The Future of Higher Education in the Age of Disruption

So. When MIT decided. To commit to creating, the. MIT, Stephen, H Schwartzman, College of Computing it. Was clear to us that this was what, was neat what, we needed to do what. Was less clear to us was. Exactly how we should do it, in. A somewhat uncharacteristic. Fashion. For an academic institution, we. Basically, designed, it decided that we should simply, declare our intentions, to. Establish, this college then, set about the business of creating the college together as a community in, that, context, it seemed entirely appropriate that we can be in an event like this to. Discuss how to teach computer science not only amongst ourselves but. With. Experts, and friends from other institutions. We. Also recently, created five working groups that are contemplating. Various aspects, of the college, many. Of the members of those working groups are here to listen and learn today and I'm, confident, that we will learn that what we learned today will, indeed shape our framing of the college I, want. To thank the organizers for this. Event in particular Salman, asou and Sanjay, who. Work diligently. To pull together what looks like an extraordinarily exciting. Program and also, the speakers that we'll hear from today both our colleagues from MIT and other institutions. At. The risk of repeating some, things that I made that many of you have heard me say let me offer my reflections. On the college to, set a context, for today's discussion a, little. Over a year ago president, rife launched. Us on a campus, conversation. About what we should do about, computing. In some. Respects, we were facing enormous challenges and, it's. One, example of that was an explosion of interest and an, efficient, allocation of, recent. Allocation, resources, 40%. Of MIT undergraduates, are, majoring. Either in computer science or computer, science combined. With another degree at MIT and yet, only 7% of, the MIT, faculty are computer, science faculty. Appointed, in the computer science, Decius. Department, that's. A tremendous imbalance in allocation. Of resources in, addition. And. Very importantly, we are hearing from all corners of the Institute a narrative that basically followed, along the following lines which is that my field insert. The blank is, being transformed, by modern computational. Methods in fact I was at a dinner just last night upstairs a, meeting. Of the visiting committee for the political science department and spoke, to one of our colleagues who's using, large datasets scraped, from public records, about. Legislation, and lobbying and applying, natural language processing to, identify, how individual. Corporations. Are influencing, the development of legislation, a. Fundamentally. Different approach, to a political science, challenge. That. Story repeats, all over campus, and what our colleagues, in these disciplines, were saying to us as we had this conversation was, that they needed.

GPUs. They, needed, access. To software professionals. They needed engagement, from, our computer, science colleagues both. To think about what are some of the more advanced algorithms. That could facilitate. Their, work but, also to assist in developing curricular, offerings for. Students in their discipline, that needed to master these skills. Lastly. We saw everyone feeling, particularly. In the climate, we're in an intense, need to think more holistically. About, the societal, impact of the, technologies, they were developing, and to, think about, that before it was deployed, and thus, appropriately, shaped the deployment. Amongst. This sea of need as we. Were going through this conversation, what we realized, was, an enormous opportunity. As. Is. True for many of the institutions, that are present, in this auditorium. Today we. At MIT are blessed with an extraordinarily. Remarkable. Computer, science talent pool at, MIT and we. Have an opportunity to invest in that talent, pool to advance the fundamental, work that's being done, but. In addition we realized, that if, we could build a structure the, college which. Strengthened, the links between computer, science and the departments, that want to leverage computing. Not. Only to the advantage of those departments, but so that what we learned through those linkages would. Certainly, feed back into the research and teaching we do in computer science that if we could do that we, would really sees a really profound opera tunity while addressing. The challenges we were feeling and lastly. In creating. Something new we have a clean sheet of paper and we have the opportunity, to think what, I believe will be creatively. About, how we introduce. Integrate, a comprehension, of the societal, impact of, the, technologies, that will emerge from MIT in this College into, the education, and research, agenda. I'm. Not suggesting that this will be easy in, fact it's gonna be quite hard but. I can't think of a more transformative, opportunity, for this institution, and that makes me get up every morning very excited, about the potential of the college so.

