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 The following is a conversation with Pamela McCordick. She's an author who has written

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 on the history and the philosophical significance of artificial intelligence.

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 Her books include Machines Who Think in 1979, The Fifth Generation in 1983, with Ed Fangenbaum,

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 who's considered to be the father of expert systems, The Edge of Chaos, The Features of Women,

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 and many more books. I came across her work in an unusual way by stumbling in a quote from

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 Machines Who Think that is something like, artificial intelligence began with the ancient

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 wish to forge the gods. That was a beautiful way to draw a connecting line between our societal

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 relationship with AI from the grounded day to day science, math, and engineering to popular stories

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 and science fiction and myths of automatons that go back for centuries. Through her literary work,

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 she has spent a lot of time with the seminal figures of artificial intelligence,

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 including the founding fathers of AI from the 1956 Dartmouth summer workshop where the field

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 was launched. I reached out to Pamela for a conversation in hopes of getting a sense of

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 what those early days were like and how their dreams continued to reverberate

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 through the work of our community today. I often don't know where the conversation may take us,

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 but I jump in and see. Having no constraints, rules, or goals is a wonderful way to discover new

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 ideas. This is the Artificial Intelligence Podcast. If you enjoy it, subscribe on YouTube,

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 give it five stars on iTunes, support it on Patreon, or simply connect with me on Twitter

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 at Lex Freedman, spelled F R I D M A N. And now here's my conversation with Pamela McCordick.

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 In 1979, your book, Machines Who Think, was published. In it, you interview some of the early

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 AI pioneers and explore the idea that AI was born not out of maybe math and computer science,

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 but out of myth and legend. So tell me if you could the story of how you first arrived at the

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 book, the journey of beginning to write it. I had been a novelist. I'd published two novels.

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 And I was sitting under the portal at Stanford one day in the house we were renting for the

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 summer. And I thought, I should write a novel about these weird people in AI, I know. And then I

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 thought, ah, don't write a novel, write a history. Simple. Just go around, you know, interview them,

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 splice it together. Voila, instant book. Ha, ha, ha. It was much harder than that.

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 But nobody else was doing it. And so I thought, well, this is a great opportunity. And there were

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 people who, John McCarthy, for example, thought it was a nutty idea. There were much, you know,

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 the field had not evolved yet, so on. And he had some mathematical thing he thought I should write

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 instead. And I said, no, John, I am not a woman in search of a project. I'm, this is what I want

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 to do. I hope you'll cooperate. And he said, oh, mother, mother, well, okay, it's your, your time.

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 What was the pitch for the, I mean, such a young field at that point. How do you write

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 a personal history of a field that's so young? I said, this is wonderful. The founders of the

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 field are alive and kicking and able to talk about what they're doing. Did they sound or feel like

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 founders at the time? Did they know that they've been found, that they've founded something? Oh,

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 yeah, they knew what they were doing was very important, very. What they, what I now see in

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 retrospect is that they were at the height of their research careers. And it's humbling to me

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 that they took time out from all the things that they had to do as a consequence of being there.

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 And to talk to this woman who said, I think I'm going to write a book about you.

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 No, it was amazing, just amazing. So who, who stands out to you? Maybe looking 63 years ago,

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 the Dartmouth conference. So Marvin Minsky was there. McCarthy was there. Claude Shannon,

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 Alan Newell, Herb Simon, some of the folks you've mentioned. Right. Then there's other characters,

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 right? One of your coauthors. He wasn't at Dartmouth. He wasn't at Dartmouth, but I mean.

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 He was a, I think an undergraduate then. And, and of course, Joe Traub. I mean,

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 all of these are players, not at Dartmouth them, but in that era. Right. It's same you and so on.

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 So who are the characters, if you could paint a picture that stand out to you from memory,

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 those people you've interviewed and maybe not people that were just in the,

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 in the, the atmosphere, in the atmosphere. Of course, the four founding fathers were

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 extraordinary guys. They really were. Who are the founding fathers?

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 Alan Newell, Herbert Simon, Marvin Minsky, John McCarthy,

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 they were the four who were not only at the Dartmouth conference,

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 but Newell and Simon arrived there with a working program called the logic theorist.

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 Everybody else had great ideas about how they might do it, but they weren't going to do it yet.

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 And you mentioned Joe Traub, my husband. I was immersed in AI before I met Joe,

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 because I had been Ed Feigenbaum's assistant at Stanford. And before that,

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 I had worked on a book by edited by Feigenbaum and Julian Feldman called

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 Computers and Thought. It was the first textbook of readings of AI. And they, they only did it

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 because they were trying to teach AI to people at Berkeley. And there was nothing, you know,

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 you'd have to send them to this journal and that journal. This was not the internet where you could

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 go look at an article. So I was fascinated from the get go by AI. I was an English major, you know,

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 what did I know? And yet I was fascinated. And that's why you saw that historical,

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 that literary background, which I think is very much a part of the continuum of AI that

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 the AI grew out of that same impulse. Was that, yeah, that traditional? What, what was, what drew

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 you to AI? How did you even think of it back, back then? What, what was the possibilities,

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 the dreams? What was interesting to you? The idea of intelligence outside the human cranium,

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 this was a phenomenal idea. And even when I finished machines who think,

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 I didn't know if they were going to succeed. In fact, the final chapter is very wishy washy,

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 frankly. I don't succeed the field did. Yeah. Yeah. So was there the idea that AI began with

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 the wish to forge the God? So the spiritual component that we crave to create this other

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 thing greater than ourselves? For those guys, I don't think so. Newell and Simon were cognitive

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 psychologists. What they wanted was to simulate aspects of human intelligence. And they found

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 they could do it on the computer. Minsky just thought it was a really cool thing to do.

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 Likewise, McCarthy. McCarthy had got the idea in 1949 when, when he was a Caltech student. And

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 he listened to somebody's lecture. It's in my book, I forget who it was. And he thought,

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 oh, that would be fun to do. How do we do that? And he took a very mathematical approach.

