Pod Insights Lex Fridman Podcast Pamela McCorduck: Machines Who Think and the Early Days of AI About this episode 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 https://twitter.com/lexfridman, LinkedIn https://www.linkedin.com/in/lexfridman/, Facebook https://www.facebook.com/lexfridman, Medium https://medium.com/@lexfridman, or YouTube https://www.youtube.com/lexfridman 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 https://www.patreon.com/lexfridman.
Transcript Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.
0:00 Lex Fridman? The following is a conversation with Pamela McCordick. She's an author who has 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 Faginbaum, who's considered to be the father of expert systems, The Edge of Chaos, that features a woman, and many more books. I came across her work in an unusual way by stumbling at 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
0:42 Lex Fridman? AI, from the grounded, day-to-day science, math, and engineering, to popular stories in 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 through the work of our community today. I often don't know where the conversation may take us, but I jump in and see, having
1:25 Lex Fridman? 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 five stars on iTunes, support it on Patreon, or simply connect with me on Twitter at LexFriedman, spelled F-R-I-D-M-A-N. And now, here's my conversation with Pamela McCordick.
2:08 Lex Fridman? In 1979, your book Machines Who Think was published. In it, you interview some of the early AI pioneers and explore 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.
2:35 Speaker 01 I had been a novelist. I'd published two novels. And I was sitting under the portal at Stanford one day in 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.
3:13 Speaker 01 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, mutter, mutter. Well, okay, it's your, your time.
3:41 Lex Fridman? 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?
3:51 Speaker 01 I said, this is wonderful. The founders of the field are alive and kicking and able to talk about what they're doing.
3:58 Lex Fridman? Did they sound or feel like founders at the time? Did they know that they've been found, that they founded something?
4:05 Speaker 01 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 was amazing, just amazing.
4:33 Lex Fridman? So who stands out to you? Maybe looking 63 years ago, the Dartmouth Conference. So Marvin Minsky was there, McCarthy was there, Claude Shannon, Alan Newell, Herb Simon, some of the folks you've mentioned.
4:51 Speaker 01 Right.
4:52 Lex Fridman? Then there's other characters, right? One of your co-authors.
4:57 Speaker 01 He wasn't at Dartmouth.
5:00 Lex Fridman? He wasn't at Dartmouth, but I mean.
5:01 Speaker 01 He was, I think, an undergraduate then.
5:04 Lex Fridman? And of course, Joe Traub. I mean, all of these are players, not at Dartmouth, but in that era.
5:12 Speaker 01 Right.
5:13 Lex Fridman? CMU and so on. So who are the characters? If you could paint a picture that stand out to you from memory, those people you've interviewed and maybe not, people that were just in the.
5:24 Speaker 01 In the atmosphere.
5:26 Lex Fridman? In the atmosphere.
5:27 Speaker 01 Of course, the four founding fathers were extraordinary guys. They really were.
5:33 Lex Fridman? Who are the founding fathers?
5:34 Speaker 01 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 going to do it yet. And you mentioned Joe Traub, my husband. I was immersed in AI before I met Joe because I had been Ed Feigenbaum's assistant at Stanford. And before that, I had worked on a book edited by Feigenbaum and Julian Feldman called.
6:19 Speaker 01 Computers and Thought. It was the first textbook of readings of AI. And they they only did it because they were trying to teach AI to people at Berkeley. And there was nothing, you know, 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. You know, what did I know? And yet I was fascinated. And that's why you saw that historical, that literary background, which I I think is very much a part of the continuum of AI. That that AI grew out of that same impulse.
7:02 Lex Fridman? That, yeah, that tradition. What what was, what drew you to AI? How did you even think of it back back then? What what was the possibilities, the dreams? What was interesting to you?
7:16 Speaker 01 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. I, in fact, the final chapter is very wishy washy, frankly. I'll succeed, the field did.
