Robotic waitresses: Alibaba launches automated restaurant in Shanghai – Axios

The big picture: Robot food runners may seem like a gimmick to get diners through the door. But if they become commonplace, it will be because high labor costs make them more cost-efficient than living, breathing waiters.

The context: In some cities around the world, waitstaff are expensive enough that they’ve been done away with, turning even nicer restaurants into Chipotle-style cafeterias where diners order at a counter, grab utensils, and bus their own table.

Some are going entirely robotic:

  •, Alibaba’s main e-c0mmerce competitor, says that next month, it will open a fully automated restaurant, staffed with no human cooks or servers, reports Daisuke Harashima of Nikkei Asian Review. By 2020, it will operate about 1,000 of them, JD says.
  • The company — fiercely competing with Alibaba — has already taken the lead in commercial robotization with a wholly automated warehouse staffed with just four humans, all of whom service the robots.

But, but, but: In most cases, humans are still part of the equation. The automated Hema experience, for instance, still includes frail mortals welcoming customers, explaining the ordering system, taking payment, and — crucially — cooking the food.

Silicon Valley frustrated by intel community’s reticence on Russia – Axios

Current Time – Bulletin of the Atomic Scientists

Rachel Bronson is the President and CEO of the Bulletin of the Atomic Scientists, where she oversees the publishing programs, the management of the Doomsday Clock, and a growing set of activities around nuclear risk, climate change, and disruptive technologies. Before joining the Bulletin, she served as vice president for Studies at The Chicago Council on Global Affairs, adjunct professor of “Global Energy” at the Kellogg School of Management, and senior fellow and director of Middle East studies at the Council on Foreign Relations, among other positions. Her book, Thicker than Oil: America’s Uneasy Partnership with Saudi Arabia (Oxford University Press, 2006), has been translated into Japanese and published in paperback. Her writings and commentary have appeared in outlets including Foreign Affairs, The New York Times, The Washington Post, “PBS NewsHour,” “Charlie Rose,” and “The Daily Show.” Bronson has served as a consultant to NBC News and testified before the congressional Task Force on Anti-Terrorism and Proliferation Financing, Congress’s Joint Economic Committee, and the 9/11 Commission.

Lynn Eden is Senior Research Scholar (Emeritus) at Stanford University’s Center for International Security and Cooperation. Eden is also co-chair of US Pugwash and a member of the International Pugwash Council. Her scholarly work focuses on the military and society; science, technology, and organizations; and US nuclear weapons history and policy. Eden’s Whole World on Fire: Organizations, Knowledge, and Nuclear Weapons Devastation won the American Sociological Association’s 2004 Robert K. Merton award for best book in science and technology studies. Her current research and writing (mostly historical) ask how a specific US military planning organization has enabled very good people to plan what, if put into action, could or would result in the deaths of tens or hundreds of millions of people. In other words, how do US military officers make plans to fight and prevail in nuclear war?

AAAS Forum on Science and Technology Policy US Initiatives Advancing the Frontiers of Science & Policy Innovation – National Science Foundation (press release)

Title slide title: NSF’s 10 Big Ideas and New Models for Accelerating Research and Innovation

Slide words: Dr. France A. Córdova

Director, National Science Foundation

AAAS 2018 Science & Technology Policy Forum

Washington, DC

June 22, 2018

Slide image: photo of a colored spectrum of light

Image credit: Thinkstock

Good morning! Thank you, Kei, for that warm welcome and a special thank you to Rush Holt for the invitation to participate in this Forum once again. The National Science Foundation has a unique stake in American innovation and competitiveness, and I’m glad to offer its perspective to this important conversation.

NSF plays a distinctive role in the innovation ecosystem by supporting high-risk, long-term, curiosity-driven research making investments that ultimately drive our economy and help secure America’s long-term competitiveness. I’m heartened that these priorities continue to have the support of the Administration and Congress — and that through the new models for innovation I will share with you today — NSF is building on that confidence and support. We continue to fulfill our time-honored mandate to further the progress of science.

Slide title: NSF’s 10 Big Ideas | Research Ideas

Slide words (left): Harnessing Data for 21st Century Science and Engineering

(center): The Future of Work at the Human-Technology Frontier; Navigating the New Arctic; Understanding the Rules of Life:

Predicting Phenotype; The Quantum Leap: Leading the Next Quantum Revolution

(right): Windows on the Universe: The Era of Multi-messenger Astrophysics

Slide images (left): word graphic about data science

(center): illustration of creative teams working on giant digital tablets and communicating digitally; aerial photo of melting ice in the Arctic; photo of seedling being watered by hand; illustration of quantum computation with trapped ions

(right): aerial photo of LIGO in Livingston, LA; photo of IceCube Neutrino Observatory in Antarctica; photo of radio telescopes at ALMA in Chile

Image credits (left): James Kurose, NSF

(center): Jesus Sanz/; Roger Wakimoto, NSF; ©; Joint Quantum Institute, University of Maryland

(right): LIGO Scientific Collaboration; F. Fleming Crim, NSF (2)

To keep the U.S. on the cutting-edge of research, technology, and innovation, NSF developed bold ideas for the future, which we call our “10 Big Ideas for Future Investment.” Six of these are Big Research Ideas meant to define a set of cutting-edge research agendas that are uniquely suited to NSF’s capabilities. These initiatives are aimed at catalyzing new breakthroughs, taking advantage of decades of technological revolutions and new discoveries. They focus on areas ripe for breakthrough, building on a foundation made possible by early investments in fundamental research.

Slide title: NSF’s 10 Big Ideas | Process Ideas

Slide words: Growing Convergence Research at NSF

NSF 2026: Seeding Innovation

NSF INCLUDES: Enhancing STEM through Diversity and Inclusion

Mid-scale Research Infrastructure

Slide images (top to bottom): illustration suggesting convergence; graphic suggesting future ideas; U.S. map with photo montage of diverse people; photo of a broken bridge

Image credits (top to bottom): National Research Council of the National Academies Press; © and design by Adrian Apodaca, NSF; design by Trinka Kensill, NSF; ©

Four of the Big Ideas would implement new processes that could enable more and better research, by embracing new practioners and approaches. One important approach is convergence, which is the process of bringing together people from a variety of disciplines around a focused problem. All of our Big Research Ideas require the convergence of disciplines to successfully take root.

Slide title: Stewardship Model

Slide image: graph showing NSF BIO, CISE, EHR, ENG, GEO, MPS, SBE, OIA, OISE directorates working collaboratively with these ideas:

Data Revolution

Future of Work

The New Arctic

Quantum Leap

Rules of Life

Windows on the Universe

Image credit: NSF

To initiate its bold research agenda, NSF established a new Stewardship Model. The decisions about how agency funds are spent are coordinated by a cross-directorate team, and a single directorate — “the steward” — manages each Big Research Idea.

