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Harvard Data Science Review Podcast
What Can We Learn From The Histories of AI: A Conversation With Stephanie Dick
00:44:53
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- [00:02]
A
Welcome to the Harvard Data Science Review podcast. I'm Liberty Vittert Capito, feature editor of the Harvard Data Science Review. And joining me as always is my co host and editor in chief, Shali Meng, statistician founding editor in chief of the Harvard Data Science Review, and the person who has probably made more people rethink everything they thought they knew about Big Data than anyone alive. Today, we have a guest who approaches artificial intelligence from a direction most people in this space rarely encounter through history. Stephanie Dick is a historian of mathematics and technology with a focus on artificial intelligence and someone who has spent over 15 years asking a question that turns out to be surprisingly hard to answer. What is AI really? And where did it come from? Her work traces the origins of artificial intelligence through automated theorem proving, early facial recognition, and the first law enforcement data banks in America. And what she finds every time is that the technical choices were never just technical. They were choices about what counts as knowledge, who counts as a person, and what we're willing to let a machine decide. She also co edits the Mining the Past column right here at the Harvard Data Science Review. So in many ways, she is already part of our family. Stephanie, we are so glad to have you here with us today.
- [01:29]
B
Well, welcome, Stephanie. And as a historian of science, which is a concept, I guess most data scientists probably have a rough idea, but they may or may not know, what did you actually do? So if you can just start by telling the broad audience, which they are like, what do you actually do as a historian of science?
- [01:51]
C
What a great question, Shelley, and it's wonderful to be with you. Thank you so much for having me on the podcast. So history of science is, I guess, a strange discipline that lots of folks don't know is a standalone sort of academic undertaking. And I, in particular, am what I call a historical epistemologist, which means I'm really interested in the history of what counts as knowledge and also the history of how and what we know in different moments in time. So, for example, it was 2,500 years that the Western world believed that the right way to know about nature was to watch it take its course uninterrupted. And then there was a rupture in European society where philosophers and theologians and aristocrats were debating how we should know the world and came out believing, in fact we should do experiments, we should intervene in the way the world works and not let it run its course in order to understand it better. And historians of science want to know what's happening in those moments, what are the ruptures that change how we think we know, the practices that shape our knowledge. And my own research has been all about how the modern digital computer, the prospect of art, automation, different forms of artificial intelligence, have shaped how and what it is that we know. So that's a bit about the field and where I've positioned myself within it.
- [03:24]
B
I'm curious now, sounds like you're doing both the philosophy and history. So what does a philosopher do that you don't do?
- [03:33]
C
I mean, I come from a philosophy background, and there is certainly an entirely branch of philosophy that is also interested in knowledge questions. Epistemology. There belongs to the discipline of philosophy. But historians of science tend to hold the belief that the answers to our deep questions about knowledge actually change over time. So whereas often philosophers want to come up with more sort of generalizable, once and for all theories of what knowledge is, historians of science are open to the possibility that the answer to that question actually changes. Changes in some really fundamental ways. So I like to frame it as in, we are doing very similar work to philosophers of knowledge, but we give historical answers rather than philosophical answers to some of our core questions about what knowledge is.
- [04:32]
D
You've argued, and correct me if I'm wrong, that history offers real tools for navigating today's AI landscape, which I think a lot of people don't necessarily believe. And it's not just context or cautionary tales. So for a data scientist who thinks history is for humanists, where's the most practically useful thing your work teaches them about AI systems that they're building or using right now?
