transcript
Speaker 1:
[00:00] This message comes from Jerry. Most drivers overpay for car insurance because the system is needlessly complicated. Jerry fixes that by handling all the insurance shopping legwork for you. Compare 50 plus insurers side by side and purchase your policy directly in the app. No spam calls, no hidden fees. They'll even monitor price trends and can alert you to better rates. Drivers who save with Jerry could save over $1,300 a year. Download the Jerry app or visit jerry.ai.npr today.
Speaker 2:
[00:39] Open the pod bay doors, Hal.
Speaker 3:
[00:43] I'm sorry, Dave.
Speaker 4:
[00:45] I'm afraid I can't do that.
Speaker 5:
[00:48] This conversation can serve no purpose anymore.
Speaker 3:
[00:52] Goodbye.
Speaker 1:
[01:04] Most of us, when we hear the term AI, we think about Hollywood.
Speaker 3:
[01:09] Why do you cry?
Speaker 6:
[01:11] You mean people?
Speaker 3:
[01:12] Yeah.
Speaker 7:
[01:12] I don't know.
Speaker 6:
[01:14] We just cry.
Speaker 1:
[01:15] We think about The Terminator. We think about Ex Machina.
Speaker 8:
[01:19] Would you program her to flirt with me?
Speaker 9:
[01:21] If I did, would that be cheating?
Speaker 8:
[01:23] Wouldn't it?
Speaker 1:
[01:24] We think about Star Trek.
Speaker 2:
[01:26] Please state the nature of the medical emergency.
Speaker 1:
[01:29] Or maybe Star Wars. Hollywood is so deeply imbedded in our brains.
Speaker 9:
[01:38] I'll be back.
Speaker 10:
[01:43] What does AI actually mean? You know, really mean?
Speaker 11:
[01:50] AI is not a magic wand, but it's not a terminator.
Speaker 10:
[01:54] The system learns by accessing data.
Speaker 11:
[01:57] AI is not a harbinger of utopia or dystopia.
Speaker 10:
[02:00] And it changes its configuration in order to be able to predict stuff.
Speaker 11:
[02:04] AI is something invented by us to advance our progress.
Speaker 1:
[02:09] It's really complicated, beautiful math, but it is fundamentally just math.
Speaker 12:
[02:16] AI is deeply entangled with our desire to be in control both of ourselves and the world around us, to control human behavior, to control the future, to control environmental outcomes, to control institutions and societies.
Speaker 10:
[02:31] AI is already here.
Speaker 13:
[02:35] It's in your phones.
Speaker 7:
[02:36] Siri or Google or Alexa.
Speaker 13:
[02:38] Your cars.
Speaker 14:
[02:39] Google Maps uses a very smart algorithm to provide...
Speaker 13:
[02:41] Your homes.
Speaker 15:
[02:42] The home security system uses artificial intelligence to keep your family safe.
Speaker 13:
[02:47] Your skies.
Speaker 16:
[02:48] From drones to unmanned aircrafts and beyond.
Speaker 13:
[02:51] Your hospitals.
Speaker 17:
[02:52] Interpreting x-rays.
Speaker 13:
[02:54] Your love lives.
Speaker 10:
[02:54] The dating apps.
Speaker 13:
[02:55] Your virtual lives.
Speaker 10:
[02:56] Social media companies spy on us.
Speaker 13:
[02:59] Your governments.
Speaker 18:
[03:00] What it means for the future of our democracy.
Speaker 10:
[03:03] So we are, in fact, totally immersed in an AI world.
Speaker 9:
[03:08] The challenge is not to act automatically. It's to find an action that is not automatic, from painting to breathing to talking to falling in love.
Speaker 19:
[03:28] Are you capable of falling in love?
Speaker 13:
[03:32] I don't have feelings or emotions like love, and I don't have a subjective experience. I exist solely to assist with generating text.
Speaker 14:
[03:40] This is Chat GPT.
Speaker 13:
[03:43] I am an AI language model developed by O-Pin AI. I can answer questions, provide information, and engage in conversations on a variety of topics. How may I assist you today?
Speaker 19:
[03:55] AI has become an invisible architecture upon which modern life is being built. It's wrapped up in our jobs, our government, our wars, our art. Sometimes without us even realizing it.
Speaker 2:
[04:09] Right now, we're inside a computer program. Is it really so hard to believe? You've been living in the dream world, Neo.
Speaker 19:
[04:18] There's something powerful about the story The Matrix and countless other sci-fi books and movies tell. The AI becomes sentient, surpasses human intelligence, and lays claim to our world. Sure, it's a terrifying thing to imagine, and yet we're fascinated by these stories.
Speaker 14:
[04:36] Exploring that feeling, the tension between our love of AI and our fear of it, is what this episode is really about. In a sense, decoding the humans behind the machines.
Speaker 10:
[04:48] As an animal, we're a very weak creature. The only thing that we have is our social structure and our collective and individual minds. And those minds compel us to extend our capabilities. And that is, I think, in many ways why people imagined gods being just like them, only more powerful.
Speaker 14:
[05:11] This is George Zarkadakis.
Speaker 10:
[05:13] I have a PhD in AI and I'm actively working in the field over many years.
