 Welcome to Building Tomorrow, a show exploring the ways that tech, innovation, and entrepreneurship are creating a freer, wealthier, and more peaceful world. As always, I'm your host, Paul Matzko. And with me in the studio today are Aaron Powell, the director of Libertarianism.org, and our special guest, Caleb Watney, a tech policy fellow at the R Street Institute. Welcome to the show, Caleb. Thanks for having me on, Paul. Oh, it's a pleasure. Now, today we have Caleb on the talk with us about artificial intelligence, you know, the tech that's behind the global overlord Skynet responsible for sending Arnold Schwarzenegger back in time to serve as a meta-joke on Californians and as a cautionary tale about 1980s pop culture. We actually will talk about AI researchers' concerns about hostile AIs in a bit, but first let's start by talking about the ongoing implementation of less, shall we say, apocalypse prone AI and the ways it's benefiting human society right now. So Caleb, why don't you kick us off? What is AI? How does that differ or similar to other phrases that are commonly used in the field like machine learning or algorithmic learning algorithms? What is that? Yeah. So I think if you get, you know, 10 AI researchers in a room and ask them what is the definition of AI, you're going to get nine or 10 different answers. It is a famously sort of divisive question. Personally, I tend to take a pretty broad definition of it. I think it's helpful as a category for the really broad range of things that are automating aspects of human decision making. And so I think that would incorporate most forms of software. That's like a really low form of artificial intelligence and go all the way up to really complicated, you know, machine learning algorithms. And so some other terms like machine learning are then subsets of AI to get you further up. Think of just intelligence as a way for an agent to apply a complex, you know, or a varied sorts of solutions to a problem they're trying to solve, the greater their ability to change the kinds of methods they use, the more intelligent the agent is. And so thinking of intelligence as this, you know, scale rather than like a binary that you are artificially intelligent or you're not tends to be a more helpful framework for me at least. What's machine learning? So machine learning is sort of the ability to train machines to kind of pick up patterns in the data themselves. So if you give it, you know, like some search function as an algorithm and like a large supply of data and it's starting to like learn from the data and recognize patterns itself and pull those out, that's kind of in a very broad sense what machine learning is all about. So it's like my spam filter. Yeah, your spam filter is a great example of machine learning because you will frequently give it, you know, hints and lessons and that's part of how it learns, you know, to get better at tagging things to spam or not is you're saying, hey, this is an example of spam. This is not an example of spam. It tries to put all those examples together, see what commonalities they have, what differentiates spam in your mind and of course what other humans think and that helps slowly update its priors about what it's going to categorize as spam or not. This is why, Aaron, you haven't lost the fortune to a Nigerian businessman since the mid-90s, your spam filter helping out there. It reminds me of too, I like that non-binary approach, right, it's like robots. Those who grew up in the 1950s or the 60s, you said robot, they thought, you know, something from 50s sci-fi television, like, you know, the alien, they land in like Central Park and the alien comes out. But the day the air stood still. The day the air stood still, right, classic sci-fi. That's a robot, a fully autonomous and usually anthropoid, you know, metal man. Well, but robots are all around us, right, they build our cars, there's a gradation of robotics and the same thing, being true for artificial intelligence is I think a useful starting point. So now you mentioned pattern recognition. The first thing that came to mind was that episode of Silicon Valley where it was hot dog or not a hot dog, remember that. So pretty good that, and you can actually download an app as I understand someone wrote a program and you can actually make sure something's not a hot dog before you bite into it or I guess you want to make sure it is a hot dog. So pattern recognition, pretty good though there is that conundrum, one of our columnists Kate Sills and a piece on smart contracts knew that like there's an issue with, there's a meme going around about recognizing the difference between a dog and a muffin. And even some of our smartest algorithms can't pick up the difference. The pattern's just not dissimilar enough. They can't tell a chihuahua from a brand muffin. So they're still even, I mean, they're doing better than we would have thought even just a couple of years ago, but they're still an issue there. So on this pattern recognition, so the value of recognizing a dog from a muffin is not because the AI can now do something that we can't. It's that it can do it at scale, right? It can find, so Google image search can find us pictures of dogs and know not to give us pictures