Paul Thagard Mixed.mp3

[00:00:00] Psychologists have pointed out lots of other kinds of intelligence that I think are also important social intelligence, for example, the ability to get along with other people. If you look at major leaders, they may not be the smartest people in the room, but they're good at working with people in cooperative ways.

[00:00:40] What's up, everybody? Welcome to the artists of Data Science podcast, the only self development podcast for Data scientists. You're going to learn from and be inspired by the people, ideas and conversations that'll encourage creativity and innovation in yourself so that you can do the same for others. I also host open office hours. You can register to attend by going to bitterly dot com forward, slash a d. S o h. I look forward to seeing you all there. Let's ride this beat out into another awesome episode. And don't forget to subscribe to the show and leave a five star review.

[00:01:35] Our guest today is a philosopher who specializes in cognitive science, philosophy of mind and the philosophy of science and medicine.

[00:01:45] He's a prolific writer and has contributed to research in analogy and creativity, inference, cognition in the history of science and the role of emotion in cognition. He's authored 13 books and over 200 articles in the realms of cognitive science, philosophy of mind and the philosophy of science. So please help me in welcoming our guest today, the distinguished professor emeritus of philosophy at the University of Waterloo, Dr. Paul Daggered. Dr. Thagard, thank you so much for taking time out of your schedule to be on the show today. I really appreciate you being here. Thank you for talking with me. Dr. Tagaris, let's take this back. What kind of a kid were you in high school?

[00:02:34] Well, I was pretty nerdy, I spent a lot of time reading it, a lot of time studying, but I was more well-rounded than that. I also played a lot of sports fairly competently and at least by grade 12, even dated a lot.

[00:02:47] And you grew up in. If I recall, Saskatchewan.

[00:02:51] Yes, Saskatoon, Saskatchewan.

[00:02:53] So do you play a lot of like hockey or anything like that out there?

[00:02:56] I was never very good at hockey, but I played a lot of basketball and other sports.

[00:03:01] So when you were in high school, what did you think your future would look like?

[00:03:06] Well, I had a very early plan, quite bizarrely early. In fact, when I was 15, I got a job shelving books at the Saskatoon Public Library and I was shelving a book one day and it was called Why I'm Not a Christian by Bertrand Russell.

[00:03:23] And I thought that looks interesting because religious questions were starting to grow. And so that's what got me interested in philosophy. So I started reading philosophy and around the same time, at the same job, I was shelving books in the reference section where there was a job section and they had a job called Professor. And I thought that sounded kind of cool. And so I decided at that early age to be a philosophy professor, quite astonishingly, and actually worked out.

[00:03:50] So what kind of philosophy did you start getting into as a 15 year old?

[00:03:55] Well, there I was. I was just reading a few famous people like Bertrand Russell and Jean-Paul Sartre, I think specific. And I didn't have any specific interests when I started at the University of Saskatchewan studying philosophy. But then I got lucky and I got a scholarship to Cambridge University.

[00:04:12] And there where I did a second BA and the part of the MBA program I took was connected to logic, became tied with a program and philosophy of science. Before then, I wasn't really interested in science or philosophy of science, but I found it absolutely fascinating because going to lectures in the history of science gave me a whole different picture of the way that knowledge is structured and grows.

[00:04:36] So that's when I moved into philosophy of science as my main specialty, infuriated to distill down like philosophy of science. And to just a quick nugget for us to understand what that means. Would you be able to do that for us?

[00:04:50] The philosophy of science looks at the kinds of methods that scientists do, things like formulate theories, how do they generate theories, how do they creatively come up with new ideas?

[00:05:00] And then once they've got new ideas, they need to test them. What's the nature of the testing that they do? Sometimes in a field you'll have competing theories. How do they figure out which is the better theory?

[00:05:10] So these are some of the problems that scientists kind of take for granted because they're doing science. But what philosophers of science do is step back and think about the kinds of thinking and reasoning that scientists used to do that.

[00:05:21] And after you studied philosophy of science, you moved into cognitive science and into the philosophy of mind. Talk to us about that. How about that journey that led you from that, the second by Cambridge to the work you're doing now?

[00:05:36] I did my PhD in Philosophy of science at the University of Toronto, but I got a job teaching philosophy at the University of Michigan at Dearborn, and that allowed me to live in Ann Arbor, which is where the main University of Michigan is. And there I met some philosophers and some psychologists, and that's how I got interested in cognitive science, because there was a cognitive science program there and they were doing a combination of philosophy and psychology. And that's what first got me interested in artificial intelligence, because I started realizing that that was a whole new methodology for doing philosophy. You could build computer programs to look at how people think. So while I was in Michigan and then did a master's degree in computer science, I could build mental models.

[00:06:18] That's really fascinating. Love to get into some of the work that you've done, specifically one of your older books, The Brain and the Meaning of Life. This book actually stumbled across it on Amazon, and I picked it up and started reading it. And I was like, wow, this is awesome. So I reached out to you and glad it worked out. But let's dig into this a little bit. First question you kind of bring up fairly early on in the book the difference between a mind and a brain usually talking to us about that.

[00:06:48] Sure. The common sense view is that there are completely different things because most people belong to religious groups that think that we're going to survive our guests. So they think that doesn't matter.

[00:06:59] Once the body is gone, the brain is gone and the mind keeps on going in heaven or wherever. I don't think that's very plausible. There's no evidence that the mind can survive beyond the brain.

[00:07:09] So I think it's basically the same thing. It's a little more complicated than saying the mind is the brain, but that's a good approximation. Yeah, like that very front end of the book, all our minds, our brains, I think, is the the phrase in there. So you talk about concepts of perception and inference. Can you define this for us? And how do these enable us, our brains to know reality?

[00:07:39] It's very complicated because there's not a direct connection, so it's not as if you just see the world and it's exactly the way you think it is, because it takes a whole lot of brain processing to create a reliable image of the world. But we start with our senses. So you get information through your eyes and through your ears and through touch and movements of your body. And so that's providing Data that go into your brain. The brain has to make sense of it. It has to kind of act like the scientists doesn't to come up with a theory of what's going on. And that's what requires inference, inferences, where you take the information you get from the sensors and other things that you know and come up with new details about what's going on in the world. So you don't when you see a person, you can see their body, but you have to make inferences about what you're seeing because it's a lot of processing of the auditory information and visual information to figure out what's actually going on.

