Latest / Yaron Brook Show / Capitalism, Productiveness and Artificial Intelligence | Yaron Brook Show

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Capitalism, Productiveness and Artificial Intelligence | Yaron Brook Show · Yaron Brook Show

0:03
Speaker 1: Oh lotical fundamental principles of ledom, lactional, self interesting and
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Speaker 1: individual wats This is the ran Brook Show. Oh right,
0:22
Speaker 1: everybody walk up to your own book show on this Saturday,
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Speaker 1: June twentieth. It's Ocan. Those of you coming to Okan,
0:33
Speaker 1: I look forward to seeing you in just a few days.
0:35
Speaker 1: It's just around the corner. I'll be flying out to
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Speaker 1: New Orleans on Thursday, so there will be a I
0:43
Speaker 1: don't know about shows from New Orleans. I don't know
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Speaker 1: what the Wi fi situation is in the hotel, the
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Speaker 1: room there, and last I look at the schedule, They've
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Speaker 1: got me busy every single day all day. So I
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Speaker 1: just don't know if I'm going to have time. I'm
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Speaker 1: gonna do try to do a show at least one
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Speaker 1: while I'm out there, but it's going to be tough.
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Speaker 1: So anyway, Oakan is coming up. I'm looking forward to
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Speaker 1: seeing hopefully a lot of you at OCONN in New Orleans. Hope,
1:13
Speaker 1: hope a lot of you are making the journey out there.
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Speaker 1: All right, Uh, let's see, So we're going to talk
1:20
Speaker 1: about AI today, artificial intelligence some more, and uh, you
1:29
Speaker 1: know I told you we're going to talk a lot
1:30
Speaker 1: about AI. I think in the years to come, this
1:33
Speaker 1: is going to be a topic that is really gotten
1:37
Speaker 1: to dominate, dominates our economy, It's going to dominate uh tech,
1:43
Speaker 1: everything to do with technology. But it's going to dominate
1:47
Speaker 1: our lives because it is in a technology that has
1:52
Speaker 1: the ability to reach into every part of our life.
1:57
Speaker 1: And and so I want to talk about two things
2:00
Speaker 1: things today you know in some depth. One is the
2:07
Speaker 1: relationship between AI and capitalism, and the other is kind
2:11
Speaker 1: of a personal one in the relationship between AI and
2:14
Speaker 1: a kind of productiveness that is AI like a I
2:16
Speaker 1: can do everything, So just do I sit back and
2:21
Speaker 1: not do anything? This is kind of the UBI crowd,
2:24
Speaker 1: which is advocating what does it mean for individual productiveness?
2:28
Speaker 1: What does it mean for the virtual productiveness now that
2:31
Speaker 1: we have AI? So that's going to be the topic
2:34
Speaker 1: for today. Let me just say that I find it
2:38
Speaker 1: interesting that we don't really have we don't really have
2:42
Speaker 1: the language, we don't have the concepts really to talk
2:46
Speaker 1: about AI. So a lot of the concepts use our
2:51
Speaker 1: concept that refer to human intelligence, artificial intelligence? Is it
2:56
Speaker 1: really intelligence? Whether would that really mean? Is this such
3:02
Speaker 1: a thing? These are the philosophical questions that I don't think.
3:07
Speaker 1: I think we have answers FORUL but to some of them.
3:11
Speaker 1: But we don't have we don't have good solutions for
3:14
Speaker 1: We don't have the language, we don't have the concept
3:16
Speaker 1: to describe what it is the day I does. I mean,
3:19
Speaker 1: even even its most advanced researchers don't have really good
3:27
Speaker 1: explanations for how it works and what it does. And
3:34
Speaker 1: in essence, it's it's super interesting, right, But we are
3:41
Speaker 1: attributing it to it human features, So we're attributing to
3:47
Speaker 1: it human intelligence, human cognition, thinking, all this stuff. And
3:53
Speaker 1: I'll be using those terms, but I just want to
3:55
Speaker 1: be clear that by using those terms, I'm not graining
3:59
Speaker 1: that that's what AI actually does, because what AI actually
4:03
Speaker 1: does is something different. You know, thinking requires consciousness. Thinking
4:12
Speaker 1: requires consciousness and and and if not any consciousness, not
4:18
Speaker 1: just any consciousness. It requires a volitional consciousness. It requires
4:23
Speaker 1: a consciousness that can guide the thinking, that can guide
4:28
Speaker 1: the process and at the very least concern it on
4:31
Speaker 1: and off and can focus the pocus, can orient the process. Now,
4:36
Speaker 1: as we'll see, there is such a consciousness with AI.
4:40
Speaker 1: It's you, the user, but the AI itself, has no consciousness,
4:46
Speaker 1: has no guide. It is just responding to stimuli. Uh.
4:52
Speaker 1: And it is doing so by you know, matching, by
4:58
Speaker 1: by analyzing large sets of data data to match it
5:03
Speaker 1: to kind of the quirity that you're being asked, how
5:06
Speaker 1: it exactly does that again, it's not clear. It's let
5:11
Speaker 1: me just say, it's unbelievably good at what it does.
5:15
Speaker 1: Unbelievably good at what it does. And it can extrapolate.
5:20
Speaker 1: It has the ability to extrapolate from you know, the
5:27
Speaker 1: data that it has to giving answers to new data,
5:32
Speaker 1: which is pretty amazing. It's pretty astounding. So it doesn't
5:35
Speaker 1: just regard your date back stuff that it has in
5:37
Speaker 1: its memory banks. It's doing something to the stuff that
5:42
Speaker 1: it has in its memory backs to provide. And the
5:45
Speaker 1: fact that it gives such good answers, The fact that
5:49
Speaker 1: it can look at that it can scan images for
5:52
Speaker 1: cancer and it can see a new image that's completely
5:56
Speaker 1: different than any of the images it saw and still
5:59
Speaker 1: be able to recognize it's cancel not or what exactly
6:02
Speaker 1: is pretty amazing. The fact that it can generalize from
6:07
Speaker 1: seeing uh, you know, complex proteins and you know, project
6:15
Speaker 1: a new protein, right, imagine it's not really imagining but
6:19
Speaker 1: present a new protein. Pretty amazing. So the technology is amazing,
6:22
Speaker 1: it's stunning, and it looks a lot like it often
6:26
Speaker 1: behaves a lot like human cognition, but it is not.
6:30
Speaker 1: So again I'm gonna I'm using terminology, and we'll use
6:34
Speaker 1: terminology and that that ascribes human cognition to it. But
6:38
Speaker 1: I don't mean that. It's just we don't have concepts.
6:42
Speaker 1: We need a new set of concepts to describe what
6:46
Speaker 1: it is that AI does that and and that will
6:51
Speaker 1: allow us to actually communicate about it and differentiate it
6:57
Speaker 1: from human cognition. Anyway, that's a project not for today,
7:04
Speaker 1: I think today. Part of what I want to do
7:07
Speaker 1: is I really want to celebrate AI as a real
7:11
Speaker 1: testament to capitalism, and not really just a testament to capitalism,
7:16
Speaker 1: but really a testament to reason, to free minds, to
7:21
Speaker 1: the ability of human beings when they are free to
7:25
Speaker 1: do amazing things. And it's also a testament to a culture,
7:30
Speaker 1: a particular kind of culture, a culture that respects reason,
7:36
Speaker 1: a culture that respects the human mind, a culture that
7:41
Speaker 1: allows for exploration and questioning and innovation and challenging the
7:48
Speaker 1: status quote and just exploring and it. You know, most
7:56
Speaker 1: of the world doesn't have that kind of a culture.
8:02
Speaker 1: Most of the world doesn't have that kind of a culture.
8:06
Speaker 1: America still does and we you know, so AI is
8:12
Speaker 1: really a testament to that. And note that this culture
8:17
Speaker 1: that respects reason, and the kind of culture that produces,
8:21
Speaker 1: the kind of companies that are producing I and the
8:23
Speaker 1: kind of AI that's being produced, this is independent of politics.
8:29
Speaker 1: This is this is a deeply rooted Enlightenment, Enlightenment idea,
8:36
Speaker 1: Enlightenment cultural influence that is still here, that is still around,
8:44
Speaker 1: and it guides us in spite of rotten politics and
8:48
Speaker 1: inspite often of the people who are doing the good
8:50
Speaker 1: work having rotten political views. So I mean, I mean,
9:00
Speaker 1: as I said, I'm blown away by AI. You guys
9:02
Speaker 1: should all be using it for for all kinds of stuff. Research,
9:08
Speaker 1: quite you know, anything you're not sure about, a medicine,
9:12
Speaker 1: you're not sure about a U is something you're gonna buy.
9:16
Speaker 1: You just ask it to do the exploration for you.
9:19
Speaker 1: I mean, it's anything. But of course, at work, think
9:23
Speaker 1: about an app you'd like to create. Maybe you can
9:26
Speaker 1: you can actually have the AI build it for you.
9:28
Speaker 1: There's so many things that you can do on a
9:31
Speaker 1: day to day basis about this, And I'm blown away
9:36
Speaker 1: because you know, again this is its ability to do
9:40
Speaker 1: all those things is really is as a testament to
9:42
Speaker 1: the human mind and what we can produce when we
9:46
Speaker 1: are left free to do so. And one of the
9:49
Speaker 1: great virtues of technology as we know is that it's
9:52
Speaker 1: relatively speaking free when people are free to think, feed, experiment,
9:58
Speaker 1: fee to build knowledge and institutions and accumulate capital and
10:04
Speaker 1: invest that capital. And it went free to fail because
10:08
Speaker 1: AI is filled with failure in its development, and then
10:13
Speaker 1: free to make money off of these ideas, and you know,
10:19
Speaker 1: free to take an abstract theory and try to turn
10:24
Speaker 1: it into a value that can be sold in the marketplace.
10:27
Speaker 1: That's amazing. And note that even though let's say China,
10:33
Speaker 1: for example, is an authoritarian political system, it has given
10:40
Speaker 1: its people, particularly in tech, enough freedom, enough ability to
10:45
Speaker 1: raise capital, enough ability to try things out and to
10:49
Speaker 1: fail and to invest and to commercialize and to make
10:52
Speaker 1: money that even China has a thriving, thriving AI industry.
11:03
Speaker 1: Now you could argue a lot of it is in
11:05
Speaker 1: a sense they can hand it, in the sense that
11:06
Speaker 1: they've copied ideas but they've copied ideas, improved on them,
11:09
Speaker 1: worked on them, brought their own engineering entrepreneurial skills to them,
11:13
Speaker 1: and prod use something. So we live in a world,
11:19
Speaker 1: not just America. We live in a world. You can
11:22
Speaker 1: take out a few areas of the world that don't
11:24
Speaker 1: have this, but we live in a world that has
11:27
Speaker 1: a heavy dose of enlightenment in a sense of a
11:29
Speaker 1: heavy dose of respect for reason and science and engineering
11:32
Speaker 1: and knowledge and even entrepreneurship. So, you know, I looked
11:44
Speaker 1: a little bit into the history of AI. I was curious,
11:47
Speaker 1: how to this start, where did it come from? And really,
11:53
Speaker 1: you know, trying to understand what thinking entailed, what knowledge entails.