7:37 Lex Fridman? Yeah. So was there the idea that AI began with the wish to forge the God? So the the spiritual component that we crave to create this other thing greater than ourselves?
7:52 Speaker 01 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.
8:10 Speaker 01 Minsky 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, 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. Minsky was hybrid, and Newlin Simon were very much cognitive psychology. How can we simulate various things about human cognition?
8:50 Speaker 01 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 a cell, the intelligence of the brain, not just human brain, but the intelligence of any kind of brain. Cephalopods, 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, Franz de Waal, say,
9:34 Speaker 01 "AI taught us the questions to ask."
9:39 Lex Fridman? Yeah, this is what happens, right? It's when you try to build it, is when you start to actually ask questions. It puts a mirror to ourselves.
9:47 Speaker 01 Yeah, right.
9:48 Lex Fridman? So, you were there in the middle of it. It seems like not many people were asking the questions that you were, or trying to look at this field the way you were.
9:59 Speaker 01 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 everything you could think of. The NSF, the DARPA.
10:23 Speaker 01 There was an Air Force place that doled out money. And each of them said, well, that was, 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, you know, 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 at MIT.
11:04 Speaker 01 He had, he was a wealthy man and he liked what he called crackpot ideas. And he considered.
11:10 Lex Fridman? This a crackpot idea?
11:11 Speaker 01 This a crackpot idea. And he was willing to support it. I am ever grateful. Let me say that.
11:17 Lex Fridman? You know, some would say that a history of science approach to AI, or even just a history, or anything like the book that you've written, hasn't been written since. 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?
11:44 Speaker 01 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 place, one place to another? He doesn't have to crew people to do that, the crewmen. Yeah, it's, it's magic. It's robots. Oh, I thought, how interesting. So.
12:29 Speaker 01 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 he, 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?
13:12 Speaker 01 He had other ideas for raising money, too.
13:16 Lex Fridman? But if we actually reach back once again, I think people don't actually really know that robots do appear, or the ideas of robots. You talk about the Hellenic and the Hebraic points of view.
13:28 Speaker 01 Oh, yes.
13:29 Lex Fridman? Can you tell me about each?
13:32 Speaker 01 I defined it this way. The Hellenic point of view is, robots are great. You know, they are party help. They help this guy Hephaestus, this god Hephaestus, and his forge. I presume he made them to help him, and so on and so forth. And they welcome the whole idea of robots. The Hebraic view has to do with, I think it's the Second Commandment. Thou shalt not make any grave an image. In other words, you better not start imitating humans, because that's just forbidden. It's the Second Commandment.
14:12 Speaker 01 And a lot of the reaction to artificial intelligence has been a sense that this is, this is somehow wicked. This is somehow blasphemous. We shouldn't be going there. Now,
14:33 Speaker 01 You can say, yeah, but there are going to be some downsides. And I say, yes, there are, but blasphemy is not one of them.
14:39 Lex Fridman? You know, there is a kind of fear that feels to be almost primal.
14:45 Lex Fridman? 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 creating life in general. It's the Frankenstein idea.
15:06 Speaker 01 There's the annotated Frankenstein on my coffee table. It's a 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.
15:24 Lex Fridman? Maybe you can speak to that fear, the psychology, if you've 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 there by these algorithms, control the behavior of the masses. There's these concerns. But it, to me, feels like the fear that people have is deeper than that. So have you thought about the psychology of it?
16:04 Speaker 01 I think in a superficial way I have. There is this notion that if we
16:12 Speaker 01 produce a machine that can think, it will outthink us and therefore replace us.
16:19 Lex Fridman? I guess that's a primal fear of almost kind of a kind of mortality. So around the time you said you worked with, at Stanford with Ed Faginbaum. 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?
16:53 Speaker 01 Oh, since sixty five.
16:55 Lex Fridman? Sixty five. So maybe it doesn't have to be about him. It could be bigger, but because he was a key person in expert systems, for example. How is that? How are these folks who you've interviewed in the seventies, seventy nine changed through the decades?
