Speaker 1: Welcome to Tech Stuff, a production from iHeartRadio. Hey there,
Speaker 1: and welcome to tech Stuff. I'm your host, Jonathan Strickland.
Speaker 1: I'm an executive producer with iHeartRadio, and how the tech
Speaker 1: are you? You know, often when we talk about tech,
Speaker 1: we reference various thought experiments, hypothetical situations, philosophical problems, and
Speaker 1: game theory. And this can get a little bit confusing
Speaker 1: if you've never actually studied any of those things, and
Speaker 1: people are just kind of off handedly spouting off terms.
Speaker 1: So today I thought i'd cover a small handful of
Speaker 1: them as a sort of foundation for future discussions. Keep
Speaker 1: in mind, there are tons of these, and I'm only
Speaker 1: covering like the teeniest, tiniest number of them. Some of
Speaker 1: these I have talked about extensively another episode, so I'll
Speaker 1: try to go a little light with them on this episode.
Speaker 1: But some of them are brand new to me, or
Speaker 1: I had only heard the name of them but had
Speaker 1: never actually researched the actual scenario or thought experiment. Now,
Speaker 1: these thought experiments in general, not the ones specifically we're
Speaker 1: talking about today, but the practice of thought experiments date
Speaker 1: back quite a ways, at the very least to ancient Greece,
Speaker 1: because we have records of them, so back then they
Speaker 1: were used to conceptualize complex mathematical problems and to give
Speaker 1: people a chance to consider consequences outside of a real
Speaker 1: world situation. But first up, I thought we would talk
Speaker 1: a little bit about game theory because we actually saw
Speaker 1: a real world version of game theory play out just
Speaker 1: a couple of weeks ago. And arguably you could say
Speaker 1: this is only tangentially related to technology, but it did
Speaker 1: have and continues to have a massive impact on tech.
Speaker 1: So the game theory situation that we're going to really
Speaker 1: talk about is known as the prisoner's dilemma. Folks were
Speaker 1: referencing this one in the wake of the Silicon Valley
Speaker 1: bank collapse, which is why I say this is only
Speaker 1: tangentially related to tech, because it's really about a run
Speaker 1: on the bank, and that bank just happened to be
Speaker 1: a really important bank to the tech industry. But the
Speaker 1: dilemma has its roots in the mid twentieth century, and
Speaker 1: the basic version goes something like this. A pair of
Speaker 1: suspected criminals are caught by police. They become the prisoners,
Speaker 1: and the police plan to interrogate each of the prisoners separately.
Speaker 1: The prison sentence for these criminals if they are found guilty,
Speaker 1: which is just taken as as the most likely outcome
Speaker 1: would be ten years. So if they are convicted, they
Speaker 1: get ten years in prison. However, each of them is
Speaker 1: told separately that there are different possible outcomes depending upon
Speaker 1: their cooperation or lack thereof with the police. They just
Speaker 1: aren't allowed to talk to each other, so the two
Speaker 1: prisoners have no way to communicate with one another. They
Speaker 1: have to make up their minds individually, and the four
Speaker 1: possible outcomes are this, neither suspect talks, neither prisoner confesses,
Speaker 1: and in that they each get only two years in
Speaker 1: prison because the cops won't have enough evidence to put
Speaker 1: them away for the more serious crime. So they'll have
Speaker 1: to go to prison, but i'll just be two years,
Speaker 1: not the full ten. However, if one of them stays
Speaker 1: silent but the other one confesses, well, then the one
Speaker 1: who confesses gets to go free and the silent prisoner
Speaker 1: has to serve the full ten years of the sentence.
Speaker 1: If both prisoners confess, then each of them will get
Speaker 1: five years. So you get your four possible outcomes. Suspects
Speaker 1: A and B keep the trap shut and each of
Speaker 1: them serve two years. Suspect A talks but Bee keeps quiet,
Speaker 1: which means A goes off scott Free and B goes
Speaker 1: to the pokey for ten years. Suspect A holds their tongue,
Speaker 1: but Suspect B sings like a canary, and that time
Speaker 1: Suspect B strolls out to freedom and Suspect A rots
Speaker 1: away in prison, or they both go blabbing and they
Speaker 1: both end up serving five years. Now, collectively, the most
Speaker 1: beneficial outcome is to serve only two years by not
Speaker 1: talking at all, and then the next best outcome for
Speaker 1: both individuals collectively would be that they both talk and
Speaker 1: they both have to serve five years, but not the
Speaker 1: full ten. However, individually, if we're not talking about collectively, individually,
Speaker 1: the best outcome is you talk and hope the other
Speaker 1: one doesn't. That way, you can put on your dance
Speaker 1: and shoes, just strut on out of the building and
Speaker 1: the other one's days behind. But worst case scenario, you talk,
Speaker 1: the other person also talks, and you just serve half
Speaker 1: the full sentence each. Actually, worst case scenario is you
Speaker 1: decide not to talk and the other person does talk,
Speaker 1: and then you're looking at ten years in jail. Well,
Speaker 1: when it came to Silicon Valley Bank, the real world
Speaker 1: scenario went like this. If everyone had remained chill, their
Speaker 1: money would have been safe. SBB had over extended its
Speaker 1: investment and government backed securities, which would take years to mature.