- [05:08]
C
I think we're hearing all the time from the people who develop technologies, that the technologies are going to change the world in some fundamental way. We are told that the Internet will change the world, the printing press will change the world, the computer will change the world. AI will change the world. Changing the world is really difficult and complicated, and often it happens in ways that we don't expect and can't anticipate. And one of the, I think, most significant gifts of historical understanding is a sense of what really causes change in the world. What kinds of interventions transform a society or transform people's perspectives and opinions. And we find ourselves, I think, in a quite unprecedented historical moment right now, wherein it's difficult to anticipate where we're going. And I think that historical understanding can also offer some really significant grounding in unprecedented and disorienting historical moments. And I say this to my students a lot, but a lot of what gets called innovative is Actually, historically speaking, quite conservative in the sense that these inventions reproduce the social order around them. They recreate many things about the society they were introduced to. They create more wealth and power for people who already have it. They reinforce the ways that we're already going about doing things. And so in that sense, some of the very new looking technologies we have on hand might not be as transformative or innovative as we hope. Historical understanding can both help us make sense of the moment that we're in and make sense of how we arrived in this moment together. And it also offers us a sense of what kinds of interventions lead to more meaningful transformation formation over and against others. So, kind of counterintuitively, I think that historical understanding is really the handmade of innovation and of creating new futures, because it's through understanding our history that we can really look at the world around us and recognize it, and that we can have a proper theory of social change that allows us to go forward and make the changes that we want to see.
- [07:36]
B
Well, as a specific example, and Steph, that I had the great fortune to hear you talk about the three acts of AI and love to have you to share with the data science community about your take on the historical development of AI, particularly these different acts and your new thinking about agentic AI.
- [07:58]
C
Thank you, Shelley. You know, it's my favorite thing to do is to give a three act historical arc for the artificial intelligence moment that we find ourselves in. And I think for lots of people, AI was just a kind of esoteric or science fictional idea until 2022 with the launch of ChatGPT. And I think it is really important, if not essential, to realize that AI has been more than one thing. It is more than one thing today. And you can look through history to see, see and to recognize that kind of pluralism of intelligences that we're talking about when we talk about AI. So Act 1, where I often begin this history is in the wake of the Second World War in the 1950s and 60s. And during this time, the hope was that artificial intelligence and this new technology of the modern digital computer would be able to improve human judgment and human decision making in the context of an increasingly uncertain and scary global situation, with the possibility of nuclear war, with the possibility of geopolitical confrontation between the United States and the Soviet Union. It was in that context that we first hear about AI. But in that moment, what intelligence itself meant for the early AI researchers was a way of thinking. Every moment in the history of AI is also a different moment in the history of what we think intelligence itself is defense. Intellectuals and logicians and mathematicians in this moment try to theorize human intelligence as being about our capacity for right reasoning, for rationality, for inference and deduction, for a kind of rule boundaries processing of information. And they hoped that they'd be able to describe the human reasoning capacity as a set of rules and that they could impart those rules for reasoning to the computer, which by following them would reproduce human intelligence. And there were some initial successes with this paradigm, especially in areas like chess and logic. But it turned out both that human intelligence seems to be a lot more complicated than just our capacity for reasoning, and also that this approach would only really work in highly formal and rule bound domains, which most of the world, as you were just saying, Xiao Li, is not like that. Things are messier than we might hope. So a second paradigm of AI starts to take shape. Out of a critique of the first one, some researchers started to say, well, human intelligence must be a lot more complicated than just our ability to think in a certain way. Surely our intelligence is also a function of what it is we know. And the second act, the second dominant paradigm of AI was called expert systems. And it was all about trying to get human expert knowledge out of human experts again into a set of rules, usually for navigating a conditionally branching tree that capture something of the learned experience of a human expert. This worked really well in certain domains. Again, for example, folks who've ever used TurboTax, that's my favorite example of a kind of legacy expert system that takes you through a branching set of questions about if you're single or married, if you own a home or you don't, if you filed taxes last year or you didn't, and so on. And that tree is supposed to bring you to an optimal tax claim without having to go to an accountant. But just like the earlier paradigm, this one struggled too, in large part for the reason, again, that human intelligence, human knowledge turned out to be really resistant to extraction and formulation as a set of conditionally branching rules. And so that paradigm also gave way to Act 3, which late in the 20th century took us away from a desire to reproduce human intelligence according to formal rules in the computer. And instead the hope was that the computer could find a different path towards intelligence by coming up with its own rules for what to do based on patterns and correlations in massive data sets on which they're trained. And so with Act 3, I think the most significant inflection point is that we stopped trying to model human intelligence. Some people even threw out the very idea that human intelligence is rule bound in that way. And we move towards this kind of data driven pattern recognition style of intelligence in machine learning and large language models. So I think it's so helpful to know that these different paradigms of AI have been at work in the past and also that they still are in the present. It might be that in a lot of situations, an automated reasoning engine is the right choice over and against a machine learning model, for example, and also to see that it's disagreements about the very nature of our own intelligence that have propelled a lot of this history as well.
