Lex Fridman Podcast - Pamela McCorduck: Machines Who Think and the Early Days of AI

Episode Date: August 23, 2019

Pamela McCorduck is an author who has written on the history and philosophical significance of artificial intelligence, the future of engineering, and the role of women and technology. Her books include Machines Who Think in 1979, The Fifth Generation in 1983 with Ed Feigenbaum who is considered to be the father of expert systems, the Edge of Chaos, The Futures of Women, and more. Through her literary work, she has spent a lot of time with the seminal figures of artificial intelligence, includes the founding fathers of AI from the 1956 Dartmouth summer workshop where the field was launched. This conversation is part of the Artificial Intelligence podcast. If you would like to get more information about this podcast go to https://lexfridman.com/ai or connect with @lexfridman on Twitter, LinkedIn, Facebook, Medium, or YouTube where you can watch the video versions of these conversations. If you enjoy the podcast, please rate it 5 stars on iTunes or support it on Patreon.

The following is a conversation with Pamela McCordock. She is an author who is written on the history and the philosophical significance of artificial intelligence. Her books include Machines Who Think in 1979, the fifth generation in 1983 with Ed Fagenbaum, who is considered to be the father of expert systems, the edge of chaos, they features a woman, and many more books. The edge of chaos, they feature as a woman and many more books. I came across her work in an unusual way by stumbling in a quote from machines who think that is something like artificial intelligence began with the ancient wish to forge the gods. That was a beautiful way to draw a connecting line between our societal relationship with

AI from the grounded, day-to-day science, math, and engineering to popular stories and science fiction and myths of automatons that go back for centuries. Through her literary work, she has spent a lot of time with the seminal figures of artificial intelligence, including the founding fathers of AI from the 1956 Dartmouth summer workshop where the field was launched. I reached out to Pamela for a conversation in hopes of getting a sense of what those early days were like and how their dreams continue to reverberate to the work of our community today. I often don't know where the conversation may take us but I jump

in and see. Having no constraints, rules, or goals is a wonderful way to discover new ideas. This is the Artificial Intelligence Podcast. If you enjoy it, subscribe on YouTube, give it 5 stars and iTunes, support it on Patreon, or simply connect with me on Twitter, at Lex Friedman spelled F-R-I-D-M-A-N, and now his my conversation with Pamela McCordock. In 1979, your book, Machines Who Think, was published. In it, you interviewed some of the early AI pioneers and explored the idea that AI was born not out of maybe math and computer science but out of myth and legend. So tell me if you could

the story of how you first arrived at the book, the journey of beginning to write it. I had been a novelist. I'd published two novels and I was sitting under the portal at Stanford one day, and the house we were renting for the summer, and I thought, I should write a novel about these weird people in AI, I know. And then I thought, ah, don't write a novel, write a history. Simple, just go around, you know, interview them,

splice it together, voila, instant book. Ha, ha, ha. It was much harder than that. But nobody else was doing it. And so I thought, oh, this is a great opportunity. And there were people who, John McCarthy, for example, thought it was a nutty idea. There were much, you know, the field had not evolved yet, so on. And he had some mathematical thing he thought I should write instead. And I said, no, John, I am not a woman in search of a project. I'm, this is what I want to do. I hope you'll cooperate. And he said, oh, mother, mother, well, okay, it's your time. And-

What was the pitch for the, I mean, such a young field at that point, how do you write a personal history of a field that's so young? I said, this is wonderful. The founders of the field are alive and kicking and able to talk about what they're doing. Did they sound or feel like founders at the time? Did they know that they've been found, that they have founded something. Oh, yeah, they knew what they were doing was very important, very. What they, what I now see in retrospect is that they were at the height of their research careers.

And it's humbling to me that they took time out from all the things that they had to do as a consequence of being there. And to talk to this woman who said, I think I'm going to write a book about you. No, it's amazing, just amazing. So who stands out to you? Maybe looking 63 years ago, the Dartmouth Conference, the so Marvin Minsk, he was there was there, McCarthy was there, Claude Shannon, Alan Newell, Herb Simon, some of the folks you've mentioned. Then there's other characters, right? One of your co-authors. He wasn't at Dartmouth. He wasn't at Dartmouth, but I mean, he was there. I think an undergraduate. And of course, Joe,

drop, I mean, all of these are players, not a darma, but in that era. Right. It's same you and so on. So who are the characters? If you could paint a picture that stand out to you for memory, those people you've interviewed and maybe not, people that were just in the atmosphere. In the atmosphere. Ah, of course, the four founding fathers were extraordinary guys. They really were. Who are the funny fathers?

