Pamela McCorduck: Machines Who Think and the Early Days of AI
Guests: Pamela McCorduck
Full transcript · Read the full conversation in order, with its original context.
§01Transcript · 00:00–23:42
The following is a conversation with Pamela McCordick. She's an author who has written on
the history and the philosophical significance of artificial intelligence. Her books include
Machines Who Think in 1979, The Fifth Generation in 1983 with Ed Feigenbaum, who's considered to
be the father of expert systems, The Edge of Chaos that features women, and many more books.
I came across her work in an unusual way by stumbling in a quote from Machines Who Think
that is something like, artificial intelligence began with the ancient wish to forge the gods.
That was a beautiful way to draw a connecting line between our societal relationship with AI
from the grounded day to day science, math and engineering, to popular stories and science
fiction and myths of automatons that go back for centuries. Through her literary work,
she has spent a lot of time with the seminal figures of artificial intelligence, including
the founding fathers of AI from the 1956 Dartmouth summer workshop where the field was launched.
I reached out to Pamela for a conversation in hopes of getting a sense of what those early
days were like, and how their dreams continue to reverberate through the work of our community
today. I often don't know where the conversation may take us, but I jump in and see. Having no
constraints, rules, or goals is a wonderful way to discover new ideas. This is the Artificial
Intelligence Podcast. If you enjoy it, subscribe on YouTube, give it five stars on iTunes,
support it on Patreon, or simply connect with me on Twitter, at Lex Friedman, spelled F R I D M
A N. And now, here's my conversation with Pamela McCordick. In 1979, your book Machines Who Think
was published. In it, you interview some of the early AI pioneers and explore the idea that
AI was born not out of maybe math and computer science, but out of myth and legend. So, tell me
if you could the story of how you first arrived at the book, the journey of beginning to write it.
I had been a novelist. I'd published two novels, and I was sitting under the portal at Stanford
one day, the house we were renting for the summer. And I thought, I should write a novel about these
weird people in AI, I know. And then I thought, ah, don't write a novel, write a history. Simple.
Just go around, interview them, splice it together, voila, instant book. Ha, ha, ha. It was
much harder than that. But nobody else was doing it. And so, I thought, well, this is a great
opportunity. And there were people who, John McCarthy, for example, thought it was a nutty
idea. The field had not evolved yet, so on. And he had some mathematical thing he thought I should
write instead. And I said, no, John, I am not a woman in search of a project. This is what I want
to do. I hope you'll cooperate. And he said, oh, mutter, mutter, well, okay, it's your time.
What was the pitch for the, I mean, such a young field at that point. How do you write
a personal history of a field that's so young? I said, this is wonderful. The founders of the
field are alive and kicking and able to talk about what they're doing. Did they sound or feel like
founders at the time? Did they know that they have founded something?
Oh, yeah. They knew what they were doing was very important. Very. What I now see in retrospect
is that they were at the height of their research careers. And it's humbling to me that they took
time out from all the things that they had to do as a consequence of being there. And to talk to
this woman who said, I think I'm going to write a book about you. No, it was amazing. Just amazing.
So who stands out to you? Maybe looking 63 years ago, the Dartmouth conference,
so Marvin Minsky was there, McCarthy was there, Claude Shannon, Alan Newell, Herb Simon,
some of the folks you've mentioned. Then there's other characters, right? One of your coauthors
He wasn't at Dartmouth.
He wasn't at Dartmouth.
No. He was, I think, an undergraduate then.
And of course, Joe Traub. All of these are players, not at Dartmouth, but in that era.
Right.
CMU and so on. So who are the characters, if you could paint a picture, that stand out to you
from memory? Those people you've interviewed and maybe not, people that were just in the
In the atmosphere.
In the atmosphere.
Of course, the four founding fathers were extraordinary guys. They really were.
Who are the founding fathers?
Alan Newell, Herbert Simon, Marvin Minsky, John McCarthy. They were the four who were not only
at the Dartmouth conference, but Newell and Simon arrived there with a working program
called The Logic Theorist. Everybody else had great ideas about how they might do it, but
But they weren't going to do it yet.
And you mentioned Joe Traub, my husband. I was immersed in AI before I met Joe
because I had been Ed Feigenbaum's assistant at Stanford. And before that,
I had worked on a book edited by Feigenbaum and Julian Feldman called Computers and Thought.