Speaker 1: And this was because SVB wasn't issuing as many loans
Speaker 1: once interest rates had gone up significantly. Venture capitalists weren't
Speaker 1: seeking loans so much. Plus they were already flush with cash,
Speaker 1: and loans are the way that banks make money for
Speaker 1: the most part. So instead the banks were investing in
Speaker 1: longer term investments that would have a modest payout, but
Speaker 1: it would have a payout once the investments matured. But
Speaker 1: that would mean that everyone If they just you know,
Speaker 1: cooled their jets and kept their money in place, things
Speaker 1: would probably have been fine. SBB would have likely survived,
Speaker 1: but instead the prisoners the customers of SVB in this case,
Speaker 1: chose to pull their money out because the thinking went
Speaker 1: something like this, if everyone else takes out their money
Speaker 1: and I don't, and then then SBB could shut down,
Speaker 1: and then my money will be stuck, it'll stop existing,
Speaker 1: or I won't be able to get to it, and
Speaker 1: I need my money. It's what lets me buy all
Speaker 1: that stuff like private jets and politicians. So I'm gonna
Speaker 1: go get my money out before i lose out on
Speaker 1: that option. The problem was is that some very big
Speaker 1: players who had a whole lot of money in SBB
Speaker 1: did this, including the heads of venture capitalist groups, who
Speaker 1: then urged all of their clients to do the same,
Speaker 1: and so there was a run on the bank and
Speaker 1: SVB could not cover all the withdrawals without selling off
Speaker 1: assets at a huge loss, and that put SVB in
Speaker 1: a very precarious situation, in fact, precarious enough that the
Speaker 1: US government had to swoop in and take over the
Speaker 1: bank and guarantee all the customers that they would still
Speaker 1: be able to access their money. So enough prisoners took
Speaker 1: the sure thing principle and they screwed over everybody else
Speaker 1: in the process, which not a big surprise because generally
Speaker 1: there's an agreement that taking the tactic of confessing makes
Speaker 1: the most sense from a game theory perspective, and that
Speaker 1: loyalty has no place in the game, that if you
Speaker 1: are loyal, the best you can hope for is two
Speaker 1: years in prison and the worst is ten. So it
Speaker 1: makes more sense to confess and either screw over the
Speaker 1: other person or you both get screwed. So that's the
Speaker 1: thinking behind the prisoner's dilemma, and like I said, we
Speaker 1: kind of saw it play out with the collapse of
Speaker 1: the Silicon Valley Bank. Now, one of my favorite thought
Speaker 1: experiments that has some connection to the tech industry is
Speaker 1: the Ship of Theseus, which dates back to at least
Speaker 1: a century of around five hundred to four hundred BC.
Speaker 1: And the Ship of Theseus idea goes like this, So
Speaker 1: you had the Greek hero Theseus. He had a ship,
Speaker 1: and he ended up docking that ship, and people were
Speaker 1: preserving the ship for his eventual return. And long after
Speaker 1: the hero himself had faded away, the ship remained preserved.
Speaker 1: But of course, over time, pieces of the ship need
Speaker 1: to be replaced. You know, maybe the sails rip and tear,
Speaker 1: so you need to put new sails on the ship.
Speaker 1: Maybe rot sets into part of the deck, so you
Speaker 1: have to rip that out and replace it with new
Speaker 1: planking and so on. And eventually, over the course of time,
Speaker 1: maybe it's decades, maybe it's even centuries, you gradually replace
Speaker 1: every single piece of the ship, so you ultimately arrive
Speaker 1: at a point where no element in the ship of
Speaker 1: Theseus is the original component. Theseus himself never touched anything
Speaker 1: on the ship at this point, So would you still
Speaker 1: call it the same ship? If not, when did it
Speaker 1: officially stop being the ship of theseus? Because obviously, if
Speaker 1: you took possession of the ship right after Theseus got
Speaker 1: tossed off a cliff by lack of medes, and then
Speaker 1: on your first day you had to replace a sale,
Speaker 1: you would still call it the ship of Theseus? Right,
Speaker 1: is just one sale that you replaced. The ship itself
Speaker 1: is still the same, And you know a single sale
Speaker 1: does not change the identity of the overall ship. But
Speaker 1: is there a point where that does happen where the
Speaker 1: ship's identity changes? In tech, one way this thought experiment
Speaker 1: can manifest is when a company undergoes digitalization. Companies that
Speaker 1: have been around for decades have various systems and processes
Speaker 1: in place that predate digital realization. And I always struggle
Speaker 1: over that word, so you're gonna hear me stumble a lot.
Speaker 1: But as a result you to modernize. The leaders of
Speaker 1: these companies have to decide when, if ever, to convert
Speaker 1: old processes into new ones in order to stay current
Speaker 1: and to avoid problems with outdated legacy components. Whenever I
Speaker 1: look at really big companies that have been around for
Speaker 1: like a century, I am often left wondering how they
Speaker 1: handled these transitions, or even if they tried to, because
Speaker 1: these legacy systems are often crucial to the business. The
Speaker 1: business grew around these systems, and so changing the system
Speaker 1: is hard because you have so much other stuff that
Speaker 1: grew up around it and depends upon it, and the
Speaker 1: hardware becomes outdated. It can even get difficult or sometimes
Speaker 1: impossible to maintain or replace old equipment simply because no
Speaker 1: one makes that thing anymore. You know that particular computer
Speaker 1: system may not even be available at any rate, so
Speaker 1: you have to figure out a different way to do things.
Speaker 1: Digitalization makes it easier to track progress and identify bobblenecks
Speaker 1: and such, but it also might mean having to take
Speaker 1: a slightly different approach to try and get a similar
Speaker 1: result as your legacy systems. So the new version isn't
Speaker 1: perfectly recreating the old one. It's just trying to reach
Speaker 1: the same conclusion. But in the process new things might
Speaker 1: pop up, unexpected complications or diversions, and thus he might
Speaker 1: be left wondering if the IBM of today is really
Speaker 1: the same company as the IBM that was founded in
Speaker 1: nineteen eleven. Actually nineteen eleven was the founding of the
Speaker 1: Computing Tabulating Recording Company, which was the precursor to IBM,
Speaker 1: But you get my point. There are some thought experiments
Speaker 1: that are specific to computing problems. One of those is
Speaker 1: called the two Generals problems, which focuses on the issues
Speaker 1: you face when you try to establish communications across unreliable connections.
