Making Sense with Sam Harris - #280 — The Future of Artificial Intelligence

Episode Date: April 22, 2022

Sam Harris speaks with Eric Schmidt about the ways artificial intelligence is shifting the foundations of human knowledge and posing questions of existential risk. If the Making Sense podcast logo in your player is BLACK, you can SUBSCRIBE to gain access to all full-length episodes at samharris.org/subscribe.   Learning how to train your mind is the single greatest investment you can make in life. That’s why Sam Harris created the Waking Up app. From rational mindfulness practice to lessons on some of life’s most important topics, join Sam as he demystifies the practice of meditation and explores the theory behind it.

Thank you. of the Making Sense podcast, you'll need to subscribe at SamHarris.org. There you'll find our private RSS feed to add to your favorite podcatcher, along with other subscriber-only content. We don't run ads on the podcast, and therefore it's made possible entirely through the support of our subscribers. So if you enjoy what we're doing here, please consider becoming Okay, jumping right into it today. Today I'm speaking with Eric Schmidt. Eric is a technologist, entrepreneur, and philanthropist. He joined Google in 2001 and served as its CEO and chairman from 2001 to 2011, and as executive chairman and Technical Advisor thereafter. In 2017, he co-founded Schmidt Futures, a philanthropic initiative that bets early on

exceptional people who are helping to make the world better. He is the host of Reimagine with Eric Schmidt, his own podcast, and most recently he's the co-author of a new book, The Age of AI and Our Human Future. And that is the topic of today's conversation. We cover how AI is affecting the foundations of our knowledge and how it raises questions of existential risk. So we talk about the good and the bad of AI, both narrow AI and ultimately AGI, Artificial General Intelligence. We discuss breakthroughs in pharmaceuticals and other good things, but we also talk about cyber war and autonomous weapons, and how our thinking about containing the risk here by analogy to the proliferation of nuclear

weapons probably needs to be revised. Anyway, an important conversation, which I hope you find useful. And I bring you Eric Schmidt. I am here with Eric Schmidt. Eric, thanks for joining me. Glad to be with you. So we have, I think we have a hard out at an hour here. So amazingly, that's a short podcast for me. So I'm going to be, there's going to be a spirit of urgency hanging over the place. And we will be efficient in covering the fascinating book that you have written with Henry Kissinger and Daniel Huttenlocher. That's right. And Dr. Kissinger, of course, is this former Secretary of State,

and Dan Huttenlocher is now the Dean of Artificial Intelligence and Computer Science at the Schwartzman Center at MIT. He's a proper computer scientist. Yeah, and that book is The Age of AI and Our Human Future, where you cover most of what I have said about AI thus far, and every case where I have worried about our possible AI future has been focused on the topic of AGI, artificial general intelligence, which you discuss briefly in the book, but it's not your main focus. So I thought maybe we could save that for the end, because I would love to get your take on AGI. But there are far more near-term concerns here and considerations that we could cover. And you are quite well placed to cover them because, if I'm not mistaken, you ran Google for, what was it, 10 years? That's correct. What was your background before that?

How did you come to be the CEO of Google? Well, I'm a computer scientist. I have a PhD in the area, and I worked for 45 years in tech in one way or the other, a whole bunch of companies. Larry and Sergey brought me in as the early CEO of the company, and we built it together. After a decade, I became chairman, Larry became CEO, and then he replaced himself with Sundar, who's now doing a phenomenal job at Google. So I'd say collectively, this group, of which I'm a member, built one of the

great companies. I'm really proud of that. Yeah, and obviously Google is quite involved in developing AI. I just saw just the other day that there's a new, I think it's a 540 billion parameter language model that is beating the average human at something like 150 cognitive tests now. And it seems like the light is at the end of the tunnel there. I mean, it's just going to be a larger model that's going to beat every human at those same tasks. But before we get into some of the details here, I just want to organize our general approach to this. There are three questions that Kant asked in his critique of pure reason, I think it was, which seem unusually relevant to the development of AI. unusually relevant to the development of AI. The first is, what can we know? The second is,