12:03
Speaker 1: You know, what philosophy has done from day one is
12:06
Speaker 1: really the beginning. It's you know, what is thought? What
12:11
Speaker 1: are the rules? What is logic? What are the rules?
12:16
Speaker 1: How do we find similarities and differences? Which is a
12:19
Speaker 1: big part of cognition. Human cognition is similarities and differences? Right,
12:25
Speaker 1: How do we solve problems? How do we analyze them?
12:27
Speaker 1: How do we formalize that? Is there a way to
12:29
Speaker 1: formalize that? And you know that's philosophy has been asking
12:36
Speaker 1: those kind of questions forever, and you know, logic and
12:42
Speaker 1: math and probability theory and computation and electrical engineering. If
12:47
Speaker 1: we go into the twentieth century, all of that is accumulated,
12:49
Speaker 1: is accumulating in terms of knowledge. People start thinking, well,
12:55
Speaker 1: can we create a machine that emulates what human beings? Do?
13:01
Speaker 1: You know? This is the birth of computers in the
13:04
Speaker 1: mid part of the century. You know, the first IBM
13:07
Speaker 1: machines are the first calculators are the first way, Well,
13:10
Speaker 1: how do we solve problems? How do we get a
13:11
Speaker 1: machine to solve a problem? It's not really solving a problem,
13:15
Speaker 1: it's going through the process that will result in a solution.
13:21
Speaker 1: And Alan Turing asks, can machine think? Is there a
13:30
Speaker 1: mathematical process that thinking is a consequence of? Now again,
13:37
Speaker 1: I'm not sure he asked the right questions in the
13:38
Speaker 1: right way philosophically, but he's you know, an important link
13:43
Speaker 1: in the creation of computers. You know, and starting in
13:52
Speaker 1: the nineteen fifties, there was a a you know, a
13:57
Speaker 1: group of scientists of thinkers launched the field of they
14:02
Speaker 1: called artificial intelligence. Can they figure out rules and procedures
14:10
Speaker 1: that emulate thinking? Thinking is not a mystical thing, it's
14:15
Speaker 1: not revelation. If you remember, if knowledge was achieved through revelation,
14:23
Speaker 1: there's something a lot of artificial intelligences to be out.
14:28
Speaker 1: What artificial intelligence denies, sadly, at least those who think
14:32
Speaker 1: that it's just a is that it is a true thinker,
14:38
Speaker 1: or those who think they can create a true thinker,
14:40
Speaker 1: is consciousness and volition. But what they accept is that
14:50
Speaker 1: knowledge is to be found in reality and it's a process.
14:55
Speaker 1: It's a process that the might engage in it. So
14:59
Speaker 1: they've implicitly adopted the like that the rejection of revelation,
15:05
Speaker 1: the rejection of another dimension, the rejection of faith, tradition,
15:14
Speaker 1: revelation is a source of truth, is a source of knowledge.
15:18
Speaker 1: They've adopted something they call reason. And now the debate
15:22
Speaker 1: is what is reason? What what is reason? What does
15:24
Speaker 1: reason actually mean? And and you know, uh, the conclusion
15:30
Speaker 1: to that philosophical debate is will ultimately determine, I think,
15:35
Speaker 1: how far we can go with AI and and how
15:38
Speaker 1: we as individuals relate to it. But this is these
15:45
Speaker 1: are the terms of the debate that we should be embracing. Right,
15:48
Speaker 1: we're talking about reason. We're not we don't we're not
15:51
Speaker 1: arguing about faith. Anymore. That's in a sense taking for granted.
15:55
Speaker 1: And this is that, you know, we live in a
15:58
Speaker 1: in a strange world in which a lot of the
16:01
Speaker 1: people who are going to benefit from these who benefit daily,
16:04
Speaker 1: moment by moment from these massive products of reason, our computers,
16:11
Speaker 1: our technology, the internet that you know, an AI a
16:15
Speaker 1: people of faith and tradition and religion and God and
16:19
Speaker 1: all of that, and they don't see, they don't see
16:22
Speaker 1: the contradiction. So starting in the nineteen fifties, you know,
16:28
Speaker 1: there's this idea, can we create artificial intelligence? Can we
16:31
Speaker 1: create a machine that doesn't just compute but does more
16:35
Speaker 1: than that. It can analyze data, analyzed language, can take
16:41
Speaker 1: in stimuli from outside and analyze that. In a sense,
16:47
Speaker 1: can quote see not really see, but quote see see
16:50
Speaker 1: again is a feature of consciousness, not a feature of
16:54
Speaker 1: a machine. But can it do something like seeing? And
17:01
Speaker 1: it's been up and down since then, right, I mean,
17:04
Speaker 1: in the early decades, AI was dominated by symbolic reasoning, logic,
17:09
Speaker 1: rules of logic, search all kinds of theorems. It was
17:14
Speaker 1: trained to play games based on these rules, attempts to
17:19
Speaker 1: create language systems, build these complicated language systems that fed
17:23
Speaker 1: into the machine with the hope that it could do
17:27
Speaker 1: something with them once it got the logical rules. This
17:32
Speaker 1: resulted in some successes, but a lot of failures, a
17:36
Speaker 1: lot of overconfidence. I remember in the eighties we were
17:39
Speaker 1: just moments away from machines waking up. By the way,
17:44
Speaker 1: that's been true every single decade, and we're still waiting
17:47
Speaker 1: for it. But we're closer now, Not that it's going
17:49
Speaker 1: to happen, but at least the machines are a closer
17:54
Speaker 1: to seeming like they're going to wake up than ever before.
18:06
Speaker 1: But you know, there were periods where funding dried up,
18:13
Speaker 1: where you know, the industry collapsed, the expectations collapsed, but
18:19
Speaker 1: people kept tinkering, they kept trying, they kept prodding. You know,
18:25
Speaker 1: this was a problem people wanted to solve, and there
18:28
Speaker 1: was enough capital, enough interest, enough entrepreneurs, enough minds thinking
18:31
Speaker 1: about this, enough scientists and engineers. It kept going. Universities
18:37
Speaker 1: continued doing the research. Corporations continue doing the research. Private
18:42
Speaker 1: companies were founded and experimented. Military government agencies funded some
18:47
Speaker 1: of the work. Foundations and research institutions funded other parts
18:51
Speaker 1: of the work. Individual scientists kept pushing and prodding and
18:55
Speaker 1: figuring this out. And of course at the same time
18:59
Speaker 1: computer hardware guy better and better and better, software got
19:05
Speaker 1: better and better and better, and there was more and
19:11
Speaker 1: more and more capital available to invest more and more
19:14
Speaker 1: and more into experimenting with us. So you know, this
19:24
Speaker 1: is not a This is a product of the markets
19:28
Speaker 1: of market trying, failing, retrying, looking at another angle, seeing
19:33
Speaker 1: that doesn't work, constant innovation and experimentation, constant trial and
19:37
Speaker 1: error and with new ideas, capital flowing to the best
19:41
Speaker 1: ideas of the moment, and and you know, constant moving forward,
19:46
Speaker 1: slow progress, but slow. You know, it looks very differently
19:52
Speaker 1: than a government five year plan. It's a process of
20:00
Speaker 1: discovery and a lot of that discovery happens from failure.
20:10
Speaker 1: It's a process a competition and a lot of persistence
20:16
Speaker 1: and a lot of really really curious, innovative smart minds
20:23
Speaker 1: that want to do this, that are passionate about it.
20:30
Speaker 1: And you know, we got in the sixties he had
20:34
Speaker 1: these rule based and logic based and then there was
20:36
Speaker 1: something called expert. You know, they came up with expert
20:39
Speaker 1: systems in the seventies and eighties that try to capture
20:42
Speaker 1: just specialized knowledge for doctors, engineers, or chemists, or stuff
20:46
Speaker 1: like that. You know, some businesses adopted them. They were useful,
20:52
Speaker 1: but not you know, they want anything that anybody would
20:58
Speaker 1: confuse with intelligence or interesting or powerful tools. They were
21:03
Speaker 1: tools and they were not flexible, and they could do
21:06
Speaker 1: one thing and they couldn't do a lot beyond that.
21:11
Speaker 1: But again, better hardware and now algorithms, better statistical models,
21:17
Speaker 1: better algorithms with more variables, because we have the computing power,
21:23
Speaker 1: so why not I mean some of these some of
21:26
Speaker 1: these models now have a trillion, a trillion variables. An
21:32
Speaker 1: algorithm with a trillion variables, Yeah, it blows my mind. Right,
21:39
Speaker 1: I can run a regression with five stuff, you know,
21:41
Speaker 1: but an algorithm with a trillion variables, what is it doing?
21:47
Speaker 1: The statistical analysis got better, and of course again computing power,
21:52
Speaker 1: more and more and more computing power. Nineteen ninety seven,
21:57
Speaker 1: IBM's Blue Deep Blue beats have a cusp power of
22:00
Speaker 1: the world champion chess player for the first time in chess,
22:07
Speaker 1: and it was I mean, people were blown away. People
22:12
Speaker 1: were blown away. I mean a computer could now predict
22:17
Speaker 1: way in advance the number of options. I mean, chess
22:22
Speaker 1: is predictable in the end. If you can run a
22:26
Speaker 1: big enough big enough model that can look enough into
22:29
Speaker 1: the future, that can take enough itsuations into account. So
22:35
Speaker 1: you could build a specialized computing computer with enormous processing power.
22:41
Speaker 1: It could do this, and then what they beat, the
22:44
Speaker 1: world championing Go Go is even more options than chess.
22:48
Speaker 1: It's even more complicated than chess. And then we got
22:55
Speaker 1: the Internet in the nineteen nineties. So the Internet provided Suddenly,
22:58
Speaker 1: what the Internet did is it provided these models with
23:02
Speaker 1: massive quantities of data web pages everywhere, you know, uploaded
23:09
Speaker 1: articles and books and essays, and just just a ton
23:13
Speaker 1: of information and literally data, you know, numbers and graphs,
23:19
Speaker 1: and so Internet explodes. Then it creates new markets, it
23:28
Speaker 1: creates new companies, creating incentives, new infrastructure, big data centers,
23:34
Speaker 1: the cloud, and now you can imagine, you know, these
23:41
Speaker 1: algorithms have more stuff that they can look at. Chips
23:48
Speaker 1: get even faster, memory becomes even cheaper, networks become much larger,
23:58
Speaker 1: and then you get of all things, graphic processes, these
24:02
Speaker 1: processes designed for video games and for visual computing. But
24:08
Speaker 1: it turns out that they're essential for the kind of
24:11
Speaker 1: networks what they call new old networks that run the
24:16
Speaker 1: AI models. So who knew that gaming would lead to AI.
