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Elon Musk BRUTALLY Honest Interview LEAVES Audience Speechless · Elon Musk Thinking

0:00
Speaker 1: Latest interview of Elon Musk.
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Speaker 2: Bill Gates said, there is no one in our time
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Speaker 2: who has done more to push the bounds of science.
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Speaker 3: Innovation than you.
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Speaker 1: What's kind of going to say, well.
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Speaker 2: That's a nice thing to have anyone say about you.
0:11
Speaker 2: Nice coming from Bill Gates. But oddly enough, when it
0:13
Speaker 2: comes to AI, actually, for around a decade, you've almost
0:17
Speaker 2: been doing the opposite and saying, hang on, you need
0:20
Speaker 2: to think about what we're doing and what we're pushing here,
0:22
Speaker 2: and what do we do to.
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Speaker 3: Make this safe?
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Speaker 2: And actually maybe we shouldn't be pushing as faster or
0:26
Speaker 2: as hard as we are. Like, I mean, you've been
0:29
Speaker 2: doing it for a decade, Like what was it that
0:31
Speaker 2: caused you to think about it that way? And you know,
0:33
Speaker 2: why do we need to be worried?
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Speaker 1: Yeah? I've been somewhat of a Cassandra for quite a
0:36
Speaker 1: while where people would I tell you, like, we should
0:39
Speaker 1: really be concerned about AI, and that'd be like, what
0:40
Speaker 1: are you talking about? Like I've never really had any
0:42
Speaker 1: experience with it since I was immersed in technology. I
0:46
Speaker 1: have been immersed in technology for a long time. I
0:48
Speaker 1: could see it coming, so, but I think this year
0:51
Speaker 1: was that there's been a number of breakthroughs. I mean,
0:53
Speaker 1: you know, the point in which someone can see a
0:55
Speaker 1: dynamically created video of themselves, you know, like it, so
0:58
Speaker 1: you can make a video of you anything in real time,
1:01
Speaker 1: or me. And so there's sort of the deep pic
1:03
Speaker 1: videos which are really incredibly good, in fact, sometimes more
1:06
Speaker 1: convincing than real ones and deep real and then and
1:09
Speaker 1: then obviously things like chat GPT were quite remarkable, and
1:12
Speaker 1: now I saw a GPT one, GP two, GPT three,
1:15
Speaker 1: GPD four that you know, the whole sort of lead
1:17
Speaker 1: up to that, so it was easy for me to
1:20
Speaker 1: kind of see where it's going. If you just sort
1:21
Speaker 1: of extrapolate the points on a curve and as seeing
1:24
Speaker 1: that tread will continue, then we will have profound artificial intelligence,
1:28
Speaker 1: and obviously at the level that far exceeds human intelligence.
1:31
Speaker 1: So but I'm glad to see at this point that
1:33
Speaker 1: people are taking safety seriously, and I like, I say
1:36
Speaker 1: thank you for holding this AI safety conference. I think
1:38
Speaker 1: actually it will go down in history as being very important.
1:41
Speaker 1: I think it's really quite profound, and I do think
1:44
Speaker 1: overall that the potential is there for artificial intelligence AI
1:47
Speaker 1: to have most likely a positive effect and to create
1:51
Speaker 1: a future of abundance where there is no scarcity of
1:53
Speaker 1: goods and services. But it is somewhat of the magic
1:57
Speaker 1: gene problem where if you have a magic gene that
1:59
Speaker 1: can grab all the wishes, usually those stories don't end well.
2:03
Speaker 1: Too careful what you wish for, including wishes.
2:05
Speaker 2: So you talked a little bit about the summit and
2:08
Speaker 2: thank you for being engaged in it, which has been
2:10
Speaker 2: great and people enjoyed having you there pulling in this dialogue. Now,
2:13
Speaker 2: one of the things that we achieved today in the
2:16
Speaker 2: meetings between the companies and the leaders was an agreement
2:21
Speaker 2: that externally, ideally, governments should be doing safety testing of
2:26
Speaker 2: models before their relief. I think this is something that
2:28
Speaker 2: you've spoken about a little bit. It was something we
2:30
Speaker 2: worked really hard on because you know, my job in
2:32
Speaker 2: government is to say, hang on, there is a potential risk,
2:35
Speaker 2: and not a definite risk, but a potential risk of
2:38
Speaker 2: something that could be bad.
2:40
Speaker 3: My job is to protect.
2:41
Speaker 2: The country and we can only do that if we
2:43
Speaker 2: develop the capability we need in our safety institute and
2:46
Speaker 2: then go in and make sure we can test the.
2:47
Speaker 3: Models before they are released.
2:49
Speaker 2: Delight of that happened today, But you know, what's your
2:51
Speaker 2: view on what we should be doing. Right, You've talked
2:53
Speaker 2: about the potential risk. Right again, we don't know, but
2:56
Speaker 2: you know, what are the types of things governments like
2:58
Speaker 2: ours should be doing to manage and degate against those risks.
3:01
Speaker 1: Well, I generally think that it is good for government
3:03
Speaker 1: to play a role when the public safety is at risk. So,
3:07
Speaker 1: you know, really, for the vast majority of software, the
3:10
Speaker 1: public safety is not at risk. If the app crashes
3:12
Speaker 1: on your phone, your laptop, it's not a massive catastrophe.
3:15
Speaker 1: But when talking about digital superintelligence, I think, which does
3:20
Speaker 1: pose a risk to the public, then there is a
3:23
Speaker 1: role for government to play to safeguard the interest of
3:25
Speaker 1: the public. And this is of course true in many fields,
3:29
Speaker 1: you know, aviation, cars, you know, I deal with regulators
3:32
Speaker 1: throughout the world because of stalling being communications, rockets being aerospace,
3:37
Speaker 1: and cars, you know, being vehicle transport. So I'm very
3:40
Speaker 1: familiar with dealing with with regulators, and I actually agree
3:42
Speaker 1: with the vast majority of regulations. There's a few that
3:44
Speaker 1: I disagree with from time to time, but zero point
3:46
Speaker 1: one percent, probably less than one percent of regulations I
3:49
Speaker 1: disagree with. So there is some concern from people in
3:52
Speaker 1: Silicon Valley who who have never dealt with regulators before,
3:55
Speaker 1: and they think that this is going to just crush
3:57
Speaker 1: innovation and slow them down and be annoying. But and
4:00
Speaker 1: it will be annoying, It's true. They're not wrong about that.
4:03
Speaker 1: But I think there's we've learnt over the years that
4:06
Speaker 1: having a referee is a good thing. And if you
4:09
Speaker 1: look at any sports game, there's always a referee, and
4:12
Speaker 1: nobody's suggesting, I think to have a sports game without one.
4:15
Speaker 1: And I think that's the right way to think about this,
4:17
Speaker 1: is for for governed to be a referee, to make
4:19
Speaker 1: sure the sportsmanlike conduct and that the public safety is
4:24
Speaker 1: you know, is addressed. That we care about the public
4:26
Speaker 1: safety because I think there might be at times too
4:28
Speaker 1: much optimism about technology. And I speak I say that
4:30
Speaker 1: as a technologist. I mean, so I ought to know
4:32
Speaker 1: and like I said that, on balance, I think that
4:35
Speaker 1: the AI will be a forceful good most likely, but
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Speaker 1: the probability of it going bad it's not zero percent. Yeah,
4:42
Speaker 1: so we just need to mitigate the downside potential.
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Speaker 3: And then how you talk about referee and that's.
4:47
Speaker 1: What we're demonstrate there.
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Speaker 3: Yeah, well, there we go.
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Speaker 2: I mean you know, and we talked about this in
4:51
Speaker 2: demos and I discussed this a long time ago, like
4:53
Speaker 2: literally facing right and actually Demoster his credit and the
4:57
Speaker 2: credit of people in the industry.
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Speaker 3: Did say that to us.
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Speaker 2: You say it's not right. Yeah, that Demis and his
5:01
Speaker 2: colleagues are marking their own homework.
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Speaker 3: Right. There needs to be someone independent, and.