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 Minsky was hybrid. And Newell and Simon were very much cognitive psychology. How can we

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 simulate various things about human cognition? What happened over the many years is, of course,

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 our definition of intelligence expanded tremendously. I mean, these days,

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 biologists are comfortable talking about the intelligence of cell, the intelligence of the

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 brain, not just human brain, but the intelligence of any kind of brain, cephalopause. I mean,

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 an octopus is really intelligent by any, we wouldn't have thought of that in the 60s,

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 even the 70s. So all these things have worked in. And I did hear one behavioral

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 primatologist, Franz Duval, say AI taught us the questions to ask.

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 Yeah, this is what happens, right? It's when you try to build it, is when you start to actually

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 ask questions, if it puts a mirror to ourselves. So you were there in the middle of it. It seems

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 like not many people were asking the questions that you were trying to look at this field,

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 the way you were. I was solo. When I went to get funding for this, because I needed somebody to

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 transcribe the interviews and I needed travel expenses, I went to every thing you could think of,

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 the NSF, the DARPA. There was an Air Force place that doled out money. And each of them said,

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 well, that was very interesting. That's a very interesting idea. But we'll think about it.

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 And the National Science Foundation actually said to me in plain English,

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 hey, you're only a writer. You're not an historian of science. And I said, yeah, that's true. But

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 the historians of science will be crawling all over this field. I'm writing for the general

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 audience. So I thought, and they still wouldn't budge. I finally got a private grant without

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 knowing who it was from from Ed Fredkin at MIT. He was a wealthy man, and he liked what he called

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 crackpot ideas. And he considered this a crackpot idea. This a crackpot idea. And he was willing to

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 support it. I am ever grateful. Let me say that. You know, some would say that a history of science

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 approach to AI, or even just a history or anything like the book that you've written,

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 hasn't been written since. Maybe I'm not familiar. But it's certainly not many.

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 If we think about bigger than just these couple of decades, a few decades, what are the roots

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 of AI? Oh, they go back so far. Yes, of course, there's all the legendary stuff, the

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 Golem and the early robots of the 20th century. But they go back much further than that. If

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 you read Homer, Homer has robots in the Iliad. And a classical scholar was pointing out to me

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 just a few months ago. Well, you said you just read the Odyssey. The Odyssey is full of robots.

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 It is, I said. Yeah, how do you think Odysseus's ship gets from place one place to another? He

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 doesn't have the crew people to do that, the crew men. Yeah, it's magic. It's robots. Oh, I thought.

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 How interesting. So we've had this notion of AI for a long time. And then toward the end of the

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 19th century, the beginning of the 20th century, there were scientists who actually tried to

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 make this happen some way or another, not successfully, they didn't have the technology

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 for it. And of course, Babbage, in the 1850s and 60s, he saw that what he was building was capable

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 of intelligent behavior. And he, when he ran out of funding, the British government finally said,

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 that's enough. He and Lady Lovelace decided, oh, well, why don't we make, you know, why don't we

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 play the ponies with this? He had other ideas for raising money too. But if we actually reach back

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 once again, I think people don't actually really know that robots do appear or ideas of robots.

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 You talk about the Hellenic and the Hebraic points of view. Oh, yes. Can you tell me about each?

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 I defined it this way, the Hellenic point of view is robots are great. You know, they're party help,

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 they help this guy, Hephaestus, this God Hephaestus in his forge. I presume he made them to help him,

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 and so on and so forth. And they welcome the whole idea of robots. The Hebraic view has to do with,

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 I think it's the second commandment, thou shalt not make any graven image. In other words, you

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 better not start imitating humans, because that's just forbidden. It's the second commandment.

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 And a lot of the reaction to artificial intelligence has been a sense that this is

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 this is somehow wicked. This is somehow blasphemous. We shouldn't be going there. Now, you can say,

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 yeah, but there're going to be some downsides. And I say, yes, there are. But blasphemy is not one of

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 them. You know, there's a kind of fear that feels to be almost primal. Is there religious roots to

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 that? Because so much of our society has religious roots. And so there is a feeling of, like you

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 said, blasphemy of creating the other, of creating something, you know, it doesn't have to be artificial

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 intelligence. It's creating life in general. It's the Frankenstein idea. There's the annotated

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 Frankenstein on my coffee table. It's a tremendous novel. It really is just beautifully perceptive.

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 Yes, we do fear this and we have good reason to fear it, but because it can get out of hand.

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 Maybe you can speak to that fear, the psychology, if you thought about it, you know,

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 there's a practical set of fears, concerns in the short term, you can think of, if we actually

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 think about artificial intelligence systems, you can think about bias of discrimination in

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 algorithms or you can think about their social networks, have algorithms that recommend the

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 content you see, thereby these algorithms control the behavior of the masses. There's these concerns.

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 But to me, it feels like the fear that people have is deeper than that. So have you thought about

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 the psychology of it? I think in a superficial way I have. There is this notion that if we

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 produce a machine that can think, it will outthink us and therefore replace us.

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 I guess that's a primal fear of almost kind of a kind of mortality. So around the time you said

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 you worked with Ed Stamford with Ed Faganbaum. So let's look at that one person throughout his

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 history, clearly a key person, one of the many in the history of AI. How has he changed in general

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 around him? How has Stamford changed in the last, how many years are we talking about here?

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 Oh, since 65. So maybe it doesn't have to be about him. It could be bigger, but because he was a

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 key person in expert systems, for example, how are these folks who you've interviewed

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 in the 70s, 79, changed through the decades?

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 In Ed's case, I know him well. We are dear friends. We see each other every month or so.

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 He told me that when machines who think first came out, he really thought all the front

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 matter was kind of baloney. And 10 years later, he said, no, I see what you're getting at. Yes,

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 this is an impulse that has been, this has been a human impulse for thousands of years

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 to create something outside the human cranium that has intelligence.

