back to indexPamela McCorduck: Machines Who Think and the Early Days of AI | Lex Fridman Podcast #34
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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,
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you know, whether the course on romantic poetry should be one semester or two.
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So Herb laughed. He said, yes, I know what you mean. He said, but, you know, you could do something
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about that. Dot, that was his wife, Dot and I used to have a salon at the University of Chicago every
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Sunday night. And we would have essentially an open house. And people knew it wasn't for a small
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talk. It was really for some topic of depth. He said, but my advice would be that you choose
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the topic ahead of time. Fine, I said. So the following, we exchanged mail over the summer.
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That was US post in those days because you didn't have personal email. And I decided I would organize
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it. And there would be eight of us, Alan Nolan, his wife, Herb Simon, and his wife, Dorothea.
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There was a novelist in town, a man named Mark Harris. He had just arrived and his wife, Josephine.
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Mark was most famous then for a novel called Bang the Drum Slowly, which was about baseball.
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And Joe and me, so eight people. And we met monthly and we just sank our teeth into really
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hard topics. And it was great fun. How have your own views around artificial intelligence changed
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in through the process of writing machines who think and afterwards the ripple effects?
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I was a little skeptical that this whole thing would work out. It didn't matter. To me, it was
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so audacious. This whole thing being AI generally. And in some ways, it hasn't worked out the way I
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expected so far. That is to say, there is this wonderful lot of apps, thanks to deep learning
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and so on. But those are algorithmic. And in the part of symbolic processing,
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there is very little yet. And that's a field that lies waiting for industrious graduate students.
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Maybe you can tell me some figures that popped up in your life in the 80s with expert systems,
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where there was the symbolic AI possibilities of what most people think of as AI. If you dream
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of the possibilities of AI, it's really expert systems. And those hit a few walls and there
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were challenges there. And I think, yes, they will reemerge again with some new breakthroughs and so
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on. But what did that feel like, both the possibility and the winter that followed, the
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slowdown in research? This whole thing about AI winter is, to me, a crock.
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It's no winters. Because I look at the basic research that was being done in the 80s, which is
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supposed to be, my God, it was really important. It was laying down things that nobody had thought
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about before. But it was basic research. You couldn't monetize it. Hence the winter.
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Science research goes and fits and starts. It isn't this nice, smooth,
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oh, this follows this, follows this. No, it just doesn't work that way.
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Well, the interesting thing, the way winters happen, it's never the fault of the researchers.
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It's the some source of hype, over promising. Well, no, let me take that back. Sometimes it
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is the fault of the researchers. Sometimes certain researchers might overpromise the
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possibilities. They themselves believe that we're just a few years away, sort of just recently talked
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to Elon Musk and he believes he'll have an autonomous vehicle in a year and he believes it.
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A year? A year, yeah, would have mass deployment of a time.
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For the record, this is 2019 right now. So he's talking 2020.
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To do the impossible, you really have to believe it. And I think what's going to happen when you
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believe it, because there's a lot of really brilliant people around him, is some good stuff
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will come out of it. Some unexpected brilliant breakthroughs will come out of it. When you
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really believe it, when you work that hard. I believe that and I believe autonomous vehicles
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will come. I just don't believe it'll be in a year. I wish. But nevertheless, there's
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autonomous vehicles is a good example. There's a feeling many companies have promised by 2021,
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by 2022 for GM. Basically, every single automotive company has promised they'll
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have autonomous vehicles. So that kind of overpromise is what leads to the winter.
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Because we'll come to those dates, there won't be autonomous vehicles, and there'll be a feeling,
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well, wait a minute, if we took your word at that time, that means we just spent billions of
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dollars, had made no money. And there's a counter response to where everybody gives up on it.
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Sort of intellectually, at every level, the hope just dies. And all that's left is a few basic
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researchers. So you're uncomfortable with some aspects of this idea. Well, it's the difference
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between science and commerce. So you think science, science goes on the way it does?
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Science can really be killed by not getting proper funding or timely funding. I think
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Great Britain was a perfect example of that. The Lighthill report in the 1960s.
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The year essentially said, there's no use of Great Britain putting any money into this.
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It's going nowhere. And this was all about social factions in Great Britain.
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Edinburgh hated Cambridge, and Cambridge hated Manchester, and somebody else can write that
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story. But it really did have a hard effect on research there. Now, they've come roaring back
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with deep mind. But that's one guy and his visionaries around him.