That's The aspiration, for our College in the journey we've embarked, upon I look. Forward to hearing from today's. Remarks, that are gonna help us point in the right direction so with, that let me now introduce our, keynote speaker, dr. Farnum. Gahan, Ian, Farnham. Was appointed the 10th president of Carnegie Mellon University, in March of 2018. He's. A nationally. Recognized, computer, scientist, entrepreneur. Public. Servant and higher, education leader, in that regard we're extraordinarily fortunate. That, he would take time to, join us today. He, first joined CMU, as vice president for research in 2014. And later assumed. The role of provost, and chief academic officer, from May, 2015. To, June 2017. In July, 2017. He stepped up at CMU, to serve as interim president before. In, the infinite wisdom of their board they've tapped him to be the president. Prior. To coming at CMU, he. Led the National Science Foundation Directorate for computer, and information science. And engineering from. 2011-2014. And. Before serving, in NSF, he, was the Edward S Davidson, collegiate, professor at the University, of Michigan where, he served as chair for, computer science and engineering from. 2007. To 2011, and his, director, of the software systems laboratory from, 2019. 97. To 2000, in addition. To his academic and government, work he co-founded, in, 2001. The, internet security, company Arbor Networks where, he served as chairman until its acquisition, in 2010. He, holds a PhD in computer science for the University, in Austin honking, horns, he's. A fellow of the ACM, the, I Triple E and the Triple A yes on. A personal, note I would say that I've come to know him very well through. A form, of what I consider to be group therapy, which is when collections, of Provost get together. In. That. Setting I've really can, sincerely, say that I've benefited tremendously from, his advice wisdom. And friendship and today. Forum is gonna address us on the future of higher education in, the age of disruption, please, join me in extending, a warm welcome, to Farnum. Good. Morning and it's good to be with you this. Morning and, Marty, thank you so much for that kind introduction. I. Should. Tell, Marty, that having.

Served As president, and also as Provost, Provost, job is the hardest job on campus Marty, I think. You know that already. Once. Again thank you very much for the invitation first. On behalf, of Carnegie. Mellon University I, would, like to congratulate, the entire MIT, community as, you. Celebrate. The, launch of the Stephen Schwarzman, college, of computing this week I want, to especially, extend. The warm congratulations. To. President Reif for, his extraordinary, leadership and, also. To Steve, Schwarzman for, his generosity vision. And continued, commitment to the. Future, of higher. Education and. Economic. Prosperity of our country please, join me in congratulating, the. Entire MIT community. MIT. Continues. To be a world-class institution. That offers a distinctive, education. And cutting edge research of, course and this. Latest development, will. Certainly, increase, its, impact, in the changing, world I'm also grateful I should say to. Announce and the organizing committee for the invitation, to deliver this, morning's keynote, the. Theme of today is centered. Around the importance, of education. And. And, especially. Within the context, of the. Unprecedented, advances, that we have seen in technology, so. This morning what I would like to talk about is the changing, role of higher education. In this age, of disruption, with, a particular focus on the. Way, computation. And data are. Underpinning. These changes, to, begin I think we all recognize and, as Marty pointed out we're. In the midst of a global transformation. That's catalyzed, by rapid, acceleration. Of, digital. Technologies. Including, unprecedented. Access to. Computation. And data, the. Scale and scope and pace. Of these advances. Are truly, unprecedented. In human history. In. Particular. You. See if I can find my clicker oh. Thank. You very much. To. Put this in in in perspective. If you look at this scale we're not only dealing with the singular technology. But rather a set, of interrelated breakthroughs. This dynamic. Of. Interrelated. Technologies, necessitates. Cross. Collaboration, across disciplines when, you look at the scope of it the impact, of these, emergent technologies, or ubiquitous. Reaching, almost every sector of our economy with. A wide range of applications. From healthcare to finance, the transportation. To energy. Manufacturing. And far beyond and of course the pace of it I don't. Need to tell this audience is, that the pace of innovation innovation, of course is accelerating, dramatically. This, requires new strategies, for partnership. Not, only within a camp community, but also across, to. Government. As well as industry partners. Let's. Consider for a moment just, what we have seen in the last 10 years we. Could have carefully. Imagined. That just. About 10 years ago, imagine. A day if I said to you by. Integrating, biomedical clinical, and scientific data. We. Can predict, the onset of diseases, identify. Unwanted. Drug, interactions, automated. Diagnosis. And, personalized. Therapeutics. Imagine. A day that by coupling, roadway. Sensors, clinical. And and I should say roadway sensors traffic, cameras. And individual, GPS, devices we. Can reduce traffic, congestion and, generate, significant. Savings in time and fuel, efficiency. Imagine. A day that by accurately. Predicting. Natural disasters. Such, as hurricanes and. Tornadoes. We can employ life-saving. Preventive, measures. That. Mitigate, their potential, impact. Imagine. A day by using biometrics, and unconstrained, facial. Recognition techniques. We. Can correlate the spared data streams, to. Enhance, Public Safety. Imagine. A day that by using autonomous. Technologies, we. Can have our cars drive us safely and securely, without the danger, of or at least mitigating, the danger of traffic. Accidents, caused by human error and. Just. Imagine a day that by. Cataloging. Data from millions of photos and and videos. Posters on social media from conflict, areas we can move rapidly. To, investigate, and understand, human impact, of conflicts. Disasters. And political, violence and. Finally. Imagine a day that, by integrating. Emerging. Technologies, such as ni AI, enable, learning techniques, and inverted, classrooms. We. Can achieve personalized. Outcome. Based education. Now. All. Of these. Applications. And advances. I talked about they're, not science fiction in. This. Audience could have tested that that every one of these scenarios is, possible, at. Least to some extent in in some cases we can do this today and, that's.