Alan Newell, Herbert Simon, Marvin Minsky, John McCarthy. They were the four who were not only at the Dartmouth conference, but Newell and Simon arrived there with a working program called the Logic Theorist. Everybody else had great ideas about how they might do it, but they weren't gonna do it yet. And you mentioned Joe Traub, my husband. I was immersed in AI before I met Joe because I had been Ed Faganbaum's assistant

at Stanford. And before that, I had worked on a book by edited by Faganbaum and Julian Feldman called Computers and Thought. It was the first textbook of readings of AI. They only did it because they were trying to teach AI to people at Berkeley. You'd have to send them to this journal and that journal. This was not the internet where you could go look at an article. So I was fascinated from the get-go by AI. I was an English major. What did I know? And yeah, I was fascinated. And that's why you saw that historical, that literary background, which I think is very much a part of the continuum of AI, that AI grew out of that same impulse. Yeah, that traditional. What was, what drew you to AI? How did you even think of it back then?

What was the possibilities, the dreams? What was interesting to you? The idea of intelligence outside the human cranium, this was a phenomenal idea. And even when I finished machines who think, I didn't know if they were going to succeed. In fact, the final chapter is very wishy-washy, frankly. I'll succeed the field did.

Yes, there was the idea that AI began with the wish to forge the God. So the spiritual component that we crave to create this other thing greater than ourselves. For those guys, I don't think so. Newell and Simon were cognitive psychologists. What they wanted was to simulate aspects of human intelligence, and they found they could do it on the computer.

Minski just thought it was a really cool thing to do. Likewise McCarthy, McCarthy had got the idea in 1949 when he was a Caltech student, and he listened to somebody's lecture, In 1949 when he was a Caltech student and He listened to somebody's lecture. It's in my book. I forget who it was and he thought oh that would be fun to do How do we do that and he took a very mathematical approach? Minski was hybrid and Nuland Simon were very much cognitive psychology. How can we simulate various things about human cognition? What happened over the many years is, of course,

our definition of intelligence expanded tremendously. I mean, these days biologists are comfortable talking about the intelligence of cell, the intelligence of the brain the intelligence of the brain, not just human brain, but the intelligence of any kind of brain. Sephlopods, I mean, an octopus is really intelligent by any, we wouldn't have thought of that in the 60s, even the 70s. So all these things have worked in. And I did hear one behavioral primatologist, François Deval, say, AI taught us the questions to ask.

Yeah, this is what happens, right? It's when you try to build it, is when you start to actually ask questions, if it puts a mirror to ourselves. Yeah, this is what happens, right? It's when you try to build it, is when you start to actually ask questions, if it puts a mirror to ourselves. Yeah, right. So you were there in the middle of it. It seems like not many people were asking the questions that you were trying to look at this field the way you were.

I was so low. I, when I went to get funding for this, because I needed somebody to transcribe the interviews and I needed travel expenses, I went to every thing you could think of, the NSF, the DARPA. There was an Air Force place that doled out money, and each of them said, well, that was very interesting. That's a very interesting idea, but we'll think about it. And the National Science Foundation actually said to me in plain English, hey, you're only

a writer. You're not a historian of science." And I said, yeah, that's true, but the historians of science will be crawling all over this field. I'm writing for the general audience, so I thought. And they still wouldn't budge. I finally got a private grant without knowing who it was from. From Ed Fredkin and MIT, he had, he was a wealthy man. And he liked what he called crackpot ideas. And he considered this a crackpot idea. This a crackpot idea.

And he was willing to support it. I am ever grateful. Let me say that. Some would say that a history of science approached AI, or even just a history, or anything like the book that you've written hasn't been written since But I mean, I don't maybe I'm not familiar, but it's certainly not many If we think about bigger than just these couple of decades few decades, what what are the roots of

AI? Oh, they go back so far. Yes, of course, there's all the legendary stuff, the golem and the early robots of the 20th century. But they go back much further than that. If you read Homer, Homer has robots in the Iliad. And a classical scholar was pointing out to me just a few months ago. Well, you said you just read the Odyssey. The Odyssey is full of robots. It is, I said. Yeah, how do you think Odysseus' ship gets from one place to another. He doesn't have the crew people

to do that, the crewman. Yeah, it's magic. It's robots. Oh, I thought, how interesting. So we've had this notion of AI for a long time. And then toward the end of the 19th century, the beginning of the 20th century, there were scientists who actually tried to make this happen some way or another, not successfully. They didn't have the technology for it. And of course, Babbage in the 1850s and 60s, he saw that what he was building was capable of intelligent behavior. And when he ran out of funding, the British government finally said, that's enough. He and Lady Lovelace decided, oh, well, why don't we make, you know, why don't we play the ponies with this?