- [13:40]
D
You co edit the Mining the Past column at the Harvard Data Science Review and work very, very closely with many data scientists. When you look at the history of how data has been collected and trusted, or in a lot of cases not trusted, what patterns do you see repeating themselves over and over again and certainly going to be repeating themselves even in the current era of really large scale AI data training.
- [14:16]
C
I think a huge part of the history of AI leads us to this kind of sad desire to bypass ourselves. There's this hope in the Cold War that whereas people are too limited, too slow, too hungry, too emotional, too arrogant, too whatever, make decisions that computers can be trusted in some way. And similarly, there has been a long arc of hope and opportunism and excitement and optimism that data driven computing technologies are also going to be able to solve some of our problems by bypassing human bias or by just making decisions based on the data. And I think there's a lot of hope that data driven decision making can do better than human decision making in a lot of environments and contexts. And I certainly think that's true. But the main takeaway from the history of data, I think, is that all data are human data. We actually don't get to bypass ourselves in some of these fundamental ways, even when working with data driven tools. There's a professor at nyu, Lisa Gittleman, who has a fantastic quote and book called Raw Data is an oxymoron, by which she essentially means that all data that we have reflect and represent a set of human values and priorities and decisions. Someone has to decide which variables are important, what we should be counting. There is always more than one way to count, for example, in the history of the census. And we have a column at Mining the Past written by Dan Bouc, who is a historian of quantification and statistics and the census. And the census offers us a perfect example of this, where different data were gathered about Caucasian men between the ages of 25 and 45 for many, many years than was gathered about everyone else for the reason that white men between those age ranges were seen as both the military and the economic capacity of the nation. And so the most sort of raw data that we have about the population has actually always been a reflection of a set of values and priorities and the worldview of the state at any given moment. And all data are like this. They carry traces of human decision making, human values, human choices. There's always other data that we might gather. So. So I think one of the things we try to highlight in several of the Mining the Past columns is this profound lesson in humanizing our data and seeing them as reflective of human decisions, human values, human worldviews, and to recognize there are always other ways that we might count other variables to consider, other frameworks to see the world through.
- [17:26]
B
Well, thank you, Steph. It is true that most data scientists may or may not realize how rich these historical lessons are, but you have also worked directly in this line work. Right. I understand that you have research projects examining the New York State identification and intelligence system. Right. And where. Now this gets to a point where, as you've said, philosophers always emphasize there's no such thing as raw data. And one of them, for example, I think you studied how the categories of, say, like criminality itself is technologically constructed. Right. Because it is a human constructed concept. So can you speak a little bit about how the lessons learned from the, say, 1960 systems for the. Currently, there are lots of debates about the, you know, predictive policing algorithms. Right. There's a lot of those issues in a very intensive debate. There's all kinds of arguments how the historical lessons you have learned, you have investigated can shed some lights on those topics of really great current interest and the future.