[00:08:32] So part of me here for kind of a naive ish question, so different brains can kind of perceive reality in different ways. Right. So how is it possible for the brain to discern that what it's looking at is like the one objectively true reality if all we have is our perception of it?

[00:08:54] Well, it's not always easy if you've had too much to drink. For example, you can make a lot of false entry instance, but our brains are all pretty much the same. And so you might wonder, well, how do you how is your perception? The same as my perception. And the answer is all members of the human species have essentially the same kind of brain and same kind of sense organs. Sometimes there are problems with them, but by and large the same. And so the information is coming in and you've got various ways of checking it. So if you're looking at something, say a tomato, is that a tomato? Well, you can see what it looks like. You can taste it. You can touch it, you can smell it. Put all sorts of ways of collecting information that give you a pretty good reason to believe, yes, that is a tomato, not an absolutely guarantee you can make. It's a fake tomato and someone's playing a trick on you. But you've got a pretty good reason to believe when you infer that that's the tomato and all humans also have these emotions.

[00:09:47] Right. And you talk quite nicely in your book about emotions. Can you kind of give us a rundown of the systems in our brain that make emotions possible?

[00:09:58] Well, many people think that somehow emotions get in the way of thinking. I've heard many people say, are you being rational? You're being emotional, but that's just completely wrong. Emotions are actually really important to rationality because they give you an idea of what's important when you're wandering around the street or even your house.

[00:10:14] You can pay attention to many different sorts of things, but you need to pay attention to what matters to you, what's going to keep you alive, what's going to keep you happy. And emotions do provide that kind of focus on the brain and not just human brains, but lots of other animals, too, are very well evolved to play that role. So there's different parts of the brain that have different functions. You've got pleasure centers that will tell you what is going to be making you happy, what's going to feel good. But you also have pain centers. You also have areas like the amygdala, which are very good for making judgments about emotions such as fear and sadness. So it really requires a whole bunch of different brain areas working together. But when they do that, they're not just doing emotions, stuff, they're interacting and really valuable ways with all the inferences you're making to try to indicate what's worth paying attention to, what's worth making inferences about. So emotions are really crucial for helping you evaluate your situation, determine what's worth thinking about, then ultimately help you decide what to do, because it's not just a matter of taking in perceptions and inference.

[00:11:20] You also have to decide what you're going to do in the world to keep yourself alive and happy.

[00:11:26] And you talk about cognitive appraisals and body perceptions as well. Can you kind of define those for us?

[00:11:35] Well, the perceptions are originating with what you get from the census. So light comes into your eye and hits the retina and that stimulates cells. That sends signals to the back of your brain. And that's where a lot of the visual processing starts in your nose. You've got receptors and they detect molecules in different shapes. And that's where smell begins. In the case of tongues, you've got your sensors on the tip of your tongue. And so all this goes into the brain and then the brain has to somehow make sense of it. It has to be able to figure out what various interpretations makes the most sense, given a combination of what senses are telling you, but also what you have from past experience. Because all of us, once, once we're past being babies, have got large amounts of experiences that we can draw on.

[00:12:23] So the brain puts it all together into a coherent picture that says, for example, this is a tomato or no, that's an apple or that's a cucumber. Thanks for digging into that for us. I found this fascinating in your book. You don't tell your brain what to do and your brain doesn't tell you what to do. You are a brain deciding what to do in your physical and social environment. I'd love to get into how the brain makes decisions.

[00:12:52] No, go ahead. Go ahead. Oh, yeah. So question is, how does the brain make decisions? Well, it's really complicated because, of course, it's taking information from the senses, as we've been talking about. But it also has to make different kinds of inferences about what's true. But the most important thing it has to do for decisions is try to figure out how to accomplish your goals. So any decision of any complexity is going to require you to look at different kinds of goals. So, for example, you've got two job offers and you're trying to decide which job to take. Well, that's usually not simple because different jobs have got different strengths. One might be more interesting to you than another offers more money. So the brain has to figure out how to balance those things. So emotions are a big part of that, but so is also doing inferences about the consequences. If you take the first job, is that going to make you happy in the short run, but unhappy in the long run or the other one where maybe you get money but you really need money? What you need money for? How much money do you need? So you have to do all these kinds of inferences and the misses.

[00:13:55] The thing about the brain is it's not like a normal computer. We just make one inference at a time. We do this, this, this, this. What the brain does is make these inferences in parallel. What that means is you've got billions of cells in your brain. They're all firing simultaneously and they're all interacting simultaneously. Every one of these 80 billion neurons interacting with ten thousand other neurons. So it's very different from when we talk verbally where we say one thing at a time. It's very different from what a standard computer does, which is one thing at a time. The brain can do billions of things at a time. And what it's doing is a kind of balancing act. It's balancing your different goals, different interests you have. And then finally it comes to consciousness. Most of what the brain does is unconscious. You don't know what's happening. You just can't observe it at all about consciousness. Provides you a tiny window where after you've done this unconscious deliberation, for example, of one job versus another, and then you can say, oh, yeah, that's what I should do.

[00:14:58] I should take job A for example, we talked about you representing actions and goals. And we have to take into consideration what know the rules and the decisions that we make. Why is it that some people get into this analysis paralysis where they have goals that maybe are aligned with each other and there's various courses of action they could take to get there, but they don't know which path to take?

[00:15:24] Well, it's a good exercise to do with Benjamin Franklin actually proposed this. He said if you've got a tough decision, make a piece of paper and have two charts of two two tables and you list the strength in mind and minuses with the pluses and minuses of different things. But quite often people do that and they see that there's more pluses in one side, more minuses, and then they ignore it and do something else. And the reason is that means that they simply haven't done the calculation. Right. The analysis is actually very hard because you don't always know what's most important to you. It gets even more complicated if you're doing a decision that's so not just about a job, but suppose it's a job that requires you to move and you've got a partner to take into account, then you're balancing not just the merits of different jobs, but the importance of the relationship as well.