It was the first textbook of readings of AI. And they only did it because they were trying to teach
AI to people at Berkeley. And there was nothing, you'd have to send them to this journal and that
journal. This was not the internet where you could go look at an article. So I was fascinated from
the get go by AI. I was an English major. What did I know? And yet I was fascinated. And that's
why you saw that historical, that literary background, which I think is very much a part
of the continuum of AI, that AI grew out of that same impulse. That traditional, what was,
what drew you to AI? How did you even think of it back then? What was the possibilities,
the dreams? What was interesting to you? The idea of intelligence outside the human cranium,
this was a phenomenal idea. And even when I finished Machines Who Think,
I didn't know if they were going to succeed. In fact, the final chapter is very wishy washy,
frankly. Succeed, the field did. Yeah. So was there the idea that AI began with the wish to
forge the gods? So the spiritual component that we crave to create this other thing greater than
ourselves. For those guys, I don't think so. Newell and Simon were cognitive psychologists.
What they wanted was to simulate aspects of human intelligence,
and they found they could do it on the computer. Minsky just thought it was a really cool thing
to do. Likewise, McCarthy. McCarthy had got the idea in 1949 when he was a Caltech student.
And he listened to somebody's lecture. It's in my book. I forget who it was. And he thought,
oh, that would be fun to do. How do we do that? And he took a very mathematical approach.
Minsky was hybrid, and Newell and Simon were very much cognitive psychology. How can we simulate
various things about human cognition? What happened over the many years is, of course,
our definition of intelligence expanded tremendously. These days, biologists are
comfortable talking about the intelligence of the cell, the intelligence of the brain,
not just human brain, but the intelligence of any kind of brain. Cephalopods, I mean, an octopus is
really intelligent by any amount. We wouldn't have thought of that in the 60s, even the 70s.
So all these things have worked in. And I did hear one behavioral primatologist, Franz De Waal,
say, AI taught us the questions to ask. Yeah, this is what happens, right? When you try to build it,
is when you start to actually ask questions. It puts a mirror to ourselves. Yeah, right. So you
were there in the middle of it. It seems like not many people were asking the questions that
you were, or just trying to look at this field the way you were. I was so low. When I went to
get funding for this because I needed somebody to transcribe the interviews and I needed travel
expenses, I went to everything you could think of, the NSF, the DARPA. There was an Air Force
place that doled out money. And each of them said, well, that's a very interesting idea.
But we'll think about it. And the National Science Foundation actually said to me in plain English,
hey, you're only a writer. You're not a historian of science. And I said, yeah, that's true. But
the historians of science will be crawling all over this field. I'm writing for the general
audience, so I thought. And they still wouldn't budge. I finally got a private grant without
knowing who it was from, from Ed Fredkin at MIT. He was a wealthy man, and he liked what he called
crackpot ideas. And he considered this a crackpot idea, and he was willing to support it. I am ever
grateful, let me say that. Some would say that a history of science approach to AI, or even just a
history, or anything like the book that you've written, hasn't been written since. Maybe I'm
not familiar, but it's certainly not many. If we think about bigger than just these couple of
decades, few decades, what are the roots of AI? Oh, they go back so far. Yes, of course, there's
all the legendary stuff, the Golem and the early robots of the 20th century. But they go back much
further than that. If you read Homer, Homer has robots in the Iliad. And a classical scholar was
pointing out to me just a few months ago, well, you said you just read the Odyssey. The Odyssey
is full of robots. It is, I said? Yeah. How do you think Odysseus's ship gets from one place to
another? He doesn't have the crew people to do that, the crewmen. Yeah, it's magic. It's robots.
Oh, I thought, how interesting. So we've had this notion of AI for a long time. And then toward the
end of the 19th century, the beginning of the 20th century, there were scientists who actually
tried to make this happen some way or another, not successfully. They didn't have the technology for
it. And of course, Babbage in the 1850s and 60s, he saw that what he was building was capable of
intelligent behavior. And when he ran out of funding, the British government finally said,
that's enough. He and Lady Lovelace decided, oh, well, why don't we play the ponies with this? He
had other ideas for raising money too. But if we actually reach back once again, I think people
don't actually really know that robots do appear and ideas of robots. You talk about the Hellenic
and the Hebraic points of view. Oh, yes. Can you tell me about each? I defined it this way. The
Hellenic point of view is robots are great. They are party help. They help this guy Hephaestus,
this god Hephaestus in his forge. I presume he made them to help him and so on and so forth.
And they welcome the whole idea of robots. The Hebraic view has to do with, I think it's the
second commandment, thou shalt not make any graven image. In other words, you better not
start imitating humans because that's just forbidden. It's the second commandment. And
a lot of the reaction to artificial intelligence has been a sense that this is somehow wicked,
this is somehow blasphemous. We shouldn't be going there. Now, you can say, yeah, but there are going
to be some downsides. And I say, yes, there are, but blasphemy is not one of them.
