Model-based assessments for long-term climate strategies
Model-based assessments for long-term climate strategies, Published online: 22 April 2019; doi:10.1038/s41558-019-0453-5Many countries are formulating a long-term climate strategy to be submitted to the United Nations Framework Convention on Climate Change by 2020. Model-based, multi-disciplinary assessments can be a key ingredient for informing policy makers and engaging stakeholders in this process.
Beginning in 2020, the timing of MIT’s Hooding and Commencement ceremonies will move from June to late May in most years. The 2020 ceremonies will be held on Thursday, May 28 and Friday, May 29. (Projected dates for doctoral hooding and Commencement ceremonies that will occur between 2021 and 2025 are available via the MIT Registrar.)
Faculty approved these updates to the academic calendar at the faculty meeting on April 17. The vote marked the culmination of months of community engagement and input-gathering conducted by the Office of the Vice Chancellor.
To accommodate the change, the Patriots’ Day student holiday will be reduced from four days to a three-day-weekend. The timing of the Independent Activities Period (IAP) will remain the same, and Registration Day will occur on the last day of IAP (Friday). The first day of spring classes will be on a Monday, and the last day of classes will be on a Tuesday. There will be four days of finals that straddle the weekend, but there will be an additional reading day. The resulting format will include two reading days, one exam day (Friday), two reading days, three exam days (Monday-Wednesday). The number of spring term teaching days remains at 65.
“The goal was to make adjustments to our academic calendar to enable a longer summer period and do so in a way that preserves what we heard was most important to the community,” said Vice Chancellor Ian Waitz, who, along with Registrar and Senior Associate Dean Mary Callahan, worked with hundreds of community members to devise a solution that best accommodates students, faculty, and staff. “With this change, we are helping the students who have to delay employment and internship opportunities, or extend their housing rental agreements, because of a June graduation date.”
Waitz, who is also the Jerome C. Hunsaker Professor of Aeronautics and Astronautics, noted that the new timing will assist faculty who currently need to put off early summer research and conference travel. In addition, the change will allow MIT to start maintenance and capital projects in residence halls and classrooms, and launch campus summer programs, sooner than is possible now.
Given the complex nature of the academic calendar, Waitz and Callahan acknowledged that the three proposals they considered, and the final version that was approved, come with positives and negatives. Even though the majority of people who provided feedback preferred the version that was approved, there are some aspects of the new format that can be further improved through proactive problem-solving and clear communication. For example, a group of staff is meeting now to develop different accommodation strategies for graduating students and their families, who may face conflicts with religious holidays or athletic schedules as a result of the new ceremony date.
“We will help the community understand and work through the implementation of this change recognizing that an academic calendar shift may create some challenges,” said Callahan.
Throughout the fall and spring, Callahan and Waitz engaged faculty by meeting with dean's group, academic council, undergraduate officers, heads of house, the OME Faculty Advisory Committee, the Committee on Graduate Programs, the Faculty Policy Committee, the Committee on the Undergraduate Program, and Committee on Academic Performance. They worked with students from the Undergraduate Association and the Graduate Student Council as well as within the faculty governance structure, the Undergraduate and Graduate academic administrators, staff from the Office of Minority Education (OME), Global Education, and Student Support Services (S3), and consulted the faculty, students, and staff members of the Commencement Committee. In addition, the community had an opportunity to provide feedback via an online survey and a student forum hosted by the Office of the Vice Chancellor.
For additional information, please refer to these Frequently Asked Questions.
Lockheed Martin and MIT International Science and Technology Initiatives (MISTI) have announced the creation of the MIT-Lockheed Martin Seed Fund to promote early-stage collaborations between MIT faculty and researchers with universities and public research institutions in Israel. The seed fund will also take place in Germany, and additional countries will be considered after the pilot year of 2019.
The MIT-Lockheed Martin Seed Fund, to be sponsored by Lockheed Martin with more than $150,000, includes two to four projects for Israel and two to four projects for Germany. MIT will administrate the fund within the MIT International Science and Technology Initiatives program in the Center for International Studies. This funding may be used for travel, meeting, and workshop costs, inclusive of visits to Lockheed Martin and MIT facilities in the U.S. Furthermore, the seed fund includes one MIT program student internship in Israel as part of the MIT-Israel program and one MIT student internship in Germany as part of the MIT-Germany program.
