Teaching Computing in Arts and Humanities

Teaching Computing in Arts and Humanities

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I'm a ghost in Rio I'm the Associate Dean of humanities Arts. And Social Sciences. We. Have some fabulous, speakers they all gave me a sneak preview so, I'm very excited. What's. Gonna happen is the, reach going to speak for ten minutes and then at the end we're, gonna have some, time for discussion and. We'll, proceed an alphabetical, order our. First speaker, is alberto. Jávea who's. An economist, so he's, an econometrician. And. He. Is an empirical, micro. Economist, and he's. A pioneer. Of synthetic, controls, which is this important. Way of answering counterfactual, questions and, economics. So. He's, a professor. In, the Economics Department and, he's also associate. Director, of our, Institute of. Data systems. And society. Okay. Good. Morning and thank you I wish Dean for the introduction. I'll I'm. Not in computer science and, then an economist, and batam I'm going to use my. My, time to talk about a recent, confluency. Named methods. And goals between. Computer. Science and the Social Sciences that. In my view is making. That. Other students in the social sciences, note about computation increasingly, important, but, also that you know like him. Computer. Science students, know more. About these social science, aspects, of the problems that they that. They are studying. So. Reflected, of this confluence that I'm going, to talk, about like, I guess many of you know that it MIT we. Have a now a new. Undergraduate. Major, in, computer. Science economics and data science, this, is a very. Popular one as. I was saying said I am I am. And. Faculty. At the Department of Economics, and many, of my students. Many of our students that they have undergraduate, students, come from from, this major. Also. At the graduate. Level our, PhD. Students in economics, and PhD students in political, science now have the opportunity, to have the option of a graduating, with a dual degree in economics, and data science, or a degree, in, political science and data science and we offered a team in, collaboration. With the MIT. Statistics. And data science, center and, this is also something that has generated a lot of interest, a, lot of attention, Efrain from, our students. Aside. From being at the Department of Economics and also associate director of ITSs that this MIT stated, for data systems, and society, the. Mission, of idss, is to combine, expertise. In like an Information, Systems and, their social and efficient sciences to, advance like a education. And. Research, with. The goal of a you know addressing the most. Pressing, more challenging, associated, problems we. Try to do that using data. And, using analytical, methods and within. Idss, like, one. Of them one. I guess I should be there. You go that's what is this poor. One. Of them programs. We have is them the. Ph.d program in socio-economic, system, and what we try to do there precisely to train our students. In the intersection, between information, system and the. Social sciences. So. You. Can ask like why all this assignment on the part of our students, and on the part of the university, to create this space of confluence, between, the. Social Sciences and, computer. Science well, you. Know from, the previous speaker I think it is pretty obvious like a for. Once like a the, way we operate in like an social, and economic environments has completely, changed in. The last two, decades like, I now we use like automated systems, and did, our platforms, to like communicate with each other and buy. Goods and services, and and you. Know to change how our commuting. Route if they if there, is traffic right, and this, is like a quite, drastic, technological. Change and social change but also like an you. Know like one that may have you. Know like him profound. Implications. For policy, and and. Then that's, something that I associate, silence a care. Quite, a lot about and now we, have like a bottom.

People, In social science, and in computer, science like that care about problems, like him electronic. Commerce, or, like a algorithmic. Furnace or the preparation of fake news in in the heated bathrooms okay, and I'm. Understanding. This problems, and you know if we, want to understand this problems we need data we need models of human behavior but. We also need an understanding of how the system works so. Um. This. Confluence, of them you know of. Goals. Also cause create the conference, of methods I think, I if I tell you that them you, know in teaching a course on you know like about, complex. Dynamic systems and networks, or causal inference, and prediction call, it alarm in game. Theory mechanism, design or you. Know artificial, intelligent, ania and dynamic. Discrete choice methods, like, perhaps you don't know if I'm doing this man Department of Economics, or I'm doing this and like in in you, know the permanent critic or sorry or department. Of computer science okay. So, um. So. This. Is a dis. A conference, of methods also is um is facilitating. Not only them you. Know like a communication. Across fields but also teaching, across field a suit something today that we, take we tried to, do here. You. Know another tech term enforce that is. That. Is I am receiving, the Social Sciences any. Same you, know like a reshaping. What did you know our students, should know is the change in that nature. Of data, you. Know in an economy's like you. Know like a beta. Is one of the primary ingredients of, a you knows of science, and you know in the past we use like him you, know like a beta, that came from Inspira, mental sources like a service, horses like animistic, administrated. Sources, okay, and these datasets them to be like a very granular in terms of you know they described, individuals, in. The sample very well but, they tend, to have very limited information about, how these individuals, like hey interact. With each other okay, and because. Of that people in the social sciences, are more and more interested in going, with some things called like a big data sources okay. And then for, you. Know like often, in computer, science like, a people. Will think okay so what is this data big sorry, what is big data big, data is data that is big and. You. Know for me as a social scientist as does that characterization is not particularly. You. Know appealing but, we may think about big data what you know in, in, different, ways we may think up I like to think about a big data say you know as a beta environment that has like a three. Component like, a first. Is like a bus, conductor of, data that are collected routinely, by automated. Systems, okay. Second, is like a large completed, computing, capabilities, allow us to analyze, this data in. Real time and theories. Like we can use this. This same ultimate assistant, that collected data, to deploy. Sophisticated. Personalized, intervention. Okay. So like him for. For. Me as some you, know as an economist, when I see, this not like a new. And, big. Data environment where I think this at. The. Bar is going to receive the Social Sciences in the next half you know couple of date decades, is how, to use these large amount of data how to use the dislike, a large. Computing capabilities, to, make have a public. Policy more. Efficient as martyrs. And to, addressed you know like a complex. Little challenge that we have and, that. Is, what we have to you. Know like train our students. For like I think that they you know as, you said in the previous I'm. In. The previous intervention, in the previous talk and you know other students are going to be. In this like a heavy. Interdisciplinary. Environments, and we need to providing with the tools and the metals that will be that they they. Should have in order to be able, to, successfully. Address this type of. So. Our next speaker is Mike, Casper. Who, is an expert, in computer-aided.