Been As a result of advances, that we've seen in science and technology over. The last couple, of decades in fact if we step back and, look. At what's happening as a. Result of the unprecedent, emerging, technologies, we, see that there are catalyze in fact by three major trends, one obviously, is, enormous. Expansion. That we've seen in our computation. Storage, and connectivity, again. At, the same time what we're seeing is that exponential. Growth in power and reduction in cost of computation storage. And bandwidth just. To consider that there's essentially, a supercomputer, in, everyone's. Pocket, and it's always on and it's always connected the. Second trend of course has to do with digitization, data, explosion, and advances, that we're seeing in machine learning we're. In a period of course that's called a period of air at data and information, that's enabled, by experimental, methods, observational. Studies scientific, instruments, email. Video images. Click, streamed Internet transaction, and so on and so on data. Represents. Of course a transformative. Currency. It's. A new currency for science, for engineering, for commerce, and education. And and. And is transforming, almost every business model, in industries, it's also accelerating, the pace of discovery in, almost every, field of inquiry. And finally. The third major trend that we've seen over the last decade. Or 20 years I should say is the, ubiquitous. Deployment. Of sensors, that has enabled smart. Systems, all around us complemented. Of course with, advances, that we're seeing in automation and robotics, bottom. Line is that were deeply, integrating. Computation. Data and control into, physical systems, and the melding, of a few world. Excuse-me. Melding, of cyber and a physical world has become a reality. The, truth is is that that, the digital innovation, that. We're seeing is, not. Just additive. It's. It. The. Combination. Of those is leading. To advances, that are exponential. In nature in, fact often. There is a major. Gap. Between milestones. That we have seen in the past has been reduced in recent decades and the, breakthrough that we see are coming to fruition in, a matter of sometimes. Years. And months today, in some cases computational. Technologies, are at stripping, essentially, the, performance, of even the, most experienced. Human beings consider, for example advances. We've seen in speech, recognition in computer. Vision in, facial, recognition in, robotic. Surgery and in. Other cases they're, augmenting. And that's what's the beauty of it is our cognitive. And our, physical. Capabilities. As we're seeing this in medical, diagnosis. In, financial. Market, analysis, in recommendation, systems, and the list goes on in. Fact the, future is, even brighter than I have, described, to you we're, now facing, a future that. The impossible. Seems, very. Achievable, we're, only, a few years away from groundbreaking. Discoveries, that. Are potentially, going to transform, our, system, of healthcare and our understanding of, human brain just give you an example of it we're, working toward for example, a greater understanding of. New brain areas and kinds, of synaptic, changes that occur during, learning, disease. States and treatment, conditions. Over. All these, type. Of technologies, are trained are poised, to transform, our entire healthcare system to. Go from something that was very reactive, and, episodic. To. A healthcare system that's much much more proactive its evidence-based, and focuses, on, quality. Of life. We. Can also envision, for example, much. Smarter cities and connect that cities by, 2050. It's estimated that two-thirds of, the world's. Population. Is projected to be about nine point seven billion will, live in urban areas, just. Imagine, that we can transform, our, cities. Through. Essentially. Introduction, an integration, of technologies. But, it's going to require us, not. Just to bring scientists, and engineers together, but you're gonna have to bring Public Policy and you're gonna have to have private and public partnerships. That. Are finding innovative solutions. To transform, our cities and urban areas and. Of course when you look at for example the area of global decision-making. There, are, now. Approaches. That bring layers of global data into, interactive, visual.