For the inaugural year, the seed fund will focus on proposals that fit within Lockheed Martin’s Advanced Manufacturing priorities to identify emerging innovative technologies around but not limited to:
• manufacturing process control;
• modeling of materials and processes;
• novel materials for extreme environments; and
• automation of the "Factory of the Future."
This collaboration brings the ability to align projects around Lockheed Martin’s areas of technology interest and interface with top global universities under a structured program which may lead to sponsored research under a separate agreement. Collaborating faculty will have the opportunity move forward their joint projects as well as engage with Lockheed Martin facilities in the U.S. and Israel.
The new Lockheed Martin-MISTI initiative joins the collaboration made in recent years with Israel’s Ministry of Education, Ministry of Science and Technology, and the Rashi Foundation to promote STEM-related programs from kindergarten throughout high school to higher education.
Headquartered in Bethesda, Maryland, Lockheed Martin is a global security and aerospace company that employs approximately 105,000 people worldwide and is principally engaged in the research, design, development, manufacture, integration, and sustainment of advanced technology systems, products, and services.
MIT International Science and Technology Initiatives creates applied international learning opportunities for MIT students that increase their ability to understand and address real-world problems and bolsters MIT’s research mission by promoting collaborations between MIT faculty members and their counterparts abroad. Since 2008, MISTI’s Global Seed Fund program has awarded $17.7 million to over 800 faculty projects. MISTI is housed within the MIT School of Humanities, Arts, and Social Sciences.
The work of a science writer, including this one, includes reading journal papers filled with specialized technical terminology, and figuring out how to explain their contents in language that readers without a scientific background can understand.
Now, a team of scientists at MIT and elsewhere has developed a neural network, a form of artificial intelligence (AI), that can do much the same thing, at least to a limited extent: It can read scientific papers and render a plain-English summary in a sentence or two.
Even in this limited form, such a neural network could be useful for helping editors, writers, and scientists scan a large number of papers to get a preliminary sense of what they’re about. But the approach the team developed could also find applications in a variety of other areas besides language processing, including machine translation and speech recognition.
The work is described in the journal Transactions of the Association for Computational Linguistics, in a paper by Rumen Dangovski and Li Jing, both MIT graduate students; Marin Soljačić, a professor of physics at MIT; Preslav Nakov, a senior scientist at the Qatar Computing Research Institute, HBKU; and Mićo Tatalović, a former Knight Science Journalism fellow at MIT and a former editor at New Scientist magazine.
From AI for physics to natural language
The work came about as a result of an unrelated project, which involved developing new artificial intelligence approaches based on neural networks, aimed at tackling certain thorny problems in physics. However, the researchers soon realized that the same approach could be used to address other difficult computational problems, including natural language processing, in ways that might outperform existing neural network systems.
“We have been doing various kinds of work in AI for a few years now,” Soljačić says. “We use AI to help with our research, basically to do physics better. And as we got to be more familiar with AI, we would notice that every once in a while there is an opportunity to add to the field of AI because of something that we know from physics — a certain mathematical construct or a certain law in physics. We noticed that hey, if we use that, it could actually help with this or that particular AI algorithm.”
This approach could be useful in a variety of specific kinds of tasks, he says, but not all. “We can’t say this is useful for all of AI, but there are instances where we can use an insight from physics to improve on a given AI algorithm.”
Neural networks in general are an attempt to mimic the way humans learn certain new things: The computer examines many different examples and “learns” what the key underlying patterns are. Such systems are widely used for pattern recognition, such as learning to identify objects depicted in photos.
But neural networks in general have difficulty correlating information from a long string of data, such as is required in interpreting a research paper. Various tricks have been used to improve this capability, including techniques known as long short-term memory (LSTM) and gated recurrent units (GRU), but these still fall well short of what’s needed for real natural-language processing, the researchers say.