- [18:38]
C
Thank you so much, Shali, for the question. And this is one that I could talk about for a really long time. And I think there are two main takeaways for the current moment. And the first lesson is that there's no automation without transformation and that every time we start using computers to take over tasks that people were doing before, whether that's investigating crimes, whether that's identifying license plates, whether that's matching fingerprints, all of these tasks were redefined while we tried to develop computers that could do them. This project led me to look at what was actually going on in the algorithms that were developed as early as the 1960s to match license plates, to match fingerprints, to match faces or photographs of faces. Doing something like photograph matching with the computers of the 1960s was a really significant technical challenge. These machines had 60 kilobytes of memory and were incredibly limited in their operating capacity. So when New York State started trying to develop an algorithm for matching photographs, the first thing they did was completely redefine faces and completely redefine what it would mean to match two faces. In the context of this early system for police data, faces are redefined as a set of distance measurements between points on the face. So the outside of the eyes, the outer points of the mouth, the various points along the hairline taken in pairs to become a set of measurements. The early algorithms developed in the context of this police databasing system would calculate and aggregate the differences between those distances across photographs and propose that the smallest distance was the likeliest match between two faces. This is a too perfect example of the fact that often when we redefine a task so that the computer can do it, we are oversimplifying it or reducing it, or taking out so many of the relevant variables. We often make hard problems solvable by really simplifying them so that we haven't solved the hard problem, we've solved the easy redefinition of the problem. And that was very true in this case. And Woody Bledsoe, who designed this algorithm, knew this was hugely problematic for recognizing faces, but the New York State Police did not. When the algorithm traveled to them from the University of Texas at Austin, where Bledsoe was working, the nuance about this technical approach sort of fell away. And the algorithm was seen as proof that police would be able to automate identification, automate mugshot matching, using these technologies. So it highlights both that there's a transformation that goes in to making our tools work for us and take on these tasks that often that transformation is reductive. And also in this way, the transformation sometimes involves computer scientists building their literal selves into the tools that they are developing. In this case, Woody Bledsoe and his team came up with what they called the standard head. And that was a set of three dimensional measurements of a head that they would assume of all heads in their database in order to simplify the calculations that allowed the matching and comparison of those distance measurements. And that set of assumptions, the introduction of the standard head, that's what made that problem solvable in the 60s. And even though we have way better tools now, and we have way more sophisticated sensibilities about automation, we're still engaged in these transformations. Whenever we automate and introduce AI, we have to pay more attention to what those transformations entail, and we have to make those Transformations with intention instead of just introducing what are often poor assumptions to make hard problems easier to solve.
- [23:03]
D
I work in facial recognition and facial shape analysis. And so it was funny when you said that people have literally put themselves in the system. My face is very much literally in the system. But I think it begs the question that you're now really embarking on this sort of large scale program and a, I believe it's called the Ritual and Algorithm that explores sort of the entanglements between mathematical, psychological and occult theories of the human mind in the 20th century. And that's a, it's a pretty striking combination. What drew you to the occult as a lens for sort of understanding computing? And what does that reveal the purely technical history would miss?
- [23:56]
C