Speaker 1: So when networks this becomes a big deal. Right. The
Speaker 1: basis of Internet connections largely falls to figuring out the
Speaker 1: most reliable way to deliver information to another system that's
Speaker 1: connected to that network. But the basic two General's problems
Speaker 1: goes something like this. You have an enemy that's in
Speaker 1: control of a centralized valley, and you have two generals.
Speaker 1: Each of them are in charge of their own army.
Speaker 1: Each army is in a valley that neighbors this central valley,
Speaker 1: So essentially you're flanking the enemy. You've got one army
Speaker 1: on the left, one army on the right. They're both
Speaker 1: in their own valleys, and the enemy is in the
Speaker 1: valley in between the two so your goal is to
Speaker 1: establish a time for both generals to attack the enemy
Speaker 1: in the middle at the same time, because the enemy
Speaker 1: is too well entrenched and too strong for either army
Speaker 1: to defeat it on its own. If only one of
Speaker 1: your army's attacks, it's going to get wiped out. So
Speaker 1: really the only hope for your victory is to have
Speaker 1: a coordinated attack. Now complicating things is that the only
Speaker 1: way the two generals are able to communicate with one
Speaker 1: another is to send a messenger across enemy lands to
Speaker 1: reach the other general, and any messenger runs the risk
Speaker 1: of being caught in the process. So let's say that
Speaker 1: you determine before they set out. The General A is
Speaker 1: in charge of establishing an attack time, and so General
Speaker 1: A writes, we attack at dawn in three days and
Speaker 1: sends a messenger out to travel to General B. Well,
Speaker 1: General A doesn't know if the messenger makes it to
Speaker 1: General B. So in three days at dawn, there's a
Speaker 1: risk the General B never got the message, and if
Speaker 1: General A attacks, it's going to mean a loss because
Speaker 1: B won't be participating in a simultaneous attack. But what
Speaker 1: if General B did get the message and then sends
Speaker 1: a confirmation back message received, we attack in three days
Speaker 1: at dawn. Well, that messenger might end up being intercepted.
Speaker 1: So now in three days time, General B isn't sure
Speaker 1: if General A knows that everything is good to go
Speaker 1: as scheduled. So maybe General B hesitates to avoid defeat
Speaker 1: because General B doesn't know if generally is aware of this.
Speaker 1: Of course, General AA, receiving the reply could try to
Speaker 1: send their own message back to General B. But maybe
Speaker 1: that messenger gets cut along the way, and this goes
Speaker 1: back and forth, and the challenges You cannot be confident
Speaker 1: that any one message made it to the correct destination,
Speaker 1: So how you design a communication system where you're reasonably
Speaker 1: certain that messages are going through becomes a challenge. The
Speaker 1: thought experiment shows that uncertainty in communication systems aren't a
Speaker 1: problem that can necessarily be outright solved, but it perhaps
Speaker 1: can be mitigated to a point where all sides are
Speaker 1: comfortably communicating. Okay, we've got more to say about thought
Speaker 1: experiments and philosophy, but let's take a quick break. All right,
Speaker 1: we're back. Let's talk a little bit more about computing
Speaker 1: thought experiments. We just mentioned that it's hard to be
Speaker 1: certain about communications in uncertain situations. You know, it's you
Speaker 1: can do the best you can and limit the chances
Speaker 1: for messaging to fall through, but you can't ensure that
Speaker 1: it is perfect. That's the purpose of the two generals
Speaker 1: thought experiment. But let's talk about a different one. Let's
Speaker 1: talk about philosophers and PISKEETI. Seriously, there is a thought
Speaker 1: experiment called the dining philosopher's problem. This one is more
Speaker 1: about the sharing of computational resources and avoiding a deadlock situation,
Speaker 1: or a situation in which one computational process is hogging
Speaker 1: all the resources and all the other processes that need
Speaker 1: to run that same machine can't. So to understand this,
Speaker 1: let's first recall that back in the old days, before
Speaker 1: we got to microcomputers and many computers, a computer system
Speaker 1: generally consisted of a big, centralized mainframe computer that you
Speaker 1: would access through a data terminal, a dumb terminal, which
Speaker 1: could include very basic input devices like a keyboard and
Speaker 1: a very basic output device like a monitor. But the
Speaker 1: dumb terminal wouldn't have any computational ability itself, like it
Speaker 1: would look kind of like a desktop computer, but instead
Speaker 1: it's literally just a monitor and a keyboard. It's connecting
Speaker 1: to this centralized computer, which likely has lots of other
Speaker 1: dumb terminals connected to it, with other people also accessing
Speaker 1: the centralized computer. So what you really have is a
Speaker 1: shared computational resource that is distributed across all these different
Speaker 1: dumb terminals. Computers typically handled all this by dealing with
Speaker 1: each terminal one at a time, in sequence, but at
Speaker 1: really fast speeds, so it felt that it was pretty
Speaker 1: responsive and that you were doing everything more or less
Speaker 1: in real time, and it was called time sharing. Every
Speaker 1: person at a data terminal was sharing time with this computer.
Speaker 1: Time was really precious with these things too. But how
Speaker 1: do you make sure that all the different processes slash
Speaker 1: terminals are able to access the computer fairly? How do
Speaker 1: you avoid situations where all the demands are coming in
Speaker 1: at a point that effectively locks the entire system where
Speaker 1: it can't do anything. This brings us to the dining
Speaker 1: philosopher's problem. So imagine we've got ourselves a big round
Speaker 1: table and we have placed five plates around this table.