The team came up with an alternative system, which instead of being based on the multiplication of matrices, as most conventional neural networks are, is based on vectors rotating in a multidimensional space. The key concept is something they call a rotational unit of memory (RUM).
Essentially, the system represents each word in the text by a vector in multidimensional space — a line of a certain length pointing in a particular direction. Each subsequent word swings this vector in some direction, represented in a theoretical space that can ultimately have thousands of dimensions. At the end of the process, the final vector or set of vectors is translated back into its corresponding string of words.
“RUM helps neural networks to do two things very well,” Nakov says. “It helps them to remember better, and it enables them to recall information more accurately.”
After developing the RUM system to help with certain tough physics problems such as the behavior of light in complex engineered materials, “we realized one of the places where we thought this approach could be useful would be natural language processing,” says Soljačić, recalling a conversation with Tatalović, who noted that such a tool would be useful for his work as an editor trying to decide which papers to write about. Tatalović was at the time exploring AI in science journalism as his Knight fellowship project.
“And so we tried a few natural language processing tasks on it,” Soljačić says. “One that we tried was summarizing articles, and that seems to be working quite well.”
The proof is in the reading
As an example, they fed the same research paper through a conventional LSTM-based neural network and through their RUM-based system. The resulting summaries were dramatically different.
The LSTM system yielded this highly repetitive and fairly technical summary: “Baylisascariasis,” kills mice, has endangered the allegheny woodrat and has caused disease like blindness or severe consequences. This infection, termed “baylisascariasis,” kills mice, has endangered the allegheny woodrat and has caused disease like blindness or severe consequences. This infection, termed “baylisascariasis,” kills mice, has endangered the allegheny woodrat.
Based on the same paper, the RUM system produced a much more readable summary, and one that did not include the needless repetition of phrases: Urban raccoons may infect people more than previously assumed. 7 percent of surveyed individuals tested positive for raccoon roundworm antibodies. Over 90 percent of raccoons in Santa Barbara play host to this parasite.
Already, the RUM-based system has been expanded so it can “read” through entire research papers, not just the abstracts, to produce a summary of their contents. The researchers have even tried using the system on their own research paper describing these findings — the paper that this news story is attempting to summarize.
Here is the new neural network’s summary: Researchers have developed a new representation process on the rotational unit of RUM, a recurrent memory that can be used to solve a broad spectrum of the neural revolution in natural language processing.
It may not be elegant prose, but it does at least hit the key points of information.
Çağlar Gülçehre, a research scientist at the British AI company Deepmind Technologies, who was not involved in this work, says this research tackles an important problem in neural networks, having to do with relating pieces of information that are widely separated in time or space. “This problem has been a very fundamental issue in AI due to the necessity to do reasoning over long time-delays in sequence-prediction tasks,” he says. “Although I do not think this paper completely solves this problem, it shows promising results on the long-term dependency tasks such as question-answering, text summarization, and associative recall.”
Gülçehre adds, “Since the experiments conducted and model proposed in this paper are released as open-source on Github, as a result many researchers will be interested in trying it on their own tasks. … To be more specific, potentially the approach proposed in this paper can have very high impact on the fields of natural language processing and reinforcement learning, where the long-term dependencies are very crucial.”
The research received support from the Army Research Office, the National Science Foundation, the MIT-SenseTime Alliance on Artificial Intelligence, and the Semiconductor Research Corporation. The team also had help from the Science Daily website, whose articles were used in training some of the AI models in this research.
Vivienne Sze, an associate professor in the Department of Electrical Engineering and Computer Science (EECS), has received the 2018-2019 Harold E. Edgerton Faculty Achievement Award.
The award, announced at today’s MIT faculty meeting, commends Sze for “her seminal and highly regarded contributions in the critical areas of deep learning and low-power video coding, and for her educational successes and passion in championing women and under-represented minorities in her field."
Sze’s research involves the co-design of energy-aware signal processing algorithms and low-power circuit, architecture, and systems for a broad set of applications, including machine learning, computer vision, robotics, image processing, and video coding. She is currently working on projects focusing on autonomous navigation and embedded artificial intelligence (AI) for health-monitoring applications.