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 I think it's very hard when you're down at the algorithmic level, and you're just trying to

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 make something work, which is hard enough to step back and think of the big picture.

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 It reminds me of when I was in Santa Fe, I knew a lot of archaeologists, which was a hobby of mine,

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 and I would say, yeah, yeah, well, you can look at the shards and say, oh,

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 this came from this tribe and this came from this trade route and so on. But what about the big

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 picture? And a very distinguished archaeologist said to me, they don't think that way. You do

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 know they're trying to match the shard to the to where it came from. That's, you know, where did

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 this corn, the remainder of this corn come from? Was it grown here? Was it grown elsewhere? And I

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 think this is part of the AI, any scientific field. You're so busy doing the hard work. And it is

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 hard work that you don't step back and say, oh, well, now let's talk about the, you know,

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 the general meaning of all this. Yes. So none of the, even Minsky and McCarthy,

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 they, oh, those guys did. Yeah. The founding fathers did early on or pretty early on. Well,

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 they had, but in a different way from how I looked at it, the two cognitive psychologists,

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 Newell and Simon, they wanted to imagine reforming cognitive psychology so that we would really,

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 really understand the brain. Yeah. Minsky was more speculative. And John McCarthy saw it as,

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 I think I'm doing, doing him right by this. He really saw it as a great boon for human beings to

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 have this technology. And that was reason enough to do it. And he had wonderful, wonderful fables

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 about how if you do the mathematics, you will see that these things are really good for human beings.

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 And if you had a technological objection, he had an answer, a technological answer. But here's how

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 we could get over that. And then blah, blah, blah, blah. And one of his favorite things was

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 what he called the literary problem, which of course, he presented to me several times.

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 That is, everything in literature, there are conventions in literature. One of the conventions

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 is that you have a villain and a hero. And the hero in most literature is human. And the villain

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 in most literature is a machine. And he said, no, that's just not the way it's going to be.

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 But that's the way we're used to it. So when we tell stories about AI, it's always with this

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 paradigm. I thought, yeah, he's right. Looking back, the classics, RUR is certainly the machines

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 trying to overthrow the humans. Frankenstein is different. Frankenstein is a creature.

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 He never has a name. Frankenstein, of course, is the guy who created him, the human Dr. Frankenstein.

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 And this creature wants to be loved, wants to be accepted. And it is only when Frankenstein

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 turns his head, in fact, runs the other way. And the creature is without love

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 that he becomes the monster that he later becomes.

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 So who's the villain in Frankenstein? It's unclear, right?

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 Oh, it is unclear. Yeah.

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 It's really the people who drive him, by driving him away, they bring out the worst.

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 That's right. They give him no human solace. And he is driven away, you're right.

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 He becomes, at one point, the friend of a blind man. And he serves this blind man,

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 and they become very friendly. But when the sighted people of the blind man's family come in,

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 you got a monster here. So it's very didactic in its way. And what I didn't know is that Mary Shelley

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 and Percy Shelley were great readers of the literature surrounding abolition in the United

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 States, the abolition of slavery. And they picked that up wholesale. You are making monsters of

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 these people because you won't give them the respect and love that they deserve.

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 Do you have, if we get philosophical for a second, do you worry that once we create

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 machines that are a little bit more intelligent? Let's look at Roomba, the vacuum cleaner,

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 that this darker part of human nature where we abuse

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 the other, somebody who's different, will come out?

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 I don't worry about it. I could imagine it happening. But I think that what AI has to offer

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 the human race will be so attractive that people will be won over. So you have looked deep into

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 these people, had deep conversations, and it's interesting to get a sense of stories of the

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 way they were thinking and the way it was changed, the way your own thinking about AI has changed.

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 As you mentioned, McCarthy, what about the years at CMU, Carnegie Mellon, with Joe?

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 Sure. Joe was not in AI. He was in algorithmic complexity.

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 Was there always a line between AI and computer science, for example? Is AI its own place of

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 outcasts? Was that the feeling? There was a kind of outcast period for AI.

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 For instance, in 1974, the new field was hardly 10 years old. The new field of computer science

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 was asked by the National Science Foundation, I believe, but it may have been the National

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 Academies, I can't remember, to tell our fellow scientists where computer science is and what

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 it means. And they wanted to leave out AI. And they only agreed to put it in because Don Knuth

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 said, hey, this is important. You can't just leave that out. Really? Don? Don Knuth, yes.

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 I talked to Mr. Nietzsche. Out of all the people. Yes. But you see, an AI person couldn't have made

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 that argument. He wouldn't have been believed, but Knuth was believed. Yes.

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 So Joe Trout worked on the real stuff. Joe was working on algorithmic complexity,

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 but he would say in plain English again and again, the smartest people I know are in AI.

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 Really? Oh, yes. No question. Anyway, Joe loved these guys. What happened was that

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 I guess it was as I started to write machines who think, Herb Simon and I became very close

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 friends. He would walk past our house on Northumberland Street every day after work.

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 And I would just be putting my cover on my typewriter and I would lean out the door and say,

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 Herb, would you like a sherry? And Herb almost always would like a sherry. So he'd stop in

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 and we'd talk for an hour, two hours. My journal says we talked this afternoon for three hours.

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 What was on his mind at the time in terms of on the AI side of things?

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 We didn't talk too much about AI. We talked about other things. Just life.

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 We both love literature and Herb had read Proust in the original French twice all the way through.

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 I can't. I read it in English in translation. So we talked about literature. We talked about

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 languages. We talked about music because he loved music. We talked about art because he was

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 he was actually enough of a painter that he had to give it up because he was afraid it was interfering

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 with his research and so on. So no, it was really just chat chat, but it was very warm.

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 So one summer I said to Herb, you know, my students have all the really interesting

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 conversations. I was teaching at the University of Pittsburgh then in the English department.

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 And, you know, they get to talk about the meaning of life and that kind of thing.