17:20 Speaker 01 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, this 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
18:04 Speaker 01 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?
18:49 Speaker 01 Was it grown elsewhere? And I think this is part of the AI, any scientific field. You're so busy doing the, the hard work, and it is hard work, that you don't step back and say, oh, well, now let's talk about the, you know. The general meaning of all this, yes.
19:10 Lex Fridman? So none of the even Minsky and McCarthy, they...
19:16 Speaker 01 Oh, those guys did, yeah. The founding fathers did.
19:18 Lex Fridman? Early on or later?
19:20 Speaker 01 Pretty early on, but in a different way from how I looked at it. The two cognitive psychologists, Newell and Simon, they wanted to imagine reforming cognitive psychology so that we would really, really understand the brain.
19:42 Speaker 01 Minsky was more speculative.
19:46 Speaker 01 And John McCarthy saw it as,
19:50 Speaker 01 I think I'm 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.
20:02 Speaker 01 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. One of the conventions is that you have
20:44 Speaker 01 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. You know, looking back, the classics, R U R is certainly the machines trying to overthrow the humans. Frankenstein is different. Frankenstein is...
21:23 Speaker 01 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, that he becomes the monster that he later becomes.
21:56 Lex Fridman? So who's the villain in Frankenstein? It's unclear, right?
22:00 Speaker 01 Oh, it is unclear, yeah.
22:02 Lex Fridman? It's really the people who drive him. By driving him away, they bring out the worst.
22:11 Speaker 01 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 they, he serves this blind man, and they become very friendly. But when the sighted people of the blind man's family come in, "Ah, you've got a monster here." So it's, 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.
22:56 Speaker 01 You know, you are making monsters of these people because you won't give them the respect and love that they deserve.
23:03 Lex Fridman? 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 Roomba, that vacuums the cleaner. That, that this darker part of human nature, where we abuse...
23:24 Lex Fridman? The other, the, the, somebody who's different will come out.
23:30 Speaker 01 I don't worry about it. I could imagine it happening.
23:35 Speaker 01 But I think that what AI has to offer the human race will be so attractive that
23:44 Lex Fridman? Yeah.
23:44 Speaker 01 people will be won over.
23:46 Lex Fridman? So you have looked deep into these people, had a deep conversations and it's
23:52 Speaker 01 Yeah.
23:52 Lex Fridman? 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 is, um, what, what about the years at CMU Carnegie Mellon with Joe and with
24:10 Speaker 01 Sure. Uh, Joe was not in AI. He was in algorithmic complexity.
24:20 Lex Fridman? Was there always a line between AI and computer science? For example, is AI its own place of outcasts? Was that the feeling?
24:28 Speaker 01 There was a kind of outcast period for AI. For instance, in 1974, the new field was hardly
24:41 Speaker 01 10 years old. Uh, 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, uh, to, you know, tell us, tell your fellow scientists where computer science is and what it means. And they wanted to leave out AI.
25:05 Speaker 01 And they only agreed to put it in because Don Knuth said, hey, this is important. You can't just leave that out.
25:14 Lex Fridman? Really? Don did?
25:15 Speaker 01 Don Knuth, yes.
25:16 Lex Fridman? I talked to Mrs. Njus, uh, out of all the people.
25:20 Speaker 01 Yes. But you see, an AI person couldn't have made that argument. It wouldn't have been believed, but Knuth was believed. Yes.
25:27 Lex Fridman? So, uh, Joe Trout worked on the real stuff.
25:32 Speaker 01 Uh, Joe was working on algorithmic complexity, but he would say in plain English again and again, the smartest people I know are in AI.
25:41 Lex Fridman? Really?
25:42 Speaker 01 Oh, yes. No question. Anyway, Joe loved these guys. What happened was that
25:51 Speaker 01 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 Northumberland 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 talked this afternoon for three hours.