“In the domain of deep learning, [Sze] created the Eyeriss chip for accelerating deep learning algorithms, building a flexible architecture to handle different convolutional shapes,” the Edgerton Faculty Award selection committee said in announcing its decision. “Eyeriss is also the first deep neural network accelerator to exploit data statistics of the network to further reduce energy consumption twofold, a substantial accomplishment in this field.” In addition, the committee noted that Sze’s work on High Efficiency Video Coding (HEVC) influenced the development of standards now in widespread use, including the improvements that provide the enabling technology for video-accessing on iPhones and Android phones worldwide.
Sze’s educational contributions include a popular conference tutorial on hardware architectures for deep neural networks, which she has turned into a regularly offered MIT subject that can be used to satisfy the EECS doctoral qualifying procedures. In addition, students praise “her ability to connect theory and practice through enjoyable and helpful lectures,” the selection committee noted.
Finally, the committee acknowledged Sze’s efforts in promoting the inclusion and advancement of women and under-represented minorities in the field. Most recently, she served as a technical co-chair of Rising Stars in EECS 2018, which brought 76 of the world’s top women graduate students and postdoctoral researchers to MIT for an intensive two-day workshop on academic careers. (As a PhD student, Sze participated in the inaugural Rising Stars in EECS workshop, held at MIT in 2012.)
Sze received a bachelor’s degree with honors from the University of Toronto and a master’s degree and PhD from MIT, all in electrical engineering. From 2010 to 2013, she was a member of the technical staff in the R&D Center at Texas Instruments (TI), where she designed low-power algorithms and architectures for video coding. In that role, she represented TI on the Joint Collaborative Team on Video Coding during the development of HEVC, which later received an Engineering Emmy Award. Sze returned to MIT in 2013 as an assistant professor of electrical engineering and computer science and was promoted to associate professor without tenure in July 2017. She is also a principal investigator in the Research Lab of Electronics, has co-authored more than 60 publications in proceedings of refereed conferences and journals, and holds more than 25 issued patents.
Other honors include a 2018 Google Faculty Research Award, a 2018 Facebook Faculty Award, a 2018 Qualcomm Faculty Award, and a 2016 Young Investigator Research Program Award from the Air Force Office of Scientific Research, among others. In 2011, she received MIT’s Jin-Au Kong Outstanding Doctoral Thesis Prize in Electrical Engineering.
The annual Edgerton Faculty Award was established in 1982 as a tribute to Institute Professor Emeritus Harold E. Edgerton in recognition of his active support of junior faculty members. Each year, a committee presents the award to one or more non-tenured faculty members to recognize exceptional contributions in research, teaching, and service.
The 2018-2019 Edgerton Award Selection Committee was chaired by Professor Ian Hunter, the George N. Hatsopoulos Professor in Thermodynamics in the Department of Mechanical Engineering. Committee members included Catherine Drennan, professor of chemistry and biology; Jay Scheib, Class of 1949 Professor of Theater; Antoinette Schoar, the Michael M. Koerner (1949) Professor of Finance and Entrepreneurship at the MIT Sloan School of Management; and Andrew Scott, professor of architecture and urbanism.
Before computers, no sane person would have set out to count gender pronouns in 4,000 novels, but the results can be revealing, as MIT’s new digital humanities program recently discovered.
Launched with a $1.3 million grant from the Andrew W. Mellon Foundation, the Program in Digital Humanities brings computation together with humanities research, with the goal of building a community “fluent in both languages,” says Michael Scott Cuthbert, associate professor of music, Music21 inventor, and director of digital humanities at MIT.
“In the past, it has been somewhat rare, and extremely rare beyond MIT, for humanists to be fully equipped to frame questions in ways that are easy to put in computer science terms, and equally rare for computer scientists to be deeply educated in humanities research. There has been a communications gap,” Cuthbert says. “That's the genesis of this new approach to computation in humanities.”
Educating bilinguals: students fluent in the humanities and computation
While traditional digital humanities programs attempt to provide humanities scholars with some computational skills, the situation at MIT is different: Most MIT students already have or are learning basic programming skills, and all MIT undergraduates also take some humanities classes. Cuthbert believes this difference will make MIT’s program a great success.