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Speaker 2: That's why we've developed the Safety Institute here. Do you
5:08
Speaker 2: think governments can develop the expertise. One of the things
5:11
Speaker 2: we need to do is they hang on you know,
5:13
Speaker 2: Denis and Sam all the others. I've got a lot
5:15
Speaker 2: of very smart people doing this. Governments need to quickly
5:19
Speaker 2: tool up capability wise, personnel wise, which is what we're doing.
5:22
Speaker 2: I mean, do you think it is possible for governments
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Speaker 2: to do that fast enough, given how quickly the technology
5:27
Speaker 2: is developing, or what do we need to do to
5:28
Speaker 2: make sure we do it quick enough?
5:29
Speaker 1: No, I think it's a great point you're making. The
5:31
Speaker 1: pace of AI is faster than any technology I've seen
5:35
Speaker 1: in history by far, and it seems to be growing
5:37
Speaker 1: in capability by at least fivefold peraps tenfold per year.
5:41
Speaker 1: It'll certainly grow by an automatitude next year. Yeah, so,
5:45
Speaker 1: and government isn't used to moving at that speed. But
5:48
Speaker 1: I think even if there are not firm regulations, even
5:51
Speaker 1: if there's not even if there isn't an enforcement capability,
5:54
Speaker 1: so we're having insight and being able to highlight concerns
5:56
Speaker 1: to the public will be very powerful. So even if
6:00
Speaker 1: that's all that's accomplished, I think that will be very good.
6:02
Speaker 3: Okay, y well, hopefully we can do better than that.
6:04
Speaker 1: Hopefully.
6:04
Speaker 2: Yeah, yeah, no, but that's how fair. Actually, we were
6:07
Speaker 2: talking before it was striking. You know, you're someone who
6:09
Speaker 2: spent their life in technology. They're living More's law and
6:13
Speaker 2: what it was interesting over the last couple of days
6:16
Speaker 2: talking to everyone who's doing the development of this, and
6:18
Speaker 2: I think you concur with this is just the pace
6:21
Speaker 2: of advancement here is unlike anything all of you have
6:24
Speaker 2: seen in your careers and technology.
6:26
Speaker 3: Is that fair because you've got these kind.
6:28
Speaker 2: Of compounding effects from the hardware and the data and
6:31
Speaker 2: the personnel.
6:32
Speaker 1: Yeah, I mean the two currently the two leading centers
6:35
Speaker 1: for a development are the San Franusco area and the
6:37
Speaker 1: sort of London area, And there are many other places
6:40
Speaker 1: where it's being done, but those are the two leading areas.
6:41
Speaker 1: So I think if you know, if the United States
6:44
Speaker 1: and the UK and China are sort of aligned on safety,
6:48
Speaker 1: that's all going to be. That's really that's where that's
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Speaker 1: where the leadership is general.
6:52
Speaker 2: And you actually you mentioned China there. So I took
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Speaker 2: a decision to invite China to summit over the last
6:58
Speaker 2: three days, and it was not easy decision. A lot
7:00
Speaker 2: of people criticize me for you know, my viewers, if
7:03
Speaker 2: you're going to try it essential serious conversation, you need to.
7:05
Speaker 2: But what would your thoughts? You do business all around
7:07
Speaker 2: the world. You just talked about it there. Should we
7:10
Speaker 2: be engaging with them? Can we trust them? Is that
7:12
Speaker 2: the right thing to have done?
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Speaker 1: If we don't, if China is not on board with
7:16
Speaker 1: AI safety, it's somewhat of a moot situation. The single
7:19
Speaker 1: biggest objection that I get to a kind of AI
7:22
Speaker 1: regulation or sort of safety controls are well, China is
7:25
Speaker 1: not going to do it, and therefore they will just
7:27
Speaker 1: jump into the lead and exceed us all. But actually
7:30
Speaker 1: China is willing to participate in a safety and thank
7:33
Speaker 1: you for inviting them, and they you know, I think
7:36
Speaker 1: we should thank China for for attending. When I was
7:38
Speaker 1: in China earlier this year, My main subject of discussion
7:41
Speaker 1: with the leadership in China was AI safety and saying
7:44
Speaker 1: that this is really something that they should care about,
7:47
Speaker 1: and they took it seriously and you are too, which
7:50
Speaker 1: is great. And having them here I think was essential. Really,
7:52
Speaker 1: if they're not participants, it's pointless.
7:54
Speaker 2: It's yeah, I know that's and I think we were pleased.
7:57
Speaker 2: I think they were engaged yesterday in the discussions and
7:59
Speaker 2: actually ended up signing the same communicy that everyone else did,
8:02
Speaker 2: which is a good stop right. And I said, if
8:05
Speaker 2: we need everyone to approach us in a similar way,
8:07
Speaker 2: if we're going to have a realistic chance of resolving it.
8:10
Speaker 2: I was going to You talked about innovation earlier and
8:12
Speaker 2: regulation being annoying.
8:14
Speaker 3: There was a good debate today we had about.
8:16
Speaker 2: Open source, and I think you've kind of been a
8:18
Speaker 2: proponent of algorithmic transparency and making some of the ex
8:21
Speaker 2: algorithms public and actually we were talking about every hint
8:25
Speaker 2: and on the way in. He's particularly been very concerned
8:28
Speaker 2: about open source models being used.
8:30
Speaker 3: By bad actors.
8:32
Speaker 2: You've got a group of people who say they are
8:34
Speaker 2: critical to innovation happening in that distributed way.
8:37
Speaker 3: Look, it's a trick.
8:38
Speaker 2: There's probably no perfect answer, and there is a tricky balance.
8:40
Speaker 2: What are your thoughts on how we should approach this
8:42
Speaker 2: open source question or where should we be targeting whatever
8:46
Speaker 2: regulatory or monitoring that we're going.
8:48
Speaker 1: To do well. The open source algorithms and data tend
8:52
Speaker 1: to lag the closed source by six to twelve. But
8:55
Speaker 1: given the rate of improvement that there's actually there for
8:57
Speaker 1: quite a big difference between the closed source in the
9:00
Speaker 1: open If things are improving by a factor of let's
9:02
Speaker 1: say five or more than being a year behind is
9:05
Speaker 1: you're five times worse. So it's a pretty big difference.
9:08
Speaker 1: And that might be actually an okay situation, but it
9:10
Speaker 1: certainly we'll get to the point where you've got open
9:12
Speaker 1: source AI that can do that, that will start to
9:15
Speaker 1: approach human level intelligence, Will PAPS succeeded. I don't know
9:18
Speaker 1: quite what to do about it. I think it's somewhat
9:19
Speaker 1: inevermentle there will be some amount of open source, and
9:22
Speaker 1: I guess I would have a slight bias towards open
9:24
Speaker 1: source because at least you can see what's going on.
9:26
Speaker 1: It wears closed source. You don't know what's going on now.
9:29
Speaker 1: It should be said with AI that even if it's
9:30
Speaker 1: an open source, do you actually know what's going on?
9:32
Speaker 1: Because if you've got a gigantic data file and you
9:35
Speaker 1: know sort of billions of days of weights and parameters,
9:38
Speaker 1: you can't just read it and see what it's going
9:40
Speaker 1: to do. It's a gigantic file of inscrutable numbers. You
9:43
Speaker 1: can test it when you run it. You can test it.
9:46
Speaker 1: You can run a bunch of tests to see what
9:47
Speaker 1: it's going to do. But it's probabilistic as opposed to deterministic.
9:51
Speaker 1: It's not like traditional programming, where you've got it you've
9:53
Speaker 1: got very discrete logic and the outcome is very predictable
9:56
Speaker 1: and you can read each line and see what each
9:57
Speaker 1: line's going to do. A neural net is just a
10:00
Speaker 1: much probabilities. I mean, it sort of ends up being
10:02
Speaker 1: a giant Comma separated value file. It's like our digital
10:05
Speaker 1: guide is a CSP file. Really, Okay, that is kind
10:08
Speaker 1: of what it is.