26:23 Lex Fridman? What was on his mind at the time if in terms of on the AI side of things?
26:28 Speaker 01 Oh, we didn't talk too much about AI. We talked about other things.
26:31 Lex Fridman? His life.
26:32 Speaker 01 We both loved literature and Herb had read Proust in the original French twice all the way through. I can't, I've 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, 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. So, no, it was really just chat, chat. But it was very warm. So one summer, I said to Herb, you know, my students have all the really interesting
27:16 Speaker 01 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. 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 small talk.
28:00 Speaker 01 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.
28:20 Lex Fridman? Right.
28:21 Speaker 01 And we, I decided I would organize it. And there would be eight of us. Alan Nolan, 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.
29:03 Lex Fridman? How have your own views around artificial intelligence changed in through the process of writing machines who think and afterwards, the ripple effects?
29:15 Speaker 01 I was a little skeptical that this whole thing would work out. It didn't matter to me. It was so audacious.
29:22 Lex Fridman? This whole thing being AI.
29:24 Speaker 01 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 algorithmic.
29:45 Lex Fridman? Yeah.
29:46 Speaker 01 And.
29:48 Speaker 01 In the part of symbolic processing, there is very little yet. And that's a field that lies waiting for industrious graduate students.
30:03 Lex Fridman? Maybe you can tell me some figures that popped up in your life in the eighties 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 of AI, it's really expert system. And those hit a few walls and there was 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 in research?
30:38 Speaker 01 You know, this whole thing about AI winter is to me, a crock.
30:43 Lex Fridman? Snow winters.
30:45 Speaker 01 Because I look at the basic research that was being done in the eighties, which is supposed to be, 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.
31:02 Lex Fridman? Hence the winter.
31:04 Speaker 01 You know, research, scientific research, goes and fits and starts. It isn't this nice, smooth, oh, this follows this, follows this. No, you know, it just doesn't work that way.
31:16 Lex Fridman? The interesting thing, the way winters happen, it's never the fault of the researchers. It's the, it's the some source of hype, overpromising. Well, no, let me take that back. Sometimes it is the fault of the researchers. Sometimes certain researchers might overpromise 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, will have autonomous vehicles in a year, and he believes it.
31:47 Speaker 01 A year?
31:48 Lex Fridman? A year, yeah. Would have mass deployment of autonomous.
31:50 Speaker 01 For the record, this is 2019 right now. So he's talking 2020.
31:56 Lex Fridman? To do the impossible, you really have to believe it.
32:00 Lex Fridman? And I think what's going to happen when you believe it, because he has 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.
32:13 Speaker 01 I believe that, and I believe autonomous vehicles will come. I just don't believe it'll be in a year. I wish.
32:20 Lex Fridman? But nevertheless, there is autonomous vehicles is a good example. There's a feeling many companies have promised by 2021, by 2022, Ford, GM. Basically, every single automotive company has promised 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 there'll be a feeling, well, 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.
33:04 Lex Fridman? And all that's left is a few basic researchers. So you're uncomfortable with some aspects of this, this idea?
33:12 Speaker 01 Well, it's the difference between science and commerce.
33:17 Lex Fridman? So you think science prevail, science goes on the way it does?
33:22 Speaker 01 Oh, science can really be killed by not getting proper funding or timely funding.
33:31 Speaker 01 I think Great Britain was a perfect example of that. The Lighthill Report in,
33:38 Speaker 01 I don't remember the year, essentially said, there's no use Great Britain putting any money into this, it's going nowhere. And this was all about social factions in Great Britain.
33:54 Speaker 01 Edinburgh hated Cambridge and Cambridge hated Manchester.
34:00 Speaker 01 Somebody else can write that story. But it really did have a hard effect on research there. Now, they've come roaring back with DeepMind.
34:12 Lex Fridman? Yeah.
34:12 Speaker 01 But that's one guy and his visionaries around him.
