Risk Bites we'll be diving
    avoid potentially disastrous mistakes in the future. Risk Bites we'll be diving

  • 00:58

    further into some of the possible risks of AI in later episodes. But to tee
    further into some of the possible risks of AI in later episodes. But to tee

  • 01:03

    things off, here are 10 things we should probably be thinking about now. One:
    things off, here are 10 things we should probably be thinking about now. One:

  • 01:07

    machines that make it harder to think for ourselves.
    machines that make it harder to think for ourselves.

  • 01:11

    Of course, apps like Google and Alexa and Siri are already doing this to some
    Of course, apps like Google and Alexa and Siri are already doing this to some

  • 01:16

    extent. But the more we rely on smart machines to do stuff for us, the more we
    extent. But the more we rely on smart machines to do stuff for us, the more we

  • 01:21

    risk losing skills that we may one day regret. Two: machines that take away jobs.
    risk losing skills that we may one day regret. Two: machines that take away jobs.

  • 01:28

    This is a huge issue as smart machines begin to outperform smart people. The big
    This is a huge issue as smart machines begin to outperform smart people. The big

  • 01:35

    question is whether we can keep on inventing jobs that humans do better, or
    question is whether we can keep on inventing jobs that humans do better, or

  • 01:39

    if we need to consider a future without paid jobs. Three: machines that learn
    if we need to consider a future without paid jobs. Three: machines that learn

  • 01:45

    from our worst habits. As we train smart machines, will they end up as opinionated,
    from our worst habits. As we train smart machines, will they end up as opinionated,

  • 01:52

    biased, and anti-social, as some of their human trainers? The evidence so far is
    biased, and anti-social, as some of their human trainers? The evidence so far is

  • 01:57

    that, unless we work hard on avoiding this, sadly they probably will.
    that, unless we work hard on avoiding this, sadly they probably will.

  • 02:02

    Four: Machines that make decisions we don't understand.
    Four: Machines that make decisions we don't understand.

  • 02:07

    Imagine failing a job interview, or flunking a class, or being fined; all
    Imagine failing a job interview, or flunking a class, or being fined; all

  • 02:12

    because a machine made a decision that no one could explain, but you had to live
    because a machine made a decision that no one could explain, but you had to live

  • 02:17

    with anyway. Scarily, were already on the slippery slope of giving smart machines
    with anyway. Scarily, were already on the slippery slope of giving smart machines

  • 02:22

    the authority to make such decisions without being accountable for them. Five:
    the authority to make such decisions without being accountable for them. Five:

  • 02:28

    Machines that don't understand what's important to people. Imagine an AI that
    Machines that don't understand what's important to people. Imagine an AI that

  • 02:33

    has the power to change your life, but it has its own ideas about what's important
    has the power to change your life, but it has its own ideas about what's important

  • 02:38

    and what's acceptable. This is great if you both agree; but it's a nightmare if
    and what's acceptable. This is great if you both agree; but it's a nightmare if

  • 02:43

    you don't. Six: Machines that kill people. Of course, machines kill people all the
    you don't. Six: Machines that kill people. Of course, machines kill people all the

  • 02:50

    time. But usually there's a person making the final decision. What happens though
    time. But usually there's a person making the final decision. What happens though

  • 02:54

    when we replace that person with another machine? Seven: Machines that alter their
    when we replace that person with another machine? Seven: Machines that alter their

  • 03:01

    own instructions. Interestingly, we want machines that can think for themselves
    own instructions. Interestingly, we want machines that can think for themselves

  • 03:06

    when solving complex problems. But what if they get too smart? Eight: machines
    when solving complex problems. But what if they get too smart? Eight: machines

  • 03:12

    that make smart-dumb decisions. We know that smart people can make
    that make smart-dumb decisions. We know that smart people can make

  • 03:17

    decisions that look really stupid with hindsight; especially when they can't
    decisions that look really stupid with hindsight; especially when they can't

  • 03:21

    appreciate the full consequences of their actions. Sadly, from what we know of
    appreciate the full consequences of their actions. Sadly, from what we know of

  • 03:25

    complex systems, smart machines are likely to have exactly the same problem.
    complex systems, smart machines are likely to have exactly the same problem.