Slide title: NSF Partnership Programs

Valley of Death

Slide words: Resources Invested, Public funds, Private funds

GOALI Grant Opportunities for Academic Liaison with Industry

IUCRC Industry University Cooperative Research Center

PFI Partnerships for Innovation

I-Corps Innovation Corps

SBIR/STTR Small Business Innovation Research/Small Business Technology Transfer

Slide image: graph of the “Valley of Death” or period of transition from high initial investment of public funds with Basic Research to Proof-of-Concept with a financial dip to Early Stage Prototype and Product Development to an increase in private funds and Commercialization

Image credit: NSF

At NSF, we fund researchers in fundamental science, and we also help them on to translate their work into new products and services. We’re proud to have a long history of innovation initiatives that link basic research with industry and application, and I’d like to highlight a few of those efforts.

Our Grant Opportunities for Academic Liaison with Industry (GOALI) program sets the stage for university-industry collaboration by enabling academics to conduct research in an industrial setting, or an industry scientist or engineer to bring commercial perspectives to a university lab. Similarly, our Partnerships for Innovation (PFI) program leverages public-private partnerships between academia and industry to accelerate the transition of technologies from lab bench to market. Providing focused entrepreneurial training, the I-Corps program brings teams of graduate and undergraduate students together with faculty and experienced business mentors. And finally, NSF’s Small Business Innovation Research program provides seed investment to small technology businesses to help bring innovative technology to the market.


Slide words:





















Slide image: map of the U.S. showing the locations of the NSF-funded centers, sites, labs and infrastructure

Image credit: NSF

In addition to these initiatives, we also have a large network of science and engineering centers, laboratories, and other large facilities. Our Engineering Research Centers, or ERCs, integrate engineering research and education with technological innovation. In response to last year’s NAE report on a new Vision for Center-Based Engineering Research, we have issued a solicitation for planning grants to strengthen the team-building process. We expect to issue a new solicitation for Generation-4 ERCs later this year.

NSF also supports more than 75 Industry University Cooperative Research Centers (IUCRCs), which have an average of 10 partners. The IUCRCs leverage every $1 of NSF funds with $7 of industry and other government agency funds to support basic, use-inspired research on a diverse range of topics. As you can see, these resources are spread throughout the entire country.

Slide words (top clockwise from top left):


Harnessing Data for 21st Century Science and Engineering Science and Engineering

NSF 18-542 – Partnerships between Science and Engineering Fields and the NSF TRIPODS Institutes

NSF 18-047 (DCL) – Signals in the Soils

The Future of Work at the Human-Technology Frontier

NSF 18-548 – Future of Work at the Human -Technology Frontier: Advancing Cognitive and Physical Capabilities (FW-HTF)

NSF 17-598 – Cyberlearning for Work at the Human-Technology Frontier

Windows on the Universe: The Era of Multi-messenger Astrophysics

NSF 17-561 – Division of Physics: Investigator-Initiated Research Projects (PHY)

NSF 16-574 – Astronomy and Astrophysics Research Grants (AAG)

The Quantum Leap: Leading the Next Quantum Revolution

NSF 18-046 (DCL) – Enabling Quantum Leap Achieving Room-Temperature Quantum Logic through Improved Low-Dimensional Materials

NSF 18-051 (DCL) – Enabling Quantum Leap in Chemistry (QLC)

Understanding the Rules of Life: Predicting Phenotype

NSF 18-031 (DCL) – Rules of Life (RoL): Forecasting and Emergence in Living Systems (FELS)

Navigating the New Arctic

NSF 18-048 (DCL) – Stimulating Research Related to Navigating the New Arctic (NNA)

(bottom clockwise from top left):


Mid-scale Research Infrastructure

NSF 17-592 – Mid-Scale Innovations Program in Astronomical Sciences (MSIP)

NSF 2026: Seeding Innovation

NSF INCLUDES: Enhancing STEM through Diversity and Inclusion

NSF 17-111 (DCL) – Expand the NSF INCLUDES National Network

NSF 18-529

NSF 17-591

Growing Convergence Research at NSF

NSF 17-065 (DCL) – Growing Convergence Research at NSF [New DCL coming soon]

NSF 18-058

Slide images: (top clockwise from left) word graphic about data science; illustration of creative teams working on giant digital tablets and communicating digitally; aerial photo of LIGO in Livingston, LA; illustration of quantum computation with trapped ions; photo of seedling being watered by hand; photo of radio telescopes at ALMA in Chile; photo of IceCube Neutrino Observatory in Antarctica; aerial photo of melting ice in the Arctic

(bottom clockwise from left) photo of a broken bridge; graphic suggesting future ideas; U.S. map with photo montage of diverse people; illustration suggesting convergence

Image credits: (top clockwise from left) James Kurose, NSF; Jesus Sanz/; LIGO Scientific Collaboration; Joint Quantum Institute, University of Maryland; ©; F. Fleming Crim, NSF (2); Roger Wakimoto, NSF

(bottom clockwise from left) ©; © and design by Adrian Apodaca, NSF; design by Trinka Kensill, NSF; National Research Council of the National Academies Press

Since unveiling the 10 Big Ideas in 2016, we’ve worked diligently to push our bold research agenda forward. As you can see from the yellow arrows in this image, weve provided numerous calls even in the current fiscal year for innovative new proposals involving the Big Ideas, and Id like to offer a few highlights.

Undoubtedly, more surprises are in store for us as we settle into this new era.

Slide title: Quantum Leap

Slide images (left to right): photo of a circuit board with CPU Motherboard; computer generated abstract fractal.

Image credits (left to right): Maxx-Studio/; sakkmesterke/

One is the investment we made in quantum research. EPiQC (pronounced epic) is a new $10 million collaborative project that unites experts in algorithms, software, hardware, and education to develop more efficient quantum algorithms to run on quantum machines. Overall, EPiQC will increase the efficiency of practical quantum computations by 100 to 1000 times, effectively bringing quantum computing out of the laboratory and into practical use much sooner than if we were to pursue these advances independently.

It is important to note that investing in quantum research is not new for NSF; we are simply making a larger, deeper commitment to this important area of research to take it to the next level. Our FY19 request is for 30 Million new dollars in this area. Many of our key country partners around the world are also making significant investments in quantum centers and research. The European Union intends to invest $1.2 billion in quantum studies. Canada is investing $170M in three university research centers. And Japan has pledged $125 million for quantum basic research, while China intends a $100 million quantum investment. We’re glad that NSF has funded awards collaborating with all these countries in quantum research.