You know, there is a kind of fear that feels to be almost primal. Is there religious roots to that?
Because so much of our society has religious roots. And so there is a feeling of, like you
said, blasphemy of creating the other, of creating something, you know, it doesn't have to be
artificial intelligence. It's creating life in general. It's the Frankenstein idea.
There's the annotated Frankenstein on my coffee table. It's a tremendous novel. It really is just
beautifully perceptive. Yes, we do fear this and we have good reason to fear it,
but because it can get out of hand. Maybe you can speak to that fear,
the psychology, if you've thought about it. You know, there's a practical set of fears,
concerns in the short term. You can think if we actually think about artificial intelligence
systems, you can think about bias of discrimination in algorithms. You can think about their social
networks have algorithms that recommend the content you see, thereby these algorithms control
the behavior of the masses. There's these concerns. But to me, it feels like the fear
that people have is deeper than that. So have you thought about the psychology of it?
I think in a superficial way I have. There is this notion that if we produce a machine that
can think, it will outthink us and therefore replace us.
I guess that's a primal fear of almost kind of a kind of mortality. So around the time you said
you worked at Stanford with Ed Feigenbaum. So let's look at that one person. Throughout his
history, clearly a key person, one of the many in the history of AI. How has he changed in general
around him? How has Stanford changed in the last, how many years are we talking about here?
Oh, since 65.
65. So maybe it doesn't have to be about him. It could be bigger. But because he was a key
person in expert systems, for example, how is that, how are these folks who you've interviewed in the
70s, 79 changed through the decades?
In Ed's case, I know him well. We are dear friends. We see each other every month or so. He told me
that when Machines Who Think first came out, he really thought all the front matter was kind of
bologna. And 10 years later, he said, no, I see what you're getting at. Yes, this is an impulse
that has been a human impulse for thousands of years to create something outside the human
cranium that has intelligence. I think it's very hard when you're down at the algorithmic level,
and you're just trying to make something work, which is hard enough to step back and think of
the big picture. It reminds me of when I was in Santa Fe, I knew a lot of archaeologists,
which was a hobby of mine. And I would say, yeah, yeah, well, you can look at the shards and say,
oh, this came from this tribe and this came from this trade route and so on. But what about the big
picture? And a very distinguished archaeologist said to me, they don't think that way. No,
they're trying to match the shard to where it came from. Where did the remainder of this corn
come from? Was it grown here? Was it grown elsewhere? And I think this is part of any
scientific field. You're so busy doing the hard work, and it is hard work, that you don't step
back and say, oh, well, now let's talk about the general meaning of all this. Yes.
So none of the even Minsky and McCarthy, they...
Oh, those guys did. Yeah. The founding fathers did.
Early on or later?
Pretty early on. But in a different way from how I looked at it. The two cognitive psychologists,
Newell and Simon, they wanted to imagine reforming cognitive psychology so that we would really,
really understand the brain. Minsky was more speculative. And John McCarthy saw it as,
I think I'm doing him right by this, he really saw it as a great boon for human beings to have
this technology. And that was reason enough to do it. And he had wonderful, wonderful
fables about how if you do the mathematics, you will see that these things are really good for
human beings. And if you had a technological objection, he had an answer, a technological
answer. But here's how we could get over that and then blah, blah, blah. And one of his favorite things
was what he called the literary problem, which of course he presented to me several times.
That is everything in literature, there are conventions in literature. One of the conventions
is that you have a villain and a hero. And the hero in most literature is human,
and the villain in most literature is a machine. And he said, that's just not the way it's going
to be. But that's the way we're used to it. So when we tell stories about AI, it's always
with this paradigm. I thought, yeah, he's right. Looking back, the classics RUR is certainly the
machines trying to overthrow the humans. Frankenstein is different. Frankenstein is
a creature. He never has a name. Frankenstein, of course, is the guy who created him, the human,
Dr. Frankenstein. This creature wants to be loved, wants to be accepted. And it is only when
Frankenstein turns his head, in fact, runs the other way. And the creature is without love,
that he becomes the monster that he later becomes.
So who's the villain in Frankenstein? It's unclear, right?
Oh, it is unclear, yeah.
It's really the people who drive him. By driving him away, they bring out the worst.
That's right. They give him no human solace. And he is driven away, you're right.