Systems, That. Are going to allow us to better, understand, environmental and, population, changes, and when, it comes to deforestation, it comes through refugee, flows sea level rises, surface water changes, pandemics. Urban growth and so on and so on and of, course the area of Transportation is, completely, being transformed. So. What. I'm sharing, with you is something that I think to. A large extent, the academic, community and as Marty mentioned the. Computer science community has. Recognized. Computational. Data intensive approaches. Are underpinning. Our economic, prosperity and global security they're, accelerating the pace of discovery and, innovation, across. Nearly all fields. Of inquiry and are, crucial, to achieving our, major societal. Priorities. I think there's broad recognition that, is happening. Of. Course. Technological. Innovations, have always disrupted. The status code and. Underpin. Dynamic. Economic changes, today. However, as I mentioned earlier, the scale of the scope and the pace of and, the impact, is unprecedented and, it's disrupting, many. Markets, and industries, adoption. Is happening, as breakthrough. Speed. And scale and of, course we're seeing acceleration, of the economic, impact and a. Society and its, structure including. Our education, system must, adapt, to this, new paradigm, in, fact I should. Set back and tell you that while in this country we have enjoyed, having, the. Gold standard, for higher education which, is a model, for the rest of the world. To copy if you will throughout. Our history every. Period, of significant. Technological change. Has been met with corresponding. Waves of innovation, in. Education. In. Fact if you think back a hundred years ago or so. Or. 100, years or so plus ago land-grant. Universities, to expand access consider. German-style, university. Ideas. That were brought to this country, consider, for example the core unit Carnegie unit for standardization, of higher education. At the California, master plan and, in, fact I would go as far as saying that many. Universities. In this country that are leading. Or. Higher education including. Cornell, Johns Hopkins MIT. University. Of Chicago in fact have. Run. Experiments. And some of them are actually were created, as part of an experiment, to. Deal. With changes that we see in technology, and the transformation, of higher education, that we have seen throughout. Our history so. The. Current. Environment. That were in I would, argue that we're at the cusp, of the next transformation, of higher education. Are we at a tipping point I'm, not sure but, we're probably at a very close to it as I. Mentioned, there's unprecedented. Pace of societal, changes, due. To advances, that we see in technology, there's. Of course greater, pressure, on higher education as, as an engine of progress in knowledge-based. Economy, and many of our higher. In. Academic, institutions in this country are at the center of that of course and of course we're seeing. A shift from. Industrial. Somewhat. Transactional. Model of Education that's based on, tradition. And rigid, pathways, to a much much more personalized, outcome. Based model, model of education, in. Fact there have been a number of studies in recent, years, that have looked at the impact of technology on education. On. The, nature of work force on business models and income, inequalities. And so on in fact, one of those, highly. Influential group was a report, by. Your. Colleague, Eric from MIT and my colleague tom from CMU, who. Co-authored. And called led this national. Report that was commissioned, by the national side academies. Of science engineering and medicine and, this. Report, which was titled information, technology, and the u.s. work force where.