“What we have that's an amazing opportunity is a large number of people who love building things with computers and want to connect those to their interests and make an impact,” he says. “Our students very much want to change the world.”
They can do that — even as first-year students — because humanities research has many open questions that can be solved with just six months or a year of programming skills, he says.
“The wonderful thing we can do is implement a lot from scratch because we have the programming skills to do that,” says Stephan Risi, one of two postdocs who works in what the students informally call the “Digital Humanities Lab,” or “DH Lab” for short. This gives the MIT researchers more latitude to explore new questions as they arise. “We’re not bound by software others have produced.”
A novel research project
To illustrate the kind of work the lab can do, the program enlisted a team of 24 students (mostly first-years) through the MIT Undergraduate Research Opportunities Program (UROP) to study gender representation in 19th century English literature. The team assembled metadata, applied grammar-parsing tools, did web scraping, wrote analysis tools, and ultimately examined 4,217 books — a total of 326.9 million words.
One interesting finding from the "Gender/Novels" experiment was that — regardless of the sex of the author and "no matter how we cut the data,” as Cuthbert says — roughly two-thirds of all male pronouns were in the subject position, whereas women were more often the object of the sentence. What these new data tell us — about men, women, and society — is up to human scholars to decide, but this project provides a window into the ways computational work can support humanities research.
Detecting research with high social value
This first project also illustrates the pedagogical benefits of working in the lab.
“One of the interesting things about the lab is it's hard to sift through which ideas have merit,” says lab UROP and first-year student Dina Atia, contrasting the humanities research to her work in science, technology, engineering, and math (STEM) fields. “Most STEM research is very fact-based but can lack important social takeaways.”
Fellow UROP and first-year student Ifeoluwapo Ademolu-Odeneye says she enjoyed the opportunity to put her computer skills to work outside the classroom. “I have developed a lot as a computer scientist doing this,” she says, adding that she has also learned to apply critical thinking skills to make decisions about the humanities content. “At first, I asked Professor Cuthbert about everything. Later he threw questions back at us, which has been good for developing as a researcher myself.”
First-year student Mayowa Songonuga, who just started her UROP in the lab this spring and is working on a new project — The History of Computing at MIT — agreed that the hands-on work is very valuable. “There is more to it than just the technology,” she says. “I haven't had the chance to research something like this before.”
The productive swerve in research
While the UROP students were designing algorithms and building a website, they also read and analyzed 19th century English literature and tackled questions such as how to teach the computer the difference between a novel and a travel log. The lab intentionally fosters this dual-stream process, Cuthbert says, because it provides rich opportunities to change the direction of research to follow some newly discovered path.
This ability to make what Cuthbert calls “a productive swerve” is often critical to fruitful research, but has been hampered in the digital humanities to date because complex digital projects are too often done by computational experts at a remove from the humanities scholar.
Students collaborate with leading humanities scholars
To further entwine the disciplines, the program next plans to bring humanities faculty on board for joint projects with students. In 2019-20, associate professor of literature Sandy Alexandre and professor of political science Evan Lieberman will be devoting six hours a week to the lab, teaching students about their research while learning some computational methods themselves.
An added benefit of this collaboration is that it should make the programming work less demanding, Cuthbert says, because creating a simple user interface can be extremely time-consuming. “We’re hoping the faculty will learn enough about the technical operation of their projects that we can devote more staff time to digging deeper,” he says.
Master class lectures by experts who combine humanities and tech
Beginning in 2020, the Program in Digital Humanities will reach out to the wider community — at MIT and in Cambridge and Boston. The plan, Cuthbert says, is to develop a lecture series based on the master class model. Outside experts who combine technology and the humanities in their profession will come to the lab to work with students and then give a public lecture.
The overall goal, Cuthbert says, is to meet a target set by Melissa Nobles, the Kenan Sahin Dean School of Humanities, Arts, and Social Sciences: “to connect the great things going on in computation with the amazing things happening in MIT’s humanities, arts, and social science fields.”