He becomes, at one point, the friend of a blind man. And he serves this blind man,
and they become very friendly. But when the sighted people of the blind man's family come in,
ah, you've got a monster here. So it's very didactic in its way. And what I didn't know
is that Mary Shelley and Percy Shelley were great readers of the literature surrounding abolition
in the United States, the abolition of slavery. And they picked that up wholesale. You are making
monsters of these people because you won't give them the respect and love that they deserve.
Do you have, if we get philosophical for a second, do you worry that once we create
machines that are a little bit more intelligent, let's look at Roomba, the vacuums, the cleaner,
that this darker part of human nature where we abuse the other, the somebody who's different,
will come out?
I don't worry about it. I could imagine it happening. But I think that what AI has to offer
the human race will be so attractive that people will be won over.
So you have looked deep into these people, had deep conversations, and it's interesting to get
a sense of stories of the way they were thinking and the way it was changed, the way your own
§02Transcript · 23:42–46:21
thinking about AI has changed. So you mentioned McCarthy. What about the years at CMU, Carnegie
Mellon, with Joe? Sure. Joe was not in AI. He was in algorithmic complexity.
Was there always a line between AI and computer science, for example?
Is AI its own place of outcasts? Was that the feeling?
There was a kind of outcast period for AI. For instance, in 1974, the new field was hardly 10
years old. The new field of computer science was asked by the National Science Foundation,
I believe, but it may have been the National Academies, I can't remember,
to tell your fellow scientists where computer science is and what it means.
And they wanted to leave out AI. And they only agreed to put it in because Don Knuth said,
hey, this is important. You can't just leave that out.
Really? Don, dude?
Don Knuth, yes.
I talked to him recently, too. Out of all the people.
Yes. But you see, an AI person couldn't have made that argument. He wouldn't have been believed.
But Knuth was believed. Yes.
So Joe Traub worked on the real stuff.
Joe was working on algorithmic complexity. But he would say in plain English again and again,
the smartest people I know are in AI.
Really?
Oh, yes. No question. Anyway, Joe loved these guys. What happened was that I guess it was
as I started to write Machines Who Think, Herb Simon and I became very close friends.
He would walk past our house on Northumberland Street every day after work. And I would just
be putting my cover on my typewriter. And I would lean out the door and say,
Herb, would you like a sherry? And Herb almost always would like a sherry. So he'd stop in
and we'd talk for an hour, two hours. My journal says we talked this afternoon for three hours.
What was on his mind at the time in terms of on the AI side of things?
Oh, we didn't talk too much about AI. We talked about other things.
Just life.
We both love literature. And Herb had read Proust in the original French twice all the
way through. I can't. I've read it in English in translation. So we talked about literature.
We talked about languages. We talked about music because he loved music. We talked about
art because he was actually enough of a painter that he had to give it up because he was afraid
it was interfering with his research and so on. So no, it was really just chat, chat.
But it was very warm. So one summer I said to Herb, my students have all the really
interesting conversations. I was teaching at the University of Pittsburgh then in the English
department. They get to talk about the meaning of life and that kind of thing. And what do I have?
I have university meetings where we talk about the photocopying budget and whether the course
on romantic poetry should be one semester or two. So Herb laughed. He said, yes, I know what you
mean. He said, but you could do something about that. Dot, that was his wife, Dot and I used to
have a salon at the University of Chicago every Sunday night. And we would have essentially an
open house and people knew. It wasn't for a small talk. It was really for some topic of
depth. He said, but my advice would be that you choose the topic ahead of time. Fine, I said.
So we exchanged mail over the summer. That was US Post in those days because
you didn't have personal email. And I decided I would organize it and there would be eight of us,
Alan Noland, his wife, Herb Simon and his wife Dorothea. There was a novelist in town,
a man named Mark Harris. He had just arrived and his wife Josephine. Mark was most famous then for
a novel called Bang the Drum Slowly, which was about baseball. And Joe and me, so eight people.
And we met monthly and we just sank our teeth into really hard topics and it was great fun.
TK How have your own views around artificial intelligence changed
through the process of writing Machines Who Think and afterwards, the ripple effects?
RL I was a little skeptical that this whole thing would work out. It didn't matter. To me,
it was so audacious. AI generally. And in some ways, it hasn't worked out the way I expected
so far. That is to say, there's this wonderful lot of apps, thanks to deep learning and so on.
But those are algorithmic. And in the part of symbolic processing, there's very little yet.
And that's a field that lies waiting for industrious graduate students.