24:26
Speaker 1: But that's the beauty of markets. That's what you will
24:30
Speaker 1: never get in central planning. At every point in the chain,
24:35
Speaker 1: there's an entrepreneur looking for opportunities, seeing connections, seeing how
24:40
Speaker 1: we can use the resources that are being created by
24:42
Speaker 1: the previous generation of entrepreneurs in order to create the
24:45
Speaker 1: next generation of innovation. That is a beautiful process. It's
24:53
Speaker 1: minds constantly thinking about value creation, about new opportunities, and
24:59
Speaker 1: again about using the resources that are being created by
25:02
Speaker 1: the previous generation to create the future generation. That is
25:07
Speaker 1: the dynamism of capitalism. There is no there's no way
25:12
Speaker 1: to mimic that. There's no way to recreate that, not
25:15
Speaker 1: even inside an AI. So it's it's unpredictable, not because
25:25
Speaker 1: it's random. It's unpredictable because it depends on human cognition.
25:29
Speaker 1: It depends on human values, it depends on human choices,
25:33
Speaker 1: It depends on a particular entrepreneur at a particular point
25:36
Speaker 1: in time, finding a particular opportunity and seeing it using
25:40
Speaker 1: his mind. And it depends on a culture. Again, this
25:45
Speaker 1: is why the United States is so good at this.
25:48
Speaker 1: It depends on a culture that supports that process, that
25:52
Speaker 1: allows that process to happen, at least in tech, and
25:56
Speaker 1: that is because it views the process as a positive.
26:04
Speaker 1: Is willing to is willing as an individual choice, not
26:08
Speaker 1: as a you know, governmental program, willing to devote the resources,
26:13
Speaker 1: the capital, and willing to take the risks both in capital,
26:19
Speaker 1: in jobs and the entrepreneur self. And it is time
26:22
Speaker 1: and effort because there's an upside at the other side,
26:26
Speaker 1: which is again capitalism. Right, you invest, you take risks.
26:31
Speaker 1: It could be successful, it could be a failure. But
26:35
Speaker 1: that's what a free economy does, and that's what cannot
26:38
Speaker 1: be mimic because it depends on individuals making choices, individuals
26:43
Speaker 1: using their minds individually, based on their values and based
26:50
Speaker 1: on what they you know, based on the facts of
26:54
Speaker 1: reality they identify, they discover. Give me one second. So
27:17
Speaker 1: twelve twelve, by twenty twelve, we have some real breakthroughs
27:22
Speaker 1: in terms of machine what they call machine learning, particularly
27:27
Speaker 1: in image recognition. A neural network trained on large data
27:32
Speaker 1: sets and run on powerful GPUs dramatically that performs every
27:37
Speaker 1: other approach in terms of recognition image recognition, and suddenly
27:44
Speaker 1: capital flows into GPUs and neural networks and people invest
27:53
Speaker 1: heavily in the accommodation of data, computation, neural networks in
27:58
Speaker 1: an effort to solve problems. And this is the birth
28:03
Speaker 1: of the current generation I think of of AI again,
28:09
Speaker 1: depending on these really advanced chips, massive quantities of data,
28:15
Speaker 1: sophisticated software, sophisticated codeing research that came from universities, that
28:23
Speaker 1: came from private companies, real competition between innovators who wanted
28:29
Speaker 1: to break through, and capital and talent flowing into this industry.
28:40
Speaker 1: And you know, they have been breakthroughs, have to breakthroughs,
28:45
Speaker 1: have to break through since then. And the consequence of
28:51
Speaker 1: that is kind of the only what three years ago,
28:57
Speaker 1: only three years ago, the uh the release of open
29:10
Speaker 1: eyes Chat GBT, which then created this massive competition between
29:19
Speaker 1: all the companies that have already been working on AI
29:21
Speaker 1: and rushing to catch up with chat GBT and overtaking
29:25
Speaker 1: GDP Chat GBT, and then the competition of startups to
29:29
Speaker 1: compete with the established players, and then massive competition and
29:34
Speaker 1: the other players in order to create uses, build applications
29:40
Speaker 1: that use all this. And it's just exploded. And you know,
29:48
Speaker 1: right now, with the building of data centers, you're seeing
29:51
Speaker 1: you know, maybe more capital invested in this than anything ever.
29:56
Speaker 1: I don't know if this is how this compares to
29:58
Speaker 1: the railroads, but this is, this is a massive investment
30:03
Speaker 1: in the future of this industry. And the future industry
30:08
Speaker 1: now depends on even more computing power, which rests on
30:14
Speaker 1: being able to network more chips together, which means massive
30:18
Speaker 1: data centers. But you know what we have today is
30:27
Speaker 1: a massive integration created not through central planning, no central planning,
30:34
Speaker 1: through the marketplace. You know, of taking a theorem deviced
30:40
Speaker 1: by mathematicians and statisticians, chip designs, entrepreneurs having ideas based
30:47
Speaker 1: on the chips and the math who can raise capital,
30:52
Speaker 1: Programmers writing the code to take advantage and to make
30:56
Speaker 1: the mathematical theorem into reality. Manufacturers building the hard ware,
31:04
Speaker 1: Investors funding all of this, Companies building products, Consumers judging
31:10
Speaker 1: their value, Competitors constantly improving, and now scale going big.
31:20
Speaker 1: I mean, as you look at this with all the
31:25
Speaker 1: challenges now in the world in which we live, with
31:33
Speaker 1: all the stupidity and the bad philosophy and the explicit
31:38
Speaker 1: anti reason ideologies, and the philosopher kings and the politicians,
31:44
Speaker 1: and even it even hurts sometimes listening to the people
31:48
Speaker 1: who run these AI companies. In spite of all that,
31:52
Speaker 1: you have to look at this and say, wow, this
31:56
Speaker 1: is such a beautiful processing right now is such a
32:01
Speaker 1: manifestation of what freedom brings us. Freedom and combined with
32:08
Speaker 1: the respect for reason, what it brings us and what
32:12
Speaker 1: it makes possible. I mean, now, just imagine a world
32:15
Speaker 1: where this kind of dynamism was everywhere, in every field,
32:19
Speaker 1: in every aspect of our lives. That would be hard. Yeah,
32:24
Speaker 1: I mean, we can focus on the lud heights, or
32:28
Speaker 1: we can focus on the creators, on the producers, on
32:32
Speaker 1: the builders, on the makers. And that's really before we
32:49
Speaker 1: talk about the lud eightes, before we talk about the politicians,
32:52
Speaker 1: before we talk about all that crap. We really should
32:55
Speaker 1: be focused on just the beauty of it. You know,
33:02
Speaker 1: Capitalism is just an amazing thing. Going back to I
33:06
Speaker 1: don't know, the invention of the steam engine, the first
33:09
Speaker 1: users of the steam engine, and how the steam engine
33:12
Speaker 1: slowly enters into factories, and how people slowly figure out
33:17
Speaker 1: how to use it in more and more and more
33:19
Speaker 1: and more sophisticated ways, and then that turns into diesel
33:24
Speaker 1: engines and internal combustion engines, and how all of this
33:29
Speaker 1: combined with new energy sources like fossil fuels, and how
33:33
Speaker 1: all of that slowly builds towards the wealth creation of
33:38
Speaker 1: the nineteenth and twentieth centuries. It's just such a magnificent project.
33:42
Speaker 1: It's such a beautiful thing. And there's a realm in
33:47
Speaker 1: our lives right now where this is happening, and that
33:55
Speaker 1: is in tech. It's been happening to some extent in
33:58
Speaker 1: tech for a while, but you know, tech has become
34:01
Speaker 1: more regulated and more controlled, and and and and but
34:06
Speaker 1: the latest manifestation of an unregulated of the unregulated side
34:09
Speaker 1: of tech is this is a We've had amazing breakthroughs
34:19
Speaker 1: in computing and in the Internet, in telecommunication, and the
34:24
Speaker 1: fact that I could do this and you're watching it live,
34:27
Speaker 1: and you're watching it in high quality live it is
34:32
Speaker 1: you know, I am old enough to remember when this
34:35
Speaker 1: was literally impossible and only imagined. And then you know,
34:41
Speaker 1: maybe you could download a video, but it takes a
34:43
Speaker 1: couple of hours to download it and watch it, and
34:45
Speaker 1: then early life streaming, which is awful, And then and
34:49
Speaker 1: now just you know, short of a short of my
34:54
Speaker 1: technical problems once in a while, is it's just seamless,
35:00
Speaker 1: This amazing ability all of that has been created in
35:06
Speaker 1: a particular geographic area by engineers and entrepreneurs and scientists
35:13
Speaker 1: and capitalists, financiers, venture capitalists who had the vision and
35:20
Speaker 1: the freedom and the respect for human reason, of respect
35:24
Speaker 1: for human reason, you know. Tech, the world of tech
35:29
Speaker 1: that we live in is the manifestation of the enlightenment
35:32
Speaker 1: in our world today and again, a beautiful thing to see,
35:42
Speaker 1: and it's under attack, as you'd expect, because we live
35:46
Speaker 1: in a culture that politically and culturally and philosophically has
35:51
Speaker 1: not caught up, indeed has abandoned all that. So we'll
35:57
Speaker 1: have plenty of time to talk about the lad heights
36:02
Speaker 1: and to talk about who's going to control all this,
36:05
Speaker 1: because that's a big debate right now, and we're seeing
36:07
Speaker 1: this around the anthropic debate right now. AI has become
36:11
Speaker 1: a powerful enough tool now that politicians are interested in it.
36:15
Speaker 1: They want to regulate it, they want to control it,
36:17
Speaker 1: or they want to leave it free, but not leave
36:20
Speaker 1: it free because they love freedom. They want to leave
36:21
Speaker 1: it free because they realize that's how you get the
36:23
Speaker 1: next powerful tool. But they're keeping the eye on it
36:26
Speaker 1: because they want to grab it when the opportunity arises,
36:29
Speaker 1: they want to, they want to, they want to use it.
36:31
Speaker 1: We'll talk about all that. And and then it has
36:35
Speaker 1: redistribution of wealth issues because AI is creating massive amounts
36:39
Speaker 1: of wealth and it's going to create huge amounts of wealth,
36:44
Speaker 1: and that of sets people always has capitalism of setch people.
36:50
Speaker 1: But we'll talk about all that. I want to I
36:52
Speaker 1: want to shift focus now from kind of a celebration
36:57
Speaker 1: of the relationship between capitalism and freedom and and the
37:01
Speaker 1: reason and the mind and freedom of the mind and
37:03
Speaker 1: freedom to think and AI and two kind of the
37:07
Speaker 1: more personal, the more personal, I mean, AI can do
37:13
Speaker 1: amazing things. It can you know, probably not yet, really
37:24
Speaker 1: really well. But Evison has a bunch of Everson here.