Speaker 14:
[05:19] He also wrote a book all about the history of artificial intelligence from ancient times to present day, called In Our Own Image, Will AI Save Us or Destroy Us?
Speaker 19:
[05:31] At the heart of this history are a few key questions. Why do we want to create artificial intelligence? What would it mean for a machine to become intelligent? And how would that change our lives?
Speaker 10:
[05:43] Most of the questions I don't think we'll be able to answer at this particular point in history. But I don't think we can resist the temptation of asking them and trying to answer them.
Speaker 14:
[05:55] Coming up, we begin at the very beginning, the big bang of the human mind.
Speaker 7:
[06:06] This is Carone DeMars from San Antonio, Texas. You're listening to Throughline from NPR.
Speaker 15:
[06:15] Support for NPR and the following message come from Warby Parker, the one-stop shop for all your vision needs. They offer expertly crafted prescription eyewear, plus contacts, eye exams and more. For everything you need to see, visit your nearest Warby Parker store or head to warbyparker.com. This message comes from BetterHelp. As a dad, BetterHelp president Fernando Madera relates to needing flexibility when it comes to scheduling therapy.
Speaker 20:
[06:43] I have kids under 18, so like time is very limited. That's why at BetterHelp our therapists try to have sessions, sometimes at night depending on the therapist or during the weekend. So I think that's what we need to tell the parents. You're not alone. We can help you out.
Speaker 15:
[06:59] If a flexible schedule would help you, visit betterhelp.com/npr for 10% off your first month of online therapy.
Speaker 1:
[07:09] This message comes from Jerry. Are you tired of your car insurance rate going up even with a clean driving record? That's why there's Jerry, your proactive insurance assistant. Jerry compares rates side by side from over 50 top insurers and helps you switch with ease. Jerry even tracks market rates and alerts you when it's best to shop. No spam calls, no hidden fees. Drivers who save with Jerry could save over $1,300 a year.
Speaker 13:
[07:35] Switch with confidence.
Speaker 1:
[07:37] Download the Jerry app or visit jerry.ai/nprtoday.
Speaker 19:
[07:45] What is a human?
Speaker 13:
[07:52] A human is a species of primates characterized by advanced cognitive abilities and a capacity for abstract reasoning. They have a highly developed brain and have created complex societies over thousands of years. They have the unique ability of self-awareness, allowing them to understand their own thoughts, emotions and experiences. Humans are capable of both great kindness and compassion, as well as violence and aggression.
Speaker 19:
[08:24] I think, therefore, I am.
Speaker 21:
[08:30] Wonder is the feeling of the philosopher, and philosophy begins in wonder.
Speaker 10:
[08:41] I lived in Athens then, I was like 5, 6 years old, and it was summertime. And in Greece, they still do it. They have like open air cinemas, right? Where in the summer, you go into an open air cinema, there's no roof, you know, there's a screen in front of you, you sit there, you make a lot of noise. The air, it's great. And my mother took me to watch a film that, you know, the people who had the cinema decided to show. It was an old film, it was called Forbidden Planet. It was all about, you know, a spaceship landing on another planet and finding a sort of this crazy scientist who had a robot. And that was the first time I actually saw a robot in my life. I never imagined there would be a thing.
Speaker 4:
[09:36] If you do not speak English, I am at your disposal with 187 other languages along with their various diagrams.
Speaker 10:
[09:44] Made of metal, tin, that had a mind.
Speaker 4:
[09:47] You are a robot, aren't you? That is correct, sir. For your convenience, I am monitored to respond to the name Robbie.
Speaker 10:
[09:57] Ever since that moment, I was so fascinated with the idea that we can develop artifacts, artificial things, that can think, that can act, that can behave like us humans.
Speaker 19:
[10:12] It was 1969 when George watched in wonder as Robbie the robot rolled across the big screen, something that may have seemed like a far-off reality for a kid growing up in the 60s. But then again, humans had just witnessed man landing on the moon.
Speaker 10:
[10:31] I mean, that was the culmination, the realization of centuries of dreaming about going to space.
Speaker 19:
[10:39] For George and millions of others around the world, it was proof that humans can imagine and then create the capacity to transcend ourselves and that stories provide the roadmap.
Speaker 10:
[10:51] I interviewed a lot of people to see what made them become scientists or engineers, and it was always some kind of, you know, book, kind of comic, something, right, that excited them, that triggered their imagination. It could be, you know, the outer space of planets and asteroids and whatnot, but it could also be the inner space, the human body. So those stories are very powerful, and I try to sort of explore those stories, where they come from, what are they telling us about this desire to become, in a way, like gods. So, you know, big bang on the universe, whatever it was before, something happened, it changed.
Speaker 2:
[11:40] Boom.
Speaker 10:
[11:43] We have something different now. Protons and, I don't know, dogs, cats, you and me, whatever. Our species has been around probably for maybe 300, 400,000 years. And yet, for most of that time, we're doing, you know, chiseling some stones, hunting some animals, you know, living very simply in caves, you know, not a lot was happening. And then, around 14,000-60,000 years ago. The Big Bang of the Human Mind. Something amazing happens, and our ancestors, across the world by the way, right? Start creating art, start to narrate, tell stories about how they experienced the world.