[00:16:12] And so you've got a really difficult kind of a balancing act to do there. And is is it because, you know, everything leading up to one point about the past is done. It's fixed, the past is written. But the future. It's kind of like a multiverse of unfolding possibilities, right? So it's very difficult when you're here now to kind of think through and imagine through what might unfold from a decision that you take right now.

[00:16:40] That's one of the problems. But there's problems about the past, too. We can always remember how we acted before. We don't always remember what was successful or what was it. We often don't learn from our mistakes. So the past is problematic as well. But the biggest problem, of course, in these big decisions is taking a wild guess about what's actually going to accomplish your goals when you've got very limited data. And look at the scientists right now, the government's trying to make decision about covid-19. It's incredibly difficult because there are so many unknowns that are involved and they've got different, sometimes competing goals. They want to open the economy. On the other hand, they want to slow down the spread of the disease. And so you've got this kind of balancing act that's very hard to accomplish.

[00:17:24] And why is it that we don't learn from our past as well as we should?

[00:17:28] Well, sometimes you just don't remember and we don't understand the mistakes we made in the past. So remember, conscious decision making very only. You can only keep a small number of things in mind at once, whereas everything that you've done in the past may potentially be relevant. But you can think that that's where pencil and paper is useful because you can start writing down things and it provides an external memory which is much larger than the very limited short term memory of operating in our brains.

[00:17:56] So let's say somebody is facing a decision that they're comparing a challenging time, deciding which action to take, which is a good thing for them to do is maybe, like you said, put pen and paper and maybe draw out decision tree and talk about, OK, if I take this action, then this might happen. This might happen, this might happen, then maybe assign probabilities to each thing that they think like this and event will occur with probability 10 percent or so and so forth. Does that.

[00:18:24] Well you can do that. But some of this is bogus because you don't actually know the probabilities. They don't know the utilities. And so if you think you could turn this into a mathematical calculation, the way economists want to do it, really fooling yourself because you just don't know the numbers with that kind of case. It's garbage in, garbage out. So you can make up numbers, you can make up guesses, but you really don't know. That's why I think it has to be more a matter of emotional coherence. You have to get an emotional reaction that will do the balancing in your brain based on things you may not easily access. You may not realize how important a particular kind of work is to you or how important your relationship is to you. That's all has to be sorted out. I've actually developed a new technique for decision making since I wrote that book that I used. When I decided to retire. I figured this is a really important decision. I was going to really retire because I was going to keep on writing books. But when I stopped teaching, which I liked, the way I did it was to have a weekly vote because I figured, you know, want to make a decision based on a momentary whim. Moods change from time to time. You think about different sorts of things.

[00:19:30] But I decided that what I would do is vote every week. And so every Sunday, four months, I took a vote. At this moment, do I want to retire or not retire? And when I first started doing it, the votes came out. Don't retire.

[00:19:44] But then after a while, the votes started to turn the other direction so that I was pretty sure I wanted to retire. So was a way of trying to bring all the relevant factors and not just rely on a particular state of mind at that particular moment. So I want to go back to this thing about probabilities. Do you think the brain itself, do we have trouble conceptualizing probabilities and what they mean?

[00:20:08] The probabilities are wonderful invention, but you remember their invention. There was Pascal in the 17th century who got figured out probabilities. Male teacher and hacking used to joke that if you knew probability theory in Rome, you could have opened the whole empire because you could have won all the games of chance. People didn't really figure out probability till the 17th century. Now it's a great tool when you've got Data to get some handle on what the probabilities are. The brains aren't set up that way. Brains evolved long before Pascal invented probability theory and we do things much more crudely. Basically, we go with emotions. Even we're trying to figure out whether something is true or not is not just whether it's good or not. We often go on emotions and that's often good when you've got to make quick decisions. But would you get a complex decision with lots of factors that can get you in a lot of trouble? So if you're in situations such as the medical world where there's loads of Data and you can use the probabilities, that's the way you should go. There's wonderful formulas to help you make good, sophisticated statistical inferences. But life usually isn't like that. We don't have the probabilities and we don't actually have the built in operating system to use those probabilities very well. And you can try to do it. You can try to calculate expected utility the way an economist.

[00:21:26] That's usually not possible in real life decisions, and when it comes to these emotional reactions that we have, is there any way we can tame these emotional reactions so we can be a bit more reasonable or thoughtful in how we are reacting?

[00:21:41] Well, taming isn't quite the right word. That's the way Plato thought of it. He thought the mind was kind of like a chariot and you had to end the charioteer had to keep the emotional horse under control. But that's not right, because as neuroscientists like Damasio pointed out, the emotions are actually incredibly valuable because they did give us an idea of what really matters. But, of course, you need to be careful with them because sometimes they go out of control. So you never want to make a decision when you're really angry, for example, because that's going to distort your decisions. So you got to watch out for extremes of emotions such as extreme anxiety, or people are often told not to make a decision for a year after when they're suffering for grief. And so when emotions are extreme.

[00:22:24] But if you avoid those extremes, the emotions are actually going to give you a much better idea of what matters to you, what you want to be guiding your decisions and some kind of pseudo numerical calculation. So in your book, you go into some great detail regarding six suggestions for making bad decisions, which I thought was great, because usually people want to give you suggestions of making good decisions. So talk to us about the fact this framework for making bad decisions.

[00:22:56] I thought it would be fun to keep track of ways in which people screw up, but it is just a way of putting it more amusing form.

[00:23:05] So I told you that the way I made my retirement decision was to do it actually over a period of a year and a half with US Weekly Folk, the people rush into things. I've seen people retire by because they get mad at their department chair and say, I quit. That's a horrible way to make a decision. So the first way to screw it up is to do it really rapidly and rush into something. People do the same thing when they make really important personal relationships like fall madly in love and get engaged right away with these rapid decisions. So the first way to mess it up is to make rapid decisions. Another way is to not collect a lot of information and sometimes often you can get more information so you don't have to buy the first car that looks good and that you can use sources of reliable information and consumer reports and find out whether that car is a lemon.