37:30
Speaker 1: I haven't yet reviewed his songs, and I was supposed
37:32
Speaker 1: to do it today and I didn't, so I apologize.
37:35
Speaker 1: I don't know these I don't know why these reviews
37:38
Speaker 1: seem to fall out of my memory when the weekend
37:42
Speaker 1: comes around. But after Okan, I should have a lot
37:44
Speaker 1: more time to do the reviews. But anyway, he's got
37:47
Speaker 1: a bunch of AI generated music and songs and lyrics,
37:52
Speaker 1: and you know, you know, people are going to go
37:56
Speaker 1: pooh pooh, pooh it. But why would it be bad
38:00
Speaker 1: if they I as the capacity to look at songs,
38:03
Speaker 1: and it's given some kind of information about what's successful
38:06
Speaker 1: or works and what doesn't, what resonates with people and
38:09
Speaker 1: what doesn't, what people like and what they don't. Can't
38:13
Speaker 1: they I find the common denominators among the things that
38:16
Speaker 1: are good and recreate those and something new. It's interesting.
38:24
Speaker 1: Could he write a novel? It certainly can code. Could
38:29
Speaker 1: it design a building or a car? It certainly can
38:34
Speaker 1: analyze data in ways that we had no chance of
38:39
Speaker 1: doing before. It can analyze images, at least of a
38:46
Speaker 1: certain type. Certainly it can calculate. It can translate. It
38:51
Speaker 1: can translate instantaneously, which is pretty amazing. Although you know
38:57
Speaker 1: it just just again computing poshare computing. Translation is just
39:01
Speaker 1: dictionary stuff. Although you you you also have to have
39:04
Speaker 1: some context. It's not like you're translating a sentence. It's
39:07
Speaker 1: not like translating every word in the sentence and repeating it.
39:11
Speaker 1: It's it's you have to have the context of the
39:12
Speaker 1: language and understand what is the sentence translated into a sentence.
39:18
Speaker 1: And yet it can do it, you know, on the spot.
39:22
Speaker 1: You can actually buy now kind of uh hippods which
39:28
Speaker 1: which will do translations for you, simultaneous translations, so it
39:35
Speaker 1: can do a lot, it can do a lot. Then
39:37
Speaker 1: maybe you have to exert quite a bit of effort
39:40
Speaker 1: today in order to do Certainly, if you're a programmer,
39:44
Speaker 1: this is impacting your life. Coding today is a very
39:49
Speaker 1: very different thing than it was even a few months ago,
39:53
Speaker 1: you never mind a few years ago or a year ago.
39:58
Speaker 1: And the question is what does this do to the
40:01
Speaker 1: virtue of productiveness? Because I mean, there are people that
40:05
Speaker 1: are out there arguing that, look, sooner, we're gonna have
40:09
Speaker 1: robots who are going to have AI built in, who
40:12
Speaker 1: are going to be able to do a lot of
40:13
Speaker 1: the physical work in addition to a lot of the
40:16
Speaker 1: so called mental work, and a lot of wealth will
40:20
Speaker 1: just be pressure button and stuff will be produced, so
40:29
Speaker 1: a lot of stuff won't require human intervention, we're told,
40:33
Speaker 1: and we will all be able to have all our
40:37
Speaker 1: basic needs taken care of, and a lot of us
40:42
Speaker 1: won't have a job, we are told. So what is productiveness?
40:47
Speaker 1: How does that even fit in? What does it mean?
40:52
Speaker 1: Does is it still a virtue? And how do we
40:54
Speaker 1: apply it? How do we apply Yeah, I see you're
41:08
Speaker 1: talking about arts and AI. Yeah, I mean, I don't
41:13
Speaker 1: think AI can create great arts, but we'll be able to.
41:17
Speaker 1: But how many people know what great art looks like?
41:20
Speaker 1: How many people can differentiate? Very few? Very few? For
41:26
Speaker 1: most people, AI produced art will be just as good
41:30
Speaker 1: as what is being produced today by humans, I think.
41:41
Speaker 1: So let's talk about productiveness for a little bit. What
41:45
Speaker 1: does it mean? I Productiveness doesn't mean work certainly doesn't
41:51
Speaker 1: mean physical labor or sweating or hard work. How hard
41:56
Speaker 1: did you work? It doesn't mean, you know, just filling
42:03
Speaker 1: ours with stuff to do. It's you don't measure it
42:09
Speaker 1: by how hard things are to Productiveness means the rational
42:18
Speaker 1: creation of values. It means using your mind and your actions.
42:24
Speaker 1: Of course, to create values, you have to understand reality
42:30
Speaker 1: and then shape it, reshape it in the service of
42:33
Speaker 1: human life. And that can be economic, productively productive, Economically,
42:42
Speaker 1: it can be you know, just just productive ness can
42:46
Speaker 1: be also in the household, which doesn't have economic ramifications.
42:54
Speaker 1: A mother raising her children. It is exercising doing it
42:58
Speaker 1: well and thoughtfully is exercising a virtual productiveness, even if
43:04
Speaker 1: it doesn't get calculated in the productivity measures of a
43:07
Speaker 1: culture of the economic productivity measures in a culture because
43:11
Speaker 1: it doesn't. So it's using your mind to figure out
43:23
Speaker 1: how to pursue values, how to make human life better
43:28
Speaker 1: in some way. And you know a lot of people
43:36
Speaker 1: today are worried because they think that AI is going
43:41
Speaker 1: to do all that for them. I mean, who needs
43:45
Speaker 1: a surgeon when you could have a robot that is
43:48
Speaker 1: far more accurate, far more accurate than a surgeon is
43:56
Speaker 1: ever going to be, and can analyze more pieces of
44:02
Speaker 1: data than a surgeon. Can? You know, who needs mechanics
44:14
Speaker 1: when cause electric cause? And their basically pieces of software
44:17
Speaker 1: with a battery. So what if productiveness looks like And
44:25
Speaker 1: here the point is that it doesn't go away. AI
44:31
Speaker 1: doesn't abolish productiveness, it doesn't get rid of it. It
44:34
Speaker 1: just raises the standard. And here a lot of people
44:42
Speaker 1: are going to find themselves in trouble. AI to some
44:49
Speaker 1: extent is going to be a real challenge in our
44:54
Speaker 1: world now every technology is people who want to stick
44:58
Speaker 1: to the way things were always done, fall behind, don't succeed,
45:03
Speaker 1: are miserable and angry and upset. And since the nineteen centie,
45:08
Speaker 1: since industal revolution, there's always been a class of people
45:11
Speaker 1: that have chosen the path of resistance to new technology
45:16
Speaker 1: and chosen to be angry and upset and pissed off
45:19
Speaker 1: at the new technology and at their own lives and
45:22
Speaker 1: at the world and at capitalism. I mean, this is
45:25
Speaker 1: the source of much of Marxism's appeal because but it
45:39
Speaker 1: becomes even more exacerbated when the machines can actually do
45:42
Speaker 1: my work for me. But you're not going to be satisfied,
45:49
Speaker 1: and you're not going to have any self esteem, and
45:52
Speaker 1: you're not going to live as a human being unless
45:56
Speaker 1: you find a way to do work. Yes, UBI could
46:04
Speaker 1: solve a lot of people's problems and create more problems
46:08
Speaker 1: than it will solve. Yes, it'll solve the problem of
46:11
Speaker 1: people maybe losing their particular job, but it'll deny them
46:20
Speaker 1: the virtual productiveness and deny them the self esteem that
46:23
Speaker 1: comes with it, deny them the pride, the work, the enjoyment,
46:29
Speaker 1: the challenge, because they'll be taken care of. But for
46:37
Speaker 1: rational human being, what AI does is it changes how
46:46
Speaker 1: we think about certain tasks. Because anything routine, anything that
46:56
Speaker 1: has been systematized, ay, I will do, and even some
47:00
Speaker 1: non routines things. But what can't AI do. He I
47:07
Speaker 1: can't actually think It can't make a value judgment. It
47:16
Speaker 1: can't actually actually integrate, It can't choose your goals. It
47:24
Speaker 1: won't identify or move you towards. It will help you achieve,
47:28
Speaker 1: but it won't identify the value your values. It can't
47:33
Speaker 1: ask the right questions. It's an unbelievably powerful tool, but
47:41
Speaker 1: it's a tool for the thinker, it's a tool for
47:46
Speaker 1: the judger, it's a tool for the valuer. If you
47:53
Speaker 1: are none of those things. If you don't like thinking,
47:56
Speaker 1: if you don't like judging, if you don't like valuing,
48:03
Speaker 1: if you don't know how to do it, if you
48:05
Speaker 1: don't want to do it, then yeah, you are going
48:11
Speaker 1: to have to figure out how to survive. And it
48:17
Speaker 1: looks like we're moving towards the world in which the
48:19
Speaker 1: government will provide it for you. You have to provide
48:27
Speaker 1: the purpose, the values, the integrations, the goals. You have
48:34
Speaker 1: to provide the judgment. And that is a beautiful thing
48:38
Speaker 1: for people who are engaged, who are focused, who are rational,
48:45
Speaker 1: who are ambitious. This is going to be a technology
48:50
Speaker 1: that makes those people super effective. And it's going to
48:53
Speaker 1: be fun because it's going to take out all the drudgery,
48:58
Speaker 1: all the repetition. We humans get to do only what
49:02
Speaker 1: we humans are good at and are uniquely qualified to do,
49:08
Speaker 1: and that is so cool. So, you know, to be
49:15
Speaker 1: to deal with they are, we're going to have to
49:17
Speaker 1: become in a sense, more conceptual, more clear about what
49:20
Speaker 1: our concepts mean, more judgmental in a positive sense of judging,
49:26
Speaker 1: more creative, more entrepreneur more purposeful. That's what's going to
49:29
Speaker 1: be required of us. But we live in a culture
49:33
Speaker 1: that demeans that, that rejects that. And what happens to
49:38
Speaker 1: all of the secondhanded people out there, what happens to
49:41
Speaker 1: all the non thinkers out there? Now, They're already not
49:45
Speaker 1: happy and they never will be. There's already, you know,
49:49
Speaker 1: there's still crying about the previous technological revolutions. There's still
49:54
Speaker 1: crying about losing their assembly line jobs. They're crying about
49:58
Speaker 1: losing their steel worker jobs. They're frustrated. The whole rise
50:10
Speaker 1: of populism on the left and on the right can
50:13
Speaker 1: be explained by the infrstration. But deforestration is about the
50:18
Speaker 1: nineteen eighties. Keep up, guys, this is going to be bigger,
50:26
Speaker 1: more disruptive, and they are going to be income inequality
50:32
Speaker 1: consequences the people who are engaged with AI, who know
50:38
Speaker 1: will learn how to use it, who improve themselves exponentially
50:42
Speaker 1: as a consequence of using it, are going to create
50:45
Speaker 1: a lot of value and are going to get much richer.