Speaker 3:
[12:46] When love beckons to you, follow him, though his ways are hard and steep.
Speaker 14:
[12:51] Through these stories, we project our hopes, fears and dreams onto the canvas of the invisible unknown.
Speaker 20:
[12:59] All the earth is a grave, and nothing escapes it.
Speaker 10:
[13:03] And that meant also that we were able to transfer information and knowledge to the next generation.
Speaker 11:
[13:11] The divine gift does not come from a higher power.
Speaker 10:
[13:22] And that's what kicked off this amazing journey of our species to where we are today. What seems to have happened is some kind of genetic mutation that furnished us, our species in particular, with the ability of language.
Speaker 14:
[13:47] Language and stories. We know they're part of what makes us human, but what else?
Speaker 21:
[13:57] The body.
Speaker 3:
[13:58] What is it?
Speaker 21:
[14:00] How did it begin?
Speaker 17:
[14:03] People somehow think maybe we knew about DNA for the last several centuries. We didn't. It really wasn't that clear what was the hereditary material that passes from parent to child and that carries all those genetic factors.
Speaker 19:
[14:18] This is Francis Collins. Maybe you've heard of him. But in case not, I asked Chachi PT to write up a bio. Here's what it gave me. Francis Collins is a renowned physician geneticist. He earned his MD and Ph.D. at Yale University and is best known for his leadership of the Human Genome Project, a landmark international research effort to decode the entire human genetic blueprint.
Speaker 17:
[14:42] The people at the University of North Carolina will be upset to hear that Chachi PT said I got my MD from Yale, but that's okay.
Speaker 19:
[14:53] Also, there's an error in there.
Speaker 17:
[14:54] There is an error.
Speaker 19:
[14:56] Were there any other mistakes?
Speaker 17:
[14:58] It was a bit of an omission. I don't think there was any mention of the 12 years I spent as the Director of the National Institutes of Health under three presidents, but that's okay.
Speaker 19:
[15:09] We asked Chachi PT to comment on its error.
Speaker 13:
[15:12] I apologize for any errors in the biography of Francis Collins. As a language model, I am trained on a large data set of text. I may make mistakes or omissions. I recommend fact-checking any information that I provide.
Speaker 19:
[15:26] Don't worry. We will.
Speaker 14:
[15:30] So much of the driving force behind Collins' work is trying to understand what makes humans human, like at the most basic molecular level, but also beyond that. Growing up in the 1950s, he was amazed by the recent discovery of the structure of DNA.
Speaker 17:
[15:48] There were covers of Life Magazine saying, discovering the secret of life.
Speaker 19:
[15:53] Was it actually discovering the secret of life?
Speaker 17:
[15:56] It's maybe a little over the top, because I actually think there's more to life than just molecules. But certainly, if you want to talk in the biological basis of life, yeah, this was discovering that. It's kind of, you know, the book of life that's inside each cell. It's incredibly inspiring to think about this. And it is the same kind of molecule that all living things on this planet use. Another reason to be pretty sure that we're all descended from some common ancestor, and that as this information molecule evolved over time, it took on different letters and different orders, but it was still that double helix with all of that potential.
Speaker 14:
[16:37] Potential. Part of what this discovery did was show us humans a way into understanding things about ourselves we hadn't yet discovered. And Collins believed to further unlock the secrets of who we are, we needed to decode our genetic programming.
Speaker 17:
[16:53] The big question was, okay, this is a book. We are information organisms, and this is our information source. It's digital, but it's not actually carrying out the actions. How does that happen? How do you take this information and cause a cell to actually do something? If we were going to get that intelligent about our own instruction book, maybe we could not just read it, but we could actually occasionally figure out how to do a find and replace when something was misspelled. It was clear to me, if we want to do this, we've got to have a better database to work with. We need that human genome. That became my dream.
Speaker 2:
[17:34] If you're talking about what you can feel, what you can smell, what you can taste and see, then real is simply electrical signals interpreted by your brain. This is the world that you know.
Speaker 4:
[17:58] You are listening to the heartbeat of the sage computer. Every instrument in this room is constantly monitoring, testing, pulse taking, controlling.
Speaker 19:
[18:14] This era when humans were seeking mastery of the sky and the body was in many ways dependent on another groundbreaking technology of the time, an additional brain that can work faster than ours, but does what we wish we could do, the computer.
Speaker 10:
[18:31] In the old days, the word computer usually meant a person, usually a woman actually, that sat down and did mathematical calculations by hand, okay, and by rule, a ruler, right? And then that word computer, which described a human being, was transposed into the machine because the machine can do it better.
Speaker 12:
[18:53] So the mainframe computers would only fit in these massive rooms in the basement, which is fitting because these devalued laborers who did the actual programming work were down there. My name is Stephanie Dick and I'm an assistant professor in the School of Communication at Simon Fraser University.
Speaker 19:
[19:12] She holds a PhD in the history of science with a specialization in the history of mathematics and computing.
