Latest / Luke's English Podcast / 909. The Existential Threat of AI to Human Civilisation 😃 (Topic _ Vocabulary)(M4A_128K)

Transcript

909. The Existential Threat of AI to Human Civilisation 😃 (Topic _ Vocabulary)(M4A_128K) · Luke's English Podcast

• 0:00
  Speaker 1: Hello, listeners, Welcome back to Luke's English podcast. How are
• 0:02
  Speaker 1: you today? I hope you're doing fine, You're doing all right.
• 0:05
  Speaker 1: I hope so lovely day here. Not that it's important
• 0:09
  Speaker 1: really in the grand scheme of things, but I thought
• 0:12
  Speaker 1: you might like to know. The sun is shining. It's
• 0:14
  Speaker 1: quite a nice, warm afternoon here in October when I'm
• 0:18
  Speaker 1: recording this slightly unseasonably warm, but pleasant. Nonetheless, let me
• 0:23
  Speaker 1: tell you about this episode that you're about to listen to.
• 0:26
  Speaker 1: So first of all, there's a PDF for this one,
• 0:29
  Speaker 1: and you can get it by just clicking the link
• 0:31
  Speaker 1: that you'll find in the episode description. Just check the
• 0:35
  Speaker 1: show notes and you'll find a link there for the PDF,
• 0:37
  Speaker 1: which you can download if you want to, and if
• 0:41
  Speaker 1: you can follow along with me, you can read while
• 0:44
  Speaker 1: you listen, or you can just go back and check
• 0:47
  Speaker 1: the PDF later. Because there will be loads of vocabulary
• 0:52
  Speaker 1: in this episode and a vocabulary list and texts which
• 0:56
  Speaker 1: have the vocab highlighted and stuff like that. It would
• 1:00
  Speaker 1: be I think a good idea for you to go
• 1:02
  Speaker 1: back to the PDF and check out all that vocab
• 1:06
  Speaker 1: and perhaps you know, review it later. It will definitely
• 1:10
  Speaker 1: help you remember it anyway, link in the description for
• 1:13
  Speaker 1: the pdf, and I'm going to start reading from the
• 1:15
  Speaker 1: pdf right now. So here we go in five four
• 1:19
  Speaker 1: three two one pure. So this is a podcast for
• 1:24
  Speaker 1: learners of English around the world. In my episodes, I
• 1:28
  Speaker 1: talk about almost anything, but my aim is always to
• 1:31
  Speaker 1: help you learn English and perhaps to entertain or stimulate
• 1:36
  Speaker 1: you a bit in the process. In this episode, I'll
• 1:39
  Speaker 1: be talking about the cheerful subject of the existential threat
• 1:44
  Speaker 1: of AI to human civilization. So I'm going to discuss
• 1:49
  Speaker 1: the subject of artificial intelligence and the impact it will
• 1:53
  Speaker 1: have on human civilization in the foreseeable future, both good
• 1:57
  Speaker 1: and bad. I'll explore and discuss the topic in some detail,
• 2:02
  Speaker 1: and will explain some vocabulary as we go. Hopefully this
• 2:05
  Speaker 1: will be interesting as well as useful for your English
• 2:09
  Speaker 1: as it will give you plenty of words and phrases
• 2:12
  Speaker 1: that you can use to discuss this subject which everybody
• 2:15
  Speaker 1: is talking about. AI is everywhere at the moment. But
• 2:20
  Speaker 1: can you say it? Yes, can you actually say AI?
• 2:26
  Speaker 1: I've noticed a lot of my students talking about AI
• 2:30
  Speaker 1: in English lessons. The topic comes up a lot, but
• 2:34
  Speaker 1: my students don't actually pronounce this correctly. So I think
• 2:38
  Speaker 1: this would be a good place to start. Just pronounce AI, right,
• 2:43
  Speaker 1: so A, I repeat it after me AI. But what
• 2:49
  Speaker 1: happens when those two letters or sounds are spoken one
• 2:54
  Speaker 1: after the other quickly, is it sounds like this AI? AI?
• 3:00
  Speaker 1: All right? AI otherwise known as AI. So we're talking
• 3:05
  Speaker 1: about AI artificial intelligence. So AI is everywhere, and we'll
• 3:10
  Speaker 1: only be more present in our lives in the future.
• 3:14
  Speaker 1: Of course, we're all aware of chat GPT, and a
• 3:17
  Speaker 1: lot of us use it all the time now, including me.
• 3:20
  Speaker 1: I use it as an English teacher, especially to generate
• 3:23
  Speaker 1: texts to present language in context. It's incredibly useful as
• 3:27
  Speaker 1: a way of generating example sentences in English, as well
• 3:30
  Speaker 1: as plenty of other interesting texts and things. So we're
• 3:37
  Speaker 1: all used to a certain amount of AI in our
• 3:40
  Speaker 1: lives now, and I should say that obviously chat GPT
• 3:43
  Speaker 1: is just one small example of the various forms of
• 3:48
  Speaker 1: AI that exist and will exist. But we're all used
• 3:52
  Speaker 1: to a certain amount of AI in our lives now,
• 3:55
  Speaker 1: and we're probably aware that there are various opinions of
• 3:58
  Speaker 1: AI going around, both positive and negative. Like with most technology,
• 4:03
  Speaker 1: it'll probably have positive and negative impacts. But I wonder
• 4:07
  Speaker 1: if we all really realize the full extent that AI
• 4:13
  Speaker 1: will change our world over the next few years and decades.
• 4:17
  Speaker 1: I mean, it's insane really if you look at the
• 4:20
  Speaker 1: different predictions, and without being alarmist or paranoid or anything,
• 4:27
  Speaker 1: we could be looking at deeply profound changes to everything
• 4:31
  Speaker 1: as a result of the continued development of AI. Alarmist,
• 4:36
  Speaker 1: if you are alarmist, it means that you are perhaps
• 4:41
  Speaker 1: taking a slightly panicked view of things, like you're raising
• 4:48
  Speaker 1: the alarm, ringing an alarm as if to say, this
• 4:51
  Speaker 1: is an emergency, this is really serious, when in fact
• 4:55
  Speaker 1: it's not that serious and it's not really an emergency.
• 4:58
  Speaker 1: So that's what it means. To be alarmist, is to
• 5:00
  Speaker 1: perhaps maybe overreact about something, and you know, overreact about
• 5:06
  Speaker 1: the seriousness or the level of emergency going on. So
• 5:13
  Speaker 1: without being alarmist or paranoid or anything, we could be
• 5:17
  Speaker 1: looking at deeply profound changes to everything as a result
• 5:20
  Speaker 1: of the continued development of AI. That's what this episode
• 5:24
  Speaker 1: is about. That's what I really want to talk about today.
• 5:28
  Speaker 1: And I was inspired to do this episode after I
• 5:30
  Speaker 1: watched an interview with one of the world's leading experts
• 5:34
  Speaker 1: on AI. I don't know. You might have seen it too.
• 5:38
  Speaker 1: It's a video that's been going around on YouTube. BBC
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  Speaker 1: News Night interview with what with who is described as
• 5:48
  Speaker 1: the Godfather of AI. So I found it absolutely fascinating
• 5:52
  Speaker 1: as well as quite disturbing. So it's an interview with
• 5:55
  Speaker 1: the Nobel Prize winning godfather of AI, Jeffrey Hinton. The
• 6:00
  Speaker 1: interview took place earlier this year on BBC Newsnight, which
• 6:04
  Speaker 1: is an evening news and current affairs TV show on
• 6:07
  Speaker 1: the BBC. The interview was then uploaded to the BBC
• 6:10
  Speaker 1: News Night YouTube channel this month October. That's the month
• 6:14
  Speaker 1: when I'm recording this, which is where I saw it.
• 6:18
  Speaker 1: So this is from the video description. This is a
• 6:20
  Speaker 1: description of the interview. So Jeffrey Hinton, former vice president
• 6:25
  Speaker 1: of Google and sometimes referred to as the Godfather of AI,
• 6:30
  Speaker 1: has recently won the twenty twenty four Nobel Physics Prize.
• 6:34
  Speaker 1: So this guy used to be vice president of Google
• 6:40
  Speaker 1: and is or has been referred to as the godfather
• 6:44
  Speaker 1: of AI. So this is a person who is definitely
• 6:48
  Speaker 1: an expert in this subject. He recently won the twenty
• 6:51
  Speaker 1: twenty four Nobel Physics Prize. He resigned from Google in
• 6:55
  Speaker 1: twenty twenty three, he resigned. That means he quit his job.
• 7:00
  Speaker 1: I'm not sure exactly the reasons why, but he stepped
• 7:02
  Speaker 1: down from his position at Google last year and has
• 7:07
  Speaker 1: warned about the dangers of machines that could outsmart humans. Okay,
• 7:14
  Speaker 1: by the way, if you're looking at the PDF, you'll
• 7:16
  Speaker 1: see that some words and phrases are highlighted in a
• 7:18
  Speaker 1: sort of orange highlight highlight highlighter. And these are bits
• 7:24
  Speaker 1: of vocab that I'll be explaining as I go along,
• 7:27
  Speaker 1: and which are summarized as well at the end of
• 7:29
  Speaker 1: the episode. So he resigned from Google in twenty twenty
• 7:35
  Speaker 1: three and has warned about the dangers of machines that
• 7:38
  Speaker 1: could outsmart humans. So to outsmart humans means to be
• 7:43
  Speaker 1: more intelligent than humans. So he's talking about the danger,
• 7:47
  Speaker 1: the possible, the possibility that machines or ai in the
• 7:51
  Speaker 1: future could become more intelligent than humans, could outsmart humans.
• 7:56
  Speaker 1: So let me summarize the video and then go through
• 7:59
  Speaker 1: the sco of the interview, giving my explanations and comments,
• 8:03
  Speaker 1: and of course explaining and highlighting certain bits of vocabulary.
• 8:07
  Speaker 1: If there's time, I'll do a vocabulary recap at the end,
• 8:10
  Speaker 1: and certainly on the PDF you'll see a full vocabulary
• 8:13
  Speaker 1: list with definitions, examples and comments. So that BBC Newsnight
• 8:21
  Speaker 1: interview is available on YouTube. I'm not going to play
• 8:24
  Speaker 1: the audio of the interview directly in this episode, but
• 8:27
  Speaker 1: I can give you a summary of the video, and
• 8:30
  Speaker 1: I will read from the transcript of the video. So
• 8:33
  Speaker 1: here's a summary of the video. It's this, Basically, experts
• 8:37
  Speaker 1: believe AI will exceed human intelligence in five to twenty years,
• 8:43
  Speaker 1: raising concerns about potential control and existential threats. Okay, so
• 8:50
  Speaker 1: experts believe that AI will exceed human intelligence in five
• 8:53
  Speaker 1: to twenty years. It'll exceed human intelligence, meaning it will
• 8:57
  Speaker 1: it will become greater than right, it'll go higher. Human
• 9:02
  Speaker 1: intelligence is here at this level, and AI will get
• 9:06
  Speaker 1: to a higher level than that. It will exceed human
• 9:09
  Speaker 1: intelligence in up to twenty years, raising concerns, meaning making
• 9:15
  Speaker 1: people concerned. So if you raise concerns about something, it
• 9:18
  Speaker 1: means that people's concerns or worries about a certain subject
• 9:24
  Speaker 1: are raised. They go up. Right, to raise something means
• 9:28
  Speaker 1: to make something go up. Rise means go up. So
• 9:31
  Speaker 1: people's concerns rise, and this information raises people's concerns about
• 9:37
  Speaker 1: potential control and existential threats. Potential means it's possible, it
• 9:43
  Speaker 1: could happen in the future. It's something that is that's
• 9:48
  Speaker 1: a possibility for the future. So potential threats threats that
• 9:51
  Speaker 1: could happen in the future. We're talking about potential control
• 9:55
  Speaker 1: threats meaning whether we will be able to control this technology.
• 10:01
  Speaker 1: And existential threats meaning threats to our very existence as
• 10:06
  Speaker 1: the human race, which sounds a little bit what's the
• 10:09
  Speaker 1: word for it, As I said, paranoid, may be alarmist,
• 10:12
  Speaker 1: but actually when you hear what this man has to say,
• 10:16
  Speaker 1: you realize that it's actually quite reasonable and quite rational
• 10:20
  Speaker 1: to have these fears about these threats potential dangers. So
• 10:27
  Speaker 1: here are the highlights of what was said in the interview,
• 10:31
  Speaker 1: and I will expand on these things later in the episode,
• 10:34
  Speaker 1: but here's a general overview of the main points. So,
• 10:38
  Speaker 1: first of all, AI is expected to exceed human intelligence soon.
• 10:42
  Speaker 1: As I said, many experts are concerned about the existential
• 10:47
  Speaker 1: threat posed by advanced AI. The existential threat posed by
• 10:52
  Speaker 1: advanced AI that AI represents, or is or poses, presents
• 10:59
  Speaker 1: a big threat to human civilization. Our very existence is
• 11:04
  Speaker 1: threatened by this so it poses an existential threat. Large
• 11:10
  Speaker 1: language models may operate similarly to human brains. So when
• 11:15
  Speaker 1: we talk about large language models, we're talking about things
• 11:17
  Speaker 1: like chat GPT, which are forms of AI that are
• 11:22
  Speaker 1: based on in putting massive amounts of language data into
• 11:29
  Speaker 1: the system. Right, So essentially, chat GPT is a large,
• 11:34
  Speaker 1: large language model. The basis of its intelligence is language.
• 11:38
  Speaker 1: That it is fed massive samples of language, which it
• 11:44
  Speaker 1: then processes, and this is the basis of its intelligence.
• 11:48
  Speaker 1: Soe it's difficult to understand that. I find that really
• 11:52
  Speaker 1: complicated to understand, but I also find it absolutely fascinating
• 11:56
  Speaker 1: Because of this, large language models like chat GPT may
• 11:59
  Speaker 1: operate similarly to human brains. So this is essentially the
• 12:04
  Speaker 1: idea that something like chat GPT and the human brain
• 12:09
  Speaker 1: might actually operate in a really similar way because maybe
• 12:15
  Speaker 1: language is the key to understanding the way that human
• 12:19
  Speaker 1: minds or human brains process things. That language is absolutely
• 12:24
  Speaker 1: central to our intelligence. And if you create a machine
• 12:28
  Speaker 1: that sort of is based on language, that uses its
• 12:33
  Speaker 1: ability to process language as the core of its intelligence,
• 12:38
  Speaker 1: you might end up with something that's very similar to
• 12:40
  Speaker 1: a human brain, which I find fascinating. We continue, so
• 12:46
  Speaker 1: the risk of AI taking autonomous lethal actions is significant,
• 12:52
  Speaker 1: which is rather terrifying. So the risk of artificial intelligence
• 12:57
  Speaker 1: taking autonomous legal at not legal autonomous lethal actions. So
• 13:03
  Speaker 1: lethal actions are actions which will kill people, So lethal
• 13:10
  Speaker 1: refers to something that could kill So lethal actions are
• 13:15
  Speaker 1: things that could kill people, and autonomous legal lethal actions.
• 13:19
  Speaker 1: Autonomous means that the AI chooses to do it on
• 13:22
  Speaker 1: its own right. It doesn't need to be told to
• 13:26
  Speaker 1: do it. It just kind of does it on its own,
• 13:28
  Speaker 1: or maybe does it as a consequence of some other
• 13:31
  Speaker 1: instruction or order that it's being given. But basically we're
• 13:34
  Speaker 1: talking about AI choosing to kill people and the risk
• 13:38
  Speaker 1: of this is significant, significant meaning sort of large in
• 13:44
  Speaker 1: its importance. Okay, so there's there's actually a large chance
• 13:50
  Speaker 1: that that AI could actually decide to kill humans. Again,
• 13:57
  Speaker 1: sounds paranoid and alarmist, but you know, just wait and see.
• 14:01
  Speaker 1: So international regulation of military AI applications is lacking. So
• 14:08
  Speaker 1: international regulation this is basically applying rules to control things,
• 14:13
  Speaker 1: so you know, different jurisdictions global or local. You know,
• 14:19
  Speaker 1: whether it's like EU law or some sort of global convention.
• 14:25
  Speaker 1: These This is what we mean by regulation. So systems
• 14:29
  Speaker 1: of rules and laws to control things. So international regulation
• 14:33
  Speaker 1: of military AI. This is artificial intelligence used in military applications.
• 14:39
  Speaker 1: So when we say applications, that means not applications on
• 14:41
  Speaker 1: your phone, but in uses the ways in which in
• 14:46
  Speaker 1: this case, AI is used, So that's military applications that
• 14:50
  Speaker 1: would be using AI for military purposes. So regulation of
• 14:55
  Speaker 1: this is lacking. So there's not enough control or regulation
• 14:59
  Speaker 1: that would limit the application or use of AI in
• 15:04
  Speaker 1: military situations. This, this is one of the most terrifying
• 15:09
  Speaker 1: aspects of this. Also, AI could widen the wealth gap, right,
• 15:16
  Speaker 1: the gap This is the gap between which the rich
• 15:19
  Speaker 1: and the poor. Okay, that the fact that there's you know,
• 15:23
  Speaker 1: a huge difference, a huge disparity between those people who
• 15:28
  Speaker 1: are rich and those people who are poor. And I
• 15:32
  Speaker 1: mean really the way it works is that there's an
• 15:34
  Speaker 1: increasingly small number of people who are becoming increasingly rich.