[00:23:55] So another way to screw up is to don't bother collecting information, but with your gut that Donald Trump is really good at that. He doesn't be what scientist always goes to. He makes decisions with no information at all. Another thing is to only consider some of your goals. So just focus on one goal. For example, if you're thinking about a job just obsessed with how much money does it pay? Well, it's not rational. Money is important.

[00:24:22] But there's lots of other things that are important to it's got to be satisfying. It's got to be compatible with your personal life. So if you just focus on one goal, you're likely to screw up your decisions. Another way is to not take into account what other people are thinking. So another really good thing to do is just ask other people because they're not going to be blind to some sort of thing. If you are, you can you can really valuable. But it can be really valuable to talk over a decision with somebody who knows you and knows what your values are and can help you do that. So that was another one. And I can't read the other ones that I've written down here. So that's most of. Thank you for sharing those. Very entertaining. I enjoyed that. So audience wants to know that they're relying on your answer here. Dr. Thagard, why is life worth living and what is the meaning of it all?

[00:25:11] Well, that's the really big question. And there have been some people who say, well, it isn't because there's even a South African philosopher who wrote a book called Better Never to have Been Born. And so he said people shouldn't have children because their lives will just be crap. And he's still around, which is a little puzzling, but obviously doesn't think that lives are worth living at all. So that's the skeptical answer. The skeptical answer sometimes comes to people because they're abandoning religion. So if you're religious. Well, there's not a question because it's the meaning of life is established by God. God made you and gave you a place in the world and a purpose. So religion provides one way of having it all taken care of because the meaning, the purpose of life comes from the religion comes from God. But for those of us who are religious, the question is just really legitimate. What what is the meaning of life? And in my book, I came up with a fairly simple slogan. The whole answer is more complicated than that. But to put it in a slogan, I think the meaning of life is love, work and play. That originated actually with the comment of Freud, where somebody asked him, he said, well, life is basically meaningless, but love.

[00:26:26] Work are good things to pursue. Well, I think that's enough to be able to say that the meaning of life is love and work. These are the things that people care about, what they care about, their personal relationships. And that doesn't have to be just romantic love. It's also families, extended families, but also friendships. These are all things that I think make lives enormously worth living for children or parents or friends. If you've got these people as part of your lives, then that's a by itself enough to make your life meaningful. Work, I think, is also important for lots of people because it involves a sense of accomplishment. People have a need for achievement, at least a lot of people do. And if work is a main way of getting that, and so if you can get into a kind of job that provides satisfaction, then that's also an enormous source of meaningful life. So I've been really delighted to be a philosophy professor and I'm happy that I'm continuing to write books. And so that gives me that sense of achievement and purpose that is really important. But in keeping with the relationships in my life that are really important. I used to think that Freud had it with just love and work.

[00:27:36] But then I had a friend who I told this theory to and he said, we don't. What I really like is hiking in the mountains. And I realized, well, that's interesting.

[00:27:45] That's play. That's neither love nor work we play is important to lots of people have things in their life like sports or watching television or reading good novels. And I think that's also a valuable source. So I think if you can have a reasonable balance of love, work and play, it actually fits well with psychological theories about basic needs. And then that seems to me all the answer. One needs to say, well, why love love? Because you can do some combination of those I find interesting.

[00:28:14] But as a data scientist, I do a lot of very quantitatively heavy lifting. It's hard work. It's it's meaningful work. I enjoy it. But I find that sometimes the biggest breakthroughs I have when I'm working on a really tough problem actually happen while I'm playing. Why is that?

[00:28:30] Ok, that's not a theory about the meaning of life. It's a really interesting question about problem solving. So there's a common phenomenon and the people have a difficult problem to solve and then they can't do it and then leave it aside. And you mentioned playing sports or something and then the idea pops into your head. For me, I often get solutions to problems when I leave them and then sleep and wake up in the morning. And I've already got the answer. So psychologists call it incubation ideas. There's an incubation period, but there's a much better explanation of that from a neural point of view, because your brain doesn't stop working consciously. You're not solving the problem or dealing with the Data issue anymore. Consciously, you're playing sports or you're sleeping, but your brain doesn't stop when you sleep. The evidence for that is your brain is your eyes, your dreams. But lots of other other kinds of thinking is going on as well. So there's all sorts of unconscious processing go on. So remember, consciousness is just a tiny tip of the iceberg of what's happening in your brain. That processing is being going on in parallel with these billions of neurons. So even though consciously you're playing sports or consciously having a crazy dream or being unconsciously asleep, there's lots of processing goes on. And so some of the inferences that are required to solve that problem are happening way below the point of consciousness. And then when you're playing the sport or when you wake up in the morning, then the answer pops into your head magically, it seems. But it's not magic. It's unconscious processing by these billions of neurons working in parallel.

[00:30:05] I was reading something recently, transient hypofrontality. Is that the same phenomenon or is this something completely different? I've never heard that term before. OK, actually check what kind of books are reading then. Thank you very much for that. I really appreciated breaking that down for us. I'd love to get into your latest book, Bots and Beasts. So in that book you talk about three aspects of the concept of intelligence. Can you talk about these and tell us what they are?

[00:30:34] I wrote this book on intelligence concerned with machines and animals and humans. And a lot of people would expect I'd have to start with a definition of intelligence because people say, well, define your terms. How can you talk about intelligence if you can't define it? But that shows a misunderstanding about the nature of concepts. There are dozens of definitions of intelligence floating around there, but frankly, none of them are very good. But that makes complete sense if you understand the psychology of concepts. So when psychologists I've done experimental studies of concepts, they don't look for definitions because they know that those are very rare outside of outside of mathematics. But they notice three other aspects. One is examples. So you may not be able to give a strict definition of intelligence in terms of necessary and sufficient conditions like.

[00:31:25] Some intelligent people you can name, you can name Einstein, for example, or Marie Curie or Jane Austen or Martin Luther King and give lots of examples of intelligent people. So examples provide us with a hook onto a concept, even if we can't say generally what sister.

[00:31:44] So that's the first aspect is example's the second aspect is features.

[00:31:50] By features.