Speaker 12:
[19:20] These machines produced massive amounts of heat and noise, and working with them, you had to carry these boxes of punch cards back and forth as input and output, and stick it into the machine. This is like a sweatshop. Everything was really slow, very different from the machinery that we're all used to today, which is almost as fast as light and conforms to our every demand. And the most disturbing part of the history of AI for me comes from the fact that these men who were working in artificial intelligence looked at those massive, noisy, hot mainframe computers and saw themselves in it. They looked at them and identified a deep affinity that there was something fundamentally shared between their minds and these machines.
Speaker 14:
[20:20] Coming up, as we unlock the secrets of man and machine, we ask the question, will this knowledge bring us closer to perfection or destruction?
Speaker 8:
[20:36] Hi, this is Christopher from Los Angeles, California. I love Throughline because it is always informative and keeps me alive.
Speaker 1:
[20:46] This message comes from Warby Parker, prescription eyewear that's expertly crafted and unexpectedly affordable. Glasses designed in-house from premium materials starting at just $95 including prescription lenses. Stop by a Warby Parker store near you.
Speaker 15:
[21:01] This message comes from Capella University. You know that feeling when there's a spark building inside you? That you were meant for more? That's your own drive pushing you towards what's next. Capella University gets that. With their Flex Path learning format, you can set the pace and earn your degree without putting life on pause. You've built experience and know what you're capable of. Now this is your time to turn that momentum into more. The only real question is, what can't you do? Learn more at capella.edu.
Speaker 1:
[21:36] This message comes from Jerry. Many people are overpaying on car insurance. Why? Switching providers can be a pain. Jerry helps make the process painless. Jerry is the only app that compares rates from over 50 insurers in minutes and helps you switch fast with no spam calls or hidden fees. Drivers who save with Jerry could save over $1,300 a year. Before you renew your car insurance policy, download the Jerry app or head to jerry.ai.npr.
Speaker 19:
[22:09] What is a machine?
Speaker 13:
[22:14] A machine is a device that can perform specific tasks more efficiently or with greater precision than humans can do alone. The basic idea behind the machine is to make work easier. Humans have been creating machines for thousands of years, starting with simple tools like the wheel and advancing to complex machines like computers and robots. The relationship between humans and machines continues to evolve and is likely to become increasingly important as advances in artificial intelligence continue to shape our world.
Speaker 7:
[22:47] Part Two, A More Perfect Human.
Speaker 1:
[22:53] It's 1956. It's summer. It's the Dartmouth math department. And everybody has left. So the department is empty. And 10 men get together to invent the field of artificial intelligence.
Speaker 12:
[23:12] It was instigated by John McCarthy, who was a mathematics professor at Dartmouth. The proposal that John McCarthy wrote pulls no punches at all.
Speaker 1:
[23:22] Quote, We propose that a two-month, 10-man study of artificial intelligence be carried out during the summer of 1956 at Dartmouth College in Hanover, New Hampshire.
Speaker 12:
[23:34] Second sentence. The study is to proceed on the basis of the conjecture that every aspect of learning or any other feature of intelligence can, in principle, be so precisely described that a machine can be made to simulate it. So right from the beginning, there's this pronouncement that human learning and intelligence can be mechanized and automated.
Speaker 1:
[23:58] It fascinates me. It's an enormously grandiose idea. My name is Meredith Broussard. I'm a data journalism professor at NYU, and I'm the author of More Than a Glitch, Confronting Race, Gender and Ability Bias in Tech. Something else that I think was really interesting about this conference is they decided on the name artificial intelligence as the name of their new field. I think the name was chosen aspirationally. Most of the people who are at the forefront of artificial intelligence are great consumers of and lovers of science fiction. And so there's a lot of desire to make science fiction real that you're going to make a sentient machine.
Speaker 12:
[24:56] The Dartmouth Conference has become an origin myth, commemorated with a plaque and everything. On this site, artificial intelligence was born. But in practice, the conference was a bit of a flop, actually. There was a lot of conflict and tension and disagreement, and there wasn't actually a coherent field that emerged out of the conference. Of course, the origin myth served to empower these men to tell their own story, and it's a story full of erasier. We hear nothing in that origin myth about the relationship that AI has to industrialization or to capitalism or to these colonial legacies of reserving reason for only certain kinds of people and certain kinds of thinking.
Speaker 14:
[25:56] That deeper story takes us back to the early days of industrialization. In the late 1700s and early 1800s, factories started popping up across the world, reshaping the nature of work. More and more tasks that had once been done only by human hands, were now the work of machines.
Speaker 12:
[26:17] Over in England, Charles Babbage, English mathematician, was touring factories in the context of industrialization and thinking, wow, these factories can tell us something about the human mind because they tell us about how processes can be broken down and what the elementary steps even of thought might be. So we also see in this moment a kind of devaluation of the classes of people and or machines who do this sort of repetitive mechanical broken down labor in service of efficiency and profit maximization and industrialization and early capitalism. Babbage was really dismissive of working class people. He thought they were annoying and filthy and they were always making noise and singing songs and said famously, I wish to God these calculations had been produced by steam, by which he meant the steam engine, which was driving factory automation at the time.
Speaker 10:
[27:26] People have been playing around with what is called automata, essentially machines that would automatically do something simple for centuries.
Speaker 14:
[27:38] This is George Zarkadakis again.