34:18 Lex Fridman? 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, where's that coming from?
34:30 Speaker 01 Oh, because I just don't think it's true.
34:33 Lex Fridman? There was a particular periods of time. It's a romantic notion, certainly.
34:39 Speaker 01 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, not, it can't continue totally regardless, but it almost regardless of what's saleable and what's not, what's monetizable and what's not.
35:16 Lex Fridman? So the winter is just something that happens on the commerce side? And the science marches? That's a beautifully optimistic, inspiring message. I agree with you. I think if we look at the key people that work in AI, that work in key scientists in most disciplines, they continue working out of the love for science, no matter the, you could always scrape up some funding to stay alive. And they continue working diligently. But there certainly is a huge amount of funding now. And there's a concern on the AI side and deep learning.
35:56 Lex Fridman? There's a concern that we might, with overpromising, hit another slowdown in funding, which does affect the number of students, you know, that kind of thing.
36:04 Speaker 01 Yeah, no, it does.
36:06 Lex Fridman? So the kind of ideas you had the machines to think, did you continue that curiosity through the decades that followed?
36:13 Speaker 01 Yes, I did.
36:14 Lex Fridman? And what was your view, historical view of how AI community evolved, the conversations about it, the work? Has it persisted the same way from its birth?
36:26 Speaker 01 No, of course not. It's just, 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'd gotten 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. And we all know the downside of this. We all know that if you're using an algorithm
37:09 Speaker 01 to make decisions based on a gazillion human decisions baked into it are all the mistakes that humans make, the bigotries, the shortsightedness, and so on and so on.
37:25 Lex Fridman? 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.
37:41 Speaker 01 Right.
37:41 Lex Fridman? It's a, I mean, it's another fascinating topic of just sort of emergent complexity from chaos. Nobody knows how it happens, really, but it seems to where all the interesting stuff does happen. So how did, first, not your novel, but just complexity in general and the work at Santa Fe, fit into the bigger puzzle of the history of AI? Or maybe even your personal journey through that?
38:11 Speaker 01 One of the last projects I did
38:15 Speaker 01 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.
38:59 Speaker 01 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 to, the institute was very small then. It was in some kind of office complex on Old Santa Fe Trail. Everybody kept their door open.
39:44 Speaker 01 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.
40:09 Speaker 01 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, OK, your instincts are OK. 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.
40:52 Speaker 01 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, oh, 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. And he said, oh, well, 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.
41:37 Speaker 01 I hadn't thought about that, that that was a road not taken that I wish I'd taken.
41:42 Lex Fridman? Well, you know what? That's just on that, on that point, it's quite brave for you as a, as a, as a writer, as a, as sort of coming from a world of literature, the literary thinking and historical thinking. I mean, just from that world and bravely talking to quite, I assume, large egos in, in, in AI or in complexity and so on.
42:10 Lex Fridman? How'd you do it? Like, where did you? I mean, I suppose they could be intimidated of you as well, because it's two different worlds.
42:19 Speaker 01 I don't think, I never picked up that anybody was intimidated by me.
42:23 Lex Fridman? But how were you brave enough? Where did you find the guts to sort of?
42:25 Speaker 01 God, just dumb, dumb luck. I mean, I, this is an interesting rock to turn over. I'm going to write a book about it. And you know, people have enough patience with writers. If they think they're going to end up in a book, that they let you flail around and so on.
42:41 Lex Fridman? Well, but they also look if the writer has, there's like, if there's a sparkle in their eye, if they get it.
42:48 Speaker 01 Yeah, sure.
42:48 Lex Fridman? Right. When were you at the Santa Fe Institute?
42:52 Speaker 01 The time I'm talking about is 1990, yeah, 1990, 91, 92. But we then, because Joe was an external faculty member, were 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.
43:17 Speaker 01 But I loved the institute and I loved the, again, the audacity of the ideas. That really appeals to me.