Are We and where do we go from here. Argued. That recent advances, in computing, and communication, technologies, have. Had and will continue to have a profound impact on society, and will. Impact our, affect almost every occupation. This, is creating, large economic, benefits. But is also leading to significant, changes for. Our workforce, so. Looking. At this context, before, we examine how we can incorporate these changes, into. Our higher education system I want, to take a very quick look at. Some of the challenges we face in higher education and you see the context, that it provides for the discussion, later. On today one. Challenge. I think that, everyone recognizes has, to do with college affordability, and, access the, second has to do with increasing, demand for, college. Educated workforce, but in particular as again. Marty. Mentioned demand. For students, have who, have computational. And and. Data. Intensive. Knowledge. And, finally. Adaptability. As, we see the rise of automation. In. The workforce so let me spend a couple of minutes on each of these topics. Let. Me first shock, you. By. Sharing some. Data. Let's. Look a little closer at access, and affordability the. Runway, cost of education, or saw, why. So many Americans are increasingly concerned. About their. Children's, future, in. This country and there's no doubt that higher education in this country has been a pathway to. Social mobility I think that's been one of the reasons that we have benefited. As, society. There. Is undoubtedly also, some skepticism, about the value of higher education, I'm gonna refute that in a moment. Consider, the fact that aggregate. Student, debt has tripled from, 2006. From. About 500 million dollars to about 1.5. Trillion. Dollars, that was a t folks in, 2018. I'm. Sorry a. Five. Point trillion, that's right. I'm. Sorry 1.5, trillion thank you in, 2018. It. Should have been billion you're right thank. You, there's. A correction on this slide, although. I've shown this a few times nobody, else caught it. Maybe. That was wishful thinking, I'm not sure. But. More seriously, consider, the fact that this five point at 1.5 trillion, dollars, is actually. Larger. That the entire. Credit, card debt of. Our. Nation, that's. Really staggering. And. By the way every time I show this slide that. One point five, trillion goes up by a hundred million every. Every year is going up by about 100 million, the. Second, data point. The. College tuition, in this country has risen by about five hundred thirty eight percent comparing. The consumer price index increase, of one hundred and twenty one percent which. Is again fairly. Significant. If you consider, that, in. Fact. Despite. The rising cost however there. Is no denying, that the kind of social mobility that. Education, provides, this. Graph breaks. Down essentially, wage trends, over time by. Education, level a, chasm. Has opened up and it's actually growing between, the. Best educated, and the least educated in, our country our, most. Educated, citizens who continue to see their wages rise robustly. Since. The early 70s, and and. I think if you just look at the, salaries, of freshmen, that are coming from, MIT. Or CMU, or many other institution, in this room you can see that there, are six figure salaries for undergraduates, for example in computer science or computer engineering.

But. Are less, educated citizens, on the other hand have seen their real income fall since the early 70s, in. Fact labor economies, predict, that the next wave of disruption, of innovation, that we're, going to see that's going to have an impact on higher education there's. Going to further growth the inequity that has placed strain, on our national. Politics. Let. Me build on that for a moment the other data point that's driving, the urgency. Of our conversation. And in. Fact the. Initiative. That current, that MIT. Has taken, is the increasing, demand, and, relevance. For a college degree, there, was a Georgetown study, a couple of years ago that showed that the number of people, with. At least some, post-secondary, credentials, have, increased, by about one percent a year but. The demand for these workers is growing about by two percent, a year annually. But. For much of the 20th century supply, of college-educated, workers, has kept up with the, demand but for the past three decades or so the, supply has not kept up in. Fact today. While, the job market, is churning and the future is constantly, evolving and we, hear all this, sort of concerns about. Automation. Displacing. Workers there's, also one thing that's patently clear, this. Is a future, that needs higher education, more than ever before. Let's. Talk about the. Issue of demand, to. Understand, this point. Consider. For example and this is a couple of studies have pointed out to. This. Result. And I'm going to share with you that's 65, percent of students. Entering elementary. School now will. One day work in jobs that do not exist today. Think. About that actually. That shouldn't be as surprising, to us because, if you consider over. The last ten years all. The kind of jobs that have been as a result of advances, that we have seen in, technology. That, they didn't even exist ten years ago it shouldn't be surprising to us that a five or six year old would.