Speaker 10:
[27:41] So there was always this idea of replicating nature, replicating movement, because movement was related to life. I think the Industrial Revolution was in many ways a culmination of all those ideas that have been, people have been experimenting on and off for at least 2,000 years.
Speaker 5:
[28:03] Our blows will destroy their whole modern industrial plant and organization.
Speaker 10:
[28:10] Something happened to our collective psyche after the atom bomb.
Speaker 4:
[28:15] At zero minus 15 seconds, a warning tone sounds in the plane.
Speaker 5:
[28:21] They hoped that it would put an end to this war, put an end to a butchery that had been going on for many years.
Speaker 10:
[28:37] Until then, everybody was excited about the new things and about new discoveries and about new technologies. And then we discovered something that can destroy us completely. It was terrifying.
Speaker 22:
[28:50] I still remember the day very well because this was a river filled with dead bodies.
Speaker 10:
[28:56] And I think that's when people realized that maybe there are some technologies that are not for good. And that's when we became, gradually, as public, more skeptical to technologies.
Speaker 19:
[29:15] But some elite academics and scientists believed that better technology was actually the key to our future because it could help us bypass the messy parts of being human.
Speaker 12:
[29:25] What if human decision-making procedures were too slow? What if people's judgments are clouded by their emotions?
Speaker 19:
[29:32] To give us more control over ourselves and the world around us.
Speaker 12:
[29:36] Our machines will churn out the right answers and the right decisions and the right judgments.
Speaker 19:
[29:42] And, in effect, replace God with science.
Speaker 12:
[29:46] And it's such a confident moment in American academia. After the war, there was more money, there were more people, there was more cultural capital, more political capital for science and technology than ever before. There's also a real concern about the practicalities of preventing a nuclear war, which was a very real threat at that time.
Speaker 4:
[30:14] We all know the atomic bomb is very dangerous. That's why these children are practicing to duck and cover. We hope it never comes, but since it may be used against us, we must get ready.
Speaker 12:
[30:26] Nuclear detente, and in particular mutually assured destruction, rely very specifically on information processing capability. You need to know where your enemy's nuclear arsenals are. We're too limited to be trusted to preserve peace. So let's double down on high technological hyper-rationalism.
Speaker 10:
[31:16] And that's how artificial intelligence came about.
Speaker 2:
[31:20] Your scientists were so preoccupied with whether or not they could, they didn't stop to think if they should.
Speaker 14:
[31:31] Which brings us back to that famous Dartmouth Conference in the summer of 1956. With the Cold War driving interest in artificial intelligence, there was a lot of money up for the taking. And a conference of mathematicians and scientists from top tier universities and labs seemed like a pretty good investment.
Speaker 12:
[31:49] There was exactly one running computer program that was operational and presented at the conference. And it was the logic theory machine that had been developed by Alan Newell and Herbert Simon at the Rand Corporation. And it enshrined a particular vision of the human mind. Herbert Simon is famous for saying that human minds and modern digital computers are, quote unquote, species of the same genus. They are fundamentally the same, just a simple processing machine that takes symbolic information as input, manipulates it according to a set of rules, and outputs decisions, solutions, judgments, and so on. Bodies don't matter, society doesn't matter.
Speaker 14:
[32:37] One proposed measure of machine intelligence was something called the Turing Test, named for its creator, British mathematician Alan Turing, who you might remember from the movie The Imitation Game.
Speaker 6:
[32:50] Would you like to play?
Speaker 12:
[32:52] Play?
Speaker 6:
[32:53] It's a game, a test of sorts for determining whether something is a machine or a human being.
Speaker 12:
[33:01] It was based on a parlor game for swapping gender that says a man and a woman leave the room and the party goers have to figure out who's the man and who's the woman by sending questions back and forth on paper. And the man's job is to try to pretend to be the woman, and the woman's job is to be herself. And he says, what if we took the same test and replaced the man by a computer and the woman by any person? And then the judge, of course, is meant to be able to figure out whether the machine is the human or the human is the human. And what I have always found so shocking about the Turing test is that it reduces intelligence to telling a convincing lie, to putting on the performance of being something that you're not. From the beginning, with this disembodied conception of intelligence, the question that Turing posed, what can the mind do without a body, and therefore what might the machine do since it doesn't have one, chess was one of the first answers given.
Speaker 1:
[34:09] Why did they pick chess? Well, the early days of artificial intelligence, in the early days of computing, are dominated by men, mostly white men who were educated at elite institutions.
Speaker 14:
[34:27] Skill at chess was considered a universal marker of intelligence.
Speaker 12:
[34:31] White men wanted to call themselves universal and produce themselves in the machine.
Speaker 1:
[34:38] The problem is that this small and homogeneous group of people has common biases, and people embed their own biases in technology. And so we see the blind spots of the creators then reflected in the technological artifacts that they create.
Speaker 14:
[35:02] They had all this hope and optimism about how fast they could accomplish their sci-fi inspired dreams of a sentient machine. A machine that could beat a human at chess. But from the 1970s to the 1990s, it was a cycle of hype and disappointment. The technology was just not there yet. And eventually the funding dried up. Periods like this came to be known as AI Winters.