We also see enthusiasm from both the Administration and Congress for the acceleration of U.S. quantum research efforts. For example, both houses of Congress are working on legislation to support the coordination of U.S. government quantum R&D. The White House Office of Science and Technology Policy recently hosted the “Artificial Intelligence for American Industry” summit, which I was delighted to take part in. The summit offered an opportunity to discuss the promise of AI and the policies we will need to realize that promise to maintain U.S. leadership. I look forward to serving as co-chair of the National Science and Technology Council Select Committee on Artificial Intelligence and to build with my inter-agency colleagues on these important efforts.

Slide Title: Convergence

Slide images (clockwise from left): photo of Argus II prosthesis for an artificial retina; fluorescent image of 3D microbot fish; photo using synthetic biology to study biochar

Image credits (clockwise from left): NSF; W. Zhu and J. Li, UC San Diego Jacobs School of Engineering; Jeff Fitlow, Rice University

Within a call for proposals for convergence research, NSF received hundreds of proposals from researchers across the country. Their proposals responded to broad societal challenges, such as human health, the environment, energy, food production and the economy. And their research includes new approaches to bioinspired design, innovative sensing strategies, engineered solutions to developing adaptive and self-evolving materials, and next generation intelligent machines. Out of the 247 prospectuses we received, 14 PIs were invited to submit full proposals for funding. We expect to fund most of these innovative projects this fiscal year.

While turning the Big Ideas into practice through programs like these, it became clear to us that some areas were ripe for accelerating because they are on a faster track to realizing innovative deliverables. To facilitate this growth, we developed a new organizational structure focused around two of our Big Research Ideas the Future of Work at the Human Technology Frontier and Harnessing the Data Revolution. We call this new entity a Convergence Accelerator.

Slide title: What is a Convergence Accelerator?

Slide words:

  • A new organizational structure intended to leverage external partnerships to accelerate convergent and translational activities in an area of national importance
  • A home for application-driven basic research
  • Advances ideas from concept to deliverables

Key Characteristics

  • Fed by basic research & discovery
  • Adopts convergent approach
  • Cohorts, integrated teams
  • Intentional in outcomes
  • Proactively and intentionally managed
  • Seed investment, competition
  • Intensive education
  • Mentorship
  • Attracts partnerships
  • Fixed term

A Convergence Accelerator leverages external partnerships to accelerate convergent research in an area of national or scientific importance. What’s new and exciting about this venture is that we are applying an accelerator model used in the private world to accelerate start-ups to basic research that is more application-driven. We believe this innovative approach can advance ideas from concepts to results quickly.

The Accelerators feature characteristics that set the stage for outcomes in both research and innovation. And because they are continually fed by basic research and discovery, they will be on the cutting-edge of science. NSF will provide the seed investments; we already have industry partners who are engaged in these two efforts.

Slide title: How Do CAs Differ from Foundational Research?

Slide words:

  • CAs are intentional in outcomes, more goal-oriented
  • CAs foster a range of approaches, solutions
  • CAs feed on the tension between top-down strategic direction and bottom-up creative approaches

Unlike the foundational research NSF usually invests in, the Accelerators strive toward an intentional outcome through a range of approaches that address a common challenge. The Accelerator model does this by allowing for both a top-down, strategic direction, and a bottom-up creative approach.

Slide title: Why NSF-Sponsored CAs?

Slide words:

  • NSF funds basic research; private accelerators target start-ups
  • We want to accelerate the process of convergent research, yet still have deliverables

  • NSF is directed toward outcomes that are not niche areas
  • Achieving the goals will push translation farther, faster

  • NSF will convene cohorts of teams with unique skill sets around broad national goals

By applying this model to our mandate, our goal is to accelerate the process of convergent research, while achieving clear deliverables. We will direct our accelerators toward outcomes that don’t fall within a niche market area, as private startups or incubators and accelerators traditionally do. Our cohorts of teams will bring a range of diverse skills to bear on broad national goals that require convergent approaches. The intention is to advance convergent research farther and faster, and this is a unique and creative way to do so.

Slide title: Convergence Accelerator Phases

Slide image: sequential graph with the following words:

NSF PIs, partners, basic research results

0: Team Seeding

  • Organic or through structured workshops
  • Multi-disciplinary
  • Diverse membership


1: Team Formation

  • Cohorts of ~20 teams in 3-5 tracks
  • ~6 months
  • Ideation
  • Convergence
  • Team dynamics


2: Accelerated Research

  • Large grants to selected teams
  • Semi-annual or annual reviews
  • Maintain cohort structure



Image credit: NSF

This diagram shows the phases of a Convergence Accelerator. The three-stage process begins with team seeding, where we will build cohorts of a dozen or more multidisciplinary teams representing a variety of sectors. In the team formation phase, groups will receive considerable training to deepen their understanding of convergence, team dynamics and ideation all the factors that go into forging solutions to a challenging goal.

Following this phase, teams will deliver a pitch. Based on the quality of that pitch and on the evaluation of the 6-month incubation period, some of the groups will receive grants of one to two million dollars to carry out their research.

In the final stage, the cohort structure will remain along with a semiannual review. At the conclusion, a prize will be awarded to the best outcome overall.

Slide title: Unique NSF Expertise, Combined in New Ways, Designed to Decrease Time to Discovery

Slide words:

  • Convergence Accelerators build on NSF innovations and best practices
  • Network model: I-Corps (Teams and Cohorts)

    Collective Impact: NSF INCLUDES

    Team Development: Ideas Labs

    Industry-inspired Workshop on Quantum (Mar. 2018): Industry wants more similar workshops on HDR and FW-HTF topics (and URoL)

  • Convergence Accelerators add new dimensions
  • Selection by pitch, instead of 15-page research proposal

    Competition for monetary prizes

The Convergence Accelerators model builds on best practices we’ve been developing at NSF over the last several years. Our I-Corps network model consists of teams coming together in an intensive educational environment. The collective impact model of NSF INCLUDES involves multiple teams working to address a common challenge. We also have considerable experience in forming Ideas Labs and developing teams. And, it was through our recent industry-inspired workshop in quantum that we saw firsthand the community’s interest in NSF sponsoring more workshops, particularly on the first two Convergence Accelerators.

What IS new to the Accelerators model is the inclusion of a 15-minute pitch rather than a 15-page research proposal and the competition for monetary prizes. While new to NSF, these strategies have been used by others with great success.

Slide title: How Will the research in a CA be defined?

Slide words:

  • NSF will start with a few “Tracks” that define focus areas within the accelerator
  • Each track will have specific goals (outcomes, deliverables)
  • NSF will host workshops both to form teams and to solicit additional tracks recommended by the community

NSF will start with “tracks” that define focused areas of research within the Accelerators. Those tracks will have specific goals in terms of outcomes and deliverables. We’ll begin workshops this summer to form teams and tracks that are recommended by the research community.