50:51
Speaker 1: And the people who are going to allow themselves to
50:52
Speaker 1: be replaced by AI and who are going to settle
50:55
Speaker 1: for living on the government dole, are just going to stagnate.
51:01
Speaker 1: And the risk is that group is going to grow.
51:05
Speaker 1: And as that group grows, the resentment will grow, the
51:09
Speaker 1: hatred will go, the fear will grow, and we know
51:13
Speaker 1: the kind of political outcomes that that can lead to.
51:21
Speaker 1: So people who like to wait for instructions, copy what
51:24
Speaker 1: others do, follow routines, don't like to judge. AI is
51:30
Speaker 1: going to expose them. But if you like thinking, if
51:37
Speaker 1: you like principles, if you like choosing goals and choosing
51:41
Speaker 1: values and integrating knowledge and taking responsibility and pushing and
51:46
Speaker 1: you know and you can you can tolerate failure and
51:49
Speaker 1: you can, but you keep you want to, you want
51:53
Speaker 1: to push yourself. AI is like going to be amazing
51:58
Speaker 1: for you, amazing for you. One group is going to
52:05
Speaker 1: use AI to substitute for their own mind. The other
52:09
Speaker 1: group is going to use AI to grow their capacity,
52:16
Speaker 1: their mind's capacity exponentially. You know, take a student high school.
52:33
Speaker 1: You can use AI to cheat of aid and avoid
52:36
Speaker 1: learning anything, or you can use AI as a tutor
52:42
Speaker 1: to master topics that your teachers can't even imagine or
52:48
Speaker 1: can't can't it can't get the whole class there. I mean,
52:51
Speaker 1: I've seen some of these AI tutors. They're pretty damn amazing,
52:54
Speaker 1: and they're only going to get better. And you'll be
52:57
Speaker 1: able to learn anything, not by holding a cost, but
53:00
Speaker 1: by downloading an app and it'll guide you literally guide
53:04
Speaker 1: you and tell you when you made a mistake and
53:06
Speaker 1: give you tips on how to correct and how to
53:08
Speaker 1: move in the right direction. It really is amazing. So
53:17
Speaker 1: in every aspect, if you're.
53:19
Speaker 2: A thinker, if you're a doer, if you're if you're ambitious, hey,
53:26
Speaker 2: I is going to enhance your life dramatically.
53:32
Speaker 1: But if you are not, you're in trouble. And this
53:38
Speaker 1: is the thing, and we'll pick this up in a
53:41
Speaker 1: future show, but this is the thing. This is going
53:44
Speaker 1: to create enormous, enormous pressure political pression, populist political pressure
53:52
Speaker 1: from those who can't and don't against those who can
53:59
Speaker 1: and do, and it's it's going to be incredibly you know,
54:08
Speaker 1: and and and this look AI is going to get
54:11
Speaker 1: better and better and it's going to get you know,
54:13
Speaker 1: and it's going to have, you know, have its failures
54:16
Speaker 1: and have its successes and and of course politics could
54:19
Speaker 1: kill much of the progress pretty early on. Uh, but
54:25
Speaker 1: you know it just project twenty yars on in the future,
54:30
Speaker 1: we're robots and I are given you know, assuming the
54:33
Speaker 1: government just doesn't screw it all up now. And it's
54:39
Speaker 1: it's going to be again amazing for some and a
54:41
Speaker 1: and a and a nightmare for others. So no, productiveness
54:46
Speaker 1: doesn't change. It just gets focused. It gets focused on
54:50
Speaker 1: what's essential. It gets focused on what's human. It gets
54:53
Speaker 1: focused on the mind. And again here I'm not talking
54:56
Speaker 1: about being brilliant or having a high IQ. People love
54:59
Speaker 1: measuring everything IQ. Intelligence matters, it doesn't. What matters is
55:04
Speaker 1: not intelligence. I mean intelligence matters, but it's not what's important.
55:09
Speaker 1: What's important here is the willingness to think, the focus
55:15
Speaker 1: what manage his ambition, would manage he his entrepreneurship at
55:20
Speaker 1: the at the personal level, how do I make myself better?
55:24
Speaker 1: How do I create values for me? At whatever level
55:27
Speaker 1: of intelligence you can, this is going to be a
55:30
Speaker 1: huge bonanza for you. So the question right now for
55:42
Speaker 1: people to ask is not how do I protect myself
55:44
Speaker 1: from a I. The question everybody should be asking is
55:50
Speaker 1: how do I use AI to become more productive? To
55:56
Speaker 1: achieve more of my goals, to create more, to create
56:00
Speaker 1: sustain more of my values. How can I learn faster,
56:05
Speaker 1: think better, build more, reach more people, solve harder problems,
56:08
Speaker 1: create more values. That's what somebody living on the basis
56:20
Speaker 1: of the virtue of productiveness, that's how they approach this issue.
56:28
Speaker 1: So these are exciting times. They're going to be volatile times,
56:34
Speaker 1: and in some way they're going to be scary times
56:37
Speaker 1: at times. But we need to fight for freedom. And
56:45
Speaker 1: right now one of the most crucial areas in which
56:48
Speaker 1: to fight for freedom is to fight for AI freedom.
56:55
Speaker 1: The vultures are circling. They will vulture. That's not a
56:59
Speaker 1: good analogy, no, anyway, The politicians are so cling, they're
57:06
Speaker 1: looking for the opportunity right now. You know, I just so.
57:10
Speaker 1: Tyler Cowen wrote an article for Free passing. His is
57:15
Speaker 1: national security. We should leave AI free because if we don't,
57:19
Speaker 1: China will beat us. What a lousy, what a lousy, lousy, lousy,
57:24
Speaker 1: defensive AI should leave AI free because look at what
57:29
Speaker 1: it can produce, look at what it creates, look at
57:33
Speaker 1: how it can make each one of our lives so
57:35
Speaker 1: much better, each one of our lives so much better.
57:43
Speaker 1: So win for a fight. I know that Alex Epstein
57:49
Speaker 1: is going to be focused on this fight. That's great.
57:53
Speaker 1: I intend to do as much as I can about this.
57:57
Speaker 1: I also want to deepen out feel understanding of AI.
58:02
Speaker 1: So the Institute is going to do a bunch of
58:04
Speaker 1: programs with philosophers and people in the AI field to
58:08
Speaker 1: try to better understand AI and to deepen it, deepen
58:12
Speaker 1: understanding of it. So I, yeah, I am. I'm excited.
58:17
Speaker 1: I hope you are too, and I hope you stay
58:21
Speaker 1: excited and join me in the battle. Keep it free,
58:28
Speaker 1: keep it, keep Bernie and Trump's hands off of it. Hey,
58:32
Speaker 1: I thought I wouldn't have to mention his name today,
58:35
Speaker 1: but there it is, all right, guys. That is That
58:40
Speaker 1: is my spiel for this Saturday. Reminder that tomorrow we
58:45
Speaker 1: have nikos. Also a reminder that we have goals to meet,
58:56
Speaker 1: goals to meet, and we're running behind today, so please
59:01
Speaker 1: jump in with support. Stickers is great questions even better
59:08
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59:11
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59:15
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59:17
Speaker 1: I'll be going to Ocon so there won't be a
59:19
Speaker 1: lot of shows. Bet we're going to try, so please
59:22
Speaker 1: consider making a contribution some support that would be greatly
59:30
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59:34
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59:42
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59:47
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59:53
Speaker 1: to all you guys for the stickers. You can ask
59:56
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Speaker 1: the show that's in the past, because you're ready in.
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Speaker 1: You're ready in les. Let's start. We've got a few
1:01:54
Speaker 1: twenty dollars questions. We don't have a lot of questions.
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Speaker 1: This would be short. So if you want to support
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Speaker 1: the show, jump in now before the show ends, and
1:02:03
Speaker 1: then you won't be able to. We will start with
1:02:07
Speaker 1: not your average algorithm. How much can artificial intelligence really
1:02:11
Speaker 1: break through an override heavily taxed and regulated late stage
1:02:17
Speaker 1: mixed economy. Well, I mean it is already. It's creating,
1:02:24
Speaker 1: you know, hundreds of billions dollars worth of value. It is.
1:02:29
Speaker 1: It is going to dramatically increase productivity. You know, it's
1:02:34
Speaker 1: think about AI as a tool to get around regulation.
1:02:38
Speaker 1: Think about AI as a tool to be more efficient
1:02:44
Speaker 1: at dealing with regulation. So let's say you have to
1:02:48
Speaker 1: file a thirteen D every time you have five percent
1:02:51
Speaker 1: of a particular stock at a hedge fun and imagine
1:02:54
Speaker 1: if you have an AI that automatically now falls out
1:02:56
Speaker 1: the form and sends it over instead of having a
1:03:00
Speaker 1: clients officer that that's half of what they do is
1:03:02
Speaker 1: fill out forms. And now you have an AI that
1:03:05
Speaker 1: does it. It's got access to all your trades, your
1:03:09
Speaker 1: entire database, it knows exactly when you go over five
1:03:11
Speaker 1: percent of a stock and you immediately file the necessary
1:03:14
Speaker 1: filings with the SEC. So it actually makes it more
1:03:19
Speaker 1: makes it easier to deal with regulations at every level.
1:03:23
Speaker 1: I mean, the only challenge AI faces, I mean to me,
1:03:26
Speaker 1: in the United States, the biggest challenge from the you know,
1:03:31
Speaker 1: sort of direct regulation of AI. The biggest challenge AI
1:03:35
Speaker 1: faces from the government right now is constraints and immigration.
1:03:39
Speaker 1: Legal high skill immigration is being constrained dramatically, that is,
1:03:45
Speaker 1: and just a willingness of talent to come to the
1:03:48
Speaker 1: United States because of the atmosphere were created around immigration
1:03:52
Speaker 1: that is incredibly destructive to AI. But everything I mean,
1:04:00
Speaker 1: you know, think about how many accountants we employed today
1:04:03
Speaker 1: to do our taxes, to file our returns. All of
1:04:06
Speaker 1: that can be can be turned over to an AI.
1:04:09
Speaker 1: Now again, it's going to take some time to get
1:04:13
Speaker 1: an AI that can do it without error and can
1:04:15
Speaker 1: do it with the right judgment, and maybe it's still
1:04:18
Speaker 1: going to require accountants to look over it. Think about investing,
1:04:22
Speaker 1: I mean, we all pay a lot of money to
1:04:25
Speaker 1: UH I don't know money managers to invest our retirement funds.