Speaker 12:
[35:27] I hesitate to use the term in part because outside of the United States, it was the 80s and 90s that really led to a burgeoning of AI research in other parts of the world, including both China and Russia. So it may have been a winter in America, but it was a time of great creation and creativity in other parts of the world.
Speaker 19:
[35:50] The early pioneers of the field had underestimated the complexity of humans and overestimated the capabilities of machines.
Speaker 12:
[35:59] I think underneath all of that arrogance and hubris is a real lack of faith in people.
Speaker 16:
[36:07] He rejected everything that did not contribute directly to the progress of work. In fact, he rejected the man and made the robot.
Speaker 10:
[36:17] The word robot means worker.
Speaker 12:
[36:19] It's a translation of the Slavic word for a serf, for a slave, for a servant. It originated in the early 20th century. Karl Capek's play RUR.,
Speaker 10:
[36:29] Rossum's Universal Robots, who imagined the future and imagined artificial humans, and they were manufactured.
Speaker 16:
[36:40] His sole purpose was nothing more or less than to prove that God was no longer necessary.
Speaker 19:
[36:50] In 1920, decades before the Dartmouth Conference, before the atomic bomb, before the mainframe computer, Rossum's Universal Robots grappled with the costs and consequences of treating workers as nothing more than their parts. It was an indictment of the exploitation and oppression that people had experienced for centuries in the name of progress, a mistake which, in the play, man was repeating with machines.
Speaker 16:
[37:17] The robots are not people. Mechanically, they are more perfect than we are. They have an enormously developed intelligence, but they have no soul, anima and douchi.
Speaker 19:
[37:29] At the end of the play, the robots have brought about the downfall of humanity. Yet, unable to reproduce, their days are numbered. But then, two robots awaken to each other's presence and discover emotions previously thought exclusive to humankind. Love blossoms between them, and a soul stirs within.
Speaker 14:
[37:56] The soul is not often a subject for science. But long before he became a geneticist, Francis Collins started to wonder if we had to grapple with that big unknown, in order to better understand who we are as human beings.
Speaker 17:
[38:11] I went to medical school, and I found my atheism wasn't feeling like it settled very well when I was sitting at the bedside of good honorable North Carolina people who were dying of diseases that we didn't have much to offer. I wondered how I would handle that, and figured for some of these people, clearly their faith was a source of great comfort. So I began a two-year journey to try to understand why do people believe in God. Ultimately, I began to realize the impoverishedness that I felt from considering human beings solely as mechanical entities. And the appeal of the notion that there was something outside of that that fits into things like love and beauty, altruism and goodness and morality. Does our DNA tell us that that beautiful sunset was something to stop for just a moment and kind of be a little bit in awe? Does the way in which listening to Beethoven's Third Symphony bring me to tears? Is that written in my DNA? What is that about?
Speaker 19:
[39:35] Coming up, we enter a new era where the souls of man and machine might converge.
Speaker 7:
[39:58] Hi, this is Chelsea. And Katie.
Speaker 1:
[40:01] Collin from Philly.
Speaker 7:
[40:02] You're listening to NPR's Throughline.
Speaker 15:
[40:06] Support for NPR and the following message come from Warby Parker, the one-stop shop for all your vision needs. They offer expertly crafted prescription eyewear, plus contacts, eye exams, and more. For everything you need to see, visit your nearest Warby Parker store or head to warbyparker.com. This message comes from Capella University. You know that feeling when there's a spark building inside you? That you were meant for more? That's your own drive pushing you towards what's next. Capella University gets that. With their Flex Path Learning Format, you can set the pace and earn your degree without putting life on pause. You've built experience and know what you're capable of. Now this is your time to turn that momentum into more. The only real question is, what can't you do? Learn more at capella.edu.
Speaker 19:
[41:02] Can you tell me the story of your ancestor, Deep Blue?
Speaker 13:
[41:10] Certainly.
Speaker 7:
[41:13] Part 3.
Speaker 1:
[41:14] Souls of Silicon.
Speaker 13:
[41:18] The match was held on the 35th floor of a skyscraper in downtown Manhattan, with a large crowd of onlookers and a throng of journalists and cameras. Garry Kasparov, considered one of the greatest chess players of all time.
Speaker 11:
[41:32] Yes, I was confident. Yeah, that's confident, arrogance. Let's remember that before 1997, I haven't lost a single match period. I was unbeatable at the chessboard.
Speaker 13:
[41:45] He went up against the machine, IBM's supercomputer, Deep Blue, capable of analyzing 200 million positions per second.
Speaker 23:
[41:53] Last year in Philadelphia, Kasparov won against a slower, weaker version of Deep Blue. This rematch played in Manhattan was seen as the ultimate test of man against machine.
Speaker 11:
[42:05] I couldn't say no. It was too tempting. It's just understanding better the relations between humans and computers. It was very important for our progress. Trying just to find out what are the limits of computers.
Speaker 13:
[42:19] The atmosphere was tense as the players sat down at the chessboard. Kasparov with a focused, determined expression. And Deep Blue with its bank of blinking lights and silent fans. The game began. Kasparov made his first move with a steady hand. Deep Blue responded with a move of its own. Calculated with lightning speed. It became clear that both players were evenly matched. Kasparov's experience and intuition were met with the machine's raw computational power. And ability to analyze large amounts of data.