He had other ideas for raising money, too. But if we actually reach back once again, I think people don't actually really know that robots do appear or ideas of robots. You talk about the Hellenic and the Hibberic points of view. Oh yes. Can you tell me, buddy? I defined it this way. The Hellenic point of view is robots are great. You know, they are party help. They help this guy, have festus this God,estus in his forage. I presume he made them to help him and so on and so forth. And they welcomed the whole idea of robots. The Brayak view has to do with,

I think it's the second commandment, thou shalt not make any graven image. In other words, you better not start imitating humans because that's just forbidden. It's the second commandment. And a lot of this is somehow wicked. This is somehow blasphemous. We shouldn't be going there. Now, you can say, yeah, but there are going to be some downsides. And I say, yes, there are, but blasphemism is not one of them. You know, there is a kind of fear that feels to be almost primal. Is there religious roots to that? Because so much

of our society has religious roots, and so there is a feeling of, like you said, blasphemy, of creating the other, of creating something, you know, it doesn't have to be artificial intelligence, it's just creating life in general. It's the Frankenstein idea. There's the annotated Frankenstein on my coffee table. I get this tremendous novel. It really is just beautifully perceptive. Yes, we do fear this and we have good reason to fear it, but because it can get out of hand. Maybe you can speak to that fear of this

ecology, if you thought about it, you know, there's a practical set of fears, concerns in the short term, you can think of if we actually think about artificial intelligence systems, you can think about bias of discrimination in algorithms. So you can think about their social networks have algorithms that recommend the content you see, their bodies, algorithms control the behavior of the masses, their these concerns. But to me, it feels like the fear that people have is deeper than that. So have you thought about the psychology of it? I think in a superficial way I have, there is this notion that if we produce a machine

that can think, it will outthink us and therefore replace us. I guess that's a primal fear of almost a kind of mortality. So around the time you said you work with, it's Stanford with the Ed Faganbaum. So let's look at that one person. Throughout his history, clearly a key person, one of the many in the history of AI, how has he changed in general around

him, how has Stanford changed in the last, how many years are we talking about here? Since that case. 65. 65. So maybe it doesn't have to be about him, it could be bigger, but because he was a key person and expert systems, for example, how is that? How are these folks who have you've interviewed in the 70s, 79 changed through the decades? In Ed's case, I know him well. We are dear friends. We see each other every month or so.

He told me that when machines who think first came out, he really thought all the front matter was kind of baloney. And ten years later, he said, no, I see what you're getting at. Yes, this is an impulse that has been a human impulse for thousands of years to create something outside the human cranium that has intelligence. I think it's very hard when you're down at the algorithmic level. And you're just trying to make something work, which is hard enough to step back and think of the big picture. It reminds me of when I was in Santa Fe,

I knew a lot of archaeologists, which was a hobby of mine. And I would say, yeah, yeah, well, you can look at the shards and say, oh, this came from this tribe, and this came from this trade route and so on. But what about the big picture and a very distinguished archaeologist said to me, they don't think that way. You do? No, they're trying to match the shard to the, to where it came from. That's, you know, where did this corn, the remainder of this corn come from? Was it grown here? Was it grown elsewhere? And I think this is part of the AI,

any scientific field. You're so busy doing the hard work, and it is hard work, that you don't step, I can say, oh, well, now let's talk about the general meaning of all this. Yes. So none of the even mince game McCarthy, they oh, those guys did. Yeah, the founding fathers did early on or pretty early on. Oh, they had, but in a different way from how I looked at it, the two cognitive psychologists,

Nule and Simon, they wanted to imagine reforming cognitive psychology so that we would really, really understand the brain. Yeah. Minski was more speculative. And John McCarthy saw it as, I think I'm doing, doing him right by this, he really saw it as a great boon for human beings to have this technology. And that was reason enough to do it. And he had wonderful, wonderful fables about how if you do the mathematics, you will see that these things are really good for human beings. And if you had a technological

objection, he had an answer, a technological answer. But here's how we could get over that and then blah blah blah blah. And one of his favorite things was what he called the literary problem, which of course he presented to me several times, that is everything in literature, there are conventions in literature. inventions is that you have a villain and a hero. And the hero in most literature is human, and the villain in most literature is a machine. And he said, no, that's just not the way it's going to be. But that's the way we're used to it. So when we tell stories about AI, it's always with this paradigm, I thought, yeah, he's right. Looking back, the classics, RUR,

is certainly the machines trying to overthrow the humans. Frankenstein is different. Frankenstein is a creature. He never, he never has a creature. He never, he never has a name. Frankenstein, of course, is the guy who created him, the human, Dr. Frankenstein. This creature wants to be loved, wants to be accepted, and it is only when Frankenstein turns his head, in fact, runs the other way. And the creature is without love. And that he becomes the monster that he later becomes.