10:09
Speaker 2: Yeah, Now, at that point you've just made is one
10:11
Speaker 2: that we have been talking about a lot, because again
10:15
Speaker 2: conversation with the people are developing their technology.
10:17
Speaker 3: Make the point that you've just made. It is not
10:19
Speaker 3: like normal.
10:19
Speaker 2: Software where there's predictability about inputs improving leading to this
10:24
Speaker 2: particular output improving, and as the models iterate and improve,
10:28
Speaker 2: we don't quite know what's going to come out the
10:30
Speaker 2: other end. I think we would agree with that, which
10:31
Speaker 2: is why I think there is this bias that we
10:34
Speaker 2: need to get in there while the training runs are
10:35
Speaker 2: being done before the models are released, to understand what
10:38
Speaker 2: is this new iteration broad about in terms of capability,
10:41
Speaker 2: which it sounds like you would agree with. I was
10:44
Speaker 2: going to shift goes a little bit on You know,
10:46
Speaker 2: you've talked a lot about human consciousness human agency, which
10:49
Speaker 2: it actually might strike people as strange given that you
10:52
Speaker 2: are known for being such a brilliant innovator and technologist,
10:55
Speaker 2: but it's quite heartfelt when I hear you talk about
10:57
Speaker 2: it and the importance of maintaining that agency in technology
11:01
Speaker 2: and preserving human consciousness.
11:03
Speaker 3: Now it kind of links.
11:04
Speaker 2: The thing I was going to ask is when I
11:06
Speaker 2: do interviews or talk to people out and about in
11:08
Speaker 2: this job about AI, the thing that comes up most
11:10
Speaker 2: actually is it probably not so much the stuff we've
11:12
Speaker 2: been talking about, but jobs. It's what is AI mean
11:15
Speaker 2: for my job? Is it going to mean that I
11:17
Speaker 2: don't have a job, or my kids are not going
11:19
Speaker 2: to have a job. Now, answer as a policymaker.
11:22
Speaker 3: As a leader is actually, AI is already.
11:25
Speaker 2: Creating jobs, and you can see that in the companies
11:27
Speaker 2: that are starting. Also, the way it's being used is
11:31
Speaker 2: a little bit more as a co pilot necessarily versus
11:34
Speaker 2: replacing the person. There's still human agency, but it's helping
11:36
Speaker 2: you do your job better, which is a good thing.
11:39
Speaker 2: And as we've seen with technological revolutions in the past,
11:43
Speaker 2: clearly there's change in the labor market.
11:45
Speaker 3: The amount of jobs.
11:46
Speaker 2: I was quoting an MIT study today that they did
11:49
Speaker 2: a couple of years ago, something like sixty percent of
11:51
Speaker 2: the jobs at that moment didn't exist forty years ago.
11:53
Speaker 3: So hard to predict.
11:54
Speaker 2: And my job is to create an incredible education system,
11:57
Speaker 2: whether it's at school, whether it's retraining people at any
12:00
Speaker 2: point in their career, because ultimately, if we've got a
12:02
Speaker 2: skill population, they'll be able to keep up with the
12:05
Speaker 2: pace of change and have a good life. But you
12:07
Speaker 2: know that it's still a concern, and you know, what
12:10
Speaker 2: would your kind of observation be on AI and the
12:14
Speaker 2: impact of labor markets and people's jobs and how they
12:16
Speaker 2: should feel about that as they think about this.
12:18
Speaker 1: Well, I think we are seeing the most disruptive force
12:21
Speaker 1: in history here, you know, where we have for the
12:24
Speaker 1: first time, we will have the first time something that
12:26
Speaker 1: is smarter than the smartest human and that I mean
12:29
Speaker 1: it's hard to say exactly what that moment is, but
12:31
Speaker 1: there will come a point where no job is needed.
12:34
Speaker 1: You can have a job if you want to have
12:35
Speaker 1: a job for sort of personal satisfaction, but the AI
12:39
Speaker 1: will be able to do everything. So I don't know
12:42
Speaker 1: if that makes people comfortable uncomfortable. You know. That's why
12:44
Speaker 1: I say, if you wish for a magic genie that
12:46
Speaker 1: gives you any wishes you want and there's no limit,
12:48
Speaker 1: you don't have those three limits three wish limit, not
12:50
Speaker 1: since you have as many wishes as you want. So
12:53
Speaker 1: it's both good and bad. One of the challenges in
12:55
Speaker 1: the future will be how do we find meaning in
12:57
Speaker 1: life if you have a magic genie that can do
13:00
Speaker 1: everything you want. I do think we it's hard you
13:02
Speaker 1: know when when? When this new technology, it tends to
13:05
Speaker 1: have usually follow an S curve. In this case, we're
13:08
Speaker 1: going to be on the exponential portion of the S
13:09
Speaker 1: curve for a long time and we have to ask
13:12
Speaker 1: for anything, and it won't be if we won't have
13:14
Speaker 1: universal basic income, we'll have universal high income. So in
13:17
Speaker 1: some in some sense, it will be somewhat of a
13:19
Speaker 1: leveler or an equalizer, because really I think everyone will
13:21
Speaker 1: have access to this magic gene and you're able to
13:24
Speaker 1: ask any question. It will certainly be good for education.
13:26
Speaker 1: It'll be the best tutor you could, the most patient
13:28
Speaker 1: tutor sit there all day, and there will be no
13:31
Speaker 1: shortage of goods and services, will be an age of abundance.
13:33
Speaker 1: I think if I'd recommend people read in Banks, the
13:36
Speaker 1: Banks culture books are probably the best in visioning. In fact,
13:40
Speaker 1: not probably, they're definitely by far the best envisioning of
13:42
Speaker 1: an a future. There's nothing even close. So I'd recommend,
13:45
Speaker 1: really recommend Banks. I'm a very big fan. All these
13:48
Speaker 1: books are good. Does not say which one all of them,
13:50
Speaker 1: So that's that'll give you a sense of what is
13:53
Speaker 1: a I guess a fairly utopian pro toopian future with
13:57
Speaker 1: with AI, Yeah, which is.
13:58
Speaker 2: Good from an as you said, it's a universal high income,
14:01
Speaker 2: which is a nice phrase, and that's it's good for
14:03
Speaker 2: a materialistic sense of abundance. Actually, that kind of then
14:07
Speaker 2: leads to the question.
14:08
Speaker 3: That you pose. Right, I'm someone who believes, you know,
14:10
Speaker 3: work gives you meaning. I write a lot about that.
14:12
Speaker 3: As you know, I think work is a good thing.
14:14
Speaker 2: It, you know, gives people purpose in their lives. And
14:16
Speaker 2: if you then remove a large chunk of that, you know,
14:19
Speaker 2: what does that mean?
14:20
Speaker 3: And where do you get that?
14:21
Speaker 2: You know, where do you get that drive, that motivation,
14:23
Speaker 2: that purpose. I mean, you're talking about it. You work
14:25
Speaker 2: a lot of our you know.
14:27
Speaker 1: As I was mentioning when we were talking earlier, I
14:29
Speaker 1: have to somewhat engage in the liberty suspension of disbelief
14:31
Speaker 1: because I'm putting so much blood, sweat and tears into
14:34
Speaker 1: a work project in burning the you know, three am oil.
14:37
Speaker 1: Then I'm like, wait, why am I doing this? I
14:39
Speaker 1: can just wait for the AI to do it. I'm
14:40
Speaker 1: just lashing myself for no reason. It must be a
14:43
Speaker 1: glutton for punishment.
14:45
Speaker 2: Called demos and tell them to hurry up and then
14:46
Speaker 2: you can have a holiday.
14:47
Speaker 3: Right, it's a plan. Yeah, No, it's a Look, it's
14:50
Speaker 3: a tricky.
14:50
Speaker 2: It's a tricky thing because I think, you know, part
14:52
Speaker 2: of our job is to make sure that we can
14:55
Speaker 2: navigate to that very I think largely positive place and
14:58
Speaker 2: help people through it between now and then, because these
15:01
Speaker 2: things bring a lot about a change in the labor
15:03
Speaker 2: market as we've seen.