Slide title: Example Accelerator

“Tracks”: Harnessing the Data Revolution

Slide words:

  • Advanced science data infrastructure that is interoperable and has an open architecture (makes it easier to access and link heterogeneous data products)
  • Open Knowledge Network an open semantic information infrastructure to discover new knowledge from multiple disparate knowledge sources

Slide image: word graphic about data science

Image credit: James Kurose, NSF

As I mentioned previously, Harnessing the Data Revolution is one of the Big Ideas we’ve chosen to accelerate. One example of an Accelerator track within this area is an advanced science data infrastructure that is interoperable and has open standards that enable the use of heterogenous data across many disciplines.

Another example is an open knowledge network. This is a higher-level, open semantic information infrastructure to help in the discovery of new knowledge from various sources. Through these exemplars, we see some of the characteristics that define the accelerators: intentionality, clear outcomes, milestones, deliverables, and agility.

Slide title: Example Accelerator “Tracks”:

Future of Work at the Human-Technology Frontier

Slide words:

  • Smart manufacturing environment: Adaptive collaboration between humans and machines using artificial intelligence
  • The Instrumented Classroom: Intelligent cognitive assistants in a smart classroom to enhance student learning
  • Cybersecurity at scale: Identifying and mitigating vulnerabilities using artificial intelligence

Slide image: illustration of creative teams working on giant digital tablets and communicating digitally

Image credit: Jesus Sanz/

Within the Future of Work at the Human Technology Frontier, we have three examples of Accelerator tracks: smarter manufacturing environments, smarter classrooms, and smarter cybersecurity at scale. We envision building on our basic research in this Big Idea to develop smarter, adaptive collaborations between humans and machines using artificial intelligence. We’ll also build on our knowledge of work conducted in classrooms by teachers and students. Lastly, cybersecurity at scale is an example of a track that identifies and mitigates vulnerabilities using artificial intelligence. Again, these examples all build on important foundational research to accelerate the creation of new and smart workplace environments.

Slide title: Potential Partnership Model for Convergence Accelerators

Slide image: graphic with the following words: Convergence Accelerator Research Domain

(left) Academic Fundamental Research

(center) Pre-Competitive Research

(right) Industry Applied and Competitive Research

Partnership Domain

  • Use-Inspired
  • Fundamental Research
  • Jointly Funded
  • Non-exclusive IP access
  • Trusted relationships
  • Delivery of value

One very exciting aspect of the Accelerators is the model they provide for partnerships. At the core of this initiative is the belief that there is no bright line between basic and applied research. The two feed on and reinforce each other to produce better outcomes.

To achieve the best results, we must bring academics who have a deep understanding of fundamental research together with people from industry, foundations, and elsewhere who have a deep understanding of the applications and associated challenges. This allows those in the application arena to comprehend the opportunities enabled by fundamental research. It also empowers those doing fundamental research to better align their studies to market needs. The result is an acceleration of research for societal impact.

Partnerships are central to achieving these goals, and we’re encouraged that the Accelerators provide both an opportunity and a model to forging these important alliances. International partnerships will also be incorporated into these efforts so that we can capitalize on innovative ideas from our colleagues around the globe and leverage each other’s interests.

Slide title: Do the right things and do things right.

When striving to achieve real impact, it is not enough to have a Big Idea; we must also implement it well. I like this saying. Through programs like I-Corps, INCLUDES and the Convergence Accelerators, NSF is exploring new ways to approach complex challenges that call on the collective wisdom of new coalitions. We’re excited that these efforts also offer an opportunity to observe what works best and to apply those lessons to improving our implementation.

For example, the Convergence Accelerators borrow ideas from the INCLUDES program like collective impact, theory of change and learning agendas — all well-tested implementation models in the social sciences. As we uncover what lessons the Accelerators offer, we can apply those insights to the INCLUDES program and vice-versa. The result is a process that enhances efficiency and outcomes.

NSF is an ideal testbed for new models of implementation as we are a home to so many exciting pilot programs. This is yet another in a long line of cutting-edge approaches we are proud to pioneer.

Slide title: THE NSF 2026 IDEA MACHINE

Slide words:

  • Entrants suggest new “Big Ideas” for future investment
  • Open to all
  • Public comments; blue-ribbon panel
  • Best ideas receive public recognition, cash prizes, and other awards

Finding “Big Ideas 2.0”: Identifying new directions for research.

Step 01 Competition opens/ entries accepted

Step 02 NSF staff select 30 competitive entries

Step 03 Videos invited & posted online

Step 04 Public comments collected; NSF analysis added

Step 05 Blue-Ribbon Panel picks 12 entries for remote interviews

Step 06 Blue-Ribbon Panel recommends 6 entries to NSF

Step 07 NSF staff add analysis/ recommendations

Step 08 NSF Leadership selects 2-4 winning entries

Step 09 Prizes awarded for winning ideas

Step 10 New Big Idea funding opportunities developed

Image credit: NSF

As we look to the future, we must envision questions that will build on the progress we forge today. Two years ago, NSF staff generated the original 10 Big ideas to raise these transformative research questions. This year, we are reaching out to the scientific community, the public, industry, and other stakeholders to conceive the next set of Big Ideas collaboratively. We are doing this through a project called the NSF 2026 Idea Machine.

This initiative is a prize competition that invites participants to submit original, transformative research challenges that they believe should drive our long-term research agenda. We call it the NSF 2026 Idea Machine because the year 2026 is our nation’s 250th birthday, and we hope to see the impact of the research agendas we receive through this process, while celebrating that milestone. We’re very excited to launch the Idea Machine this August. Through this effort and your input, we intend to create a bold new research agenda for NSF, or Big Ideas 2.0! Let’s advance these goals together and implement powerful new models for convergent science. Thank you!

Slide title: NSF’s 10 Big Ideas and New Models for Accelerating Research and Innovation

Slide words: Dr. France A. Córdova

Director, National Science Foundation

AAAS 2018 Science & Technology Policy Forum

Washington, DC

June 22, 2018

Slide image: photo of a colored spectrum of light

Image credit: Thinkstock

UCR Receives $400000 To Support Women In High-Tech Fields – Inland Empire

Riverside, Ca. — The University of California, Riverside, has received a $400,000 gift from the Center for Advancing Women in Technology, or CAWIT, to support a new data science degree to increase the numbers of women and underrepresented groups in high-tech fields. The new program at UC Riverside is one of several at different universities funded by CAWIT’s Technology Pathways Initiative, or TPI.

The new interdisciplinary computing degree program will be implemented in the Marlan and Rosemary Bourns College of Engineering and the College of Natural & Agricultural Sciences, or CNAS.