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 And what do I have? I have university meetings where we talk about the photocopying budget and,

27:15.200 --> 27:20.160
 you know, whether the course on romantic poetry should be one semester or two.

27:21.200 --> 27:25.760
 So Herb laughed. He said, yes, I know what you mean. He said, but, you know, you could do something

27:25.760 --> 27:33.920
 about that. Dot, that was his wife, Dot and I used to have a salon at the University of Chicago every

27:33.920 --> 27:42.400
 Sunday night. And we would have essentially an open house. And people knew it wasn't for a small

27:42.400 --> 27:51.440
 talk. It was really for some topic of depth. He said, but my advice would be that you choose

27:51.440 --> 27:59.200
 the topic ahead of time. Fine, I said. So the following, we exchanged mail over the summer.

27:59.200 --> 28:09.120
 That was US post in those days because you didn't have personal email. And I decided I would organize

28:09.120 --> 28:16.880
 it. And there would be eight of us, Alan Nolan, his wife, Herb Simon, and his wife, Dorothea.

28:16.880 --> 28:27.040
 There was a novelist in town, a man named Mark Harris. He had just arrived and his wife, Josephine.

28:27.840 --> 28:33.040
 Mark was most famous then for a novel called Bang the Drum Slowly, which was about baseball.

28:34.160 --> 28:43.360
 And Joe and me, so eight people. And we met monthly and we just sank our teeth into really

28:43.360 --> 28:52.160
 hard topics. And it was great fun. How have your own views around artificial intelligence changed

28:53.200 --> 28:57.520
 in through the process of writing machines who think and afterwards the ripple effects?

28:58.240 --> 29:04.400
 I was a little skeptical that this whole thing would work out. It didn't matter. To me, it was

29:04.400 --> 29:16.160
 so audacious. This whole thing being AI generally. And in some ways, it hasn't worked out the way I

29:16.160 --> 29:25.760
 expected so far. That is to say, there is this wonderful lot of apps, thanks to deep learning

29:25.760 --> 29:35.680
 and so on. But those are algorithmic. And in the part of symbolic processing,

29:36.640 --> 29:45.600
 there is very little yet. And that's a field that lies waiting for industrious graduate students.

29:46.800 --> 29:53.040
 Maybe you can tell me some figures that popped up in your life in the 80s with expert systems,

29:53.040 --> 30:01.840
 where there was the symbolic AI possibilities of what most people think of as AI. If you dream

30:01.840 --> 30:08.000
 of the possibilities of AI, it's really expert systems. And those hit a few walls and there

30:08.000 --> 30:12.960
 were challenges there. And I think, yes, they will reemerge again with some new breakthroughs and so

30:12.960 --> 30:18.640
 on. But what did that feel like, both the possibility and the winter that followed, the

30:18.640 --> 30:25.520
 slowdown in research? This whole thing about AI winter is, to me, a crock.

30:26.160 --> 30:33.200
 It's no winters. Because I look at the basic research that was being done in the 80s, which is

30:33.200 --> 30:39.520
 supposed to be, my God, it was really important. It was laying down things that nobody had thought

30:39.520 --> 30:44.880
 about before. But it was basic research. You couldn't monetize it. Hence the winter.

30:44.880 --> 30:53.680
 Science research goes and fits and starts. It isn't this nice, smooth,

30:54.240 --> 30:59.200
 oh, this follows this, follows this. No, it just doesn't work that way.

30:59.200 --> 31:03.600
 Well, the interesting thing, the way winters happen, it's never the fault of the researchers.

31:04.480 --> 31:11.920
 It's the some source of hype, over promising. Well, no, let me take that back. Sometimes it

31:11.920 --> 31:17.200
 is the fault of the researchers. Sometimes certain researchers might overpromise the

31:17.200 --> 31:23.760
 possibilities. They themselves believe that we're just a few years away, sort of just recently talked

31:23.760 --> 31:30.240
 to Elon Musk and he believes he'll have an autonomous vehicle in a year and he believes it.

31:30.240 --> 31:33.520
 A year? A year, yeah, would have mass deployment of a time.

31:33.520 --> 31:38.640
 For the record, this is 2019 right now. So he's talking 2020.

31:38.640 --> 31:44.800
 To do the impossible, you really have to believe it. And I think what's going to happen when you

31:44.800 --> 31:49.520
 believe it, because there's a lot of really brilliant people around him, is some good stuff

31:49.520 --> 31:54.640
 will come out of it. Some unexpected brilliant breakthroughs will come out of it. When you

31:54.640 --> 31:59.520
 really believe it, when you work that hard. I believe that and I believe autonomous vehicles

31:59.520 --> 32:05.280
 will come. I just don't believe it'll be in a year. I wish. But nevertheless, there's

32:05.280 --> 32:11.680
 autonomous vehicles is a good example. There's a feeling many companies have promised by 2021,

32:11.680 --> 32:18.000
 by 2022 for GM. Basically, every single automotive company has promised they'll

32:18.000 --> 32:22.480
 have autonomous vehicles. So that kind of overpromise is what leads to the winter.

32:23.040 --> 32:28.320
 Because we'll come to those dates, there won't be autonomous vehicles, and there'll be a feeling,

32:28.960 --> 32:34.160
 well, wait a minute, if we took your word at that time, that means we just spent billions of

32:34.160 --> 32:41.600
 dollars, had made no money. And there's a counter response to where everybody gives up on it.

32:41.600 --> 32:49.600
 Sort of intellectually, at every level, the hope just dies. And all that's left is a few basic

32:49.600 --> 32:56.800
 researchers. So you're uncomfortable with some aspects of this idea. Well, it's the difference

32:56.800 --> 33:04.160
 between science and commerce. So you think science, science goes on the way it does?

33:06.480 --> 33:14.800
 Science can really be killed by not getting proper funding or timely funding. I think

33:14.800 --> 33:22.080
 Great Britain was a perfect example of that. The Lighthill report in the 1960s.