Speaker 1: There's a chair at each plate, and in between the
Speaker 1: plates there is a single fork, so you have plate
Speaker 1: for plate, fork plate fork plate, etc. So five plates,
Speaker 1: five forks, five chairs. So far, so good, But the
Speaker 1: problem is that the philosophers who are coming to dine
Speaker 1: are there to eat spaghette. And the big old heap
Speaker 1: and plate of spaghett is glorious, but the only way
Speaker 1: to eat it is to use two forks simultaneously. So
Speaker 1: you need a fork in each hand in order to
Speaker 1: be able to wind up enough spaghett to shove into
Speaker 1: your gob and you can eat your spaghetti. When you're
Speaker 1: not eating, you can think, because you're a philosopher, so
Speaker 1: you're either thinking or you're eating. That's all you're doing
Speaker 1: at this table. But obviously if you go without eating
Speaker 1: for too long, you'll starve yourself to death. Now, since
Speaker 1: you need two forks to eat, and there are only
Speaker 1: five forks at the table, if you grab the fork
Speaker 1: on your left and the fork on your right, it
Speaker 1: means that the people sitting to your left and to
Speaker 1: your right they can't eat right they have access at
Speaker 1: most to one fork. They don't have access to the
Speaker 1: second one, because those are the ones that are in
Speaker 1: your hands. Once you put the forks down, they become available,
Speaker 1: and then the people on either side can pick that
Speaker 1: fork up and potentially eat unless of course, the fork
Speaker 1: on their opposite side has already been taken by the
Speaker 1: other two people at the table, so they could be
Speaker 1: out of luck, and you have to figure out how
Speaker 1: to juggle this. Worse than that, though, Let's say that
Speaker 1: you've set a rule that whenever a fork is laid down,
Speaker 1: you pick it up immediately, so that you're always at
Speaker 1: least half ready to start eating. But everyone follows this rule,
Speaker 1: and at the very beginning of the meal, everybody reaches
Speaker 1: over to their right and picks up a fork. Well,
Speaker 1: now all five forks are in hand, five different hands
Speaker 1: in fact, which means no one can eat or think
Speaker 1: because they only have one fork in their hand. They
Speaker 1: need two forks to eat. If they set down the fork,
Speaker 1: then they're going to lose it, so they're holding onto it.
Speaker 1: It deadlocks the whole system. So how do you fix this? Well,
Speaker 1: they are actually different solutions to this problem, and they're
Speaker 1: all meant to try and avoid deadlock. And then there
Speaker 1: are other solutions that are meant to ensure fairness, because
Speaker 1: that's not a guarantee in this system. For example, you
Speaker 1: might set a rule that ends up assigning a number
Speaker 1: to each one of the forks and maybe the rule
Speaker 1: is that you can only grab the lower number that's
Speaker 1: in front of you first, and then you can grab
Speaker 1: whichever fork has a higher number, and everyone grabs their
Speaker 1: lowest number. But someone's going to be left without being
Speaker 1: able to do that because they will only be left
Speaker 1: with the number five fork. There is no lower number four.
Speaker 1: They cannot follow the rule because the number four has
Speaker 1: been grabbed by someone else. This allows one person to
Speaker 1: grab the number five fork because they've already grabbed the
Speaker 1: lower number, and they can eat. Then they can sit
Speaker 1: down their fork, and this can then continue with everybody
Speaker 1: getting a chance, assuming you have other rules in place
Speaker 1: to help guide things. Now that's just one approach, mind you,
Speaker 1: there are lots of others, Like there's one where there's
Speaker 1: an arbiter who is there to determine when each person
Speaker 1: is allowed to eat. They essentially are the ones given
Speaker 1: permission to grant the privilege of eating too specific people
Speaker 1: and to make sure that no one overheats like. That's
Speaker 1: another approach. So the point of the whole thought experiment
Speaker 1: is to get people considering the challenges using a limited
Speaker 1: number of resources for multiple entities in such a way
Speaker 1: that no one goes without for too long, and there's
Speaker 1: a means of managing things. It's meant to give computer
Speaker 1: scientists heads up on things they have to consider when
Speaker 1: they're designing their systems. So it's really thought experiments that's
Speaker 1: where they're really valuable. Is that it's before you started
Speaker 1: to build anything, right, you haven't dedicated asset and time
Speaker 1: and effort to building something. You're thinking it through first
Speaker 1: and saying, how do I avoid this perceived problem so
Speaker 1: that we don't actually encounter it in the wild and
Speaker 1: then have to figure out a solution. How can I
Speaker 1: solve it just by thinking about it? That is what
Speaker 1: these thought experiments give you the opportunity to do, assuming
Speaker 1: that the thought experiment is constructed properly, which is not
Speaker 1: always the case. There are thought experiments that later on
Speaker 1: people picked apart and said, this thought experiment is predicated
Speaker 1: upon assumptions that we can't be certain are true, and
Speaker 1: therefore you can't really use this thought experiment without acknowledging
Speaker 1: that it could just all be for nothing because the
Speaker 1: actual primases aren't proven. But then there are the various
Speaker 1: thought experiments and ethics problems that come into play when
Speaker 1: you start to talk about artificial intelligence. Now, I've said
Speaker 1: many times in this show, AI covers a huge amount
Speaker 1: of ground. It's a very I think AI is a
Speaker 1: dangerous term. Not dangerous in the sense of it potentially
Speaker 1: being harmful to humans, but rather it's such a huge
Speaker 1: discipline that it's very easy to be reductive when you're
Speaker 1: talking about AI, and to think that when you say AI,
Speaker 1: are just talking about machines, thinking as if they were people.
Speaker 1: That's one version of what AI could be. It's generally
Speaker 1: referred to as strong AI. But AI covers a lot
Speaker 1: of ground. It is a multidisciplinary technology, and it encompasses
Speaker 1: relatively constrained concepts like computer vision or language recognition, and
Speaker 1: then it ranges all the way up to big ideas
Speaker 1: like strong or general AI capable of processing information in
Speaker 1: a way that it is at least human like. Well,
Speaker 1: one of the elements, in fact, one that's closest to
Speaker 1: strong AI, that's in the thought experiment world, is the
Speaker 1: thought experiment of an artificial brain or artificial mind. What
Speaker 1: would it take to produce an artificial brain? So something
Speaker 1: that we have created that is capable of some form
Speaker 1: of thought, something that we would recognize as thought. So
Speaker 1: there's a question about whether or not it's even possible
Speaker 1: to create an actual artificial brain and what that could entail.