So who's the villain in Frankenstein? It's unclear, right? Oh, it is unclear. Yeah. It's really the people who drive him by driving him away. They bring out the worst. That's right. They give him no human solace. And he is driven away. You're right. He becomes at one point, the friend of a blind man. And he serves this blind man and they become very friendly. But it went, the sighted people of the blind man's family come in, ah, you got a monster here. So it's very didactic in its way. And what I didn't know is that Mary Shelley and Percy Shelley were great readers of

the literature surrounding abolition in the United States, the abolition of slavery. And they picked that up wholesale. You know, you are making monsters of these people because you won't give them the respect and love that they deserve. Do you have, if we get philosophical for a second, do you worry that once we create machines that are a little bit more intelligent? Let's look at Rumba, that vacuum is the cleaner, that this darker part of human nature where we abuse the other, the, the, the somebody who's different will come out. I don't worry about it. I could imagine it happening.

But I think that what AI has to offer, the human race will be so attractive that people will be one over. So you have looked deep into these people, have deep conversations. And it's interesting to get a sense of stories of the way they were thinking and the way it was changed, the way your own thinking about AI has changed. So you mentioned McCarthy, what about the years at CMU, Carnegie Mellon, with Joe and was a Joe was not in AI. He was in algorithmic complexity. Was there always a line between AI and computer science, for example? Is AI its own place of outcasts?

Was that the feeling? There was a kind of outcast period for AI. For instance, in 1974, the new field was hardly 10 years old. The new field of computer science was asked by the National Science Foundation, I believe, but it may have been the National Academies, I can't remember, to tell us, tell, are your fellow scientists where computer science is and what it means. And they wanted to leave out AI. And they only agreed to put it in because Don Knuth said, Hey, this is important. You can't just leave that out.

Really Don? Don Knuth. Yes. I talked to him recently, just out of all the people. Yes. But you see, an AI person couldn't have made that argument. He wouldn't have been believed, but Knuth was believed. Yes. So, uh, Joe Trab worked on the real stuff. Joe was working on algorithmic complexity, but he would say in plain English, again, the smartest people I know are in AI. Really? Oh,, no question. Anyway, Joe, love these guys. What happened was that I guess it was, as I started to write machines who think, Herb Simon and I became very close friends.

He would walk past our house on North Emberland Street every day after work. And I would just be putting my cover on my typewriter. And I would lean out the door and say, herb, would you like a sherry? And herb almost always would like a sherry. So he'd stop in. And we'd talk for an hour, two hours. My journal says we talk this afternoon for three hours. Well, as in his mind at the time, in terms of an AI side of things, we didn't talk too much about AI.

We talked about other things. It's life. We both love literature and Herb had read, proust in the original French twice all the way through. I can't. I read it in English in translation. So we talked about literature, we talked about languages, we talked about music because he loved music, we talked about art, because he was actually enough of a painter that he had to give it up because he was afraid it

was interfering with his research. And so on. No, it was really just chat, chat, but it was very warm. So one summer, I said to her, you know, my students have all the really interesting conversations. I was teaching at the University of Pittsburgh then in the English department. You know, they get to talk about the meaning of life and that kind of thing. And what do I have? I have university meetings where we talk about the photocopying budget and, you know, whether the course on romantic poetry should be one semester or two. So herb laughed. He said,

yes, I know what you mean. He said, but, you know, you could do something about that. to me. He said, but you know, you could do something about that. Dot, that was his wife, Dot and I used to have a salon at the University of Chicago every Sunday night. And we would have essentially an open house. And people knew it wasn't for a small talk. It was really for some topic of depth. He said, but my advice would be that you choose the topic ahead of time, fine, I said. So the following, we exchanged mail over the summer that was US Post in those days

because you didn't have personal email. Right. And I decided I would organize it. And there would be eight of us, Alan Nuland, his wife, Herb Simon, and his wife, Dorothea. There was a novelist in town, a man named Mark Harris, he had just arrived, and his wife, Josephine. Mark was most famous then for a novel called Bang the Drum Slowly, which was about baseball. And Joe and me, so eight people. And we met monthly. And we we just sank our teeth into really hard topics and it was great fun. really hard topics and it was great fun. How have your own views around artificial intelligence

changed through the process of writing machines who think and afterwards the ripple effects? I was a little skeptical that this whole thing would work out. It didn't matter. To me, it was so audacious. The whole thing being AI. AI, generally. Yeah.

