REIN: Hello everyone, and welcome to Episode 189 of Greater Than Code. I am your co-host, Rein Henrichs, and I'm here with my friend, John Sawers. JOHN: Thanks, Rein. And I'm here with Arty Starr. ARTY: And I'm here with Astrid Countee. ASTRID: Thanks, Arty. And I'm going to introduce our guest today, which is Deborah Berebichez. Deborah Berebichez is a physicist, TV host, and data scientist. She's the first Mexican woman to graduate with Physics Ph.D. from Stanford University. Dr. Berebichez is the co-host of Discovery Channel's Outrageous Acts of Science TV show, which started in 2012, where she uses her physics background to explain the science behind extraordinary engineering feats. She also appeared as an expert on the Travel Channel, NOVA. CNN, Fox, MSNBC, and numerous international media outlets. Welcome to the show, Debbie. DEBBIE: Thank you. It's an honor for me to be here. REIN: We don't always get a real celebrity on the show, so this is exciting for us. DEBBIE: [Giggles] ASTRID: That is true. The first question we often ask is, what is your superpower and how did you get it? DEBBIE: One of my superpowers is having excellent memory, a photographic memory. I remember entire conversations and what the person was wearing at the time and where were we. It's just, yeah, uncanny and people often get scared, like my classmates growing up, I would meet them like 30 years later and I would be like, "Yeah. And then the time that we were in that class and you mentioned a quote by this author." And they're like, "Oh, my God, this is so scary. How could you possibly remember that?" REIN: It's like the opposite of how my memory works. [Laughter] DEBBIE: I think I have a very detail-oriented brain. And my father was really strict with me about getting good grades and being [inaudible] at school. And so, I think I probably became very alert of everything that was around me because I needed to do so well. Otherwise, my dad wouldn't be happy. And so, I started remembering and looking at every detail, being super observant of every minute thing, which actually comes in quite handy when you're coding and you have to find the mistake in your code. And I'm meticulously obsessed with little details. ASTRID: So that means that you never had that whole 'I [inaudible] hours to find that missing semicolon moments'? DEBBIE: Well, I am the one who wants to do those things. ASTRID: [Laughs] DEBBIE: "Help me find the mistake. I can't find them." I'm like, "Me. Me." I can just glance at a code and see the semicolon that's missing. ASTRID: Oh, my God. That is a superpower right there. [Laughs] DEBBIE: I love doing that. I have fun doing taxes, doing like brain puzzles that require memory and stuff. I love all those crazy things that people hate. [Laughs] REIN: So, you're a physicist and a data scientist and a TV host. DEBBIE: Correct. REIN: What do you do in your free time? DEBBIE: These days, I spend a lot of time with my kids, with my young kids, and I actually try to teach them science in fun ways. For my three-year old daughter, we have all the science books you can imagine because my husband is a physicist, so everybody gifted us with science books, because what else do you give to scientists? REIN: The one thing we already have. [Laughter] DEBBIE: Exactly. And so, we have like quantum mechanics for babies and thermodynamics for toddlers and you name it. [Laughs] REIN: I could use quantum dynamics for babies, I think. DEBBIE: [Laughs] It's fun. And I started doing live book reviews with my daughter on Instagram. And we have tons of fun. So, that's part of my free time. ASTRID: I did get a Physics for Babies book at my baby shower, which was awesome. It's like, "This is a ball. This is a ball with energy." It's great. [Laughter] DEBBIE: Yeah, they're cool. We really like an author named Ruth Spiro. It's a whole series. So she has like babies and eyesight and optics and aerospace engineering for babies and quarks, Baby Loves Quarks!, that's my daughter's favorite book. I also have the [inaudible] for babies, which actually I don't really like. But anyway, there's like a ton of books and I'm super excited because I just bought a pretty inexpensive microscope. I forgot the name of it. But I'm going to do a review of it. Basically, it's a piece of paper with a magnifying glass and it's for underserved communities in general. But it costs only like 20 some dollars, and you have a microscope in your hands. And you can put all kinds of samples, like the video shows them analyzing what a flower, a pollen and all kinds of things from your backyard and from the kitchen, whatever, all kinds of samples, and see what we can look at with my daughter and get her excited about that. REIN: There's a lot of data in the world today more than ever before, and it's sort of growing exponentially. And it seems like everyone who wants to operate in the world needs to understand data to some extent. Agree or disagree? DEBBIE: You're right. There's a ton of data in the world and things just get more and more complex. And if we don't strive to have a general public data literacy, then it can really lead to disasters. Because, I mean, just to give you an example with Covid right now, there's so many graphs and statistics that have been manipulated to show because it became a political issue and just to show either that the growth of the spread was not as fast as it was and whatnot. So, they only show you part of the data which is misleading, or they cut the Y, the vertical axis so that they manipulate what you visually see and have a gut feeling about. And if we're not educated into how to read those things and ask critical questions on what we're seeing, we're not going to be critical thinkers. And the citizens will always be manipulated by whatever the agenda is from political parties or marketing, advertising programs and whatnot. And so, I do think it's critical, just like one literacy in terms of being able to read and write. We want literacy for data, just the basics. And data literacy, we all often hear the title data literacy and we think, "Oh, that's for the experts, that's for the technical people." But no way, no more. I can tell you that we've all been in airports and we've all seen when you go to the restroom, there are these little machines that have smiley faces or sad face all the way to a frowny face that you can signal and give feedback on what the state of the restroom is like. Did you have a good experience and whatnot. You press a button. Well, that's a data collector. And where is it going to? Well, a janitor is having to have contact with that data without being a data scientist. The janitor used to get a schedule for when he or she would need to clean that restroom, and that may not have been often enough. But now that janitor gets a direct data-based signal that tells them, "Okay. Now, enough people have complained, frowny faces, about the state of the bathroom. So please go and clean." Similarly, a cashier is not what it used to be. They are like collecting data, and every time they ring your purchase, that is going to a database telling the tech team what are the purchases that are most popular, what are the substitutes that people order instead of those purchases and all kinds of data that can lead to different actions. I gave two very simple examples, but of course, data literacy goes from that level all the way to the data scientists who are like at the cutting edge, looking at new tools and technologies to bring back to the company so that they can