• 15:39
  Speaker 1: So you get you get to a situation where there's
• 15:42
  Speaker 1: a kind of a small minority, very small minority, who
• 15:46
  Speaker 1: have most of the money, and then all the other people,
• 15:51
  Speaker 1: relatively speaking, have very little money. So you end up
• 15:55
  Speaker 1: with this big gap between poor people and rich people.
• 15:59
  Speaker 1: So there is this huge imbalance in society so AI
• 16:03
  Speaker 1: could widen this gap, make it wider, make it bigger,
• 16:07
  Speaker 1: affecting society negatively. And plumbing may be one of the
• 16:13
  Speaker 1: safest jobs in the Age of AI. Plumbing refers to
• 16:19
  Speaker 1: all of the systems in someone's home, normally a home
• 16:22
  Speaker 1: or a building or something. All the systems that deal
• 16:25
  Speaker 1: with water, So all the pipes in your kitchen, all
• 16:29
  Speaker 1: the pipes in your bathroom that transfer water around your apartment,
• 16:34
  Speaker 1: something as basic and mechanical as that. So that's plumbing.
• 16:40
  Speaker 1: This might be actually one of the safest jobs that
• 16:43
  Speaker 1: you could choose to do in the future in the
• 16:45
  Speaker 1: Age of AI. Okay, so this is it's pretty serious stuff. Okay,
• 16:51
  Speaker 1: it's pretty serious stuff. It's pretty fascinating stuff. It's slightly disturbing.
• 16:56
  Speaker 1: Let's get into the interview transcript. Okay. So now I'll
• 16:59
  Speaker 1: read out the trans script of the interview. Listen and follow,
• 17:02
  Speaker 1: and I'll go through this word by word afterwards. So
• 17:05
  Speaker 1: first of all, let's just let me just read through
• 17:07
  Speaker 1: the script. I'll kind of reconstruct the interview. See if
• 17:11
  Speaker 1: you can follow it all. And yeah, we'll be going
• 17:14
  Speaker 1: through this script line by line later in the episode. Okay,
• 17:19
  Speaker 1: So let's get let's get started. So here's the transcript.
• 17:22
  Speaker 1: So Faisal Islam, that's the interviewer talks to Jeffrey Hinton
• 17:27
  Speaker 1: about the threat of AI on BBC News Night. That
• 17:31
  Speaker 1: doesn't mean the fact that AI could be a threat
• 17:34
  Speaker 1: to BBC Newsnight. This just means that the interview was
• 17:37
  Speaker 1: on BBC News Night. I think you understand right, So
• 17:40
  Speaker 1: here we go. This is this is where the script begins.
• 17:43
  Speaker 1: So I began by asking Jeffrey Hinton whether he thought
• 17:47
  Speaker 1: the world is getting to grips with the issue of
• 17:50
  Speaker 1: AI or if he's as concerned as ever. So this
• 17:54
  Speaker 1: is Jeffrey Hinton. I'm still as concerned as I have been,
• 17:58
  Speaker 1: but I'm very pleased that the world is beginning to
• 18:00
  Speaker 1: take it seriously. In particular, they're beginning to take the
• 18:04
  Speaker 1: existential threat seriously. That these things will get smarter than
• 18:08
  Speaker 1: us and we have to worry about whether they'll want
• 18:11
  Speaker 1: to take control away from us. That's something we should
• 18:15
  Speaker 1: think seriously about. And people now take that seriously. A
• 18:20
  Speaker 1: few years ago they thought it was just science fiction.
• 18:24
  Speaker 1: And the interviewer, and from your perspective, from having worked
• 18:28
  Speaker 1: at the top of this, having developed some of the
• 18:31
  Speaker 1: theories underpinning all of this explosion in AI that we're seeing.
• 18:36
  Speaker 1: That existential threat is real. Yes, so some people think
• 18:42
  Speaker 1: these things don't really understand they're very different from us,
• 18:46
  Speaker 1: they're just using some statistical tricks. That's not the case.
• 18:51
  Speaker 1: These big language models, for example, the early ones, were
• 18:55
  Speaker 1: developed as a theory of how the brain understands language,
• 19:00
  Speaker 1: the best theory we've currently got of how the brain
• 19:03
  Speaker 1: understands language. We don't really understand either how they work
• 19:08
  Speaker 1: or how the brain works in detail, but we think
• 19:11
  Speaker 1: probably they work in fairly similar ways. What is it
• 19:16
  Speaker 1: that's triggered your concern. It's been a combination of two things.
• 19:21
  Speaker 1: Playing with the large chatbots, particularly one at Google before
• 19:25
  Speaker 1: chat GPT four, but also with GPT four. They're clearly
• 19:29
  Speaker 1: very competent. They clearly understand a lot, They have a
• 19:33
  Speaker 1: lot more knowledge than any person. They're like a not
• 19:36
  Speaker 1: very good expert at more or less everything. So that
• 19:40
  Speaker 1: was one worry, and the second was coming to understand
• 19:44
  Speaker 1: the way in which they're a superior form of intelligence.
• 19:47
  Speaker 1: Because you can make many copies of the same neural network.
• 19:51
  Speaker 1: Each copy can look at a different bit of data,
• 19:55
  Speaker 1: and then they can all share what they've learned. So
• 19:58
  Speaker 1: imagine if we had ten thousand people. They could all
• 20:01
  Speaker 1: go off and do a degree in something, share what
• 20:05
  Speaker 1: they'd learned efficiently, and then we'd all have ten thousand degrees.
• 20:09
  Speaker 1: We'd know a lot. Then we can't share knowledge nearly
• 20:13
  Speaker 1: as efficiently as different copies of the same neural network can.
• 20:18
  Speaker 1: So the key concern here is that it could exceed
• 20:22
  Speaker 1: human intelligence, indeed massively exceed human intelligence. Very few of
• 20:27
  Speaker 1: the experts are in doubt about that. Almost everybody I
• 20:32
  Speaker 1: know who's an expert on AI believes that they will
• 20:35
  Speaker 1: exceed human intelligence. It's just a question of when. And
• 20:40
  Speaker 1: at that point it's really quite difficult to control them. Well,
• 20:44
  Speaker 1: we don't know. We've never dealt with something like this before.
• 20:48
  Speaker 1: There are a few experts, like my friend Jan Lucun,
• 20:52
  Speaker 1: who thinks it'll be no problem. We'll give them the
• 20:55
  Speaker 1: goals and it'll be no problem. They'll do what we say,
• 20:58
  Speaker 1: they'll be subservient to us. There are other experts who
• 21:02
  Speaker 1: think absolutely they'll take control. Given this big spectrum of opinions,
• 21:09
  Speaker 1: I think it's wise to be cautious. I think there's
• 21:12
  Speaker 1: a chance they'll take control, and it's a significant chance.
• 21:16
  Speaker 1: It's not like one percent, it's much more Interviewer. Could
• 21:22
  Speaker 1: they not be contained in certain areas like scientific research,
• 21:26
  Speaker 1: but not, for example, the armed forces? Maybe? But actually,
• 21:31
  Speaker 1: if you look at all the current legislation, including the
• 21:34
  Speaker 1: European legislation, there's a little clause in all of it
• 21:38
  Speaker 1: that says that none of this applies to military applications.
• 21:42
  Speaker 1: Governments aren't willing to restrict their own uses of it
• 21:45
  Speaker 1: for defense. Interviewer. I mean there's been some evidence, even
• 21:50
  Speaker 1: in current conflicts, of the use of AI in generating
• 21:53
  Speaker 1: thousands and thousands of targets. Yes, I mean that's happened
• 21:58
  Speaker 1: since I mean, I mean that's happened since you started
• 22:01
  Speaker 1: warning about AI. Is that the sort of pathway that
• 22:04
  Speaker 1: you're concerned about? I mean, that's the thin end of
• 22:09
  Speaker 1: the wedge. What I'm most concerned about is when these
• 22:12
  Speaker 1: things can autonomously make the decision to kill people. So
• 22:17
  Speaker 1: robot soldiers, yeah, those or drones and the like. And
• 22:23
  Speaker 1: it may be we can get something like the Geneva
• 22:25
  Speaker 1: Conventions to regulate them, but I don't think that's going
• 22:29
  Speaker 1: to happen until after very nasty things have happened. There's
• 22:33
  Speaker 1: an analogy here with the Manhattan Project and with Oppenheimer.
• 22:38
  Speaker 1: If we restrain ourselves from military use in the G
• 22:41
  Speaker 1: seven advanced democracies, what's going on in China, what's going
• 22:45
  Speaker 1: on in Russia? Interviewer, Yes, it has to be an
• 22:50
  Speaker 1: international agreement. But if you look at chemical weapons, the
• 22:54
  Speaker 1: International Agreement for chemical weapons has worked quite well. I mean,
• 22:59
  Speaker 1: do you have any of whether the shackles are off
• 23:01
  Speaker 1: in a place like Russia. Well, Putin said some years
• 23:06
  Speaker 1: ago that whoever controls AI controls the world. So I
• 23:10
  Speaker 1: imagine they're working very hard. The West is probably well
• 23:15
  Speaker 1: ahead of them in research. We're probably still slightly ahead
• 23:19
  Speaker 1: of China, but China is putting more resources in in
• 23:23
  Speaker 1: terms of military uses of AI. I think there's going
• 23:26
  Speaker 1: to be a race. Interviewer. It sounds very theoretical, this argument,
• 23:33
  Speaker 1: this thread of argument, But you really are quite worried
• 23:37
  Speaker 1: about extinction level events. We should distinguish these different risks.
• 23:43
  Speaker 1: The risk of using AI for autonomous lethal weapons doesn't
• 23:47
  Speaker 1: depend on AI being smarter than us. That's a quite
• 23:50
  Speaker 1: separate risk from the risk that the AI itself will
• 23:54
  Speaker 1: go rogue and try to take over. I'm worried about
• 23:58
  Speaker 1: both things. The autonomous weapons are clearly going to come,
• 24:02
  Speaker 1: but whether AI goes rogue and tries to take over
• 24:06
  Speaker 1: is something we may be able to control or we
• 24:09
  Speaker 1: may not. We don't know, and so at this point,
• 24:13
  Speaker 1: before it's more intelligent than us, we should be putting
• 24:16
  Speaker 1: huge resources into seeing whether we're going to be able
• 24:20
  Speaker 1: to control it. Interviewer, what sort of society do you
• 24:24
  Speaker 1: see evolving? Which jobs will still be here? Yes, I'm
• 24:29
  Speaker 1: very worried about AI taking over lots of mundane jobs,
• 24:33
  Speaker 1: and that should be a good thing. It's going to
• 24:36
  Speaker 1: lead to a big increase in productivity, which leads to
• 24:39
  Speaker 1: a big increase in wealth, and if that wealth was
• 24:43
  Speaker 1: equally distributed, that would be great, but it's not going
• 24:46
  Speaker 1: to be in the systems we live in. That wealth
• 24:50
  Speaker 1: is going to go to the rich, not to the
• 24:52
  Speaker 1: people whose jobs get lost, and that's going to be
• 24:55
  Speaker 1: very bad for society, I believe. So it's going to
• 24:59
  Speaker 1: increase the gap between rich and poor, which increases the
• 25:02
  Speaker 1: chances of right wing populists getting elected. Interviewer, So, to
• 25:07
  Speaker 1: be clear, you think that the societal impacts from the
• 25:11
  Speaker 1: changes in jobs could be so profound that we may
• 25:14
  Speaker 1: need to rethink the politics of I don't know the
• 25:17
  Speaker 1: benefits system inequality absolutely, universal basic income, yes, I certainly
• 25:25
  Speaker 1: believe in universal basic income. I don't think that's enough though,
• 25:29
  Speaker 1: because a lot of people get their self respect from
• 25:31
  Speaker 1: the job they do, and if you put everybody on
• 25:34
  Speaker 1: universal basic income, that solves the problem of them starving
• 25:39
  Speaker 1: and not being able to pay the rent, but it
• 25:41
  Speaker 1: doesn't solve the self respect problem. So what you just
• 25:46
  Speaker 1: try to the government needs to get involved. I mean,
• 25:51
  Speaker 1: it's not how we do things in Britain. We tend
• 25:54
  Speaker 1: to sort of stand back and let the economy decide
• 25:57
  Speaker 1: the winners and losers. Yes. Actually I was consulted by
• 26:02
  Speaker 1: people in Downing Street and I advised them that universal
• 26:06
  Speaker 1: basic income was a good idea interviewer, And there is
• 26:10
  Speaker 1: you said ten to twenty percent risk of them taking over.
• 26:14
  Speaker 1: Are you more certain that this is going to have
• 26:16
  Speaker 1: to be addressed in the next five years next parliament? Perhaps?
• 26:21
  Speaker 1: My guess is between five and twenty years from now
• 26:24
  Speaker 1: there's a probability of about half that we'll have to
• 26:27
  Speaker 1: confront the problem of them trying to take over. Interviewer.
• 26:31
  Speaker 1: Are you particularly impressed by the efforts of governments so
• 26:35
  Speaker 1: far to try and rain this in. I'm impressed by
• 26:39
  Speaker 1: the fact that they're beginning to take it seriously. I'm
• 26:42
  Speaker 1: unimpressed by the fact that none of them is willing
• 26:45
  Speaker 1: to regulate military uses, and I'm unimpressed by the fact
• 26:48
  Speaker 1: that most of the regulations have no teeth. Interviewer. Do
• 26:53
  Speaker 1: you think that the tech companies are letting down their
• 26:55
  Speaker 1: guard on safety because they need to be the winner
• 26:59
  Speaker 1: in this race? For AI? I don't know about the
• 27:02
  Speaker 1: tech companies in general. I know quite a lot about
• 27:05
  Speaker 1: Google because I used to work there. Google was very
• 27:08
  Speaker 1: concerned about these issues, and Google didn't release the big chatbots.
• 27:12
  Speaker 1: It was concerned about its reputation if they told lies.
• 27:16
  Speaker 1: But as soon as open ai went into business with
• 27:19
  Speaker 1: Microsoft and Microsoft put chatbots into being, Google had no choice.
• 27:25
  Speaker 1: So I think the competition is going to cause these
• 27:27
  Speaker 1: things to be developed rapidly, and the competition means that
• 27:30
  Speaker 1: they won't put enough effort into safety. Interviewer. People parents
• 27:37
  Speaker 1: talk to their children, give them advice on the future
• 27:40
  Speaker 1: of the economy, what jobs they should do, what degrees
• 27:43
  Speaker 1: they should do. It seems like the world is being
• 27:46
  Speaker 1: thrown up in the air by this by the world
• 27:50
  Speaker 1: that you're describing. What would you advise somebody to study
• 27:53
  Speaker 1: now to kind of surf this wave. I don't know,
• 27:57
  Speaker 1: because it's clear that a lot of mid level into
• 28:00
  Speaker 1: actual jobs are going to disappear. And if you ask
• 28:03
  Speaker 1: which jobs are safe, my best bet about a job
• 28:06
  Speaker 1: that's safe is plumbing. Because these things aren't yet very
• 28:10
  Speaker 1: good at physical manipulation. That will probably be the last
• 28:15
  Speaker 1: thing they're very good at. And driving. What about driving? No, driving, No,
• 28:20
  Speaker 1: that's hopeless. That's been slower than expected, but it's going
• 28:24
  Speaker 1: to go. Journalism might last a little bit longer. But
• 28:28
  Speaker 1: I think these things are going to be pretty good
• 28:29
  Speaker 1: journalists quite soon, and probably quite good interviewers too. Okay, well,
• 28:35
  Speaker 1: thank you very much for your time. You're welcome. Right,
• 28:40
  Speaker 1: So there you go. That was the script of the
• 28:42
  Speaker 1: interview itself. Now I will go through that transcript word
• 28:46
  Speaker 1: by word in a moment, helping you to understand everything.
• 28:50
  Speaker 1: But first let's look at the key insights from the interview.
• 28:54
  Speaker 1: So this is another summary, let's say, of the main
• 28:57
  Speaker 1: points of the interview, but just expanded slightly with some
• 29:00
  Speaker 1: of my thoughts and comments. Excuse me while I drink
• 29:02
  Speaker 1: some tea. Excuse me, well I drink some tea. Excuse me,
• 29:09
  Speaker 1: little Jimmy Hendrix moment. Okay, So here are those key
• 29:13
  Speaker 1: insights from the interview. First of all, impending intelligence shift
• 29:17
  Speaker 1: shift here, meaning a movement, obviously a movement from humans
• 29:22
  Speaker 1: to AI. Most experts agree that AI will surpass human
• 29:26
  Speaker 1: intelligence in the coming years, highlighting the urgency or importance
• 29:31
  Speaker 1: of understanding its implications. Super Intelligent AI might be a
• 29:35
  Speaker 1: threat to us, or it might not, but since there's
• 29:38
  Speaker 1: potentially a fifty to fifty chance of it being dangerous
• 29:41
  Speaker 1: to us, it would be wise to be cautious about it.