So this project really started for me when I was reading the letters, the archival letters of one of the 20th century's most important logicians, Kurt Godel, who immigrated from Hungary and was based at the Institute for Advanced Study at Princeton. And I learned from those letters that Kurt Godel had been reading Carl Jung, who is one of the most important students of Sigmund Freud and one of the most important psychoanalysts of the 20th century. And Goethe was getting really interested in some of the occult ideas that Jung was interested in, including this idea that Jung has of what he called synchronicity, which was the idea that our state of mind, the state of mind of a person might actually shape the outcome of events in the world in ways that we don't have a good scientific explanation for and that look like occult phenomena. And Godel very famously has other interests in the occult. He writes about ghosts in the context of the 20th century and certain other sort of mystical phenomena. And this led me to a really interesting question, which is, you know, what does this central thinker about formalization and deduction and mathematical proof and certainty and their limitations, what role does the occult play in his understanding of the human condition? And so to make a long story short, I went along my way, reading Godel's letters and then reading some of this surrounding texts of Jung's and many others. And that led me down a bit of a separate rabbit hole which I've called Mid Century Men Searching, which is that I uncovered this list of about 50 books, starting with Carl Jung's Man's Search for Meaning. And I found about 45 other books all with the same title. It's Man's Search for Meaning, Man's Search for God, Man's Search for the Soul, God's Search for Man, the Dual Search of Man for go and God for man and man's search for meaning and man's search for. And it all led me to this sense that of course, the middle of the 20th century is this existential crisis where people are looking for meaning, they are looking at the human condition in new ways, and they're all, at the sort of core of all of this anxiety are a set of questions about where does meaning come from in a world full of so much uncertainty and suffering and so much of what looks like injustice? And I now am convinced that artificial intelligence should be seen in large part as a response to that crisis of meaning and that existential crisis in the Second World War, and that a lot of people who were active in AI research were also very actively asking these bigger questions about meaning, one more step towards the project. This led me in turn to think about the fact that on the surface, what we call ritual and what we call algorithm are very similar actually. Often ritual, like algorithm, is highly disciplined. People who engage in ritual often have a set of very precise steps they are meant to follow, or very precise rules or prescriptions for what they're meant to do. But ritual, through its history in religion, in the arts, in many parts of human society, ritual is a history of meaning making. We engage in ritual in order to deepen our sense of meaning, deepen our spiritual relationship to the world or ourselves. In some ways, an algorithm, even though it looks kind of the same in the sense of bringing discipline and routine and procedure into what we're doing, it gets accused of doing the exact opposite. In all of these mid century men searching books, there's this fear that algorithm makes us mindless, makes us passive, takes away our agency, takes away our sense of meaning. So I think AI, then and now, is a meaning making project, or rather it is pushing us into a meaning making problem. Xiao Li has heard me say this before, but, you know, we used to write things down because they were important. We would write because that was the best way to save what we cared about and to communicate it. But now the written word is completely saturated with what AI has generated. I don't think writing is a good signal anymore for what we care about, for what matters the most. We are needing to find new places to look, to make collective meaning. So no matter how much we automate, we will always be left with that job, that meaning making job. And I think there's a lot to explore philosophically and historically about that. There's also a lot to explore about how some of our leaders in technology and mathematics and other parts of 20th century life, how they are making sense of meaning making and the sorts of decisions that they have made about the human condition in that context.
- [29:41]
B
Well, thank you, Steph. You really make me think. I think that you have something really profound here. But in what sense? That is something that the machine cannot mimic. Right? Because people talk about AGI. I think I probably know your take on AGI. So let's get into a little bit, right? This whole artificial general intelligence. And it doesn't have to be human, obviously, but I think when you have enough data, let's say we all engage more and more into the meaning search thing, which we all do, but they become more pronounced. We will leave lots of data, lot of trace, how we search for it. We'd be writing more about them, we'll talk more about them. And these machines obviously are good at collecting all our data, hopefully at some point, and then mimic the pattern. We could just escalate, right? We do more, it does more. Right. So at what point or more kind of intrinsically, what is the kind of the search process of the meaning truly belong to human in a sense that it cannot be mimicked by any other intelligence? Or maybe that could. It's just we don't know yet.