develop improved algorithms for whatever their goal is. So, I do think data literacy is important, especially Forrester did an analysis and it showed that 80% of jobs by 2030 are going to be affected by automation or some kind of digital data transformation. So, if you're not prepared for that, we are going to be in real bad shape. JOHN: Yeah. I like the example of the janitor because it's not just something that affects people that work at tech companies or people in data science departments or scientists. It comes down to how you understand how a disease is progressing in your community. The number of log scale charts I've seen about Covid that I didn't notice until I read the fine print [inaudible] log scale, I'm like, "Oh, it's not so bad. Oh, no! This is terrible." And just the fact that it wasn't called out or that it was used as some sort of default or it may not even be intentional manipulation, but it's just that poor presentation of data now really impacts the real world. DEBBIE: Thank you, John. Yeah, absolutely. I agree. I think at best, it could be a mistake in how people grasp things. But at worst, it can be intentionally deceiving. And that is really dangerous. In the case of Covid, it could even mean life or death, because if we don't take the right steps that the data is informing us on, then we may lead to many more cases that could have been prevented. ASTRID: So, in terms of the first step to take towards data literacy, especially for somebody who maybe find statistics overwhelming and sees all the different charts as just another form of misinformation or another way to just manipulate people, what do you suggest as their first step so that they can start to have a little more ownership of what it is that they take it and how they process that? DEBBIE: Sure. I give that workshop, it's called Statistics and the Art of Deception. Statistics is a pretty dry field, I would say. Very few people have fun in their like Intro to Statistics courses in college. And I don't blame them because it's taught in a dry way without fun examples and it hasn't really been transformed. And that's a shame because it's really a very, very important field and topic to understand. So, yes, I would start with educating people with examples on the news and on TV and marketing examples of when people have made these very visible mistakes or intentional deception graphs. And so I think it's called vertical measures or -- I'll get the actual URL for you. But it's this great blog that shows examples. For example, correlation is not causation. A lot of people think that because two things move together similarly over time, then they're related, one. And secondly, a stronger statement is one must be causing the other. And that could not be further from the truth. In fact, they have all these beautiful graphs. I saw one the other day. I think you will see the price of margarine was highly correlated, meaning it moved in time, very much like the rate of divorce in Maine. So obviously, to any skeptical person, this is ridiculous. Like there they are moving together by chance because there is no such thing as margarine in any world causing the divorce rate to go up, or margarine consumption. But however, those graphs are meant for people to laugh because they're so obviously not correlated because of that particular factor. They just by chance happen to be moving in similar ways. And so, I think when you see those examples and statistics can actually become quite fun, and you do experiments. Statistics, we don't think of it as an experimental science, but I very much like it when you get the students to stand up in class and move around and create samples. Like, "Everybody that is a flat Earther, go to that corner. And everyone else, go to that other corner." And they start seeing how to form statistical samples why it's so important to have a diversity of opinions. And that translates to how we create our algorithms for them to be not biased or as little biased as possible because there's no such thing in algorithm that's not biased. But we try to relate those intuitive things that we get from playing with statistics to how the data products that we use today, like Uber, Waze, Google Maps, Yelp, et cetera, how they are created and what biases are behind them. So just to answer your question, I'm happy to work with people in giving that workshop or we, at Metis, have a data literacy course that we offer that's really good. And just reading and trying to get acquainted with the basics of data literacy. What is a data product? What assumptions does the model that it's using have that I should know about? And what are the typical applications of this and what can I do with these data? What are the insights that are going to be helpful for me? Just asking those four questions, I think, would be greatly beneficial as a starting point. REIN: It reminds me a little bit of some of the current problems with reproducibility in social sciences and some of the correlations in the studies that are supposedly statistically significant. When the news publishes these results, they say, "Drinking red wine cures cancer." And then it turns out that there are a whole bunch of dots everywhere, and if you draw the line, it's barely moving in one direction. These things where actually the correlation is incredibly small, like the effect, if it exists, is incredibly small and that it's sensationalized by the media. DEBBIE: You know, you just said basically one of the most frustrating aspects of scientific illiteracy today, and that is that people think that science is about facts. And remember that data science has the word science in it. So, we forget sometimes that it uses the scientific method. So, when I say science, I'm also referring to the topics that we study with data science. So, when people see a scientific study telling them, "Use your mask or else you're going to be more at risk of contracting Covid." And then the next week, they see another article from Europe saying, "No, do not use your mask," then you get to comment and add it to that anti-science in the public. And very much just like what you said, examples of do coffee or wine is good for? Oh, no. Coffee and wine are the greatest stressors to the body. So, what do we do with these conflicting facts? People get very frustrated and then they say things such as, "Why should I trust scientists? They change their mind all the time." It's not that. What we fail to see is that the scientific method is designed so that you come closer and closer. And it's a series of iterations to come increasingly closer to the truth. Now, when we gain more evidence, we add it to the study. And that's why our minds change, because we were able to consider one more factor or we were able to get rid of factors that were not the direct cause of that phenomena. So, while the goal is to arrive at a fact, it doesn't mean that it's immutable in those steps. It means that our opinion on things, which is evidence-based, is informed by new data. And changing our mind is actually a sign of openness and dealing with information in an intelligent way. Sometimes, the masses think that scientists should just have one very fixed opinion and never change it. But it couldn't be further from the truth. Scientists are the people who should change opinion the most, because we're constantly discovering new things and new data. And if we train the public to expect somebody who has a steady opinion about how coffee affects us or whatnot without