1:04:29
Speaker 1: But what if you know, if if those managers are
1:04:33
Speaker 1: using some basic rules of you know, creating an efficient
1:04:39
Speaker 1: portfolio based on based on algorithm. Right, it's all based
1:04:45
Speaker 1: on algorithm, based on trying to trying to mimic the
1:04:48
Speaker 1: what's called the efficient frontier to get to that point
1:04:51
Speaker 1: where you you get the maximum return for the for
1:04:54
Speaker 1: the least amount of risk kind of balancing all of that.
1:04:59
Speaker 1: I mean imagine that that. You know, manumations in the
1:05:01
Speaker 1: future will basically say, we'll ask you on a scale
1:05:06
Speaker 1: from one to ten. I mean they'll do more of this,
1:05:08
Speaker 1: but scale of one to ten, what is your risk
1:05:10
Speaker 1: preference and they'll explain what that means. That is what
1:05:13
Speaker 1: risk actually means in this context, and you'll give them
1:05:16
Speaker 1: a number and they'll plug it in and the AI
1:05:18
Speaker 1: will allocate your investments automatically based on the risk I mean,
1:05:26
Speaker 1: I mean there's so much there's not an industry where
1:05:30
Speaker 1: this is. You know, you're gonna have AI coaches and
1:05:37
Speaker 1: AI teachers and AI psychologists and all kinds of stuff. Now,
1:05:44
Speaker 1: it won't replace human beings in a sense that human
1:05:46
Speaker 1: beings will find other things to do, and you will
1:05:48
Speaker 1: still need that judgment to sit over the I. But
1:05:52
Speaker 1: the AI tools are going to increase productivity. So now
1:05:58
Speaker 1: I mean even with the taxes. Look take in took
1:06:02
Speaker 1: down all the taxes that are out there, how much
1:06:03
Speaker 1: we are taxed, and just look at how much capital
1:06:07
Speaker 1: is out there available, capital to invest. Now, some of
1:06:12
Speaker 1: you're going to say it's all money that's being printed,
1:06:14
Speaker 1: but that's just not true. It's wealth being created. I mean,
1:06:22
Speaker 1: SpaceX just raised seventy five billion dollars, largest IPO in
1:06:26
Speaker 1: human history. Where did that seventy five billion dollars come from?
1:06:31
Speaker 1: People have money, they have capital, they have resources. So
1:06:36
Speaker 1: in spite of the taxes, we're all saving. Then this
1:06:40
Speaker 1: is why I keep telling you stop being so pessimistic.
1:06:43
Speaker 1: Stop using the regulatory state and the taxes and the
1:06:45
Speaker 1: mixed economy to justify not succeeding in life. Yes, all
1:06:53
Speaker 1: of those things make it harder. All of those things
1:06:55
Speaker 1: will make it harder for AI to progress. All of
1:06:59
Speaker 1: those things would make it harder for AI to have
1:07:02
Speaker 1: the kind of impact on productivity that it could have.
1:07:06
Speaker 1: Doesn't mean it won't happen. It still will happen. It'll
1:07:09
Speaker 1: happens slower and less than it would otherwise, but it'll
1:07:12
Speaker 1: still happen. Just like in a free market, it would
1:07:16
Speaker 1: be a lot easier for you to go out there
1:07:17
Speaker 1: and build, you know, start your business or follow your
1:07:23
Speaker 1: career that you want. But you can still do it.
1:07:27
Speaker 1: You can still do it. It's just harder and riskier and
1:07:31
Speaker 1: more difficult. And that's harder, right and uh uh, and
1:07:36
Speaker 1: you'll suffer more failures, but you can still do it.
1:07:43
Speaker 1: Look at how the Internet has changed everything. Look at
1:07:46
Speaker 1: how computers have changed anything. Look at how biotech is
1:07:49
Speaker 1: still advancing in spite of the regulations and the taxes,
1:07:52
Speaker 1: and our capital is flowing to all of that in
1:07:53
Speaker 1: spite of the taxes. Capital is still available. Now. It
1:07:57
Speaker 1: could be that it all collapses tomorrow because the dollar,
1:08:02
Speaker 1: this inflation, whatever it could be. I just don't see
1:08:05
Speaker 1: it happening, all right, Andrew fifty dollars, Thank you, Andrew.
1:08:14
Speaker 1: Are there any positive intellectual trends in the culture. If not,
1:08:17
Speaker 1: have you observed any germs of rationality that could turn
1:08:21
Speaker 1: into such trends? Well, I mean, objectivism is the biggest
1:08:25
Speaker 1: positive intellectual in the force in the culture. It's not
1:08:30
Speaker 1: a trend yet, and I don't see any big positive trends.
1:08:36
Speaker 1: I think, you know, the kind of there's a kind
1:08:40
Speaker 1: of coalescing of intellectuals who are fed up with the
1:08:45
Speaker 1: crazy left and the crazy right. Focused kind of on
1:08:49
Speaker 1: the scent. I think the Stephen Pinkers and Glass and
1:08:56
Speaker 1: Noah Smith I think his name is and and you know,
1:08:59
Speaker 1: from both right, and those are primarily in the left.
1:09:02
Speaker 1: I think, you know, there was on the right from
1:09:05
Speaker 1: Joanah Goldberg and others who are better, more Enlightenment oriented,
1:09:12
Speaker 1: recognize the value of the Enlightenment and support the Enlightenment.
1:09:15
Speaker 1: They don't have a voice because social media speak of
1:09:20
Speaker 1: a voice, because social media highlights the crazies, the nuts,
1:09:25
Speaker 1: and because politics they have no power politically, politics is
1:09:33
Speaker 1: dominated by the in a sense, the crazies. But they
1:09:40
Speaker 1: are there the biggest, you know, the big bulk of
1:09:46
Speaker 1: not off my intellectuals, because the intellectuals are mostly crazy
1:09:50
Speaker 1: leftists and then there's some crazy rightists. But there's a
1:09:53
Speaker 1: there's a good group of pro Enlightenment centric they call
1:09:57
Speaker 1: themselves Centris. It's meaningless who I think a force for
1:10:02
Speaker 1: good overall. But I don't know that's a trend. I
1:10:05
Speaker 1: don't I'm not sure what trends are. You know, I
1:10:08
Speaker 1: can see bad trends on the right, and I can
1:10:10
Speaker 1: definitely see bad trends on the left. Both of those,
1:10:14
Speaker 1: It's hard to see what's happening outside of those because
1:10:17
Speaker 1: they suck up all the oxygen in terms of the energy.
1:10:21
Speaker 1: And again the social media. All right, hop A Campbell.
1:10:35
Speaker 1: I'm in a lot of online chat rooms with the
1:10:38
Speaker 1: new Right, and eugenics seems to be making a comeback.
1:10:42
Speaker 1: What's the appeal An excuse to hate other groups of people,
1:10:46
Speaker 1: an attempt to gain a feeling of automatic superiority. Yes,
1:10:50
Speaker 1: I agree with you. Eugenics is definitely making a comeback.
1:10:53
Speaker 1: It never completely went away. The people behind eugenics, the
1:10:57
Speaker 1: money behind eugenics, has always been in the back ground.
1:11:00
Speaker 1: This kind of you can you can draw a line,
1:11:04
Speaker 1: a direct line from the eugenics movement of the nineteen teens,
1:11:09
Speaker 1: in the twenties and thirties to the eugenics rise today.
1:11:16
Speaker 1: Who is funding them, the books that are being funded,
1:11:18
Speaker 1: all of that. It's pretty scary. But I think the
1:11:23
Speaker 1: primary source of the appeal of eugenics is tribalism. It's
1:11:29
Speaker 1: just an attempt to define your tribe, and tribalism is
1:11:33
Speaker 1: a consequence not of just hate for other people. It's
1:11:38
Speaker 1: the appeal is a massive insecurity. How am I going
1:11:46
Speaker 1: to survive? You know, I don't know how to use
1:11:51
Speaker 1: my mind. Particularly an AI is just going to exacerbate this.
1:11:56
Speaker 1: How am I going to survive? You know, the number
1:12:00
Speaker 1: of jobs is shrinking and all these immigrants are coming,
1:12:04
Speaker 1: and you know, how am I supposed to live? Who's
1:12:07
Speaker 1: going to tell me how to live? Well? People gravitate
1:12:11
Speaker 1: towards people who look like them and then demonize the
1:12:14
Speaker 1: people who look differently from them. So I think it
1:12:18
Speaker 1: comes from a deep alienation and frustration with the world
1:12:25
Speaker 1: around them that comes from inability or unwillingness if you will,
1:12:34
Speaker 1: to use their mind, and therefore they're stuck with emotions
1:12:39
Speaker 1: there for their stuck with failure. You know again this
1:12:41
Speaker 1: is it's a consequence of the missing link. And if
1:12:46
Speaker 1: you you know, societies do not develop a respect for reason,
1:12:55
Speaker 1: uh tribal and tend to be in group outgroup, and
1:13:03
Speaker 1: you know, eugenics is just an in group outgroup thing.
1:13:05
Speaker 1: And this is why everybody's so focused on IQ is
1:13:09
Speaker 1: to measure everything because they don't want to acknowledge what
1:13:13
Speaker 1: really matters. What really matters is what you do with it,
1:13:20
Speaker 1: how you use it, your willingness to focus your willingness
1:13:24
Speaker 1: to think, because you know, if it's just like c
1:13:30
Speaker 1: then it's easy to judge people, it's easy to judge yourself.
1:13:37
Speaker 1: But if it's something amorphous like oh, the choice to
1:13:41
Speaker 1: think volition, they don't know how to deal with that.
1:13:46
Speaker 1: And I'm not sure what that means to themselves, to
1:13:49
Speaker 1: their own character and well being. But I think it
1:13:52
Speaker 1: comes to fundamentally an inferioity complex which drives them towards tribalism,
1:13:58
Speaker 1: and then tribalism just that it's simple for tribalism to
1:14:02
Speaker 1: get to eugenics. Eugenics just a pseudo scientific way of
1:14:08
Speaker 1: justifying your tribalism. Author says most people get rich to
1:14:14
Speaker 1: show off. I wanted to get rich to disappear. I
1:14:17
Speaker 1: don't know anybody who got I mean, I really don't
1:14:20
Speaker 1: know people who get rich to show off. I really don't.
1:14:27
Speaker 1: I don't know. I mean, maybe that's true, but I
1:14:32
Speaker 1: don't think so. I know a number of people who,
1:14:36
Speaker 1: you know, worked hard and started companies, built stuff, and
1:14:40
Speaker 1: became rich. None of that was the show off. It
1:14:45
Speaker 1: was because they were enjoying it. They were having fun,
1:14:47
Speaker 1: they were challenging themselves, they were pursuing their career they were,
1:14:52
Speaker 1: and getting rich was a by product of everything else
1:14:56
Speaker 1: that they were doing. And if they wanted to get
1:14:58
Speaker 1: you know, and if the what was driving them was wealth,
1:15:01
Speaker 1: it was wealth in order to build and you know,
1:15:04
Speaker 1: in order to buy an house, house and live in
1:15:09
Speaker 1: the kind of life that they want. Maybe that's what
1:15:11
Speaker 1: you mean by disappear. But you know, I don't know.