of muffins and all of that. But it seems like, I mean, a lot of the more valuable uses of machine learning or of AI is becoming good at pattern recognition is to find stuff that we weren't able to find. To identify patterns that we couldn't identify by looking at lots of data or looking, or to try to use AI to figure out what the potential causes of certain health ailments are. So we're using it for research as opposed to automation. How do you do that though? Because as you said, with the spam filter, the way my spam filter works is, and I used to have in the 90s I had one called, God, I can't remember. The spam pop file, I think it was, that you installed on your own computer and routed your mail through it. And it just used a Bayesian filter, and so you just trained it just over time. And it got remarkably good, but that required me training it. So how do you get an AI to be good at something that by definition we can't train it on? Yeah, so there's a bunch of different techniques that machine learning researchers will use to try to improve the functionality. When humans are sort of directly involved in telling the algorithm, this is a good thing to do, this is not a good thing. It's usually called supervised machine learning. And unsupervised is when you're trying to give it some more automated function. So as an example, OpenAI has a number of different programs that will try to learn various games. And so they have one that learned how to play chess. And counter to how previous algorithms have learned to play chess, which is usually from watching a whole bunch of human games, having humans program into it. These are the kinds of strategies you should be looking for. Rather, it just had the algorithm play another version of itself for billions of hours, and that slowly had to become better. And obviously, you could learn slowly as it played billions of games that these kinds of moves increase the probability of winning these ones don't. And just through that brute force, it was able to come up with patterns and strategies and techniques that humans hadn't even thought of. And so today in chess tournaments, one way that you might be able to find out if a human is cheating and using a computer to help them is if their move seems too original. Because if their move is too original, then it's unlikely that a human would have discovered that already. That's really smart, actually. Well, and it's a reminder. You're looking for deviation from a norm, which is what pattern matching and the ability to go through that many calculations a second allow you to do. Some of the applications are really quite exciting. We did a episode on DNA databases, things like Ancestry.com, 23andMe. And when we were discussing that 23andMe had just signed a deal with GlaxoSmithKline, it's a major pharmaceutical company. And their goal, I mean, they're years away from this, but their goal is to use AI to look at genetic markers. So for each person, there are so many different genetic variants on your basic chromosome that like to actually parse through that for a real doctor, a specialist to parse through that would just be impossible. It's just too much volume. It can be done. So if you can train the AI to look through someone's entire genetic code and look for these patterns and maybe look for patterns that even people haven't picked up on yet, and then take a basic prescription, tweak it for that individual person. You can make the drug more effective potentially than the generic variation. You can make it have fewer symptoms, have a lower symptom rate. There's some really cool, exciting stuff just using that basic pattern recognition and the ability to just absorb vacuum of data like that. Yeah, I think one weird thing we're seeing is there are a whole bunch of potentially interesting applications that we don't have access to right now because the search costs for sorting through so much information is just too high. And so trying to run individual clinical trials to see how this specific drug interacts on these 15 different types of genomes or whatever, that's just like unfeasible. You can't run clinical trials on that many things, but if you can sort of model what that would look like ahead of time on a computer, and then you can run billions of simulations beforehand, you can find which possible solutions are gonna be the most promising and then run human clinical trials on those. And so it's really, I think, just expanding out the production frontier of what are our search costs? Like how expensive is it to search for new, really informational, intense solutions? That's really cool. So you've written about, so there's the pattern recognition side of AI, but there's also the kind of decision process automation side of it where it's like, make a decision for me so I don't have to go through that process or you can do it faster or maybe you can do it more accurately. And you wrote a while back about one that seems incredibly counterintuitive, it was kind of interesting, which was criminal justice. So can you tell us a bit about how we might use AI in criminal justice? Sure, and kind of to take a step back on what's the broader point here, yeah, AI tends to be very useful for pattern recognition and then also in