Speaker 1: Some argue that what it will take is just a
Speaker 1: sufficiently complex computer system that's emulating how our brains work,
Speaker 1: so like an artificial neural network. If we were able
Speaker 1: to build an artificial neural network that was big enough
Speaker 1: and fast enough on powerful enough computer systems, then potentially
Speaker 1: we would see the formation of an artificial brain. That's
Speaker 1: how that argument goes. And maybe we wouldn't even need
Speaker 1: to do an actual artificial neural network. Maybe the collective
Speaker 1: interconnections of the Internet could allow an intelligence to emerge,
Speaker 1: you know, maybe it would even be transitory in nature.
Speaker 1: Maybe it would be an intelligence that emerges and fades away,
Speaker 1: and maybe so quickly that we can't even ever recognize it,
Speaker 1: that it's elements of an intelligence that because it's so transitional,
Speaker 1: we don't recognize it as such. And maybe it is
Speaker 1: possible to create a brain or mind out of such
Speaker 1: complex connections between high end computer systems. But the truth
Speaker 1: of the matter is we don't have a full understanding
Speaker 1: of how our minds work, the actual gray matter that's
Speaker 1: in our heads. We don't have a full understanding of that. So,
Speaker 1: because we don't fully understand how our brains work, there
Speaker 1: are some who argue that you know that it's possible
Speaker 1: there's some element in our minds that we have yet
Speaker 1: to identify. They will be necessary for us to understand
Speaker 1: if we are to ever realize a true artificial brain
Speaker 1: that without this unknown but perhaps fundamental component, it just
Speaker 1: won't happen. Maybe we would fall upon it by accident,
Speaker 1: or maybe we'll hit a limit that we just can't
Speaker 1: get around without first having a deeper understanding of how
Speaker 1: our own brains work. Alternatively, it might be possible to
Speaker 1: create an artificial brain or mind without attempting to simulate
Speaker 1: or replicate how human minds work. Proponents of this argument
Speaker 1: I pointed out that for much simpler tasks relatively speaking,
Speaker 1: like human flight, we ultimately abandoned technologies that we're attempting
Speaker 1: to replicate how birds fly. I'm sure you've seen old
Speaker 1: film footage of experiments in heavier than air flight where
Speaker 1: people had strapped wings to their arms and they were
Speaker 1: flapping them up and down, or they had some mechanical
Speaker 1: contraption that was moving wings up and down and it
Speaker 1: was all an attempt to replicate how birds fly in
Speaker 1: the air, but these didn't really work, and ultimately we
Speaker 1: found that going with a fixed wing aircraft design and
Speaker 1: abandoning our foolish attempts to replicate what birds are doing,
Speaker 1: we could actually succeed. We ended up creating successful flying
Speaker 1: machines even though we were not directly mimicking birds and nature.
Speaker 1: So by that argument, you could say, well, maybe creating
Speaker 1: an artificial brain won't involve mimicking our own neurological systems
Speaker 1: at all. Maybe it'll be through some other means, such
Speaker 1: as those complex connections on the Internet, for example, where
Speaker 1: intelligence would be an emergent property. So that is another
Speaker 1: another approach toward looking at an artificial brain. As for
Speaker 1: what I believe, I think that with enough complexity and power,
Speaker 1: maybe we could see something like an artificial brain emerge,
Speaker 1: But I honestly don't know. I do think that the brain,
Speaker 1: the mind is completely engulfed and encompassed by the gray
Speaker 1: matter in our heads. I don't think there's anything metaphysical
Speaker 1: that's going on there. That's my own personal belief, but
Speaker 1: I don't know that for sure. It's just my belief
Speaker 1: partially backed up by the fact that people who have
Speaker 1: encountered some form of brain injury often have very different
Speaker 1: experiences from that point forward. And to me, that means
Speaker 1: that consciousness and experience are very tightly locked with the
Speaker 1: actual organ of the brain. But that doesn't mean that,
Speaker 1: you know, there's not something else going on that I'm
Speaker 1: missing that would be necessary. I just don't know. All Right,
Speaker 1: we're going to take another break. When we come back.
Speaker 1: We've got a few more thought experiments we need to
Speaker 1: talk about, including some golden oldies. Okay, let's talk about
Speaker 1: the Turing test, because you could argue that this is
Speaker 1: kind of a thought experiment. So Alan Turing based this
Speaker 1: off a game called the imitation game, and the idea
Speaker 1: behind this is that you have a contestant who gets
Speaker 1: to communicate with someone without being able to see or
Speaker 1: hear this person. So maybe they're typing things out on
Speaker 1: a typewriter. They submit it and then they get a
Speaker 1: typed response, and their goal is to try and figure
Speaker 1: out with whom they are communicating. So one version of
Speaker 1: the imitation game has them talking to someone who could
Speaker 1: be a man or could be a woman, and if
Speaker 1: it is a woman, the woman is posing as if
Speaker 1: she were a man. So it's the contestants job to
Speaker 1: sess out whether or not the person on the other
Speaker 1: end of the communication chain is actually a man or
Speaker 1: a woman pretending to be a man. So let's ignore
Speaker 1: the dated concept of binary approach to gender that you know,
Speaker 1: obviously that's definitely changed since then. Touring was suggesting that
Speaker 1: you could play this same sort of game, but instead
Speaker 1: of having a woman pose as a man, you could
Speaker 1: have a machine posing as a human. The contestant would
Speaker 1: have to decide whether or not they were interviewing a
Speaker 1: person or a machine pretending to be a person. And
Speaker 1: if the machines were reliably able to fool contestants into
Speaker 1: thinking that they are chatting with another human being, then
Speaker 1: the machine would be said to pass the Turing test. Now,
Speaker 1: Turing wasn't actually saying that such a machine, essentially a chatbot,
Speaker 1: is intelligent or was capable of thought. Instead, he was
Speaker 1: saying the machine could simulate intelligence to a degree that
Speaker 1: a person might not be able to tell the difference.