[00:31:51] I don't mean defining features where you can say something's intelligent if and only if it has these features. It's much looser. The typical features, features that occur in most cases of intelligence. And so that's where you can look at things like being able to solve problems and learn and make decisions and do reasoning and according to my account, also have feelings. So these are typical features. They're not required always for intelligence, but their typical features. So that's the second aspect. The third aspect is explanation, because a big role of what concepts do for us is provide explanations.

[00:32:30] They say why things happen. So we use the concept of intelligence all the time and explanations. We use it to explain, for example, why somebody was able to do something. How could he do a challenging job like being a data scientist? Well, he because he or she is intelligent. So it's explanatory. Or we sometimes say that one person is more intelligent than another. So you've got these ways of being explanatory with the concept as well. So the three aspects of the concept that apply to intelligence are examples and features and explanations.

[00:33:09] What artists I would love to hear from. You feel free to send me an email to the artists of Data Science at Gmail dot com. Let me know what you love about the show. Let me know what you don't love about the show and let me know what you would like to see in the future. I absolutely would love to hear from you. I've also got open office hours that I will be hosting and you can register by going to Bitly.com/adsoh dot com forward, slash a d. S o h. I look forward to hearing from you all and look forward to seeing you in the office hours. Let's get back to the episode.

[00:33:59] You to us about some of the kinds of intelligence then that contribute to human intelligence.

[00:34:05] People sometimes think that there's just one kind of intelligence, which is what IQ tests measure, IQ tests measure. Some of the aspects are very good at figuring out how good you are with language or how good with your mathematics and abstract reasoning. But psychologists have pointed out lots of other kinds of intelligence that I think are also important social intelligence, for example, the ability to get along with other people. If you look at major leaders, they may not be the smartest people in the room, but they're good at working with people in cooperative ways. One aspect of social intelligence is emotional intelligence, which requires you to understand your own emotions and the emotions of other people and to be able to empathically work with lots of other kinds of cognitive intelligence to that. I think out there look at bodily intelligence. If you consider a great basketball player, for example, who can make split second decisions in the middle of the air. That's intelligence because that's doing something that requires problem solving and learning.

[00:35:03] So that's another sort of intelligence that's most mysterious to me since I have none of it is musical intelligence. One of my sons is able to hear a tune and then play it on six different instruments.

[00:35:17] And that's just magic to me. But there does seem to be also music, intelligence. So these are things that I think definitely count as intelligence, but don't fall under the narrow rubric of IQ. So when it comes to things like like you mentioned, the physical intelligence, like shooting the basketball and then musical intelligence, let's say practicing these types of intelligence, I feel like you can grow, cultivate and develop them through deliberate practice, let's say. But that emotional intelligence, how can we grow and develop that part of our intelligence?

[00:35:54] That's a really interesting one. All of these kinds of intelligence involve a combination of innate ability and learning takes both of them, because if you're going to be good at them, you can probably have to have some innate ability that's built into your brain at birth. But then it gets way better when you learn from experience. That's obviously with basketball. You don't become LeBron James just by having any talent, obviously an enormous amount of it. But he also practices he works as hard as anybody in the game to improve. But emotional intelligence can be somewhat like that, too. So you can get better at it by training. What kind of training? While there's some evidence that one way you get better emotional intelligence is to read really good novels, not crap, just thrillers with five words, sentences and no sweat. But if you're reading really good novels like Lamees Love, for example, or by good writers like Margaret Atwood, you can learn a lot about people and about their emotions. And so that that's one way there are other ways you can adjust by interacting with people. And so if you're interacting with other people, you've got emotional intelligence, you pick things up for them. One of the keys to emotional intelligence is empathy, which is getting a sense of other people's feelings. And people probably are born with some innate differences there. But everybody can get better at it by thinking about it, watching what other people do, paying attention to other people, talking with people and reading first rate novels. So I think empathy can be improved as well as we get more experience with other people. Some people simply don't have it. If you look psychopaths, for example, are narcissistic and they just are probably incapable of empathy. And they really miss out on what sometimes they can be very successful when they're ruthless. But people don't like them. People don't work with them. People start to hate them eventually.

[00:37:47] Thank you very much for that. Never thought about that would be really good fiction and you could develop that emotional intelligence muscle. So you also talk about a really fascinating paper that you came across by Marvin Minsky in 1978. Talk to us about what that paper was about and what was it that you found interesting about it.

[00:38:09] This is going way back to really the first year where I stumbled across cognitive science because of the psychologists that I met. And a number of the psychologists were making references to this guy and hadn't even heard of this field called artificial intelligence. But there is connections happening between the psychologists who interest me and people doing A.I., which is trying to get computers to do that. And Marvin Minsky was one of the founders of artificial intelligence. He was one of the six people who got the field going at a conference in nineteen fifty six. And he went on to become the director of the Artificial Intelligence Lab at MIT. And through psychology, I came across this paper. He wrote about frames and provide a different way of thinking about thinking for me because I was trained in philosophy. So I was trained in logic. And so I was trained to think of. Inference is being a step by step serial process, you believe this and this and this and this. That's because that's the way logic works. But Minsky had a very different picture, which was much more consistent with the way psychologists think, because it's more a matter of pattern matching than a bunch of patterns in your head that you've acquired.

[00:39:17] Psychologists tend to call them schemas. Minsky calls them frames. And so the frame was a computational schema or pattern that you could apply to different situations. It's a really powerful way of thinking of how you do things. And I suppose you go into a restaurant you've never been to before, but you can look around and see pretty quickly what kind of restaurant it is. Is it a fast food restaurant? Is there really classy restaurant as a sort of middle steakhouse? So you classify it. How do you classify things? We don't do it by doing a series of logical deductions. What you do instead is you take this pattern, which means he called the frame and you pattern match and realize, oh, this fits the frame for fast food restaurant and then you know what to do because then you're not going to go looking for a fancy table and a waiter. You're going to go up to the counter and make an order at the counter.