automating or helping us sort of create a more rigorous model for how we think through decisions ourselves. So the specific example in criminal justice is in pre-trial detention decisions. Where essentially a judge has to look at, but before the trial they have to decide is this defendant going to have to post bail? Are they going to just be released without bail? Are there gonna be various levels of community surveillance? Or if we think that they're a very big risk of either running away before their trial or committing another crime before their trial, we can keep them in pre-trial detention. And as a portion of the population is incarcerated, jail has been a large portion of that, especially in the growth in the last 20, 30 years. And so, and constitutionally, these are people that are still innocent because they're innocent before proven guilty. And so it's really just kind of like a fundamentally a risk prediction of what's the likelihood of them leaving town before their trial or committing another crime. And it seems like we have pretty good indication that judges are very bad at making those kinds of predictions. They'll systematically under-rate the risk of the very high-risk defendants, and they'll systematically over-rate the risk of very low-risk defendants. And so, by just having more accurate predictions about what is that likelihood, you can get, I think, simultaneously lower crime rates and lower jail populations. If we're gonna have minority report, it might as well be an effective minority report. So this is so that last weekend I watched, but this is before I knew we were having this conversation, I watched an episode of Rift Tracks on Amazon Prime and the movie, so this is the new version of Mr. Science Theater, was called Cyber Tracker. It was a vehicle for Don the Dragon Wilson, who was a short-lived martial arts star. It was terrible, but the whole premise was there's this company that is replacing judges with AI, and there's then evil senators and cahoots with them, and then lots of kicking. But that everyone was up in arms about this, right? Because this is like you're taking away our humanity. And that seems like a real, I mean, that's a concern not just with this, but with a lot of the AI stuff that we've talked about. It shows up in autonomous vehicles, too, that we seem to be perfectly happy with getting plowed into by drunk drivers all the time, but if we're gonna get plowed into a whole lot less by a computer, just because it's a computer, that's way worse than the mindless humans doing it. So how do we, is that inevitable? Is there something we can do to get around that? And how much do you think that limits really effective and positive change in both the near and long term? Yeah, I think it's worth differentiating between situations where it may be more likely that the computer completely replaces the human, which seems more likely in autonomous vehicles and drivers, versus times when the AI can partner with humans and improve human decision making, which seems more likely in the case of judges. So we're not recommending or advocating here that we remove all human judges from the courtroom, and we just let algorithms put everything out. It's about trying to give them more accurate baselines of risk. And so judges are implicitly making these decisions already subconsciously. They're looking at the defendant, they're looking at their rap sheet, what they've been accused of, what's the background, how they skipped crime before, and they're implicitly making a risk calculation already. And so human decision making, though, is incredibly volatile, we're subject to all sorts of biases. There's really good evidence that judges when their undergraduate football team loses that weekend, for that entire week, they're gonna give harsher punishments, or be more likely to incarcerate rather than let the defendant go. And in many ways, algorithms allow us to systematize human decision making. It's the process of when you write down the process by which you're making a decision, it allows you to examine it externally, check it for bias, in a way that you can't when it's all just happening in your head. And so again, there's lots of aspects of human decision making that you can't capture in that process. But insofar as that can be, in addition to the more subjective parts of the criminal justice reasoning, I think it can be a tool that can improve outcomes. Yeah, especially if you have a situation, there will be, people keep track of, particularly onerous and kind of infamous judges who will come up with inventive penalties, right? And you have to wear a sign and stand on the corner and be publicly shamed. And this is the judge that always shames people, or the kind of idiosyncrasies of judges, which is impervious to inspection, like you mentioned, right? You can't look inside the judge's mind and figure out exactly why they're making decisions they're making. You can pick up on patterns, and some of those patterns are real disturbing. Football teams, but racial gender prejudice, the subconscious bias will pop up frequently as well, that essentially a black and a white, defendant or whatever accused will receive different bail assessment risks, despite having a very similar profile