“We are excited to join TPI with the goal of advancing diversity in computing through undergraduate studies in data science,” said Sharon L. Walker, interim dean of Bourns.

“UC Riverside’s new degree program demonstrates a strong commitment to prepare students for 21st century jobs by equipping them with computing knowledge and skills,” said Belle Wei, CAWIT chairwoman and the Carolyn Guidry Chair of engineering education and innovative learning and former dean of the College of Engineering at San José State University.

The United States faces a workforce gap between demand for and supply of workers with computing and technology skills. The U.S. Bureau of Labor Statistics projects 1.4 million computer science jobs by 2020–with only 400,000 computing graduates available to fill them.

This workforce gap is exacerbated by a gender gap. Women represent roughly 58 percent of college graduates, but only 18 percent of graduates with bachelor’s degrees in computing.

TPI funds interdisciplinary degree programs that integrate computing and information technology into majors that already attract large numbers of women. Many study areas related to data science, such as astronomy, biology, and economics, historically enroll equal numbers of men and women.

“Through cross-campus collaboration, we will create new interdisciplinary degree programs that integrate curricula in computer science, statistics, and domain areas in which data science can be applied,” said Kathryn Uhrich, dean of the College of Natural and Agricultural Sciences.

An integral part of the data science program will be outreach activities aimed at establishing data science-related curricula, projects and activities in K-12 education, with particular emphasis on targeting women and underrepresented student populations.

The data science program offered as an intercollege major by Bourns and CNAS is modeled on the university’s existing neuroscience major. Program organizers intend to have a “soft rollout” in fall 2018 with a fully operational degree in fall 2019. They expect about 50 new students to enroll in the program each year.

A faculty committee headed by Vassilis J. Tsotras (Computer Science and Engineering) will run the data science program. Other committee members include Daniel Jeske, Wenxiu Ma, and Shuheng Zhou (Statistics); Evangelos E. Papalexakis and Christian Shelton (Computer Science and Engineering).

TPI universities include Cal Poly San Luis Obispo, San Francisco State University, San José State University, UC Berkeley, UC Davis, and UC Riverside. TPI university pilot programs are supported by a $3 Million gift from TPI industry partners Intel Corporation, KLA-Tencor Foundation, and Salesforce, which provide mentoring and internships for participating students.

“Our partnership with CAWIT provides UC Riverside faculty with opportunities to develop and pilot interdisciplinary computing degree programs more quickly with initial funding support from corporate sponsors,” said Peter Hayashida, vice chancellor for University Advancement. “Together, we will open doors to new and highly relevant learning opportunities for more students, including those who have traditionally been underrepresented in engineering and the sciences.”

About UC Riverside

The University of California, Riverside ( is a doctoral research university, a living laboratory for groundbreaking exploration of issues critical to Inland Southern California, the state and communities around the world. Reflecting California’s diverse culture, UCR’s enrollment is now nearly 23,000 students. The campus opened a medical school in 2013 and has reached the heart of the Coachella Valley by way of the UCR Palm Desert Center. The campus has an annual statewide economic impact of more than $1 billion. To learn more, call (951) UCR-NEWS.

Google Is Building a City of the Future in Toronto. Would Anyone Want to Live There? – POLITICO Magazine

TORONTO—Even with a chilly mid-May breeze blowing off Lake Ontario, this city’s western waterfront approaches idyllic. The lake laps up against the boardwalk, people sit in colorful Adirondack chairs and footfalls of pedestrians compete with the cry of gulls. But walk east, and the scene quickly changes. Cut off from gleaming downtown Toronto by the Gardiner Expressway, the city trails off into a dusty landscape of rock-strewn parking lots and heaps of construction materials. Toronto’s eastern waterfront is bleak enough that Guillermo del Toro’s gothic film The Shape of Water used it as a plausible stand-in for Baltimore circa 1962. Says Adam Vaughan, a former journalist who represents this district in Canada’s Parliament, “It’s this weird industrial land that’s just been sitting there—acres and acres of it. And no one’s really known what to do with it.”

That was before Google.

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This past October, a coalition of the Toronto, Ontario and Canadian governments contracted with Sidewalk Labs, a sister company of Google, to come up with a $50 million design for a dozen acres on the waterfront’s far eastern end. The idea is to reimagine Toronto’s derelict waterfront as “the world’s first neighborhood built from the internet up,” as Sidewalk describes it. The neighborhood, called Quayside, would leapfrog the usual slow walk of gentrification to build an entire zone, all at once, as a “smart city,” a sensor-enabled, highly wired metropolis that can run itself.

Toronto’s choice of the Google-affiliated firm immediately captured the attention of urban planners and city officials all over the world; magazine stories trumpeted “Google’s Guinea-Pig City” and “A Smarter Smart City.” Still in its early days, the partnership has left people curious but wary. Google? What does a tech company know about running a real live city?

In one sense, what’s perhaps surprising is that it has taken this long. Silicon Valley’s innovators have long had side obsessions with making the world a better place, driven largely by the confidence that their own brainpower and a near-total disregard for tradition can break old logjams. PayPal co-founder Peter Thiel helped seed the “seasteading” movement to create offshore libertarian paradises; the tech incubator YCombinator is currently running a public-policy experiment in Oakland, California, giving residents a guaranteed monthly stipend to see how it might improve their quality of life.

The notion of the feedback-rich “smart city” has circulated for years, and in practice has mostly taken the shape of centuries-old cities like New York or Boston adopting sensor-enabled stoplights or equipping their residents with an app for spotting potholes. But the real dream, a place whose constant data flow lets it optimize services constantly, requires something different, a ground-up project not only woven through with sensors and Wi-Fi, but shaped around waves of innovation still to come, like self-driving cars. Thanks to a host of technological advances, that’s practical now in a way it never has been before. Mass-produced sensors now cost less than a dollar apiece, even for hobbyists; high-speed broadband and cheap cloud computing mean that a city can collect and analyze reams of data in real time.

In Toronto, Sidewalk sketches out a picture of a neighborhood where intelligent “pay-as-you-throw” garbage chutes separate out recyclables and charge households by waste output; where hyperlocal weather sensors could detect a coming squall and heat up a snow-melting sidewalk. Apps would tell residents when the Adirondack chairs on the waterfront are open, and neighbors would crowdsource approvals for block-party permits, giving a thumbs-up or thumbs-down based on the noise the gathering was expected to produce. Traffic signals could auto-calibrate to ease pedestrian congestion during public events, or to ensure a smooth rush hour. The data from such systems would feed back into the city, which would constantly learn, optimizing its own operations from month to month, year to year. Sidewalk promises “the most measurable community in the world.”

The idea is to reimagine Toronto’s derelict waterfront as “the world’s first neighborhood built from the internet up.”