33:22.080 --> 33:27.360
 The year essentially said, there's no use of Great Britain putting any money into this.

33:27.360 --> 33:35.600
 It's going nowhere. And this was all about social factions in Great Britain.

33:36.960 --> 33:44.400
 Edinburgh hated Cambridge, and Cambridge hated Manchester, and somebody else can write that

33:44.400 --> 33:53.760
 story. But it really did have a hard effect on research there. Now, they've come roaring back

33:53.760 --> 34:01.360
 with deep mind. But that's one guy and his visionaries around him.

34:01.360 --> 34:08.320
 But just to push on that, it's kind of interesting, you have this dislike of the idea of an AI winter.

34:08.320 --> 34:15.440
 Where's that coming from? Where were you? Oh, because I just don't think it's true.

34:16.560 --> 34:21.360
 There was a particular period of time. It's a romantic notion, certainly.

34:21.360 --> 34:32.960
 Yeah, well, I admire science, perhaps more than I admire commerce. Commerce is fine. Hey,

34:32.960 --> 34:42.960
 you know, we all got to live. But science has a much longer view than commerce,

34:44.080 --> 34:54.000
 and continues almost regardless. It can't continue totally regardless, but it almost

34:54.000 --> 34:59.600
 regardless of what's saleable and what's not, what's monetizable and what's not.

34:59.600 --> 35:05.840
 So the winter is just something that happens on the commerce side, and the science marches.

35:07.200 --> 35:12.560
 That's a beautifully optimistic inspired message. I agree with you. I think

35:13.760 --> 35:19.440
 if we look at the key people that work in AI, they work in key scientists in most disciplines,

35:19.440 --> 35:25.360
 they continue working out of the love for science. You can always scrape up some funding

35:25.360 --> 35:33.120
 to stay alive, and they continue working diligently. But there certainly is a huge

35:33.120 --> 35:39.840
 amount of funding now, and there's a concern on the AI side and deep learning. There's a concern

35:39.840 --> 35:46.160
 that we might, with over promising, hit another slowdown in funding, which does affect the number

35:46.160 --> 35:51.280
 of students, you know, that kind of thing. Yeah, it does. So the kind of ideas you had

35:51.280 --> 35:56.400
 in machines who think, did you continue that curiosity through the decades that followed?

35:56.400 --> 36:04.800
 Yes, I did. And what was your view, historical view of how AI community evolved, the conversations

36:04.800 --> 36:11.520
 about it, the work? Has it persisted the same way from its birth? No, of course not. It's just

36:11.520 --> 36:21.360
 we were just talking. The symbolic AI really kind of dried up and it all became algorithmic.

36:22.400 --> 36:29.520
 I remember a young AI student telling me what he was doing, and I had been away from the field

36:29.520 --> 36:34.080
 long enough. I'd gotten involved with complexity at the Santa Fe Institute.

36:34.080 --> 36:40.960
 I thought, algorithms, yeah, they're in the service of, but they're not the main event.

36:41.680 --> 36:49.200
 No, they became the main event. That surprised me. And we all know the downside of this. We

36:49.200 --> 36:58.240
 all know that if you're using an algorithm to make decisions based on a gazillion human decisions

36:58.240 --> 37:05.040
 baked into it, are all the mistakes that humans make, the bigotries, the short sightedness,

37:06.000 --> 37:14.000
 so on and so on. So you mentioned Santa Fe Institute. So you've written the novel Edge

37:14.000 --> 37:21.200
 of Chaos, but it's inspired by the ideas of complexity, a lot of which have been extensively

37:21.200 --> 37:30.160
 explored at the Santa Fe Institute. It's another fascinating topic of just sort of

37:31.040 --> 37:37.600
 emergent complexity from chaos. Nobody knows how it happens really, but it seems to wear

37:37.600 --> 37:44.160
 all the interesting stuff that does happen. So how do first, not your novel, but just

37:44.160 --> 37:49.520
 complexity in general in the work at Santa Fe fit into the bigger puzzle of the history of AI?

37:49.520 --> 37:53.520
 Or it may be even your personal journey through that.

37:54.480 --> 38:03.040
 One of the last projects I did concerning AI in particular was looking at the work of

38:03.040 --> 38:12.960
 Harold Cohen, the painter. And Harold was deeply involved with AI. He was a painter first.

38:12.960 --> 38:27.600
 And what his project, Aaron, which was a lifelong project, did, was reflect his own cognitive

38:27.600 --> 38:34.800
 processes. Okay. Harold and I, even though I wrote a book about it, we had a lot of friction between

38:34.800 --> 38:44.560
 us. And I went, I thought, this is it, you know, the book died. It was published and fell into a

38:44.560 --> 38:53.040
 ditch. This is it. I'm finished. It's time for me to do something different. By chance,

38:53.040 --> 38:59.280
 this was a sabbatical year for my husband. And we spent two months at the Santa Fe Institute

38:59.280 --> 39:09.200
 and two months at Caltech. And then the spring semester in Munich, Germany. Okay. Those two

39:09.200 --> 39:19.520
 months at the Santa Fe Institute were so restorative for me. And I began to, the institute was very

39:19.520 --> 39:26.240
 small then. It was in some kind of office complex on old Santa Fe trail. Everybody kept their door

39:26.240 --> 39:33.440
 open. So you could crack your head on a problem. And if you finally didn't get it, you could walk

39:33.440 --> 39:42.480
 in to see Stuart Kaufman or any number of people and say, I don't get this. Can you explain?

39:43.680 --> 39:51.120
 And one of the people that I was talking to about complex adaptive systems was Murray Gellemann.