what should we do? And the third is, what is it reasonable to hope for? And I think those are really do capture almost every aspect of concern here, because as you point out in the book, concern here because, as you point out in the book, AI really promises to, and it has already begun to shift the foundations of human knowledge, right? So the question of what we can know and how we can know it is enormously salient now, and maybe we can talk about some of those examples. But obviously, this question of what should we do and what can we reasonably hope for captures the risks we're running in developing these systems. And we're running these risks well short of producing anything like artificial general intelligence. And it's interesting that we're on a path now where we're really not free to decline to produce this technology. I mean, to my eye, there's really no break to pull. I mean,

we're in a kind of AI arms race now. And the question is how to put that race for more intelligence on a footing that is not running cataclysmic risk for us. So before we jump into the details, I guess I'd like to get your general thoughts on how you view the stakes here and where you view the field to be at the moment. Well, of course, we wrote the book, Age of AI, precisely to help answer the questions you're describing, which are perfectly cast. And what's happened in the book, which is written roughly a year ago and then published, happened in the book, which is written roughly a year ago and then published, we described a number of examples to illustrate the point. One is the development of new moves in the game of Go, which is 2,500 years, which were discovered by a computer, which humans had never discovered.

It's hard to imagine that humans wouldn't have discovered these strategies, but they didn't. And that calls the question of, are there things which AI can learn that humans cannot master? That's a question. The second example that we use is the development of a new drug called Halicin, which is a broad spectrum antibiotic, which could not be done by humans, but a set of neuroscientists, biologists, and computer scientists put together a set of programs that ultimately searched through 100 million different compounds and came up with candidates

that were then subsequently tested, advancing drugs at an enormous rate. That's another category of success in AI. And then the third is what you've already mentioned, which is large language models. And we profile in the book GPT-3, which is the predecessor of the one you described. And it's eerie. On the back cover of our book, we say to the GPT-3 computer, are you capable of human reasoning? And it answers, no, I am not. You may wonder why I give you that answer. And the answer is that you are a human reasoning machine,

whereas I am a language model that's not been taught how to do that. Now, is that awareness or is that clever mimicry? We don't know. But each of these three examples show the potential to answer Kant's questions. What can we know? What will happen? What can we do about it? Since then, this past few weeks, we've seen the announcement that you mentioned of this enormous large language model, which can beat humans on many things. And we've also seen something called DALE, which is a text-to-art program. You describe roughly what you want, and it can generate art for you. Now, these are the beginnings of the impact of artificial intelligence on us as humans.

So Dr. Kissinger, Dan, and myself, when we looked at those, we thought, what happens to society when you have these kinds of intelligence? Now, they're not human intelligence. They're different kinds of intelligence in everyday life. And we talk about all the positives, of which there are incredible positives. Better materials, better drugs, more efficient systems, better understanding, better monitoring of the earth, additional solutions for climate change. There's a long, long list which I can go through. Very, very exciting. And indeed, in my personal philanthropy, we are working really hard to fund AI-enabled science discoveries. We recently announced a grant, a structure with a guy named James Manyika, he's a friend of mine, of $125 million to actually go and fund research on the really hard problems in AI, the ones that you're mentioning and others, and also the economic impacts and so forth. So I think people don't really know. The real question is, what happens when these systems become more commonplace? Dr. Kissinger

says, if you look at history, when a system that is not understandable is imposed on people, they do one of two things. They either invent it as a religion or they fight it with guns. So my concern, and I'll say it very directly, is we're playing with the information space of humans. We're experimenting at scale without a set of principles as to what we want to do. Do we care more about freedom? Do we care more about efficiency? Do we care more about education and so forth? And Dr. Kissinger would say, the problem is that these decisions are being made by technical people who are ignorant of the philosophical questions that you so ably asked. And I agree with him, speaking as an example of that. So we recommend, and indeed I'm trying to now fund,

that people begin in a multidisciplinary way to discuss the implications of this. What happens to national security? What happens to military intelligence? What happens to social media? What happens to your children when your child's best friend is a computer? And for the audience who might be still thinking about the killer robot, we're not building killer robots, and I hope we never do. This is really about information systems that are human-like, that are learning, they're dynamic, and they're

emergent, and they're imprecise, being used and imposed on humans around the world. That process is unstoppable. It's simply too many people working on it, too many ways in which people are going to manipulate it, including for hostile reasons, too many businesses being built and too much success for some of the early work. Yeah. Yeah. I guess if I can just emphasize that point, the unstoppability is pretty interesting because it's just anchored to this basic fact that intelligence is almost by definition the most valuable thing on earth, right? And if we can get more of it, we're going to. And we clearly can. And all of these narrow intelligences we've built thus far, you know, all that are effective, that come to market, that we pour resources into,