Musical. Analysis. So. He is the creator of music 21 which, is. Very. Broadly used toolkit, for. Computer. Based musical, analysis. Mike. Is professor. In the music. Department and, he's, also faculty. Director, of digital, humanities at. MIT. Thank. You all for coming so. I'll not, wear, my music hat today I'll wearing humanity's hat, and to. Say a little bit about humanists. Humanists. Are people. Who tell stories about the world and. That's about our world the past the, present occasionally. The future but, and. We. Sometimes couch, our writing. Of stories, in language, that says you know something like well what we construct, new narratives, that invert, perpetuated. Hierarchies. Of privilege, and but, really we write stories and. And. It's, another way of saying that I'm writing a new story a, different, story hopefully a better one but at the heart story. Now. Sometimes our stories are about really big things, the. French Revolution. The structure of families, in Africa. What. Does the Sistine, Chapel really mean and. Quite. Often though they're about very small things that we hope to lead to something bigger in. My training as a music historian, I once wrote 40 pages on one. Sheet. Of music paper it. Happened to be blank a blank, sheet, yet, from that you know I was able to get into the structure of economies. Of monks in Padua and an italian, foreign relations in the 15th century and so, on but whatever. It is it's a story. Now. What distinguishes, us from, screenwriters. And novelists, whom we hope write better, stories than we do is. That our stories need to be about this world about. Facts real, events, at least as much as we can discern them and stories. They're based on evidence that we discovered, analyses. That we conducted. And stories. That could not have been told before. In. This research, process, which we might call data gathering good, humanists, behave kind of like the scientists, and engineers we're, talking about and. When. I say that stories, are the essence of the humanities, project of course all researchers, create. Stories. But. To the extent that the story the narrative is, fundamental. To the project I think is what, connects disciplines. To the humanities. It is why MIT, the, undergraduates, learned, to write in the humanities first, before, taking, writing and speaking classes in their own disciplines. What. Also unites the humanities with the sciences, engineering, and the arts is love.

Of Experimenting, with the new new, new and. That, includes new digital technologies. There. Are a lot of us tech nerds in the humanities, and not just here at MIT. There's. Also a great yearning in the humanities for ways to use technology, to make us better researchers. And better, teachers. But. Computers, and humanities, are not always an easy fit because. Of all the amazing brilliant. Spectacular. Things that computers do telling. Stories is not one of them or at least not today. Retrieving. Information finding. Patterns identifying. Outliers these, are the tasks that right now our computers, are great at doing but asking, how come, is, not, something, comes naturally, to our series, and Alexa's and Google's and beans, and. I kind. Of reminds me those of us of a certain age can, remember our friends firing up Netscape, and typing, why is it, that dot dot dot. Into, AltaVista. Before, they learned like the rest of us that if you just stick to the nouns you're gonna get a much better result. So. How'd. It go from data, gathering, machine, learning and analysis. To. Writing, stories, that answer the questions of why and, how. Is not. At all obvious, it's. Something that needs to be learned and if, something needs to be learned it's something that needs to be taught. Earlier. This year we started at MIT a major set of projects, related, to connecting, programming. And emerging. Digital technologies, with, research, and teaching in the humanities, the programs. In digital humanities MIT, launched, last September with, a mission to use code, to, encourage communication, across the humanities CS. Tech world divide and build, a community of practitioners fluent. In both languages. We. Wanted to assemble, and train a group of people who understood, how to write code, and employ, algorithms, to help understand, the questions about the world that humanists, already had and equally. Importantly, to, how, to read, data to come up with questions and narratives, that, we didn't know were important, before computational.

Analysis. Our. Aim over the next three years is to have our work affect, the humanities, faculty the, graduate, students, Boston. And the world but. You have to start somewhere and. What. We thought about starting, with was, what's, a group of people who are incredibly. Smart incredibly. Motivated, but too naive, to know that everyone else has said this is too hard, so. We started with freshmen. The. Lab project cindy h this year have brought together 30 undergraduates, almost, all first years to think about problems, in literature, and history they have too much information for one researcher, to keep in her his head at one time so. The first project we threw our students, at was. Looking. As much in learning as much as we can through computational, methods about, gender relations, in 19th century novels. So. We tasked the students build, the first major corpus, focused, solely on novels. From the from, england that were originally written in english in the 19th century assemble. All the necessary metadata, to classify, them apply, gender classifiers, to characters, look at clusters of pronouns, connections. Of certain words primarily. With people one gender or the other apply, grammar processing, parsing, tools oh and read as much literature, from the humanities, about the topic as you can also. Build, a website learned to codes in, standards, when teams peer, reviewed each other's code Lauren get web frameworks, for many learned Python for the first time z, 3950. Queries accessibility. Standards, oh and let's analyze some jane, austen, by hand just, so we learn how to do that and do. All this while taking intro CS calculus. Physics, Oh writing, class biology. And since, we're talking about freshmen learn to do your own laundry. We. Gave them two months, and. They. Did it together. With a my amazing, postdocs, Lisa Talia Ferry and Stefan, Ricci they, assembled, a repository, of 4200. Novels, from the American Revolution, to the end of the public domain, 327. Million words in all and analyzed. It in making. Our project. Research, and teaching our intertwined, classes. And workshops on, technical. Computer. Science and literary, topics led. Directly to new discoveries. One. Lesson we learned was, that as much as we, all loved playing, with the latest machine learning classifiers, and other, cutting-edge, technologies. The. Field of digital humanities. And. Humanities, and connected. Computers is so wide open that, often the simplest, programming. Techniques were, still able to come up with the most interesting. And the most compelling, results and I want to talk about one, of them just very briefly. We. After. All this work on classifying, genders if we said well let's just count pronouns. And let's look at how pronouns, are connected to each other and one of the things that, reinforced. Sort. Of something that we already thought we knew was, that male authors, much more often talk, about men and. Female. Authors talk about men and women about equally. Now. That that's something that that, I think, will not come as too much of a surprise but once we looked at, what. Where, in the sentence, do female, and male characters, appear. Are they, subject, pronouns he or she or are they object pronouns, he, him.