43:31 Lex Fridman? I think that there, there's this feeling, much like in great, great institutes of neuroscience, for example, that it's, they're, they're in it for the long game of understanding something fundamental about reality and nature.
43:47 Speaker 01 Yeah.
43:49 Lex Fridman? And that's really exciting. So if we start not to look a little bit more recently,
43:56 Lex Fridman? how, you know, AI is really popular today.
44:02 Lex Fridman? How is this world, you mentioned algorithmic, but in general, is the spirit of the people, the kind of conversations you hear through the gray eye and so on, is that different than the roots that you remember?
44:14 Speaker 01 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.
44:24 Lex Fridman? You know, a lot of folks, especially young, 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 at which breakthroughs in AI happen?
44:45 Speaker 01 I really don't, because look at deep learning. That was a, Geoffrey 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 can't, I wouldn't even try.
45:15 Lex Fridman? Well, let me ask you to, not to 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's 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 were there. I remember reading quotes in the 60s and those continued. How have your own views, maybe if you look back, about the timeline of a singularity changed?
45:54 Speaker 01 Well, I'm not a big fan of the singularity as Ray Kurzweil has presented it.
46:03 Lex Fridman? How would you define the Ray Kurzweil? Sort of, how would you, how do you think of singularity in those?
46:10 Speaker 01 If I understand Kurzweil's view, it's sort of, there's going to be this moment when machines are smarter than humans and, you know, game, game over. However, the game over is, I mean, do they put us on a reservation? Do they, et cetera, et cetera. 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, it's not going to come like some great edible crossroads, you know, where they meet each other and our offspring Oedipus says, your dad. It's just not going to happen.
46:55 Lex Fridman? Yeah, so it's already game over with calculators, right? They're already out, do 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, as a friend. You can have deep connections with, that kind of passing the Turing test and beyond. Those kinds of ideas, have you dreamt of those?
47:24 Speaker 01 Oh, yes, yes, yes.
47:26 Lex Fridman? Those possibilities?
47:27 Speaker 01 In a book I wrote with Ed Feigenbaum, 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, here's what the geriatric robot does. It doesn't just clean you up and feed you and wheel 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.
48:10 Speaker 01 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.
48:23 Speaker 01 I would love something like that.
48:26 Lex Fridman? Yeah, I mean, for me, that deeply excites me. So I think there's a lot of us.
48:32 Speaker 01 Lex, you gotta 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.
48:48 Speaker 01 When it went into the book, they took my hide off in the New York Review of Books. This is just what we have thought about these people in AI. They're inhuman. Oh, come on, get over it.
49:02 Lex Fridman? Don't you think that's a good thing for the world? That AI could potentially?
49:06 Speaker 01 Moi, 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.
49:23 Lex Fridman? Let me, let me linger on that point. Do you really, though?
49:27 Speaker 01 Yeah, I do. Here's why.
49:28 Lex Fridman? Don't you want it to push back a little bit?
49:31 Speaker 01 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.
49:53 Lex Fridman? So basically?
49:54 Speaker 01 Just roll yourself in the corner and shut up.
49:57 Lex Fridman? Unfortunately, us humans, when we're assistants, we, we care, we're still, even when we're assisting others, we care about ourselves more.
50:05 Speaker 01 Of course.
50:06 Lex Fridman? And so you create more frustration and a robot, AI assistant can really optimize the experience for you. 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 you disagree with that, but there's a.
50:34 Lex Fridman? I think there's a push and pull with humans. A little tension, a little mystery that, of course, that's really difficult for AI 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.
51:11 Speaker 01 So to speak.
51:13 Lex Fridman? So to, so to, so to speak, yeah, so to speak. Yeah, that to me is really exciting. But so if we look at that, 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. Turing dreamt of it.
51:38 Lex Fridman? He had a date timeline. Do you have, how have your own timeline evolved on past?
51:44 Speaker 01 I don't even think about it.
51:46 Lex Fridman? You don't even think?