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But just to push on that, it's kind of interesting, you have this dislike of the idea of an AI winter.
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Where's that coming from? Where were you? Oh, because I just don't think it's true.
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There was a particular period of time. It's a romantic notion, certainly.
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Yeah, well, I admire science, perhaps more than I admire commerce. Commerce is fine. Hey,
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you know, we all got to live. But science has a much longer view than commerce,
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and continues almost regardless. It can't continue totally regardless, but it almost
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regardless of what's saleable and what's not, what's monetizable and what's not.
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So the winter is just something that happens on the commerce side, and the science marches.
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That's a beautifully optimistic inspired message. I agree with you. I think
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if we look at the key people that work in AI, they work in key scientists in most disciplines,
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they continue working out of the love for science. You can always scrape up some funding
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to stay alive, and they continue working diligently. But there certainly is a huge
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amount of funding now, and there's a concern on the AI side and deep learning. There's a concern
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that we might, with over promising, hit another slowdown in funding, which does affect the number
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of students, you know, that kind of thing. Yeah, it does. So the kind of ideas you had
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in machines who think, did you continue that curiosity through the decades that followed?
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Yes, I did. And what was your view, historical view of how AI community evolved, the conversations
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about it, the work? Has it persisted the same way from its birth? No, of course not. It's just
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we were just talking. The symbolic AI really kind of dried up and it all became algorithmic.
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I remember a young AI student telling me what he was doing, and I had been away from the field
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long enough. I'd gotten involved with complexity at the Santa Fe Institute.
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I thought, algorithms, yeah, they're in the service of, but they're not the main event.
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No, they became the main event. That surprised me. And we all know the downside of this. We
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all know that if you're using an algorithm to make decisions based on a gazillion human decisions
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baked into it, are all the mistakes that humans make, the bigotries, the short sightedness,
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so on and so on. So you mentioned Santa Fe Institute. So you've written the novel Edge
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of Chaos, but it's inspired by the ideas of complexity, a lot of which have been extensively
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explored at the Santa Fe Institute. It's another fascinating topic of just sort of
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emergent complexity from chaos. Nobody knows how it happens really, but it seems to wear
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all the interesting stuff that does happen. So how do first, not your novel, but just
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complexity in general in the work at Santa Fe fit into the bigger puzzle of the history of AI?
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Or it may be even your personal journey through that.
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One of the last projects I did concerning AI in particular was looking at the work of
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Harold Cohen, the painter. And Harold was deeply involved with AI. He was a painter first.
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And what his project, Aaron, which was a lifelong project, did, was reflect his own cognitive
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processes. Okay. Harold and I, even though I wrote a book about it, we had a lot of friction between
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us. And I went, I thought, this is it, you know, the book died. It was published and fell into a
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ditch. This is it. I'm finished. It's time for me to do something different. By chance,
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this was a sabbatical year for my husband. And we spent two months at the Santa Fe Institute
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and two months at Caltech. And then the spring semester in Munich, Germany. Okay. Those two
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months at the Santa Fe Institute were so restorative for me. And I began to, the institute was very
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small then. It was in some kind of office complex on old Santa Fe trail. Everybody kept their door
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open. So you could crack your head on a problem. And if you finally didn't get it, you could walk
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in to see Stuart Kaufman or any number of people and say, I don't get this. Can you explain?
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And one of the people that I was talking to about complex adaptive systems was Murray Gellemann.
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And I told Murray what Harold Cohen had done. And I said, you know, this sounds to me
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like a complex adaptive system. And he said, yeah, it is. Well, what do you know? Harold's
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Aaron had all these kissing cousins all over the world in science and in economics and so on and
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so forth. I was so relieved. I thought, okay, your instincts are okay. You're doing the right thing. I
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didn't have the vocabulary. And that was one of the things that the Santa Fe Institute gave me.
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If I could have rewritten that book, no, it had just come out. I couldn't rewrite it. I would have
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had a vocabulary to explain what Aaron was doing. Okay. So I got really interested in
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what was going on at the Institute. The people were again, bright and funny and willing to explain
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anything to this amateur. George Cowan, who was then the head of the Institute, said he thought it
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might be a nice idea if I wrote a book about the Institute. And I thought about it. And I had my
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eye on some other project. God knows what. And I said, oh, I'm sorry, George. Yeah, I'd really love
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to do it. But, you know, just not going to work for me at this moment. And he said, oh, too bad.