[00:40:07] So it was a powerful way of thinking about how you can deal with situations in the world by pattern matching rather than logical inference. It's very interesting. You also talk about some marvelous machines in your book, talking about what they do, how they work, how well do they satisfy benchmarks and how they fall short of human intelligence. And what I found really interesting, because it's really relevant to, I think, a lot of the audience here that a scientist would be a recommender systems. So talk to us about that.

[00:40:44] I take that example because everyone's familiar with recommender system. So on Netflix, you watch a movie and then next time you sign on, it recommends similar movies. Are you done shopping on Amazon for books or something else? And the next time it's going to say, why don't you consider this book? Because it's got an algorithm that will do this. And these are really sophisticated machine learning algorithms. They've managed to train on vast amounts of data and produce patterns of human behavior that they can use to predict what people are going to like in the future. So one of the reasons I was interested in this is that it's a kind of analogy, which is something I did a lot of work on, including computer models. They are giving you suggestions about what's analogous to what you liked in the past. And they're doing it not just based on your experience, your experience, but on whole packages of experience that they've got from millions and millions of users.

[00:41:39] So with the recommender systems do is look at what you've done in the past. Look what you bought, look what how you've rated things.

[00:41:47] And on that basis and comparison with other people, take a guess. And they're often very good guesses at what you're likely to like in the future. And how does this fall short of human intelligence?

[00:42:01] Well, it's very good for its purpose because Netflix and Amazon love a lot of money out of this. But if you compare it to the kinds of thinking that people do, it falls short in a number of ways. One is it's not really doing analogy. It's just going on simple features humans can do. Amazingly complicated analogies are used in scientific discovery they used in decision making. They're used in creating music where you have very complicated structures and what Netflix or Amazon do with the recommender systems just boil it down to something much more simple, just a bunch of features. So the sort of thing you can train a neural network on, but you're not doing complex analogical thinking the way that people can when they do really major discoveries in fields like science. So that's one way it falls short. Another way it's obviously really different is emotion. When you recommend a movie to someone else or a book that you read to someone else, you do it because you liked it. It's evoked your emotions. It's about happiness, maybe made you sad for part of the time, but it also made you feel excited or stimulated. And so when you like something, it's because of your emotions and you recommend things to other people because you've got empathy with them. And so you think they would like it to. But Netflix and Amazon know nothing about emotions. And so they're not doing the same kind of thinking that people are doing a good feeling for the good substitute for it. But not having the full level of intelligence that people have when we're able to use rich, complex analogies and we're able to mingle them with emotions to give people, I think, much richer kinds of suggestions and humans, we're able to do it on much less Data, for example.

[00:43:41] Right. I can just tell you the name of one movie that that I like. And just based on that one title, you might be able to infer that, oh, you might like this movie. That movie, that movie. Right.

[00:43:51] Well, especially if I know you and I know your tastes. So I've got two sons. One of them recommend recommends a. I go for it right away. The other tends to like action shows in comic books and now I know that doesn't work for me. So it's a combination of knowing the source and knowing the extent to which their experience and their preferences and their emotional preferences match yours.

[00:44:17] You also talk about some amazing animals in your book. One that I found really interesting was the the octopus and how it's intelligent in its own way. Would you mind talking to us about that?

[00:44:29] I put in octopuses because they're astonishing. First of all, there are mollusks, so mice. So things like snails, which you don't expect to be particularly intelligent, but evolution produce octopuses with amazingly large brains. Their brains have got up to half a billion neurons. And so there's many neurons is more than a cat as much as a dog. And these brains are completely unlike our brains. Our brains are very centralized, obviously, in our heads. But the neurons in the in the octopus not only operate in its head, they're distributed across its arms. It's just like a mini brains, operating neurons with neurons in each of its arms. So it's got a much more distributed kind of brain than us.

[00:45:12] And it uses this brain power to do quite amazing things so octopuses can solve complex problems like opening jars. They're also really good at escaping from aquariums because they can look around and figure out what's going to be the way out. They also can recognize particular people, one octopus in one aquarium to drop the dislike for one particular keeper.

[00:45:33] So other people walk by did nothing. This creature walks by the octopus with squirt so they can do very complicated kinds of perceptions and problem solving and learning, which is just astonishing for a mollusk. Do they kind of do the same type of pattern matching as us or is it a different way?

[00:45:51] Well, obviously they don't have language, so they can't deal with verbal concepts by restaurants. But sure, they're doing pattern matching the way you recognize a person, the way that they could tell one keeper from another is it's a whole pattern. Whereas what does the face look like? What is the hair looks like? I don't know exactly which features it's noticing, but it's a pattern and that it's working with and the same thing enables it to sometimes solve really complicated problems like finding shells that it can carry around to make a home for itself. So they they can really use patterns to solve problems in a way that the animals with simpler brain organization like snails just can't.

[00:46:29] Thank you very much. I want to get into now ethics of artificial intelligence. And you talk about this in your book as well. You use the principles of the of medical ethics as a framework for ethics, namely autonomy, justice benefit, avoiding harm. So why use this framework?

[00:46:51] Well, let me say first how this problem arose. About 10 years ago, artificial intelligence really started to take off. We feel it's been around since nineteen fifty six, but it's had a lot of ups and downs at times and it looks like it was good. And then but those methods didn't work out. But around 10 years ago, new methods were being developed. The most prominent has been deep learning that suddenly we're having some amazing successes and suddenly cars were driving themselves, which hadn't been possible before. Suddenly, companies like Netflix and Amazon and many, many other Google were coming up with really good algorithms for doing really complicated things. I'll consider, for example, machine translation that's been around for a long time and used to be very good. Now it's really quite good or things like voice recognition, which is a really hard problem because people's voices are so different, their sounds are so different, but now it's really good. People talk to Aleksa or Siri or Google and it works really well. So around ten years ago, it was starting to appear that artificial intelligence is really going to take off. And a lot of people in the field very responsibly realized, wow, this could be dangerous. This could be something that could have huge implications for human beings. And really famous people like Elon Musk and Stephen Hawking. Bill Gates is saying, hey, we got to watch out. The computers might get so smart that they're going to take over the world. And we're not sure we want that.