Or Her, the. Female. And male authors had, exactly, almost exactly, the same usage. But. Men, are being put in the subject position doing. Something, too and women. Are being put in the in the object position more often so. This is a sort. Of an idea of some of the work that we're working on. I'm. Looking at a time so I'll skip over one slide. So. One of the qualities of the d-h lab that has, there's. Really. Created the great results is, the diversity of the lab 3/4. Of the members of the lab our women, and one quarter are members, of underrepresented minority. Groups and by the way we did not announce what, the project we were gonna be working on before the members signed up so. To build a more diverse Cs world bring. More humanities, applications. To the table I want. To end by saying what, we're doing now because it's relevant to all of us who are here on building. The Schwartzman college in partnership. With the libraries, were digitizing, analyzing. And text mining and making, public thousands. Of documents relating. To the founding of the computation, center at MIT in the 1950s. We. Hope that, by, looking. At how it was founded what, opportunities. Were seized. We can duplicate, and replicate that, today. And when we look at what mistakes, were made we, can avoid them in the future the. Future of Humanities depends. On our ability to bring, in computational. Resources. And I think the, future of computations. Ability, to affect positive societal. Change, depends. On bringing the, humanities, to the table I. Think. Both futures at MIT are very bright so, thank you. Our. Next speaker is Eric domain, who's a mathematician, you, may, have heard. Of him because of his work on the mathematics, of origami. He, is the only. Mathematician. I know of who has work. In the permanent collection, of the MoMA in New York, investments. Or nion institutions, in Washington. He's. A, professor, of computer, science and, electrical engineering at, MIT. Thanks. So. We're. Here to talk about, interactions. Between art and education and, I want to throw a third, circle. Into the Venn diagram, which is research and I think a lot of you. Know this, is why, we're here at MIT is to do all three of these things maybe. You don't think of the the red one but. That's. The, topic today and I think there's. A lot of really exciting things we can do at the intersection, between these. Three and. Let. Me talk about the, intersection between art and research I, think. I. Do, a lot of work at, that boundary, and I think it's a really fun way to work that.

By Doing. Art we get inspired to do new research and by doing research we get inspired to do new, art and I'll give you some examples of that through origami, and glassblowing, and, on, the education, side and research I think we. Should be doing more research in our educational. Setting like we just heard in that Freshman, Seminar it sounds like great research endeavor. Unsolved. Problems, are fun and exciting, and they're why they're. Why we're here and I think it's a great way to motivate students. To learn stuff, I've. Written over, 50. Papers, from. Classes. And. With students in those classes and, I think it's a great way to teach students, to collaborate as well so. Let. Me tell you a little bit about origami, this. Is a field that started, as essentially. An art form. And now. Has lots of practical applications, on the science side and lots of interesting mathematics, and computer science. Every. Advanced. Origami artists, has, to learn the, mathematics and the geometry of, how to lay, out parts of the paper to make, their. Desired, form this is an example by Jason ku who as a high school student designed, this hyper accurate, butterfly. By learning. Mathematical. Theory for how to do that wasn't. A mathematician at the time although he then became, an MIT student and now he teaches computer science at MIT. And. So. That's an exciting application. Of, computer. Science to art, and. A more, recent example, of a technique. For this we call organizer, this has just finished last year and it. The. Input to this algorithm, is an arbitrary 3d, model like the bunny in the top-left the. Output is a crease pattern like, that thing in the bottom left it, takes about 10 hours to fold but who uses almost. A quarter of the material, of that square of paper and folds into exactly, the 3d model you asked for and and in, particular this algorithm, comes up with crease. Patterns and designs that no human, could come up with and so this opens. Up a whole new, world. Of possibilities for, origami. Art and. So that's exciting but there's also lots of applications, sorry let's, go to education, so. This. Is a class I teach at MIT called, 6 8 4 9 and, you see the kind, of spirit, going, from the top of a conceptual design I want to make my, logo, that. Algorithm turns out into a crease pattern then, you fold it by hand into that 3d model, an. Exciting thing for me about this class is it's taken, by a lot of design. Students, from Department of Architecture and they're, just excited, about what. Kinds of geometric. Designs are possible with these types of algorithms so we have to find a way we have found a way to teach. What's, ostensibly a mathematics. And algorithms class. To, design, students in addition to the regular computer. Science math students, so, that's a lot of fun. There's. Also lots of practical applications, for. Origami, this is an example with. Led, by Daniela Roose who's here and, a collaboration with Harvard and Penn to make robots. Out of flat material an origami provides, a way to do that 2d to 3d transformation. And so the result is with ten. Or twenty dollars worth of materials, and a laser cutter you, can make a. Custom. Robot, in. Just a few hours so. That's really exciting. I'm. Going the wrong way. That's an exciting thing in particular for education, you can imagine in a robotics class everyone, gets to make their own custom, robot instead of using off-the-shelf robots. Even in a grade. School setting this could be really fun, still. Lots of work to do, and, one, example I wanted to show is pleat, folding this is something you've been working on for, about 20 years and we've gone back and forth between the. Science and the art of Fleet, folding the. Story begins in the 1920s. When I was very young. And. This. Is the Bauhaus. In late 1920s. Josef. Albers had, this design, class. Where. Some. Student, folded, these models we don't know exactly who, and. It's really, an idea and he was using paper, folding as a way to explore design without. Worrying too much about material. And constructability and being, able to prototype really quickly. So. We. Explored. That. Idea. And these are the pieces mentioned, in MoMA and the Renwick gallery and Smithsonian, and. So. What. Really excited us here is we didn't. Understand, the mathematics, of curved, crease folding, we didn't understand the research side so, we, were stuck we just switched gears and entered, the. Art side and my. Background is in computer science and math my dad's, background, my dad is, here. His. Background is in visual arts and when I started working in geometry he got excited, and said.