1:15:17
Speaker 1: I don't know what getting rich means. Is that a million?
1:15:20
Speaker 1: Is that five million, ten million, one hundred million, a billion,
1:15:23
Speaker 1: a trillion? What does it mean these days to get rich?
1:15:25
Speaker 1: I don't know. And you know, I think I think
1:15:30
Speaker 1: most people get rich as a by product a being
1:15:35
Speaker 1: productive of working. And they're not working in order to
1:15:39
Speaker 1: show off. I just don't think that's the case. They're
1:15:44
Speaker 1: working because they enjoy it, they love it. They guess
1:15:48
Speaker 1: self esteem out of it. All right, don't forget to
1:15:54
Speaker 1: like the show, don't forget to share the show. But
1:15:56
Speaker 1: liking is the easiest thing you can do because it
1:15:59
Speaker 1: doesn't ask you anything. It just press a button and
1:16:03
Speaker 1: and it's done, And it really helps the algorithm to
1:16:07
Speaker 1: promote the show. And this is the kind of show.
1:16:11
Speaker 1: Maybe you should you can share Uh, it's it's not
1:16:14
Speaker 1: not particularly political. It's about the beauty of capitalism, it's
1:16:17
Speaker 1: about artificial intelligence. It's somewhat educational, So think about sharing
1:16:22
Speaker 1: the show. Also, you can hype the show if you're
1:16:25
Speaker 1: on a if you're listening to the show on a phone,
1:16:29
Speaker 1: on a mobile device, there is a hype feature. Again,
1:16:34
Speaker 1: hyping the show really helps you with the algorithm. The
1:16:37
Speaker 1: algorithm loves hyped shows, So please do that to help
1:16:41
Speaker 1: us spread the show. And of course don't forget to
1:16:44
Speaker 1: support the show financially. We're on I'll deep into our
1:16:47
Speaker 1: second hour and we're not quite where we need to
1:16:51
Speaker 1: be for the second hour. So and of course the
1:16:55
Speaker 1: more questions there are, the more hours these shows can be.
1:16:59
Speaker 1: You'll the time. The strengths on the show is how
1:17:01
Speaker 1: many questions you're willing to ask, how much money you
1:17:04
Speaker 1: will need to put forward? James, is China less regulated
1:17:10
Speaker 1: than the US and that you need fewer permits to
1:17:13
Speaker 1: open a business, But the government decides off a traval
1:17:16
Speaker 1: to your business shouldn't exist. It can crush you without
1:17:19
Speaker 1: cause or do process. Business owners in China have less
1:17:23
Speaker 1: protection from the courts or simultaneously having less of a
1:17:28
Speaker 1: regulatory burden to comply with than American businesses. I think
1:17:32
Speaker 1: that's right. I think you've described it accurately. It's also
1:17:35
Speaker 1: true that there's very little visibility. Transparency is the world.
1:17:42
Speaker 1: There's very little transparency into what the government's going to do,
1:17:46
Speaker 1: how they're going to treat you, when they're going to
1:17:48
Speaker 1: increase regulations, or when they're going to change the regulations,
1:17:51
Speaker 1: or when they're going to, as you said, just shut
1:17:53
Speaker 1: you down and close you down completely. You have very
1:17:57
Speaker 1: very limited protections against government acts against you or against
1:18:02
Speaker 1: the actions are powerful people against you who might have
1:18:05
Speaker 1: more sway in the courts than you do. But in
1:18:09
Speaker 1: terms of day to day actions, in terms of day
1:18:12
Speaker 1: to day stuff, as long as you're not involved in
1:18:15
Speaker 1: criticizing the government, the governess generally leaves you alone. It's
1:18:19
Speaker 1: not interested in what you're doing. As long as you
1:18:21
Speaker 1: don't criticize the government, and as long as you realize
1:18:23
Speaker 1: that they can't come and shut you down for whatever
1:18:26
Speaker 1: reason they want to whenever they like. So uh and
1:18:30
Speaker 1: and you know, pseudo rights are property rights are pseudo
1:18:33
Speaker 1: property rights. The government can come and take your property
1:18:36
Speaker 1: rights anytime they want to. But there is enough freedom
1:18:40
Speaker 1: there for entrepreneurs to start businesses and to build stuff
1:18:44
Speaker 1: and to grow stuff. And that's what we that's what
1:18:47
Speaker 1: you have in China. It's why it can be as
1:18:52
Speaker 1: successful as it is. Barry, thank you for the sticker.
1:18:56
Speaker 1: I really appreciate it. So yeah, I think you described
1:19:01
Speaker 1: it really well. James chasbad. How long before we have
1:19:06
Speaker 1: emotional support droids like b too Emo from Ando, although
1:19:11
Speaker 1: maybe maybe how clean they are should be adjustable that
1:19:17
Speaker 1: droid was said to eleven. Yeah, I think we already
1:19:22
Speaker 1: have them. I mean similar ones. You know, there are
1:19:25
Speaker 1: all kinds of now robotic pets that people have. I
1:19:31
Speaker 1: saw this. Somebody had this, you know, furry things, just
1:19:35
Speaker 1: a football thing and it vibates a little bit and
1:19:39
Speaker 1: if you hold it closer to vibate, and it is
1:19:42
Speaker 1: supposedly the vibation changes a little bit with your mood,
1:19:46
Speaker 1: maybe because of your pulse or something like that. And
1:19:49
Speaker 1: if you put it on a table and it'll move
1:19:53
Speaker 1: a little bit. It doesn't do much, but people are
1:19:56
Speaker 1: buying these things, so I know, five hundred bucks or something,
1:20:00
Speaker 1: you're gonna get more and more and more sophisticated. You've
1:20:02
Speaker 1: got these pet robotic dogs, and I think I think
1:20:07
Speaker 1: the the the pet robot industry is going to be massive, massive.
1:20:15
Speaker 1: I mean, it's so much better than a living dog. Right.
1:20:18
Speaker 1: You don't have to take it out, you don't have
1:20:20
Speaker 1: to pick up it's poop, you don't have to feed it,
1:20:23
Speaker 1: you don't have to worry when you travel what happens
1:20:25
Speaker 1: to it. I mean and and uh, yet when you
1:20:28
Speaker 1: come home and you open the door, it'll run up
1:20:31
Speaker 1: to you and jump up onto you. So it's you know,
1:20:44
Speaker 1: it'll it know, it'll it'll probably have these uh, these
1:20:47
Speaker 1: fake eyes that that you know, have these particular expressions
1:20:52
Speaker 1: that they look at you. You know, it'll be amazing,
1:20:54
Speaker 1: It'll be amazing. I might even get a pet if
1:20:57
Speaker 1: I could take out all the hassles involved with the
1:21:00
Speaker 1: bad maybe I take it and it would be incredibly
1:21:03
Speaker 1: soft and cuddly, right, I mean, it could be. It
1:21:07
Speaker 1: could be made of of real fur. It could be
1:21:10
Speaker 1: you could have you could change its fur, you could
1:21:13
Speaker 1: you could change its skin periodically. It's not fake. It's
1:21:20
Speaker 1: just different. There's nothing fake about it. It's fake in
1:21:26
Speaker 1: the sense that it's not really valuing you. But dogs
1:21:30
Speaker 1: are just programmed to value you. There's no free will.
1:21:33
Speaker 1: There any free will you're attributing to your dog. It
1:21:37
Speaker 1: chooses to love me and not the neighbor.
1:21:40
Speaker 2: Is it is you?
1:21:43
Speaker 1: You know, placing what do you call it? Making it
1:21:50
Speaker 1: like a human? It's not. So you can do the
1:21:53
Speaker 1: same to an AI, make it seem like it's human.
1:21:59
Speaker 1: So I'm looking forward to the day of really really sophisticated, beautiful,
1:22:06
Speaker 1: really beautiful. And it can be new shapes, right, We're
1:22:10
Speaker 1: not limited to the shapes of existing animals. You can
1:22:13
Speaker 1: create new pets of any shape, and they can be supportive,
1:22:18
Speaker 1: and they can be and yeah, you'd be able to
1:22:21
Speaker 1: set set how excited they should get when you walk
1:22:25
Speaker 1: in through the door, or how you know, clingy they are.
1:22:31
Speaker 1: Some people like clinging, some people don't. Generally, they'll be programmable.
1:22:36
Speaker 1: You'll be able to give them a personality. You'll be
1:22:38
Speaker 1: able to determine what kind of a personality they have.
1:22:42
Speaker 1: You know, you might be skeptical right now, but twenty
1:22:46
Speaker 1: years down the road, you'll all will have AI robotic pets.
1:22:53
Speaker 1: I'm telling you it's And if you want to make money,
1:22:57
Speaker 1: put aside all the other stuff. If you just want
1:22:59
Speaker 1: to make money, this is where you should put your
1:23:03
Speaker 1: money in. This is this is the investment to This
1:23:06
Speaker 1: is the investment you have to make because this is
1:23:14
Speaker 1: where this is where a lot of a lot of
1:23:20
Speaker 1: money will be made because there'll be a lot of
1:23:22
Speaker 1: demand for this. You heard it here on the Iran
1:23:25
Speaker 1: book Show when the pet robots I pos happen buy in.
1:23:31
Speaker 1: I'm kidding. No investment advice is provided by the Ion
1:23:34
Speaker 1: book Show ever. All right, a jill jed up. Sorry,
1:23:48
Speaker 1: I'm mispronouncing that terribly. He says, one hundred Canadian dollars.
1:23:52
Speaker 1: My treasury has enough double loons to make a contribution. Now,
1:23:59
Speaker 1: happy catter day. I don't know what that means. All right,
1:24:05
Speaker 1: somebody will translate for me. Thank you really really appreciate that.
1:24:08
Speaker 1: Thank you, thank you, thank you. That is that is.
1:24:12
Speaker 1: Jennifer disagrees right now. I'm I'm betting you that twenty
1:24:17
Speaker 1: years from now, you know, Jennifer is going to have
1:24:21
Speaker 1: a robotic pet. That's just my prediction. Alex hierro On,
1:24:29
Speaker 1: have you looked into the cries about how what are
1:24:32
Speaker 1: you usage by AI data centers? It sounds like made
1:24:35
Speaker 1: up wiries to me, Yes, definitely made up wiries. I
1:24:38
Speaker 1: have looked into it. I did a segment on a
1:24:40
Speaker 1: show a couple of weeks ago on it. You know,
1:24:44
Speaker 1: I had data on how much water almond farm farmers
1:24:49
Speaker 1: of almonds use up in California. It's just astronomical amounts.