TK Maybe you can tell me some figures that popped up in your life in the 80s with expert systems
where there was the symbolic AI possibilities of what most people think of as AI,
if you dream of the possibilities of AI, it's really expert systems. And those hit a few walls
and there was challenges there. And I think, yes, they will reemerge again with some new
breakthroughs and so on. But what did that feel like, both the possibility and the winter that
followed the slowdown in research? BG Ah, you know, this whole thing about AI winter is to me
a crock. TK Snow winters.
BG Because I look at the basic research that was being done in the 80s, which is supposed to be,
my God, it was really important. It was laying down things that nobody had thought about before,
but it was basic research. You couldn't monetize it. Hence the winter.
TK That's the winter. BG You know, research,
scientific research goes and fits and starts. It isn't this nice smooth,
oh, this follows this follows this. No, it just doesn't work that way.
TK The interesting thing, the way winters happen, it's never the fault of the researchers.
It's the some source of hype over promising. Well, no, let me take that back. Sometimes it
is the fault of the researchers. Sometimes certain researchers might over promise the
possibilities. They themselves believe that we're just a few years away. Sort of just recently
talked to Elon Musk and he believes he'll have an autonomous vehicle, will have autonomous vehicles
in a year. And he believes it. BG A year?
TK A year. Yeah. With mass deployment of a time.
BG For the record, this is 2019 right now. So he's talking 2020.
TK To do the impossible, you really have to believe it. And I think what's going to happen
when you believe it, because there's a lot of really brilliant people around him,
is some good stuff will come out of it. Some unexpected brilliant breakthroughs will come out
of it when you really believe it, when you work that hard. BG I believe that. And I believe
autonomous vehicles will come. I just don't believe it'll be in a year. I wish.
TK But nevertheless, there's, autonomous vehicles is a good example. There's a feeling
many companies have promised by 2021, by 2022, Ford, GM, basically every single automotive
company has promised they'll have autonomous vehicles. So that kind of over promise is what
leads to the winter. Because we'll come to those dates, there won't be autonomous vehicles.
BG And there'll be a feeling, well, wait a minute, if we took your word at that time,
that means we just spent billions of dollars, had made no money, and there's a counter response to
where everybody gives up on it. Sort of intellectually, at every level, the hope just
dies. And all that's left is a few basic researchers. So you're uncomfortable with
some aspects of this idea. TK Well, it's the difference between science and commerce.
BG So you think science goes on the way it does?
TK Oh, science can really be killed by not getting proper funding or timely funding.
I think Great Britain was a perfect example of that. The Lighthill report in,
I can't remember the year, essentially said, there's no use Great Britain putting any money
into this, it's going nowhere. And this was all about social factions in Great Britain.
Edinburgh hated Cambridge and Cambridge hated Manchester. Somebody else can write that story.
But it really did have a hard effect on research there. Now, they've come roaring back with Deep
Mind. But that's one guy and his visionaries around him. BG But just to push on that,
it's kind of interesting. You have this dislike of the idea of an AI winter.
Where's that coming from? Where were you? TK Oh, because I just don't think it's true.
BG There was a particular period of time. It's a romantic notion, certainly.
TK Yeah, well. No, I admire science, perhaps more than I admire commerce. Commerce is fine. Hey,
you know, we all gotta live. But science has a much longer view than commerce and continues
almost regardless. It can't continue totally regardless, but almost regardless of what's
saleable and what's not, what's monetizable and what's not. BG So the winter is just something
that happens on the commerce side, and the science marches. That's a beautifully optimistic
and inspiring message. I agree with you. I think if we look at the key people that work in AI,
that work in key scientists in most disciplines, they continue working out of the love for science.
You can always scrape up some funding to stay alive, and they continue working diligently.
But there certainly is a huge amount of funding now, and there's a concern on the AI side and
deep learning. There's a concern that we might, with over promising, hit another slowdown in
funding, which does affect the number of students, you know, that kind of thing.
RG Yeah, it does. BG So the kind of ideas you had in Machines Who Think,
did you continue that curiosity through the decades that followed?
RG Yes, I did. BG And what was your view, historical view of how AI community evolved,
the conversations about it, the work? Has it persisted the same way from its birth?
RG No, of course not. It's just as we were just talking, the symbolic AI really kind of dried up
and it all became algorithmic. I remember a young AI student telling me what he was doing,
and I had been away from the field long enough. I'd gotten involved with complexity at the Santa
Fe Institute. I thought, algorithms, yeah, they're in the service of, but they're not the main event.