Oh That I see, a, kind of symmetry between solving. Research, problems, and solving art problems, and that's so. Then we started working together. I keep it auto advancing, here and. So. I taught him to become a mathematician and, then he taught me to become an artist and so now, when, we get stuck on a math problem we can switch over and do. Dude. Art instead and get unstuck and as a result of that, exploration, on the art side we were able to characterize, how curved, crease folding at least to a large extent works, mathematically. So, over the last few years we could have better, and better understanding of this just, really exciting so. I'm in a nice example of how. We can be more productive going, back and forth between these two worlds I'll, show you one more example which is in the world of glassblowing, my. Dad and I are also glass blowers he was actually the father of glassblowing in Canada also, father me, and. This. Is an example of a beautiful. Glass piece made here, at the MIT glass slab by lino Tagliapietra, during. A visit he's, the world's, best glass blower and, he. For his color patterns, he uses this idea called glass cane and there's, a set, of traditional, glass cane designs which are shown on the right and. They are for. Allah for centuries. They've, been the only patterns, out there and. So, we want. On the research side we are curious you know can we do other patterns, and so we came up with this software and used, it to design, new. Glass, cane, patterns, and. Then. We make them out of glass and they look cool so success. And. But. This project has been even. More successful, than we imagined. Because, the software we published. It's called virtual glass you can download it and play with it it's free is. Now, used, on the education, side to teach glass, blowers how to blow glass it is still, the only software, for computer-aided design of glassblowing and so artists. Use it to. Sort. Of pre visualize what pieces. They're going to make and educators. Use it to teach how glassblowing. Works and. So. The. Point is at this intersection, between art education, and research you get a lot, of really exciting things you might be initially, inspired in that case by research. Sort, of or designing new art side on its we're at that intersection and. Have accidental. Implications. In education, and if, you're comfortable, bouncing. Between all of these circles, I think. You get stuck a lot less if you have a hard time solving, a math problem you can make a sculpture about it or. You could teach share. It with your students and say hey look here's a cool-looking problem let's work on it. If you get startup stuck making a sculpture you have making sculptures are hard sometimes they're impossible and you can prove it that. Leads to new research problems, and so, I think this is an exciting space. To play in and happy, to be in this panel Thanks. Our. Next speaker is. Iran. It, goes see who, is. Mathematician. Entrepreneur. And, technologist. So, he's a co-founder. Of, harmonics. Music, systems which, makes real-time. Music, generating. Computer. Programs so, famously. Guitar. Hero and rock back. And. He's a professor. Of the practice in, our music department. Thanks. Agustin I'm. Actually going to switch over to my computer. For. A. Reason. You'll see soon. Can. We switch over. All, right. So. I I, was, thinking about. How. I. Music. I'm in the music, department. But. I'm a music technologist, so I, was, thinking well I teach. Computing. With music, but, maybe I could also title this talk teaching, music with computing, because at least I think of it as two sides of the same coin I. Teach, a class called interactive, music systems, it was the class I designed after finishing. My stint at harmonics with guitar hero and rock band and all that stuff and trying to sort of synthesize. Everything I learned there, in industry and. Figure out how to teach it and. I. Was. Sort of thinking of. Well. Okay I want to teach computation. I actually want these kids to learn how to program well, you, know I. Learned a lot of how to program at MIT but, there's. Something about the practical experience that. I wanted to share so, so. I cover topics like data abstraction capsulation, real-time systems HCI. And especially, code quality but, it's all applied to music so. We. Talked about music and music modeling and sound synthesis, and and, actually. Get into how, do you mathematically, model. Composition. Or performance. Or music, theory, and. Of course it's all wrapped up in music, which is an art form. But. There's a lot of design elements as well so art and design there's, kind of an interesting interplay, there along, with computation, we talk about aesthetics, and interaction. Design and style and and that kind of thing and so I want to show you an example. About. I don't, know third into the course I have, I, have, an example I point out which is let's. Build a metronome does everyone know what a metronome is yeah.

Hopefully You know that annoying thing that that all is always off like, you play and it seems to never ticket, the right right. Well that tells you you need to practice some more. Okay. So oh. Look. It's code. I. Have. A class called metronome I thought. Since we this was a, conference. On computing, I should actually have some computing so this is some, code here if you don't know Python that's okay this, all still be fairly clear, so. What happens here I have I, haven't the beginnings of a class of a metronome and I have a function called on beat when, I call. That. Function oh it. Plays a tick sound. Okay. So that's great so I have a beginning of that but it's I need more than that right in fact, one. Of the things we learned about is is. Function. Pointers and callbacks and how you do, scheduling. With computation, well you can see this this function here called on beat it sets up some parameters and calls play note that's that tick that you heard but, I actually want this thing to play more than one note not. At the same time but at some, point later so I have, a. Scheduling. Function. Which. I will call, schedule. And. I want to schedule this function, on. Beat, okay. Oh so. Dot. On beat when. Do I want this to happen well. Not now but later okay, later well, what is later later is now. Now. Is the tick now in in, computational, music what we sometimes do is is divide, beats, into sub, subunits, much, like you divide a minute into seconds we divide, a beat in two, ticks in this particular case I'm saying, that one, beat is 480, ticks okay. So I have the tick which is now plus, I have, set up my beat length here which is exactly 480, so it's one beat later okay, and that's later so. Let's see if that works. Ah-ha. Okay. So the function is being called and then telling itself to. Call itself over again the. Wonderful thing about scheduling. When, you're in the musical context, is that we have this thing called a tempo a tempo is simply a mapping. Between tick and time. And so. I can. Raise the tempo and increase the speed of that or slow it down. Okay. Great. So we have a metronome and. That's fine but we can probably do more than just a metronome right in fact what I want to do is take this whole thing and normally. I do not recommend cut and paste coding but I'm going to do it anyway because I kind of lacking time here and let's create a new kind of thing here which I will call not a metro. But the lead line okay, so I'm actually about to create an instrument of virtual instrument I don't want to sound like a metronome so I wanted to sound more like oh I don't know a saxophone.