• 29:45
  Speaker 1: How and why would it be a threat to us
• 29:48
  Speaker 1: is a question. Now. It might not be the sci
• 29:50
  Speaker 1: fi version, which is basically like what we've seen in
• 29:53
  Speaker 1: the movies. You know, AI suddenly decides that humans must
• 29:56
  Speaker 1: die for some reason. So it might not be that version,
• 29:59
  Speaker 1: but A could still destabilize our society severely in a
• 30:04
  Speaker 1: variety of ways. Control challenges. As AI becomes more competent,
• 30:11
  Speaker 1: controlling its actions may become increasingly difficult, raising ethical and
• 30:15
  Speaker 1: existential questions. So this is the classic science fiction storyline
• 30:20
  Speaker 1: that we've seen in films like The Terminator and the matrix,
• 30:24
  Speaker 1: the AI becomes self aware and can't be controlled, and
• 30:28
  Speaker 1: then it sees humans as a threat to its existence,
• 30:32
  Speaker 1: or perhaps it identifies humanity as a problem that needs
• 30:35
  Speaker 1: to be solved. It's kind of like the Avengers Age
• 30:38
  Speaker 1: of Ultron situation. Tony Stark develops a form of artificial
• 30:44
  Speaker 1: intelligence as a tool to help protect the earth, but
• 30:50
  Speaker 1: for some reason, it becomes evil and decides that humanity
• 30:55
  Speaker 1: needs to be stopped. Right, to be fair, maybe that
• 31:01
  Speaker 1: will happen. Maybe superintelligent AI will see humans as a
• 31:05
  Speaker 1: problem that needs to be solved. Continuing the PDF to
• 31:09
  Speaker 1: be fair, over population and human actions are probably largely
• 31:13
  Speaker 1: to blame for environmental and societal collapse, So who knows.
• 31:19
  Speaker 1: Maybe AI will consider us to be a threat and
• 31:22
  Speaker 1: will take action to stop us destroying everything. Perhaps it
• 31:26
  Speaker 1: will be like Thanos from Adventures Infinity War and will
• 31:29
  Speaker 1: decide that half of humanity needs to be wiped out
• 31:32
  Speaker 1: in order to guarantee the survival of the human race
• 31:34
  Speaker 1: as a whole. I mean, who knows. But as I said,
• 31:37
  Speaker 1: it might not be as simple as AI choosing to
• 31:39
  Speaker 1: kill all humans. It might be that human life is
• 31:42
  Speaker 1: put in danger as a consequence of the things AI does,
• 31:46
  Speaker 1: especially if we can't control it, cognitive parallels cognitive meaning
• 31:52
  Speaker 1: relating to the way the mind works, parallels meaning similar things.
• 31:58
  Speaker 1: The structure of large language model like chat GPT mirrors
• 32:02
  Speaker 1: cognitive processes in the human brain, so mirrors it. It's
• 32:06
  Speaker 1: like very similar to it reflects it's almost exactly the
• 32:10
  Speaker 1: same as cognitive processes or thinking processes in the human brain,
• 32:15
  Speaker 1: suggesting they may possess unique forms of understanding. So maybe
• 32:19
  Speaker 1: even AI large language models like this might might have
• 32:25
  Speaker 1: quite a profound understanding of how we think and what
• 32:30
  Speaker 1: it's like to be human. Maybe our ability to process
• 32:35
  Speaker 1: languages is central to how our intelligence works, and making
• 32:39
  Speaker 1: AI that processes language the same way as we do
• 32:42
  Speaker 1: is a way of kind of reverse engineering human like intelligence. Basically,
• 32:47
  Speaker 1: perhaps AI will think just like humans do. Maybe it
• 32:52
  Speaker 1: will be a lot more similar to humans than we realize.
• 32:57
  Speaker 1: We could be looking at intelligences that opera in a
• 33:00
  Speaker 1: similar way to human minds, but with much greater capacity
• 33:04
  Speaker 1: for complex decision making, multitasking, memorizing, processing, and calling upon
• 33:10
  Speaker 1: a much wider resource of knowledge that most than most
• 33:15
  Speaker 1: human individuals can have in their brains. So these things
• 33:19
  Speaker 1: could think just like humans, but with a way greater
• 33:23
  Speaker 1: capacity for everything, memorizing, drawing upon collective knowledge, processing things quickly, multitasking,
• 33:34
  Speaker 1: you know, sort of superhuman stuff. Then we move on
• 33:39
  Speaker 1: to the autonomous weapons risk, So the potential for AI
• 33:42
  Speaker 1: to autonomously make lethal decisions that means choosing to do
• 33:47
  Speaker 1: it on its own poses or presents a severe risk
• 33:53
  Speaker 1: necessitating immediate regulatory attention. So this means that we really
• 33:58
  Speaker 1: need to start stepping in and making global laws that
• 34:03
  Speaker 1: limit the use of AI in military conflicts. And it's
• 34:09
  Speaker 1: obvious why, right, I mean, we're looking at an arms race,
• 34:13
  Speaker 1: just like you know, just like the old fashioned arms
• 34:17
  Speaker 1: race of the Cold War, the space race and all
• 34:20
  Speaker 1: that stuff. But you know there's a significant risk of
• 34:30
  Speaker 1: you know, because any regulations about AI, they always have
• 34:36
  Speaker 1: the clause in the law that sort of say, actually,
• 34:40
  Speaker 1: but none of these rules apply to military AI because
• 34:44
  Speaker 1: everyone is so concerned about defending themselves or trying to
• 34:49
  Speaker 1: compete with rivals on Earth who might be developing the
• 34:56
  Speaker 1: technology more quickly, because no one wants to be in
• 34:59
  Speaker 1: a situation where suddenly they're faced with an enemy who's
• 35:02
  Speaker 1: got more sophisticated artificial intelligence. And using it for military purposes,
• 35:07
  Speaker 1: because then you know, they take over the world, right,
• 35:10
  Speaker 1: But we need to find a way for Earth to
• 35:13
  Speaker 1: agree on a set of regulations, a bit like the
• 35:16
  Speaker 1: way we've done for chemical weapons. There are international laws
• 35:20
  Speaker 1: that regulate the use of chemical weapons. There are, bizarrely,
• 35:24
  Speaker 1: there are laws and rules for war. You know, there
• 35:29
  Speaker 1: are such things as war crimes. War is a defined thing,
• 35:33
  Speaker 1: and we try to make sure that nations, even when
• 35:38
  Speaker 1: they're fighting against each other, comply with these laws as
• 35:43
  Speaker 1: much as possible. And in a sense, when a nation
• 35:47
  Speaker 1: breaks those laws, they instantly become the bad guy. You know,
• 35:52
  Speaker 1: it's interesting the way that nations wage war and take
• 35:56
  Speaker 1: military actions, and they're constantly sort of managing their position
• 36:04
  Speaker 1: in the conflict and trying not to be the bad guy,
• 36:07
  Speaker 1: or trying not to prove that they are let other
• 36:10
  Speaker 1: people prove that they're the bad guy in the situation,
• 36:13
  Speaker 1: and certainly breaking certain international laws that regulate military conflicts,
• 36:19
  Speaker 1: that's a definite sign that if you're doing that, then
• 36:23
  Speaker 1: you're you know, you're probably the bad guy in the
• 36:26
  Speaker 1: situation anyway. So we need to take immediately immediate action
• 36:32
  Speaker 1: in terms of making regulations, laws that limit the use
• 36:37
  Speaker 1: of AI in military situations. So, continuing the PDF, AI
• 36:43
  Speaker 1: is definitely being used for military purposes, and it's already
• 36:46
  Speaker 1: being used, and this is terrifying when you consider how
• 36:50
  Speaker 1: efficient and effective it could be. And that's relating to
• 36:58
  Speaker 1: not just AI being like Ultron from the Avengers movie,
• 37:06
  Speaker 1: which is a form of technology that becomes self aware,
• 37:08
  Speaker 1: like Ultron or Skynet in the Terminator films, that it
• 37:14
  Speaker 1: becomes self aware and then decides to have a robot
• 37:17
  Speaker 1: versus humans war. Rather, it's regular making sure that humans
• 37:24
  Speaker 1: controlling AI don't aren't able to use that AI to
• 37:29
  Speaker 1: wipe out their enemies in extremely efficient and quite horrifying ways.
• 37:37
  Speaker 1: What a jolly subject for an episode of the podcast.
• 37:40
  Speaker 1: Perhaps I'll go back to telling terrible jokes in the
• 37:43
  Speaker 1: next episode, but you know, I just feel like I
• 37:45
  Speaker 1: had to talk about this. I watched this video and
• 37:47
  Speaker 1: I was just like stunned by it. I mean, all
• 37:51
  Speaker 1: this sort of thing has been in my mind, and
• 37:53
  Speaker 1: I'm sure that you're the same that you read these things,
• 37:55
  Speaker 1: and it's just kind of like shocking that the things
• 37:58
  Speaker 1: that they're talking about the stuff that's normally just reserved
• 38:02
  Speaker 1: for science fiction. You think, oh, it's just a kind
• 38:05
  Speaker 1: of a dramatic science fiction story. But this is this
• 38:09
  Speaker 1: stuff is becoming alarmingly close to reality. Let's move on
• 38:14
  Speaker 1: to the next point, which is global regulation gaps, which
• 38:17
  Speaker 1: is what I've been talking about. Current international agreements lack enforceability.
• 38:23
  Speaker 1: To enforce law means to basically apply law and actually
• 38:27
  Speaker 1: make it work. For example, the police can enforce law,
• 38:30
  Speaker 1: or courts can enforce law, essentially finding people guilty of
• 38:37
  Speaker 1: crimes if these laws are broken. So this is this
• 38:40
  Speaker 1: is how we talk about the enforceability enforcing law, applying
• 38:44
  Speaker 1: law in the real world. So, current international agreements lack
• 38:48
  Speaker 1: enforceability regarding military uses of AI. So it's basically the
• 38:53
  Speaker 1: the the laws are not quite strong enough or clear
• 38:57
  Speaker 1: enough to be able to actually control people's behavior, potentially
• 39:02
  Speaker 1: leading to unchecked advancements by nations. Right unchecked meaning unregulated.
• 39:11
  Speaker 1: The idea of humans controlling lethal AI with no regulations
• 39:15
  Speaker 1: is more frightening than the idea of AI autonomously trying
• 39:18
  Speaker 1: to wipe out humans. It's more of a realistic concern
• 39:22
  Speaker 1: that one side or one faction in a situation would
• 39:26
  Speaker 1: suddenly get their hands on extremely advanced and sophisticated AI weaponry,
• 39:33
  Speaker 1: and they would use it with devastating consequences, just like
• 39:38
  Speaker 1: in the nuclear age. There is already an arms race
• 39:41
  Speaker 1: underway involving AI, in which competing power blocks are racing
• 39:46
  Speaker 1: to develop lethal AI systems to prevent one faction having supremacy.
• 39:51
  Speaker 1: The result could be that AI becomes armed, dangerous, fully developed,
• 39:56
  Speaker 1: and poised to wreak havoc. There's a happy thought for you. Also,
• 40:01
  Speaker 1: economic disparities. So it's not just sky net and terminators
• 40:06
  Speaker 1: being sent back from the future to kill housewives or whatever.
• 40:10
  Speaker 1: We're talking about economic effects as well, right, which could
• 40:14
  Speaker 1: be I mean, is likely to be just as damaging.
• 40:19
  Speaker 1: So the rise of AI threatens to deepen existing economic equalities.
• 40:24
  Speaker 1: So economic inequalities. Did I say inequalities? I think so.
• 40:29
  Speaker 1: So existing economic inequalities. So again talking about the gap
• 40:33
  Speaker 1: between the rich and the poor, the haves and the
• 40:36
  Speaker 1: have nots. The rise of AI is not going to
• 40:39
  Speaker 1: balance this out. In fact, it's going to exacerbate it.
• 40:43
  Speaker 1: It's likely to make this much worse, prompting considerations for
• 40:48
  Speaker 1: universal basic income and societal reformation. Universal basic income is
• 40:54
  Speaker 1: essentially that everyone gets given a certain amount of money
• 40:57
  Speaker 1: to live on. Everyone is given even if they don't
• 41:01
  Speaker 1: have work. Everyone is provided with enough money to help
• 41:04
  Speaker 1: them survive. Right. This is the concept of universal basic income,
• 41:10
  Speaker 1: because if you don't, then you will see large, large
• 41:14
  Speaker 1: sections of society essentially falling into terrible levels of poverty
• 41:19
  Speaker 1: as a result of the inequality, the economic inequality in society.
• 41:24
  Speaker 1: So you essentially hold up all of human civilization by
• 41:28
  Speaker 1: just you know, basically just giving them money. And it
• 41:30
  Speaker 1: also makes sure that the money moves through the economy.
• 41:35
  Speaker 1: So what we mean here is that when a small
• 41:38
  Speaker 1: minority of people get basically all the money, Like this
• 41:44
  Speaker 1: is when at the end of a game of monopoly,
• 41:47
  Speaker 1: when your sister, who this time has won because she
• 41:50
  Speaker 1: got all the orange properties, all the red properties, and
• 41:53
  Speaker 1: she got the dark blue properties and the purple ones
• 41:56
  Speaker 1: as well, why not she won all. She's got all
• 41:58
  Speaker 1: of that stuff, and in the end she has everything,
• 42:01
  Speaker 1: she has all the money. The game is over at
• 42:04
  Speaker 1: that point. And it's the same in the economy. If
• 42:06
  Speaker 1: you get to that situation where you've got your zero
• 42:09
  Speaker 1: point one percent who have everything pretty much, they have
• 42:13
  Speaker 1: all of the property, they've got all the money, they
• 42:15
  Speaker 1: own all the businesses, everything society will not work. It
• 42:21
  Speaker 1: will fall apart. Because you need the wheels to turn,
• 42:26
  Speaker 1: you need the whole system to keep going. You need
• 42:29
  Speaker 1: you know, those business owners need customers to spend their money.
• 42:34
  Speaker 1: You need liquidity in the system, you need the money
• 42:37
  Speaker 1: to constantly be moving. And so it would necessitate essentially
• 42:41
  Speaker 1: these people who've got everything going, all right, well, we'll
• 42:43
  Speaker 1: give you, will give you loads of loads of money.
• 42:47
  Speaker 1: It wouldn't be loads. We'll give you money just to
• 42:49
  Speaker 1: keep the economy turning. It's a bit like if your
• 42:54
  Speaker 1: sister wins a game of monopoly and she goes, no, no,
• 42:57
  Speaker 1: I don't want to stop, So I'll just give you
• 43:00
  Speaker 1: odds more money every round. I'll give you five hundred
• 43:03
  Speaker 1: pounds just so we can keep playing the game. It's
• 43:07
  Speaker 1: kind of like that. But Jeffrey Hinton is that his name.
• 43:11
  Speaker 1: He actually believes universal basic income is a good idea ultimately,
• 43:15
  Speaker 1: So we were talking about prompting considerations for universal basic
• 43:21
  Speaker 1: income and societal reformation, reformation meaning reforming society, changing society
• 43:27
  Speaker 1: in some important, big way. Job security. While many intellectual
• 43:34
  Speaker 1: jobs may vanish, right, there's a lot of them, including
• 43:39
  Speaker 1: English teaching quite possibly although what do you think would you?
• 43:44
  Speaker 1: Could you? Really? When will you give up Luke's English
• 43:52
  Speaker 1: Podcast for the latest AI driven show which you can
• 43:56
  Speaker 1: create yourself. You know, that's the future of all of
• 44:01
  Speaker 1: this stuff, that instead of having to wait for the
• 44:04
  Speaker 1: next episode of Luke's English Podcast, and instead of having
• 44:06
  Speaker 1: to essentially accept what I give you, you could probably
• 44:13
  Speaker 1: just go into some podcast making software. I mean, you
• 44:15
  Speaker 1: can do it already. You go into some podcast making
• 44:19
  Speaker 1: software and just type in, can you make an episode
• 44:21
  Speaker 1: of Luke's English podcast about the subject to if I
• 44:23
  Speaker 1: don't know donuts? And make it thirty minutes long to
• 44:29
  Speaker 1: fit my morning commute to work, okay, and include a
• 44:33
  Speaker 1: joke every forty five seconds and it will do it
• 44:39
  Speaker 1: and it'll be exactly what you wanted. And you could
• 44:42
  Speaker 1: just do that every single time, and then after maybe
• 44:45
  Speaker 1: the first ten times you've generated them, you just say
• 44:48
  Speaker 1: to it, just keep making episodes like this, you know,
• 44:51
  Speaker 1: the kind of thing I like, Just go on and
• 44:53
  Speaker 1: it'll keep doing it and then there's that or will
• 44:57
  Speaker 1: you continue to listen to the real Luke's English Podcast. So,
• 45:00
  Speaker 1: I mean, that's just an example of how my job
• 45:04
  Speaker 1: as a podcaster could vanish my job as an English teacher,
• 45:07
  Speaker 1: of course, could I mean it's already under threat from
• 45:12
  Speaker 1: AI language models. I mean I use it now as
• 45:15
  Speaker 1: a teaching assistant. But at some point that teaching assistant
• 45:18
  Speaker 1: is going to get to a stage where it can
• 45:21
  Speaker 1: just replace me completely. But you know, could you would you?