- [30:57]
C
Thank you. I love this question so much, and you're absolutely right that I'm an AGI skeptic, but it's because I reject the premise that we have AGI. I think there's this sort of idea that humans represent a kind of place, plastic form of intelligence that could work always and everywhere and put itself towards many different kinds of problems. And there's been this long desire for intelligence and machinery that's not domain specific and so on. But for me, there are so many, and this is, I think perhaps the core finding of the discipline of the history of science is that there is more than one way to know. There is more than one way to know out there in the world. People are making knowledge in really different ways that all have really profound value. And I would add to that that for me, intelligence is also a pluralism. I really love the discussion we had recently in the HDSR about whether or not it makes sense to talk about the planet as having a kind of intelligence. And if planet Earth is intelligent, it's intelligent in a way that has to do with relationships and the dynamism of ecosystems and the give and take and balance of the natural world. That's a really different way of thinking about what intelligence might be from the kind of logico, deductive, rational decision Making models of human intelligence, which are often actually quite brittle by comparison. And so I think that for me, this idea that there's something called AGI or artificial superintelligence, or the singularity on the horizon that we're approaching, is both incorrect, but also it's a fundamentally missed opportunity. Because I love to embrace a world where we think about a pluralism of intelligences, machine, human, animal, hybrid, and that our task in the future is about thinking through how to put all these different intelligences into relation with one another to unlock value and so on. So I'm of the mind, that there are many different things called intelligence. And I'm very open to the idea that there are forms of intelligence that are very alien to us, that could be at work already in our world, or that we have yet to meet in the universe. And yet I think that human intelligence will also have some very unique specificities that are born out of the specificity of our experience in the universe. In philosophy, there's often a debate about whether the human mind belongs to the brain, whether it's separate in some kind of dualistic sense, Cartesian sense of the mind being immaterial, or my personal philosophy, which is that the mind is embodied. I think that human intelligence comes from. It comes from our experience. And there was this tragic way in which in the 20th century, our bodies, our emotions, our spirituality, so many facets of the human being were deemed not just irrelevant to our intelligence, but they were deemed an impediment that if only we were less emotional, if only we were less spiritual, if only we were less hungry, if only we were less tired, if only we were less sickly, if only we were less mortal, right? We could be so much more intelligent. But my view, and this was also the view of Ha Wang, who I mentioned earlier, is that human intelligence is actually a product of the very specific ways that we suffer and live and feel and navigate the world. I think human wisdom, which is profound, comes from a lot more than just data driven pattern matching. It comes from storytelling and narrating and trying to find and make meaning out of our particular experience. Experience. So I absolutely think that every other form of intelligence in the universe can engage in meaning making behaviors and projects, but that human meaning making will always fall to us. Because we are the only creatures in the history of the universe who have had this particular experience of mind and body and planet. And it's our job to make meaning with that in the way that it might be the job of other intelligences somewhere to make meaning out of their experience of the universe. And as you were saying, I used to critique AI by saying AI has no access to the world, none at all. All it has is the data we give it. And the data is always, as we were talking about earlier, it's always partial, it's always incomplete, it always reflects human priorities and decision making. And even with agentic AI that can become increasingly physical, interact with its environment, generate some of its own data, stay up to date in terms of real time data generation, that's going to improve the behavior of our data driven models. But I don't think it stands in for the embodied experience of life in the world. And Alan Turing actually, in a way that I love, sort of gave us that idea two years before he wrote his article on the Turing test that says we should call a computer intelligent if it can sufficiently often fool us into thinking that it is a person in a kind of discursive test. And two years before he published that article that reduced intelligence to conversation. Essentially, he said, you know, the surest way to make an intelligent machine is, would be to take all the parts of a man and reproduce them by machinery and then let this giant machine man roam around the countryside learning things for itself. And even Turing says it would learn a lot, but it would still have no relationship to sex or sports or food or desire or all of these other human things that Turing also thought that shaped the human perspective on life, the world, and any meaning making project we might engage in. So I think there's no way out, there's no way out of the call to make meaning out of our own lives. Not through technology or anything else. That will be a constant struggle. And it's one that I'm thrilled that we're taking on right now. I think a lot of us are overdue for an audit of where meaning comes from in our lives. And I think that AI is encouraging all of us to ask those really fundamental questions about where meaning comes from in our relationships and our learning and our work. So this is a moment for meaning and for fundamentals. And AI is nothing but a support and a motivator for doing that incredibly essential human work.
- [38:19]
B
Well, thank you, Steph. And I just want to put you on the spot, hope you don't mind, to give the audience a one sentence description, not definition, but description of what is intelligence in your mind as the way you define it.
- [38:38]
C
Intelligence is relations.