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I think it would make an interesting book. Well, he was right and I was wrong. I wish I'd done it.
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But that's interesting. I hadn't thought about that, that that was a road not taken that I wish
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I'd taken. Well, you know what? That's just on that point. It's quite brave for you as a writer,
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as sort of coming from a world of literature, the literary thinking and historical thinking. I mean,
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just from that world and bravely talking to quite, I assume, large egos in AI or in complexity and so
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on. How'd you do it? Like, where did you? I mean, I suppose they could be intimidated of you as well,
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because it's two different worlds. I never picked up that anybody was intimidated by me.
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But how were you brave enough? Where did you find the guts to just dumb, dumb luck? I mean,
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this is an interesting rock to turn over. I'm going to write a book about and you know,
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people have enough patience with writers, if they think they're going to end up at a book
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that they let you flail around and so on. It's well, but they also look if the writer has.
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There's like, if there's a sparkle in their eye, if they get it. Yeah, sure. Right. When were you
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at the Santa Fe Institute? The time I'm talking about is 1990. Yeah, 1990, 1991, 1992. But we then,
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because Joe was an external faculty member, we're in Santa Fe every summer, we bought a house there.
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And I didn't have that much to do with the Institute anymore. I was writing my novels,
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I was doing whatever I was doing. But I loved the Institute and I loved
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the, again, the audacity of the ideas. That really appeals to me.
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I think that there's this feeling, much like in great institutes of neuroscience, for example,
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that they're in it for the long game of understanding something fundamental about
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reality and nature. And that's really exciting. So if we start not to look a little bit more recently,
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how AI is really popular today. How is this world, you mentioned algorithmic, but in general,
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is the spirit of the people, the kind of conversations you hear through the grapevine
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and so on, is that different than the roots that you remember? No, the same kind of excitement,
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the same kind of, this is really going to make a difference in the world. And it will, it has.
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A lot of folks, especially young, 20 years old or something, they think we've just found something
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special here. We're going to change the world tomorrow. On a time scale, do you have
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a sense of what, of the time scale at which breakthroughs in AI happen?
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I really don't, because look at deep learning. That was, Jeffrey Hinton came up with the algorithm
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in 86. But it took all these years for the technology to be good enough to actually
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be applicable. So no, I can't predict that at all. I can't, I wouldn't even try.
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Well, let me ask you to, not to try to predict, but to speak to the, I'm sure in the 60s,
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as it continues now, there's people that think, let's call it, we can call it this
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fun word, the singularity. When there's a phase shift, there's some profound feeling where
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we're all really surprised by what's able to be achieved. I'm sure those dreams are there.
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I remember reading quotes in the 60s and those continued. How have your own views,
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maybe if you look back, about the timeline of a singularity changed?
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Well, I'm not a big fan of the singularity as Ray Kurzweil has presented it.
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But how would you define the Ray Kurzweil? How would you, how do you think of singularity in
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those? If I understand Kurzweil's view, it's sort of, there's going to be this moment when
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machines are smarter than humans and, you know, game over. However, the game over is,
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I mean, do they put us on a reservation? Do they, et cetera, et cetera. And
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first of all, machines are smarter than humans in some ways, all over the place. And they have been
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since adding machines were invented. So it's not, it's not going to come like some great
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edible crossroads, you know, where they meet each other and our offspring, Oedipus says,
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you're dead. It's just not going to happen. Yeah. So it's already game over with calculators,
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right? They're already out to do much better at basic arithmetic than us. But, you know,
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there's a human like intelligence. And that's not the ones that destroy us. But, you know,
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somebody that you can have as a, as a friend, you can have deep connections with that kind of
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passing the Turing test and beyond, those kinds of ideas. Have you dreamt of those?
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Oh, yes, yes, yes.
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Those possibilities.
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In a book I wrote with Ed Feigenbaum, there's a little story called the geriatric robot. And
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how I came up with the geriatric robot is a story in itself. But here's, here's what the
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geriatric robot does. It doesn't just clean you up and feed you and will you out into the sun.
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It's great advantages. It listens. It says, tell me again about the great coup of 73.