But with it comes a host of new questions, points out Vaughan, the Toronto MP. Day to day, a truly smart city runs on data and algorithms rather than civic decisions made by humans. So, who owns all the data produced by the city of the future? Who controls it? Whose laws apply?

These have been mostly abstract questions for urban-studies seminars so far, as cities adopt relatively small-bore innovations, like a streetlight system in Chicago that self-reports malfunctions to keep the lights on in high-crime areas. But there are already hints of darker potential. The ruler of Dubai says his plan to collect data on citizens is intended to “make Dubai the happiest city on earth,” but skeptics of the United Arab Emirates’ human rights record aren’t so sure what will happen when all its cellphone-obsessed residents are being tracked by an authoritarian state. “The reality is that conversation is coming to cities anyway,” Vaughan says. “Let’s have it now.”

Fans of what’s become known as Sidewalk Toronto say there are few better places to have this conversation than Canada, a Western democracy that takes seriously debates over informational privacy and data ownership—and is known for managing to stay polite while discussing even hot-button civic issues. Hitching up with tech companies that are flush with both cash and grand visions might be cities’ best chance to leap into the future, or at least to turbocharge their lagging districts. But some aren’t so sure cities will get the better end of the deal. Google is already buying up chunks of the Bay Area and New York; its power and public appeal could easily overwhelm cash-strapped local governments even before it becomes the repository for all that citizen data. Some urbanists and good-government advocates fret that going down the aisle with big corporations might be a short-term salvation that, generations from now, will have set cities on the wrong path.


Anthony Townsend, an urban planner and forecaster, has spent most of his career on the question of how the thoughtful application of technology can help cities solve their problems, including challenges of sustainability and inclusion. He is a champion of the idea that cities should engage in careful “digital master planning,” thinking through for themselves the role of technology in urban life. He’s not shy about touting the importance of the movement, especially for sustainable living—in a 2013 book, Smart Cities: Big Data, Civic Hackers, and the Quest for a New Utopia, he wrote: “The real killer app for smart cities’ new technologies is the survival of our species”—but also warns that cities are in danger of blindly and haphazardly embracing innovations presented to them by industry.

In Toronto, he likes the approach Sidewalk has taken so far: Rather than designing its own tech fantasy from scratch, Sidewalk started by asking Toronto for its own vision for its future, through a full year of public consultations, and it is using that to shape the company’s plans. He wonders, though, whether Quayside is ambitious enough—whether its accumulation of existing technology really amounts to the kind of breakthrough cities need. Modern cities, with money and an educated population, should be the labs for big new ideas about living, and Sidewalk is their best shot right now. “I look at the bar that Google and [parent company] Alphabet have set for innovation, I look at what was proposed for Toronto, and I think, where are the moonshots?” he wonders.

Other observers counter that Sidewalk is aiming high—that aggregating all that technology in one place could be its own kind of breakthrough. One analogy that Sidewalk itself uses is to ancient Rome: The Romans didn’t invent the aqueduct, but their engineering skill meant that their capital had a totally unprecedented supply of clean water, allowing them to build a city like nothing else that had ever existed. Modern cities thrive on information, but none has built itself around data infrastructure in a similar way; connecting a bevy of smaller-scale innovations through a common networked digital platform could be a hugely powerful innovation in itself. Sidewalk’s former chief operating officer, borrowing a term from the software world, calls it a chance to “reimagine the full stack.” Bruce Katz, co-author of the 2018 book The New Localism and founder of the Brookings Institution’s Metropolitan Policy Program, says “when you put it all together, you are talking about the future being fast forwarded.”

Google is not the first company to try reimagining a city. Epcot Center, the Florida theme park, has its roots as a real city-building idea. The name is an acronym for the Experimental Prototype Community of Tomorrow, and it began as Walt Disney’s vision of a carefully engineered urban paradise of the 1960s, one that would “take its cue from the new ideas and new technologies that are now emerging from the creative centers of American industry.” “I don’t believe there’s a challenge anywhere in the world that’s more important to people everywhere than finding solutions to the problems of our cities,” Disney said in a 1966 promo film introducing what would become Disney World. No less than the New York “master builder” Robert Moses called Disney’s vision for Epcot “overwhelming.” But it wouldn’t come to pass. Disney died of lung cancer shortly after shooting that film, and Disney, the company, balked at being in the city-building business. It ended up as a theme park.

But a great deal has changed since Disney’s time. Cities themselves have more money and energy than ever; rather than building from scratch, like Disney did, modern smart-city builders want to harness the energy and dynamism of existing cities. And today’s “new technologies” are more seamlessly integrated into people’s daily lives than even Disney might have imagined. The corporations behind the technologies, like Google, have the power and reach to envision changes on a scale far beyond a theme park.

A truly smart city runs on data and algorithms rather than civic decisions made by humans. So, who owns all the data? Whose laws apply?

Google had been interested in city-building for some time—former Google CEO Eric Schmidt has said that founders Larry Page and Sergey Brin began dreaming about reengineering cities “years ago”—but its venture into urbanism got more concrete in 2014, when Schmidt reached out to Dan Doctoroff, an investor and philanthropist who had been a deputy mayor of New York during Michael Bloomberg’s administration and energetically drove its rebuilding after the 9/11 attacks. (“Dan Doctoroff has done more to change the face of this city than anyone since Robert Moses,” Bloomberg once said.) Doctoroff was available: He had become CEO of Bloomberg’s media company and then left when the former mayor unexpectedly returned.

Doctoroff formed Sidewalk Labs and headquartered it in Hudson Yards, a brand-new neighborhood on Manhattan’s once-seedy far west side that took root amid what was left over from a failed 2012 Olympic bid that Doctoroff had helmed. (There was supposed to be a stadium in that spot.) And the company started looking for land.

Doctoroff had learned in New York that it’s far easier to build where there are no people, and where higher levels of government can’t tell you no, and Toronto had a rare opportunity on the table. Appealingly, it was on the waterfront, and the city was ready for it to change. It even had a new government structure in place to oversee the property, an entity called Waterfront Toronto, made up of three layers of government—city, provincial and national—which itself is the product of a failed Olympic bid. Its job was to make something happen. That so much of government was already on board with the waterfront project was hugely appealing to Sidewalk. Canada’s youthful prime minister, Justin Trudeau, was invested in the idea; he had already been talking to Google’s leadership about the possibility of a second Silicon Valley north of the border.

But in Schmidt’s view, there was more than that in Toronto’s favor. The local tech sector was booming. In fact, the very artificial intelligence that powers post-search Google was in large part pioneered at the University of Toronto. Toronto, whose population is half foreign-born, is a magnet for immigrants, and “technology is powered by immigrants,” said Schmidt. Doctoroff would later testify that Sidewalk had scoured the world, and “out of the entire world, the single place that we thought was the best was Toronto.”