1:24:55
Speaker 1: A bunch of other you know, just farming usages that
1:24:58
Speaker 1: uses huge amounts of water in places that are prone
1:25:02
Speaker 1: to droughts, and nobody, nobody seems to care. In comparison,
1:25:07
Speaker 1: data centers barely use any water at all. So you know,
1:25:11
Speaker 1: it's it's an issue you want to when you build
1:25:14
Speaker 1: the data center. You want to think about where you're
1:25:17
Speaker 1: going to get the water farm, and how you're going
1:25:18
Speaker 1: to recycle it to the best you can and all
1:25:20
Speaker 1: of that. But think about if water was a private
1:25:23
Speaker 1: resource and water was prized based on how available it was.
1:25:28
Speaker 1: A you'd have an incentive to discover a new water,
1:25:31
Speaker 1: more water, build more desalination plans for example, and b
1:25:35
Speaker 1: the pricing mechanism will take care of it. There would
1:25:38
Speaker 1: be no almond farmers, no zero almond farmers in California.
1:25:42
Speaker 1: Even though it's a huge export business. They sell most
1:25:45
Speaker 1: of the almonds to China. Ryan what happens as more
1:25:50
Speaker 1: and more people rely on AI as a source of truth.
1:25:53
Speaker 1: I am concerned about AI being wrong seven point three
1:25:57
Speaker 1: percent of the time, but rather people engaging their minds
1:26:00
Speaker 1: even less because AI has the answers. This is warring, Yes,
1:26:04
Speaker 1: I I think it is wobbing. Generally, AI should worry us,
1:26:12
Speaker 1: particularly when it comes to people who are not engaged
1:26:17
Speaker 1: in thinking. It'll take their jobs and it would be
1:26:21
Speaker 1: hard for them to replace it, and they'll they'll they'll
1:26:24
Speaker 1: they'll be residentful and hate for it. And also they'll
1:26:26
Speaker 1: turn to AI for truth. They'll treat AI as the Bible,
1:26:30
Speaker 1: as God, as revelation. And that's a problem. And this
1:26:35
Speaker 1: is where let's this is where I really, really, really
1:26:39
Speaker 1: hope we have real competition and we have alternatives and
1:26:45
Speaker 1: we have choices when it comes to AI, so that
1:26:48
Speaker 1: we don't have one authority. And this is why we
1:26:51
Speaker 1: do not want government involved, so that one of the
1:26:55
Speaker 1: parameters on which this competition is truth or on issues
1:26:58
Speaker 1: where there's disagreement, is real disagreement represented in the AIS.
1:27:04
Speaker 1: So competition. And yeah, a lot of bad things are
1:27:10
Speaker 1: going to happen to mindless people because of AI, and
1:27:14
Speaker 1: AI will only exacerbate the problems they already have, and
1:27:17
Speaker 1: the weaknesses they already have. That is reality. The cattered
1:27:25
Speaker 1: thing was a cat appreciation Saturday, which was a meme
1:27:30
Speaker 1: trend from two thousand and seven, way before I was
1:27:34
Speaker 1: into memes. I don't think I'm still into memes. String
1:27:39
Speaker 1: of bell Hell are you on? I used AI to
1:27:42
Speaker 1: diagnose back pain and developed a recovery plan. In comparison,
1:27:46
Speaker 1: some family have gone to the emergency room, wait for
1:27:50
Speaker 1: hours and still didn't have a doctor. In my opinion,
1:27:53
Speaker 1: the benefits outweigh the concerns. Oh yeah, absolutely, I mean
1:27:56
Speaker 1: the benefits are huge. You know, I'm if if the
1:28:00
Speaker 1: doctor asked me about medication. I used to go to
1:28:03
Speaker 1: WebMD or something like that. Now ask, I ask chat GPT,
1:28:07
Speaker 1: I get some references. I might check the references. I
1:28:10
Speaker 1: might dig deep. I might ask them a question, and
1:28:13
Speaker 1: I'll develop what are the side effects? How do you
1:28:15
Speaker 1: how do you deal with the side effects? How long
1:28:17
Speaker 1: sit the fighters? I mean all of that. It just
1:28:19
Speaker 1: has its fingertips, and yeah, it can make mistakes, but
1:28:23
Speaker 1: it also gives you the links, the resources from which's
1:28:27
Speaker 1: deriving this knowledge, this information, so you can check them out,
1:28:31
Speaker 1: which which I tend to do so I think absolutely, no,
1:28:35
Speaker 1: I'm one hundred percent favorable towards this, and I think
1:28:39
Speaker 1: it's gonna it's going to change all our lives again,
1:28:43
Speaker 1: those of us who are thinkers, going to change our
1:28:45
Speaker 1: lives for the better in ways that I don't think
1:28:51
Speaker 1: we even comprehend yet, Jennifer says, So, you are saying
1:28:57
Speaker 1: the fact the dog is alive and cont just like me,
1:29:01
Speaker 1: does not add value to the relationship as opposed to
1:29:05
Speaker 1: non alive robot may be valuable, but different. Yes, I
1:29:10
Speaker 1: mean that's true, the fact that they're alive and the
1:29:14
Speaker 1: fact that the conscience does make a difference. Jennifer's right, absolutely,
1:29:21
Speaker 1: How much of a difference ultimately, given how well they
1:29:25
Speaker 1: I will be able to fake? It is to be determined,
1:29:30
Speaker 1: to be determined. We will see, all right, God, all
1:29:39
Speaker 1: right time. Thoughts on Miller considering habeas corpus move, Oh,
1:29:48
Speaker 1: this is Miller suspending habeas corpus. Yeah, I mean, I'll
1:29:52
Speaker 1: have to do a show on that if we believe that. Right,
1:29:55
Speaker 1: it's from journalists who say they've got an insight line.
1:29:59
Speaker 1: I mean, this is just terrific. This is the authoritarian
1:30:03
Speaker 1: the jd Vance and Miller. This is authoritarian streak in
1:30:06
Speaker 1: the Trump administration which is very strong and very dangerous,
1:30:10
Speaker 1: and watch out, watch out Ryan. Should we not call
1:30:19
Speaker 1: it artificial intelligence? Seems not accurate and also scary and
1:30:24
Speaker 1: which a left end maga a right used to scare
1:30:27
Speaker 1: people about. It is their more accurate name you can
1:30:29
Speaker 1: think of? I can't, I don't. I think we need
1:30:33
Speaker 1: a new vocabulary for artificial intelligence, but I don't know
1:30:36
Speaker 1: what it would be. It's one of the things I
1:30:39
Speaker 1: think people need to think about and work on in
1:30:42
Speaker 1: the near future. We need an objectives could contribute to
1:30:46
Speaker 1: this quite a bit. How do you see AI currently
1:30:50
Speaker 1: and plant? How do you use AI currently and plan
1:30:52
Speaker 1: to use in the future. So I have a I
1:30:55
Speaker 1: do a lot of the research, you know. So I
1:30:58
Speaker 1: wanted to know about AI here Street today, So I
1:31:01
Speaker 1: asked AI about the history and I got a history.
1:31:04
Speaker 1: I've tried having it outlined talks for me, and it
1:31:08
Speaker 1: does a okay job. It doesn't. It actually does a
1:31:13
Speaker 1: pretty good job. It exaggerates certain things. I have two
1:31:18
Speaker 1: different AIS that I use. I use ch GPT straight
1:31:21
Speaker 1: which I and then I. But but I've it has
1:31:24
Speaker 1: memory and it's trained on me. I guess, and then
1:31:27
Speaker 1: I use something called uron Ai, which uses Claude, which
1:31:31
Speaker 1: is trained on a subset of my materials. So I've
1:31:36
Speaker 1: I've asked that do you want ai? I used primarily
1:31:39
Speaker 1: just to see what it knows, because if I ever
1:31:42
Speaker 1: release it, I want to see where it's getting stuff
1:31:45
Speaker 1: right or wrong. So I can see one day having
1:31:47
Speaker 1: you guys having access to UoN Ai, maybe for a
1:31:50
Speaker 1: monthly fee. Maybe everybody who gives I don't know, twenty
1:31:54
Speaker 1: dollars above gets gets access to you on Ai and
1:31:57
Speaker 1: you can ask it questions. And but I'm not quite
1:32:01
Speaker 1: comfortable enough yet to make it public, but I'm playing
1:32:04
Speaker 1: with it. Here's something I'm thinking about doing. I don't
1:32:08
Speaker 1: know if I'll do it, but I'm thinking about doing it.
1:32:10
Speaker 1: And I'm thinking of creating a substack that is a
1:32:15
Speaker 1: place to write essays where the essays will be written
1:32:18
Speaker 1: by Youuranai and I. So let's say once a week
1:32:22
Speaker 1: there'll be a Uranai essay. Now I'll review it, I'll
1:32:24
Speaker 1: edit it, I'll screen it to make sure it doesn't
1:32:27
Speaker 1: say anything I don't agree with, but it and it
1:32:29
Speaker 1: won't be under my name. It'll be under youranai's name.
1:32:34
Speaker 1: And I don't know if there's a value there. They
1:32:37
Speaker 1: might be for people who prefer reading then listening. To me,
1:32:41
Speaker 1: a lot of it will be based on the shows,
1:32:44
Speaker 1: but I'm playing with that, so I haven't quite ready
1:32:49
Speaker 1: to pull the together. The problem with AI for me
1:32:51
Speaker 1: right now is I spent so much time prepping for
1:32:54
Speaker 1: these shows and so much time prepping for teaching and
1:32:58
Speaker 1: for all the other things that I I don't have
1:33:01
Speaker 1: enough time to exploit AI the way I should. So
1:33:06
Speaker 1: for me right now, the issue is time. If I
1:33:10
Speaker 1: had more time, there are lots and lots of things
1:33:13
Speaker 1: I would like to use AI four that would make
1:33:16
Speaker 1: the show better. They would provide new types of products
1:33:21
Speaker 1: out there. It's already making preparations for talks easier, preparations
1:33:26
Speaker 1: for shows easier. I need to streamline how to do that.
1:33:30
Speaker 1: I need to get better at it. He I needs
1:33:33
Speaker 1: to get better at it. But yeah, it's it's it's
1:33:43
Speaker 1: going to be. I think for me right now, the
1:33:46
Speaker 1: biggest barrier is time. Time to figure out how to
1:33:49
Speaker 1: do it more. Andy, thank you, he says, great talk today,
1:33:52
Speaker 1: Thank you, appreciate that, and Linda says, appreciate you. You're
1:33:56
Speaker 1: on happy to contribute monthly soide to miss the poker
1:33:59
Speaker 1: game at ok on this year. Yeah you you were good. Yes.
1:34:02
Speaker 1: Last year she made made it, made it almost to
1:34:06
Speaker 1: the final table. I think she made it past me. Ali. Guys,
1:34:11
Speaker 1: thank you. I will see you guys tomorrow with Nikos
1:34:15
Speaker 1: and see some of you do'con. Have a great weekend
1:34:19
Speaker 1: by everybody.