otherwise, right? And like, so, but that process can't, you can't crack that, the human brain is a black box, whereas an algorithm, it's designed by people, we come up with that algorithm, at least in theory, and I know this gets into a question of like proprietary algorithms and all that, but in theory, that can be examined, and it can be tweaked, it can be changed, it can be debated, it can be discussed in the public sphere. I wonder how much of a check that would be though, like, I mean, so we just saw the President of the United States tweet out that he's mad at Google's algorithms for privileging content that is linked to by a lot of people over a content that is not, so he doesn't, like, Google, I mean, Google doesn't publish its algorithms, but we all, it's pretty easy to read up on the basics, the gist of how they work. Here's an AI and algorithm that's surfacing news stories. We know how it works, and yet you've got a huge portion of the country up in arms because they don't know how it works and refuse to believe the people who have told you how it works, and so it's obviously, we just kind of, we feel like we might have a tendency to read all those biases we think that the AI is gonna help us out with, we just read back into the AI's behavior and then get mad at it, yeah. Now I think that's a serious problem, and that may be sort of a reason for some amount of humility about how fast some of this change is gonna happen, because there is a cultural change that needs to happen where I'm much more okay with the idea of not driving myself around and having an autonomous vehicle drive me around because I've grown up on Uber and Lyft, and it's really not that different. It's pretty condescending towards the people who drive you at the Uber and Lyft. No, no, no, I'm just saying that the sense of freedom attached to personal car ownership doesn't mean nearly as much to me as it does to my dad or some of my older colleagues. And I think in the same way you're gonna see sort of a cultural shift where, you know, what freedom means becomes less about what you, like owning the car and more can you get where you want, when you want, how you want, like that positive aspect to do that becomes the aspect of freedom. And I'm gonna imagine that you're gonna see similar cultural changes around how okay are we are, how okay are we with algorithms making decisions and do we assign them, you know, similar levels of culpability as we do for humans or more? Well, it's like with, we already, you know, most low-cost index funds have robo-advisors. You know, the ordinary middle-class American person who has a 401k is trusting an algorithm with their life savings, right? And like that was something that would have been kind of unimaginable 10, 20 years ago, and now it's just ordinary. So evolving cultural norms and expectations, I think, but there's always gonna be a lag there and that lag, things can get messy, you get a lot of distrust and trouble. Before we move on real quick, so we were moving towards bail, flight risk assessment and whatnot, maybe a quick word, like what has the impact been? And I think my home state, I live in New Jersey, so I think New Jersey was leading the way in the kind of algorithm bail system. What have been the effects? Yeah, so New Jersey took pretty broad sweeping actions at the beginning of 2017, they passed a bill that completely got rid of cash bail and instead replaced it with a risk assessment algorithm. And so, you know, now judges have the option to either assign various levels of community surveillance, you know, someone checking up on you once a week, as far as like ankle monitoring, would be like the highest level of community surveillance. And overall, this has led to, I think, was it like 25, 28% decrease in the jail population since the legislation has gone into effect. And it's somewhat difficult to tease out how much of that is getting rid of cash bail and adding more levels of community surveillance versus what is more accurate risk prediction by the risk assessment algorithm. But I think in some ways they almost go together. I think risk assessment algorithms politically enable certain types of criminal justice reform that wouldn't have been able before. Because if you just told a population, we're just gonna get rid of bail and we're instead just gonna trust the judges to give community surveillance instead. I think a lot of people would have freaked out about that and not been willing to vote for that. But if you give them sort of a semblance of we're placing cash bail with something actively that's gonna still try to assess risk in a somewhat objective manner, I think that enables new political possibilities that weren't available before. So oddly enough, it's almost like we're using as a political tool, we're taking advantage of people's blind trust in a technology that because it's new to most people's indistinguishable from magic. And you in an article, in a response article for K to Unbound, you were talking about a kind of a fairy dust view of artificial intelligence. And so in a funny way, we're basically saying, hey, it'll be okay if we get rid of bail because we'll sprinkle some magic fairy dust algorithm on the assessment process so you can trust that it'll