Speaker 1: And after all, each individual doesn't know for sure that
Speaker 1: the people they encounter possess intelligence. If you met me
Speaker 1: and we had a conversation you wouldn't be sure that
Speaker 1: I am actually intelligent. You would know you're intelligent because
Speaker 1: you know your own experience, right, You've had your experiences,
Speaker 1: You know you possess intelligence. When you talk to someone else,
Speaker 1: you assume they also possess intelligence. But you can't know
Speaker 1: for sure because you cannot occupy their experience. But we
Speaker 1: grant the assumption that the people we encounter have intelligence.
Speaker 1: Turing was kind of cheekily suggesting that perhaps we should
Speaker 1: extend the same courtesy to machines that appear to possess
Speaker 1: the same qualities. Whether or not the machine is actually
Speaker 1: intelligent or is capable of thought is kind of moot.
Speaker 1: If the outcome seems to mimic intelligence, why shouldn't we
Speaker 1: just go ahead and say the machine is intelligent. Does
Speaker 1: it really matter if the machine can actually think or not? Now,
Speaker 1: philosopher John Searle said, heck, yeah, it matters if we
Speaker 1: say computers think and they don't. And in fact, he
Speaker 1: went so far as to say computers are not capable
Speaker 1: of having a mind to make up because ultimately they
Speaker 1: are just machines designed to follow instructions. Thus, you know
Speaker 1: a machine that follows a program, the program could be
Speaker 1: incredibly sophisticated and complicated, but it's still ultimately just a
Speaker 1: list of instructions that the computer has to follow. The
Speaker 1: computer can't divert away from those instructions. It might appear to,
Speaker 1: but it can't go off book, you know, it can't
Speaker 1: go off the script and start to improvise. And at
Speaker 1: no point does this become something as human as a
Speaker 1: mind is. So to illustrate his perspective, he proposed a
Speaker 1: thought experiment called the Chinese Room. Now I've talked about
Speaker 1: the Chinese Room and a lot of other episodes, so
Speaker 1: I'll try to keep this kind of short. Searle argues
Speaker 1: that a computer running a program is a bit like
Speaker 1: taking a non Chinese speaking person, someone who does not
Speaker 1: understand Chinese. They can't speak it or read it. You
Speaker 1: put this person into a room that just has a
Speaker 1: door with like a mail slot in it, and inside
Speaker 1: the room is a desk, there's paper, there's a pen,
Speaker 1: there's plenty of ink, and there's a giant book of instructions.
Speaker 1: So once in a while, someone shoves a piece of
Speaker 1: paper through the little mail slot in the door, and
Speaker 1: the piece of paper has a Chinese symbol written on it.
Speaker 1: The person in the room has a job. They take
Speaker 1: that piece of paper with a Chinese symbol written on it.
Speaker 1: They go through their big book of instructions looking for
Speaker 1: that symbol, and they ultimately will find it, and then
Speaker 1: they will produce a response based on what's in the book.
Speaker 1: They'll have to draw a different Chinese symbol. They just
Speaker 1: ape the instruction that's in the book. Then they put
Speaker 1: that through the mail slot in the door and they're done.
Speaker 1: On the other side of the door, you have someone
Speaker 1: who has brought a question to the room. You know,
Speaker 1: it's written in that Chinese symbol. So they submit a
Speaker 1: question and then after a bit of time, they get
Speaker 1: an answer, and to them it appears that whomever is
Speaker 1: behind the door understands Chinese symbols and can respond in kind.
Speaker 1: But the fact is the person in the room doesn't
Speaker 1: understand Chinese. They're just following very thorough instructions. But they
Speaker 1: have no idea what's being asked or even what the
Speaker 1: response means. They don't know what they're saying. They're just
Speaker 1: copying what's in the book. They're following the program. They're
Speaker 1: matching questions with answers in a language they don't understand.
Speaker 1: They don't even necessarily know that they're questions. They're just
Speaker 1: submitting whatever the corresponding response should be. So Searle says
Speaker 1: that machines are essentially doing this, that's what they're doing.
Speaker 1: They're producing responses based on input, but they have no
Speaker 1: unders standing of either the input or the response. They're
Speaker 1: just following instructions. When you engage in a conversation with
Speaker 1: chat GPT, chat GPT doesn't actually understand what you're talking about.
Speaker 1: It doesn't comprehend the questions, It doesn't understand context or
Speaker 1: anything like that. It just builds up responses based on
Speaker 1: a really sophisticated program. But these responses, even if they
Speaker 1: correctly answer your question, do not show that chat GPT
Speaker 1: actually understands what is going on. It's just producing a result.
Speaker 1: Searle says this is because the machine ultimately cannot think,
Speaker 1: It cannot be said to have a mind, and he
Speaker 1: further argues that strong AI is a dead end. We're
Speaker 1: never going to get there. It is it's inherently impossible,
Speaker 1: and there's actually a lot of discussion and debate around
Speaker 1: the Chinese Room thought experiment. There are people on different
Speaker 1: sides of the matter, arguing for or against its merits,
Speaker 1: and the repretation of it. But again, this gets into
Speaker 1: a lot of details that we don't really have time
Speaker 1: to dive into for this episode. And I have done
Speaker 1: episodes on the Chinese Room thought experiment in the past,
Speaker 1: so let's move on for a different approach to AI.
Speaker 1: Let us turn to Valentino Brightenberg, who was a neuroscientist
Speaker 1: and an important figure in the field of cybernetics. And
Speaker 1: I feel like I need to define cybernetics because I
Speaker 1: had a complete misunderstanding of what that term meant until
Speaker 1: I was doing research. Cybernetics is a discipline concerned with
Speaker 1: communications and automatic control systems in both machines and living things,
Speaker 1: as defined by Oxford Languages. The word has its origins
Speaker 1: in the Greek word for kyberneticus, which means good at steering.