In Fact most of them will have jobs that have not been invented, I. Often. Share this other data point. Which. Is almost trivial, but, somewhat, surprising, to people a student. That comes to MIT, and eighteen year old or to Carnegie Mellon. After. He or she graduates in, four, years will, be in the workforce for the next 40 to 50 years, think. About it. 40. To 50 years, imagine. The. Kind of education, and foundation, we have to give these students the, next generation, that will enable them, to thrive. In an, economy for, the next 40 or 50 years given. The context of some of the data that I've shown you. So. There are two forces of course driving this future work one. And MIT. Is in the middle of all of this of course has to do with autonomy, than the digital revolution, which. Of course many talks about how talk about how it displaces, blue-collar. Workers, performing, routine jobs but the truth is that it also has, changes. The nature of work for white-collar workers, in a knowledge-based economy. And in fact there are estimates that for example, 50. Percent of these jobs are at risk at some level for. Significant. Change. The. Second force has. To do with the gig economy we. See a much more liquid force that's. Contributing. To the. Shift that we're seeing, in. Education, that will contribute to the shift that we need to see in education, I should say, of. Course I don't need to tell you about the gig economy but. One data point that was quite intriguing, is that the. Estimates, are that much. Of the growth that we have seen in the job sector. In. The workforce over the last decade, or so has been due to the rise of the gig economy and. In. Fact one of the data points that that that supports, that is in fact the, percentage. Of the work, due to the economy has gone from 10.1%. To 15.8%, over, the last several. Years. And. As I mentioned estimated, that almost all of the employment, growth that we've seen in the US since 2005. Is due to the, economy. I'm. Not trying to depress, you on the count on the contrary. These. Trends, have the, potential impact, to shape the educational, landscape, significantly. Hence. The need for, experimentation. Hence the need for thinking. About. The. Future of the country and the future of our education, but, not continuing. To follow the path that we have been on for for. Many years these, trends of course have to have the potential to reshape, the educational, landscape, bringing. You focus, on, self-directed. Education, lifelong, learning and. Topics. Such as entrepreneurship. As a foundational. Skill so. How, do we prepare students, for a changing, workforce, and. And and workplace, I. Would. Have in, the remaining minutes that I have to, talk about the solution space in. Three, dimensions. One. Having to do with your imagining, curriculum. Second. Rethinking. Structure, and pedagogy. And. Finally. Considering. New models, of collaboration within. An institution, as well as across our academic. Institutions, and, and. External. Partners. First. About. Reimagining. The curriculum, to both, enhance digital. Core skills as well as for incorporating, human skills I. Think. It's pretty well documented, and, I know that in fact Jim Carosi who's sitting here talks, about this in, his presentations. Representing. The National Science Foundation that. The. First trend that we must be mindful of is that growing, reliance on technology and science, has drivers of, new jobs in. Fact growth, in STEM, jobs have. Outstripped. Overall job growth in this country. And a lot of that of course in in in computational. Areas but, the US. Department, of Labor Statistics Labor's. Estimates, that the STEM jobs are. I should say STEM related jobs, will, grow at almost double the rate of non-stem, jobs for. The next ten years. There's. Also an important point to highlight, that. And. And. The growth that we're seeing is not just because of the technology. Companies, virtually. Every industry and organization. Has become dependent on technology for its business and in, particular computation. And data. Centric. Approaches. You, see this in finance you see this in transportation. Health care energy production, distribution and, so on and so on. I'm. Sure most of you in. Fact in the cs community. Are. Familiar, with the two reports, that, I'm showing on the screen and. In, fact these two. Influential. Reports, have. Looked, at how do we deal in fact with broadening participation in. Computing and STEM, fields how. Can we increase the computer science core competencies, across. The educational, system the. Report was prepared by. National. Academy of Sciences Committee on the growth of computer science undergraduate enrollment, it was co-chaired, by Jerry Cohen from, CMU and CRA vice chair at the time Suzanne Hamm brush from Purdue, University it.