Which I happen to know is 65, okay, and. Let's. Just see what happens if I play it Oh in order to hook it up I need to actually. Create one so I'm instantiating the, lead line, okay. It's a lead instrument, not. A load a lead, instrument and it's going to be on channel 1 because, I want to be independent of what I heard from. The metronome and I, will also create it here okay so that. Lets me set. This up. Okay. Now some, of you might consider this music. There. Are minimalists, around us who might might perfectly like this but let's see if we can do something a little more interesting, with this lead line that we just created, for example, the. Pitch the. Pitch is marked at 60 right now which I happen to know is middle C so what, you're hearing is middle, C over and over again but let's, make, those notes a little different now if I have to choose some notes I often. Rely on this really kind of dirty trick which, is random, okay. So R, and int is a really handy function, which gives which returns a set of integers. I don't, know from. Say-. 1212. Okay. So I'm just adding a random number and. So. Who considers this music. Few. People okay that's all right now again depending on your stupidity as you might not like you might like that or not you might want this to play a little faster like for example I can have it playing. Like. That or. Maybe, you might be interesting, to change. The pitches a little bit so, so. Here we're starting to think about how we actually apply the. Rules of music to. Computation. Okay, so instead, of random int which is a little too random I will. Use random choice and, random. Choice will let me pick from a particular, array of values. Those values I think should be zero which is the root and then, four which is the major third seven, which is the. Perfect. Fifth and maybe the octave which is twelve. Okay. So, we've got something that sounds a little more reasonable or I could even go, harmonic. Blues should, we do that three change the four to three now we're in minor add a little. Bit of a flat fifth that's that blues note maybe, add the minor seventh. Alright. So we got kind of a bluesy thing going here that's, kind of cool and also you know what when, when people play. Music they tend to not. Just play the same note over and over again right as. Eighth. Notes or quarter notes but they kind of vary it up they kind of mix it around so maybe they do this. Okay. So are we getting somewhere I think we are I have I, have, another thing here which is which I called the bass okay. And I, will just bring this up over. Here just to save us there's a little bit of time. The. Bass is a similar kind of instrument but it moves a little bit more slowly, let me just hook the bass in right here bass, and. Bass. Okay. And let's, connect it oh no, it should be a two not one and, one, of the wonderful things about coding, in front of your class is, it keeps you really honest also, if you write any bugs they. Are right there to tell you oh you have, a bug over there so now, I have a bass. Okay. So, now I'm actually making, a piece I'm like in, about I don't know about five minutes I've been able to construct a piece of music the only thing that's missing well there's a lot missing but, but. One of the things that's kind of interesting here is the 60 remember that's 60 it's middle C well, why does that have to be fixed it could be a variable.

Which I will call G root I'm calling it to you cuz it's a global variable I know that's bad, okay. I know, I know okay and I set that to 60 which means I can change it I have some keys hooked up to change that value so. So. So, in my class the way I've decided to teach computation, and music is to do it like this, essentially. To show that computation, is exciting, and there, is a reason to do it because you can create real-time systems and as, you are learning about computation. You are also learning about music and the rules of music and they all kind of interplay around each other when. I have. My. Students work on projects. They. Always, do, things in two steps the first is, they, build a system they build, technology, they use their engineering skills to actually build some text and then the second thing they do because, I ask them to do it is turn, that tech, that you built into something creative and I, just wanted to show you a couple of examples of, what. That looks like one. Of the one of the things that they like to build is using, this device it's a leap motion I don't. Know if you've seen it before but. It, essentially allows you to. To, use your hand as an interface a. Special. Interface great okay so one of the the tasks is actually, this is problem set 5 pset 5 build, a harp ok so there's kind of what it looks like and you, learn about graphics, but the, the creative part is where it really gets interesting nice ask students to build different kinds of harps well. Why. Do harps, necessarily, have to have strings that are all exactly the same length or or how. About a harp that can I don't know change the the, modality. As. It happens or what about a harp, I love, this one this was around Halloween. Right. I love the tonality that the student picked in order to create this. Spider, harp or, something, that I also found quite soothing. Sort. Of a. Three-dimensional. Kind. Of Rising. Sun. Sorry. About the glitching here but. Okay. So so. You give students the opportunity to to. Build something but, then also use it in a creative way and, you get you get amazing results so. Thank. You so. I'm. Sure you can all see why Iran's class. Is basically the most oversubscribed. Class. In school. Our. Next speaker is avatar. - who's, mathematician. She's. Best known for, her work on Network flow algorithms, in for quantifying, the efficiency, of selfish. Routing. And she's. A professor. Of computer science at, for now. Thank. You very much and. This. Previous, talk was exciting. Showing more projects, than I. Will, be able to show I want to talk to you about a very. Oversubscribed. Class at Cornell called, networks. It's. A course that we have been offering for 10 plus years and, it's been taken, by 60, to 700, 600. To 700 students, per time, we are offering it. Cornell. Has very few large lectures, of the course of this sort but we have secured, one of them it's, cross listed with economics, sociology and information, science and computer science, and. There's. A really wide range of students taking it maybe, I'll come back at the end of who, the students are but I want to tell you a little bit about what the courses but, the message is what I want to get across, in the course is run, them to see see.