15:04
Speaker 1: Yeah, I think it's probably is generally a good thing
15:06
Speaker 1: because you know, there are a lot of jobs that
15:08
Speaker 1: are uncomfortable or dangerous or which sort of tedious, and
15:12
Speaker 1: the computer will have no problem doing that. We're happy
15:14
Speaker 1: to do that all day long. So you know, it's
15:16
Speaker 1: fun to cook food, but it's not that fun to
15:17
Speaker 1: wash the dishes, but the computer is perfectly happy to
15:20
Speaker 1: watch the dishes. I guess there is. You know, we
15:22
Speaker 1: still have a sports like where where humans compete and
15:25
Speaker 1: like the Olympics, and obviously a machine can can go
15:29
Speaker 1: faster than any human, but we still have we saw
15:31
Speaker 1: humans race against each other and have all, you know,
15:34
Speaker 1: have these sports competitions against each other where even though
15:36
Speaker 1: the machines are better, which there's all I guess computingity,
15:39
Speaker 1: who can be the best human at something and people
15:41
Speaker 1: do find fulfillment in that. So I guess that's perhaps
15:43
Speaker 1: a good example of how even when machines are faster
15:45
Speaker 1: than us, stronger than us.
15:46
Speaker 3: We still find a way.
15:47
Speaker 1: We still enjoy competing against other humans, so at least
15:49
Speaker 1: who's the best human.
15:50
Speaker 2: It's a good it's a good analogy, and we've been
15:52
Speaker 2: talking a lot about managing the risks. Just before we
15:55
Speaker 2: move on and finish on AI, is just talk a
15:57
Speaker 2: little bit about the opportunities. You know, you're engaged in
16:01
Speaker 2: lots of different companies. You're an obvious one, which is
16:04
Speaker 2: doing some exciting stuff. You touched on the thing that
16:06
Speaker 2: I'm probably most excited about, which is an education. Yeah,
16:09
Speaker 2: and I think many people will have seen Suth's.
16:12
Speaker 3: Video from earlier this year.
16:13
Speaker 2: Is ted talk about as you talked about, it's like
16:15
Speaker 2: a personal tutor.
16:16
Speaker 1: Yeah, an amazing personal tutor, an.
16:18
Speaker 3: Amazing personal tutor.
16:19
Speaker 2: And we know the difference in learning having that personal
16:22
Speaker 2: his tutor is incredible compared to class from learning. So
16:25
Speaker 2: if you can have every child have a personal tutor
16:27
Speaker 2: specifically for them that then just evolves with them over time,
16:30
Speaker 2: that could be extraordinary. So that you know, for me,
16:32
Speaker 2: I look at it, I think, gosh, that is within
16:34
Speaker 2: reach at this point, and that's one of the benefits
16:36
Speaker 2: I'm most excited about. When you look at the landscape
16:39
Speaker 2: of things that you see as possible, what is it
16:41
Speaker 2: that you know you are particularly excited about.
16:44
Speaker 1: I think certainly a tutors are going to be the amazing,
16:47
Speaker 1: perhaps already are. I think there's also perhaps companionship, which
16:51
Speaker 1: may seem odd because how can the computer really be
16:54
Speaker 1: your friend? But if you haven't that has memory, you know,
16:56
Speaker 1: and remembers all of your interactions and has read every
17:00
Speaker 1: you're going to actually like give it permission to read
17:02
Speaker 1: everything you've ever done, so it really will know you
17:03
Speaker 1: better than anyone, perhaps even yourself. And where you can
17:06
Speaker 1: talk to it every day and those conversations spoiled upon
17:08
Speaker 1: each other, you will actually have a great friend, as
17:11
Speaker 1: long as that friend can stay your friend and not
17:13
Speaker 1: get turned off or something. Don't turn off my friends.
17:16
Speaker 1: But I think that will actually be a real thing.
17:18
Speaker 1: And I one of my sons is sort of has
17:20
Speaker 1: some learning disabilities and has trouble making friends actually, and
17:23
Speaker 1: I was like, well, you know he ai friend would
17:26
Speaker 1: actually be a great friend.
17:28
Speaker 2: Okay, you know that was a surprising answer. That's actually
17:31
Speaker 2: it's worth worth reflecting on that. That's really interesting. I
17:34
Speaker 2: mean we're already seeing it actually as we deliver psychotherapy anyway,
17:38
Speaker 2: now doing far more by digitally and by telephone to
17:42
Speaker 2: people and it's making a huge difference, and you can
17:44
Speaker 2: see a world in which actually, you know, AI can
17:46
Speaker 2: provide that social benefit to people. Just a quick question
17:50
Speaker 2: on X and then we should open it up to everybody.
17:54
Speaker 2: You made a change when in one of the made
17:56
Speaker 2: many changes, but one of the one.
17:58
Speaker 1: Of the change that letter. Yet thing about it, you really.
18:01
Speaker 2: Do, really do one of the changes which you know
18:04
Speaker 2: kind of you know, it goes into the space that
18:05
Speaker 2: you know we have to operate in. And this balance
18:08
Speaker 2: between free speech and moderation is you know, we grapple
18:11
Speaker 2: with as politicians. You were grappling with your own version
18:14
Speaker 2: of that, and you moved away from a kind of
18:17
Speaker 2: manual human way of doing it the moderation.
18:20
Speaker 3: To the community that.
18:22
Speaker 2: Yeah, and I think that's it was an interesting change, right,
18:24
Speaker 2: It's not what everyone else has done. It would be
18:27
Speaker 2: good you know what's what was the reasoning behind that?
18:29
Speaker 2: And why do you think that is a better way
18:31
Speaker 2: to do that?
18:31
Speaker 1: Yeah. Part of the problem is if you if you
18:33
Speaker 1: empower people as sensors, then well there's going to be
18:36
Speaker 1: some amount of bias that they have and then whoever
18:39
Speaker 1: points the sensors is effectively in control of information. So
18:42
Speaker 1: then the idea behind community notice, well, how do we
18:44
Speaker 1: have a consensus driven I mean, so it's not really
18:47
Speaker 1: censoring it, but consensus driven approach to truth. How do
18:50
Speaker 1: we or how do we make things the least amount untrue?
18:53
Speaker 1: Could you say, like you can't perhaps get to pure truth,
18:56
Speaker 1: but you can aspire to be more truthful. So the
18:59
Speaker 1: thing about comurnity notes is it doesn't actually delete anything.
19:02
Speaker 1: It simply adds context. Now that context could be this
19:04
Speaker 1: thing is untrue for the following reasons. But importantly with
19:07
Speaker 1: community notes, everything is open source actually, so you can
19:10
Speaker 1: see the software, every line of the software, you can
19:13
Speaker 1: see all of the data that went into a community note,
19:16
Speaker 1: and you can independently create that community note. So if
19:19
Speaker 1: you've got if you see manipulations of data, you can
19:21
Speaker 1: actually highlight that and say, well this this there appears
19:24
Speaker 1: to be some gaming of the system, and you can
19:26
Speaker 1: suggest improvement. So it's maximum transparency, which.
19:30
Speaker 2: Is I think combined with the kind of wisdom of
19:32
Speaker 2: the crowds and trying to get to a better answer.
19:35
Speaker 1: And really one of the key elements of community notes
19:37
Speaker 1: is that in order for a note to be shown,
19:39
Speaker 1: people who have historically disagreed must agree, and there is
19:42
Speaker 1: a bit of AI usage here. So this will populate
19:45
Speaker 1: a parameter space around each contributor to the community notes,
19:49
Speaker 1: and then a parameter space so everyone's got basically these
19:52
Speaker 1: vectors associated with them, which so it's not as simple
19:55
Speaker 1: as right or left, it's saying, it's more it's several
19:58
Speaker 1: hundred vectors. That that because things are more complicated than
20:01
Speaker 1: something right, right or left, and and they will we'll
20:03
Speaker 1: do sort of inverse correlation, say like, okay, these people
20:06
Speaker 1: generally disagree, but they agree about this note. Okay, so
20:09
Speaker 1: then that so then that that that gives the note credibility. Okay, yeah,
20:13
Speaker 1: that's that's the core of it. And it's working quite well.