“We have an opportunity to create a love for humanities and an acknowledgement of the importance of humanistic research with the next generation of computer programmers,” Cuthbert says. “We are incredibly excited.”Story prepared by MIT SHASS Communications
Editorial and Design Director: Emily Hiestand
Senior Writer: Kathryn O'Neill
MIT International Science and Technology Initiatives (MISTI) Global Seed Funds (GSF) empower MIT researchers to build collaborations that tackle international problems. Funded projects unite teams of faculty and students with international peers, combining their individual strengths to address challenging issues that may have a worldwide impact.
"This international collaboration was a great encouragement to us,” says GSF awardee Yogesh Surendranath. “The current energy crisis is a global problem, so having scientists from all over the world with different backgrounds, needs, cultures, and expertise come together to address this problem at the chemical level was deeply inspiring."
This year, the 26 funds that comprise MISTI GSF received 221 applications. Over $2 million was awarded to 106 winners from 24 departments across all five schools. That brings the total amount of funding awarded to $17.7 million over the 11-year life of the program.
GSF projects have often culminated in research associated with published papers, and at times have been able to leverage those results to obtain additional research funding. Over two-thirds of the projects report at least one publication that directly resulted from the collaboration, with others referencing a publication in process.
“This worked out better than I could have ever imagined,” says GSF awardee Michael Short. “[My international collaborator] has been conducting research in my area for 25 years, yet never really published in English literature. Now we have three papers together in the last 18 months, and are preparing six more with joint results.”
GSF awardee Vincent Chan also appreciates his impactful partnership.
"As a result of our collaboration in 2017, we successfully created a new program on IoT Smart City funded by the Innovative Technology Commission of Hong Kong with matching funds from the industry” he says. “Also, HKU landed a new program on environmental sensing … and MIT is the international adviser of the project."
GSF projects contribute to the Institute’s educational mission as well. Nearly 75 percent of GSF teams include students from both the MIT and international teams. The next call for proposals will begin in late May.
MIT International Science and Technology Initiatives creates applied international learning opportunities for MIT students that increase their ability to understand and address real-world problems. MISTI collaborates with partners at MIT and beyond, serving as a vital nexus of international activity and bolstering the Institute’s research mission by promoting collaborations between MIT faculty members and their counterparts abroad.
MISTI is a program in the Center for International Studies within the School of Humanities, Arts, and Social Sciences.
In 1988, the U.S. federal government created a $3 billion, 15-year project to sequence the human genome. Not only did the project advance science, it hit the economic jackpot: In 2012, human genome sequencing accounted for an estimated 280,000 jobs, $19 billion in personal income, $3.9 billion in federal taxes, and $2.1 billion in state and local taxes. And all for a price of $2 per year per U.S. resident.
“It’s an incredible rate of return,” says MIT economist Simon Johnson.
It’s not just genomics that pays off. Every additional $10 million in public funding granted to the National Institutes of Health, according to one MIT study, on average produces 2.7 patents and an additional $30 million in value for the private-sector firms that own those patents. When it comes to military technology, each dollar in publicly funded R&D leads to another $2.50-$5.90 in private-sector investment.
In general, “Public investment in science has very big economic returns,” says Johnson, who is the Ronald A. Kurtz Professor of Entrepreneurship at the MIT Sloan School of Management.
Yet after a surge in science funding spurred by World War II, the U.S. has lowered its relative level of public investment in research and development — from about 2 percent of GDP in 1964 to under half of that today.
Reviving U.S. support of science and technology is one of the best ways we can generate economic growth, according to Johnson and his MIT economist colleague Jonathan Gruber, who is the Ford Professor of Economics in MIT’s Department of Economics. And now Johnson and Gruber make that case in a new book, “Jump-Starting America: How Breakthrough Science Can Revive Economic Growth and the American Dream,” published this month by PublicAffairs press.
In it, the two scholars contend that pumping up public investment in science would create not only overall growth but also better jobs throughout the economy, in an era when stagnating incomes have caused strain for a large swath of Americans.
“Good jobs are for MIT graduates, but they’re also for people who don’t finish college. They’re for people who drop out of high school,” says Johnson. “There’s a tremendous amount of anxiety across the country.”