Worked, In fact it builds, on the work that was published in CRE is next generation CS report which was chaired by. Tracy. Camp I when I acknowledged, their work, and. And. This report, had, first. Of all identified, this. Is of course as a major issue but, equally important. Highlighted. That context, matters an approach. Is taken by in one institution as not necessarily, workable. In other institutions, they the, report highlights, limiting participation. Growing programs, leveraging, resources creatively. But. Equally important, their, report is very forceful, about rethinking, organizational. And structure, for computer, science both. In terms of interdisciplinary. Collaborations. CS. Plus X which I know it's to be discussed, or X plus y is going to be discussed as one of the panels later on today as well. As considering, college of computing and new organizational, structures, that, allow a much more porous boundaries, between. Units. On on campus. And. In fact the. Report mentions, that there is no one-size-fit-all. All, institutions. Need to assess the role of CS and related. Fields and should see this as an opportunity to, plan for future success, across. The entire institution which, obviously is, the model that MIT. Is, employing. By. The way I should also highlight, that we've seen a significant. While we've seen a significant, growth in the number of undergraduates in computer science as Marty mentioned much. Of the growth is also is happening as a result of all, other students on campus who. Need to be who. Need to learn computational. Approaches, and approaches. That our data centric, so the need is fairly, broad-based across the university. But. But. Stem. Is only one part of the picture and I want to underscore, that and spend a minute or so on that in particular. The. Argument, that can, be made that a liberal arts education and, core, human skills, are just as. As. Important. In the, new economy in this. Uncertain, and constantly. Shifting of course landscape. Non-auto. Writable, and viewable human, skills should, perhaps serve. As a foundation. Foundational. Core competency, I should say these skills include things such as. Communication. Leadership, problem, solving critical thinking, organizational. Skills creativity, and so on I'm. Really, fond, of this quote from Jeff Holden who is one of the editors at. Fortune, and. In a book that he wrote a couple. Of years ago called humans, are underrated. And I'll just read the code to you it says our greatest, advantage, lies in our deepest, most, essential, human, abilities. Empathy. Creativity. Social, sensitivity, storytelling. Humor. Relationship, building, and, expressing. Ourselves with greater power than logic, can achieve so. I want to underscore. This that it is extremely, important as we think about STEM education we, also think about the, importance, of developing. The whole individual. And developing. The students. Who go out to, that to the real world having. Not only. Disciplinary. Expertise, in one area but at the same time be able to connect to other disciplines. At. The same time that we've seen the rate of progress continues, to accelerate, the, societal. Issues, and intersection, of technology and humanity will continue, to become really. Important a number of institutions including. MIT. Are looking. At the, issues of ethics, and technology. But. I want to argue that we. Need to expand, a discussion to include, discussion. Of the critical intersection, between not. Only ethics. And technology, but also addressing. Issues of security, privacy, fairness. Trust and so on, and. This has to become part of our educational, system, and it has to become part of the curriculum at. Or. Higher, at, our academic, institutions. Including. Dealing with issues such as mitigating, algorithmic, bias, the. Spread of fake news and so on and so on this argues, that in, fact we need to think about this much more holistically, to bring together an intersection. Of technology. With policy, design psychology. Economics and other disciplines I know that there's a session later on this morning at 10:45, that's going to focus on this, topic and I'm going to acknowledge my colleague David Danks who's here who's going to be serving on this panel in fact at CMU, we've, been thinking about this very hard and and deeply, integrating. Some, of these issues into, our curriculum I hope that you'll, have a chance to share some of that with you as, I'm, gonna run out of time I want to highlight the other two points very quickly the second, one has to do with rethinking, structure, and pedagogy, moving.

Away From transactional. Nature of Education. Potential disciplinary, silos that. We have I want. To argue in fact that, we need to consider. Experimentation. Assessment, and ways of scaling and eventually. Consolidation. Of new educational, models and instructors if you remember what I said a few minutes ago which is in the last hundred years every. Time we've had major technological, advances, what, we've seen in this country is significant. Experimentation. And and trend. And innovations. In higher ed and I absolutely, believe, we're at the cusp of one. Of those moments. For, example consider learning. As a lifelong. Endeavor should, be rethink, the relevance, of a four-year degree should. Be focused, on outcome, and competency, not just a transactional, model that we have bought into another. Example, is to rethink, disciplinary. Silos, if. The future is going to be increasingly. Interdisciplinary. Department. Boundaries, may need to become much much more porous in fact, not. To make the department heads and the deans in this room nervous. Our. Departments. And colleges, as, important, as they, were a few decades ago. Some. Of you are saying no so I will. Hold my. Comment. Any. Further. So. These. Connections. That we have to build in across. Departments, and across colleges, are becoming extremely important. In. Fact every, time we experimented, with this at Carnegie Mellon what we have seen is that the response, of our students, and the, response, of, essentially. The external, world people who hire our students has been tremendous, we. Experiment with that in our neuroscience, program we introduced, and neuroscience. Undergraduate. Such that you can come into the, program having. Essentially, the same set of foundational courses, but, you can get a degree from our Science. College with, a biology, focus. In neuroscience, or you can get a degree from our social science and humanities, college with, a focus on cognitive. Neuroscience. Or you can actually get a degree from our computer science College with the focus on computational. Neuroscience, and it's been received, extremely, well another, example that I'm really is, id8 which is at intersection, of, technology. Design. Media and art, and, we. Launched as a minor and the, result of that has been. About. 850. To 900 undergraduates. Out of a, 7,000. Population, in. Our, institution. Are minoring, and taking, courses in our id8 program, and there are a number of other examples. Such, as that and I know MIT has also experimented, with similar, things, the. Other important, point related, to pedagogy. Is that, there. Is in. Fact an important, role for technology that. Could not be understated. Technology. Enhanced learning and potential. Disruption of it on innovation. I think, of a grand, challenge for education, to be one. That each student, has a dedicated. Tutor, or teacher, delivering. Personalized. Learning and marginal, cost in. Fact, studies have been shown that. Could have a significant. Impact on learning outcomes so this desire for personalization, desired, were better learning outcomes desire for control and controlling. Cost and access can, potentially be addressed by technology. Enhanced, learning finally. The item that I want to highlight has to do with considering, new models of engagement with the private sector in government and this, is really beyond his scope of the discussion, today but I want to just get this off my chest and plant. A seed with you we, need new collaboration, models we need new policies, public policies, that. In fact build. That. Support building, human capital, let. Me just share a couple of that with you in. This. Country we, need to start thinking as, rethink, as human capital development as, long-term investment, by the private sector in fact, we need to think about it in the public sector of the government, tax, incentives, for and fiscal policies, for investing, in human capital in fact much of our tax policy, in this countries it supports. Capital. Investment but not human capital development, another. Example, of it that and I'm quite, fond of is. Thinking. About creative. Options for financial, such as income share.