20:16
Speaker 1: I get to see a note actually be present for
20:18
Speaker 1: more than a few hours, but that that is incorrect.
20:21
Speaker 1: So the batting average is extremely good. And when I
20:23
Speaker 1: ask people say, oh, they're worried about community notes sort
20:25
Speaker 1: of being disinformation, like send me one, and then they can't,
20:28
Speaker 1: So so I think it's I think it's quite good.
20:29
Speaker 1: I mean, the general aspiration is with the X platform
20:33
Speaker 1: is to inform and entertain the public and to be
20:36
Speaker 1: as accurate as possible. And as truthful as possible, even
20:39
Speaker 1: if someone doesn't like the truth. You know, people don't
20:41
Speaker 1: always like the truth at always, but that's the aspiration.
20:44
Speaker 1: And I think if we are, if we stay true
20:47
Speaker 1: to the truth, then I think we'll find that people
20:49
Speaker 1: use the system to learn what is going on and
20:53
Speaker 1: to I think actually truth pays, so I think it'll
20:56
Speaker 1: be well, I mean, assuming you don't want to engage
20:58
Speaker 1: in self delusion, then I think it's the smart move.
21:01
Speaker 3: Excellent, very helpful. Right, let's open it up to all
21:04
Speaker 3: our guests here, and.
21:05
Speaker 2: We've got some microphones that'll complete I find you have,
21:07
Speaker 2: we got yes, go for it.
21:09
Speaker 3: Thank you, good evening.
21:11
Speaker 4: Alice Pentink from an entrepreneur. First, thank you for a
21:13
Speaker 4: fascinating conversation. I suppose a question for each of you,
21:16
Speaker 4: Prime Minister. The UK has some of the best universities
21:19
Speaker 4: in the world, We have the talent. What will it
21:21
Speaker 4: take for the UK to be a real breeding unicorn companies?
21:25
Speaker 4: Being a founder in the UK is still a non
21:27
Speaker 4: obvious career choice for the most exceptional technical talent. What
21:31
Speaker 4: are the cultural elements that we need to put into
21:32
Speaker 4: place to change this?
21:34
Speaker 3: Thank you? You want to go first?
21:35
Speaker 1: Go for it. Sure, well, you're right that there are
21:37
Speaker 1: cultural elements where you know, the culture should celebrate creating
21:41
Speaker 1: new companies and there should be a bias towards supporting
21:45
Speaker 1: small companies because the ones that need nurturing. The larger
21:48
Speaker 1: companies really don't need nurturing. So you know, just you
21:51
Speaker 1: can think of it's sort of like a garden. If
21:52
Speaker 1: it's a little sprout that needs nurturing, if it's a
21:55
Speaker 1: mighty ogre, doesn't need quite as much. So I think
21:57
Speaker 1: that is a mindset change that is important. But I
22:00
Speaker 1: should mention that London is you know, London and San
22:04
Speaker 1: Francisco or the Bay Area are really the two centers
22:07
Speaker 1: for AI. So that so London is actually doing very
22:09
Speaker 1: well on that front. That the two most I said,
22:12
Speaker 1: the two leading locations on Earth. You know, San Francisco's
22:15
Speaker 1: probably head of London, but London's really very strong. But
22:18
Speaker 1: London Area greater London home counties, I guess. So I'm
22:21
Speaker 1: just saying objectively this is the case. But you do
22:23
Speaker 1: need that. You need the infrastructure, you need the landlords
22:25
Speaker 1: who are willing to rent to new companies. You need
22:28
Speaker 1: more firms and accounts that are willing to support new
22:30
Speaker 1: companies and it's generally it is a mindset change, and
22:33
Speaker 1: I think some of that is happening, but I think
22:35
Speaker 1: really it's just culturally people need to decide this is
22:37
Speaker 1: a good thing. Yeah.
22:38
Speaker 2: Yeah, no, Actually, well thanks for what you said about
22:40
Speaker 2: the UK. It's something that we work hard on. Lots
22:42
Speaker 2: of people in the room are part of what makes
22:44
Speaker 2: this a fabulous place for companies, including Alice. So that's
22:48
Speaker 2: what i'd say is my job is to get all
22:50
Speaker 2: the you know they're nuts and bolts right, make sure
22:52
Speaker 2: that all of you are starting companies can raise the
22:54
Speaker 2: capital that you need everything from. You know, you'll see
22:57
Speaker 2: funding with our incredible you know EI tax reliefs all
23:01
Speaker 2: the way through.
23:01
Speaker 3: To your late stage rounds.
23:03
Speaker 2: And we need reform of our pension funds and the
23:06
Speaker 2: Chancellor's got a bunch of incredible reforms to unlock capital
23:09
Speaker 2: from all the people who have it and deployed into
23:12
Speaker 2: growth equity.
23:12
Speaker 1: Right.
23:13
Speaker 2: That is a work in progress. We're not there yet,
23:15
Speaker 2: but I think we're making good progress. We need talent,
23:17
Speaker 2: we need people, so that means an education system that
23:20
Speaker 2: prioritizes the things that matter.
23:22
Speaker 3: And you've seen my reforms.
23:23
Speaker 2: I go on about more maths, more maths, more maths,
23:26
Speaker 2: But I think that it's important but also attracting the
23:28
Speaker 2: best and the brightest fit. If you look at our
23:30
Speaker 2: fastest growing companies in this country, and I think it's
23:33
Speaker 2: probably the same in the US, over half of them
23:36
Speaker 2: have a non British founder, right, And so that tells
23:39
Speaker 2: you we've got to be a place that is open
23:41
Speaker 2: to the world's best and brightest entrepreneurial talent. So the
23:44
Speaker 2: visa regime that we've put in place, I think, does
23:46
Speaker 2: that makes it easy for those people to come here.
23:48
Speaker 2: And then actually it's the thing that we've spent the
23:50
Speaker 2: beginning of the session talking about the regulation right, making
23:53
Speaker 2: sure that we've got a regulatory system that's pro innovation.
23:57
Speaker 1: That.
23:57
Speaker 2: Yeah, of course we always need guardrails on the things
24:00
Speaker 2: that will worry us, but we've got to create a
24:02
Speaker 2: space for people to innovate and do different things.
24:04
Speaker 3: Now, those are all my jobs.
24:06
Speaker 2: The thing that is tougher is the thing that Elon
24:08
Speaker 2: talked about, which is culture. Right, It's how do you
24:10
Speaker 2: transpose that culture from places like Silicon Valley across the
24:14
Speaker 2: world where people are unafraid to give up the security
24:18
Speaker 2: of a regular paycheck to go and start something and
24:21
Speaker 2: be comfortable with failure.
24:22
Speaker 3: You talk about that a lot. I think you talked about.
24:24
Speaker 2: It more when you were playing games, right, But you've
24:27
Speaker 2: got to be comfortable failing and knowing that that's just
24:30
Speaker 2: part of the process. And that is a tricky cultural
24:32
Speaker 2: thing to do overnight, but it's an important part of
24:35
Speaker 2: I think creating that.
24:36
Speaker 3: Kind of environment.
24:37
Speaker 1: Yeah, if you don't succeed with your first startup, it
24:40
Speaker 1: shouldn't be a sort of a catastrophic career ending exactly thing,
24:43
Speaker 1: it should be you know, well good I think should
24:46
Speaker 1: like should be like, well, you know, you gave it
24:48
Speaker 1: a good shot, you know, in now try.
24:49
Speaker 3: Again and exactly.
24:50
Speaker 1: Yeah. And it's so one thing I'm going to mention is,
24:53
Speaker 1: like I've seen, creating a company is sort of a
24:55
Speaker 1: high risk, high reward situation. But I don't know quite
24:59
Speaker 1: what the how works. In the UK I think is
25:00
Speaker 1: probably better than contenttle Europe, but I'm not sure how
25:03
Speaker 1: does the new give it. But if somebody's basically going
25:06
Speaker 1: to risk their life savings and with the beast majority
25:09
Speaker 1: of startups fail, so I mean you hear about the
25:11
Speaker 1: startups that succeed, but most companies are most startups consist
25:15
Speaker 1: of you know, a massive amount of work followed by failure.