are superhuman, more or less right out of the gate, right? I mean, it's not a question of, I mean, human level not a question of human level intelligence is a bit of a mirage because the moment we get something that's general, it's going to be superhuman. And so we can leave the generality aside. All of these piecemeal intelligences are superhuman. And the example you give of the new antibiotic, halicin, it's fascinating because it's not just a matter of doing human work faster. If I understand what happened in that case, this is an AI detecting patterns and relationships in molecules already known to be safe and efficacious as antibiotics, you know, safe and efficacious as antibiotics, and detecting new properties that human beings very likely would never have conceived of and may, in fact, be opaque to the people who built

the AI and may remain opaque. I mean, one of the issues you just raised is the issue of transparency. Many of these systems are built in such a way as to be black boxes, and we don't know how the AI is doing what it's doing in any specific way. It's just training against data and against its own performance so as to produce a better and better result, which qualifies as intelligent and even superhumanly so. intelligent and even superhumanly so, and yet it may remain a black box. Maybe we can just close the loop on that specific problem here. Are you concerned that transparency is a necessity when decision-making is important? I mean, just imagine the case where we have something like an AI oracle that we are convinced makes better decisions than any person or even any group of people, but we don't actually know the details of how it's making those decisions, right? So this is, I mean, you can just multiply examples as you like, but just questions of who should get out of prison, the likelihood of recidivism in the case of any

person, or who's likely to be more violent at the level of conviction, right? What should the prison sentence be? I mean, it's very easy to see that if we're shunting that to a black box, people are going to get fairly alarmed that any differences in outcome that are not transparent. Perhaps you have other examples of concern, but do you think transparency is something that, I mean, one question is, is it technically feasible to render black boxes transparent when it matters? And two, is transparency as important as we intuitively may think it is? Well, I wonder how important transparency is for the simple fact that we have teenagers among our midst, and the teenagers cannot explain themselves at all, and yet we tolerate their

behavior with some restrictions because they're not full adults. But we wouldn't let a teenager fly an airplane or operate on a patient. So I think a pretty simple model is that at the moment, these systems cannot explain how they came to their decision. There are many people working on the explainability problem. Until then, I think it's going to be really important that these systems not be used in what I'm going to call life safety situations. And this creates all sorts of problems, for example, in automated war, automated conflict, cyber war, those sorts of

things where the speed of decision-making is faster than what humans can. What happens if it makes a mistake? And so, again, we're at the beginning of this process. And most people, including myself, believe that the explainability problem and the bias problems will get resolved because there's just too much money, too many people working on it, maybe at some cost, but we'll get there. That's historically how these things work. You start off with stuff that works well enough, but it shows a hint of the future and then it gets industrialized. I'm actually much more focused on what's it like to be human when you have these specialized systems floating around. My favorite example here is Facebook, where they changed their feed to AMP it using AI. And the AI that they built was around engagement.

And we know from a great deal of social science that outrage creates more engagement. And so, therefore, there's more outrage on your feed. Now, that was a clearly deliberate decision on part of Facebook, presumably thought it was a good product idea, but it also maximized their revenue. That's a pretty big social experiment, given the number of users that they have, which is not done with an understanding, in my view, of the impact of political polarization. Now, you sit there and you go, okay, well, he doesn't work at Facebook. He doesn't really understand. But many, many people have commented on this problem. This is an image of what happens in a world where all of the information around you can be boosted or manipulated by AI to sell

to you, to anchor you, to change your opinion, and so forth. So we're going to face some interesting questions in the information space, the television and movies and things you see online and so forth. Do there need to be restrictions on how AI uses the information it has about you to pitch to you, to market to you, to entertain you? These are questions. We don't have answers. But it makes perfect sense that in the industrialization of these tools, the tools that I'm describing, which were invented in places like Google and Facebook, will become available to everyone and every government. So another example is a simple one, which is the kid is a two-year-old and gets a toy and the toy gets upgraded every year and the kid gets smarter. The toy is now, the kid is now 12