changing their mind when new data comes in, then that is definitely not a good scientist, and then we're in trouble. And so, I think that's a very, very important thing what you just said. We really need to know that science itself is about coming closer and closer to the truth by bringing new models and new data in and discarding stuff that we see is no longer an important factor. JOHN: Yeah, I think that ties in with the sort of popular concept of the -- [inaudible] shows up in fiction, this doesn't show up in an actual science -- of the fact of an experiment being a failure because it's only a failure if you got absolutely no information out of that experiment, which is pretty rare. But the popular thought of it is that either it confirms your thing or it's a failure. And unfortunately, I think it's pretty common for people who have not done science. DEBBIE: John, thank you for saying that. That's very smart. I can't tell you how many people got their PhDs out of a failure. And by failure, I mean a negative result. I was at Stanford and one of our professors, Blas Cabrera, his whole career, he had been looking for the magnetic monopole, meaning like one magnetic charge and there isn't such a thing. But at one point in his career, he thought he'd discovered it and they were about to publish it and they told everyone. And it was very kind of sad and funny at the same time. But he eventually, they reproduced the experiment and he wasn't sure, and there's no magnetic monopole. A lot of people could say, "Wow, what a failure." No, on the contrary, he saved a bunch of research from many different scientists because he knew that his negative result was in fact incredibly important for us to have a clear picture of what happens with the electromagnetic forces. So yes, negative results are very important. This brings me to education again, because there's a great psychologist at Stanford by the name of Carol Dweck, and she wrote a book called Mindset about the difference in education of boys with girls and having to do with failure, John, what you just said, because the way we conditioned boys is to, not [inaudible] a failure, but to be okay with it. The growth mindset is all about having a flexible brain that can grow in knowledge. And even if you're not an expert right now, try and try again until you become an expert in whatever you desire. And that's typically how boys are brought up. And so, they take risks. They take classes where they're not already good at the topic and they experience failure. However, they don't take it personal or it's sort of ingrained in the process that failure is a good thing. However, Carol Dweck states that the way we encourage women to pursue their educational path is more with a fixed mindset, meaning you have a fixed amount of intelligence that cannot grow and stretch. And so, you better stay doing the things that you're already good at, which are going to be fewer, because, of course, if you don't gain new skills, you stay with the ones that people already crazy for. And that creates people who, especially in the sciences and in data science, feel very inadequate and have an impostor complex many times because data science and science is all about failing many times that the successful data scientists are the ones that after failing to find something with that one model, they could try a different one and then they fail again and they try a different one, and so on and so on until they find the one that gives them the right information. If you don't have a growth mindset and you're not used to practicing failure for the greater positive success, then you're going to not pursue those deals and that's a really sad thing, because many people, if they tried it, they would fall in love with doing data analysis. ASTRID: I think your point, Debbie, about growth mindset is really important. I was reading something actually this morning on Medium and it was about a woman who's now an oncologist, but she was recounting when she first started college. She went to Brown and she had actually gotten accepted into a joint undergraduate and medical school appointment. And in her first chemistry class that she took, her first test, she got a C. And when she went to talk to her guidance counselor, which was a mandatory thing, the guidance counselor questioned whether or not she could pursue her undergraduate degree, and asked her about what her plans are. And she had told him, "I want to be a doctor." And he questioned whether or not she would be able to make it as a doctor, not knowing that she had already been accepted to medical school. But his response to her made her question herself, and she ended up not pursuing her MD after she finished and she went and got a Masters in Public Health. And only after working a while in that field and meeting other women, especially in her case, other black women who encouraged her, she went back to medical school. But she was talking about how for her, she had never had a C before. It was a shocking moment for her. It was her first year and first semester in college. But also, the response that she was getting from people who seemed to be authority figures in the area made her question, "These things that I thought I could be good at, maybe I'm wrong. Maybe I can't be good at those things." And it seems like a lot of people also have that experience of, they want to try to do something that they've never done before and then they get shut down by someone who seems to be an authority figure, maybe unintentionally, but just asking questions a certain way, but sometimes intentionally. And I think that we talk a lot about things that individuals can try to do to make sure that they continue to succeed, but it seems like there's also a need for us to start to be more cognizant of our systems that we build around these things so that people can try again and people can fail. Because one of things she talked about was, "It would have been nice to have the option of having a mistake and not been dismissed," that she needed to be able to go through something but still have the option on the other side not to be excellent at every step. And I know with programing, this is a big thing, especially people like me who switched into this field, not started out this way. The first thing you think is, "Oh, my gosh. Look at all these people, they're so good. I can't be and do what they do. I don't know how I'm going to keep moving." And there are a lot of people who do try to encourage you. But one of the bigger hurdles a lot of people come up with is when they get on their first programing team and then they have that whoever coder who is like, "I don't even know why you're here. I wish that you could not touch my code. I don't even want to do your code review because it's so awful." How to keep going and how to find other communities of people who will encourage you, because it seems like in general, in the sciences, this is a reoccurring issue of, "I want to do this. I'm trying. And then I get all this pushback and now I don't know what to do next." DEBBIE: Wow. Astrid, great story. And I can't tell you how much I resonate with it. I can share that when I was growing up in Mexico City, I experienced the same type of bias and just lack of confidence in my own skills because I was told by every teacher, every classmate, even my own parents that love me very much, said, when I said, "I want to study physics and math, I'm very curious." They said, "Oh, it's not very appropriate for a girl and you don't