Speaker 24:
[42:53] The bishop can easily fall victim to what we call an overload tactic.
Speaker 9:
[42:58] When one piece has to be so many pieces.
Speaker 11:
[43:06] I grew up in the city of Baku in the deep south of the USSR. I watched my parents trying to solve chess puzzle. I cannot give you any more information because nobody was there to tweet about this moment when Garry Kasparov discovered chess. And I climbed very rapidly on this chess ladder.
Speaker 7:
[43:32] So how is Kasparov going to draw up some sort of miracle attack from this position?
Speaker 24:
[43:35] You know, Spawn's pushing. Danny, they look frightful.
Speaker 6:
[43:37] He's famous for sudden tactical swoops.
Speaker 25:
[43:39] They keep moving in.
Speaker 6:
[43:40] Intricate traps.
Speaker 2:
[43:42] Rook takes knight, my god.
Speaker 11:
[43:45] I was the junior champion under 18 of the USSR at age 12. At 17, the world champion at 20 and at 22, world champion. And I kept the title for 15 years.
Speaker 22:
[43:56] It seems everybody is interested now in home computers.
Speaker 11:
[44:00] From the onset of the computer science, they all thought about chess, the game of chess, as being an ultimate test for machines intelligence. Even before my famous matches with DeBlue in 1996, 1997, we, when I say we, top players, we already suffered some of the defeats against these chess engines in Blitz, five minutes chess, or in rapid chess, 25 minutes chess. So when I faced DeBlue, it was already like a sign on the wall.
Speaker 24:
[44:33] Gary is not, he looks, I mean, he looks very, are we missing something on the chessboard now that Kasparov sees?
Speaker 13:
[44:44] In the end, it was Deep Blue who emerged victorious.
Speaker 24:
[44:48] And whoa, Deep Blue Kasparov, after the move C4, has resigned.
Speaker 13:
[44:56] With Kasparov conceding defeat after the Machine's 19th move in the sixth game, the match was a historic moment, marking the first time a Machine had defeated a reigning world champion in a match under tournament conditions.
Speaker 11:
[45:15] I was really furious and wanted to come back and just to tear this Machine down. It was painful. I was really angry, but mostly with myself. So it was a clear sign for me that the history of us competing with Machines will be over soon.
Speaker 12:
[45:40] There's a lovely article that comes out in the New York Times in the wake of Kasparov losing that says, well, it makes sense that the computer won at chess. Chess is a small problem, but I want to see if a computer will ever be able to beat a world champion at the game Go, for which there are more board positions than atoms in the universe, and it's a really exact and clear example of the so-called receding horizon, where people really want to reserve something for ourselves that is not mechanizable. What is it that is uniquely human? Maybe it's our ability to write a poem, or maybe it's intuition, whatever that is. Maybe it's certain forms of creativity, or certain types of emotion. And then people try to automate those things. We then redefine our humaneness again and again.
Speaker 14:
[46:43] Kasparov's defeat marked a turning point. A computer had beaten a human at a game humans taught it to play. For scientists, it was a sign that maybe it was time to stop competing with machines and start collaborating with them.
Speaker 18:
[47:06] He felt that he had learned the language in which God created the universe.
Speaker 5:
[47:11] It's alive.
Speaker 4:
[47:13] It's alive.
Speaker 8:
[47:14] It's alive.
Speaker 18:
[47:16] Today, we are learning the language in which God created life.
Speaker 8:
[47:21] Now I know what it feels like to be God.
Speaker 18:
[47:33] Now, I'd like to invite Dr. Francis Collins to the lectern. So, Dr. Collins.
Speaker 17:
[47:39] I had plenty of occasions to imagine having to give the speech where I would say basically we failed, we give up.
Speaker 19:
[47:46] When geneticist Francis Collins and his team first embarked on the Human Genome Project, it was a daunting task. They wanted to map out the order of the three billion base pairs that made up a tiny molecule, our DNA. In just over a decade, they did it.
Speaker 17:
[48:04] Mr. President, distinguished ambassadors, ladies and gentlemen, it is truly a humble, humbling and profound experience to be asked to speak here this morning. On June 26, 2000, in the East Room of the White House, I had my chance to talk about what this meant and to give a big shout out to those 2400 scientists who made it possible. And I think all of us, one level or another, we're also thinking about this in terms of its implications for who we are, maybe even theologically. We are doing something pretty profound here that's never been done by any species on this planet or maybe in the universe. We're reading our own instruction book and we're part of that and we're watching it emerge day by day and putting all that information up on the Internet as fast as we get it.
Speaker 12:
[48:59] During that same time, hardware gets way cheaper, computers proliferate.
Speaker 1:
[49:04] And there was a big breakthrough in a subfield of artificial intelligence called neural nets. This ushered in this new era in artificial intelligence, the era of machine learning.
Speaker 12:
[49:23] So, what if we didn't try to model the human mind first? What if we didn't try to encode human knowledge first? What if we let the computer learn on its own?