39:51.120 --> 39:58.960
 And I told Murray what Harold Cohen had done. And I said, you know, this sounds to me

39:58.960 --> 40:06.080
 like a complex adaptive system. And he said, yeah, it is. Well, what do you know? Harold's

40:06.080 --> 40:12.560
 Aaron had all these kissing cousins all over the world in science and in economics and so on and

40:12.560 --> 40:21.200
 so forth. I was so relieved. I thought, okay, your instincts are okay. You're doing the right thing. I

40:21.200 --> 40:25.920
 didn't have the vocabulary. And that was one of the things that the Santa Fe Institute gave me.

40:25.920 --> 40:31.040
 If I could have rewritten that book, no, it had just come out. I couldn't rewrite it. I would have

40:31.040 --> 40:37.680
 had a vocabulary to explain what Aaron was doing. Okay. So I got really interested in

40:37.680 --> 40:47.440
 what was going on at the Institute. The people were again, bright and funny and willing to explain

40:47.440 --> 40:54.800
 anything to this amateur. George Cowan, who was then the head of the Institute, said he thought it

40:54.800 --> 41:02.160
 might be a nice idea if I wrote a book about the Institute. And I thought about it. And I had my

41:02.160 --> 41:08.960
 eye on some other project. God knows what. And I said, oh, I'm sorry, George. Yeah, I'd really love

41:08.960 --> 41:13.840
 to do it. But, you know, just not going to work for me at this moment. And he said, oh, too bad.

41:13.840 --> 41:18.560
 I think it would make an interesting book. Well, he was right and I was wrong. I wish I'd done it.

41:18.560 --> 41:24.080
 But that's interesting. I hadn't thought about that, that that was a road not taken that I wish

41:24.080 --> 41:32.400
 I'd taken. Well, you know what? That's just on that point. It's quite brave for you as a writer,

41:32.400 --> 41:39.680
 as sort of coming from a world of literature, the literary thinking and historical thinking. I mean,

41:39.680 --> 41:52.640
 just from that world and bravely talking to quite, I assume, large egos in AI or in complexity and so

41:52.640 --> 42:00.560
 on. How'd you do it? Like, where did you? I mean, I suppose they could be intimidated of you as well,

42:00.560 --> 42:06.160
 because it's two different worlds. I never picked up that anybody was intimidated by me.

42:06.160 --> 42:11.040
 But how were you brave enough? Where did you find the guts to just dumb, dumb luck? I mean,

42:11.680 --> 42:16.160
 this is an interesting rock to turn over. I'm going to write a book about and you know,

42:16.160 --> 42:21.840
 people have enough patience with writers, if they think they're going to end up at a book

42:21.840 --> 42:27.840
 that they let you flail around and so on. It's well, but they also look if the writer has.

42:27.840 --> 42:33.440
 There's like, if there's a sparkle in their eye, if they get it. Yeah, sure. Right. When were you

42:33.440 --> 42:44.480
 at the Santa Fe Institute? The time I'm talking about is 1990. Yeah, 1990, 1991, 1992. But we then,

42:44.480 --> 42:49.920
 because Joe was an external faculty member, we're in Santa Fe every summer, we bought a house there.

42:49.920 --> 42:55.600
 And I didn't have that much to do with the Institute anymore. I was writing my novels,

42:55.600 --> 43:04.320
 I was doing whatever I was doing. But I loved the Institute and I loved

43:06.720 --> 43:12.960
 the, again, the audacity of the ideas. That really appeals to me.

43:12.960 --> 43:22.160
 I think that there's this feeling, much like in great institutes of neuroscience, for example,

43:23.040 --> 43:29.840
 that they're in it for the long game of understanding something fundamental about

43:29.840 --> 43:36.800
 reality and nature. And that's really exciting. So if we start not to look a little bit more recently,

43:36.800 --> 43:49.280
 how AI is really popular today. How is this world, you mentioned algorithmic, but in general,

43:50.080 --> 43:54.320
 is the spirit of the people, the kind of conversations you hear through the grapevine

43:54.320 --> 44:00.160
 and so on, is that different than the roots that you remember? No, the same kind of excitement,

44:00.160 --> 44:07.120
 the same kind of, this is really going to make a difference in the world. And it will, it has.

44:07.120 --> 44:13.280
 A lot of folks, especially young, 20 years old or something, they think we've just found something

44:14.080 --> 44:21.040
 special here. We're going to change the world tomorrow. On a time scale, do you have

44:22.000 --> 44:27.120
 a sense of what, of the time scale at which breakthroughs in AI happen?

44:27.120 --> 44:39.040
 I really don't, because look at deep learning. That was, Jeffrey Hinton came up with the algorithm

44:39.920 --> 44:48.960
 in 86. But it took all these years for the technology to be good enough to actually

44:48.960 --> 44:57.680
 be applicable. So no, I can't predict that at all. I can't, I wouldn't even try.

44:58.320 --> 45:05.440
 Well, let me ask you to, not to try to predict, but to speak to the, I'm sure in the 60s,

45:06.000 --> 45:10.320
 as it continues now, there's people that think, let's call it, we can call it this

45:11.040 --> 45:17.120
 fun word, the singularity. When there's a phase shift, there's some profound feeling where

45:17.120 --> 45:23.040
 we're all really surprised by what's able to be achieved. I'm sure those dreams are there.

45:23.040 --> 45:29.200
 I remember reading quotes in the 60s and those continued. How have your own views,

45:29.200 --> 45:34.960
 maybe if you look back, about the timeline of a singularity changed?

45:37.040 --> 45:45.760
 Well, I'm not a big fan of the singularity as Ray Kurzweil has presented it.