Indeed, spurring growth across the country is a key theme of “Jump-Starting America.” Technology-based growth in the U.S. has been focused in a few “superstar” cities, where high-end tech jobs have been accompanied by increased congestion and sky-high housing prices, forcing out the less well-off.
“The prosperity has been concentrated in some places where it’s become incredibly expensive to live and work,” Johnson says. That includes Silicon Valley, San Francisco, New York, Los Angeles, Seattle, the Washington area, and the Boston metro area.
And yet, Johnson and Gruber believe, the U.S. has scores of cities where the presence of universities combined with industrial know-how could produce more technology-based growth. Some already have: As the authors discuss in the book, Orlando, Florida, is a center for high-tech computer modeling and simulation, thanks to the convergence of federal investment, the growth of the University of Central Florida, and local backing of an adjacent research park that supports dozens of thriving enterprises.
The Orlando case is “a modern version of what once made America the most prosperous nation on Earth,” the authors write, and they believe it can be replicated widely.
“Let’s spread it around the country, to take advantage of where the talent is in the U.S., because there’s a lot of talent away from the coastal cities,” Johnson says.
“Jump-Starting America” even contains a list of 102 metropolitan areas the authors think are ripe for investment and growth, thanks to well-educated work forces and affordability, among other factors. At the top of the list are Pittsburgh, Rochester, and three cities in Ohio: Cincinnati, Columbus, and Cleveland.
The authors’ list does not include any California cities — where affordability is generally a problem — but they view the ranking as a conversation-starter, not the last word on the subject. The book’s website has an interactive feature where readers can tweak the criteria used to rank cities, and see the results.
“We’d like people to challenge us and say, maybe we should think of the criteria differently,” Johnson says. “Everyone should be thinking about what have we got in our region, what do we need to get, and what kind of investment would make the difference here.”
A dividend on your investment
“Jump-Starting America” has received praise from scholars and policy experts. Susan Athey, an economist at Stanford University, calls the book “brilliant” and says it “brings together economic history, urban economics, and the design of incentives to build an ambitious proposal” for growth. Jean Tirole, of the Toulouse School of Economics, says the book gives a boost to industrial policy, by showing “how the government can promote innovation while avoiding the classic pitfalls” of such interventions.
For their part, Johnson and Gruber readily acknowledge that public investment in R&D is just one component of long-term growth. Continued private-sector investment, they note, is vital as well. Still, the book does devote a chapter to the limits of private investment, including the short-term focus on returns that has led many firms to scale back their own R&D operations.
“We’re very pro-private sector,” Johnson says. “I’m a professor of entrepreneurship at Sloan, and I work a lot with entrepreneurs around the world and venture capitalists. They will tell you, quite frankly … their incentives are to make money relatively quickly, given their time horizons and what their investors want. As a result they are drawn to a few sectors, including information technology, and within that more software than hardware these days.”
As a sweetener for any program of public science investment, the authors also suggest that people should receive a kind of annual innovation dividend — a return on their tax dollars. In effect, this would be a scaled-up version of the dividend that, for instance, Alaskans receive on that state’s energy revenues.
That would be a departure from current U.S. policy, but ultimately, Johnson and Gruber say, a departure is what we need.
“We don’t find the existing policies from anyone compelling,” Johnson says. “So we wanted to put some ideas out there and really start a debate about those alternatives, including a return to a bigger investment in science and technology.”
In February, the Institute established five working groups to generate ideas for different components of the structure and operation of the new MIT Stephen A. Schwarzman College of Computing. The Organizational Structure working group is charged with recommending ways to organize the college’s departments and programs, establish its governance, and link it academically with MIT’s five schools, among other considerations. To get a glimpse at the group’s goals and progress, MIT News recently spoke with co-chairs Nelson Repenning, the associate dean of leadership and special projects and the Distinguished Professor of System Dynamics and Organization Studies at the MIT Sloan School of Management; and Asu Ozdaglar, the School of Engineering Distinguished Professor of Engineering and head of the Department of Electrical Engineering and Computer Science (EECS). Three community forums for all five working groups are being held Wednesday, April 17, and Thursday, April 18.