Speaker 1: I didn't know that before I researched this episode. So
Speaker 1: you could describe the act of a human picking up
Speaker 1: a teacup from a saucer on the table as a
Speaker 1: cybernetics series of actions. And you would first think of
Speaker 1: like the human brain as a controller and it receives
Speaker 1: information from a sensor the human's eyes, and this gives
Speaker 1: information about the teacup, where the teacup is located, its
Speaker 1: distance from the human in question, the teacup's orientation with
Speaker 1: reference to the humans position, etc. This information is called feedback,
Speaker 1: So the feedback goes to the controller, and then the
Speaker 1: controller uses the feedback to make a decision in order
Speaker 1: to achieve a desired outcome, in this case picking up
Speaker 1: the teacup. But this actually happens in stages. Right. You
Speaker 1: might as an outward observer, you might see this human
Speaker 1: lean forward and reach out their arm and then open
Speaker 1: their hand, and then take the teacup and then lift it.
Speaker 1: So this is actually a series of steps. The goal
Speaker 1: for the controller is to take the behavior that we're
Speaker 1: observing as outsiders, they leaning forward in the reaching of
Speaker 1: the hand and so forth, and to bring that into
Speaker 1: alignment with the desired behavior of just picking up the teacup.
Speaker 1: This discipline plays an important part not just in our
Speaker 1: understanding of organisms in their behavior, but also how you
Speaker 1: could create things like artificial limbs that interface with our
Speaker 1: brains and have those artificial limbs behave similarly to an
Speaker 1: organic limb. We have seen some really incredibly sophisticated robotic
Speaker 1: limbs that can do this sort of thing, but they
Speaker 1: have to really be grounded in this study to move
Speaker 1: in a way that's natural and actually achieves whatever the
Speaker 1: outcome is that the person who is attached to that
Speaker 1: limb wants it to do. This is not something that
Speaker 1: just automatically happens. You have to build it in. So
Speaker 1: in the mid nineteen eighties, Brightenberg published a book called Vehicle.
Speaker 1: In this book, he presented hypothetical self operating machines. So
Speaker 1: these were not actual machines, they were just sort of
Speaker 1: a thought experiment. He said, what imagine if you had
Speaker 1: a machine that did this, and they would exhibit behaviors
Speaker 1: that could become increasingly intricate and complicated and dynamic, But
Speaker 1: ultimately you could start to boil down those behaviors as
Speaker 1: following simpler rules. And if you just understood all the
Speaker 1: different rules in all the different situations, you would be
Speaker 1: able to even predict what something would do to some degree. So,
Speaker 1: for example, a machine might have an optical sensor and
Speaker 1: it can detect if something is in front of the machine.
Speaker 1: So imagine you've mounted the sensor to the front of
Speaker 1: a little four wheeled vehicle, and if the sensor doesn't
Speaker 1: detect anything in front of it, it allows power to
Speaker 1: go to the motor that drives those wheels, and the
Speaker 1: little robotic car will move forward. But if something gets
Speaker 1: in its way, then maybe it cuts power to the
Speaker 1: motors and the wheels stop turning, or maybe it changes
Speaker 1: the rate at which different wheels turn so that it
Speaker 1: can rotate a bit, it can turn out the way
Speaker 1: of whatever the obstacle is. I'm sure you've had experience
Speaker 1: with little toys that do this sort of thing where
Speaker 1: there's some sort of simple optical sensor so that if
Speaker 1: it gets close to a wall, it stops and turns
Speaker 1: and moves in a different direction. Heck, your typical robot
Speaker 1: vacuum cleaner will do this, right, So this is something
Speaker 1: that we've had some experience with at this point. And
Speaker 1: Brightenberg actually went on further and hypothesize that you could
Speaker 1: have machines that would follow slightly more complicated rules in
Speaker 1: such a way that it could imply motivations behind movements,
Speaker 1: things that we would normally associate with humans, like fear
Speaker 1: or aggression, but really it could just be the machine
Speaker 1: responding to different situations in a predetermined way. So let
Speaker 1: me give you a simple example. Maybe you've got this
Speaker 1: optical sensor that's on the front of this little four
Speaker 1: wheeled vehicle, and when it detects something, it tries to
Speaker 1: determine whether or not the thing ahead of it is
Speaker 1: bigger or smaller than it is. If it's smaller, maybe
Speaker 1: it accelerates toward it, as if to intimidate it. And
Speaker 1: if it's larger, maybe it turns and accelerates away from
Speaker 1: the object, as if it's in fear. Now, Brightenberg's hypothetical
Speaker 1: vehicles didn't really need any cognitive processes. They would just
Speaker 1: follow these simple instructions. But if you were to put
Speaker 1: them in a complex enough environment with enough different sets
Speaker 1: of instructions, these behaviors would potentially be very dynamic and complicated,
Speaker 1: perhaps complicated enough to imply a deeper intelligence, even though
Speaker 1: ultimately they were just following simple rules. Speaking of vehicles,
Speaker 1: let's talk about the trolley problem. And you've likely heard
Speaker 1: of this one. It's one of the more famous thought
Speaker 1: experiments that relates to ethic. The basic version is that
Speaker 1: there's a trolley hurtling down some tracks, and the breaks
Speaker 1: on the trolley aren't working, and if the trolley keeps going,
Speaker 1: it will hit a group of five people, killing all
Speaker 1: of them. But you're standing at a switch. If you
Speaker 1: throw the switch, the trolley will divert onto a separate
Speaker 1: set of rails and strike one person, killing that one,
Speaker 1: but the other five people will be saved. So do
Speaker 1: you throw the switch dooming one person and saving five?