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Tell me again about how awful or how wonderful your grandchildren are and so on and so forth.
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And it isn't hanging around to inherit your money. It isn't hanging around because it can't get
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any other job. This is its job and so on and so forth. Well, I would love something like that.
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Yeah. I mean, for me, that deeply excites me. So I think there's a lot of us.
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Lex, you got to know it was a joke. I dreamed it up because I needed to talk to college students
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and I needed to give them some idea of what AI might be. And they were rolling in the aisles
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as I elaborated and elaborated and elaborated. When it went into the book,
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they took my hide off in the New York Review of Books. This is just what we've thought about
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these people in AI. They're inhuman. Oh, come on. Get over it.
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Don't you think that's a good thing for the world that AI could potentially...
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Why? I do. Absolutely. And furthermore, I want... I'm pushing 80 now. By the time I need help
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like that, I also want it to roll itself in a corner and shut the fuck up.
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Let me linger on that point. Do you really, though?
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Yeah, I do. Here's what.
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But you wanted to push back a little bit a little.
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But I have watched my friends go through the whole issue around having help in the house.
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And some of them have been very lucky and had fabulous help. And some of them have had people
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in the house who want to keep the television going on all day, who want to talk on their phones all day.
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No. So basically... Just roll yourself in the corner.
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Unfortunately, us humans, when we're assistants, we care... We're still...
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Even when we're assisting others, we care about ourselves more.
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And so you create more frustration. And a robot AI assistant can really optimize
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the experience for you. I was just speaking to the point... You actually bring up a very,
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very good point. But I was speaking to the fact that us humans are a little complicated,
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that we don't necessarily want a perfect servant. I don't... Maybe you disagree with that.
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But there's... I think there's a push and pull with humans.
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A little tension, a little mystery that, of course, that's really difficult for you to get right.
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But I do sense, especially in today with social media, that people are getting more and more
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lonely, even young folks. And sometimes, especially young folks, that loneliness,
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there's a longing for connection. And AI can help alleviate some of that loneliness.
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Some. Just somebody who listens. Like in person.
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So to speak, yeah. So to speak.
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Yeah, that to me is really exciting. But so if we look at that level of intelligence,
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which is exceptionally difficult to achieve, actually, as the singularity, or whatever,
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that's the human level bar, that people have dreamt of that too. Touring dreamt of it.
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He had a date timeline. Do you have how of your own timeline evolved on past time?
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I don't even think about it.
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You don't even think?
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No. Just this field has been so full of surprises for me.
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That you're just taking in and see a fun bunch of basic science?
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Yeah, I just can't. Maybe that's because I've been around the field long enough to think,
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you know, don't go that way. Herb Simon was terrible about making these predictions of
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when this and that would happen.
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And he was a sensible guy.
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Yeah. And his quotes are often used, right, as a...
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That's a legend, yeah.
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Yeah. Do you have concerns about AI, the existential threats that many people like Elon Musk and
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Sam Harris and others that are thinking about?
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Oh, yeah, yeah. That takes up a half a chapter in my book.
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I call it the male gaze.
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Well, you hear me out. The male gaze is actually a term from film criticism.
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And I'm blocking on the woman who dreamed this up.
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But she pointed out how most movies were made from the male point of view, that women were
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objects, not subjects. They didn't have any agency and so on and so forth.
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So when Elon and his pals, Hawking and so on,
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okay, AI is going to eat our lunch and our dinner and our midnight snack too,
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I thought, what? And I said to Ed Feigenbaum, oh, this is the first guy.
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First, these guys have always been the smartest guy on the block.
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And here comes something that might be smarter. Ooh, let's stamp it out before it takes over.
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And Ed laughed. He said, I didn't think about it that way.
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But I did. I did. And it is the male gaze.
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Okay, suppose these things do have agency. Well, let's wait and see what happens.
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Can we imbue them with ethics? Can we imbue them with a sense of empathy?
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Or are they just going to be, I don't know, we've had centuries of guys like that?
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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.
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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.
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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?
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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
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to write. And I used something from the Santa Fe Institute, which is the sand pile effect,
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that you drop sand very slowly onto a pile, and it grows and it grows and it grows until
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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.
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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.
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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.
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Let's hope. Oh, wow, that is so beautiful. Pamela, thank you so much for talking to
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us. Oh, it's been a great pleasure. Thank you.