• 45:25
  Speaker 1: Would you reject me that easily anyway. While many intellectual
• 45:30
  Speaker 1: jobs may vanish practical skills like plumbing, which is what
• 45:34
  Speaker 1: I talked about before, fixing repairing water pipes in a
• 45:39
  Speaker 1: home or in the street, something like that will likely
• 45:43
  Speaker 1: remain safe, emphasizing the need for future job readiness. Readiness
• 45:48
  Speaker 1: means being ready. Okay, so that's again an overview of
• 45:52
  Speaker 1: the things that were said in the interview. We're going
• 45:54
  Speaker 1: to go through the interview in a bit and look
• 45:56
  Speaker 1: at the vocab and stuff. But at this point I
• 45:59
  Speaker 1: would like to ask you what do you think? What
• 46:02
  Speaker 1: do you think of all this? What's your reaction now
• 46:04
  Speaker 1: when you watch a video like this with an expert
• 46:07
  Speaker 1: giving serious warnings or just listen to me rambling on
• 46:10
  Speaker 1: about it, or what is your reaction? And you could
• 46:14
  Speaker 1: write your reaction in the comments section. Now, maybe you
• 46:18
  Speaker 1: feel like you want to make criticisms like these ones.
• 46:22
  Speaker 1: And I'm about to read out some comments which are
• 46:25
  Speaker 1: based on things I read in the comments section under
• 46:28
  Speaker 1: the BBC video. So maybe you want to make criticisms
• 46:32
  Speaker 1: like this. Maybe you want to say things like this.
• 46:34
  Speaker 1: Maybe he, maybe this expert should have done something about
• 46:38
  Speaker 1: this earlier when he had the chance. He's the godfather
• 46:41
  Speaker 1: of AI, he helped to develop all this. Maybe he
• 46:44
  Speaker 1: should have thought about this and done something about it
• 46:47
  Speaker 1: when he had the chance. Maybe you criticize this expert,
• 46:53
  Speaker 1: or maybe you'll say something like this, as a former
• 46:55
  Speaker 1: Google employee, he's a mainstream globalist blah blah blah blah blah,
• 46:59
  Speaker 1: something like that. You probably get triggered by the fact
• 47:02
  Speaker 1: he works for Google, and you might go off on
• 47:04
  Speaker 1: some rant about him being whatever, some globalist thing. I
• 47:08
  Speaker 1: don't know, I don't know, do you you know? Are
• 47:11
  Speaker 1: you triggered by any of it? Or maybe you're you're
• 47:15
  Speaker 1: one of those sorts of people who sort of says,
• 47:17
  Speaker 1: you know what, I think, it's all overblown nonsense, it's
• 47:21
  Speaker 1: all alarmist nonsense, there's nothing to be afraid of, or
• 47:26
  Speaker 1: something like new technology is always disruptive, but it's all right.
• 47:31
  Speaker 1: No one. Everyone was worried when the printing press was invented,
• 47:36
  Speaker 1: everyone was concerned when the radio is invented, When manufacturing
• 47:40
  Speaker 1: systems were invented, the luodites smashed the machinery. You know,
• 47:46
  Speaker 1: I don't know do you think like that or or
• 47:50
  Speaker 1: are you more like me? Personally? I take it all
• 47:53
  Speaker 1: seriously and just accept it for what it is, an
• 47:55
  Speaker 1: expert giving his expert opinions. But I personally don't quite
• 48:00
  Speaker 1: know what to do about it all myself. Anyway, let's
• 48:04
  Speaker 1: take a closer look by studying the transcript of the interview,
• 48:07
  Speaker 1: and I can comment and explain some language. So we're
• 48:10
  Speaker 1: going to go back up to the top of the
• 48:12
  Speaker 1: transcript now, and I'm going to fly through it and
• 48:14
  Speaker 1: focus on those highlighted bits of vocabulary, okay, and just
• 48:19
  Speaker 1: focus on clarifying those things. So the interviewer said, I
• 48:23
  Speaker 1: began by asking Jeffrey Hinton whether he thought the world
• 48:26
  Speaker 1: is getting to grips with the issue of AI. So
• 48:28
  Speaker 1: if you get to grips with something, it basically means
• 48:31
  Speaker 1: you get it under control. Okay. To get it under control,
• 48:35
  Speaker 1: for example, I need to get to grips with this,
• 48:38
  Speaker 1: what would it be I need to really, I really
• 48:42
  Speaker 1: need to get to grips. So let's say someone has
• 48:46
  Speaker 1: introduced a new computer system at work and you need
• 48:50
  Speaker 1: to understand how it works and really get on top
• 48:53
  Speaker 1: of it. You might say, I really need to get
• 48:56
  Speaker 1: to grips with this new system. I'm just going to
• 48:58
  Speaker 1: spend a couple of hours this afternoon just working it out.
• 49:01
  Speaker 1: I really need to get to grips with it. Now
• 49:03
  Speaker 1: you can imagine that this expression involves well it you
• 49:07
  Speaker 1: just don't imagine it. You can just remember that it
• 49:10
  Speaker 1: involves the word grip, which means to hold something in
• 49:13
  Speaker 1: your hands without its slipping. So if you hold something,
• 49:18
  Speaker 1: you get it under control, right, So that means getting
• 49:23
  Speaker 1: to grips with something. I'm trying to think of another example.
• 49:25
  Speaker 1: Let's say you buy a new laptop or you've got
• 49:28
  Speaker 1: a new phone, and you don't understand the operating system,
• 49:30
  Speaker 1: so you need to spend some time getting to grips
• 49:32
  Speaker 1: with it, meaning getting it all under control, understanding how
• 49:36
  Speaker 1: it works, and being able to use it. You get
• 49:41
  Speaker 1: to grips with some homework that you've been given, meaning
• 49:45
  Speaker 1: you read the task and you try and understand what
• 49:47
  Speaker 1: you've got to do. You're preparing for the il's test.
• 49:51
  Speaker 1: You really need to get to grips with the test,
• 49:53
  Speaker 1: meaning understand how it works and get it all under
• 49:55
  Speaker 1: your control. In this case, is the world getting to
• 49:58
  Speaker 1: grips with the issue of AI. Are we getting it
• 50:01
  Speaker 1: all under ConTroll? Do we understand it? Can we control it? Okay?
• 50:06
  Speaker 1: And then we've got and from your perspective, from having
• 50:10
  Speaker 1: worked at the top of this meaning at the top
• 50:13
  Speaker 1: of the artificial intelligence movement, having developed some of the
• 50:17
  Speaker 1: theories underpinning all of this explosion in AI. So the
• 50:22
  Speaker 1: theories underpinning the explosion in AI. If something underpins something,
• 50:28
  Speaker 1: it basically kind of works as a foundation. I guess
• 50:32
  Speaker 1: this must come from like making clothes, making clothing, tailoring clothes.
• 50:41
  Speaker 1: That when you let's say, if you're making a shirt,
• 50:44
  Speaker 1: you would use pins metal pins underneath parts of the shirt.
• 50:48
  Speaker 1: You underpin the shirt, holding the different parts of the
• 50:51
  Speaker 1: shirt in place before you then stitch the shirt together.
• 50:56
  Speaker 1: So that's underpinning a shirt. It's kind of like the
• 51:00
  Speaker 1: the the connections or the parts that hold the whole
• 51:05
  Speaker 1: thing together. It's a bit like foundation for a building.
• 51:14
  Speaker 1: So you have ideas underpin something. For example, what are
• 51:17
  Speaker 1: the ideas and concepts that underpin my approach to doing
• 51:21
  Speaker 1: this podcast. A lot of it's based on the training
• 51:26
  Speaker 1: that I've received as an English teacher over the years,
• 51:29
  Speaker 1: So the idea that language should be presented in context,
• 51:33
  Speaker 1: but also stuff I've learned as a teacher that it
• 51:35
  Speaker 1: helps when the language learning process is personalized, both in
• 51:39
  Speaker 1: the way that you consume language, so it's easier to
• 51:43
  Speaker 1: essentially acquire language when it's presented to you in a
• 51:47
  Speaker 1: personal way rather than an impersonal way, and plenty of
• 51:51
  Speaker 1: other things as well. These are ideas that underpin my
• 51:55
  Speaker 1: approach to doing this podcast. So when we talk about
• 51:57
  Speaker 1: theories or ideas underpinning something, it means the things that
• 52:01
  Speaker 1: are that form this the underlying structure that defines the
• 52:07
  Speaker 1: way that something works, like the foundations of something. Okay,
• 52:14
  Speaker 1: So the interviewee developed some of the theories that underpin
• 52:21
  Speaker 1: this explosion in AI. Okay, So, Jeffrey Hinton said, some
• 52:25
  Speaker 1: people think these things don't really understand meaning some people
• 52:29
  Speaker 1: think that these AI systems don't really understand things, that
• 52:34
  Speaker 1: they're very different from us, that they're using some statistical tricks.
• 52:39
  Speaker 1: So statistical refers to the words statistics. Statistics is basically
• 52:44
  Speaker 1: like data, either quantitative data just numbers, or qualitative data
• 52:49
  Speaker 1: more values, you know, different types of data information. This
• 52:55
  Speaker 1: is statistics, right, So some people believe that these AI
• 53:01
  Speaker 1: systems are basically just using statistical tricks, like, for example,
• 53:07
  Speaker 1: maybe when they produce language, that they are just producing
• 53:13
  Speaker 1: sentences based on frequency. For example, well, after this word,
• 53:19
  Speaker 1: the most common next word is this, so's I'll just
• 53:23
  Speaker 1: add this, that they're just using statistical tricks to perform
• 53:30
  Speaker 1: these acts of intelligence. But Jeffrey disagrees. He thinks they're
• 53:34
  Speaker 1: not just using tricks, that there is actual processing going
• 53:38
  Speaker 1: on that is very similar to the way that the
• 53:40
  Speaker 1: human mind processes things. Which is really interesting again talking
• 53:45
  Speaker 1: about science fiction because you think of films like a
• 53:48
  Speaker 1: Blade Runner, and the whole idea of Blade Runner is
• 53:53
  Speaker 1: do synthetic humans or androids do they actually have emotions?
• 54:01
  Speaker 1: They are so similar to humans, they even have memories
• 54:05
  Speaker 1: which have been implanted, right. Their emotions are just as
• 54:11
  Speaker 1: powerful as emotions that humans feel. So what's the difference then,
• 54:15
  Speaker 1: really between a synthetic person who is experiencing emotions in
• 54:19
  Speaker 1: the same way that a human does, a synthetic person
• 54:23
  Speaker 1: that is essentially thinking and operating in exactly the same
• 54:26
  Speaker 1: way that a human does. What's the difference between that
• 54:29
  Speaker 1: and a human? Then? Really, and don't those synthetics, those replicants,
• 54:36
  Speaker 1: don't they have as a result, somehow the same rights
• 54:40
  Speaker 1: as humans. In fact, aren't they even more than human?
• 54:45
  Speaker 1: There's that famous speech at the end of Blade Runner
• 54:48
  Speaker 1: where Rutger Hower is saying to Harrison Ford's character, I've
• 54:53
  Speaker 1: seen things that you people could never understand. I have
• 54:57
  Speaker 1: insights about the universe that you couldn't comprehend. I am
• 55:05
  Speaker 1: you know, I'm more than a human. I'm greater than
• 55:09
  Speaker 1: a human. I am more human than human. Just an
• 55:14
  Speaker 1: interesting idea that maybe these ais, maybe they should have rights,
• 55:21
  Speaker 1: which is again a terrifying thought, because if any of
• 55:24
  Speaker 1: you have seen The Animatrix, which is the animated there
• 55:28
  Speaker 1: was an animated series of short films based in the
• 55:31
  Speaker 1: Matrix universe, and one of the stories in The Animatrix
• 55:35
  Speaker 1: talks about how the war between the machines and the
• 55:39
  Speaker 1: humans happened, and a principal key moment in it is
• 55:44
  Speaker 1: a legal case where it's decided in the courts that
• 55:48
  Speaker 1: artificial intelligences don't have the same rights as humans. And
• 55:54
  Speaker 1: this was an important groundbreaking case in law that stated
• 56:00
  Speaker 1: that these intelligences don't have the same kinds of rights
• 56:03
  Speaker 1: as humans. And this was the basis upon which the
• 56:07
  Speaker 1: AI felt there was an injustice and that was like
• 56:10
  Speaker 1: the start of a big conflict and ultimately led to
• 56:14
  Speaker 1: a horrific war between the humans and the AI, which
• 56:18
  Speaker 1: the AI won. So should AI be considered similar to humans?
• 56:25
  Speaker 1: How do you make the distinction ultimately when they think
• 56:29
  Speaker 1: just like us, they you know, anyway, it's a philosophical question.
• 56:35
  Speaker 1: But anyway, Jeffrey says that they are. So people think
• 56:38
  Speaker 1: that these systems don't really think, they don't really understand things,
• 56:42
  Speaker 1: they just use statistical tricks. But he's arguing that their
• 56:46
  Speaker 1: intelligence is far more sophisticated and human like than we realize.
• 56:54
  Speaker 1: The interviewer said, what is it that's triggered your concern?
• 56:57
  Speaker 1: I've already used the word triggered in this episode. To
• 57:00
  Speaker 1: be triggered on the Internet is when you see something
• 57:04
  Speaker 1: or read something that makes you angry, you know, and
• 57:08
  Speaker 1: it's often something that sort of angers you on a
• 57:11
  Speaker 1: political level, or maybe you get offended by something, right,
• 57:17
  Speaker 1: So that's one use of the word triggered, but really
• 57:20
  Speaker 1: triggered just means to trigger something, is just to cause
• 57:24
  Speaker 1: something to happen, like in a gun. Right, In the gun,
• 57:28
  Speaker 1: you've got the barrel of the gun where the bullet
• 57:30
  Speaker 1: goes down. You've got the handle which you hold in
• 57:33
  Speaker 1: your hand, and then there's the trigger, which is the
• 57:36
  Speaker 1: thing that your finger pulls which causes the mechanism to
• 57:40
  Speaker 1: fire the bullet from the gun. Right, So that's to
• 57:43
  Speaker 1: trigger the gun. A trigger essentially, then is a button
• 57:46
  Speaker 1: that causes a reaction, and in this case as a verb,
• 57:52
  Speaker 1: to trigger something is exactly that to cause something to happen. Right,
• 57:58
  Speaker 1: So in this case, what's triggered your concern? Meaning what
• 58:01
  Speaker 1: has caused you to be concerned? Like this? So you
• 58:04
  Speaker 1: can imagine a finger pulling the trigger and the trigger,
• 58:09
  Speaker 1: let's say the trigger triggers the mechanism of the gun. Also,
• 58:15
  Speaker 1: something might trigger Jeffrey to have these concerns. What was
• 58:21
  Speaker 1: it that triggered his concern? It was a combination of
• 58:23
  Speaker 1: two things. Playing with GPT four chatbots and also something else.
• 58:31
  Speaker 1: Understanding the way in which these things are a superior
• 58:35
  Speaker 1: form of intelligence. Understanding the way that these AI systems
• 58:41
  Speaker 1: are essentially multineural networks something like that. So when he
• 58:46
  Speaker 1: got a more profound understanding of the way that they work,
• 58:49
  Speaker 1: that's what triggered him to be concerned. He said, they're
• 58:54
  Speaker 1: clearly very competent. If something or someone is competent, it
• 58:58
  Speaker 1: means they are cap bull of doing things, they're good
• 59:01
  Speaker 1: at doing things. So, for example, if you are running
• 59:04
  Speaker 1: a business, you want staff who are competent, meaning staff
• 59:08
  Speaker 1: who can do things, and they can do things. Well,
• 59:11
  Speaker 1: what do you think of Sarah? I think she's very competent.
• 59:14
  Speaker 1: I think that, you know, she's definitely one of the
• 59:17
  Speaker 1: staff members that we need to hold on to. I
• 59:20
  Speaker 1: think she's a very competent teacher. She's very good at
• 59:24
  Speaker 1: problem solving. She lessens plans very carefully. I've observed during
• 59:28
  Speaker 1: lessons a few times, and she's a very competent teacher.
• 59:31
  Speaker 1: She knows her grammar, She's able to manage the classes
• 59:33
  Speaker 1: very effectively, very competent. What do you think of Bill Well? Bill,
• 59:37
  Speaker 1: on the other hand, is a different story. First of all,
• 59:40
  Speaker 1: he's always late. Secondly, I observed him last week and
• 59:44
  Speaker 1: I watched him spelling on the board, and his spelling
• 59:47
  Speaker 1: is atrocious. He can't spell, he doesn't know the grammar,
• 59:50
  Speaker 1: he doesn't really seem to care about the students, and
• 59:54
  Speaker 1: in fact doesn't show any signs of actually knowing what
• 59:57
  Speaker 1: he's doing at all. He's completely rubbish. He's a useless teacher,
• 1:00:00
  Speaker 1: is I would say, completely incompetent. He can't use the photocopier,
• 1:00:04
  Speaker 1: he doesn't know how to say good morning, he rarely
• 1:00:07
  Speaker 1: dresses appropriately for the job. He's a slob, he's rude,
• 1:00:12
  Speaker 1: he's lazy, and he's frequently absent. So he's perhaps one
• 1:00:16
  Speaker 1: of the most incompetent teachers I've ever met. So I
• 1:00:19
  Speaker 1: think that we should probably, So what do you think
• 1:00:21
  Speaker 1: we should? Probably? I think we should keep Bill because
• 1:00:23
  Speaker 1: he's a man. What this situation became suddenly very sexist,
• 1:00:31
  Speaker 1: just a little example of what happens in the patriarchal
• 1:00:36
  Speaker 1: workplace anyway. Anyway, obviously Sarah would be the competent one.