Agreements, And so on but the list goes on and I wanted to just to share that with you in this country we need to have really fairly. Progressive policies. Towards. Supporting, the next generation, as they look at education, as the. Source. Of mobility, a. Couple. Of slides before I lap up I was, asked to share also some thoughts on our experiences. And and of course well. When I first start by saying that. Local. Context, matters and that's, my last bullet that's actually the most important, thing there, is no single, recipe, for. In fact. Creating. The kind of higher education, that's, much much more porous, provides. Our students, the, kind of, foundational. Knowledge and. Competencies. That they need so. The local context matter the organizational. Cultural, budgetary, environment of, every institution, is different, what. We have observed is, that intellectual, and and practical, justifications. Are often, mutually, reinforcing. By. That I mean whether. For, intellectual, reasons, such as we, need to make sure students have computational. And data intensive skills, or. Practical. Reasons as Marty, mentioned 40. Percent of the students on this campus or, major. In computer science or related, fields it turns out these are mutually reinforcing, and their opportunity. Opportunities. For all academic institutions. We. And when I say we I mean computer. Scientists. Have. To take a much more expansive, but inclusive. View of computing. And I think that's been recipe, for success for institutions, like Carnegie Mellon. Also. We need to keep disciplinary. Boundaries as I've mentioned porous, and that allows us to strengthen connections, between academic. Units. Furthermore. Continuous. Experience experimentation. And risk-taking, requires, stakeholder. Commitments, but, I can't underestimate the. Importance. Of. Experimentation. In in, this environment and finally.

Again. For my fellow computer scientists, we have to be very, very sensitive and I've lived through this in my career to the perception, that computer, science. May. Become insular. By becoming a separate. Larger, unit on a campus, I think, to a large extent, the experiments, that we have had in other institutions shows, that's. Probably a perception, but we have to be really aware of it and we have to be very sensitive to that notion. To. Wrap up. Horace. Mann in. 1848. Said education, beyond. All other devices of human origin, is the greatest, great, equalizer, and the balance, wheel of social, machinery indeed. Higher. Education, is unique in its power to catalyze, social, mobility, it. Can bridge social. Economic. Racial, geographical. Divides not like no other force, but. If you want a education. To continue, to be an active force, for equality and not. The. Inadvertent I should say engine for inequality, we need to commit ourselves to, major. Transformations. The. Future is arriving. Faster, than ever before and it's, looking vastly, different, what we've seen it so, we must embrace a system, that. Allows these unbounded. Connections. Across organization. The disciplines, it, further. Encourages. And nurtures continuous. Innovation, new models and of, course supports, lifelong. Learning as a, guiding. Principle with, that once again I want to congratulate our colleagues at MIT on, the. Launch of the computing. College and I, look forward to watching their success in the future thank you very much.

2019-03-06 02:06

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