The Future of Higher Education in the Age of Disruption

[APPLAUSE] So when MIT decided to commit to creating the MIT Stephen A. Schwarzman College of Computing, it was clear to us that this was 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 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 convene 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 what we learn today will, indeed, shape our framing of the College. I want to thank the organizers for this event, in particular Saman, Asu, and Sanjay, who worked 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 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 Reif launched us on a campus conversation about what we should do about computing. In some respects, we are facing enormous challenges. And one example of that was an explosion of interest and an inefficient 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 EECS department. That's a tremendous imbalance in allocation of resources. In addition, and very importantly, we're 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 data sets 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 shape 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 realize that if we could build a structure, the college would strengthen 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 learn through those linkages would certainly feed back into the research and teaching we do in computer science. And if we could do that, we would really seize a really profound opportunity 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 integrate a comprehension to the societal impact of the technologies that will emerge from MIT and this college into the education and research agenda. I'm not suggesting that this will be easy. In fact, it's going to 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 and the journey we've embarked upon. I look forward to hearing from today's remarks that are going to help us point in the right direction. So with that, let me now introduce our keynote speaker Dr. Farnam Jahanian. Farnam 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. And 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 for 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 tapped him to be the president. Prior to coming into CMU, he led the National Science Foundation directorate for computer and information science and engineering from 2011 to 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 is director of the software systems laboratory from 1997 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 of Texas in Austin. Hook 'em, horns. He's a fellow of the ACM, the IEEE, and the AAAS. 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 provosts get together. In that setting, I've really-- can sincerely say that I've benefited tremendously from his advice, wisdom, and friendship. And today Farnam is going to address us on the future of higher education in the age of disruption. Please join me in extending a warm welcome to Farnam. [APPLAUSE]
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 A. Schwarzman College of Computing this week. I want to especially extend a 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 the economic prosperity of our country. Please join me in congratulating the entire MIT community. [APPLAUSE] 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 Anant 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 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-- let me see if I can find my clicker.
Oh, thank you very much. To put this in perspective, if you look at this scale, we're not only dealing with a 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 emerging technologies are ubiquitous, reaching almost every sector of our economy with a wide range of applications from health care to finance to 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, of course, is accelerating dramatically. This requires new strategies for partnership, not only within a campus 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 scarcely 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-- 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 lifesaving preventative measures that mitigate their potential impact. Imagine a day by using biometrics and unconstrained facial recognition techniques we can correlate disparate 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. Just imagine a day that by cataloging data from millions of photos and videos posted 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 AI enabled 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 fact, this audience can attest to that, every one of these scenarios is possible. At least to some extent, 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 unprecedented emerging technologies, we see that they're catalyzed, in fact, by three major trends. One, obviously, is an 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 data and information that's enabled by experimental methods, observational studies, scientific instruments, email, video, images, click streams, 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 it's transforming almost every business model and industry. 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 we're deeply integrating computation data and control into physical systems and a melding of, if you will-- excuse me, melding of cyber and the physical world has become a reality. The truth is that the digital innovation that we're seeing is not just additive. It's 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 breakthroughs that we see are coming to fruition in a matter of sometimes years and months. Today, in some cases, computational technologies are out-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 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 health care 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. Overall, these type of technologies are poised to transform our entire health care system to go from something that was very reactive and episodic to a health care system that's much, much more proactive, is evidence based, and focuses on quality of life. We can also envision, for example, much smarter cities and connected cities. By 2050, it's estimated that 2/3 of the world's population-- it's projected to be about 9.7 billion-- will live in urban areas. Just imagine that we can transform our cities through, essentially, introduction and integration of technologies. But it's going to require us not just to bring scientists and engineers together, but you're going to have to bring public policy and you're going 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, when it comes to 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 and 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 is broad recognition that is happening.