and there's 10 years from now, there's a great toy. And this toy is smart enough in non-human terms to be able to watch television and decide if the kid likes the show. So the toy is watching the television and the toy says to the kid, I don't like this show, knowing that the kid's not going to like it. And the kid goes, I agree with you. Now, is that okay? Probably. Well, what happens if that same system that's also learning learns something that's not true? And it goes, you know, kid, I have a secret. And it goes, tell me, tell me, tell me. And the secret is something which is prejudicial or false or bad or something like that. We don't know how to describe, especially for young people, the impact of these systems on

their cognitive development. Now, we have a long history in America of having school boards and textbooks which are approved at the state level. Are the states going to monitor this? And you sit there and you say, well, no parent would allow that. But let's say that the normal behavior of this toy is smart enough, understands the kid behavior of this toy, it's smart enough, understands the kid well enough to know the kid's not good at multiplication. So the kid's bored, and the toy says, I think we should play a game. Kid goes, great. And of course, it's a game which strengthens his or her multiplication capability. So on the one hand, you want these systems to make people smarter, make them develop, make them more serious adults, make the adults more productive. Another example would be my physics friends. They just want a system to read all the physics books every night and make suggestions to them. in a situation, at least with kids and with elderly who are isolated, where these tools are going to have an out-of-proportion impact

on society as they perceive it. We've never run that experiment. Dynamic, emergent, and not precise. I'm not worried about airplanes being flown by AI because they're not going to be reliable enough to do it for a while. Now, we should also say for the listeners here that we're talking about a term which is generally known as narrow AI. It's very specific, and we're using specific examples, drug discovery, education, entertainment. But the eventual state of AI is called general intelligence, where you get human kind of reasoning. In the book, what we describe that as the point where the computer can set its own objective. And today, the good news is the computer can't

choose its objective. At some point, that will not be true. choose is subjective. At some point, that will not be true. Yeah. Yeah. Well, hopefully we'll get to AGI at the end of this hour, but I think we should talk about the good and the bad in that order, and maybe just spend a few minutes on the good, because the good is all too obvious. Again, intelligence is the most valuable thing on earth. It's the thing that gives us every other thing we want, and it's the thing that safeguards everything we have. And if there are problems we can't solve, well, then we can't solve them. But if there are problems that can be solved, the way we will solve them is through greater uses of our intelligence. And if, you know, insofar as we can leverage artificial intelligence to solve those problems, we will do that, more or less regardless of the attendant risks. And that's the problem, because the attendant risks are increasingly obvious, and it seems not at all trivial. And we've already proven we're capable of implementing massive technological change without really thinking about the consequences at all. You cite the massive

psychological experiment we've performed on all of humanity with no one really consenting, that is social media. And the effects are ambiguous at best. I mean, there's some obviously bad effects, and it's not even straightforward to say that democracy or even civilization can survive contact with social media. I mean, that remains to be seen given how divisive some of its effects are. I consider social media to be far less alarming than the prospect of having an ongoing nuclear doctrine anchored to a proliferating regime of cyber espionage, cyber terrorism, cyber war, all of which will be improved massively by layering AI onto all of that. will be improved massively by layering AI onto all of that. So before we jump into the bad,

which is really capturing my attention, is there anything specifically you want to say about the good here? I mean, if this goes well, what are you hoping for? What are you expecting? Well, there are so many positive examples that we honestly just don't have time to make a list. I'll give you a few. In physics and math, the physicists and mathematicians have worked out the formulas for how the world works, at least at the scientific level. But many of their calculations are not computable by modern computers. They're just too complicated. An example is how do clouds actually work is a function of something called the Navier-Stokes equations, which for a normal-sized cloud would take 100 million years for a computer to figure out. But using an AI system, and there's a group at Caltech doing this,

they can come up with a simulation of the things that they care about. of the things that they care about. In other words, the AI provides enough accuracy in order to solve the more general climate modeling problem. If you look at quantum chemistry, which is sort of how do chemical bonds work together, not computable by modern methods. However, AI can provide enough of a simulation that we can figure out how these molecules bind, which is the halicin example. In drug discovery, we know enough about biology that we can basically predict that if you do these compounds with this antibody, we can make it stronger, we can make it weaker, and so forth in the computer, and then you go reproduce it in the lab. There's example after example where AI is being used from existing data