Network. Network, effects, all around us and I think I don't have to convince you all that. Their network effects, in. Everything. And we live in that when someone, does something far. Away that. Somehow indirectly affects, a lot of other people around. But. Students, tend not to see this and the. Goal of the course is to then walk away seeing, the, network effect, around, in the everyday life. Tools. The. Mathematical, tools are graph theory and game series, but we basically to, them and I said I'm a mathematician so, this is a pretty mathematical. Course, but. We go, from applications. In various different vert places. From markets, for contact, contagion. For small, word phenomenon, and lots, of other things. But. I want them to go away this is how. The word is a connected. Place how things. Stay connected online, rumors. Propagate, how fats propagate, how. Markets. Are affected, and a. Bunch of other topics, I gave, you I will, give you a couple, examples of. What are the topics we're covering but maybe one to start with but, is I think our best tool, to, achieve. What. I want that is I want the students, to see the world around them as a place. Where this is happening part. Of the course project, is the. Students writing mini essays, which actually, two of the mini Assessor, in a form of a blog post and. I give you a pointer here to our blog which you can check out this. Is pointing to the current. Run of the course that is the 2018. For. Incarnation. What. They what they have to do is every twice, in the semester they have to write a blog post about something, that, we didn't, cover in the class so. They now have to mention a topic that we did cover in the test they have to mention a topic we haven't not covered, in class but. It has to mathematically. Connect to something that we did cover in class and the. Way we do this is a blog post is asked. Somehow. Random ordering of the alphabet based, on the first letter of your last name we, give you a week then, on this week you have to post, a blog post and you get some credit for this blog post if it's indeed connects, to something in the class if it is something that happened in the world out there maybe you're supposed to give us a pointer in the news or a pointer pointer to company, and, it's. Rolling over the semester there, are some unlucky guys whose last net last. Name starts is their own bladder and they first right but, they can you. Know take inspiration from last semesters, run and the, other people have can read the previous blog post we certainly get a culture where people do read each other's blog post I don't mean oh 700, of them only, RTS have to read those 700, of them but. They, read each other's book post and I learn an insane, amount about the word by reading this blog post they name companies, they name product they, named all kinds, of things that are really really interesting, and I think this blog post more, than any of the topics recovered is what, gets them to oh yeah, it's all around it's. Everywhere, I, think it's very very successful. Typical. Topics, coming, from the news they, come it can come from the technology, word and, actually, they're allowed to come from their personal life tour the first two are probably more common so, a couple examples of.

Pictures, To be using, we definitely, use a lot of Facebook and introducing, graphs and networks and, talking about how, do you discover, connect. How do you discover structures. And, friendship structures, and what people's friendship. Such yours are like so on topic is some, of them explore their own neighborhood. And does, it have, the same sort of pattern we. Certainly talked about the webpage and couldn't have searched. Among, maps and. Structure. Of webpages is connected. We. Do talk a lot about spreading. Rumors or. Spreading things on a network whether it's, remorse, or anything, else, technology. We. Do talk about the market, and effect of technology and, both in terms of abstract, models and also in terms of very concrete who's. In good position, in networks. We. Talk about something, that was mentioned as something that's closer to my my, field, is. How. Strategic, interaction, of people can, have a, surprising. Effect what, I have in the bottom here is what's called the brace paradox, it's a products, where if. You know drivers, goes through a network and the naturally. Naturally. The. Action again I don't know oh. Thank. You but. I can I missed it. That's, okay I. Can. Tell you inverts but the, the. Best paradox the fact is that you, can oh, I. See I can go forward. Whispered, okay facts is that sometimes, in our network, if everyone, naturally, chooses, the shortest, paths in the network one, intuition, would suggest that, hey they're, optimizing, their writing they're doing a good job. Going. As fast as they can and if you put in an extra link in the middle then the selfish, optimization. Will turn into a disaster and, well, visit the link it was shorter, mr., link selfish, optimization, will not read them to to, wrap. Themselves in a wrong way and have, an extra in this example 25, minutes extra today, wrapping. At. The end of the course virtually. All the students, see the, strategic, interactions, around them they see the, strategic. Interaction, in our everyday political life, like, again lot of the blog posts are about the current politics, like, you know what's happening between Trump, and the, Congress, and, and, what, the games theory describing, this they. See the game theory around. The everyday interaction, with their friends and they, see the network and connectedness, and effects and I think this is something they take home whatever. Level they are so. Here is a little, summary I promised, in the beginning so. Cornell, has seven colleges so. The three, biggest one and engineering, Arts and Sciences actually other a run accident, Sciences engineering and, quality of Agriculture, and life sciences but, we also have smaller.