And in some ways, it hasn't worked out the way I expected so far. That is to say, there is this wonderful lot of apps thanks to deep learning and so on. But those are alchemy. And in the part of symbolic processing, there's very little yet. And that's a feel that lies waiting for industry, an industrious graduate students. Maybe you can tell me some figures that popped up in your life in the 80s with expert systems where there was the symbolic AI possibilities of what's, you know, that what most people think of as AI, if you dream of the possibilities, AI is really expert system.

And those hit a few walls and there were challenges there. And I think, yes, they will reemerge again with some new breakthroughs and so on. But what did that feel like both the possibility and the winter that followed the slowdown and research? Ah, you know, this whole thing about AI winter is to me a crock. I went there is to me a crock. It's no winters. Because I look at the basic research that was being done in the 80s, which is supposed to be, oh my God, it was really important. It was laying down things that nobody had thought about before, but it was basic research.

You couldn't monetize it. Hence the winter. Hence the winter. You know, research, scientific research, goes and fits and starts. It isn't this nice smooth. Oh, this follows this follows this. No, it just doesn't work that way. The interesting thing, the way winter's happened, it's never the fault of the researchers. It's the, it's the, the some source of hype over promising. Well, no, let me take that back. Sometimes it is the fault of the researchers. Sometimes certain researchers might over promise

the possibilities they themselves believe that we're just a few years away sort of just recently talked to Elon Musk and he believes he'll have an autonomous vehicle, we'll have autonomous vehicles in a year and he believes it. A year? A year, yeah. I've massed deployment of a time for the record. This is 2019 right now. So he's talking 2020. To do the impossible, you really have to believe it.

And I think what's going to happen when you believe it, because there's a lot of really brilliant people around him is Some good stuff will come out of it Some unexpected brilliant breakthroughs will come out of it when you really believe it when you work that hard But I believe that and I believe autonomous vehicles will come. I just don't believe it will be in a year Yeah, I wish but nevertheless there's a house vehicles is a good example There's a feeling many companies have promised by 2021 by 2022 for GM, basically every single automotive company's promise they'll have autonomous vehicles. So that kind of over promise is what leads to the winter because we'll come to those dates.

There won't be autonomous vehicles and they'll be a feeling, we'll wait a minute, if we took your word at that time, that means we just spent billions of dollars had made no money and there's a counter response to where everybody gives up on it. Sort of intellectually, at every level, the hope just dies. And all that's left is a few basic researchers. So you're uncomfortable with some aspects of this idea. Well, it's a difference between science and commerce. So you think science goes on the way it does?

Science can really be killed by not getting proper funding or timely funding. I think Great Britain was a perfect example of that. The Light Hill report in, you know, remember the year essentially said there's no use Great Britain putting money into this. It's going nowhere. And this was all about social factions in Great Britain. Ed Merrow hated Cambridge and Cambridge hated Manchester. Somebody else can write that story.

But it really did have a hard effect on research there now they've come roaring back with deep mind, but that's One guy and his vision areas around him, but just to Push on that it's kind of interesting you have this dislike of the idea of an AI winter. The... Where's that coming from? Where were you? Oh, because I just don't think it's true. There was a particular period of time. There's a romantic notion certainly. Yeah, well. No, I admire science. Perhaps more than I admire commerce.

Commerce is fine. Hey, you know, we all got to live. But science has a much longer view than commerce and continues almost regardless. It can't continue totally regardless, but it almost regardless of what's salable and what's monetizable and what's not. So the winter is just something that happens on the commerce side and the science.

So it seems to me. March is, that's a beautifully optimistic inspired message. I agree with you. I think if we look at the key people that work in AI, that work in key scientists, most disciplines, they continue working out of the love for science, no matter if you can always scrape up some funding to stay alive. And they continue working diligently. But there are certainly a huge amount of funding now. And there's a concern on the AI side and deep learning. There's a concern that we might with over promising,

hit another slow down funding, which does affect the number of students, you know, that kind of thing. Yeah. It really does. So the kind of students, you know, that kind of thing. Yeah. It really does. So the kind of ideas you had in machines, you think, did you continue that curiosity through the decades that followed? Yes, I did.