45:45.760 --> 45:52.480
 But how would you define the Ray Kurzweil? How would you, how do you think of singularity in

45:52.480 --> 45:58.880
 those? If I understand Kurzweil's view, it's sort of, there's going to be this moment when

45:58.880 --> 46:06.320
 machines are smarter than humans and, you know, game over. However, the game over is,

46:06.320 --> 46:10.800
 I mean, do they put us on a reservation? Do they, et cetera, et cetera. And

46:10.800 --> 46:19.840
 first of all, machines are smarter than humans in some ways, all over the place. And they have been

46:19.840 --> 46:27.280
 since adding machines were invented. So it's not, it's not going to come like some great

46:27.280 --> 46:34.320
 edible crossroads, you know, where they meet each other and our offspring, Oedipus says,

46:34.320 --> 46:41.040
 you're dead. It's just not going to happen. Yeah. So it's already game over with calculators,

46:41.040 --> 46:47.920
 right? They're already out to do much better at basic arithmetic than us. But, you know,

46:47.920 --> 46:55.840
 there's a human like intelligence. And that's not the ones that destroy us. But, you know,

46:55.840 --> 47:01.520
 somebody that you can have as a, as a friend, you can have deep connections with that kind of

47:01.520 --> 47:07.680
 passing the Turing test and beyond, those kinds of ideas. Have you dreamt of those?

47:07.680 --> 47:08.880
 Oh, yes, yes, yes.

47:08.880 --> 47:10.160
 Those possibilities.

47:10.160 --> 47:17.520
 In a book I wrote with Ed Feigenbaum, there's a little story called the geriatric robot. And

47:18.880 --> 47:24.240
 how I came up with the geriatric robot is a story in itself. But here's, here's what the

47:24.240 --> 47:29.520
 geriatric robot does. It doesn't just clean you up and feed you and will you out into the sun.

47:29.520 --> 47:42.720
 It's great advantages. It listens. It says, tell me again about the great coup of 73.

47:43.520 --> 47:52.080
 Tell me again about how awful or how wonderful your grandchildren are and so on and so forth.

47:53.040 --> 47:59.440
 And it isn't hanging around to inherit your money. It isn't hanging around because it can't get

47:59.440 --> 48:08.320
 any other job. This is its job and so on and so forth. Well, I would love something like that.

48:09.120 --> 48:15.600
 Yeah. I mean, for me, that deeply excites me. So I think there's a lot of us.

48:15.600 --> 48:20.880
 Lex, you got to know it was a joke. I dreamed it up because I needed to talk to college students

48:20.880 --> 48:26.880
 and I needed to give them some idea of what AI might be. And they were rolling in the aisles

48:26.880 --> 48:32.160
 as I elaborated and elaborated and elaborated. When it went into the book,

48:34.320 --> 48:40.240
 they took my hide off in the New York Review of Books. This is just what we've thought about

48:40.240 --> 48:44.400
 these people in AI. They're inhuman. Oh, come on. Get over it.

48:45.120 --> 48:49.120
 Don't you think that's a good thing for the world that AI could potentially...

48:49.120 --> 48:58.800
 Why? I do. Absolutely. And furthermore, I want... I'm pushing 80 now. By the time I need help

48:59.360 --> 49:04.160
 like that, I also want it to roll itself in a corner and shut the fuck up.

49:06.960 --> 49:09.920
 Let me linger on that point. Do you really, though?

49:09.920 --> 49:11.040
 Yeah, I do. Here's what.

49:11.040 --> 49:15.120
 But you wanted to push back a little bit a little.

49:15.120 --> 49:21.520
 But I have watched my friends go through the whole issue around having help in the house.

49:22.480 --> 49:29.760
 And some of them have been very lucky and had fabulous help. And some of them have had people

49:29.760 --> 49:35.120
 in the house who want to keep the television going on all day, who want to talk on their phones all day.

49:35.760 --> 49:38.960
 No. So basically... Just roll yourself in the corner.

49:38.960 --> 49:45.760
 Unfortunately, us humans, when we're assistants, we care... We're still...

49:45.760 --> 49:48.400
 Even when we're assisting others, we care about ourselves more.

49:48.400 --> 49:49.280
 Of course.

49:49.280 --> 49:56.800
 And so you create more frustration. And a robot AI assistant can really optimize

49:57.360 --> 50:03.040
 the experience for you. I was just speaking to the point... You actually bring up a very,

50:03.040 --> 50:07.120
 very good point. But I was speaking to the fact that us humans are a little complicated,

50:07.120 --> 50:14.560
 that we don't necessarily want a perfect servant. I don't... Maybe you disagree with that.

50:14.560 --> 50:21.360
 But there's... I think there's a push and pull with humans.

50:22.240 --> 50:28.080
 A little tension, a little mystery that, of course, that's really difficult for you to get right.

50:28.080 --> 50:35.120
 But I do sense, especially in today with social media, that people are getting more and more

50:35.120 --> 50:42.000
 lonely, even young folks. And sometimes, especially young folks, that loneliness,

50:42.000 --> 50:47.760
 there's a longing for connection. And AI can help alleviate some of that loneliness.

50:48.560 --> 50:52.880
 Some. Just somebody who listens. Like in person.

50:54.640 --> 50:54.960
 That...

50:54.960 --> 50:55.680
 So to speak.

50:56.240 --> 51:00.080
 So to speak, yeah. So to speak.

51:00.080 --> 51:07.120
 Yeah, that to me is really exciting. But so if we look at that level of intelligence,

51:07.120 --> 51:12.560
 which is exceptionally difficult to achieve, actually, as the singularity, or whatever,

51:12.560 --> 51:18.320
 that's the human level bar, that people have dreamt of that too. Touring dreamt of it.

51:19.520 --> 51:26.320
 He had a date timeline. Do you have how of your own timeline evolved on past time?

51:26.320 --> 51:27.520
 I don't even think about it.

51:27.520 --> 51:28.240
 You don't even think?

51:28.240 --> 51:35.520
 No. Just this field has been so full of surprises for me.

51:35.520 --> 51:39.120
 That you're just taking in and see a fun bunch of basic science?

51:39.120 --> 51:45.840
 Yeah, I just can't. Maybe that's because I've been around the field long enough to think,

51:45.840 --> 51:52.240
 you know, don't go that way. Herb Simon was terrible about making these predictions of

51:52.240 --> 51:53.840
 when this and that would happen.