Waterfront Toronto issued a request for proposals for the site, with a tight turnaround of six weeks. Sidewalk scrambled and generated a plan hundreds of pages long, complete with quirky line drawings of local features, that offered a sweeping vision of a neighborhood built from the ground up—actually, from below the ground up—to be a home for innovation. It got the contract. At a news conference last October to announce the partnership,Trudeau argued that Torontonians had little choice but to get engaged in the debate about what could happen on the waterfront: “We know the world is changing, and the choice we have is either resist it and be frightened by it”—here, the PM mimed a grimace while pushing back an imagined force—“or to say, we can step up, together, and shape it.”


So far, the deal hasn’t exactly been a victory for transparency; Waterfront Toronto has declined to make the exact terms of its deal with Sidewalk public, so no one on the outside knows exactly what the city has promised Google, or vice versa. But the basic idea is for Sidewalk to go on a yearlong local listening tour, brainstorming along the way for a master development plan for the dozen-acre slice of land. If the plan is approved by Waterfront Toronto’s board, that group and Sidewalk will serve as co-master developers, with the latter in charge of funding and “innovation”—how to design streets to handle self-driving cars, or how to build underground utility channels serving as conduits for city services that haven’t even been dreamt up yet.

Charged with pulling all this off is Rohit Aggarwala, Sidewalk’s head of urban systems. Known as “Rit,” Aggarwala—like Trudeau, a spirited 46—sports rimless glasses and close-cropped hair, and is something of the brains behind the Sidewalk Labs operation. Aggarwala, too, served in New York City government, as head of its sustainability plan. (Perhaps the flagship initiative of that plan, congestion pricing to ease traffic in the heart of the city, did not come to pass.) Aggarwala, who along the way picked up a master’s degree in Canadian history, with a thesis called “American Trade and Urban Dominance in Upper Canada, 1830-1850,” said by phone that Quayside is “clearly one of the most important potential new neighborhoods in North America.”

Part of the founding team at Sidewalk Labs, Aggarwala made a study of neighborhoods, even whole cities, designed from scratch. One was the original Epcot, which he credits as a visionary project that tried to ambitiously “move the needle”—in part by trying to conquer the sometimes inhospitably hot and humid environment of inland Florida with a vast downtown dome. With cars banished below ground, humans would travel around instead via never-stopping electric-powered trollies called People Movers. And it wasn’t seen as a static achievement: Prefiguring the philosophy of “iteration” that would come to dominate Silicon Valley, Walt Disney said in the 1966 film that Epcot would “always be in a state of becoming.” It’s an important principle, says Aggarwala. Even a highly planned community isn’t done just because it’s opened its doors.

What’s more, even cities built from scratch aren’t built in a vacuum, separate from traditional politics or conventional local goals. Aggarwala’s counterpart in government is Kristina Verner, who once taught computer science at the nearby University of Windsor and now directs the innovation and sustainability portfolio for Waterfront Toronto. She considers herself a “smart-cities nerd” but says that the “smart city” branding that’s attracted so much attention is more Google’s than Toronto’s. The city itself was mainly interested in the promise of solid economic development and a quick timetable. Sidewalk’s pitch had a number of compelling points, she says: There was the financial commitment, in the form of the pledge of some $50 million poured into developing a plan for the site, and its ability to deliver that plan in a single year. Appealingly, Google itself promised to anchor the project, moving its Canadian headquarters from Toronto’s financial district down to a to-be-determined spot on the eastern waterfront. But most attractive, she says, is that Sidewalk’s proposal was soup to nuts. It was not simply a smart-cities play; it considered everything from sustainability to housing. “Sidewalk’s response was holistic in its answers to the questions posed,” Verner says.

The relationship between government and Sidewalk remains a work in progress, and some critics worry that handing over too much control to a private company will set the wrong precedent. By definition, the autonomy of a smart city means taking some hands-on day-to-day decision-making away from elected officials and civil servants. And when the complex algorithms and data-collection decisions driving those city operations are in the hands of one company, that can raise worries that too much power over our civic lives is being handed over to private interests.

“Blurring the line between what is the public sector and what is the private sector is the thematic concern here,” says Bianca Wylie, a Toronto-based open-government advocate and co-founder of the group Tech Reset Canada. “We’re talking about a lot of things that are municipal-service-delivery-related. Infrastructure—I keep hearing the word ‘infrastructure.’ What are we talking about? What kinds of products and services are we talking about? And we’re in this situation where we’re making policy on the fly with a vendor.”

This is the first, and maybe broadest, point of concern that has started to bubble up about smart cities, not just in Toronto. Using data to organize and optimize, Google’s expertise since its early days as a search company, makes a lot of sense in the online world, the argument goes, but is a far different tool when it’s applied to the chaos that makes cities cities.

Allison Arieff, a San Francisco urbanist and design writer for the New York Times, says she is reserving judgment on Quayside—after all, a shovel has yet to be put into the ground or sensor hooked up—but considers herself wary. The for-profit tech sector, shot through with libertarian disdain for government and the build-and-flip ethos of the startup world, isn’t well-matched for the kind of work it takes to run a city: long-term, and driven by civic improvement rather than stock prices. The most fundamental flaw she sees when it comes to Silicon Valley’s city-building ambitions is the gap between what data can and can’t do.The beauty of urban life is what happens organically, argues Arieff.“They really do believe in their heart and soul that it’s all algorithmically controllable,” she says, “and it’s just not.”

“They really do believe in their heart and soul that it’s all algorithmically controllable,” says urbanist Allison Arieff, “and it’s just not.”

At the street level, another worry is that Sidewalk could exacerbate a trend in urban innovation toward intentionally cutting down on actual human interactions. If even the beach chairs are wired and reservable, do neighbors ever have to meet? An example of what concerns urbanists is the “meatless and wheatless” eatery near Google’s current Toronto headquarters, at which there are separate counter locations for picking up orders made via two competing Toronto-based food apps. Local workers breeze in, flash a phone, grab their prepackaged almond lime bowls and breeze out. It’s impossibly efficient, and appealing to young tech workers who never have to take out their earbuds, but it also eliminates the most basic kind of human interaction at the heart of city living.