25:18
Speaker 1: But that's actually most companies, and so it's a high risk,
25:21
Speaker 1: high reward and so the higher reward part does need
25:24
Speaker 1: to be there for it to make sense.
25:25
Speaker 2: I think that was a very soft pitch for a
25:28
Speaker 2: tax policy. I mean, but actually I can tell you so,
25:31
Speaker 2: like AI agree, and we have so we have, I
25:34
Speaker 2: think relative to certainly European countries, but certainly the US,
25:38
Speaker 2: definitely California a much lower rate of capital gains tax right,
25:41
Speaker 2: So for those people who are risking and growing something like,
25:44
Speaker 2: we think the reward should be there at the end.
25:46
Speaker 3: So it's twenty percent capital gains tax right.
25:48
Speaker 2: And on stock options, I don't know if we've got
25:50
Speaker 2: anyone from Index Ventures in the room.
25:53
Speaker 3: So you know, Index one of our bleeding VC funds here.
25:55
Speaker 2: Okay, they do a regular report looking at most countries
25:59
Speaker 2: tax of stock options. And you know, when I was
26:03
Speaker 2: a Chancellor of Treasury sexualy equivalent, you know, we were
26:06
Speaker 2: I think down we were pretty good, but we were
26:08
Speaker 2: fourth or fifth, and I said we need to for
26:10
Speaker 2: exactly the reason that you mentioned. I was like, this
26:12
Speaker 2: has got to be the best place for renovative We
26:14
Speaker 2: need to move that up and I think in the
26:15
Speaker 2: last iteration of that report we had because of the
26:18
Speaker 2: changes that Jeremy and I had made, we have moved
26:20
Speaker 2: up to I think second from memory. So hopefully they
26:23
Speaker 2: actually give you and everyone else some comfort that we recognize.
26:26
Speaker 2: That's important because when people work hard and risk things, yeah, they.
26:29
Speaker 3: Should be able to enjoy the rewards and award.
26:31
Speaker 1: Yeah.
26:31
Speaker 2: And I think we have a We very much have
26:33
Speaker 2: a tax system that supports that. And those are the
26:36
Speaker 2: values that you know, I believe in, and I think
26:38
Speaker 2: most of us in this room probably do as well. Right,
26:40
Speaker 2: next next question, I've got seven front of me, and
26:42
Speaker 2: then I'll come over here.
26:43
Speaker 1: Gone, thanks very much.
26:45
Speaker 5: We've talked about some really big ideas, global changing ideas.
26:48
Speaker 5: I'm really interested, particularly in the context of creation of
26:51
Speaker 5: science and technology superhubs and so on. How does that
26:53
Speaker 5: map onto the everyday lives of people living in say Austin, Texas,
26:58
Speaker 5: to choose not aroundin the more in my case Nottingham,
27:00
Speaker 5: East Midlands. What is how do you see that evolving
27:03
Speaker 5: for people you know every.
27:05
Speaker 2: Day sort of every day effective AI for context, Elon
27:08
Speaker 2: Sis said runs are equivalent of CDs, right or Walgreens.
27:11
Speaker 2: So you know, as I visited, right, so he's got
27:14
Speaker 2: millions of people coming in the shops every day, and
27:16
Speaker 2: it's making sure how do we make this relevant? I
27:18
Speaker 2: think that your question how is this relevant to that person?
27:21
Speaker 3: You know?
27:22
Speaker 2: Maybe actually let me go I'll go first on that
27:24
Speaker 2: because I think it's a fair point. I was just
27:25
Speaker 2: going over with the team a couple of things that
27:27
Speaker 2: we're doing because I was saying, how are we doing
27:29
Speaker 2: AI right now that it's making a difference to people's lives.
27:32
Speaker 2: And we have this thing called gov dot which is
27:35
Speaker 2: which actually when we when it happened several years ago,
27:38
Speaker 2: was a pioneering thing all the government information together on
27:41
Speaker 2: one website and so you need to get a driving license, passport,
27:45
Speaker 2: any interaction with government. It was centralized in a very easy,
27:48
Speaker 2: relatively easy to use way.
27:50
Speaker 3: Better than most. So we're about to deploy AI across
27:53
Speaker 3: that platform.
27:54
Speaker 2: So that is something that I think you know, several
27:56
Speaker 2: million people a day use, right, So a large chunk
27:59
Speaker 2: of the popular is interacting with gov dot UK every single.
28:02
Speaker 3: Day to do all these day to day tasks.
28:04
Speaker 2: Right, every one of your customers is doing all those things,
28:07
Speaker 2: and so we're about to deploy AI into that to
28:10
Speaker 2: make that whole process so much easier, because you know,
28:13
Speaker 2: some people will be like, look, well I'm currently here
28:16
Speaker 2: and I've lost my passport and my flights in five hours.
28:19
Speaker 2: At the moment, that would require you know how many
28:21
Speaker 2: steps to figure out what you do. Yeah, Actually, when
28:24
Speaker 2: we deploy the AI, it should be that you could
28:25
Speaker 2: just literally say that and boom, this is.
28:28
Speaker 3: What we're going to do, walk.
28:29
Speaker 2: You through it, and that's going to benefit millions and
28:31
Speaker 2: millions of people every single day, right, Because that's a
28:33
Speaker 2: very practical way in my seat, that I can start
28:36
Speaker 2: using this technology to help people in their day to
28:38
Speaker 2: day lives, not just healthcare discoveries and everything else that
28:42
Speaker 2: we're also doing. But I thought that's quite a powerful
28:44
Speaker 2: demonstration of literally your day to day customer seeing actually
28:49
Speaker 2: their just day to day life get a little bit
28:50
Speaker 2: easier because of something that you know, Elon Demis and
28:52
Speaker 2: others in this room have helped create.
28:54
Speaker 1: Yeah, exactly. The most immediate thing is just being able
28:57
Speaker 1: to ask, like having a very smart friend that you
28:59
Speaker 1: can ask anything, you know, ask how to make something,
29:02
Speaker 1: how to solve any problem and it'll tell you so.
29:05
Speaker 1: And obviously companies are going to adopt this, so I
29:08
Speaker 1: think maybe you'll have much better customer service. I guess
29:10
Speaker 1: essentially that will probably be the first thing you notice.
29:12
Speaker 1: And then we talked about education. So having a tutor
29:16
Speaker 1: so that you're trying to understand a subject, like having
29:20
Speaker 1: a phenomenal tutor on any subject. Is that that's really
29:23
Speaker 1: pretty much there already almost. I mean we need to obviously,
29:26
Speaker 1: I need to stop hallucinating before you know it can't
29:29
Speaker 1: give you. I mean that we still have a little
29:31
Speaker 1: bit of a problem where it can give you an
29:33
Speaker 1: answer that's confidently wrong with great grammar, you know, bullet
29:36
Speaker 1: points and everything in citations. It was not real. So
29:38
Speaker 1: that's be okay, we need to make sure it's not
29:40
Speaker 1: it's not it's not giving you confidently wrong tutor answers.
29:43
Speaker 1: But but that's going to happen pretty quickly where it
29:46
Speaker 1: is actually correct.
29:47
Speaker 2: Yeah, say for any parent who was homeschooling and realizing
29:50
Speaker 2: what their kids needed to be helped with, yeah, that
29:53
Speaker 2: will come as an enormous relief.
29:54
Speaker 1: I think they're very good.
29:55
Speaker 2: Right, have we got let's go questions over here, who
29:57
Speaker 2: have got only microphones or brand that perfect.