And what was your view, historical view of how AI community evolved, the conversations about it, the work, as it persisted the same way from its birth. No, of course not. It's just as we were just talking, the symbolic AI really kind of dried up and it all became algorithmic. I remember a young AI student telling me what he was doing. And I had been away from the field long enough, I got involved with complexity at the Santa Fe Institute. I thought algorithms, yeah, they're

in the service of, but they're not the main event. No, they became the main event. That surprised me. event. That surprised me. And we all know the downside of this. We all know that if you're using an algorithm to make decisions based on a gazillion human decisions baked into it, are all the mistakes that humans make, the bigotry, the short-sightedness, someone and so on. So you mentioned Santa Fe Institute. So you've written the novel, Edge of Chaos, but it's inspired by the ideas of complexity, a lot of which have been extensively explored at the Santa Fe Institute. Right. It's another fascinating topic, just sort of emergent complexity from chaos. Nobody knows how it happens, really, but it seems to wear all the interesting stuff

that does happen. So how did first, not in novel, but just complexity in general in the work of Santa Fe, fit into the bigger puzzle of the history of AI, or maybe even your personal journey through that. One of the last projects I did concerning AI in particular was looking at the work of Harold Cohen, the painter. And Harold was deeply involved with AI. He was a painter first. And what his project, Aaron, which was a lifelong project, did was reflect his own cognitive processes. Okay. Harold and I, even though I wrote a book about it, we had a lot of friction between us.

And I went, I thought, this is it, you know, the book died, it was published and fell into a ditch. This is it, I'm finished, it's time for me to do something different. By chance, this was a sabbatical year for my husband, and we spent two months at the Santa Fe Institute and two months at Caltech, and then the spring semester in Munich, Germany. Okay. Those two months at the Santa Fe Institute were so restorative for me. And I began, the Institute was very small then. It was in some kind of office complex on Old Santa Fe trail. Everybody kept their door open. So you could crack your head on a problem, and if you finally didn't get it, you could walk in to see Stuart Kaufman or any number of people

and say, I don't get this, can you explain? And one of the people that I was talking to about complex adaptive systems was Murray Gell-Mann. And I told Murray what Harold Cohen had done. And I said, you know, this sounds to me like a complex adaptive system. And he said, yeah, it is. Well, what do you know? Harold's Aaron had all these kissing cousins all over the world in science and in economics and so on and so forth.

I was so relieved. I thought, okay, your instincts are okay. You're doing the right thing. I didn't have the vocabulary. And that was one of the things that the Santa Fe Institute gave me. If I could have rewritten that book, no, it had just come out. I couldn't rewrite it. I would have had a vocabulary to explain what Aaron was doing.

OK. So I got really interested in what was going on at the Institute. The people were, again, bright and funny and willing to explain anything to this amateur. George Cowan, who was then the head of the institute, said he thought it might be a nice idea if I wrote a book about the institute. And I thought about it, and I had my eye on some other project, God knows what. And I said, I'm sorry, George, yeah, I'd really love to do it, but you know, just not going to work for me at this moment. He said, oh, it's too bad. I think it

would make an interesting book. Well, he was right and I was wrong. I wish I'd done it. But that's interesting. I hadn't thought about that. That that was a road not taken that I wish I'd taken. Well, you know what, this just on point, it's quite brave for you as a writer, as sort of coming from a world of literature, the literary thinking, historical thinking, I mean, just from that world, and bravely talking to quite, I assume, large egos in AI or in complexity and so on. How'd you do it? Like, what did you, I mean, I suppose they could be intimidated of you as well, because it's two different worlds. I never picked up that anybody was intimidated by me. But how were you brave enough? Where did you find the guts to start?

God, just dumb. Dumb luck. I mean, this is an interesting rock to turn over. I'm gonna write a book about it. And you know, people have enough patience with writers if they think they're gonna end up at a book that they let you flail around and so on. It's well, but they also look if the writer has, there's like, if there's a sparkle in their

eye, if they get it. Yeah, sure. Right. When were you at the Santa Fe Institute? The time I'm talking about is 1990, yeah, 1990, 1990, not 192, but we then, because Joe was an external faculty member, we're in Santa Fe every summer. We bought a house there, and I didn't have that much to do with the institute anymore.

I was writing my novels. I was doing whatever I was doing. But I loved the institute, and I loved the, again, the audacity of the ideas. That really appeals to me. I think that there's this feeling, much like in great institutes of neuroscience, for example, that they're in it for the long game of understanding something fundamental about reality and nature.

And that's really exciting. So if we start not to look a little bit more recently, how AI is really popular today. How is this world you mentioned the algorithmic, but in general, is the spirit of the people, the kind of conversations you hear through the grave, and so on? Is that different than the roots that you remember? No. The same kind of excitement, the same kind of, this is really going to make a difference in the world.