Of course, technological innovations have always disrupted the status quo and underpinned dynamic economic changes. Today, however, as I mentioned earlier, the scale, 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 an acceleration of the economic impact. And the society and its structure, including our education system, must adapt to this new paradigm. In fact, I should step 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 100 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 Carnegie unit for standardization of higher education, the California Master Plan. And in fact, I would go as far as saying that many universities in this country that are leading our higher education-- including Cornell, Johns Hopkins, MIT, University of Chicago, in fact-- have run experiments. And some of them 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 we're 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 the advances that we see in technology. There's, of course, greater pressure on higher education as the engine of progress in knowledge-based economy, and many of our higher academic institutions in this country are at the center of that, of course. And of course, we're seeing this 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 of education. In fact, there've been number of studies in recent years that have looked at the impact of technology on education, on the nature of workforce, on business models, on income inequities, and so on. In fact, one of those highly influential reports was a report by your colleague, Erik, from MIT and my colleague Tom from CMU who co-authored and co-led this national report that was commissioned by the National Academies of Science, Engineering, and Medicine. And this report, which was titled information Technology and the US Workforce, 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 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 who have computational 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 runaway cost of education are 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 a society. There is undoubtedly also some skepticism  about the value of higher education. I'm going to refute that in a moment. Consider the fact that aggregate student debt has tripled from 2006 from about $500 million to about $1.5 trillion dollars-- that was a T, folks-- in 2018. [INAUDIBLE] I'm sorry? $500 million to $1.5 trillion. $5.-- 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. [LAUGHTER] Maybe that was wishful thinking. I'm not sure. But more seriously, consider the fact that this $1.5 trillion dollars is actually larger than the entire credit card debt of our nation. That's really staggering. And by the, way every time I show this slide, that $1.5 trillion goes up by $100 million. Every year it's going up by about $100 million. The second data point, the college tuition in this country has risen by about 538% comparing the consumer price index increase of 121%, 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 have continued to see their wages rise robustly since the early '70s. And I think if you just look at the salaries of freshmen that are coming from MIT or CMU or any 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 our less educated citizens, on the other hand, have seen their real income fall since the early '70s. In fact, labor economists 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 is going to further grow the inequality 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 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 1% a year, but the demand for these workers is growing about by 2% 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 that I'm going to share with you. That's 65% 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 10 years all the kind of jobs that have been created as a result of advances that we have seen in technology that didn't even exist 10 years ago. It shouldn't be surprising to us that a five or a six-year-old will, in fact, most of them, will have jobs that have not been invented yet. I often share this other data point, which is almost trivial, but somewhat surprising to people. A student that comes to MIT, an 18-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, then the digital revolution, which, of course, many talk about how it displaces blue collar workers performing routine jobs. But the truth is that it also changes the nature of work for white collar workers in a knowledge-based economy. And in fact, there are estimates that, for example, 50% 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 supports that is, in fact, the percentage of the workforce due to gig 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 gig economy. I'm not trying to depress you. On the contrary, these trends have the potential, in fact, 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 higher education, but not continuing to follow the path that we have been on for many years. These trends, of course, have the potential to reshape the educational landscape, bringing focus on self-directed education, lifelong learning, and topics such as entrepreneurship as a foundational skill. So how do we prepare our students for a changing workforce and workplace?
I want to-- in the remaining minutes that I have-- to talk about the solution space in three dimensions. One, having to do with reimagining curriculum, second, rethinking structure and pedagogy, and finally, considering new models of collaboration within an institution as well as across our academic institutions and external partners. First, about reimagining the curriculum to both enhance digital core skills, as well as incorporating human skills. I think it's pretty well-documented. And I know that, in fact, Jim Kurose, 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 as 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 computational areas. But the US department of Labor estimates that the STEM jobs-- or I should say, STEM-related jobs-- will grow at almost double the rate of non-STEM jobs for the next 10 years.
There's also an important point to highlight, that-- 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 this business, and in particular computation and data centric approaches. You see this in finance. You see this in transportation, healthcare, 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 a National Academy of Sciences Committee on the growth of computer science undergraduate enrollment. It was co-chaired by Jerry Cohen from CMU and CRA a vice chair at the time Susanne Hambrusch from Purdue University. In fact, it builds on the work that was published in CRA's Next Generation CS report, which was chaired by Tracy Camp. I want to acknowledge their work. And this report had, first of all, identified this, of course, as a major issue. But equally important, highlighted that context matters. And approaches taken by one institution is not necessarily workable in other institutions. The report highlights limiting participation, growing programs, leveraging resources creatively. But equally important, their report is very forceful about rethinking organizational structure for computer science, both in terms of interdisciplinary collaborations, CS Plus X, which I know is going to be discussed, or X Plus CS which 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 campus.
And in fact, the report mentions that there is no one-size-fits-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 happening as a result of all other students on campus who need to learn computational approaches, and approaches that are data centric. So the need is fairly broad-based across the university. 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 important in the new economy. In this uncertain, and constantly shifting, of course, landscape, non-automatable, if you will, human skills should perhaps serve as a 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 Geoff Colvin, 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 quote 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 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 the 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 our higher ed-- 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 the intersection of technology with policy, design, psychology, economics, and other disciplines. I know that there is a session later on this morning at 10:45 that's going to focus on this topic. And I want 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 deeply integrating some of these issues into our curriculum. I hope that he'll have a chance to share some of that with you. As I'm going to 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 and 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 structures. If you remember what I said a few minutes ago, which is in the last 100 years every time we've had major technological advances, what we've seen in this country is significant experimentation 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 we rethink the relevance of a four year degree? Should we focus 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, are 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 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 experimented with that in our neuroscience program. We introduced a 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 a focus on computational neuroscience. And it's been received extremely well. Another example that I'm really fond of is the IDeATe, which is at the intersection of technology, design, media, and art. And we launched it 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 IDeATe 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, desire for better learning outcomes, desire for control and controlling costs and access can potentially be addressed by technology and enhanced learning. Finally, the item that I want to highlight has to do with considering new models of engagement with the private sector and government. And this is really beyond the 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 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 or the government tax incentives fiscal policies for investing in human capital. In fact, much of our tax policy in this country 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 aid, such as income share agreements and so on. But the list goes on and I wanted to just 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 wrap up. I was asked to share, also, some thoughts on our experiences. And of course, I want to 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 matters. The organizational, cultural, budgetary environment of every institution is different. What we have observed is that intellectual 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% of the students on this campus are majoring in computer science or related fields. It turns out these are mutually reinforcing and there are 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 mentioned, porous. And that allows us to strengthen connections between academic units. Furthermore, continuous experimentation and risk taking requires stakeholder commitments. But I can't underestimate the importance of experimentation 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 great equalizer and a 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 like no other force. But if we want 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 have seen it. So we must embrace a system that allows these unbounded connections across organization and 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. [APPLAUSE]

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