  • 03:31

    Nine: Machines that decide we're not needed! What if AIs get so smart that they
    Nine: Machines that decide we're not needed! What if AIs get so smart that they

  • 03:38

    realize people are a waste of space? The good news is that were probably a long
    realize people are a waste of space? The good news is that were probably a long

  • 03:42

    way from super intelligent machines that are disdainful of mere human beings. But
    way from super intelligent machines that are disdainful of mere human beings. But

  • 03:47

    it's worth thinking about, just in case. And finally ten: Machines that use our
    it's worth thinking about, just in case. And finally ten: Machines that use our

  • 03:54

    human weaknesses to control us. What are the chances that we create machines that
    human weaknesses to control us. What are the chances that we create machines that

  • 03:59

    are smart enough to understand our human foibles and use them against us? If we're
    are smart enough to understand our human foibles and use them against us? If we're

  • 04:04

    not careful, we won't be fearing our AI lords: we'll be worshipping them! Of course,
    not careful, we won't be fearing our AI lords: we'll be worshipping them! Of course,

  • 04:10

    despite these and other risks, AI could make our lives amazingly better if it's
    despite these and other risks, AI could make our lives amazingly better if it's

  • 04:15

    developed responsibly. But this won't happen if we don't think about the
    developed responsibly. But this won't happen if we don't think about the

  • 04:19

    possible downsides up-front. The good news is that AI developers and others are
    possible downsides up-front. The good news is that AI developers and others are

  • 04:24

    already beginning to do this. But we're going to need a whole lot of input from
    already beginning to do this. But we're going to need a whole lot of input from

  • 04:28

    experts in many other areas, if the benefits of AI are to far outweigh the risks.
    experts in many other areas, if the benefits of AI are to far outweigh the risks.

All

Can Artificial Intelligence be Dangerous? Ten risks associated with AI

52,967 views

Intro:

What are the risks of artificial intelligence, and what should we be doing
about them? The idea of AI has been around for decades. But recent advances
have completely transformed what we can achieve with the technology. And as a
result, many experts believe we're teetering on the edge of an AI
revolution. This is fantastic news if you're struggling with really knotty
problems like how to cure incurable diseases, or solve world hunger, or even
design safer and faster ways of getting from A to B. Here, smart machines are
already paving the way to doing things which would have been impossible without
them. But there are downsides to creating machines that can think and act way
faster than humans; and often in ways that we don't fully understand. Because
of this, we need to be thinking now about what might possibly go wrong, so we can
avoid potentially disastrous mistakes in the future. Risk Bites we'll be diving
further into some of the possible risks of AI in later episodes. But to tee
things off, here are 10 things we should probably be thinking about now. One:
machines that make it harder to think for ourselves.
Of course, apps like Google and Alexa and Siri are already doing this to some
extent. But the more we rely on smart machines to do stuff for us, the more we
risk losing skills that we may one day regret. Two: machines that take away jobs.
This is a huge issue as smart machines begin to outperform smart people. The big
question is whether we can keep on inventing jobs that humans do better, or

Video Vocabulary

/ˈpräbəblē/

adverb

certainly.

/ˈevədəns/

noun verb

information indicating whether something is true. be or show evidence of.

/fanˈtastik/

adjective

Strange or unusual in design or appearance.

/krēˈāt/

verb

To make, cause, or bring into existence.

/ˈTHiNGkiNG/

adjective noun verb

using thought or rational judgement. Thoughts behind an idea, opinion or belief. To have an idea, opinion or belief about something.

/ˌärdəˈfiSHəl/

adjective

(Something) made by people; not created by nature.

/tran(t)sˈfôrm/

verb

make thorough or dramatic change in form.

/ˈpräbləm/

noun other

unwelcome or harmful matter. Questions to show understanding of a math concept.

/dəˈzastrəs/

adjective

Having very unfortunate or terrible consequences.

/dəˈsiZHən/

noun other

conclusion or resolution reached after consideration. Choices made after thinking; final judgment.

/ˌôlˈredē/

adverb

Having happened or been done before this time.

/ˈkwesCH(ə)n/

noun verb

What you ask about; issue. ask someone questions.

/ˈfərT͟Hər/

adjective adverb conjunction verb

more distant in space. A greater distance; at a more distant point. (Used to introduce an additional point); also. help or promote.

/imˈpäsəb(ə)l/

adjective

(Child, etc.) very annoying.

/ɡet/

verb

To send or bring someone or something home, etc..