have the skill set [inaudible] in school because you need to be a genius in order to study physics." And I had very good grades, but still it was not enough. And they said, "You better study something more feminine, like marketing or something else." Even though I was not interested in the least. And how many women have I heard from that actually end up studying marketing or law or whatnot because they were too embarrassed to get the experiment and get that C once, twice, three times, but then go on to succeed in science. And so, I always tell the women I mentor that if I was able to do it, anybody can do it. I had lack of confidence and very few people believed that I could do it. And I was able to do it. So, it's all about perseverance. And more than that, it's about knowing that you should get up and pursue your dream, no matter what. Even if you fail once, you keep going and going. And I tell you, you build a thick skin, because I recall when I was in the PhD program, when my colleagues would call their parents and say, "Oh, I'm so upset, I'm depressed. I didn't pass the exam," a lot of the parents would tell them, "Don't worry. Your father, who also has a PhD and is a professor, went through the same thing. Keep going. It's just one little failure, but you should just keep going." But when I would call home and I was depressed about not doing well in an exam, I would get the, "Well, we told you this is not good for you. Pack your bags, come back and live a normal life." And it almost made me want to do it even more. And I have met the people who succeed in life are the ones that have that strength, not necessarily the innate talent, although that's important, too. But it's that perseverance that gets you to the end. JOHN: Yeah, I do find it somewhat tragic, I guess, that that sort of perseverance is required, that you have to fight so many headwinds in order to get there. And then the further twist of the knife that once you get there, once you get into that college program, once you fight against all of that pressure, then you have to be perfect the whole time because any failure is going to be used as evidence against why you should be there in the first place or you get into that CS weed-out course that's just horrible. And then you have to further summon your personal energy to fight through that and to pick yourself up off the ground. I mean, it's just really heartbreaking that that much mental fortitude is required to do something that's already somewhat hard inherently. So, it would be great if we could find a way to stop that from happening. DEBBIE: I couldn't agree more. REIN: And also, we know that human performance is contextual. It's ecological. It has to do with the environment. It has to do with a lot more than just what's up here. And so if someone gets a C, the question isn't, why weren't they smart enough? The real question is, what was their environment like that they didn't get the support they needed. The grade people get don't seem to be very well correlated with things like intelligence. They seem to be more correlated with things like, does the professor like you and help you when you ask for help? DEBBIE: Yes, absolutely. I think we went through that when we were at Stanford. And at some point, we were two women out of 34 people in our class. And there was one woman before us, I think she was one class above us, and she had not passed the qualifying exam, which allows you to continue to the PhD, and she had left Stanford. And at one point when we also had issues with the qualifying exam, we went and asked, "Why is it, we investigated that women tend to have more problems passing the qualifying exam and end up having to leave the program after just claiming a master's degree." And we were told that physics departments were male-dominated in the US and will continue to be, which we later complained about that statement. But what was interesting was that the environment in which we took that qualifying exam was really stressful. And who knows? Maybe if they research then it could be that it was simply more stressful for women. I remember it took two days and we were in a basement where there was no light. And I remember we didn't even have chairs. It was like a bench, like a lab bench and it's quite uncomfortable. We were fed pizza at lunch, just a couple slices, and then the whole day, we were solving these really complex problems for two days, Saturday and Sunday. And who knows, I bet you, we could do research that some people, I'm not saying it's gender related, but some people just do not react well to that kind of stressful environment when they have to do a test. JOHN: There's also a wider context involved, too, which is not just the context of having the test right there, but also the context of what happens in your life while you are doing your PhD program, if you have enough support that you can have a quiet apartment where you can actually get a good night's sleep. These are all going to be affected by your socioeconomic status as well as your gender and race as well. And so, there's so many of these factors that are going to tie into that. ARTY: We've also got this world that is completely changing right now. And what we've done and always done for, I say always, but for some amount of time, we've had these institutions and structures around education and sort of this recipe for how you're supposed to engage with the world where there's these career opportunities that have these certain labels. And you're supposed to figure out, "What do I want to do with my career," and fit myself into this system of cogs. We had this idea that this is how the economy is supposed to work, that this is how education is supposed to work, because it's been sort of this system that we've grown up with. And now, everything is kind of like [explosion sound] disruption. And so, we think back to these fundamentals around science and discovery, and what is it that actually matters in life? What is it we want to do while we're here, this time in our planet? What is it we want to discover? There's something fundamentally human about discovering new things and pursuing knowledge of what it means to be a scientist. And it's not about this fitting a cog in a machine, it's about that experience of discovery. And I feel like with this disruption with education and all of these things, getting back to those fundamentals such that we can recreate new different sorts of things that are more anchored in these fundamental first principles of what it means to be a human among humans, to pursue knowledge, to try and make the world a bit better place while we're here. I feel like we need to get back to our roots and get back to really basic things in terms of human relationships and discovery and remembering why we're even here doing any of this stuff. It's all a big hamster wheel. With this sort of disruption, I'm thinking back to what are the reasons you became a scientist to begin with? Like, what inspired you to go down this path? DEBBIE: I was always a very inquisitive child and I would ask questions all the time. And my father was a civil engineer, so he was somewhat technical. And he would take me on road trips to hydraulic dams that he was working on. He would always explain the bridges and the forces on the columns and how they were constructed. And I was always fascinated by that, and I would ask lots of questions. And then I remember that I like David Bowie. And there was this guy in school who was very strange. And he was