And it will. It has. You know, a lot of the folks, especially young, you know, 20 years old or something, they think we've just found something special here. We're going to change the world tomorrow. On a time scale, do you have a sense of what of the time scale that we break through as an AI happen? of what, of the time scale at which breakthroughs in AI happened. I really don't, because look at deep learning. That was Jeffrey Hinton came up with the algorithm in 86.

But it took all these years for the technology to be good enough to actually be applicable. So no, I can't predict that at all. I wouldn't even try. Well, let me ask you to not try to predict, but to speak to the, you know, I'm sure in the 60s, as it continues now, there's people that think, let's call it, we can call it this fun word, the singularity. When there is a phase shift, there's some profound feeling where we're all really surprised by what's able to be achieved.

I'm sure those dreams are there. I remember reading quotes in the 60s and those 15, how have your own views maybe if you look back about the timeline of a singularity changed? Well, I'm not a big fan of the singularity as Ray Kurzweil has presented it. How would you define the Ray Kurzweil has presented it. How would you define the Ray Kurzweil? How do you think of singularity in those? If I understand Kurzweil's view, it's sort of, there's going to be this moment when machines are smarter than humans and game over.

However, the game over is. I mean, do they put us on a reservation, do they, etc., etc.? And first of all, machines are smarter than humans in some ways all over the place. And they have been since adding machines were invented. So it's not going to come like some great eatable crossroads, you know, where they meet each other and our offspring, Etappus says, you're dead. It's just not going to happen. Yeah, so it's already game over with calculators, right?

They're already out the duet much better at basic arithmetic than us. But you know, there's a human-like intelligence. And it's not the ones that destroy us, but, you know, somebody that you can have as a friend, you can have deep connections with that kind of passing the touring test and beyond. Those kinds of ideas. Have you dreamt of those? Oh, yes, yes, yes, those possibly. In a book I wrote with Ed Fiegenbaum, there's a little story called the Geriatric Robot. And how I came up with the Geriatric Robot is a story in itself. But here's what the Geriatric Robot does. It doesn't just clean you up and feed you

and will you out into the sun. It's great advantages. It listens. It says, tell me again about the great coup of 73. Tell me again about how awful or how wonderful your grandchildren are and so on and so forth. And it isn't hanging around to inherit your money. It isn't hanging around because it can't get any other job. This is its job and so on and so forth.

Well, I would love something like that. Yeah, I mean, for me that deeply excites me. So I think there's a lot of us. Like you got to know, it was a joke. I dreamed it up because I needed to talk to college students and I needed to give them some idea of what AI might be. And they were rolling in the aisles as I elaborated and elaborated and elaborated. When it went into the book, they took my hideoff in the New York Review of Books. This is just what we have thought about these people in AI, they're inhuman.

Come on, get over it. Don't you think that's a good thing for the world that a I could potentially? Why? I do absolutely and furthermore, I want, you know, I'm pushing 80 now by the time I need help like that. I also want it to roll itself in a corner and shut the fuck up. Let me let me linger on that point. Do you really, though? Yeah, I do. Here's what you wanted to push back a little bit, a little.

But I have watched my friends go through the whole issue around having help in the house. And some of them have been very lucky and had fabulous help. And some of them have had people in the house who want to keep the television going on all day. Who want to talk on their phones all day? No. So basically, just roll yourself in the corner. Unfortunately, I ask humans when we're assistants, we care, we're still, even when we're assisting

others, we care about ourselves more. Of course. And so you create more frustration and a robot, AI assistant can really optimize the experience for you. Wait a minute. I was just speaking to the point, you actually bring up a very, very, very good point, but I was speaking to the fact that us humans are a little complicated that we don't necessarily want a perfect servant. I don't maybe either disagree with that, but there's, I think there's a push and pull with humans, a little tension, a little mystery

that, of course, that's really difficult for you to get right but I do sense especially in today with social media that people are getting more and more lonely even young folks and sometimes especially young folks that loneliness there's a longing for connection and AI can help alleviate some of that loneliness. Some, just somebody who listens, like in person. That sort of speak, yeah, sort of speak. That to me is really exciting. But so if we look at that level of intelligence, which is exceptionally difficult to achieve actually as the singularity or whatever, that's the human level bar. That

people have dreamt of that too, touring dreamt of it. He had a date timeline. Do you have how of your own timeline evolved on past? I don't even think about it. You don't even think. No. Just this field has been so full of surprises for me. You just taken it and see. Yeah, I'm so full. Wow, that's great.