30:01
Speaker 6: Brent Herman, So you know you've spoken eloquently about abundance
30:04
Speaker 6: in the Age of abundance, so it feels obviously with
30:07
Speaker 6: AI it's everything everywhere, all at once, but with robots,
30:11
Speaker 6: and to get the age of abundance will need a
30:13
Speaker 6: lot of robots. I know you're working out on robots
30:15
Speaker 6: as well. Are there sort of constraints that we should
30:17
Speaker 6: think of our politician should be thinking of that we
30:20
Speaker 6: might get one country might get heavily behind in robots
30:23
Speaker 6: that can do all these things and end to the
30:24
Speaker 6: age of bunds and therefore be it a strategic disadvantage.
30:26
Speaker 1: Well, really, anything that can be actuated by a computer
30:29
Speaker 1: is effectively a robot. So you can think of frankly
30:32
Speaker 1: Tesla cars or robots on wheels. Anything that's connected to
30:36
Speaker 1: the Internet is effectively an endpoint actuator for artificial intelligence.
30:40
Speaker 1: So you've got Bust and Dynamics. Obviously they've been making
30:43
Speaker 1: impressive robots for a while. I think they are at
30:45
Speaker 1: this point mostly owned by Hundai, So I guess I
30:48
Speaker 1: is probably gonna make robots that are humanoid and some
30:52
Speaker 1: interesting shapes that I wasn't just beating like the one
30:54
Speaker 1: that looks like a has wheels and looks sort of
30:56
Speaker 1: like a kangaroo on wheels. I'm sure what that is,
30:58
Speaker 1: but it's a little demanded, frankly, but there's going to
31:00
Speaker 1: be all sorts of robots. You've got the company Dison,
31:03
Speaker 1: in which I think there's some pretty impressive things, and
31:07
Speaker 1: I think the UK will not be behind. Actually on
31:09
Speaker 1: that front, the UK also has ARM, which is really
31:12
Speaker 1: the best once one of the best peraps, the best
31:15
Speaker 1: in chip design in the world. Tesla uses a lot
31:18
Speaker 1: of ARM technology almost every winds actually, so I think
31:22
Speaker 1: the UK is in a strong position. Germany obviously makes
31:24
Speaker 1: a lot of robots, industrial robots. I mean, I think
31:27
Speaker 1: generally countries that make robots of any kind, even if
31:30
Speaker 1: they seem somewhat conventional, will be fine. I do think
31:33
Speaker 1: there is a safety concern, especially with humanoid robots, because
31:38
Speaker 1: at least the car can't chase you into this building,
31:41
Speaker 1: not very easily, you know, or chase you over a tree,
31:43
Speaker 1: or you know, you can sort of run up a
31:44
Speaker 1: flight of stairs and get away from a Tesla. I
31:46
Speaker 1: think it's a Stephen King movie about that pere car
31:48
Speaker 1: gets possessed so, but if you have a humanoid robot,
31:51
Speaker 1: it can basically chase you anywhere. So I think we
31:53
Speaker 1: should have some kind of hardwired local cutoff that you
31:57
Speaker 1: can't update from the internet. So anything that could be
32:00
Speaker 1: after updated from the internet obviously could be overridden. But
32:02
Speaker 1: if you have a local sort of off switch where
32:04
Speaker 1: you have to say a keyword or something, and then
32:06
Speaker 1: that puts the robot into a safe state, it's some
32:09
Speaker 1: kind of localized safe state ability and off switch, you know,
32:13
Speaker 1: where you don't have to get too close to the robot.
32:14
Speaker 1: I don't know. So we've got millions of these things
32:16
Speaker 1: going over the place.
32:17
Speaker 3: You're not selling it, just you know, I know.
32:20
Speaker 1: I'm saying this is something we should be quite concerned
32:22
Speaker 1: about because Robert can follow you anywhere. Then you know
32:25
Speaker 1: what if they just one day get a software update
32:27
Speaker 1: and they're not so friendly anymore. We've got a James
32:30
Speaker 1: Cameron movie on our house.
32:31
Speaker 2: It's actually that's it's funny you're saying that, because in
32:33
Speaker 2: our session that we had today, I just would say
32:36
Speaker 2: who they made exactly the same point right then, And
32:39
Speaker 2: so we're talking about they're talking about movies actually about
32:42
Speaker 2: mentioning James Cameron. They're talking about James cameraon movies the same.
32:44
Speaker 2: If you think about it, it's not just those movies, but
32:47
Speaker 2: any of these movies, trains, subways, metros. They said, all
32:52
Speaker 2: these movies with the same plot fundamentally all and with
32:55
Speaker 2: the person turning it off right or finding a way
32:58
Speaker 2: to shut the thing down. And they were making the
33:00
Speaker 2: same point that you were about the importance of actual.
33:03
Speaker 3: Physical off switches.
33:05
Speaker 2: Yeah, and so all the technology is great, but undamentally,
33:07
Speaker 2: the same movie has played out fifty times. We've all
33:10
Speaker 2: watched it and they're all fundamentally you know, you know
33:12
Speaker 2: point I'm revenge right, it all ends in pretty much
33:14
Speaker 2: the same way, with someone finding their way to just.
33:17
Speaker 1: Do it wrong.
33:18
Speaker 3: Which is kind of interesting that you've said a similar point. Right,
33:20
Speaker 3: it's not the it's not the obvious place you'd go to, but.
33:23
Speaker 1: Be one of the tests for the AI, which is
33:25
Speaker 1: so like blank is your favorite Gamers camera movie for
33:28
Speaker 1: Linda Blank?
33:29
Speaker 2: Yeah, excellent, right, Yes, we got over there, yep, perfect, Hi.
33:32
Speaker 7: Question for you both. So I'm a founder of a
33:35
Speaker 7: AI and mL scale up in the third Center for
33:37
Speaker 7: AI which is leads in the North of England and
33:39
Speaker 7: bit biased since the launch of chat GBT. Three months
33:43
Speaker 7: after that, we saw a real increase in phishing attacks
33:46
Speaker 7: using much more sophisticated language patterns. What do we do
33:49
Speaker 7: to protect businesses consumers so they trust this technology better
33:53
Speaker 7: and how do we bring them along that.
33:54
Speaker 1: Journey with this, Well, I think we shouldn't trusted that much. Actually,
33:57
Speaker 1: it is actually quite quite a significant allenge because we're
34:00
Speaker 1: getting to the point where even open source AI can
34:02
Speaker 1: pass human capture tests. So you know, this is are
34:05
Speaker 1: you're human identify all the traffic lights in this picture.
34:09
Speaker 1: You're like, okay, yeah, it's going to have no problem
34:12
Speaker 1: doing that. In fact, it'll do it better than the
34:13
Speaker 1: human and faster than human. So we're like, how do
34:15
Speaker 1: you know it's the point which is a better human
34:18
Speaker 1: better passing human tests than humans? Then well, what tests
34:21
Speaker 1: actually make sense? That is a real problem. I don't
34:23
Speaker 1: actually have a good solution to it.
34:25
Speaker 3: That.
34:25
Speaker 1: One of the things we're trying to figure out on
34:27
Speaker 1: the X platform is how to deal with that, because
34:30
Speaker 1: it really we really are at the point where even
34:32
Speaker 1: with open source you know, readily available AI, you don't
34:35
Speaker 1: need to be sort of leading in the field. You
34:37
Speaker 1: can actually be better than humans at passing these tests,
34:40
Speaker 1: and that's sort of why we think, well, perhaps we
34:42
Speaker 1: should sort of charge a dollar or a pound a year.
34:44
Speaker 1: It's a very tiny amount of money, but it's still
34:46
Speaker 1: makes it privatively expensive to make a million bots. So
34:50
Speaker 1: and especially if you need a million payment methods, then
34:52
Speaker 1: you run out of sort of stolen credit cards pretty quickly.
34:55
Speaker 1: So that's that's sort of where we're thinking, like we
34:57
Speaker 1: might have to sort of just charge some very tiny
34:59
Speaker 1: amount of money three cents a day effectively to deal
35:02
Speaker 1: with the onsought of AI power advance. And that is
35:07
Speaker 1: not a growing problem, but it will be I think,
35:08
Speaker 1: perhaps an insurmountable problem next year. So and then you
35:11
Speaker 1: have to worry about, well, manipulation of information is making
35:15
Speaker 1: something seem very popular when in fact it it's not,
35:17
Speaker 1: because it's getting boosted by all these likes and reposts
35:21
Speaker 1: from AI powered barts. So that's why I sort of
35:23
Speaker 1: think somewhat inevitably it leads to some small payment in
35:27
Speaker 1: order to dramatically increase the cost of a bot. So
35:31
Speaker 1: I frankly I think probably any social media system that
35:33
Speaker 1: doesn't do that will simply be over on by bot.