Colleges. Every. College, has students, that are sending to our courses, here. Is a beginning. Distribution. Of what the students. Are and I do admit, the biggest biggest. Enrollment, is from the engineering, college of the, seven hundred, two. Hundred and seventy six last year were from engineering, but. A lot of them are from the Arts and Science College there are a lot of them from the College of Agriculture, and life sciences and. Also, from, the smaller, courage, is proportionately. With. This many engineering, students, it's very important. That, we don't make, it too. Mathematical. I want this message, of the. World is a connected, place and strategic, interactions. Are affecting, all of us it's something that, every, student, can benefit, from whether, you're a psychologist. Political. Scientist, or any other form, of. You. Know scientists. Are not even a humanist, and, indeed we have enrollment from all kinds, of students, the, last majority, vast majority, of the students are either freshmen, or sophomores, and they, are unaffiliated, that, is they do not yet have a major but. Those that do have a major range, from psychology. Political science. And yes of, the engineering fields to, make this very diverse, audience. Be. Able to live, together in a single course we, use an absolute grading, scheme if you, perform. If you do the homeworks and do the plot post you can get an A and it doesn't matter how many mathematically. Inclined students, are there who can do some of these things better you, can get that get your a or, get B, or whatever grade you you deserve, in. An absolute, scheme that makes students much more comfortable. And indeed some, of the projects, are really cool and evil of coding, and someone some, student explorer day on Facebook. Friendship, page and do all kinds of interesting statistics. Out of it and clearly that person, you have to code you to have how to download the web page and you to do, more. Interesting so this takes and other students, do something, that's more and more and be you, know abstract. Level about. News, but. The, course has been very popular and a lot of students, after the fact report. That this, was one of their favorite courses at Cornell so thank you. All. Right so we have 15. Minutes for questions and what I'm going to do is I'm going to start by asking a, question, to, our panelists, and then I'll open it up so. My question, is suppose. You're. In an elevator with an undergraduate and. They ask you how. Should, I go about integrating. My. Stem. Training with. My training in humanities, Arts Social, Sciences what's. Your elevator pitch. Any. Order you want. All. Right. In. Which ways yes. You're, the student in question is at MIT. They. Want to be a computer scientist and they. Start out skeptical. About. Things like okay. So like I, think. In Fatty's are quite easy sell, like. Am you, know like him many. Students, that comes to my classroom you know like in statistics, in the in, economics, and the social sciences. They. Come after taking like many courses in like computer science and. You. Know they kind, of seek like there is a new wall in which you know a using, data to solve like social problems and to approach things. That they care about all these like egg type of sample that we were talking about like how people connect, in facebook, like you. Know like the, dynamics, of you know Kanye's traffic congestions and so on and you know different people have like a different type, of em. Like. Inclination. Sink what they care, about the things that them that, they want to do for you know in the professional, life or in their academic, lives and, it's like a really, easy to see how you know this connection, between what they are already doing many, times in computer science and what we have to offer in the social science connect you, know and getting like extra leverage to approach of these problems so. My. I don't like this an answer, but my answer is like it is really easy is, I mean and it's not it's not only that is I'm least, not only come from ask, from students they are demanding this type of education so. I don't think that we have to convince anybody to do that there they come to us already. Convinced, in this way I like. Put. In a pitch for of course in some way this is easier, at Cornell. I. Do. Admit, technically, oriented engineering. Students, often if they admitted to my tea they choose MIT but the ones that do not do this I don't, do it because they, take them by wanting, to have a full fledge University, with the social.

Sciences And human is around so, we do have a lot of students who. Are really really, interested, in this integration, and actually. I downloaded. The major. Distribution. From. My course before I was speaking and a couple kids were, interesting. Double majors like English. And computer. Science, double major or. Psychology. And computer science, double major so. Is. This sad we are there, there are students who need a little bit of convincing, and because Cornell is a full, flash University, we actually do have requirements, that they take some other courses. But. One advantage I have is I tell them to talk to their friends I, can, tell them about interesting. Courses that a lot of people liked that, have nice connections, and, we don't have faculty, at. Cornell, who, are connecting. Computing. And information and and computing. And. And. Social Sciences and computing a humanities, so, they can even take courses in. The. Computing, part of Cornell, that Connect which usually. Then encourages, them to take courses also as well after they take computational, linguistics, they tend to want to take the real linguistics, after. They take computational, humanities, they Tavano maybe take some humanity, courses. Well. Since, since we have a lot of people here who are visiting, from outside of MIT I just, want to say that MIT does, require, all the students to take a quarter of their courses in humanities. Arts and Social Sciences and. Because, we cook one of the things that comes out of this the. Students, quite often they're, in these classes they're immediately, when they're assigned you, know professional, literature for. The scholarly journals they just say wait I don't understand, this part that says well you know we can't ever estimate. You. Know how many, you. Know how many pieces of medieval, art were lost on the bases and I go well you know if course, you just use the Plus on distribution, and. And what, the students really just need is the encouragement of the confidence, that. That. They, have skills, in connecting, the humanities, and, technical. Science, and engineering, that others don't have and that. Just. To go ahead and do it and sometimes go. Ahead and do it even though you don't know what question, you're, pursuing, and size the questions will come out of that I had, a student who really. This, about ten years ago when 3d printers were you know really, really a new thing just wanted to print. Renaissance. Musical, type and I. Was like, well I'm not sure what you're gonna learn from that except how to use a 3d printer things. But heck, let's, do actually bribed me she said she would give me a copy so after, it was printed and by. Doing so just holding, this tangible object we learned so, much about things, that I thought I knew about how printing, worked at the time but it, didn't so just I would, I, guess. The elevator has already thought off so just do it. Just. I guess. The, one. Thing I'll say is and, this is kind of based on my experience when I was here. What. 25 years ago I. Mean. Back back then it was actually more, difficult to combine see, us with with other disciplines and, I, looked, around at MIT for a while and I played. Clarinet I did tons of music and intensive engineering, computer science and they were essentially, separate and. It, doesn't have to be that way anymore and I certainly tell students that and I think they kind of know that an MIT is definitely. Going more and more into that direction which is really exciting but, the main thing that I encouraged them is to. Just. Think about not. What they think they should be but, what their passion is and what they actually want to be doing because I think we all have the secret thing, that we actually know we love to do and for. Some of us we think that's not possible in the world because, the thing that I want to do is is.