Yeah, I just can't. Maybe that's because I've been around the field long enough to think, you know, don't go that way. Herp Simon was terrible about making these predictions of when this and that would happen. And he was a sensible guy. His quotes are often used right as a... A sublugion, yeah. right as a legend. Yeah. Yeah. Do you have concerns about AI, the existential threats that many people like Elon Musk and Sam Harrison, all the other things. Oh yeah. Yeah. That takes up a half a chapter in my book. I call it the male gaze.

in my book, I call it the Male Gaze. Well, you hear me out. The Male Gaze is actually a term from film criticism, and I'm blocking on the woman's who dreamed this up, but she pointed out how most movies were made from the male point of view. That women were objects, not subjects. They didn't have any agency, so on and so forth. So when Elon and his pals were talking and so on, and K, AI is going to eat our lunch and dinner and our midnight snack too, I thought, what? And I said to Ed Fuykinfell, oh this is the first guy, first these guys have always been the smartest guy on the block, and here comes something that might be smarter. Ooh, let's stamp it out before it takes over. And Ed laughed, he said, I didn't think about it that way. But I did, I did. And it is the male gaze, you know, okay, suppose these things

do have agency. Well, let's wait and see what happens. Can we imbue them with ethics? Can we imbue them with a sense of empathy? Or are they just going to be, I don't know, we've had centuries of guys like that. That's interesting that the ego, the male gaze is immediately threatened. threatened. And so you can't think in a patient calm way of how the tech could have all, and he's speaking of which, you're a 96 book, the future of women. Oh, I think at the time and now certainly now, I mean, I'm sorry, maybe at the time, but I'm more cognitive now, as extremely relevant. You and Nancy Ramsey talk about four possible futures of women in science and tech. So if we look at the decades before and after the book was released, can you tell a history sorry of women in science and tech and how it has evolved, how have things changed? Where do we stand? Not enough.

They have not changed enough. The way that women are ground down in computing is simply unbelievable. But what are the four possible futures for women in tech from the book? What you're really looking at are various aspects of the present. So for each of those, you could say, oh, yeah, we do have backlash. Look at what's happening with abortion and so on and so forth. We have one step forward, one step back. The golden age of equality was the hardest chapter to write, and I used something from the Santa Fe Institute,

which is the sandpile effect that you drop sand very slowly onto a pile, and it grows, and it grows, and it grows, until suddenly it just breaks apart. And in a way, me too, has done that. That was the last drop of sand that broke everything apart. That was a perfect example of the sandpile effect. And that made me feel good. It didn't change all of society, but it really woke a lot of people up. But are you in general optimistic about maybe after me too? I mean, me too is about a very specific kind of thing. Boy, solve that and you solve everything.

But are you in general optimistic about the future? Yes. I'm a congenital optimist. I can't help it. What about AI? What are your thoughts about the kitchen? When AI. Of course, I get asked what you worry about. And the one thing I worry about is the things we can't anticipate. There's going to be something out of left field that we will just say we weren't prepared for that.

I am generally optimistic. When I first took up being interested in AI, like most people in the field, more intelligence was like more virtue. What could be bad? And in a way, I still believe that, but I realize that my notion of intelligence has broadened. There are many kinds of intelligence,

and we need to imbue our machines with those many kinds. So you've now just finished or in the process of finishing the book, even working on them my more. What, how have you changed? I know it's just writing, but how have you changed the process? If you look back, what kind of stuff did it bring up to you that surprised you, looking at the entirety of it all. The biggest thing, and it really wasn't a surprise, is how lucky I was.

Oh, my, to be, to have access to the beginning of a scientific field that is going to change the world. How did I luck out? And yes, of course, my view of things has widened a lot. If I can get back to one feminist part of our conversation, without knowing it, it really was subconscious. I wanted AI to succeed because I was so tired of hearing that intelligence was inside the male cranium. And I thought if there was something out there that wasn't a male thinking and doing well, then that would put a lie to this whole notion of intelligence resides in the male cranium.

I did not know that until one night Harold Cohen and I were having a glass of wine, maybe two, and he said, what drew you to AI? And I said, oh, you know, smartest people. I knew great project, blah, blah, blah. And I said, and I wanted something besides male smarts. And it just bubbled up out of me, Lex. And I, what? It's brilliant actually. So, AI really humbles all of us and humbles the people that need to be humbled the most. Let's hope. Wow, that is so beautiful.

Pamela, thank you so much for talking to this. It's been a great pleasure. Thank you. you