work out. Isn't that a problem though, the fairy dust approach? I mean, I think there's different ways of selling it. I think the correct way to try to approach it is as a hammer, it's a tool that you use for a very specific purpose. And in this case, we have pretty good evidence that humans are really bad at assigning risk. Algorithms seem to do a better job in sort of the simulations that we have. And so I think it makes sense. I mean, it may also subconsciously be kind of working on people's trust in technology but I think just as much a factor on the other end, people are scared of new technology and don't wanna go with some, I mean, so that may kind of cancel out to a certain extent. That's a good point. Okay, so we made a nod towards the problem of proprietary algorithms, both for cash bail, flight systems but in other avenues. I mean, governments, state governments, city governments, the federal government are increasingly rolling out artificial intelligence for like national security, surveillance purposes, you know, to track the facial tracking and all kinds of applications. What are some of the concerns that we should have as people who love freedom and liberty about the state's use of artificial intelligence and its contracts with private providers and what kind of thing should we be wary of in that regard? For sure. So I think a mistake I've seen some people fall into is kind of to assume that whenever the government is purchasing or using algorithms, we should be holding them to the same standards that we're holding private companies to. And I think that's a mistake for two main reasons. One, usually the level of harm that the state is able to do if it messes up is much higher than a private company. Obviously, private companies do not have the ability to send you to jail if they choose to. They don't have drones that have missiles on them. Yet. Yet. You know, if Tinder makes a mistake, you end up going on a bad date. You know, that sucks, but it's not the end of the world. At the level of harm associated with usually government uses of technology tend to be much higher. And two, there's different sorts of feedback loops. So private companies are usually in competition with other companies. Again, if going back to Tinder, if they set you up on bad dates consistently and there's an alternative that doesn't set you up on bad dates, you're able to switch to that. And that kind, the knowledge that there's competition inspires Tinder to be really careful about the way that their algorithm works, to constantly check it for bias or for data error and to improve it over time. The government though, you know, if I'm a defendant, I don't get to choose which jurisdiction I wanna be tried in based on how much I like their risk assessment algorithm. And so there's not the same sort of feedback loop for improvement or for transparency or for accountability. And so I think you can totally justify stronger, if you wanna call them regulations, you can buy. I think it's really just the government using their contract power. They are having procurement contracts with these private companies. And in the same way that Google, if they're buying from a third party vendor, they're allowed to put whatever stipulations they want in their contract to ensure that it meets their standards they have. I think the government should be very willing to use the incredible power they have in procurement contracts to make sure that they have full access to check on the data and make it accountable and to the public. My brain turned off, I heard the word regulation. That's a dirty word around there. I know, I know. You heard your first Cato Institute in favor of regulation. No, that's a good, I think, there's a lot of legitimate cautions there about the state's use of AI. And it'd be a mistake to only focus on the positive potential applications where we see them and not have a cautionary note. So there's some more, some will say fantastical concerns about super intelligent AIs with a hostile intent towards humankind. So why don't we dig into two related concepts or at least they're concepts that, the first one here, the singularity develops first and then someone proposed an interesting thought experiment called Rocco's Basilisk and we'll talk about that is second. So the singularity, it's built on the presumption that artificial intelligence will increase in intelligence at the same kind of exponential rate as other areas of tech, the most famously Moore's law about superconductor, the number of transistors you can fit in like a square inch and a square millimeter. Now we're talking about molecular level transistors. So that curve that basically super semiconductor chips will become more and more transistor dense and do so essentially an exponential growing rate that the same thing will be true of artificial intelligence, which means that at some point not only will artificial intelligences be indistinguishable from human intelligences, they will surpass us and when that day comes as they become smarter and smarter and smarter than us, more capable of out innovating us that they might, I mean, so there's a optimistic use case for this which is the idea that we'll have a machine learning introduced human utopia where machines