• 1:00:41
  Speaker 1: She would be the one that you would keep. And
• 1:00:43
  Speaker 1: Bill sounds like a total disaster, and you know you've
• 1:00:45
  Speaker 1: got to let him go, haven't you, because he seems incompetent.
• 1:00:49
  Speaker 1: Jeffrey was saying that the AI systems are clearly very
• 1:00:52
  Speaker 1: competent they're very, very good at what they do. So
• 1:00:57
  Speaker 1: he talks about making many copies of the same neural network.
• 1:01:02
  Speaker 1: So neural refers to the systems in the brain, right,
• 1:01:08
  Speaker 1: the connections in the brain. A brain, like a neural
• 1:01:13
  Speaker 1: network is essentially a brain, right, a system of connections.
• 1:01:20
  Speaker 1: And he's saying that one of the reasons why AI
• 1:01:23
  Speaker 1: is more sophisticated and advanced than humans is because the
• 1:01:28
  Speaker 1: difference is that humans. Each individual human has a neural network,
• 1:01:34
  Speaker 1: meaning a brain, right, And the fact is that these
• 1:01:38
  Speaker 1: brains are not connected, and we actually find it very
• 1:01:44
  Speaker 1: difficult to share the knowledge we have in our brains.
• 1:01:47
  Speaker 1: We have all these complex difficulties with social communication, and
• 1:01:53
  Speaker 1: it's all a very inefficient system. You know, humans have
• 1:01:58
  Speaker 1: to go to university to learn and they take three
• 1:02:01
  Speaker 1: years to learn about just one subject. But what Jeffrey
• 1:02:05
  Speaker 1: is saying that these things are a superior form of
• 1:02:08
  Speaker 1: his intelligence because you can make many copies of the
• 1:02:11
  Speaker 1: same neural network. Each copy can look at a different
• 1:02:15
  Speaker 1: bit of data, and then they can all share what
• 1:02:18
  Speaker 1: they've learned. So you get ten thousand people studying ten
• 1:02:22
  Speaker 1: thousand subjects. Okay, they all go off and learn this
• 1:02:26
  Speaker 1: stuff very very quickly, and then you can actually bring
• 1:02:29
  Speaker 1: all these people together, all these neural networks. You can
• 1:02:32
  Speaker 1: connect them all together, and they instantly share everything that
• 1:02:36
  Speaker 1: they've known, that they've learned, and then each individual person
• 1:02:40
  Speaker 1: in that group of ten thousand knows ten thousand people's
• 1:02:44
  Speaker 1: worth of information. Okay, So that's why he was saying
• 1:02:49
  Speaker 1: that they are superior because you can make many copies
• 1:02:53
  Speaker 1: of the same neural network. Essentially, they can share information
• 1:02:58
  Speaker 1: with each other much more effect and effectively than humans can.
• 1:03:05
  Speaker 1: So the interviewer says, the key concern here is that
• 1:03:07
  Speaker 1: it could exceed human intelligence, and Jeffrey says, indeed, massively exceed.
• 1:03:13
  Speaker 1: So I said we've had exceed before, right, meaning become bigger, right,
• 1:03:20
  Speaker 1: and massively exceed So this means become much much bigger. Right.
• 1:03:27
  Speaker 1: He then says, very few of the experts are in
• 1:03:30
  Speaker 1: doubt about that. Very few of the experts are in
• 1:03:33
  Speaker 1: doubt about that. If you are in doubt about something,
• 1:03:35
  Speaker 1: it means you're not sure about it, right, I'm in
• 1:03:38
  Speaker 1: doubt about it. Experts are in doubt about that. Very
• 1:03:42
  Speaker 1: few of the experts are in doubt about that, meaning
• 1:03:44
  Speaker 1: most of them are sure about it. Most of them
• 1:03:47
  Speaker 1: agree with each other, and they're and they're sure that
• 1:03:50
  Speaker 1: AI will massively, massively exceed human intelligence. All right. Jeffrey
• 1:04:01
  Speaker 1: went on to say that some people believe that AI
• 1:04:05
  Speaker 1: will be subservient to us. That will give them the goals.
• 1:04:10
  Speaker 1: It will be no problem. They'll do what we say.
• 1:04:13
  Speaker 1: They'll be subservient to us. If someone is subservient to
• 1:04:17
  Speaker 1: someone else, Essentially, they they take a like a lower position,
• 1:04:25
  Speaker 1: and they allow their masters to tell them what to do.
• 1:04:29
  Speaker 1: So a servant, a servant takes a subservient position to
• 1:04:36
  Speaker 1: his or her master. Right. For example, you can say
• 1:04:39
  Speaker 1: make me a cup of tea, and they'll say, certainly, sir,
• 1:04:42
  Speaker 1: would you like two sugars? As usual, you know, and
• 1:04:45
  Speaker 1: they take a subservient position. They'll be subservient to us,
• 1:04:50
  Speaker 1: right in a lot of cultures, and traditionally women are
• 1:04:55
  Speaker 1: subservient to men right in relationships and things like that.
• 1:05:00
  Speaker 1: Not so much the case you know, in many societies. Right,
• 1:05:05
  Speaker 1: But anyway, just explaining the word, so many people think
• 1:05:09
  Speaker 1: that AIS will be subservient to us. But then there
• 1:05:13
  Speaker 1: are other experts who think that they will definitely take control.
• 1:05:17
  Speaker 1: And then he says, given this big spectrum of opinions.
• 1:05:20
  Speaker 1: So given is quite a nice word. We use it
• 1:05:24
  Speaker 1: at the beginning of sentences. Given this big spectrum of opinions,
• 1:05:29
  Speaker 1: I think it's wise to be cautious. So given is
• 1:05:34
  Speaker 1: a bit like saying because there are or because of this, right,
• 1:05:41
  Speaker 1: taking this into account, Given this big spectrum of opinions,
• 1:05:45
  Speaker 1: I think it's wise to be cautious. Think of some
• 1:05:48
  Speaker 1: other examples of given. You might say, given that, and
• 1:05:53
  Speaker 1: then a clause. Given that the sun is shining today,
• 1:05:58
  Speaker 1: I think a T shirt would be sufficient given the sunshine,
• 1:06:05
  Speaker 1: I think a T shirt would be sufficient. All right,
• 1:06:10
  Speaker 1: Given this big spectrum of opinions, I think it's wise
• 1:06:13
  Speaker 1: to be cautious because we have because there are Yeah.
• 1:06:17
  Speaker 1: See if I can get a better explanation of that,
• 1:06:23
  Speaker 1: here's a bit more information about it. So taking something
• 1:06:26
  Speaker 1: into account, or considering a particular factor, for example, given
• 1:06:29
  Speaker 1: the circumstances, I think we made the right decision. So
• 1:06:33
  Speaker 1: taking into account the circumstances, right because of or because
• 1:06:41
  Speaker 1: we have because of the circumstances, or thinking about the circumstances,
• 1:06:46
  Speaker 1: because the circumstances are the way they are, I think
• 1:06:48
  Speaker 1: we made the right decision. So This word is often
• 1:06:50
  Speaker 1: used to introduce conditions or facts that influence a conclusion
• 1:06:54
  Speaker 1: or decision. Given the nature of the situation, I think
• 1:06:59
  Speaker 1: we need to be careful. It's like saying, because the
• 1:07:02
  Speaker 1: nature of the situation is difficult, I think we need
• 1:07:05
  Speaker 1: to be careful. Given this, So it could be given
• 1:07:09
  Speaker 1: plus a noun like given this big spectrum of opinions,
• 1:07:12
  Speaker 1: or given plus a cause with that, given that there
• 1:07:16
  Speaker 1: are many different opinions, I think it's wise to be cautious.
• 1:07:20
  Speaker 1: So anyway, he's saying that because there are lots of
• 1:07:24
  Speaker 1: different opinions, there's a big spectrum of opinions, a wide
• 1:07:27
  Speaker 1: variety of opinions. He said, I think it's wise to
• 1:07:31
  Speaker 1: be cautious. So we've got opinions that say, it'll be fine,
• 1:07:35
  Speaker 1: they'll do what we tell them to do, they'll just
• 1:07:37
  Speaker 1: be subservient to us. And then there are other opinions
• 1:07:40
  Speaker 1: on the other side that say, no, actually, they will
• 1:07:41
  Speaker 1: definitely try to take control. And so, considering we've got
• 1:07:45
  Speaker 1: this wide spectrum, it's probably a good idea to be
• 1:07:48
  Speaker 1: cautious because we've got at least, I don't know what
• 1:07:51
  Speaker 1: is it, fifteen to fifty percent of opinion, saying that
• 1:07:56
  Speaker 1: this could be very dangerous, so it's probably a good
• 1:07:58
  Speaker 1: idea to be cautious. The interviewer says, could they not
• 1:08:02
  Speaker 1: be contained in certain areas like scientific research? So to
• 1:08:07
  Speaker 1: be contained meaning limited, kept in one space like for example,
• 1:08:12
  Speaker 1: you know, animals in the zoo, the lion is contained
• 1:08:16
  Speaker 1: within its cage, sadly, right to be contained meaning kept
• 1:08:21
  Speaker 1: in one area. So could AI not be contained in
• 1:08:25
  Speaker 1: certain areas like scientific research? I meaning could we not
• 1:08:28
  Speaker 1: just restrict its use to certain areas but not for example,
• 1:08:32
  Speaker 1: the armed forces? So if we could just limit it
• 1:08:35
  Speaker 1: to scientific use but not allow it to be used
• 1:08:38
  Speaker 1: in military situations? The armed forces this means the army
• 1:08:42
  Speaker 1: basically the army, the Navy, okay, the air Force. These
• 1:08:47
  Speaker 1: are the armed forces. And Jeffrey said maybe, but actually,
• 1:08:52
  Speaker 1: if you look at all the current legislation, so legislation
• 1:08:55
  Speaker 1: means laws. So we've had regulations, laws, legislate. They're all
• 1:09:01
  Speaker 1: kind of the same thing, right. Regulations are laws which
• 1:09:05
  Speaker 1: seek to try to control things, and they're often made
• 1:09:08
  Speaker 1: by governments or something like the European Union, which issues
• 1:09:12
  Speaker 1: regulations as a way of like you know, creating frameworks
• 1:09:18
  Speaker 1: legal frameworks to control things, tax regulations and so on.
• 1:09:23
  Speaker 1: Legislation means laws specifically means laws. If you look at
• 1:09:28
  Speaker 1: all the current legislation, which is uncountable law laws that's countable,
• 1:09:35
  Speaker 1: that's plural. So if you look at the current laws
• 1:09:37
  Speaker 1: or in general, the current legislation, including the European legislation,
• 1:09:42
  Speaker 1: there's a little clause in all of it that says
• 1:09:45
  Speaker 1: that none of this applies to military applications. A little clause.
• 1:09:49
  Speaker 1: A clause is a line in a legal document, in
• 1:09:56
  Speaker 1: an agreement, in a contract, or in a piece of legislation,
• 1:10:00
  Speaker 1: a single line which might provide a right or take
• 1:10:06
  Speaker 1: a right away, or you know, allow something to be
• 1:10:09
  Speaker 1: possible or not allowed. So a clause. So when you're
• 1:10:13
  Speaker 1: reading a contract, you've got to read every single clause.
• 1:10:16
  Speaker 1: You might find like fourteen point one point three says,
• 1:10:21
  Speaker 1: you know, these regulations are not limited to the use
• 1:10:25
  Speaker 1: of military interventions. You know, it might say AI must
• 1:10:31
  Speaker 1: be contained within these limitations, blah blah blah. Clause fourteen
• 1:10:37
  Speaker 1: point one point three except in the case of military applications,
• 1:10:41
  Speaker 1: which is a little clause which means that none of
• 1:10:44
  Speaker 1: this applies to military applications or military uses the way
• 1:10:48
  Speaker 1: that it applied to military situations, wars, fighting, the actions
• 1:10:57
  Speaker 1: of armed forces. Governments aren't willing to restrict their own
• 1:11:01
  Speaker 1: uses of it. If governments aren't willing, meaning they don't
• 1:11:07
  Speaker 1: want to, aren't they just don't want to do it,
• 1:11:09
  Speaker 1: They're not willing to do it. This is this word
• 1:11:11
  Speaker 1: we use all the time in business situations when we're
• 1:11:15
  Speaker 1: doing negotiations. For example, are you willing to Would you
• 1:11:19
  Speaker 1: be willing to provide a discount of twenty percent if
• 1:11:23
  Speaker 1: we order, you know, over three thousand units, we'd be
• 1:11:28
  Speaker 1: willing to give a discount, But I think twenty percent
• 1:11:30
  Speaker 1: is a little bit more than we imagined. Would you
• 1:11:33
  Speaker 1: be willing to go to fifteen percent? I think fifteen
• 1:11:35
  Speaker 1: percent is more realistic. You know that kind of thing.
• 1:11:38
  Speaker 1: It's basically, what do you want to do? Are you
• 1:11:41
  Speaker 1: willing to do this? Are you willing to do that?
• 1:11:43
  Speaker 1: Governments are not willing to? We don't, you know, it's
• 1:11:47
  Speaker 1: more appropriate language for this kind of subject, saying willing
• 1:11:50
  Speaker 1: to do this, willing to do that, or not willing to,
• 1:11:52
  Speaker 1: rather than saying want to. You know, we don't say
• 1:11:55
  Speaker 1: governments don't want to restrict their uses of it, We
• 1:11:58
  Speaker 1: say governments aren't willing to restrict their own uses of it,
• 1:12:01
  Speaker 1: So it's a slightly more formal, more serious way of
• 1:12:04
  Speaker 1: essentially saying want to or don't want to willing to.
• 1:12:08
  Speaker 1: We also say governments aren't prepared to, which is another
• 1:12:12
  Speaker 1: way of saying that they just don't want to. So
• 1:12:15
  Speaker 1: governments aren't willing to restrict their own uses of it
• 1:12:18
  Speaker 1: for defense, restrict meaning limit it. Moving on, the interviewer says,
• 1:12:26
  Speaker 1: that's happened since you started warning about AI. Is that
• 1:12:30
  Speaker 1: the sort of pathway you're concerned about? So the interviewer
• 1:12:33
  Speaker 1: asks about the way things are going to go in
• 1:12:36
  Speaker 1: the future. He said, is that the sort of pathway
• 1:12:38
  Speaker 1: that you're concerned about? I suppose this is fairly clear,
• 1:12:42
  Speaker 1: isn't it. Really You think of a pathway some way
• 1:12:44
  Speaker 1: you would walk down, for example, a pathway that takes
• 1:12:47
  Speaker 1: you down to the bottom of the garden, or a
• 1:12:49
  Speaker 1: pathway that takes you through a field or something like that.
• 1:12:55
  Speaker 1: So a pathway is just a way in which things
• 1:12:57
  Speaker 1: can move. So he's saying, is that the sort of
• 1:13:01
  Speaker 1: way that you're concerned about, Meaning the way that this
• 1:13:05
  Speaker 1: could develop, is that the kind of pathway the kind
• 1:13:09
  Speaker 1: of road that you're concerned about, and he said, I
• 1:13:13
  Speaker 1: mean it's the thin end of the wedge. Hmmm, this
• 1:13:17
  Speaker 1: is quite a nice idiom, the thin end of the wedge.
• 1:13:21
  Speaker 1: So a wedge would be a kind of like a
• 1:13:25
  Speaker 1: piece of wood, okay, that is thin at one end
• 1:13:31
  Speaker 1: and thick at the other end. Typically we use a
• 1:13:34
  Speaker 1: wedge to stick a wedge under a door and the
• 1:13:38
  Speaker 1: door can't close. You sort of wedge a door open, right,
• 1:13:44
  Speaker 1: So a wedge is a thing that's thin, a piece
• 1:13:46
  Speaker 1: of wood that's thin at one end and thick at
• 1:13:48
  Speaker 1: the other end. So it's kind of like triangular slanted
• 1:13:53
  Speaker 1: piece of wood. That's a wedge, And you'd stick a
• 1:13:57
  Speaker 1: wedge under a door. But now you know what a wedge.
• 1:14:00
  Speaker 1: We talk about the thin end of the wedge. It's
• 1:14:03
  Speaker 1: a bit like the tip of the iceberg. It's a
• 1:14:05
  Speaker 1: similar expression. The thin end of the wedge means you've
• 1:14:09
  Speaker 1: got a situation that's the wedge in this case, and
• 1:14:12
  Speaker 1: you've got at one end the thin end of the wedge,
• 1:14:14
  Speaker 1: and at the other end the thick end of the wedge.
• 1:14:16
  Speaker 1: So so in talking about artificial intelligence being used in
• 1:14:27
  Speaker 1: military situations, for example, weapons systems that can target many
• 1:14:32
  Speaker 1: different targets all at the same time, allowing governments to
• 1:14:38
  Speaker 1: send missiles out to lots of different targets all at
• 1:14:40
  Speaker 1: the same time. Is that the kind of thing that
• 1:14:43
  Speaker 1: you're worried about, He said, that's the thin end of
• 1:14:45
  Speaker 1: the wedge, meaning in terms of the situation of AI
• 1:14:49
  Speaker 1: controlling military technology, that sort of example is just the
• 1:14:54
  Speaker 1: thin end of the wedge, meaning that's just the the smaller,
• 1:15:01
  Speaker 1: maybe less serious end of the situation. And at the
• 1:15:04
  Speaker 1: other end there is a much bigger way of understanding it.