to simulate a non-computable function in science. And you say, what's he talking about? I'm talking about the fact that the scientists have been stuck for decades because they know what they want to do, but they couldn't get through this barrier. That unleashes new materials, new drugs, new forms of steel, new forms of concrete, and so forth and so on. It also helps us with climate change, for example, because climate change is really about energy and CO2 emission and so forth. These new surfaces, discoveries and so forth will make a material difference. And I'm talking about really significant numbers. So that's an example. Another example is what's happening with these large language models that you mentioned earlier, that people are figuring out a way to put a

conversational system in front of it so that you can talk to it. And the conversational system has enough state that it can remember what it's talking about. It's not like a question, answer, question, answer, and it doesn't remember. It actually remembers the context of, oh, we're talking about the Oscars, and we're talking about what happened at the Oscars, and what do I think? And then it sort of goes and it gives you a thoughtful answer as to what happened and what is possible. In my case, I was playing with one of them a few months ago. And this one, I asked the question, what is the device that's in 2001, A Space Odyssey that I'm using today? There's something from 1969 that I'm using today that was foreshadowed in the movie.

And it comes right back and says, the iPad. Now, that's a question that Google won't answer if you ask it the way I did. So I believe that the biggest positive impact will be that you'll have a system that you can verbally or by writing, ask it questions, and it will make you incredibly smarter, right? That it'll give you the nuance and the understanding and the context. And you can ask it another question and you can refine your question. Now, if you think about it in the work you do or that I do or that a scientist does or a politician or an artist, this is enormously transformative. So example after example, these systems are going to build scientific breakthroughs, scalable breakthroughs.

Another example was that a group at DeepMind figured out the folding structure of proteins. And proteins are the way in which biology works. And the way they fold determines their effectiveness, what they actually do. And it was thought to be not really computable. And using these techniques in a very complicated way with a whole bunch of protein scientists, they managed to do it. And their result was replicated in a different mechanism with different AI from something called the Baker Lab at University of Washington. The two together have given us a map of how

proteins work, which in my view is worthy of a Nobel Prize. That's how big a discovery that is. All of a sudden, we are unlocking the way biology works, and it affects us directly. But those are some positive examples. I think the negative examples... Well, let's wait, because I'm chock full of negative examples. Okay. But I'm interested in how even the positive can disclose a surprisingly negative possibility, or at least it becomes negative if we haven't of machine and human cooperation, right, and facilitation where people just get smarter by being able to have access to these tools or they get effectively smarter. But you can imagine just in the limit, more and more getting seeded to AI because AI is just better at doing these things. It's better at proving theorems. It's better at designing software. It's better, it's better, it's better.

And all of a sudden, the need for human developers at all or human mathematicians at all, or you just make the list as long as you want, it seems like some of the highest status jobs cognitively might be among the first to fall, which is to say, I certainly expect at this point to have an AI radiologist, certainly, before I have an AI plumber. And there's a lot more above and beyond the radiology side of that comparison that I think is going to fall before, you know, the basic manual tasks fall to robots. So, and this is a picture of real success, right? Because in the end, all we're going to care about is performance. We're not going to care about keeping a monkey in the loop just for reasons

of sentimentality. You know, if you're telling me that my car can drive a thousand times better than I can, which is to say that, you know, it's going to reduce my risk of getting in a fatal accident, you know, killing myself or killing someone else by a factor of a thousand if I just flip on autopilot, well then not only am I going to flip it on, I'm going to consider anyone who declines to do that to be negligent to the point of criminality. And that's never going to change. Everything is going to be in the position of a current chess master who knows that the best player on earth is never going to be a person ever again, right? Because of alpha zero.

So take that wherever you want. I disagree a little bit, and I'll tell you why. I think you're correct in about 30 years, but I don't think that argument is true in the short term. Yeah, no, I was not, just to be clear, I'm not suggesting any time frame there. I'm just saying ultimately, if we continue to make progress, something like this seems bound to happen. Yes. But what I want to say is, I defy you to argue with me that making people smarter is a bad thing. Okay? So let's start with the premise of the human assistant that is the the thing that you're using will make humans smarter it'll make it deeper better analysis better choices but at least the current technology cannot replace the essentially the free will of humans they sort of wake up in

