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

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