• 1:15:10
  Speaker 1: So that means that there could be much bigger uses
• 1:15:13
  Speaker 1: of AI in warfare. It could be much more sophisticated,
• 1:15:17
  Speaker 1: it could be a much larger threat. So you've got
• 1:15:22
  Speaker 1: the thin end of the wedge, which is like some
• 1:15:24
  Speaker 1: uses of AI in conflicts, and then you've got the
• 1:15:27
  Speaker 1: thick end of the wedge, would be a much bigger,
• 1:15:30
  Speaker 1: much larger, and much more sophisticated use of AI. Okay,
• 1:15:35
  Speaker 1: So at the thin end of the wedge is a
• 1:15:37
  Speaker 1: minor change that could lead to significant and undesirable consequences
• 1:15:42
  Speaker 1: in the future. Okay, So he's talking about a small
• 1:15:44
  Speaker 1: thing that could become a big thing. For example, allowing
• 1:15:49
  Speaker 1: this exception could be the thin end of the wedge
• 1:15:51
  Speaker 1: for more serious abuses of power. This idiom is often
• 1:15:55
  Speaker 1: used in debates to warn that small actions or changes
• 1:15:57
  Speaker 1: could escalate into bigger issues. Okay, a bit like a
• 1:16:01
  Speaker 1: slippery slope kind of situation. So he's saying that these
• 1:16:05
  Speaker 1: these uses of AI are just like the small end,
• 1:16:10
  Speaker 1: and that ultimately they could become much bigger later on.
• 1:16:14
  Speaker 1: It's the thin end of the wedge. What I'm most
• 1:16:18
  Speaker 1: concerned about is when these things can autonomously make the
• 1:16:22
  Speaker 1: decision to kill people. So we've talked about autonomously meaning
• 1:16:25
  Speaker 1: that they do it on their own. They do it
• 1:16:28
  Speaker 1: automatically autonomously, meaning on their own without being told to
• 1:16:33
  Speaker 1: do it. So robot soldiers, says the interviewer, And Jeffrey says, yeah,
• 1:16:40
  Speaker 1: those or drones and the like. So and the like
• 1:16:45
  Speaker 1: means and things like that. So the like means things
• 1:16:50
  Speaker 1: like that, So robot soldiers, Yes, those or drones and
• 1:16:54
  Speaker 1: the like or drones and things like that. Mm hmm. Remember,
• 1:17:00
  Speaker 1: so you can get a full list of all this
• 1:17:01
  Speaker 1: vocabulary that I'm describing here on the PDF that I've
• 1:17:05
  Speaker 1: mentioned already, with definitions examples and comments as well in
• 1:17:11
  Speaker 1: a long list. So I do recommend that you get
• 1:17:14
  Speaker 1: the PDF. It's completely free. You don't need to give
• 1:17:16
  Speaker 1: me your email address or anything like that. You can
• 1:17:18
  Speaker 1: just download it directly. But it might be useful as
• 1:17:22
  Speaker 1: a way of helping you to consolidate a lot of
• 1:17:24
  Speaker 1: the vocap that I'm talking to you about here. Let's
• 1:17:28
  Speaker 1: move on. If we restrain ourselves from military use in
• 1:17:32
  Speaker 1: the G seven advanced democracies, what's going on in China,
• 1:17:35
  Speaker 1: what's going on in Russia? Shout out to my Chinese
• 1:17:38
  Speaker 1: and Russian listeners who might feel a little bit triggered
• 1:17:41
  Speaker 1: by this particular moment. Anyway, if we restrain ourselves, if
• 1:17:45
  Speaker 1: you restrain yourself from doing something, or you restrain yourself
• 1:17:50
  Speaker 1: from something, it means you hold yourself back and you're like, no,
• 1:17:54
  Speaker 1: I won't do that. I think I won't use AI
• 1:17:59
  Speaker 1: in military applications. So if we restrain ourselves, what about
• 1:18:05
  Speaker 1: our competitors, will they restrain themselves? No, they won't, So
• 1:18:11
  Speaker 1: that means we can't restrain ourselves from doing that. The
• 1:18:17
  Speaker 1: interviewer said, do you have any sense of whether the
• 1:18:19
  Speaker 1: shackles are off in a place like Russia, So shackles
• 1:18:24
  Speaker 1: would be things that would restrain you or restrain someone
• 1:18:28
  Speaker 1: like they are they are metal chains, aren't they? Yeah? Yeah,
• 1:18:37
  Speaker 1: yeah yeah, yes, a bit like when someone is in prison,
• 1:18:42
  Speaker 1: they might have metal rings around their ankles attached to
• 1:18:48
  Speaker 1: chains which are then attached to the wall. These are shackles, okay,
• 1:18:53
  Speaker 1: And these shackles would certainly restrain you from doing something.
• 1:18:57
  Speaker 1: So the idiom the shackles off means that we're talking
• 1:19:02
  Speaker 1: about an unrestrained thing, something that's unrestrained, or where people
• 1:19:06
  Speaker 1: are not holding themselves back. So do you have any
• 1:19:10
  Speaker 1: sense of whether the shackles are off in a place
• 1:19:12
  Speaker 1: like Russia? Means do you think that in a place
• 1:19:15
  Speaker 1: like Russia they are not restraining themselves? And in fact,
• 1:19:18
  Speaker 1: they are really trying to develop AI systems, especially for
• 1:19:26
  Speaker 1: military applications, and they are not holding themselves back, They're
• 1:19:30
  Speaker 1: not limiting themselves. They're really trying to innovate in this area.
• 1:19:34
  Speaker 1: Do you have any sense of whether the shackles are off,
• 1:19:38
  Speaker 1: meaning that they are not restraining themselves? And he said, well,
• 1:19:41
  Speaker 1: Putin said some years ago that whoever controls AI controls
• 1:19:44
  Speaker 1: the world. So I imagine they're working very hard, so
• 1:19:47
  Speaker 1: basically saying, I expect the shackles are off. Yes, the
• 1:19:51
  Speaker 1: West is probably well ahead of them in research, well ahead,
• 1:19:55
  Speaker 1: So they're ahead of them, meaning they are in a
• 1:20:00
  Speaker 1: you know, in a in a forward position. The opposite
• 1:20:03
  Speaker 1: of that would be there behind them and well ahead
• 1:20:07
  Speaker 1: of them, well ahead of them, meaning much further forward
• 1:20:11
  Speaker 1: in research. We're probably slightly ahead of China, so we've
• 1:20:16
  Speaker 1: got well ahead of them, slightly ahead of them, right.
• 1:20:21
  Speaker 1: The interviewer says, it sounds very theoretical, this thread of argument,
• 1:20:27
  Speaker 1: that's just interesting collocation. We talk about a thread of
• 1:20:30
  Speaker 1: argument or a kind of line of argument. We actually
• 1:20:34
  Speaker 1: say a line of inquiry, series of questions. A thread
• 1:20:38
  Speaker 1: of argument is a series of statements that make up
• 1:20:41
  Speaker 1: an argument a position. So we talk about a thread
• 1:20:45
  Speaker 1: of arguments. Quite a neat phrase, because a thread suggests
• 1:20:49
  Speaker 1: a line. You know, again, going back to making clothes,
• 1:20:52
  Speaker 1: making a shirt, you use a thread, use a needle
• 1:20:55
  Speaker 1: and thread to you know, stitch the parts of the
• 1:20:58
  Speaker 1: clothing together, and you thread the needle through different parts
• 1:21:03
  Speaker 1: of the clothes, leaving this like, you know, quite a
• 1:21:07
  Speaker 1: neat line that moves in and out. So this is
• 1:21:10
  Speaker 1: a thread of argument. This sort of sophisticated way of
• 1:21:14
  Speaker 1: putting ideas together to create an argument. So it sounds theoretical,
• 1:21:19
  Speaker 1: this thread of argument, But you really are quite worried
• 1:21:23
  Speaker 1: about extinction level events? Extinction for the human race would
• 1:21:29
  Speaker 1: mean where the human race gets wiped out and there
• 1:21:32
  Speaker 1: are no humans left. That sounds a little bit alarmist,
• 1:21:38
  Speaker 1: doesn't it. It's probably unlikely to result in extinction level events,
• 1:21:43
  Speaker 1: I would imagine. I mean that's just speaking personally. Probably
• 1:21:46
  Speaker 1: what would be more likely is that you'd end up
• 1:21:48
  Speaker 1: with maybe a lot of loss, a large loss of
• 1:21:51
  Speaker 1: human life like we've seen in the past in global wars,
• 1:21:56
  Speaker 1: but where maybe a certain percentage of the human race
• 1:22:02
  Speaker 1: would survive, and that would probably be the richer ones. Anyway,
• 1:22:07
  Speaker 1: are you quite worried about extinction level events? Events at
• 1:22:11
  Speaker 1: a level that could cause the extinction of the human race.
• 1:22:14
  Speaker 1: We talk about extinction of animals. The dinosaurs are extinct.
• 1:22:19
  Speaker 1: An extinction level event for the dinosaurs was the impact
• 1:22:22
  Speaker 1: of several asteroids that hit the earth, causing all sorts
• 1:22:28
  Speaker 1: of environmental you know, challenges that most of the dinosaurs
• 1:22:33
  Speaker 1: couldn't deal with, and they became extinct. Okay, and Jeffrey said,
• 1:22:41
  Speaker 1: we should distinguish these different risks. So distinguish something means
• 1:22:45
  Speaker 1: make a difference between them. We should distinguish these different risks,
• 1:22:49
  Speaker 1: show that this risk is different to this risk. So
• 1:22:52
  Speaker 1: he talks about the risk of using AI for autonomous
• 1:22:55
  Speaker 1: lethal weapons doesn't depend on AI being smarter than us.
• 1:22:59
  Speaker 1: So there's the use of AI for weaponry by humans.
• 1:23:03
  Speaker 1: And then there's another risk, which is that AI on
• 1:23:06
  Speaker 1: its own auto autonomously choosing to attack the human race.
• 1:23:11
  Speaker 1: So these are two different risks, and he says that
• 1:23:15
  Speaker 1: he's worried about both. So the one risk is just
• 1:23:18
  Speaker 1: humans using AI to kill kill other humans, and the
• 1:23:22
  Speaker 1: other one is like AI going rogue. So he uses
• 1:23:27
  Speaker 1: this expression the risk that AI itself will go rogue
• 1:23:32
  Speaker 1: and try to take over. So to go rogue means
• 1:23:36
  Speaker 1: to kind of hmm, have you seen Mission Impossible? The
• 1:23:43
  Speaker 1: Mission Impossible films? There's one called Rogue Nation, right, Mission Impossible?
• 1:23:48
  Speaker 1: Rogue Nation. Also, it makes me think of because you know,
• 1:23:52
  Speaker 1: I understand all issues through the context of pop culture.
• 1:23:56
  Speaker 1: Movie plot lines, the Born Identity films with Matt Damon.
• 1:24:02
  Speaker 1: In those films, he goes rogue, right, So he was
• 1:24:07
  Speaker 1: a spoiler alert, He was a specially trained soldier, but
• 1:24:13
  Speaker 1: he goes rogue, meaning he goes off and starts making
• 1:24:16
  Speaker 1: his own independent decisions. Right, He no longer follows the
• 1:24:21
  Speaker 1: orders or commands that he's been given. He goes off
• 1:24:24
  Speaker 1: and starts making his own decisions. So he and he's
• 1:24:28
  Speaker 1: no longer controlled or subservient to his former military bosses.
• 1:24:34
  Speaker 1: He goes rogue. Same thing with Tom Cruise in some
• 1:24:37
  Speaker 1: of those Mission Impossible films where he realizes that his
• 1:24:41
  Speaker 1: bosses at the CIA have been compromised and they're now
• 1:24:45
  Speaker 1: working for the enemy, and he realizes that he has
• 1:24:48
  Speaker 1: to just go rogue. He's going to have to go
• 1:24:50
  Speaker 1: off on his own and make his own decisions. And
• 1:24:55
  Speaker 1: in fact, his bosses, his former bosses, are trying to
• 1:24:57
  Speaker 1: catch him, right Ethan, and he's gone rogue. Down down, down, down, down,
• 1:25:06
  Speaker 1: down down down down't And then he jumps off a
• 1:25:08
  Speaker 1: building and breaks his ankle, you know, classic Tom Cruise stuff,
• 1:25:11
  Speaker 1: and then he runs really fast in some situation. So
• 1:25:18
  Speaker 1: Jeffrey is worried that AI will actually go rogue and
• 1:25:22
  Speaker 1: try to take over, meaning to take control, to take over,
• 1:25:27
  Speaker 1: to take control of the world, take over the world,
• 1:25:31
  Speaker 1: and so at this point, before it's more intelligent than us,
• 1:25:34
  Speaker 1: we should be putting huge resources into seeing whether we're
• 1:25:37
  Speaker 1: going to be able to control it. So to put
• 1:25:39
  Speaker 1: resources into something means to invest money, to invest time,
• 1:25:43
  Speaker 1: to invest different things into trying to understand if we
• 1:25:52
  Speaker 1: can control it. So we should be putting a lot
• 1:25:54
  Speaker 1: of resources into this, putting a lot of people into
• 1:25:57
  Speaker 1: working on it, spending a lot of money on it.
• 1:26:00
  Speaker 1: Resources means things that you can use to get something done.
• 1:26:03
  Speaker 1: It could be money, it could be people, it could
• 1:26:05
  Speaker 1: be infrastructure, whatever it is. We should be putting these
• 1:26:09
  Speaker 1: resources into understanding if we can control AI to prevent
• 1:26:13
  Speaker 1: it from going rogue and trying to take over the world.
• 1:26:16
  Speaker 1: So basically we need to set up the Avengers and
• 1:26:18
  Speaker 1: we need to do that really fast. So if someone
• 1:26:20
  Speaker 1: can call Tony Stark, that would be good. Oh he
• 1:26:24
  Speaker 1: doesn't exist, Oh dear, I don't know what we're going
• 1:26:27
  Speaker 1: to do. Then yes, I'm very worried about AI taking
• 1:26:31
  Speaker 1: over lots of mundane jobs. Mundane jobs are like boring
• 1:26:36
  Speaker 1: jobs that you know people have to do but they
• 1:26:39
  Speaker 1: don't really want to do, like kind of data entry jobs.
• 1:26:44
  Speaker 1: I mean, you might have a mundane job. And sure
• 1:26:46
  Speaker 1: it might be mundane, it might be boring and always
• 1:26:50
  Speaker 1: the same, which is what mundane means, but it might
• 1:26:53
  Speaker 1: also be pretty important for you. Maybe you don't enjoy
• 1:26:56
  Speaker 1: your job, but you need it in order to get
• 1:26:58
  Speaker 1: your money, pay to pay the rent. So he's saying,
• 1:27:01
  Speaker 1: I'm very worried about AI taking over lots of mundane jobs,
• 1:27:08
  Speaker 1: and he does then say that should be a good thing,
• 1:27:11
  Speaker 1: meaning that humans don't need to do those boring tasks anymore.
• 1:27:15
  Speaker 1: It would be a good thing if the money could
• 1:27:18
  Speaker 1: if the benefits of AI were spread equally, but that's
• 1:27:23
  Speaker 1: not the case in our world as it is today.
• 1:27:26
  Speaker 1: That those people would lose their jobs and then the
• 1:27:28
  Speaker 1: money would just go to the business owners or the
• 1:27:31
  Speaker 1: rich people, and the people who'd lost their jobs would
• 1:27:33
  Speaker 1: just lose out, so it wouldn't be a good thing.
• 1:27:36
  Speaker 1: In fact, he said that AI replacing people's jobs is
• 1:27:40
  Speaker 1: going to lead to a big increase in productivity. So
• 1:27:43
  Speaker 1: productivity means being able to do a lot of work,
• 1:27:47
  Speaker 1: producing or manufacturing a lot of products, or at least
• 1:27:52
  Speaker 1: being able to do a lot in terms of services
• 1:27:55
  Speaker 1: work work related services. So for me, productivity means making
• 1:28:00
  Speaker 1: podcast episodes, and actually AI does help me with my productivity.