will do for us better than what we can do for ourselves. They'll be the end of pain and suffering will die. We'll upload our brains to the cloud. We'll have true communism. We'll have true communism, because clearly that is the only true utopia. So there's the optimistic use case here and it is worth noting this does kind of come out of Golden Age sci-fi in the 40s, 50s. This is when, I mean, it's not an accident that Turing, Alan Turing, the famous Turing test, will you be able to tell one of the markers of the advancement of artificial intelligence will be if you can't distinguish a computer from person in the conversation, which we still haven't actually passed. We can kind of fudge the test, but we're getting there, but we're not there yet. But it comes out of this post World War II, a bunch of mathematicians, sci-fi geeks and they come up with, hey, this could happen. They all expected it to happen their lifetime and obviously the pace has been slower than what was expected. But there again, there's this belief of a super intelligent AI that will surpass humankind intelligence. This leads us to Rocco's Basilisk. Now on this regard, I think Aaron, you brought this to my attention. What's your, how did you come across Rocco's Basilisk? It's been around for a little while. I have no idea probably on Twitter or some blog or it was something that everyone was talking about for a while. And I mean, very briefly, it's simply the idea that a super intelligent AI can turn against us, likely will turn against us in all sorts of ways, right? So if you task it with making the world a better place, do everything you can to make the world peaceful. Well, the least peaceful thing on the planet is us pesky humans. And so at some point, does it start, it starts getting upset with people who are going against its particular set of rules or people who are interfering with it, advancing these. And so that gets us to people who are not sufficiently positive about AI would be considered threats to this AI's accomplishment of its mission because if we're not sufficient, if we're not society isn't all keen on AI's, that's gonna slow things down, slow down advancement, whatever else. And so the AI might start picking off or otherwise punishing those people who have not, who have said nasty things about the possibilities of AI in the past. And because these AI's will then have access to all of the information because we, if we're griping about AI, we're griping about AI on Twitter and that's there forever. And so the AI will have access to that. And so then it will start going back and like, look, if you've been grouchy, Kayleigh, you've been saying nasty things about AI in the past, chances, I mean, it's better than average chance you still harbor some of those ill feelings. And so we might as well pick you off. And so the kind of outcome of this thought experiment is we all better just say nothing nice about AI starting now. Yeah, so there's a whole range of kind of possible negative consequences that come about from a super intelligence. It could be as simple as it feels totally neutral towards humans, but we give it, a poorly defined goal. Like the famous experiment is, or the famous thought experiment is a paperclip maximizer. If we just tell an AI to maximize the number of paperclips with no specified end goal or any sort of conditions on that, eventually it will just slowly consume all matter in the universe, including all of us and turn us into paperclips. And that's like one category of possible harms from super intelligences, poorly defined goal systems. Broko's Basque is usually kind of specifically a malevolent AI, which might come about, Aaron, as you alluded to, it can search back in the history of various Twitter, podcast, whatever. And if it feels that you were insufficiently devoted to bringing it about faster, it would then go back and either kill you or if you're not alive, resimulate your mind and infinitely torture you in some computer simulation. And so then, for the purposes of avoiding this horrible fate, we should all be focused and dedicating ourselves to helping bring about this malevolent AI so that when it exists, it doesn't torture us infinitely. And yeah, I think there's a lot of potential problems with it, but it's kind of a fun and interesting thought experiment. I do often wonder with these conversations, the effect of, so there's kind of a selection effect when it comes to people who do AI research. I mean, I'm not the first to observe that maybe the population of Silicon Valley is not representative of humankind as a whole that lacks diversity and not just in the literal ways, gender and race and religion and whatnot, but also in like, there's a kind of person who gravitates towards this kind of research who's maybe not the most like, doesn't have the densest social connectedness, right? They're college educated, they're mobile, they're moving, they're not rooted in place in family and tribe and neighborhood. And so that, so my point in bringing this up is like, part of me wonders if when we worry about AI futures, we're really looking in a mirror. And so essentially you have perhaps a community of folks who are inclined towards a certain level, like on the spectrum of well-adjusted