[00:48:16] Some people do want that. Some people to think that the world of computers were in charge, Wall Street were aghast of it. So a lot of people in the field of artificial intelligence as well as outside started to get really worried. And the way that lots of companies reacted was to start generating principles because they said, oh, we need some principles for figuring out how it's going to work. But there are a lot of different companies did this. At least 60 different organizations and companies have generated their own principles for artificial intelligence. So I noticed this and I started wondering, well, wow, they're all over the place and then there's overlap. But how can you make sense of these 60 different principles? Some companies only. About 10 principles like the like IBM, but other places generate dozens of them. How do you make sense of it? And then I thought of medical ethics because I've taught medical ethics and knew that field. And I knew that in that field, people often work with a small set of principles just for principles. And so I thought, well, can these four principles, which I think actually make sense, such principles of justice and equality, principles about being benefits for people avoiding harm, can these principles make sense of the vast array? And so one of the things I did in the what became the last chapter of what's BS is to show that, yes, this huge proliferation of more than 60 sets of principles of 10 to 30 apps can actually fall under this quite four principles for medical ethics.

[00:49:50] So that's one reason. The second reason is these principles have worked well within medical ethics or in medical ethics textbooks. And people use them for all sorts of really hard problems. When you see, for example, cases where you've got conflicts between people's autonomy, they want to choose their own treatment, but also question what's going to be beneficial for them or what's going to cause harm. And so you've got those people in medical ethics of sort of that up. But the third reason that I really like these four principles is that they quite delicately balance a lot of principles that have been proposed in other kinds of ethics. So for many people, the ethics they grew up with are the ethics from the religion. But these four principles actually fit well with a lot of principles that are religious. And they don't say that they came from God, but nevertheless, with a lot of the same conclusions. Why why shouldn't you kill people? Well, because you're going to cause them harm. And so a lot of the religious principles fall under there as well. And it's also a way of combining the main approaches to ethics that philosophers have talked about somewhere in terms of rights and duties, other in terms of greatest good for the greatest number. But those four principles quite concisely cover both those approaches to ethics then the next two to five years.

[00:51:06] Which principle do you think is going to be of most concern to society?

[00:51:12] Well, one principle we always have to be worried about is the avoid the harm, because I think it would be very harmful if, in fact, computers took over the world. Why? Because they're not going to make decisions that are in the interest of human beings before. Earlier, we talked about how humans make decisions using their emotions and we can understand other people's decisions by empathy with their emotions. Computers are psychopaths. They don't have emotions. They can do fabulous calculations as far as numerical calculations. They can do way faster than humans can. They're fabulous at arithmetic. They're fabulous at doing Bayesian inference with probabilities better, much better than humans are. So the great with numbers, but they're not good with Feliks. And so there's no guarantee at all that a computer that was making decisions on its own would have anything like empathy for the needs of human beings. So the idea of having a computer that took over the world, even if it had some principles you tried to build in to be good to people, there's no way it would stick because it doesn't actually care about people. So I think the highest principle there is a void Harp of the sort that can happen if you had some psychopathic computer running the world.

[00:52:25] And so that avoiding harm, it really starts with a data scientist, machine learning practitioners who are kind of behind the algorithms and the ones creating them. Right.

[00:52:35] Right. So people when they do when they build algorithms, they need to pay attention to what values are going into it. And so any any algorithm for accomplishing something has got values.

[00:52:47] Is the value that you just trying to make money for the company or are you trying to further the interests of the people who are going to be using the product?

[00:52:55] Or are your algorithms going to be enabling some particular country to dominate other countries or to practice surveillance on all the people so it can control them? So Data scientists, people in artificial intelligence need to be constantly aware of what uses their products are being used for. Is this, in fact, doing it for the good of the people generally, or is it only going to be good for a few rulers or a few people who are going to get filthy rich off using those algorithms?

[00:53:25] So how can we instill human values into the systems?

[00:53:30] It's hard to do it in the systems because you might think, well, we could just apply the four principles, principles like harm and benefits. And you can put the some of that in your in your algorithms, but you can't put so easily as well how to care about those things.

[00:53:46] So good human decision makers care about justice. They care about equity. They care about the freedom of the people involved. They care about not harming the people they're dealing with. So what you can do is. Even in the program is try to put in at least some approximation to that so that when you're doing a cost benefit calculation, it isn't just looking at one group of people say rich people and ignoring poor people. Let's make sure that it's covering people of different genders and different colors and it's actually capturing people all across the world. So the slogan that I use to sum this up, it originates with Gandhi actually in something he said is need, not greed. So a lot of decisions are made in the tech world based on greed. And sometimes it's greed for profit. Sometimes it's greed for power. Sometimes it's tied in with companies or countries that are greedy for power. So that's the greed side. The neat part is looking at what humans need. Now, this is going back to what we talked about with the meaning of life. Humans have basic needs for love, work and play, which are tied into psychological needs as well as, of course, all the biological needs we have for food, water, air, health care and how housing. And so you can make the big decisions in a way that satisfies the needs of all people rather than the greed of just a few.

[00:55:10] And when it comes to to make a decision, we talked about earlier how the brain represents actions and goals and then proceeds to make a decision that way. Does that change at all when it comes to A.I. systems?

[00:55:23] Well, it depends. Some people build a AI for human good. Some companies have been established specifically for that. But that's not always the case. When the AIs in the hand of government, some governments, when they AIs in the hands of some companies, it's sort of greed rather than need. So you've got to have leaders in these fields, including government leaders and corporate leaders, being careful to make decisions based on principles. I think the four principles pretty much cover it, and other companies have got longer, but they pretty much cover it quite well. So if what you're doing is figuring out who's going to be affected by this, what harms that are going to be, what benefits are there going to be or are you going to take away their freedom? Are you going to apply this fairly to different groups of people if you cover those fruits? Because then I think you're being quite ethical in your development of Data science or artificial intelligence.

[00:56:17] Thank you very much for that, Dr. Thagard. So one last question before you jump into a quick random round here and one hundred years into the future, what is it that you want to be remembered for?