• 1:28:05
  Speaker 1: It has led to an increase in my productivity because
• 1:28:08
  Speaker 1: it helps me to create lessons because I use it
• 1:28:12
  Speaker 1: as a way of generating English which I can then
• 1:28:15
  Speaker 1: adapt and turn into podcast episodes. M okay, So being
• 1:28:22
  Speaker 1: productive doing work, So AI is going to lead to
• 1:28:27
  Speaker 1: a big increase in productivity, which leads to a big
• 1:28:30
  Speaker 1: increase in wealth. So if we talk about wealth, we
• 1:28:32
  Speaker 1: talk about money, owning money. Being wealthy means being rich basically,
• 1:28:39
  Speaker 1: So an increase in wealth means more people having more money,
• 1:28:45
  Speaker 1: an increase in people being rich. And if that wealth
• 1:28:49
  Speaker 1: was equally distributed, meaning if everyone got on equal share,
• 1:28:54
  Speaker 1: if it was distributed, if it was passed out, given
• 1:28:57
  Speaker 1: out into society, if everyone got an equal amount, that
• 1:29:01
  Speaker 1: would be great. But it's not going to be equally
• 1:29:03
  Speaker 1: distributed because that's not the way the world works. We
• 1:29:07
  Speaker 1: have a very inequal system. So it's going to increase
• 1:29:13
  Speaker 1: the gap between rich and poor. We've talked about that,
• 1:29:16
  Speaker 1: which increases the chances of right wing populists. So these
• 1:29:21
  Speaker 1: are sort of political figures who are usually on the
• 1:29:25
  Speaker 1: right wing end of the spectrum, who essentially use basic
• 1:29:30
  Speaker 1: kind of human emotive reactionary opinions as a way of
• 1:29:37
  Speaker 1: manufacturing support for their own essentially corrupt and probably unethical
• 1:29:45
  Speaker 1: and dangerous policies. Right, that's what we mean by right
• 1:29:50
  Speaker 1: wing populists. It's a form of manipulative politics that uses
• 1:29:58
  Speaker 1: h issues that caused people to have knee jerk reactions.
• 1:30:04
  Speaker 1: For example, the previous UK government spent a lot of
• 1:30:08
  Speaker 1: their time and energy talking about how they were going
• 1:30:10
  Speaker 1: to stop illegal immigration because this, they worked out, was
• 1:30:14
  Speaker 1: the issue that a certain number of people in society
• 1:30:19
  Speaker 1: were very emotionally invested in the idea of small boats
• 1:30:22
  Speaker 1: arriving on the shores of the country every day, and
• 1:30:27
  Speaker 1: that it's illegal immigrants who are the cause of all
• 1:30:31
  Speaker 1: of the problems in society, not the fact that the
• 1:30:33
  Speaker 1: government are giving all of the money to their super
• 1:30:36
  Speaker 1: rich friends. It's the small groups of desperate people coming
• 1:30:41
  Speaker 1: to the country to get help. These are the ones
• 1:30:44
  Speaker 1: who we need to focus our energy on. And so
• 1:30:47
  Speaker 1: they would spend massive amounts of time doing these performative
• 1:30:51
  Speaker 1: acts of like policy making, which wasn't even real policy making,
• 1:30:59
  Speaker 1: just in order to get support so that they could
• 1:31:02
  Speaker 1: guarantee that they keep power, so that they could then
• 1:31:06
  Speaker 1: just keep doing the things that they were doing often
• 1:31:08
  Speaker 1: which were you know, corrupt business dealings and other things
• 1:31:11
  Speaker 1: of that nature populism. Okay, So he's basically saying, when
• 1:31:15
  Speaker 1: you get a bigger gap between rich and port, you
• 1:31:18
  Speaker 1: get more and more right wing populists in power because
• 1:31:22
  Speaker 1: essentially people, the most people who are who are getting less,
• 1:31:27
  Speaker 1: feel disenfranchised, they feel angry, and then the politicians in
• 1:31:32
  Speaker 1: power use that anger and direct it towards minority groups
• 1:31:36
  Speaker 1: or other easy targets, and it's all just an effort
• 1:31:40
  Speaker 1: to whip up emotion which they can use to maintain power.
• 1:31:46
  Speaker 1: So this is this is obviously a very pessimistic view
• 1:31:50
  Speaker 1: that with this that AI will allow the rich to
• 1:31:55
  Speaker 1: get richer, and it will allow them to manipulate the
• 1:31:58
  Speaker 1: public more and more, leading to a sort of controlled state,
• 1:32:04
  Speaker 1: which is also frightening. To be fair, the interviewer says, so,
• 1:32:10
  Speaker 1: to be clear, you think that the societal impacts from
• 1:32:13
  Speaker 1: the changes in jobs, So societal impacts. I think you
• 1:32:17
  Speaker 1: know what impacts are, like strong effects? What will be
• 1:32:20
  Speaker 1: the impacts of this technology on society? Impacts on society?
• 1:32:25
  Speaker 1: What kind of impacts societal impacts? So societal is the
• 1:32:30
  Speaker 1: adjective for society. So you could say, what will be
• 1:32:33
  Speaker 1: the impacts on society or what will be the societal impacts?
• 1:32:38
  Speaker 1: Can you say it, what will be the societal impacts
• 1:32:43
  Speaker 1: from the changes in jobs? These things could be so profound,
• 1:32:50
  Speaker 1: meaning so deep, and so significant and so heavy. We
• 1:32:58
  Speaker 1: may need to rethink the politics of the benefit system,
• 1:33:01
  Speaker 1: to think about it again, meaning find a new way
• 1:33:05
  Speaker 1: of thinking about it, to rethink it. We need to
• 1:33:08
  Speaker 1: rethink the politics of the benefit system. The benefit system
• 1:33:12
  Speaker 1: is a system that's in place that means that governments
• 1:33:16
  Speaker 1: provide benefits to people in society who need help. When
• 1:33:20
  Speaker 1: we say benefits, we mean things like childcare benefit. This
• 1:33:27
  Speaker 1: is money to help families pay for food for their
• 1:33:29
  Speaker 1: children or pay for childcare. Pensions are a form of benefits.
• 1:33:36
  Speaker 1: So this is old people who are too old to
• 1:33:38
  Speaker 1: work and they're they're given a pension which is a
• 1:33:42
  Speaker 1: certain amount of money every month or every year to
• 1:33:44
  Speaker 1: help them live. If you don't have a job, you
• 1:33:47
  Speaker 1: might have unemployment benefit of some kind, which is money
• 1:33:51
  Speaker 1: that you're given to support you while you find a job.
• 1:33:54
  Speaker 1: And so this is the benefit system. It basically is
• 1:33:57
  Speaker 1: there to support people who need support. So maybe we
• 1:34:00
  Speaker 1: need to rethink the benefit system, and maybe we need
• 1:34:05
  Speaker 1: to rethink inequality. So this is basically, look again at
• 1:34:09
  Speaker 1: the inequality the lack of equality that we have, and
• 1:34:12
  Speaker 1: just think about a new way of dealing with it.
• 1:34:15
  Speaker 1: And he says absolutely, which leads us onto universal basic income,
• 1:34:19
  Speaker 1: which I mentioned before, This idea that everyone could be
• 1:34:24
  Speaker 1: given a basic income, a basic amount of money every
• 1:34:29
  Speaker 1: month which they could use to just you know, pay
• 1:34:32
  Speaker 1: for the basic requirements of life, as a way of
• 1:34:34
  Speaker 1: preventing large sections of society dropping into poverty. And this
• 1:34:42
  Speaker 1: is a this is a thing that people are talking
• 1:34:44
  Speaker 1: about more and more, and it could be a sort
• 1:34:46
  Speaker 1: of solution to this huge inequality that we've got in society,
• 1:34:50
  Speaker 1: basically providing people with money. It's quite a controversial one
• 1:34:53
  Speaker 1: when you think about it, because you know, you just think, what,
• 1:34:55
  Speaker 1: we're just going to give people money for doing nothing,
• 1:34:57
  Speaker 1: just to prevent them from falling into poverty. And you know,
• 1:35:00
  Speaker 1: some people are of the opinion this is very bad
• 1:35:03
  Speaker 1: thing to do, that it encourages a culture of laziness
• 1:35:06
  Speaker 1: and stuff like that, and other people are saying, well, actually,
• 1:35:08
  Speaker 1: there's no other solution. We have to essentially provide people
• 1:35:12
  Speaker 1: with a basic living wage even if they're not working,
• 1:35:16
  Speaker 1: because otherwise, millions of people will live in poverty, millions
• 1:35:19
  Speaker 1: of people will starve to death because that's the situation
• 1:35:22
  Speaker 1: we're living in. So what do you want that solves
• 1:35:27
  Speaker 1: the problem of them starving meaning dying because they've got
• 1:35:31
  Speaker 1: no food and not being able to pay the rent.
• 1:35:34
  Speaker 1: The rent is the money that you need to pay
• 1:35:37
  Speaker 1: for your accommodation if you don't own your own house,
• 1:35:41
  Speaker 1: if you're renting an apartment, you have to pay the
• 1:35:43
  Speaker 1: rent every month to your landlord if you're a tenant.
• 1:35:48
  Speaker 1: So this could help prevent people from starving or prevent
• 1:35:52
  Speaker 1: people from being unable to pay the rent. And if
• 1:35:55
  Speaker 1: you can't pay the rent, you will be evicted from
• 1:35:57
  Speaker 1: your apartment and then you're on the street. With that
• 1:36:00
  Speaker 1: universal basic income, we could see people starving to death
• 1:36:04
  Speaker 1: on the streets, you know. So you know, maybe universal
• 1:36:10
  Speaker 1: basic income is a thing that people that we need
• 1:36:12
  Speaker 1: to set up in society. So what you just you
• 1:36:18
  Speaker 1: just try to The government needs to get involved, right,
• 1:36:23
  Speaker 1: get involved. If you're involved, it means you are part
• 1:36:26
  Speaker 1: of what is happening, Right, you're implicated. Although implicated in
• 1:36:32
  Speaker 1: English sounds like there's a negative sense to it if
• 1:36:37
  Speaker 1: you're implicating you'd be implicated in a crime. But no,
• 1:36:41
  Speaker 1: to be involved, meaning to be part of what's happening,
• 1:36:44
  Speaker 1: the government needs to get involved, means the government needs
• 1:36:47
  Speaker 1: to step in and maybe try and control the situation
• 1:36:50
  Speaker 1: somewhat by you know, setting up universal basic income and
• 1:36:54
  Speaker 1: looking after people who need support. Jeffrey says, that's not
• 1:36:59
  Speaker 1: really the way we do things in Britain. We tend
• 1:37:01
  Speaker 1: to stand back and let the economy decide a bit
• 1:37:04
  Speaker 1: like in America, they have a slightly more of a
• 1:37:07
  Speaker 1: small government approach. Certainly, the kind of a conservative model
• 1:37:11
  Speaker 1: in the UK is that we don't like a kind
• 1:37:15
  Speaker 1: of big government nanny state situation where the government is
• 1:37:18
  Speaker 1: dictating what happens all the time. There is a feeling
• 1:37:22
  Speaker 1: in the UK that that's not preferable. That we prefer
• 1:37:26
  Speaker 1: to let the economy decide the winners and the losers.
• 1:37:30
  Speaker 1: You know, we have that more free market economy situation.
• 1:37:34
  Speaker 1: But Jeffrey says, I was consulted by people in Downing Street.
• 1:37:38
  Speaker 1: Downing Street this is where the Prime Minister is based.
• 1:37:42
  Speaker 1: So if you talk of Downing Street, it's a bit
• 1:37:44
  Speaker 1: like in America when they talk about the White House
• 1:37:47
  Speaker 1: or Capitol Hill. Downing Street is where the prime minister is.
• 1:37:51
  Speaker 1: So I was consulted by people in Downing Street means
• 1:37:54
  Speaker 1: that people in the government asked for his advice. He
• 1:37:58
  Speaker 1: was consulted, He worked as a consultant, He gave his
• 1:38:03
  Speaker 1: advice and he advised them that universal basic income was
• 1:38:08
  Speaker 1: a good idea, but it's not a perfect idea. Because
• 1:38:11
  Speaker 1: you can provide people with money that solves that problem.
• 1:38:14
  Speaker 1: But people don't have jobs just for money. They also
• 1:38:18
  Speaker 1: have jobs for a sense of self worth, and giving
• 1:38:24
  Speaker 1: taking people's jobs away can also remove their sense of
• 1:38:28
  Speaker 1: self worth. So we might be in a situation where
• 1:38:30
  Speaker 1: people still need to do things as a way of
• 1:38:35
  Speaker 1: preventing them from just losing their sense of purpose. The
• 1:38:41
  Speaker 1: interviewer said, are you more certain that this is going
• 1:38:43
  Speaker 1: to have to be addressed in the next five years?
• 1:38:46
  Speaker 1: So to address a problem is to deal with a
• 1:38:49
  Speaker 1: problem that this is going to have to be dealt
• 1:38:51
  Speaker 1: with in the next five years. This is going to
• 1:38:53
  Speaker 1: have to be managed in the next five years. It's
• 1:38:56
  Speaker 1: going to have to be addressed in the next five
• 1:38:58
  Speaker 1: years in the next parliament. Perhaps that means in the
• 1:39:01
  Speaker 1: next term, in the next government. So you've got five
• 1:39:04
  Speaker 1: years in the UK where the government has a period
• 1:39:07
  Speaker 1: of five years, and then after that there's a general
• 1:39:09
  Speaker 1: election and all the members of Parliament get re elected
• 1:39:13
  Speaker 1: or whatever, and you get a different setup, you get
• 1:39:15
  Speaker 1: a different parliament. So he's saying that maybe this is
• 1:39:19
  Speaker 1: going to be directly addressed in the next parliament, you know,
• 1:39:24
  Speaker 1: in about five years time maybe, and he said, probably
• 1:39:27
  Speaker 1: between five and twenty years from now this will have
• 1:39:30
  Speaker 1: to be addressed. We will have to confront the problem
• 1:39:33
  Speaker 1: of AI trying to take over. To confront it means
• 1:39:36
  Speaker 1: to come face to face with it and deal with
• 1:39:39
  Speaker 1: this problem. Okay, a bit like at school if you're
• 1:39:43
  Speaker 1: getting bullied. If there's a kid at school who is
• 1:39:46
  Speaker 1: always teasing you, stealing your pocket money, hitting you around
• 1:39:50
  Speaker 1: the back of the head, and generally making your life
• 1:39:53
  Speaker 1: a misery, you might have to confront that bully, stand
• 1:39:57
  Speaker 1: up to them, face them. You know. Similarly, between five
• 1:40:02
  Speaker 1: and twenty years from now we will probably have to
• 1:40:04
  Speaker 1: confront the problem of AI trying to take over. It's
• 1:40:08
  Speaker 1: so serious, isn't it can't believe it really that relatively
• 1:40:12
  Speaker 1: soon we're going to have to come face to face
• 1:40:15
  Speaker 1: with the issue of AI trying to take over the world.
• 1:40:20
  Speaker 1: Is it even real? I'm really talking about this, I
• 1:40:23
  Speaker 1: am actually are you impressed by the efforts of governments
• 1:40:30
  Speaker 1: to try to try and rein this in? To rain
• 1:40:34
  Speaker 1: something in means to get something under control, to get
• 1:40:36
  Speaker 1: to grips with it. Rain r eign. Now this refers
• 1:40:41
  Speaker 1: to horses. Reins are the leather straps that you use
• 1:40:47
  Speaker 1: to hold onto a horse's head. You hold the reins
• 1:40:51
  Speaker 1: when you're riding a horse. You hold the reins in
• 1:40:53
  Speaker 1: your hand, and you can use it to turn the
• 1:40:55
  Speaker 1: horse's head left and right and so on. So to
• 1:40:59
  Speaker 1: rain something in is to get something under control, a
• 1:41:01
  Speaker 1: bit like when you hold the reins of a horse.
• 1:41:06
  Speaker 1: So are you impressed by the efforts of governments to
• 1:41:09
  Speaker 1: rain this in? Are you impressed by the efforts of
• 1:41:11
  Speaker 1: governments to try to get this under control? And he
• 1:41:14
  Speaker 1: gives his answer blah blah blah. He says, I'm unimpressed
• 1:41:17
  Speaker 1: by the fact that most of the regulations have no teeth.
• 1:41:21
  Speaker 1: So he's talking about the fact that most of the
• 1:41:22
  Speaker 1: laws relating to this just don't have any real power.
• 1:41:27
  Speaker 1: They have no teeth right, they don't have any real power.
• 1:41:32
  Speaker 1: That's what that expression means. Do you think that the
• 1:41:36
  Speaker 1: tech companies are letting down their guard? If you let
• 1:41:39
  Speaker 1: down your guard? So in boxing, right, you hold up
• 1:41:43
  Speaker 1: your hands, you punch with your fists, but you've got
• 1:41:47
  Speaker 1: to hold your guard up as well, which is your
• 1:41:50
  Speaker 1: other hand or even both your hands, protecting your head
• 1:41:53
  Speaker 1: or protecting your body. This is your guard. And if
• 1:41:56
  Speaker 1: you let your guard down, it means your hands go
• 1:41:59
  Speaker 1: down and suddenly you don't have any defenses anymore, and
• 1:42:02
  Speaker 1: you can easily be punched by your opponent. So do
• 1:42:07
  Speaker 1: you think that the tech companies are letting down their
• 1:42:09
  Speaker 1: guard on safety because they need to be the winner
• 1:42:13
  Speaker 1: in this race for AI? So basically, are tech companies,
• 1:42:17
  Speaker 1: in their efforts to try to be the winner in
• 1:42:23
  Speaker 1: this kind of arms race for AI, are the tech
• 1:42:27
  Speaker 1: companies not thinking about safety? Are they letting down their
• 1:42:32
  Speaker 1: guard on safety? Meaning are they allowing AI to develop
• 1:42:36
  Speaker 1: in dangerous ways because they're trying to race to become
• 1:42:39
  Speaker 1: the leader in the marketplace? And the answer is more
• 1:42:43
  Speaker 1: or less yes it is. He said Google was concerned
• 1:42:48
  Speaker 1: about its reputation, meaning about what people thought of it
• 1:42:52
  Speaker 1: and I think pretty much. The answer was probably yes.
