Teaching Computing in Arts and Humanities



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 [Applause]

5 Comments

  1. Greg Dubela said:

    Jason Ku with the crazy good butterfly.

    June 26, 2019
    Reply
  2. Igor Gabrielan said:

    arts.ai

    June 26, 2019
    Reply
  3. William Jayaraj said:

    Mind blowing knowledge about the interview connection between the art and mathematics.

    June 26, 2019
    Reply
  4. suraj Kumar said:

    I wish I was there in MIT

    June 26, 2019
    Reply
  5. MosesTheRedSeas said:

    Hey MIT My name is Moses and I’m a Freshman I will see you in the next 4 years

    June 26, 2019
    Reply

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