Speaker 1: There's actually a lot of stuff to consider here. For example,
Speaker 1: do you consider it more ethical to make an active choice?
Speaker 1: Is it akin to murdering someone? If you throw the switch?
Speaker 1: Are you killing that person? Like you're condemning them to die?
Speaker 1: But if you choose not to act, does that exonerate
Speaker 1: you from the death of the five people? You could say, well,
Speaker 1: they'd be dead if I weren't at the switch, there'd
Speaker 1: be no one to change it. They would have died
Speaker 1: either way, So it's the only thing that's different is
Speaker 1: that I happened to be at the which does that
Speaker 1: make me a bad person for not throwing the switch?
Speaker 1: There are variations of this as well that make it
Speaker 1: even more complicated. For example, one early version had the
Speaker 1: person at the switch having to choose between saving the
Speaker 1: five people or condemning their own child. To death. Other
Speaker 1: versions replaced the trolley. What if it's an incoming missile
Speaker 1: and you have the ability to divert a missile that
Speaker 1: was heading toward a city, But if you divert it,
Speaker 1: that missile is going to hit a small town instead.
Speaker 1: So if the missile hit the city, more people would die,
Speaker 1: but there could still be some survivors in the city.
Speaker 1: If it hits the town, it's going to essentially wipe
Speaker 1: out the entire town population. Fewer people overall will die
Speaker 1: because the town is smaller than the city, but essentially
Speaker 1: everyone in the town dies in the city. It's a
Speaker 1: massive but not entire part of the population. Well, what
Speaker 1: does all this have to do with technology? These are
Speaker 1: actually the sort of questions that engineers have to wrestle
Speaker 1: with as a design stuff like aonamous systems. When we
Speaker 1: look at the possibility of driverless vehicles, for example, we
Speaker 1: have to consider how the vehicle will handle emergency situations.
Speaker 1: So let's say a driverless car with passengers inside it
Speaker 1: is motoring down the road and a person steps out
Speaker 1: in the road suddenly, so it's too late for the
Speaker 1: vehicle to break. Let's say let's say does the driverless
Speaker 1: vehicle instead via ale the way, perhaps even going off road,
Speaker 1: factoring in the fact that the passengers inside the car
Speaker 1: have seat belts and have air bags and other protective
Speaker 1: measures around them, and thus prioritize the pedestrians health, or
Speaker 1: does it instead prioritize the safety of the passengers and
Speaker 1: make a decision that it puts the pedestrians safety at
Speaker 1: considerable risk. Machines do not intrinsically know this stuff. So
Speaker 1: grim as it may seem, these are things that engineers
Speaker 1: have to take into consideration as they build out complex
Speaker 1: autonomous systems. Now, let me just finish up by touching
Speaker 1: on a classic science fiction thought experiment. Are we living
Speaker 1: in a simulation? You've seen this idea explored in movies
Speaker 1: like the Matrix series, And there is an interesting thought
Speaker 1: experiment proposed by Nick Bostrom regarding simulated realities, and he
Speaker 1: posits that at least one of several possibilities must be true,
Speaker 1: namely scenario one. Humans are never going to reach a
Speaker 1: point in which they can construct a simulated reality sophisticate
Speaker 1: enough for the inhabitants of that reality to believe they
Speaker 1: are quote unquote real. So, in other words, for whatever reason,
Speaker 1: maybe we destroy ourselves before we get there. Maybe we
Speaker 1: just never develop technology sufficient enough to do it. But
Speaker 1: we aren't able to create a computer simulation so robust
Speaker 1: that the simulated beings inside of it have their own
Speaker 1: kind of self awareness. Two that there are no other
Speaker 1: civilizations out in the universe that are able to do
Speaker 1: this for whatever reason. Three that we humans will one
Speaker 1: day be able to do this, but we can't do
Speaker 1: it yet. However, we will one day be able to
Speaker 1: do it, we just haven't reached that point, and we're
Speaker 1: the first to do it, like no one else has
Speaker 1: managed to do it. Four that we're actually living in
Speaker 1: a simulation. That the idea being that if it is
Speaker 1: possible to build such a simulation where the beings inside
Speaker 1: the simulation have self awareness and can think and have
Speaker 1: emotions and all this sort of stuff that we associate
Speaker 1: with being humans and having experiences, then if that is
Speaker 1: possible to build such a thing, we are definitely in one,
Speaker 1: or at least there's a fifty fifty shop we are,
Speaker 1: because if it is possible, it would be pretty egotistical
Speaker 1: to suggest that we'd be the first to do it.
Speaker 1: That hasn't happened already, And that we are not in
Speaker 1: fact a product of such a thing. There are a
Speaker 1: lot of arguments that go into that. It's kind of fun.
Speaker 1: I would argue, ultimately it's moot because it's not a
Speaker 1: falsifiable hypothesis. And ultimately we still have our own experiences
Speaker 1: in our own lives. So even if it is a simulation,
Speaker 1: it matters to us as we're in it, like just
Speaker 1: as much as it would matter if it's not a simulation.
Speaker 1: So I say, simulation or not. Go out there, be
Speaker 1: good people, use critical thinking, use compassion, and use these
Speaker 1: thought experiments to kind of guide you a little bit
Speaker 1: and kind of suss out what's right and what's wrong
Speaker 1: and what are some possible solutions to these problems. Like
Speaker 1: I said, this is just a small collection. They're ton more.
Speaker 1: I'll probably do more episodes in the future about different ones.
Speaker 1: There's a whole bunch about quantum mechanics. But boy, howdy
Speaker 1: do those get heavy. So maybe we'll take another look
Speaker 1: at these in a future episode. For now, I hope
Speaker 1: you're all well, and I will talk to you again
Speaker 1: really soon. Text Stuff is an iHeartRadio production. For more
Speaker 1: podcasts from iHeartRadio, visit the Heart Radio app, Apple podcasts,
Speaker 1: or wherever you listen to your favorite shows.