just into physics. Already in my mind, my heroes were obscure scientists like Tycho Brahe who was a Danish astronomer, who is said to have lost his nose in a duel because he was quite anti-social. And I said to myself, because everybody made me believe that I wasn't going to be socially accepted if I studied physics, so I said, "Okay, I'll be like Tycho Brahe. Maybe I'll be locked up in a tower or in an observatory, but at least I'll have my observations with me." And so, I just thought that I was this strange person that really cared about how the world works. And it was the insatiable thirst to know why things happen the way they do. And the more I read these books, the more they became guiding light, so to speak, in my cap. And I don't really know because I didn't have a role model growing up. It was just this one person that liked physics and we liked the same music. And I just said, "Oh, wow, what is this about?" And I fell in love with him. ARTY: It's interesting how relationships affect us. I've found, like in different relationships, the different side of me will come out and then I'll see a new side of myself. And then I'll be like, "I'm really cool. I like this side of me." And even if the relationship ends up ending, sometimes just that experience and interaction, we end up falling in love with the new side of ourselves and discovering these new passions and things we get excited about. And what I'm hearing, too, is a certain resilience that you built around this pursuit in this character that you were becoming and being different than when you could kind of put yourself in the shoes, "Well, that's fine. We'll just be locked up in a tower," and you knew you could construct in narratives of coolness and individuality with your identity such that even in the face of all of these challenges, they come up, that having that resilience of that character, even if it was like this outcast weirdo character, gave you power in resilience and being able to hold up to whatever those differing opinions where you'd go, "That's fine. I'll just do my cool, unique thing." And that's great. I mean, I think those kinds of things are important with respect to resilience and having our own sort of self-love loop. And sometimes, that often happens through a relationship with someone else and falling in love with this aspect of ourselves is a great story, too. ASTRID: I have this book on my shelf called Your Story is Your Power, which I haven't read yet. So, I can't really talk about that book. But it sounds kind of similar to what you're talking about, Arty. But also, I did read Michelle Obama's book Becoming, which I thought was a really great title because her whole book is really about creating herself in these different situations. And it started me thinking about some of what you brought up, but also this idea that we separate career from ourselves. Like, I'm going to pursue a career and then I'm a different person, instead of thinking about it as an expression of who we are and as a means of us being able to experiment with who we are in the world. And thinking about it that way makes it a little bit easier, I think, to try things that you haven't tried before, if you're thinking about how you may want to grow as a person. But I also think it allows you to give a more authentic version of yourself when you are doing your work. And I think that alone can be really inspirational for other people who are looking for, "I don't know where I fit." I love what you said, Debbie, about like, "There's this weird person and they like physics. So, I think maybe I could like physics because I feel weird." I think there's a lot of people who feel off, they don't think that they have like a particular peer group and they're trying to find like some sort of beacon of what do I do with that? And I think a lot of us are kind of just going about our life as a separate thing from the work that we're pursuing. And so, it makes it even harder to try to figure out if you are that person, what looks like me or feels like me. So maybe the more that we start to kind of merge these worlds of what we do for our work and why we do that work and then who we think we are, the more that other people can start to see themselves as well. And that might result in a world where people are actually a lot more closer to the thing that they want to do, like the impact that they want to make. Because when we were having this discussion about science and how it can be exclusionary, I think the sad part about that is it is one of the best ways to try to learn new things about who you are, too. It's to use that same process of, "I'm going to test something and see how it goes. And even if it doesn't work out the way that I thought, I can still learn something about what I am, what I like." Keeping that as a separate category as there are scientists who do that, and then there's other regular people, means that the average person who may not see themselves as a scientist doesn't realize that that's a thing they could be doing. And that science is a living, breathing action. It's not a thing you become, it's a thing that you are. Like how writers are people who write, scientists are people who do experiments and have inquisitive notions about the world that they investigate. And I think if we could do a little bit more of making that obvious and not so behind walls and only with certain degrees, then I think maybe some people would have a little more access as well. DEBBIE: Right. Yes. REIN: I would kind of like to talk a little bit about the TV show, because that's super cool. DEBBIE: Yes. One thing that I didn't like about academic physics is that I had to perform all the experiments through all nights and weekends and I had very little contact with the outside world for months. I would just see my lab colleagues, if I saw them when I wasn't doing the experiments myself. So, if you're a sociable person like me, then it can be a little bit isolating. And so, I recall when I had the opportunity, I had a teacher friend, my friend Amanda, in the Silicon Valley, and she was a teacher. She said, "Come to my school and teach first or second grade level kids what it is like to be a scientist." And a lot of the kids she was teaching were from Mexico originally and they had never seen a scientist, let alone a woman and a woman from Mexico. So, it was really inspiring because they went back home and they told their parents that this was an available option for them. And then I spoke to the parents in Spanish. And they got really excited because they had really thought that a scientist was not only impossible to access, but it was something that they shouldn't learn because it was impossible to make a living. And they were very boring professionals wearing a robe and being stuck in the lab. And when I showed them that not only that there were many career options after studying science and that they were quite profitable if that was their interest, but that also it was a fascinating field. And I showed them, I created a program called The Science of Everyday Life, where I would show myself explaining the science of daily things like the physics of high heels, the chemistry in the kitchen, and a bunch of stuff. And I was writing in the Stanford newspaper and I had so much fun building a bridge between complex concepts in entertaining ways of explaining things that I just thought, "This is my calling. This is what I want to do." Yes, I want to continue to do complex science, but I get such a