the morning you have a new idea, you decide something, you say, that's a bad idea, so forth and so on. We don't know how to do that yet. And I have some speculation on how that will happen. But in the next decade, we're going to not be solving that problem, we'll be solving a different problem, which is how do we get the existing people doing existing jobs to do them more efficiently, that is smarter, better, faster. When we looked at the funding for this AI program that I've since announced, the funding, 125 million, a fair chunk of it is going to really hard computer science problems. Some of them include, we don't really understand how to explain what they're doing. As I mentioned, they're also brittle. When they fail, they can fail catastrophically. Like, why did it fail? And no

one can explain. They're hardening. There are resistance to attack problems. There are a number of problems of this kind. These are hard computer science problems, which I think we will get through. They use a lot of power, the algorithms are expensive, that sort of thing. But we have also focusing around the impact on jobs and employment and economics. We're also focusing on national security. And we're focusing on the question that you're asking, which is, what's our identity? What does it mean to be human? Before general intelligence comes, we have to deal with the fact that these systems are not capable of choosing their own outcome, but they can be applied to you as a citizen by somebody else against your own satisfaction. So the negatives before AGI are all of the form, misinformation, misleading information,

creating dangerous tools and, example dangerous viruses for the same reason that we built a fantastic new antibiotic drug it looks like you could also imagine a similar evil team of producing an incredible number of bad viruses you know things that would hurt people and you could imagine in that scenario they might be clever enough to be able to hurt a particular race or particular sex or something like that, which would be totally evil and obviously a very bad thing. We don't have a way of discussing that today. So when I look at the positives and negatives right now, I think the positives, as with many technologies, really overwhelm the negatives, but the negatives need to be looked at. And we need to have the conversation right now about, let's use social media, which is an easy whipping boy

here. I would like, so I'm clear what my political position is, I'm a very strong proponent of freedom of speech for humans. I am not in favor of freedom of speech for computers, robots, bots, so forth and so on. I want an option with social media which says, I only want to see things that a human has actually communicated from themselves. I want to know that it wasn't snuck in by some Russian agent. I want proof of providence, and I want to know there's a human. And if it's a real human who's, in fact, an idiot or crazy or whatever, I want to be able to hear their voice, and I want to be able to decide I don't agree with it. What's happening instead is these systems are being boosted. They're being pitched. They're being sold by AI. And I think that's got to be limited in some way.

I'm in favor of free speech, but I don't want only some people to have megaphones. And if you talk to politicians and you look at the political structure in the country, this is a completely unintended effect of getting everyone wired. Now, is it a human or is it a computer? Is it a Russian, a Russian compromise plan, or is it an American? Those things need to get resolved. You cannot run a democracy without some level of trust. Yeah. Yeah. Well, let's take that piece here. And obviously it extends beyond the problem of AI's involvement in it, but the misinformation problem is enormous.

What are your thoughts about it? Because I'm just imagining we've been spared thus far the worst possible case of this, which is just imagine under conditions of where we had something like perfect deep fakes, right, that were truly difficult to tell apart from real video. right, that were truly difficult to tell apart from real video, what would the controversy around the 2020 election have looked like, or the war in Ukraine and our dealings with Putin at this moment, right? Like, just imagine, you know, a perfect deep fake of Putin declaring a nuclear first strike on the US or whatever. I mean, you just. I mean, just imagine essentially a writer's room from hell where you have smart, creative people spending their waking hours figuring out how to produce media that is shattering to every open society and conducive to provoking international conflict. That is clearly coming in some form. I guess my first question is, are you hopeful that the moment that arrives, we will have the same level of technology that can

spot deepfakes? Or is there going to be a lag there of months, years that are going to be difficult to navigate? We don't know. There are people working really hard on generating deepfakes, and there are people working really hard on detecting deepfakes. And one of the general problems with misinformation is we don't have enough training data. The term here is, in order to get an AI system to know something, you have to give it enough examples of good, bad, good, bad, and eventually you can say oh here's something new and I know if it's good or bad and one of the core problems of misinformation is we don't have enough agreement on what is misinformation or what have you and the thought

experiment I would offer is President Putin in Russia has already shut down the Internet and free speech and controls the media and so forth. So let's imagine that he was further evil. If you'd like to continue listening to this conversation, you'll need to subscribe at SamHarris.org. Once you do, you'll get access to all full-length episodes of the Making Sense podcast, along with other subscriber-only content, including bonus episodes and AMAs and the conversations I've been having on the Waking Up app. The Making Sense podcast is ad-free

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