Q: Distilled, what are the key goals of your working group?
Repenning: First, we want to continue to have world-class research and teaching in computer science. Second, we want to develop a structure that will support people outside the field of computer science who are using and developing new computational innovations to do research. Computing is going to be much more tightly connected with the rest of the activity that happens at MIT, so we have to continue to infuse and support computing in all places where it’s relevant.
One challenge we face in developing such a structure is that right now, though they share a department, electrical engineering and computer science are somewhat divided. Our committee feels that categorizing everyone as either/or has outlived its usefulness and is creating needless friction. We aren’t quite sure yet how to resolve this issue, but we are working on it.
Ozdaglar: We are also discussing how to build bi-directional bridges to support interdisciplinary research between computing and other academic disciplines. Another important goal is to incorporate social science not as an afterthought, but as a critical component of future computing research.
Q: What major lessons have you learned in this process?
Repenning: Many of the fields we have at MIT move pretty fast, computer science being the most significant example now. Our organizational structure for teaching and hiring side doesn’t move fast enough to keep up. We are trying to design and reorganize a structure that can adapt to this changing landscape. It’s been challenging considering everyone’s differing views. It’s high stakes. Researchers care a lot about what they do, and don’t want their lives disrupted.
What I didn’t expect when I jumped into this is that we have a chance to set a standard here that could have impact far beyond the [MIT Schwarzman College of Computing]. There have been tectonic shifts in what we do here and, whether it’s genomics or sustainability, we may launch a new entity around another hot field in the near future. So, there will have to be a lot of thinking about how we add more dynamism to our existing organizational structure, so we’re continue to hire the right faculty and prepare students for challenges they’ll face when they graduate.
Q: Where are you now in the process and what’s left to do before submitting your summary report?
Ozdaglar: These new entities are way too complicated to try to design from a clean sheet of paper. We spent quite a bit of time going through a variety of structures we have for organizational research and technology here and elsewhere, and did a thorough diagnosis about what we liked and didn’t like about those structures. We went through EECS, CSAIL, IDSS, ORC, and IMES, and talked about the structures of entities at other schools. Our goal is to evaluate the strength and weaknesses of a bunch of different design options.
Repenning: Now, we have a pretty good sense of the landscape and are evaluating some strong designs. I will say the commitment of everyone at MIT to make this work has been remarkable. We’ve been meeting each week and everyone shows up, even people from departments that may not connect much with the new college. I think it’s a good testament to MIT’s culture.
Four MIT faculty members are among more than 200 leaders from academia, business, public affairs, the humanities, and the arts elected to the American Academy of Arts and Sciences, the academy announced today.
One of the nation’s most prestigious honorary societies, the academy is also a leading center for independent policy research. Members contribute to academy publications, as well as studies of science and technology policy, energy and global security, social policy and American institutions, the humanities and culture, and education.
Those elected from MIT this year are:
- Dimitri A. Antoniadis, Ray and Maria Stata Professor of Electrical Engineering;
- Anantha P. Chandrakasan, dean of the School of Engineering and the Vannevar Bush Professor of Electrical Engineering and Computer Science;
- Guoping Feng, the James W. (1963) and Patricia T. Poitras Professor of Brain and Cognitive Sciences; and
- David R. Karger, professor of electrical engineering.
“We are pleased to recognize the excellence of our new members, celebrate their compelling accomplishments, and invite them to join the academy and contribute to its work,” said David W. Oxtoby, president of the American Academy of Arts and Sciences. “With the election of these members, the academy upholds the ideals of research and scholarship, creativity and imagination, intellectual exchange and civil discourse, and the relentless pursuit of knowledge in all its forms.”
The new class will be inducted at a ceremony in October in Cambridge, Massachusetts.
Since its founding in 1780, the academy has elected leading “thinkers and doers” from each generation, including George Washington and Benjamin Franklin in the 18th century, Maria Mitchell and Daniel Webster in the 19th century, and Toni Morrison and Albert Einstein in the 20th century. The current membership includes more than 200 Nobel laureates and 100 Pulitzer Prize winners.