kick out of explaining those complex concepts. Fast forward, I moved to New York and I started tweeting and somebody in the early days in 2006, I believe, said, "Hey, Oprah Winfrey wrote in her magazine that she's looking for women to participate in her Women in Leadership Conference." So, I sent my project and it was called The Science of Everyday Life. The Oprah people that read it really liked it. So, I was invited to participate at the conference. And more than that, my project got selected to be presented as a keynote speech. And so, I remember Gayle King was there and 80 women that had been selected who were all leaders in their field and had great ideas for projects. It just inspired me so much and I could see the impact I could have with other people in the world. So, for the first time, I felt useful not only to my colleagues in physics, but to the rest of the world. Dr. Oz invited me to his show and then I said, "You know what? I'm going to make physics the most fun and fascinating topic. And even Oprah is going to love it. Everyone's mother is going to now care about the physics behind the makeup they're using or the cars they're driving, the gadgets they're buying." And so, I set as a goal to create more videos and more media content describing how fun and how incredibly fascinating physics is. At one point, somebody said, "Hey, they're looking for women physicists under 35 years old who live in New York that want to be on camera." I don't know how many of those were there. Maybe I was the only one. REIN: So, like all three of you showed up. DEBBIE: Yes. So, I showed up in this audition, which was incredibly stressful because they put me in front of the camera. I was sweating bullets. I had to answer questions like, "If there's a guy that gets run over by eight trucks and he survives, he doesn't break anything but one rib, how does he survive and how would you prove that with one experiment?" It turns out it was a crazy show called Humanly Impossible. I don't know if nobody else wanted to be on it or what. But the next day, even though I thought I did terrible in the interview, they called me and they said, "You're in." They didn't pay me a penny. And I had to take two weeks to film this show. I would take the subway to Brooklyn, to this studio where we would film. And it was incredible. I met a blind man who used echolocation making sounds to map his surroundings and know when he could ride a bicycle in between cars and he could play basketball in front of us and make more baskets than I ever could. It was just fascinating because of this physics principle. And then one guy jumped from 30 feet into a shallow pool of water and survived. Another guy named Nippulini and he's become quite famous. He basically carried a bunch of weight with rings attached to his nipples. Anyway, I was explaining the science behind all these crazy feats that crazy people were doing. I remember I was always following the emergency medical doctor because I was so nervous every time that something was going to happen. Anyway, the show was a great success. And from there, I realized, "Wow, this is incredible," because it generated a lot of fans writing to me. I provided my email and stuff and they would write to me to ask questions. And so now, I saw that TV had a great impact. And from then, one day I got a call by a friend that said, "Hey, there's another show in the UK looking for female physicist." And I said, "I am in." And I remember memorizing a script for it and reading it. I was alone in my apartment. I was single. I didn't have even friends who would like help me film it. And so, I put my Android phone in some furniture and filmed myself with bad lighting. And following this script, I had to explain what a trebuchet was, which is this machine that can throw cars for very long distances. And I had to learn what it was. And so, I was scared about English not being my first language and my accent and not being able to remember the details and the numbers. At one point, I was fed up and I said, "You know what? I'm just going to do it from memory, from what I already read. I'm not going to be stiff." I grabbed a glass of wine, I drank it. I pulled a lamp underneath my face, like I changed the whole light. And I just went with my heart and I was much more passionate about it, even if I might have gotten some of the concepts wrong. I don't think I did. But I just kind of relaxed and went with my passion as opposed to only trying to be perfect again. And two weeks, I didn't hear from them. And after that, they got back. They're like, "You are in." And it became the show that I currently co-host titled Outrageous Acts of Science by the Discovery Channel. It's an international show. And it was the number one show on the Science Channel, which is the channel that broadcasts it in the US. And it has been a blast. We get together once every three months to film more episodes and we basically are given a bunch of videos where people are shown doing crazy things. And we are seven scientists that explain from different perspectives the science behind all this crazy engineering feats. And it's edited in a very cool and engaging way. It can be watched on iTunes, Amazon Prime, and YouTube. And it's really a wonderful show. We have 11 seasons of it out. ASTRID: Wow, I love that story because you can tell that it seemed like once you figured out what you loved, that as long as you focused on that, all these other amazing things came to you. DEBBIE: Yes. REIN: The other thing that really stood out to me was when you were able to connect your knowledge with your passion. It wasn't enough to just know the stuff. You had to find a way to be authentic. DEBBIE: Yes. REIN: I like that. I did have a question actually from a bit earlier on when you were talking about finding ways to explain complex concepts. And so, my question is, how do you approach that? DEBBIE: Because I was a person for whom things didn't just come easily, I think I know what it's like to not understand the concept. So even when I'm studying physics and I'm reading a String Theory book or something quite complex, I argue with my husband about the very basics of it. We always go back to the basics. Whereas a lot of people don't need to, but I find the need to really dig in and see where does this theory come from? What were the assumptions that they made and why are they okay? And really go back to the basics. I studied my PhD with the Nobel Prize winner, Bob Laughlin, and he's amazing. And the reason why he got the Nobel Prize due to the fractional quantum Hall effect is because he went back to the basics. Even though he was quite lucid in physics and an amazing professor, he felt the need to go back and just retreat from society for a while and really just derive things in different ways. And that's why he came up with that theory. So, I think I learned from him that it's quite useful to do that. So, what I do, whenever I'm going to explain something for TV or for a company that I'm doing a workshop for, or a training in data science, I go back to the very basics. How would a baby, how would a toddler, how would my mom look at these graphs? How would she experience this? Where would her eyes set on this slide? And how can I make it more simple? And I actually test it. I go and I ask people that have nothing to do with data science, "Is this concept clear? What kinds of questions come up in your mind that I can explain better?" And I go lower and lower and lower level until I feel like I've mastered