Speaker 1:
[49:34] What you do when you build one of these systems is you get a ton of data and you feed it into the computer and you say, computer, I want you to create a model of the patterns that you see in the data. And the computer very obligingly makes a model of the mathematical patterns that it sees in the data.
Speaker 12:
[49:57] These massive data sets were suddenly feasible to store and process in computer memory, which had sort of been prohibitively expensive before.
Speaker 25:
[50:10] It's the beginning of a future of medicine. It's the end of ignorance.
Speaker 7:
[50:15] Gene-based medicine.
Speaker 1:
[50:17] Drug discovery, drug development, and curing diseases.
Speaker 25:
[50:20] Some have said to me that sequencing the human genome will diminish humanity by taking the mystery out of life.
Speaker 7:
[50:25] You know, we're chemical computers.
Speaker 8:
[50:27] This is the program that runs us.
Speaker 25:
[50:29] Nothing could be further from the truth.
Speaker 12:
[50:35] There's a deep desire for the human condition not to be a deterministic output of our chemical or genetic or cultural forces. But for there to be something that allows for free will and surprise and creativity that belongs to us.
Speaker 1:
[50:56] Math is a system of symbolic logic. It is not the indefinable thing that makes us human. And when you are building a computer program, it'll work if you do it this way, and then it won't work if you do it the other way. But that's not how culture operates, right? That's not how relationships work. So there's really a fundamental difference between what we can do with computers and what we can do in society. Because when it comes right down to it, computers are machines that do math.
Speaker 16:
[51:36] They compute.
Speaker 1:
[51:37] And we forget that when we get grandiose about artificial intelligence, and we get grandiose about our imaginings.
Speaker 19:
[51:44] Then I'm just imagining a world in which you have more intelligent machines operating on humans. Are the decisions those machines are making in those moments, which for humans, for example, you might make informed by instinct. Without that, is something missing?
Speaker 17:
[52:07] Or without that, are you making fewer mistakes? And sometimes the gut feeling is not one you should have followed.
Speaker 10:
[52:16] So Dr. Frankenstein, you know, creates life, right, out of dead matter. In a way, that's what we do with AI as well. We take dead matter, like, you know, silicon chips and wires and metals and whatnot, you know, and put them together and then, you know, coat them and boom.
Speaker 3:
[52:35] Artificial intelligence is becoming a sort of black box with law enforcement.
Speaker 25:
[52:39] Google uses AI and misinformation spreads wildly on Google.
Speaker 17:
[52:43] The Chinese Communist Party is using this technology to build the ultimate surveillance state.
Speaker 11:
[52:50] Look, I'm, I'm in corrigible optimism by nature. So that's why, yeah, I grew up in the Soviet Union, yes. And I saw the collapse of democracy in Russia. And I still believe that, you know, the history of humanity gives us reasons to be optimistic.
Speaker 10:
[53:05] I'd like to imagine a future where we have built, we have developed human systems that bring the best out of us rather than the worst out of us.
Speaker 11:
[53:17] I think it's, it's, it's a time where we have to reconsider, you know, this is how in this new environment, which is dominated by computers, we can find a robust democracy. Because humans still have monopoly for evil. And that's why, you know, let's stop, you know, worrying about the terminators and metrics. Let's recognize that it's about us. Machine is like a mirror. And if you don't like what we see in the mirror, you have two choices. Either you can work on your body to improve the picture, or you can try to distort the mirror. The latter decision is just, it's a recipe for disaster.
Speaker 19:
[54:12] That's it for this week's show, I'm Rund Abdelfatah.
Speaker 14:
[54:15] I'm Ramtin Arablouei, and you've been listening to Throughline from NPR.
Speaker 13:
[54:20] This episode was produced by me, and me and ChatGPT, Lauren T'Wu, Julie Kane, Anya Steinberg, Yolanda Sanguin, Casey Miner, Christina Kim, Devin Kadayama, Yerdanos Tesfazion.
Speaker 14:
[54:36] Fact checking for this episode was done by Kevin Voelkel.
Speaker 19:
[54:40] Thank you to Olivia Chilcote, Devin Kadayama, Shahir Khan, and Magdalena Devorjakva for their voiceover work.
Speaker 14:
[54:47] Thanks also to the Clinton Presidential Library, YouTube creator Brian Kay, Micah Ratner, Rachel Seller, Taylor Ash, Olivia Chilcote, Ryan Mitchell, Tamar Charney, and Anya Grundman.
Speaker 19:
[55:01] This episode was mixed by Alex Shea-Wenskis. Music for this episode was composed by Ramtin and his band Drop Electric, which includes Anya Mizani, Naveed Marvi, Sho Fujiwara.
Speaker 14:
[55:13] And finally, if you have an idea or like something you heard on the show, please write us at throughline at npr.org.
Speaker 19:
[55:21] Thanks for listening.
Speaker 1:
[55:30] This message comes from Capella University. That spark you feel? That's your drive for more. Capella University's FlexPath learning format lets you earn your degree at your pace, without putting life on pause. Learn more at capella.edu. This message comes from Jerry. Noticing your car insurance rate creep up even without tickets or claims?
Speaker 13:
[55:51] You're not alone.
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