[00:56:29] I don't think I'll be remembered a hundred years from now if if even one of my books is being read, I'd be absolutely delighted. It's hard to predict. I'm actually really concerned about what kind of condition the world's going to be like a hundred years from now. This huge, huge problems. Right now, everybody's focused on the pandemic, but that eventually will pass. But there's going to be other pandemics. So the bigger the far bigger long term problem that I think is terrifying is climate change. And other problems are that there are signs that governments are becoming more and more oppressive. See that the world, the kind of United States that Donald Trump wants to have, but lots of other leaders in the world. So if you put together dictatorial leaders and climate change and pandemics, and I'm not sure the prospects for the world is very good, I'll spend any time thinking about what is going to be reading my books a hundred years from now.

[00:57:26] Let's go ahead and jump into a quick lightning round with that. So what is it that you're currently most excited about or that you're currently exploring?

[00:57:35] Right now I'm writing a book on balance, and it's both about the physiology of balance, how it is that we manage to walk down the street without falling over. It's also about ways in which it fails. For example, a lot of people get vertigo or they get dizzy.

[00:57:49] And I'm actually trying to give a neuroscience theoretical account of how this works. There's loads of stuff about how the anatomy and physiology of the brain works to produce the sorts of balance. But I've got a new take drawing on new ideas from theoretical neuroscience on how the brain actually puts it all together. So that's the first part. The second part, though, notice is that in so many different fields, we use balanced metaphors. For example, when I was talking about the meaning of life, I talked about balancing life and work. What's the nature of that metaphor and how does it connect back to the balance that we do when we're walking down the street? How do we consider balancing is operating in lots of other things, such as nature when you talk about the balance of nature. So I'm interested in both how the brain balances us, but also how these ideas about balancing become metaphors by which we understand so much other parts of our lives.

[00:58:45] What do you believe that other people think is crazy?

[00:58:50] I couldn't think of anything. I believe that almost everybody thinks it's crazy. I guess I've got some views that aren't. Terribly popular these days, if you think about what kinds of government should we have? There seems to be a general trend toward more and more autocratic kinds of government. But I actually think that the world has come across a fairly reasonable kind of government called social democracy, where you manage to have all the freedom that comes from having a fairly open economy.

[00:59:19] But you have the social responsibility from taking into account people's needs and countries like the Scandinavian countries and Canada and its good days. And I have managed to carry out that balance really pretty well. So that's by no means a majority view. But that's something I think is quite important. If you could have a billboard put up anywhere, what would you put on it?

[00:59:42] The slogan that I told you before need not greed. I would be great to see that all about all of the freeways in Canada. As I said, it wasn't I didn't coin that. That's been around for a while and I think it originated with a single. Gandhi says the world has enough for everyone's need, but not for everyone's greed. And that boils down to need, not greed is a really nice three word slogan that I'd love to see on a billboard.

[01:00:08] I like that a lot, too. What are you currently reading?

[01:00:11] While I'm always reading lots of things, so I'm reading a lot of books connected with balance, not just the availability of balance in art and movies, for example, so that I've just written a section describing Hitchcock's great movie Vertigo. And so I read a couple of books about the making of Vertigo, because that's the kind of balanced story. Most recently today I read a book that my son gave me called Breathe and he gave it to me because he is working on balance, he thought to enjoy reading a book about another physiological process, which is quite fascinating. I didn't realize just how bad humans are as a result of the fact that we got big brains, which means which means we could get lots of food by cooking, which means we didn't need very big mouths, but now our mouths have too much teeth and our noses are very well structured and lots of people have breathing and eating problems as a result. So that's been a lot of fun.

[01:01:08] It's interesting. Speaking of Hitchcock, is that was funny coincidence. I was reading through Strategies of War by Robert Green and those earlier today and one of the chapters he was talking at great length about, Hitchcock says, interesting, that's brought up. So what song do you currently have on Repeat?

[01:01:27] I just started a couple of songs to my iTunes list. One of them is the one politically most interesting is by Mickey Gaeton Bill I only heard about a couple of weeks ago and listening to her music, she's extremely rich.

[01:01:41] She's a country singer who's black.

[01:01:43] I've never seen of a couple of black male country singers, but I've never actually heard of a black female country singer, which is really good. And the song that has been a kind of breakthrough song for is called Black Like Me, which I think is incredibly powerful and that's a current favorite of mine.

[01:01:59] Definitely have to check that out. Yeah, I've never heard of that either. So we're going to open up the random question generator just to do a few random questions here. So let's go ahead and pull this up. All right. What's an unpopular opinion you have? I think we kind of touch that on on the what what do you think that other people what do you believe other people think is crazy? But if you want to answer this, go for it.

[01:02:24] Well, I mentioned the social democracy now. Obviously, most people in the world are religious, and so we know that because their surveys of something like 80 percent of the world are religious. And so I'm an atheist. So that's an unpopular opinion.

[01:02:39] What's the story behind one of your scars?

[01:02:43] Well, I've got a few, but the most amusing one I got when I was about eight years old and my younger brother David had a nail file in his hand. And I guess I thought he shouldn't be having a nail file. So I said, give that to me. He stabbed me. I still have the scar in your group of friends.

[01:03:03] What role do you play?

[01:03:05] That's an interesting question, because there are lots of roles. People I think all my groups of friends are quite egalitarian, so there's not going to be a leader or something like that. I like everybody to be happy. I like people to smile. And so I guess I tend to tell a lot of jokes.

[01:03:24] What's the best piece of advice you have ever received?

[01:03:28] Probably something my father said to me. My father wasn't educated. He grew up in Winnipeg in a working class family and never got passed grade 11. But he had some good practical wisdom and he said something like this to me. He said, reach for the top and whatever happens, land on your feet. And the reason that's a good thing, because you want to have high aspirations. You want to try to achieve as much as you can. But you also have to realize that not everything is going to work. And so you're going to end up being OK, even if you don't manage to accomplish those very high goals that you set for yourself.

[01:04:05] I love it, Tartaglia, how can people connect with you? Where can they find you online?

[01:04:11] The easiest way is through my website, which is Pathé Garden.com.

[01:04:15] Thank you so much for taking time out of your schedule to be here to come on the show.

[01:04:20] I really appreciate everything you've discussed with us today. Thank you. Thank you very much.