the very basic pieces of what forms that complex concept. So then, I prepare the path, this logical path that starts, "Okay, from the very basics, this is what mechanics does." And then you go on to [inaudible]. Add that in and you add these other data. And this is how the concept comes about. And I think people really appreciate it because nobody's talking down to them. They're actually sort of going with me in that journey of discovery that I myself went through in order to understand this complex theory. And so, the similar questions are popping up in their head. And I have the answer for them right away. ASTRID: I love that because it makes it feel like, even for people who've been doing this for a while, it's that first step of really understanding the core concepts that makes it possible for you to understand, possible for you to teach it. So that makes it feel like it's possible for someone who's new to be able to feel it big and be a part of that world. Because it's not going to take them years and years and years just be able to have a conversation. DEBBIE: Exactly. And Astrid, you just reminded me your question about coding of something that I'm a big proponent of teaching coding as a tool and not as a means to an end, and not just as coding for coding itself. I think the mistake that people are doing these days is there's so many coding programs out there that are just built to learn the very basic and superficial aspects, the mechanics of coding, without knowing that it's just a tool to discover, to solve problems, to gain insights, and whatnot, that in a rush to get everyone, all the kids to code, they're forgetting why we're doing it. I'll give you an example. I went once to the Museum of Natural History and they had a program for underrepresented communities. It was an afternoon program where mostly young women were learning how to code. And when I was talking to them, they were really smart girls and they had been working with SQL and knowing how to query these database [inaudible], and they were so proficient at it. I was very impressed. I was even jealous thinking I wish I had that opportunity at their age. And then I go to one group who was using these tools to study the museum's turtles. And then I saw a column on the data, had the title of Weight and the numbers were 100, 150, 300, but it had no units. And so, I asked the girls, "So, how big are the turtles that you're studying?" They said, "Oh, they fit in the palm of our hands. We've held them before." I said, "Wow. And so, what are these numbers of, the weight? Is it in kilograms? Are they in pounds? What is weighed in?" They all of a sudden fell silent, even though they had been working with these data for three months. And they were great at manipulating all these query terms in SQL, they had forgotten or they had no clue. Until one girl lifts her hand and says, "Oh, yeah, I know. I know. I think it's pounds." And I said, "Oh, wow, that's a little odd because I myself, a regular sized woman and I don't weigh 200 pounds. I'm like around 120, 130 pounds. And you're telling me that these little turtles that fit in the palm of your hand weigh up to 300 pounds, like double my size. Wow!" And then they all realized what they were saying and how it did not make any sense. And eventually, we talked to one of the teachers and they said, "Oh, it's actually grams. The weight is in grams." And I was like, "Oh, okay." And that was an illustrative experience because it taught me that in our rush to teach all these young people how to code, we had forgotten that code is to solve problems. And that the best thing we can teach them is how to think critically about the world, that is how to know exactly what they're doing and why they're doing it and what the purpose of it is and what the code's goal is. And they failed to respond to that. And so, I think we should absolutely add critical thinking as one of the essential skills before we want them to master the details of coding. REIN: I just want to highlight one thing that you mentioned there, which is -- so, these girls didn't remember what the unit was. And one interpretation might be, "Oh, these girls are stupid." But the important interpretation that gets back to human performance and how it's not just about individuals is they were taught in a way that they didn't remember. DEBBIE: Oh, I absolutely can tell you that these women were bright as can be. They were very, very intelligent and performed coding in a way that maybe few people can do in only three months. They were very bright. But it's more that they're not taught about caring, why they're doing it. All the message they received is be incredibly good of coding and give me the results I want. But they weren't taught about thinking about the whole problem and the vision, the bird's eye view. What am I doing? Why am I doing it? What is it for? Who is going to use these results? All of those big questions that we need to teach people how to ask, because maybe the algorithm would change if they knew that they were going to analyze small turtles. Maybe there are different assumptions that they would make. REIN: Yeah, I don't think those girls were stupid. I also don't think a woman who gets a C on her first physics exam is stupid. ASTRID: I think it's partly like this whole loop broken everything into such small pieces that we don't really talk about big concepts anymore in the beginning. Like with this assumption that if we give you all the little pieces, you will eventually get this big concept. But I know like for me, I'm the type of person where I want to understand why I'm doing it first, because if I understand why I'm doing it, then I'm motivated to do all the little things it may take for me to get there. But if you give me a bunch of little pieces, then I don't really always know what to do with all the little pieces. And then it becomes really easy to get kind of lost in the weeds and then not really be able to understand, why do I need this or should I use this? I don't know. It makes it appear as though you're not capable or you're not intelligent. But really, the truth is there's not like a map that you have already laid out of these are the steps I'm taking to get to this endpoint, which is really what you're talking about, Debbie, the critical thinking, the problem solving. Like here is a big, nasty problem. What are we going to do? What are the first, second, third things you're going to think about? What are the steps you're going to take to test and see, is this going to work or is there something better? Yes, you can use code to do that, but you have to understand that that's what you're doing. You're not just writing beautiful code. DEBBIE: Yes. REIN: I think one of the other problems is that, and I think this is especially true of software engineers, is that we're trained to go down and in, we're trained to drill down, to look closer, to get to the tiniest detail to figure out what the problem is, when actually what you often have to do is go up and out and look more holistically, like you were saying, Astrid, at the problem. And that actually is something that ends up [inaudible] software engineers, because when they start to become more senior, they need to be able to take this more holistic view of the work they're doing. I mean, it's good all the time, but it's one of the things that gets you promoted when you go to the higher levels of being an individual contributor. ASTRID: Sure. It's also one of the reasons why lately I've been