35:35
Speaker 2: You know, I think my general answer would be, you know,
35:38
Speaker 2: we need to show that we are on top of
35:41
Speaker 2: mitigating the risks right so people can trust the technology.
35:44
Speaker 2: That's what actually the last couple of days has been
35:46
Speaker 2: about on the Safety Summit is just showing, you know,
35:49
Speaker 2: we're investing in the Safety Institute, having the people who
35:52
Speaker 2: can do the research on these things to figure out
35:54
Speaker 2: how we mitigate against them, and we have to do
35:57
Speaker 2: it fast and we have to keep iterating it because
36:00
Speaker 2: all of us probably in this room, believe that the
36:02
Speaker 2: technology can be incredibly powerful, but we've got to make
36:04
Speaker 2: sure we bring people along that journey with us, that
36:06
Speaker 2: we're handling the risks that are there. And as there's
36:09
Speaker 2: a job to do, and the last couple of days,
36:12
Speaker 2: I think we make good progress on it because we
36:14
Speaker 2: want to focus on the positives and manage these things.
36:16
Speaker 2: But that requires action, and that's what the last couple
36:19
Speaker 2: of days.
36:20
Speaker 3: Has been about. Your story, you know, analogy.
36:22
Speaker 2: There was part of the research that actually, you know,
36:25
Speaker 2: the team working on the task force here published and
36:29
Speaker 2: presented yesterday.
36:30
Speaker 3: I don't know if you.
36:30
Speaker 2: Saw it was, which is essentially that it was using
36:33
Speaker 2: AI to do to create a ton of fake profiles
36:37
Speaker 2: on social media and then infiltrate particular groups with particular information.
36:43
Speaker 2: And actually, at the moment that, as I said, to
36:44
Speaker 2: your point, and there's a cost.
36:46
Speaker 1: Free, it's getting to the point where it's like, really,
36:48
Speaker 1: you're going to have one hundred for a penny sort
36:50
Speaker 1: of thing. Ridiculous.
36:51
Speaker 2: And if you think about some of these social networks
36:53
Speaker 2: quite a neighborhood or town level, it's not that many
36:56
Speaker 2: fake profiles.
36:57
Speaker 3: Suddenly they're everywhere and.
36:58
Speaker 2: There's some local issue that might be of importance, and
37:01
Speaker 2: you know, the team of run versions of how that
37:03
Speaker 2: would look like, and suddenly they're interacting with everybody and
37:05
Speaker 2: then spreading and misinformation around. Yeah, challenge, and we literally
37:09
Speaker 2: as part of the research that we published on this
37:11
Speaker 2: information yesterday, it's a real challenge.
37:13
Speaker 1: Yeah, exactly to your point. I mean the images, it's
37:17
Speaker 1: you don't even need to steal somebody's picture because then
37:19
Speaker 1: that's traceable. But you can actually just say, create a
37:22
Speaker 1: new image of a person, realistic looking it doesn't exist,
37:25
Speaker 1: and then create a biography realistic but doesn't exist, and
37:30
Speaker 1: do that en mass and practically the only way of
37:32
Speaker 1: able to tell us that the grammar is too good
37:33
Speaker 1: give away typhos.
37:35
Speaker 2: Come on, now, I'm getting waved at because I think
37:37
Speaker 2: we are out of time. I don't we take one
37:39
Speaker 2: very brief last question and let's make it go on. Yes,
37:42
Speaker 2: sir going, you're right in front of me, go.
37:44
Speaker 8: Question for you related to the X platform. Are there
37:47
Speaker 8: simple things we can do, especially when it comes to
37:49
Speaker 8: visual media. You alluded to the fact that it's fairly
37:51
Speaker 8: straightforward and effectively free to make people like yourselves say
37:55
Speaker 8: and do things that you never said or did. Can
37:57
Speaker 8: we do something like cryptographically signed media? I'm from Adobe
38:00
Speaker 8: working on this project. Twitter was a member. Love to
38:02
Speaker 8: see X come back digitally sign media to indicate not
38:05
Speaker 8: only what was created by AI, but what came from
38:07
Speaker 8: a camera, what was real to imview, A sense of
38:09
Speaker 8: trust in media that can go viral.
38:11
Speaker 1: That sounds like a good idea. Actually, so if some
38:14
Speaker 1: way of authenticating would be good. So, yeah, that sounds
38:17
Speaker 1: like a good idea, we should probably do it.
38:18
Speaker 2: There you go, and actually on that on that point already,
38:21
Speaker 2: And this is particularly partner for people in my job,
38:24
Speaker 2: right and I've already had a situation happened to me
38:27
Speaker 2: with adopted image that goes everywhere negative.
38:30
Speaker 3: By the time everyone realizes all.
38:31
Speaker 2: That's fake and we should stop sending it the damages damage.
38:35
Speaker 3: And actually we were again reflecting today.
38:37
Speaker 2: If you think next year, you've got elections in you know,
38:40
Speaker 2: I think you know, the US, India, I think Indonesia.
38:43
Speaker 3: Probably here there you go for massive news and actually you've.
38:47
Speaker 2: Got just an enormous junk of the world's population is
38:50
Speaker 2: voting next year, right, and you've got EU elections as well.
38:54
Speaker 1: You know.
38:55
Speaker 2: Actually, just these issues are right in front of ours.
38:58
Speaker 2: Next year is where a big election across the globe,
39:00
Speaker 2: probably the first set of elections.
39:02
Speaker 3: Where this has been a real issue.
39:04
Speaker 2: Yeah, So figuring out how we manage that is I
39:07
Speaker 2: think kind of mission critical for the people who want
39:09
Speaker 2: the integrity of our democracy.
39:11
Speaker 1: Yeah. I mean some of it is quite entertaining, like
39:14
Speaker 1: the pope and the puffer jacket. Have you seen that one?
39:16
Speaker 1: That's amazing, But I mean I still write to people
39:18
Speaker 1: who think that's real. I'm like, well, one of the
39:20
Speaker 1: answer is wearing a puffer jacket in July, right, be sweating,
39:23
Speaker 1: but it actually looks quite quite dashing of things. In fact,
39:27
Speaker 1: I think AI fashion is going to be a real thing.
39:28
Speaker 1: So doing gloom like we learn in the most interesting times.
39:31
Speaker 1: And I think this is it is, you know, like
39:33
Speaker 1: eighty percent likely to be good and twenty percent bad.
39:36
Speaker 1: And I think if we're cognizant and careful about the
39:39
Speaker 1: bad part, on balance, actually it will be the future
39:41
Speaker 1: that we want or for the future that is preferable,
39:44
Speaker 1: and it actually will be somewhat of a leveler, an
39:47
Speaker 1: equalizer in the sense that you know, I think everyone
39:49
Speaker 1: will have access to goods and services and education, and
39:52
Speaker 1: so you know, I think probably it leads to more
39:55
Speaker 1: human happiness. So I guess i'd probably leave on an
39:57
Speaker 1: optimistic note.
39:58
Speaker 3: Tough act.
39:59
Speaker 2: Yeah, well, that's that is a great note to end on.
40:01
Speaker 2: I think we all want that better future. I think
40:04
Speaker 2: it's that the promise of it is certainly there. Lots
40:06
Speaker 2: of people in this room, including yourselves, are working hard to.
40:09
Speaker 3: Make it happen.
40:09
Speaker 2: Our job in government is to make sure it happen safely.
40:12
Speaker 2: But on the basis of this conversation in the last
40:14
Speaker 2: couple of days, I'm certainly leaving more confident.
40:17
Speaker 1: Thanks for listening. See you in the next episode.