51:46 Speaker 01 No.
51:48 Speaker 01 Just, this field has been so full of surprises for me.
51:55 Lex Fridman? You just taking in and see?
51:57 Speaker 01 That's right. Yeah, it's, I just can't. Maybe that's because I've been around the field long enough to think, you know, don't, don't go that way. Herb Simon was terrible about making these predictions of when this and that would happen.
52:14 Speaker 01 And he was a sensible guy.
52:16 Lex Fridman? Yeah. And his quotes are often used, right, as a?
52:21 Speaker 01 As a legend, yeah.
52:21 Lex Fridman? Yeah. Do you have concerns about AI, the existential threats of many people like Elon Musk and Sam Harris and others?
52:35 Speaker 01 Oh, yeah, yeah. That takes up a half a chapter in my book.
52:42 Speaker 01 I call it the male gaze.
52:46 Speaker 01 Well, you hear me out. The male gaze is actually a term from film criticism. And I'm blocking on the woman 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, and so on and so forth. So when Elon and his pals talking and so on came, AI is going to eat our lunch and our dinner and our midnight snack, too. I thought, what? And I said to Ed Feinkenbaum, oh, this is the first guy. First, these guys have always been the smartest guy on the block.
53:31 Speaker 01 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.
54:12 Lex Fridman? That's interesting that the ego, the male gaze is immediately threatened. And so you can't think in a patient, calm way of how the tech could evolve. And he's, speaking of which, your '96 book, The Future of Women.
54:34 Speaker 01 Oh.
54:35 Lex Fridman? I think at the time and now, certainly now, I mean, I'm sorry, maybe at the time, but I'm more cognizant of now, is extremely relevant. You and Nancy Ramsey talk about four possible futures.
54:49 Speaker 01 Right.
54:49 Lex Fridman? 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?
55:03 Speaker 01 Oh, boy.
55:03 Lex Fridman? And how it has evolved? How have things changed? Where do we stand?
55:08 Speaker 01 Not enough. They have not changed enough. The way that women are ground down in computing is simply unbelievable.
55:21 Lex Fridman? But what are the four possible futures for women in tech from the book?
55:27 Speaker 01 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.
55:45 Speaker 01 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.
56:25 Lex Fridman? But are you in general optimistic about maybe after Me Too? I mean, Me Too is about a very specific kind of thing.
56:34 Speaker 01 Boy, solve that and you solve everything.
56:37 Lex Fridman? But are you in general optimistic about the future?
56:40 Speaker 01 Yes, I'm a congenital optimist. I can't help it.
56:45 Lex Fridman? What about AI? What are your thoughts about the future of AI?
56:51 Speaker 01 Of course, I get asked, what do you worry about? And the one thing I worry about is the things we can't anticipate.
57:00 Speaker 01 You know, there's going to be something out of left field that we will just say, we weren't prepared for that.
57:08 Speaker 01 I am generally optimistic. When I first took up being interested in AI,
57:18 Speaker 01 like most people in the field, more intelligence was like more virtue. You know, what could be bad? And
57:27 Speaker 01 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.
57:41 Lex Fridman? So you've now just finished, or in the process of finishing your, the book that you've been working on, the memoir. 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?
58:09 Speaker 01 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, my view of things has widened a lot. If I can get back to one feminist part of our conversation.
58:45 Lex Fridman? Sure.
58:47 Speaker 01 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.
59:32 Speaker 01 And I said, and I wanted something besides male smarts. And it just bubbled up out of me, Lex. And I... What?
59:44 Lex Fridman? It's kind of brilliant, actually. So AI really humbles all of us and humbles the people that need to be humbled the most.
59:54 Speaker 01 Let's hope.
59:56 Lex Fridman? Wow, that is so beautiful. Pamela, thank you so much for talking today.
1:00:00 Speaker 01 Oh, it's been a great pleasure.
1:00:02 Lex Fridman? Thank you.
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