reacquainting myself with learning programing again, because I don't have to do it in my job right now, but it's something I enjoy doing. But I want to find a better way to do it for me. And one of the ways that I have found that's been helpful is to think about it more like being an artist rather than being a scientist, because being a scientist, you are trying to break things down into their smallest level and often trying to understand it at that little bitty [inaudible] place. But if you're an artist, you have a bigger goal usually. So, you're using whatever your medium is. So, if you think of programming as a medium to create something. And how that gets created can be multiple ways because it depends on what your process is. But the outcome is really what you're trying to get to. You're trying to get to 'I want this to live in the world'. And I may go about it in different ways. But the goal is this other, bigger thing that's bigger than me usually, has a deeper meaning. There's a purpose to it. It kind of has an end when it reaches its goal sort of thing, which I know that for me, it's been really helpful to think about programming more like being an artist. Like, I'm trying to create a work and I'm going to use programming to do that. So now, let me start thinking about how I go about solving out a little problem so I can actually create something. ARTY: Very cool approach. I like it. ASTRID: And it also makes me feel special when I do it. ARTY: I love that. ASTRID: Are we at reflection time, you think? REIN: Yeah, I think so. I can go first. Debbie, when you were talking about breaking down concepts and building a bridge to connect with the people who are trying to learn, I was thinking about Gordon Pask's theory of learning which he calls Conversation Theory. The sort of basic idea there is that learning isn't transferred. So, I don't pick something out of my brain and drop it into yours. It's constructed. So, this is a constructivist theory. And specifically, the way it's constructed is that you first build a bridge sort of from both sides of the bank towards the center. I'm trying to reach you when you're trying to reach me. And then this is where this metaphor sort of falls apart. Once you get there, you build up together towards what someone's trying to learn. You're taking them with you and you're building together. The formal parts of this theory talk about, like what the process of finding common ground looks like and so on. But I like the analogy, too. ARTY: I think the thing that really stuck with me was thinking about science in this process of discovery and problem solving, and how easy it is to get lost in all the details of the things we do and start taking all this data to mean whatever it is we want it to mean. We talked about all these things kind of going off the rails and yet at the same time, there's this fundamental thing that is anchoring everything of critical thinking and problem solving. And all these things are a means for some purpose. And as long as we kind of shift to this mode of operating that, we only need to know the how-to things and we teach the how-to things in absence of that context. We're always going to be kind of tumbling down this rabbit hole of challenges that come with trying to teach the how without teaching the why. And that story of what initially inspired you, Debbie, to become a scientist and become a physicist in that passion and curiosity, it was lit up like a Christmas tree. Just to see that inspiration and excitement in you and being able to teach that and help others to find that inspiration and accepting themselves through the work you do is a beautiful thing. Maybe you can help others to find their story and their power and their passion and to pursue their dreams. ASTRID: I think the thing that most stuck out to me is, Debbie, when you were telling us different stories like your curiosity as a child and the tenacity you had to have going through your PhD program and then realizing that you didn't only want to do academic work, you wanted to be more sociable, that it seems like your perseverance and just trying to be your weird self, as you called yourself, seems to be the thing that's catapulted you to all these different levels in your life and in your career. And that the more that you kind of often think about that, "I like this, I want to do this," or, "I do like this, but I don't really want to be in this basement lab. I want to be out there with people," that the more that you did that, it seems like the more your influence was able to spread and that you were able to reach more and more people, which wasn't your original reason for doing this. You were just trying to satisfy your own internal urges. So, it kind of feels like maybe without meaning to, you have this inspirational story of trying to just be yourself. And that in trying to be yourself and continue to allow yourself to evolve, that you've been able to build this amazing career, not necessarily by accident, but more so like through love and attention to trying to be a person that not everybody wanted you to be. DEBBIE: Thanks, Astrid. I think for me, what was inspiring about this recording is that I met you all today. At times, it felt like you were all friends in my living room because I've done a number of podcasts in recent weeks and this one was particularly striking in that you guys kind of weeded out the interesting details of my life story rather than focusing on buzz words, "What does she do as a Chief Data Scientist at Metis?" You were interested in the person behind the title and the professional persona. And I really, really enjoyed. You made me kind of travel back to my childhood and remember things. And each one of you has such a unique perspective. Like, John had all these great intellectual ideas. And Astrid, you were like very kind of experiential, like, "Okay, this is a story of... This is what I experience... This is what I'm doing." Really interesting. And Arty, you were more like about kind of the heart and trying to figure out the motivation and what is this all about and why are we doing the things that we're doing. So, yeah, I don't know. Everyone had like a very unique way of seeing things, and I really, really enjoyed that. REIN: Cool. Well, that means we're doing it right. DEBBIE: You, of all people, you had very insightful questions. They were very always well thought out and that was very cool. I could see your brain working. ASTRID: Rein is our resident philosopher. REIN: Yeah, a little bit. Oh, Arty, I wanted to mention. You were talking about how you wish there was more art to go with the science. I have a definition of art and science that comes from [inaudible]. His definition of science is finding similarities among things that are different. And his definition of art is finding differences among things that are similar. DEBBIE: Interesting. REIN: I thought that you might get a kick out of it. ASTRID: Say it again, Rein. It's finding similarities among things that are different, that's science. REIN: Yes. ASTRID: And then what is the opposite? REIN: Art is finding differences among things that are similar. ASTRID: That's interesting. I have to think about that. ARTY: Yeah. I feel like I need to ponder that one a bit more too. [Laughter] ASTRID: Well, Debbie, it's been amazing having you on the show today. It's been such a great conversation. I feel like we could talk forever. DEBBIE: I absolutely feel the same, seriously. Thank you so much. REIN: Thank you so much, Debbie.