51:53.840 --> 51:54.320
 Right.

51:54.320 --> 51:58.400
 And he was a sensible guy.

51:58.400 --> 52:01.600
 Yeah. And his quotes are often used, right, as a...

52:01.600 --> 52:02.880
 That's a legend, yeah.

52:02.880 --> 52:13.840
 Yeah. Do you have concerns about AI, the existential threats that many people like Elon Musk and

52:13.840 --> 52:16.160
 Sam Harris and others that are thinking about?

52:16.160 --> 52:21.200
 Oh, yeah, yeah. That takes up a half a chapter in my book.

52:21.200 --> 52:27.120
 I call it the male gaze.

52:27.120 --> 52:35.200
 Well, you hear me out. The male gaze is actually a term from film criticism.

52:36.240 --> 52:40.640
 And I'm blocking on the woman who dreamed this up.

52:41.280 --> 52:48.880
 But she pointed out how most movies were made from the male point of view, that women were

52:48.880 --> 52:55.520
 objects, not subjects. They didn't have any agency and so on and so forth.

52:56.080 --> 53:00.560
 So when Elon and his pals, Hawking and so on,

53:00.560 --> 53:05.760
 okay, AI is going to eat our lunch and our dinner and our midnight snack too,

53:06.640 --> 53:11.600
 I thought, what? And I said to Ed Feigenbaum, oh, this is the first guy.

53:11.600 --> 53:14.880
 First, these guys have always been the smartest guy on the block.

53:14.880 --> 53:20.880
 And here comes something that might be smarter. Ooh, let's stamp it out before it takes over.

53:20.880 --> 53:23.360
 And Ed laughed. He said, I didn't think about it that way.

53:24.080 --> 53:30.320
 But I did. I did. And it is the male gaze.

53:32.000 --> 53:37.120
 Okay, suppose these things do have agency. Well, let's wait and see what happens.

53:37.120 --> 53:47.040
 Can we imbue them with ethics? Can we imbue them with a sense of empathy?

53:48.560 --> 53:54.960
 Or are they just going to be, I don't know, we've had centuries of guys like that?

53:55.760 --> 54:03.600
 That's interesting that the ego, the male gaze is immediately threatened.

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 And so you can't think in a patient, calm way of how the tech could evolve.

54:13.280 --> 54:21.520
 Speaking of which, here in 96 book, The Future of Women, I think at the time and now, certainly now,

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 I mean, I'm sorry, maybe at the time, but I'm more cognizant of now, is extremely relevant.

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 And you and Nancy Ramsey talk about four possible futures of women in science and tech.

54:35.120 --> 54:42.400
 So if we look at the decades before and after the book was released, can you tell a history,

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 sorry, of women in science and tech and how it has evolved? How have things changed? Where do we

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 stand? Not enough. They have not changed enough. The way that women are ground down in computing is

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 simply unbelievable. But what are the four possible futures for women in tech from the book?

55:10.800 --> 55:16.720
 What you're really looking at are various aspects of the present. So for each of those,

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 you could say, oh, yeah, we do have backlash. Look at what's happening with abortion and so on and so

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 forth. We have one step forward, one step back. The golden age of equality was the hardest chapter

55:31.280 --> 55:37.040
 to write. And I used something from the Santa Fe Institute, which is the sand pile effect,

55:37.760 --> 55:44.480
 that you drop sand very slowly onto a pile, and it grows and it grows and it grows until

55:44.480 --> 55:56.640
 suddenly it just breaks apart. And in a way, MeToo has done that. That was the last drop of sand

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 that broke everything apart. That was a perfect example of the sand pile effect. And that made

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 me feel good. It didn't change all of society, but it really woke a lot of people up.

56:07.440 --> 56:15.760
 But are you in general optimistic about maybe after MeToo? MeToo is about a very specific kind

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 of thing. Boy, solve that and you'll solve everything. But are you in general optimistic

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 about the future? Yes, I'm a congenital optimistic. I can't help it.

56:28.400 --> 56:35.600
 What about AI? What are your thoughts about the future of AI? Of course, I get asked,

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 what do you worry about? And the one thing I worry about is the things we can't anticipate.

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 There's going to be something out of that field that we will just say,

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 we weren't prepared for that. I am generally optimistic. When I first took up being interested

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 in AI, like most people in the field, more intelligence was like more virtue. What could be

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 bad? And in a way, I still believe that, but I realize that my notion of intelligence has

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 broadened. There are many kinds of intelligence, and we need to imbue our machines with those many

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 kinds. So you've now just finished or in the process of finishing the book, even working on

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 the memoir. How have you changed? I know it's just writing, but how have you changed the process?

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 If you look back, what kind of stuff did it bring up to you that surprised you looking at the entirety

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 of it all? The biggest thing, and it really wasn't a surprise, is how lucky I was, oh my, to be,

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 to have access to the beginning of a scientific field that is going to change the world.

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 How did I luck out? And yes, of course, my view of things has widened a lot.

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 If I can get back to one feminist part of our conversation, without knowing it, it really

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 was subconscious. I wanted AI to succeed because I was so tired of hearing that intelligence was

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 inside the male cranium. And I thought if there was something out there that wasn't a male

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 thinking and doing well, then that would put a lie to this whole notion of intelligence resides

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 in the male cranium. I did not know that until one night, Harold Cohen and I were

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 having a glass of wine, maybe two, and he said, what drew you to AI? And I said, oh, you know,

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 smartest people I knew, great project, blah, blah, blah. And I said, and I wanted something

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 besides male smarts. And it just bubbled up out of me, Lex. It's brilliant, actually. So AI really

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 humbles all of us and humbles the people that need to be humbled the most.

59:37.040 --> 59:43.440
 Let's hope. Oh, wow, that is so beautiful. Pamela, thank you so much for talking to

59:43.440 --> 59:49.440
 us. Oh, it's been a great pleasure. Thank you.