No, they became the main event. That surprised me. And we all know the downside of this. We all
know that if you're using an algorithm to make decisions based on a gazillion human decisions,
baked into it are all the mistakes that humans make, the bigotries, the short sightedness,
and so on and so on. BG So you mentioned Santa Fe Institute. So you've written the novel
Edge of Chaos, but it's inspired by the ideas of complexity, a lot of which have been extensively
explored at the Santa Fe Institute. It's another fascinating topic, just sort of emergent
complexity from chaos. Nobody knows how it happens really, but it seems to where all the interesting
stuff does happen. So how did first, not your novel, but just complexity in general and the
work at Santa Fe, fit into the bigger puzzle of the history of AI? Or maybe even your personal
journey through that? RG One of the last projects I did
concerning AI in particular was looking at the work of Harold Cohen, the painter. And Harold was
deeply involved with AI. He was a painter first. And what his project, ARIN, which was a lifelong
project, did was reflect his own cognitive processes. Okay. Harold and I, even though I wrote
a book about it, we had a lot of friction between us. And I went, I thought, this is it. The book
died. It was published and fell into a ditch. This is it. I'm finished. It's time for me to
do something different. By chance, this was a sabbatical year for my husband. And we spent two
months at the Santa Fe Institute and two months at Caltech. And then the spring semester in Munich,
Germany. Okay. Those two months at the Santa Fe Institute were so restorative for me. And I began
to, the Institute was very small then. It was in some kind of office complex on old Santa Fe trail.
Everybody kept their door open. So you could crack your head on a problem. And if you finally didn't
get it, you could walk in to see Stuart Kaufman or any number of people and say, I don't get this.
Can you explain? And one of the people that I was talking to about complex adaptive systems
was Murray Gelman. And I told Murray what Harold Cohen had done. And I said, you know,
this sounds to me like a complex adaptive system. And he said, yeah, it is. Well, what do you know?
Harold Aaron had all these kids and cousins all over the world in science and in economics and
so on and so forth. I was so relieved. I thought, okay, your instincts are okay. You're doing the
right thing. I didn't have the vocabulary. And that was one of the things that the Santa Fe
Institute gave me. If I could have rewritten that book, no, it had just come out. I couldn't rewrite
it. I would have had a vocabulary to explain what Aaron was doing. Okay. So I got really interested
in what was going on at the Institute. The people were, again, bright and funny and willing to
explain anything to this amateur. George Cowan, who was then the head of the Institute, said he
thought it might be a nice idea if I wrote a book about the Institute. And I thought about it and I
had my eye on some other project, God knows what. And I said, I'm sorry, George. Yeah, I'd really
love to do it, but just not going to work for me at this moment. He said, oh, too bad. I think it
would make an interesting book. Well, he was right and I was wrong. I wish I'd done it. But that's
interesting. I hadn't thought about that, that that was a road not taken that I wish I'd taken.
Well, you know what? Just on that point, it's quite brave for you as a writer, as sort of
coming from a world of literature and the literary thinking and historical thinking. I mean, just
from that world and bravely talking to quite, I assume, large egos in AI or in complexity.
Yeah, in AI or in complexity and so on. How'd you do it? I mean, I suppose they could be
intimidated of you as well because it's two different worlds coming together.
I never picked up that anybody was intimidated by me.
But how were you brave enough? Where did you find the guts to sort of...
God, just dumb luck. I mean, this is an interesting rock to turn over. I'm going
to write a book about it. And you know, people have enough patience with writers
if they think they're going to end up in a book that they let you flail around and so on.
Well, but they also look if the writer has,
if there's a sparkle in their eye, if they get it.
Yeah, sure.
When were you at the Santa Fe Institute?
The time I'm talking about is 1990, 1991, 1992. But we then, because Joe was an external faculty
member, were in Santa Fe every summer. We bought a house there and I didn't have that much to do
with the Institute anymore. I was writing my novels. I was doing whatever I was doing.
But I loved the Institute and I loved
again, the audacity of the ideas. That really appeals to me.
I think that there's this feeling, much like in great institutes of neuroscience, for example,
that they're in it for the long game of understanding something fundamental about
reality and nature. And that's really exciting. So if we start now to look a little bit more recently,
how, you know, AI is really popular today. How is this world, you mentioned algorithmic,
but in general, is the spirit of the people, the kind of conversations you hear through the
grapevine and so on, is that different than the roots that you remember?
No. The same kind of excitement, the same kind of, this is really going to make a difference
in the world. And it will. It has. You know, a lot of folks, especially young, 20 years old or
something, they think we've just found something special here. We're going to change the world
tomorrow. On a time scale, do you have a sense of what, of the time scale at which breakthroughs
of the time scale at which breakthroughs in AI happen? I really don't. Because look at Deep Learning.