to sociopathic. There's almost an inclination towards like, I don't really feel like I need people. And so I'm worried that my AI that I designed won't feel a need for people as well. That like some of our concerns come out of the particular community. So the Rocco's Basilisk, it comes from Les Wrong, that's the name of the website, which is a big part of the rationalist community. You know, community, I enjoy a lot like Slate Star Codex, even Tyler Cowan that is rationalist adjacent. But again, it's a community that's known for, like there's almost the people as atomized individual units who talk about utils. Like I'm going to maximize my utils. What's the most efficient way I can ingest substances? So I'm going to drink lots of Soylent. In the words- Which is, I mean to tie this into your thesis is named after a product that's quite literally using other people to feed yourself. Yes. And someone that was supposed to be a bad thing in whatever 70s sci-fi movie that was featured in. Soylent Green. Yeah. Yeah, okay. Based on the Harry Harrison novel, make room, make room. There we go. I knew you'd know your sci-fi references. That was meant to be a bad thing, but now it's been turned into a branding for a very successful consumer product, which is all layers of irony. But again, maybe that's, maybe there's something unusual about the community that's doing a lot of AI research or AI is AI interested right now. And so that would be maybe an optimistic argument, which is to say that as AIs become not just a preserve of a hyper select subculture or small community, that our AIs will look more like people, which is there'll be some really good ones, some really shitty ones. It'll be the whole gamut of humanity we reflected in our AIs. So I think a lot that kind of underlines this is a lot of assumptions about what intelligence is and what it implies as you have increasing intelligence. Whether or not personality or malevolence or benevolence are inevitable consequences of increasing intelligence seems very unclear right now. I would probably lean towards no. I mean, it seems like there's a lot of things about human consciousness that we still don't understand from like a purely reductionist perspective, and maybe we will find them out, maybe we won't. Getting back to your earlier question though about is there something about these hypothetical, philosophical thought experiments that come out of the community? I think there probably is. I think what it may be is generally as a community, they have a willingness to bite bullets when they're thinking through about what are the logical consequences of axioms X, Y and Z. And I think that's an admirable trait, but it gets you to a lot of really crazy circumstances about the entire universe being devoted to computonium, which is like the hypothetical, most efficient processing unit per atom. And I think it's worth considering some of those, at least as very small possibility events, but it's also worth recognizing we have very poor track records in terms of actually being able to predict the future. It seems very likely that's going to continue. And so having kind of like a epistemic humility about what's the actual likelihood at any of these things, even if from our specific axioms, they seem entirely rational, I think it's good to take a step back. Reeves is one question I have about AI and AI advancement because so a lot of, baked in assumption of this is that there's a super intelligence that comes out of this. And a lot of the AI that we interact with, like my kids talk to Alexa all the time, and Alexa is Alexa. And Alexa lives in little pods throughout her house and may live in your houses, but it still is, it's Alexa, right? But a lot of these AIs are, they're not, it's not monolithic. There might be the AI that drives my car, which only lives in that car. And there's a lot of the AI processing that goes on on my phone only happens in my phone because Apple does that for privacy reasons largely. So how, is it a mistake to think that AI in the future will be even a big super intelligence in the first place? Or will it just always remain these kind of low level with, it's not trying to do everything. Like it's, this may be super intelligent, but it's super intelligence only enables it to drive a car. And so even if it wanted to destroy the world, the most it could do is maybe run someone over if it could even think like that. But it is like highly, highly intelligence in very narrow domains. So I think the sort of the biggest question about what future do we end up in is it a bunch of discrete AIs that are each industry specific or is there kind of one amalgamation of an AI that controls everything? The largest assumption underlying that is about whether or not recursive self-improvement is possible and what's the speed of that. In a theoretical world where you can get an AI which can then improve itself, presumably it's able to improve itself at a rate that's faster than human engineers can improve it. And then as it gets better, it can presumably improve itself even faster. And it sort of becomes this exponential curve where suddenly it is light years ahead of the competition. In that sort of world, then hypothetically, the first AI that's able to reach