Weird. Or or. No one will pay me to do it you, know where that kind of thing is certainly that's the advice we get from our parents well you have to go into computer science so that you can earn a lot of money you know and and. The. Thing is that like, like we've seen in what's happening the world today where everything is getting interconnected. And. And. How all these disciplines are related to each other really I think no matter what, seemingly, obscure, thing you think you love there. There, is going to be a place for it in the world and so you should pursue that. Eric. Do you wanna tackle the elevator challenge, sure. I mean in computer. Science what I most, often encounter, are the, computer. Science graduate students who are. And. They don't share that with anyone, because they don't know that it's okay and I think the biggest thing is just to tell them that it's okay, and. It's. Like oh can, I fit this into my reward structure I tell them oh you know there's these publication, venues where you can talk about your art or you could exhibit it and that's like a paper and and so just sort of fitting I think. In general crossing. Over these boundaries you need to explain what the other side is like because. People are typically only brought up in one one. Silo, and so they understand, the reward structure within that silo and just need to translate, across that boundary. Excellent. So we now have a few, minutes for questions. From the audience and I believe. There are microphones. No oh yes there are microphones so. If. You want to ask question raise. Your hand or stand up and a microphone, will come to you. A. Question. For Eric, so is there now a robot. That will do the folding for you so you don't have to even do that. In. Robotic folding the the main place where we've had success is in building. Sheets, of material that fold themselves, into the robotic, 3d structure and it's. Possible, but it's also difficult, there's still a lot of problems to work out. Bunny. Was folded by hand. This. Is an open question I think. There's a lot of excitement, about what computing. Computer science can bring to other, disciplines, and what new insights. Can. Come from using computational, methodologies. And. I'm wondering about the other, direction, you kind. Of touched upon that especially, Eric in. Some fields it's not as clear for instance in humanities what can computer. Science, and engineering gain. From, being. Exposed to humanities, scholarship. Skills. Methodologies. And and so on. One. Of the one, of the main things that I think. Computer. Science students, can really learn from the humanities is that. That a lot of what we're doing has. Is. Working with data where you have to come up with that balance between. Generalized. Algorithms. That work very well on everything, and domain-specific, knowledge, and, I think that working through humanity's, problems or arts problems, can. Can, really be a place, to figure out where, the, where, the boundary between how. Much domain-specific, knowledge, should, come in and the answer is almost always not, is, almost never zero, but, it's also almost. Never everything. And so trying to figure that is something, I feel like my students have learned the most and. At MIT there's a big focus on problem, sets which tend to have this, very clearly specified, problem, and there's a very clearly very, clear answer you're trying to get to and I think humanities. Offers this an arts offer this way to. Think. About problems. That may be aren't so clearly specified, and how also, how to translate. Unclear. Problems, into clear, subproblems. I think that's a really good, motivator. For that kind of creative, problem. Posing not just problem solving. Right. And we're seeing now. Areas. Where, C. CS. Or computation, is actually creating art you, know and so I think that leads to a really interesting question, which. So, far we haven't really had to answer as computer scientists which is is it is, it good art, you know like how do you judge something that is fundamentally, not really. Supposed it's about it's about taste you, know so we, have we. Have computers, that create, artwork or computers that create music is that music good and, so, I think that's an interesting question that we're, only able to start asking right now. It's. Not quite what you asked is. Humanities. But social sciences I mean, we, are living in a world and Social Sciences are impacting. Us and I guess both we, can learn from the social scientists of how to think about this. But. Also, we. Need to start thinking of how what we're creating, changes.

The Society, around us and I guess this is more the topic. Of the next panel but I guess in responding, to your question, is, a very important, thing that we, need to explicitly. Think about and learn. From social. Scientists, know how to think about this I. Guess. On this point about a social, science and part. Of the reason why part. Of the reason why we are here I believe, is that you know, engineers. Have been, extraordinarily. Successful, and, they, have created all this a great systems, that interact, with people who interact with other people and suddenly. Now like human, behavior, and human interactions. Have become a topic of something, that you know is computer. Science and you, know like we. In the social sciences we have been thinking about that for a while okay. So I think that we have something to contribute. And, put on the table so. We have time for one last question, thank, you and this is will night from technology, review, I thought. Your demonstrations, are all fantastic I especially like the computation. Of jazz and I. But. One thing I sort. Of perceived is that machine, learning is having a huge impact on, the world of computer science and. There. It is moving so quickly and there all these techniques which are, open. New possibilities in creativity, things, like Gans and I'm thinking of project magenta so I'm just curious you, know I know that's part of the vision for the Schwartzman, colleges, is to integrate, AI so I'm just curious how you, think it's, possible to sort of keep on top of that when it's moving so quickly in, computer. Science. The. Team, that came in the end of the opening. Talk, about the lifelong learning. You. Know the students, realized, given, how fast the field is moving that they're gonna have to keep on top of this they. Were also very excited, about machine. Learning boss, because, it's, a field that you know it's exciting in you but, also because, it gives them amazing opportunity, to go to some, company, and know something that the senior guys have worked there for a long time haven't like, if they go to. Any of the companies including, the start, Google, Facebook, the. Senior engineers, there didn't take a course on machine learning and didn't take a course on ganz because, those things didn't exist when they were then.

Endeavor, Students, this makes them super, excited, so you, know again I can't answer how I might even integrate, this but I think all computer science departments, whether at MIT or at Cornell, we. Are creating, coercing me creating, opportunities. For students, to learn the basics, and to realize that this is a moving field and they're gonna have to keep you. Know keeping, their eyes open of what's going on I. Think, a, little, more specifically with respect to your question about like what Google magenta, is doing and and you know ganz for generating artwork. And things like that it. It, so, I see students who get to really, excited by this right this is kind of the cutting edge of what's happening computation, and, and in. Creation, and. They want to dive in and start I want to build a system that you know kind of like what I did in about five minutes you know but hopefully better. But. But then you you start asking the questions like well why, are you doing this and is it legitimate for a computer to make artwork for us and do we enjoy it and is it asking, is the artwork which is what it's supposed to do is that is it bringing up sort of interesting, controversial, issues, or matters of opinion or matters of taste or matters of aesthetics and so. Of all these, new questions that are coming up because sort of because of where machine learning is today I think. Is, makes, it even more important, that we study the humanities as. Just. As humanities, you know, and so we. Have to we. Have to know, how, artists. Think and how to become artists we have to study music from first principles, and actually take the theory classes we have to take our history classes in and. You. Know sculpture in and, in. Visual Arts just. Just to to have those as a foundation, so that we can evaluate what, these, games are creating for us so. Let us thank our, panelists.

2019-03-02 05:53

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