That was, Jeffrey Hinton came up with the algorithm in 86. But it took all these years
for the technology to be good enough to actually be applicable. So no, I can't predict that at all.
I can't. I wouldn't even try. Well, let me ask you to, not to try to predict, but to speak to the,
you know, I'm sure in the 60s, as it continues now, there's people that think, let's call it,
we can call it this fun word, the singularity. When there's a phase shift, there's some profound
feeling where we're all really surprised by what's able to be achieved. I'm sure those dreams are
there. I remember reading quotes in the 60s and those continued. How have your own views,
maybe if you look back, about the timeline of a singularity changed?
Well, I'm not a big fan of the singularity as Ray Kurzweil has presented it.
How would you define the Ray Kurzweil? How do you think of singularity in those?
If I understand Kurzweil's view, it's sort of, there's going to be this moment when machines
are smarter than humans and, you know, game over. However, the game over is. I mean, do they put us
on a reservation? Do they, et cetera, et cetera. And first of all, machines are smarter than humans
in some ways all over the place. And they have been since adding machines were invented.
§03Transcript · 46:21–1:00:05
So it's not, it's not going to come like some great eatable crossroads, you know, where
they meet each other and our offspring, Oedipus says, you're dead. It's just not going to happen.
Yeah. So it's already game over with calculators, right? They're already out to do much better at
basic arithmetic than us. But you know, there's a human like intelligence. And it's not the ones
that destroy us, but you know, somebody that you can have as a, as a friend, you can have deep
connections with that kind of passing the touring test and beyond those kinds of ideas. Have you
dreamt of those? Oh yes, yes, yes. Those possibilities. In a book I wrote with Ed Feigenbaum,
a book I wrote with Ed Feigenbaum, there's a little story called the geriatric robot.
And how I came up with the geriatric robot is a story in itself. But here's what the geriatric
robot does. It doesn't just clean you up and feed you and wheel you out into the sun.
It's great advantages. It listens. It says, tell me again about the great coup of 73. Tell me again
about how awful or how wonderful your grandchildren are and so on and so forth.
And it isn't hanging around to inherit your money. It isn't hanging around because it can't get
any other job. This is his job. And so on and so forth. Well, I would love something like that.
Yeah. I mean, for me, that deeply excites me. So I think there's a lot of us.
Lex, you gotta know, it was a joke. I dreamed it up because I needed to talk to college students
and I needed to give them some idea of what AI might be. And they were rolling in the aisles as
I elaborated and elaborated and elaborated. When it went into the book, they took my hide off
in the New York Review of Books. This is just what we have thought about these people in AI.
They're inhuman. Come on, get over it. Don't you think that's a good thing for
the world that AI could potentially do? I do. Absolutely. And furthermore,
I'm pushing 80 now. By the time I need help like that, I also want it to roll itself in a corner
and shut the fuck up. Let me linger on that point. Do you really though?
Yeah, I do. Here's why. Don't you want it to push back a little bit?
A little. But I have watched my friends go through the whole issue around having help
in the house. And some of them have been very lucky and had fabulous help. And some of them
have had people in the house who want to keep the television going on all day, who want to talk on
their phones all day. No. Just roll yourself in the corner and shut the fuck up. Unfortunately,
us humans, when we're assistants, we're still, even when we're assisting others,
we care about ourselves more. Of course. And so you create more frustration. And a robot AI
assistant can really optimize the experience for you. I was just speaking to the point,
you actually bring up a very, very good point. But I was speaking to the fact that
us humans are a little complicated, that we don't necessarily want a perfect servant.
I don't, maybe you disagree with that, but there's a, I think there's a push and pull with humans.
You're right.
A little tension, a little mystery that, of course, that's really difficult for AI to get right. But
I do sense, especially today with social media, that people are getting more and more lonely,
even young folks, and sometimes especially young folks, that loneliness, there's a longing for
connection and AI can help alleviate some of that loneliness. Some, just somebody who listens,
like in person. So to speak. So to speak, yeah. So to speak. Yeah, that to me is really exciting.
That is really exciting. But so if we look at that, that level of intelligence, which is
exceptionally difficult to achieve actually, as the singularity or whatever, that's the human level
bar, that people have dreamt of that too. Turing dreamt of it. He had a date timeline. Do you have,
how have your own timeline evolved on past?
I don't even think about it.
You don't even think?
No. Just this field has been so full of surprises for me.
You're just taking in and see the fun about the basic science.