recursive self-improvement kind of by definition becomes the most powerful because unless there's an AI that's literally a few seconds behind it, then in a matter of hours, it's going to quickly outpace all other theoretical AIs across the matter of existence, disable all of our cybersecurity protocols because it's just infinitely smarter than us. And I think if you take that for granted, then a lot of those concerns about sort of the singularity and having one AI that sort of runs everything begin to make more sense. But that's an assumption to be questioned. And I don't think that it all seems inevitable, at least, that recursive self-improvement is possible or even if it is that it becomes this exponential curve. I mean, there's a lot of domains where as we get better at something, the next unit of improvement becomes exponentially harder. We're seeing that in Moore's law, Moore's law is slowing down because it's just getting chemically near impossible to just fit more transistors on like a micrometer. And it seems totally plausible that that would also be the case in terms of self-improvement for AI. Yeah, there's this underlying assumption that shows up in the literature about two kind of scenarios, a fast takeoff versus a slow takeoff, which is all kind of, do you see that recursive intelligence with like a hyper exponential upward curve that just so quickly outpaces anything else? Or will they become smarter at more of an evolutionary pace in the same way that human beings did? I'll be on a faster time scale. Because if you see a slow takeoff, you end up with Andrew Ng, who's a Google AI researcher. He said that if it's a slow takeoff, worrying about super intelligent, hostile AIs is a bit like worrying about overpopulation on Mars. Like, how about we get the Mars first and then we'll worry about the overpopulation problem. But if it's a fast takeoff, then well, no, you should because we're gonna go from Mars to overpopulation in hours, minutes, seconds, you know, who knows, right? And by then it's too late. So there is that kind of baseline assumption about AI growth and I don't think any of us are expert enough in the field to kind of estimate a guess at what it is, but it's something for our listeners to keep in mind. I think that's all, we've covered the basic kind of overview of cool stuff going on in AI, some of the interesting speculation about the future of AI. So, Kail, thanks for coming on the show to talk about that. Do you have anything that you're working on right now that our listeners would be interested in knowing about? Yeah, I mean, so in my work at R Street, I'm working a lot on artificial intelligence policy. I'm working on a paper right now, specifically on competition policy in AI and kind of how do we think about, you know, is it just going to be Google and Amazon who are running all the AI systems or are there levers that we can pull now to sort of increase at least the odds of healthy competition in the ecosystem? And some of those policy barriers are things like what's the supply of data scientists? If there's only 10,000 scientists coming out of top universities every year, it's a lot easier for Amazon and Google to grab them all and specifically the number that are in the United States. So there's an op-ed I recently wrote that you could link in the show notes if you wanted to about sort of the importance of immigration in the AI debate and the fact that we have a ton of really smart AI researchers that are coming through United States universities and then because we're so backlogged in high-skill visa programs, they're not able to stay here. And especially in a world where there is a fixed supply essentially of AI talent, there's sort of a zero-sum international competition aspect where every smart AI researcher we have is one less that China has. And while generally, I think a lot of the China comparisons can be overblown and it's probably not as concerning as some people make it out, I think generally I would prefer cutting edge AI to be developed in a democratic country. That's not implausible. Well, I think the number I remember from your article was or somewhere that 20 years ago, one in 10 Chinese like tech graduate students returned back to China who were a H1B visa holder. But now that rate's gone down to eight out of 10 to go back to China. So like we've seen that real switch where there's the kind of the brain drain influx is starting to shift back towards China. This ties in for our listeners to a previous episode a week or two ago about the transformation of China, the tech industry and how it's transforming both rural and urban China and the way in which they're actually attracting people who would have been engineers or executives at Yahoo and Google and Amazon who are now opting to leave just because they think the prospects for innovation are better in China now than the US. So we're seeing, I think that ties back into that conversation that we had before. But until next week, be well. Building Tomorrow is produced by Test Terrible. If you enjoy our show, please rate, review and subscribe to us on iTunes or wherever you get your podcasts. To learn about Building Tomorrow or to discover other great podcasts, visit us on the web at libertarianism.org.