• 1:42:56
  Speaker 1: Then the interviewer asks Jeffrey for his comments on what
• 1:43:00
  Speaker 1: jobs people should do, what degrees they should do. Degrees
• 1:43:04
  Speaker 1: means university qualifications, bachelor degrees, master's degrees. The interviewer says,
• 1:43:10
  Speaker 1: it seems like the world is being thrown thrown up
• 1:43:13
  Speaker 1: in the air. So if things are up in the air,
• 1:43:15
  Speaker 1: or if things are thrown up in the air, it
• 1:43:17
  Speaker 1: means they are sort of suddenly there's no order anymore,
• 1:43:21
  Speaker 1: and we're not sure about anything. Everything's up in the
• 1:43:24
  Speaker 1: air at the moment. We can't be sure about anything.
• 1:43:27
  Speaker 1: So what would you advise somebody to study? To surf
• 1:43:32
  Speaker 1: this wave? Meaning the wave, meaning that this trend in
• 1:43:37
  Speaker 1: society to surf the wave is to get yourself in
• 1:43:40
  Speaker 1: a position where you can actually get a beneficial situation
• 1:43:45
  Speaker 1: to surf the wave, so instead of being drowned by
• 1:43:48
  Speaker 1: the wave, you can somehow climb on top and you
• 1:43:50
  Speaker 1: can ride along. So basically, what jobs should people do
• 1:43:55
  Speaker 1: in order to find a good position in this situation?
• 1:44:00
  Speaker 1: And he said my best bet meaning my best prediction,
• 1:44:04
  Speaker 1: my best Yeah, my best prediction is that plumbing is
• 1:44:09
  Speaker 1: that is a good idea plumbing. I talked about it
• 1:44:11
  Speaker 1: several times already. Notice there's a bee in the middle,
• 1:44:15
  Speaker 1: but it's not plumbing. It's actually pronounced plumbing, so the
• 1:44:18
  Speaker 1: bee is silent. So again, you know, working with water
• 1:44:23
  Speaker 1: pipes and things just basic, a basic mechanical job, practical job.
• 1:44:29
  Speaker 1: Because he said, these things aren't yet very good at
• 1:44:32
  Speaker 1: physical manipulation, meaning AI systems are not very good at
• 1:44:36
  Speaker 1: the actual physical side of things. So we've seen those robots,
• 1:44:39
  Speaker 1: those Boston Dynamics robots that are able to kind of
• 1:44:42
  Speaker 1: rather clumsily open and close doors. They can maybe draw
• 1:44:46
  Speaker 1: with a pen, but they don't have the sort of
• 1:44:49
  Speaker 1: physical dexterity that most humans have with our ability to
• 1:44:53
  Speaker 1: use our fingers and thumbs and arms and stuff. So
• 1:44:57
  Speaker 1: when it comes to actually fixing fixing a leak in
• 1:45:01
  Speaker 1: someone's kitchen, a human being is still much better at
• 1:45:05
  Speaker 1: being able to squeak, open up the doors of the cupboard,
• 1:45:07
  Speaker 1: look inside, work out what the problem is, and then
• 1:45:10
  Speaker 1: get into a position where you can open up the
• 1:45:13
  Speaker 1: pipe and fix it and stuff like that. So we're
• 1:45:16
  Speaker 1: still better in terms of physical manipulation. So something like
• 1:45:19
  Speaker 1: plumbing is would be a good career, Driving says the interviewer,
• 1:45:25
  Speaker 1: and Jeffrey says, no, driving is hopeless. I mean, there's
• 1:45:28
  Speaker 1: no hope in terms of driving. So a career in
• 1:45:31
  Speaker 1: driving is there's no future in that because you know,
• 1:45:35
  Speaker 1: we're going to get driver as cars. It's taking time,
• 1:45:38
  Speaker 1: but we will get them. He even says journalism might
• 1:45:41
  Speaker 1: last a bit longer, but I think these things are
• 1:45:43
  Speaker 1: going to be pretty good journalists quite soon, and probably
• 1:45:46
  Speaker 1: quite good interviewers too. And that's where the interviewer decides
• 1:45:50
  Speaker 1: to end the interview when he says, oh, yeah, they'll
• 1:45:54
  Speaker 1: be better at interviewing than you, and the interview goes, Okay,
• 1:45:57
  Speaker 1: well that's the end of this interview. Thank you very
• 1:45:59
  Speaker 1: much for your time. You're welcome. So what do you think.
• 1:46:02
  Speaker 1: I'm going to ask you at the end here to
• 1:46:05
  Speaker 1: tell me what you think about all this, So leave
• 1:46:08
  Speaker 1: your comments in the comments section. I'm curious to know
• 1:46:11
  Speaker 1: what your reactions are to all of this. Here's what
• 1:46:14
  Speaker 1: I think. So personally, I think this. I think we
• 1:46:17
  Speaker 1: would be wise to take these comments very seriously, although
• 1:46:22
  Speaker 1: as I said, I don't know what to do. I
• 1:46:28
  Speaker 1: don't know what to do about it. I mean, what
• 1:46:31
  Speaker 1: should we do us normal people? I had no idea.
• 1:46:36
  Speaker 1: I think there's no doubt that as AI develops, it
• 1:46:39
  Speaker 1: will make many of us redundant, meaning our jobs will disappear,
• 1:46:44
  Speaker 1: so God knows what the world is going to look like.
• 1:46:46
  Speaker 1: Just for that reason alone, people often say that AI
• 1:46:50
  Speaker 1: won't completely replace a lot of jobs because people like
• 1:46:53
  Speaker 1: the human factor. But I'm not completely sure. But you
• 1:46:56
  Speaker 1: can tell me, you know, would you choose a human
• 1:47:01
  Speaker 1: as your English teacher over a very effective AI teacher?
• 1:47:07
  Speaker 1: Would you choose me simply because you like the fact
• 1:47:10
  Speaker 1: I'm a human, even though arguably I might be less
• 1:47:13
  Speaker 1: efficient and more prone to human error than some amazing
• 1:47:19
  Speaker 1: AI language teacher that could look and sound just like me.
• 1:47:22
  Speaker 1: Does it matter that it would look like me? I
• 1:47:24
  Speaker 1: don't know. When AI gets to a certain point, it
• 1:47:28
  Speaker 1: will be extremely good at being human, enough to make
• 1:47:31
  Speaker 1: it satisfying for us to interact with them. At the moment.
• 1:47:35
  Speaker 1: AI bots, and I'm talking about ones that look like humans,
• 1:47:41
  Speaker 1: so you could have an actual video conversation with it
• 1:47:44
  Speaker 1: where you can see its face and its lips moving
• 1:47:47
  Speaker 1: and its body language. At the moment, it's not very
• 1:47:49
  Speaker 1: satisfying to have conversations with that kind of thing. It's
• 1:47:54
  Speaker 1: not bad. It's certainly a lot better than it was,
• 1:47:57
  Speaker 1: but they still are not very convincing, and it's generally
• 1:48:00
  Speaker 1: kind of awkward. It's not so bad having a conversation
• 1:48:04
  Speaker 1: with chat GPT, for example, where it doesn't have a face,
• 1:48:06
  Speaker 1: it's just a voice replying to you. That's actually surprisingly amazing.
• 1:48:12
  Speaker 1: But in terms of like replicating a video call with
• 1:48:15
  Speaker 1: an English teacher, it's not there yet. So but when
• 1:48:19
  Speaker 1: it does get to that point, and that will be rapid,
• 1:48:23
  Speaker 1: eventually we'll probably be able to have essentially like video calls,
• 1:48:29
  Speaker 1: skype calls or zoom calls with an AI human and
• 1:48:34
  Speaker 1: it will be really difficult to tell that it's fake.
• 1:48:40
  Speaker 1: So we'll get there. I think people will quite gladly
• 1:48:45
  Speaker 1: switch to AI versions of many things because the quality
• 1:48:49
  Speaker 1: and the naturalness will be exceptional. It's going to be insane.
• 1:48:56
  Speaker 1: I mean that raises all sorts of other questions about
• 1:48:58
  Speaker 1: fake news and stuff of course as well, Right, how
• 1:49:01
  Speaker 1: will you know what is real anymore? Which again goes
• 1:49:05
  Speaker 1: back to that idea of the kind of the potential
• 1:49:10
  Speaker 1: for propaganda and government level control where it will be
• 1:49:16
  Speaker 1: quite possible, where the matrix will sort of come true,
• 1:49:19
  Speaker 1: where eventually, the powers that be. Maybe I'm being very paranoid,
• 1:49:24
  Speaker 1: but I don't know, the powers that be will be
• 1:49:26
  Speaker 1: able to essentially just create a completely simulated world for us.
• 1:49:30
  Speaker 1: I mean that's already happening, of course, with the way
• 1:49:34
  Speaker 1: that information is controlled all It's happened for centuries, of course,
• 1:49:38
  Speaker 1: but with the Internet, with different forms of social media,
• 1:49:42
  Speaker 1: with the way that the government you know, monitors, regulates,
• 1:49:48
  Speaker 1: controls the media that we see. It essentially defines our reality,
• 1:49:53
  Speaker 1: you know. And all you need to do is just,
• 1:49:55
  Speaker 1: you know, have a look at the state controlled media
• 1:49:57
  Speaker 1: and the narrative that it presents, and you'll see that,
• 1:50:01
  Speaker 1: you know, they are definitely controlling your reality. And when
• 1:50:06
  Speaker 1: you look at well, anyway, I won't go on, but anyway,
• 1:50:11
  Speaker 1: So it raises questions about that. But also basically, when
• 1:50:16
  Speaker 1: these simulated conversations with a human become really natural, I
• 1:50:23
  Speaker 1: think sure people will just switch to them. They'll have
• 1:50:25
  Speaker 1: no problem with it. AI will be absolutely central to
• 1:50:29
  Speaker 1: everything we do entertainment. For example, we'll be able to
• 1:50:33
  Speaker 1: create our own movies, instantly choosing all the factors we want,
• 1:50:37
  Speaker 1: for example, which actors, the themes of the movie, the style,
• 1:50:41
  Speaker 1: the storyline type anything, and generative AI will create it instantly.
• 1:50:46
  Speaker 1: It will create our perfect movie and we can just
• 1:50:49
  Speaker 1: sit down and watch it and it'll be good. We'll
• 1:50:53
  Speaker 1: be able to do the same thing with music. We'll
• 1:50:55
  Speaker 1: be able to say, you know, hey, whatever you know.
• 1:50:59
  Speaker 1: You'll just talk to you AI and say, make a
• 1:51:01
  Speaker 1: playlist of songs that upbeat, kind of sound like a
• 1:51:03
  Speaker 1: bit like the Beatles, but with a modern vibe, and
• 1:51:06
  Speaker 1: it'll just do it. Make all the songs about learning English,
• 1:51:11
  Speaker 1: and it'll do it and they'll be good. A lot
• 1:51:13
  Speaker 1: of the things will be automatic, right, A lot of things,
• 1:51:16
  Speaker 1: obviously driving, but so many other things will just be automatic,
• 1:51:20
  Speaker 1: and AI will understand what we want and will make
• 1:51:23
  Speaker 1: it happen. One day, we will look back in horror
• 1:51:27
  Speaker 1: and amazement at the way that we live now. For example, driving,
• 1:51:32
  Speaker 1: we will look back in horror and amazement that we
• 1:51:35
  Speaker 1: actually let humans drive cars on highways. We will think
• 1:51:40
  Speaker 1: this is madness. We'll think, wait, we let humans drive
• 1:51:44
  Speaker 1: cars six line six lane highways, driving eighty kilometers an
• 1:51:49
  Speaker 1: hour in opposite directions, right next to each other. And
• 1:51:53
  Speaker 1: this was normal, and people did it every day, and
• 1:51:55
  Speaker 1: people died on the roads all the time absolutely insane.
• 1:51:59
  Speaker 1: We will think a bit. I think it is bonkers.
• 1:52:03
  Speaker 1: English Teachers Chat GPT is already stunningly reactive, well informed,
• 1:52:08
  Speaker 1: and capable as a conversation partner. AI will be able
• 1:52:12
  Speaker 1: to make strategic decisions for most things that are based
• 1:52:14
  Speaker 1: on much better, clearer and efficient thinking processes, and will
• 1:52:18
  Speaker 1: probably produce better results, more quickly and efficiently than humans.
• 1:52:22
  Speaker 1: AI will understand us better than we understand ourselves, and
• 1:52:26
  Speaker 1: might be able to completely manipulate us. Think of advertising,
• 1:52:31
  Speaker 1: which is so incisive and effective that it's basically hypnosis
• 1:52:35
  Speaker 1: or mind control, and hypnotic mind control is definitely a
• 1:52:39
  Speaker 1: real thing that is practiced in various entertainment situations, but
• 1:52:43
  Speaker 1: also in military and secret service projects. So AI will
• 1:52:47
  Speaker 1: probably be able to brainwash us extremely effectively. I am
• 1:52:51
  Speaker 1: worried about what humans will do to other humans using
• 1:52:54
  Speaker 1: AI as a tool. Who will control it, what will
• 1:52:57
  Speaker 1: they choose to do with it? But you don't mean
• 1:53:00
  Speaker 1: to be just completely pessimistic. AI is also awesome. What
• 1:53:04
  Speaker 1: it can do is incredible, and it could be tremendously empowering.
• 1:53:09
  Speaker 1: You know, in medical situations, we could use AI to
• 1:53:11
  Speaker 1: help diagnose medical conditions and to provide medical care in
• 1:53:17
  Speaker 1: the most adaptive way. I already use AI a lot
• 1:53:22
  Speaker 1: in my job, as I said earlier, but it will
• 1:53:23
  Speaker 1: develop to unbelievably sophisticated levels in ways that we can't
• 1:53:27
  Speaker 1: quite comprehend now. But AI is far from being This
• 1:53:32
  Speaker 1: is maybe the most negative point in the whole episode.
• 1:53:34
  Speaker 1: I'm so sorry everyone. AI is far from being the
• 1:53:37
  Speaker 1: biggest threat to human existence at the moment. It might
• 1:53:41
  Speaker 1: even be our savior in fact, and that bigger threat is,
• 1:53:44
  Speaker 1: of course the climate catastrophe, which is another story for
• 1:53:49
  Speaker 1: another time. Now. I want to explore this subject further,
• 1:53:52
  Speaker 1: but I think it's that that's for another episode. I'm
• 1:53:56
  Speaker 1: interested in making projections and predictions for how specific AI
• 1:54:00
  Speaker 1: could change our society over the next five, ten, twenty
• 1:54:03
  Speaker 1: fifty years. I think it's fascinating. So what do you think?
• 1:54:06
  Speaker 1: Does that sound interesting? Let me know and perhaps I
• 1:54:10
  Speaker 1: can explore it in another episode. But in the meantime
• 1:54:13
  Speaker 1: leave your comments below, perhaps with your own opinion. Here
• 1:54:17
  Speaker 1: are some questions for you to consider. Are you optimistic
• 1:54:20
  Speaker 1: or pessimistic about this or some kind of combination of
• 1:54:23
  Speaker 1: the two. Do you use AI at the moment, how
• 1:54:26
  Speaker 1: do you use it Could AI be a threat to
• 1:54:29
  Speaker 1: your job one day? Or will it open new possibilities
• 1:54:32
  Speaker 1: and new opportunities for your work? Do you use AI
• 1:54:36
  Speaker 1: to help you learn English? And will English teachers all
• 1:54:39
  Speaker 1: be out of a job in the next few years?
• 1:54:41
  Speaker 1: Would you choose me over my AI alternative? There's a
• 1:54:47
  Speaker 1: question anyway. Thank you so much for listening. If you
• 1:54:50
  Speaker 1: look at the PDF, you'll see a full vocabulary list
• 1:54:53
  Speaker 1: of all the highlighted bits of vocab in a list,
• 1:54:56
  Speaker 1: and also another list with loads of all of that
• 1:55:02
  Speaker 1: those bits of vocab with definitions, examples, and commentary. So
• 1:55:07
  Speaker 1: tons more detail on the PDF, which you can just
• 1:55:11
  Speaker 1: download just freely from the episode show notes. But that's it.
• 1:55:16
  Speaker 1: That's the end of this episode. Thank you so much
• 1:55:17
  Speaker 1: for watching or listening. Quite I just dumped all sorts
• 1:55:22
  Speaker 1: of heavy things on your mind for this one. I
• 1:55:26
  Speaker 1: hope I didn't bring down your mood at all. I mean,
• 1:55:30
  Speaker 1: it's kind of terrifying, but I mean I feel actually
• 1:55:32
  Speaker 1: quite energized by the subject. I find it fascinating and
• 1:55:35
  Speaker 1: quite stimulating. But I'm curious to know what you think
• 1:55:38
  Speaker 1: about it all. Thank you very much for watching, Thank
• 1:55:40
  Speaker 1: you for listening. Speak to you in the next episode
• 1:55:44
  Speaker 1: of Luke's English podcast. But for now, it's just time
• 1:55:46
  Speaker 1: to say goodbye bye bye Bow Bow Boom.