Yeah. I just can't. Maybe that's because I've been around the field long enough to think,
you know, don't go that way. Herb Simon was terrible about making these predictions of
when this and that would happen. And he was a sensible guy.
His quotes are often used, right?
As a legend, yeah.
Yeah. Do you have concerns about AI, the existential threats that many people
like Elon Musk and Sam Harris and others are thinking about?
Yeah. That takes up half a chapter in my book. I call it the male gaze.
Well, you hear me out. The male gaze is actually a term from film criticism.
And I'm blocking on the women who dreamed this up. But she pointed out how most movies were
made from the male point of view, that women were objects, not subjects. They didn't have any
agency and so on and so forth. So when Elon and his pals Hawking and so on came,
AI is going to eat our lunch and our dinner and our midnight snack too, I thought, what?
And I said to Ed Feigenbaum, oh, this is the first guy. First, these guys have always been
the smartest guy on the block. And here comes something that might be smarter. Oh, let's stamp
it out before it takes over. And Ed laughed. He said, I didn't think about it that way.
But I did. I did. And it is the male gaze. Okay, suppose these things do have agency.
Well, let's wait and see what happens. Can we imbue them with ethics? Can we imbue them
with a sense of empathy? Or are they just going to be, I don't know, we've had centuries of guys
like that. That's interesting that the ego, the male gaze is immediately threatened. And so you
can't think in a patient, calm way of how the tech could evolve. Speaking of which, your 96 book,
The Future of Women, I think at the time and now, certainly now, I mean, I'm sorry, maybe at the
time, but I'm more cognizant of now, is extremely relevant. You and Nancy Ramsey talk about four
possible futures of women in science and tech. So if we look at the decades before and after
the book was released, can you tell a history, sorry, of women in science and tech and how it
has evolved? How have things changed? Where do we stand? Not enough. They have not changed enough.
The way that women are ground down in computing is simply unbelievable. But what are the four
possible futures for women in tech from the book? What you're really looking at are various aspects
of the present. So for each of those, you could say, oh yeah, we do have backlash. Look at what's
happening with abortion and so on and so forth. We have one step forward, one step back.
The golden age of equality was the hardest chapter to write. And I used something from
the Santa Fe Institute, which is the sandpile effect, that you drop sand very slowly onto a pile
and it grows and it grows and it grows until suddenly it just breaks apart. And
in a way, Me Too has done that. That was the last drop of sand that broke everything apart.
That was a perfect example of the sandpile effect. And that made me feel good. It didn't
change all of society, but it really woke a lot of people up. But are you in general optimistic
about maybe after Me Too? I mean, Me Too is about a very specific kind of thing.
Boy, solve that and you solve everything.
But are you in general optimistic about the future?
Yes. I'm a congenital optimistic. I can't help it.
What about AI? What are your thoughts about the future of AI?
Of course, I get asked, what do you worry about? And the one thing I worry about is the things
we can't anticipate. There's going to be something out of left field that we will just say,
we weren't prepared for that. I am generally optimistic. When I first took up
being interested in AI, like most people in the field, more intelligence was like more virtue.
You know, what could be bad? And in a way, I still believe that. But I realize that my
notion of intelligence has broadened. There are many kinds of intelligence,
and we need to imbue our machines with those many kinds.
So you've now just finished or in the process of finishing the book that you've been working
on, the memoir, how have you changed? I know it's just writing, but how have you changed
the process? If you look back, what kind of stuff did it bring up to you that surprised you,
looking at the entirety of it all? The biggest thing, and it really wasn't a surprise,
is how lucky I was. Oh, my. To have access to the beginning of a scientific field that is going to
change the world. How did I luck out? And yes, of course, my view of things has widened a lot.
If I can get back to one feminist part of our conversation. Without knowing it,
it really was subconscious. I wanted AI to succeed because I was so tired of hearing
that intelligence was inside the male cranium. And I thought if there was something out there
that wasn't a male thinking and doing well, then that would put a lie to this whole notion of
intelligence resides in the male cranium. I did not know that until one night Harold Cohen and I
were having a glass of wine, maybe two, and he said, what drew you to AI? And I said, oh,
you know, smartest people I knew, great project, blah, blah, blah. And I said, and I wanted
something besides male smarts. And it just bubbled up out of me like, what?
It's kind of brilliant, actually. So AI really humbles all of us and humbles the people that
need to be humbled the most. Let's hope.
Wow. That is so beautiful. Pamela, thank you so much for talking to me. It's really a huge honor.
It's been a great pleasure.
Thank you.