- [38:46]
D
Okay, I like it. I like it. For our mat. One question I think, you know, it's important to note, you know, your, your work spans automated Proof, facial recognition, police data banks, and now this entanglement of ritual and algorithm. And really across it all, you've shown sort of how technical systems encode assumptions about what counts as knowledge. You know, who, who counts as a person, what counts as intelligence. But if you could wave your magic wand, what's one change you would make to how AI systems are designed, governed or deployed? So one sentence, one change. And what chapter of history most convinced you that that change would be necessary?
- [39:44]
C
Oh, I want this wand. I will wave my magic wand. And in this moment, although there are many things I would love to change about artificial intelligence and the context in which it's being deployed, I would say end the sycophancy because I'm quite troubled by the degree to which we are struggling with human relationships right now. And if I'm right, that intelligence is relations, all of our intelligence is collective. It's born out of the relationships we have with other people, with people we disagree with, with institutions, with technologies. If all of these relationships shape our intelligence, we are not as intelligent right now as we could be because our relationships are struggling, our democracies are struggling. We're having difficulties in lots of contexts disagreeing, productively breaching and bridging the chasms between us. I think that Covid increased a lot of people's social anxieties. It's harder to get folks out to do hard, real time, face to face interpersonal work. We are becoming more and more siloed online and there's, I feel, a breakdown in our capacity for dialogue and for discourse course in the world right now. And then you drop into that situation tools that say to everyone, well that's the best idea I've ever heard. Or you're so right. The sycophancy, I think is going to further isolate people because we'll come to enjoy talking to these models that are so complimentary and they agree with us and they think we're so great at exactly this moment when we need to learn again and struggle through how to talk to people we don't agree with, who don't think our ideas are the best. So I think that the sycophancy is going to lead people in the wrong direction. It's going to make people defer to the tool at times that they shouldn't. I think it's further eroding our democratic and dialogic sensibilities. And so I would advocate for turning the sycophancy way down on the models if I could wave my magic wand. So that's a very special specific thing and then we could wave our wands all day about some of the bigger things.
- [42:13]
B
Well, thank you, Steph, for really a fascinating and inspiring conversation. Just to pick up your last point, I've been thinking about, you know, thinking about other intelligence. Imagine you have an alien intelligence coming observe the human, you know, society from a distance, whatever that thing called. Must think we're not very intelligent because you see, we spend so much effort, money, resources and human intelligence to create all kinds of advanced technologies to say, save people's life, to extend some people's life for another year or another month. At the same time we have all these crazy killing each other going on, right? And from just a distance perspective, say that ecosystem is not optimized just purely from a distance, right? Why you spend this much to save a little bit and then you spend all the other things doing all kinds of creative things. And the logic will not be that clear to whatever the intelligence out there. I think we don't have lots of soul searching we need to do ourselves. But your historical perspective is incredibly helpful. I'm always inspired who by listening to you. And I look forward to a lot more than we're just here. Thank you very much.
- [43:33]
C
Thank you, Shaly. I'm so grateful for the opportunity to be on the podcast and for all of the work we're doing together at the HDSI and hdsr. It's such a pleasure,
- [43:49]
A
Stephanie. This has been such a rich conversation and I think what really strikes me most is how much clearer the present looks when you when you really know about the past. The assumptions buried in these systems didn't appear out of nowhere, and you've spent your career making sure we can see them for our listeners. If today's conversation sparked something for you, we'd really encourage you to seek out Stephanie's work. Her book Making Up Minds is forthcoming, she co edits the Mining the Past column at the Harvard Data Science Room, and you can find her writing and speaking at Stephanie Dick ca she is one of those rare thinkers who can walk into a room full of engineers or a room full of humanists and make both feel like they've been handed something they didn't know they needed. Thank you so much for being here, Stephanie. And thank you all for listening to the Harvard Data Science Review podcast. If you enjoyed this episode, please subscribe, share it with someone who you think should probably be thinking more his historically about AI and we will see you next time. Harvard Data Science Review Everything Data Science and Data Science for Everyone.