This is a growing critique of the “smart cities” movement, and I put it to Aggarwala. He considers it a caricature—“It is taking it to a ridiculous extreme the idea that, ‘Oh, you can run everything by an algorithm,’” he shoots back—and thinks the critics are missing the point. Urbanists like Arieff, or the famed author and activist Jane Jacobs, value the fabric of cities for its density, life and serendipity. Aggarwala sees data as a way to protect and improve that experience, not replace it. Traffic signals already in place in many cities are timed to make it so no driver is kept waiting at a red light on an empty street in the middle of the night—making cities both safer and smoother-running. Expanding that technology could do the same for the pedestrians that urbanists love. Today’s technology, Aggarwala says, can “optimize everyone’s needs in a more rational way.” Sidewalk’s idea for snow-melting sidewalks would let residents enjoy more of the city for more of the year. Self-driving cars, integrated properly into the streetscape, could make it more humane. “If you can count on a car to go straight down the middle of the street and obey the speed limit at all times, you can redesign the street” in all sorts of ways, he says.


If all this seems like a great deal of attention directed at a tiny plot of land that takes about eight minutes to cross on foot, that’s because there is, lurking behind Quayside, far more on the table. Just about all players involved believe that if Sidewalk can be successful at Quayside, it has a shot at the adjoining 800-acre Port Lands, a swath of problem space big enough to become home to a dozen new neighborhoods in a growing metropolis. Townsend, the consultant, says of the Port Lands: “That’s a city they’re going to build there. This is just the warmup, this little piece.”

A project on that scale—a dozen neighborhoods in one of the highest-profile cities in North America—could set the tone for urban development all over the world from here on out. What sort of tone that will be, though, is the big question, and one area in which there are the fewest answers is the simple matter of information.

A truly smart city stands to radically increase the amount of data collected on its citizens and visitors, and it puts into sharp relief the responsibility a local government—and the contractors it would inevitability hire to manage some of that digital infrastructure—would have to both hold and probe that data. That dynamic quickly turns the future of the smart city from a technological question to a fundamentally civic one. Heaps of data are already piling up in cities around the world, with very little agreement on the best way to handle all that information. Toronto could well be a test bed, a suggestion Doctoroff has made himself.

At one of the series of roundtables Sidewalk Toronto has held, Doctoroff responded to a question about data management by saying, “There are cameras everywhere anyway. There’s chaos out there. Together we can bring order.” Whose “order” will it be? That’s what worries people. Take Dubai, says Ann Cavoukian, a civic-privacy specialist who is consulting for Sidewalk on the Toronto project. “It’s horrible—the antithesis of privacy. They use sensors to identify everybody and track their movements.” That city in the United Arab Emirates set out in 2014 to become what it called the world’s smartest city. The director of the effort has said, “In Dubai, we believe that happiness can be measured, and that we can aid our leadership to positively impact happiness for the city, through science and technology.”

Cavoukian served three terms as Ontario’s privacy commissioner and is now Sidewalk Labs’ lead outside privacy consultant on the Toronto project. She is known for having developed a concept called “privacy by design,” which in its simplest form argues that privacy must be baked into everything from software to cities from the very beginning. In an ideal privacy regime, the system can’t track individuals; any personally identifiable information on people living in or moving through Quayside would be scrubbed right at the sensor. So a sidewalk, for example, could track the flow of pedestrians all day without ever knowing who they were. “You strip the personal identifiers, you get rid of the privacy concerns,” Cavoukian says.

Aggarwala says that over time Sidewalk has come to appreciate “how deeply different” the Canadian view of privacy is compared with that in the United States. Canadians tend to see privacy as a fundamental human right; Americans have historically been more willing to see it as something that should be protected, with abuses punished after the fact, but which can be traded away in exchange for some benefits, like free Gmail. Google makes its billions in large part by collecting data on its users and slicing and dicing it for the benefit of advertisers. Sidewalk has already stipulated that data collected in Quayside won’t be used for advertising purposes. Says Cavoukian, “It’s not going to be a smart city of surveillance. It’s going to be a smart city of privacy, and that will be a first.”

But even if, as Cavoukian contends, stripping personally identifiable data at the sensor in most cases largely addresses privacy concerns, other worries remain. Some privacy advocates worry about the idea of collective privacy, or the idea that data can be used to know things about communities they would rather not have everyone know, like tracking residents’ movements to create a profile of the overall rhythms of a neighborhood, or even analyzing sewage for signs of concentrated drug use. That issue hasn’t come up for discussion in Quayside, Cavoukian says. (Asked about it, Aggarwala says that community-level data can be hugely useful, pointing to the value of the Canadian and U.S. censuses, which are largely uncontroversial data-collection efforts.) And then there’s the idea of what sort of entity makes the calls on how the data can be used. Waterfront Toronto’s CEO has floated the notion of a “data trust,” a third party that would make decisions about proper uses of the information collected at Quayside.

While many of those questions of data are still abstract, one particularly pressing concern is data residency—simply put, where the machines that hold the data generated by Quayside will actually reside. That matters, in part, because the laws of that place will largely govern how it can be used. The information economy depends on highly networked, decentralized systems that share data across companies, devices and borders. This data sharing has lately become a political issue, in part because the United States’ handling of data has been the subject of global suspicion since the revelations of Edward Snowden. Some countries, including China, Russia and Brazil, reacted to evidence of U.S. companies’ sharing of user data with the U.S. government by demanding so-called data localization or data sovereignty—requiring that information that affects a country’s people must be physically housed inside its borders. The United States had been making some headway advocating for open digital borders in trade negotiations, but President Donald Trump’s withdrawal from some of those agreements, such as the Trans-Pacific Partnership, means, says Daniel Castro, director of the Center for Data Innovation at the Information Technology and Innovation Foundation, “the U.S. is no longer at the forefront” of that debate.

Canadians see privacy as a fundamental human right; Americans see it as something that should be protected, with abuses punished after the fact.

When it comes to Quayside residents, Cavoukian says the data should stay in Canada—be collected, processed and stored on Canadian servers. Waterfront Toronto’s Verner, though, isn’t so sure that’s possible or even wise. As they have worked through the planning process for Quayside, she says, they have come across instances where it makes sense to let the data migrate, such as if the expert best able to solve an issue with a sensor in Toronto is located in, say, New York. Or if a company providing a service riding on top of the Sidewalk-powered digital layer is housed on the other side of the world. Keeping all the data strictly in Canada, Verner says, “is a layer of making sure we’ve got an entire help desk, call center, or whatever it is here in Canada”—and that, she says, might not be practical.

As the data debate makes clear, there’s much here that isn’t going to be in Toronto’s hands, or even Sidewalk’s, and it’s not clear that the city has worked out that issue yet. Townsend, the former Sidewalk consultant, jabs that thus far in Toronto, Sidewalk has spent more time discussing garbage innovation than data management. “This is the city-level question of our time,” he says. “And they haven’t taken the slightest step in trying to even create a vision there.”

Sidewalk officials say they agree about the stakes—“we have the opportunity here to really be innovators in terms of data governance,” says one—but say it’s still a work in progress. Verner argues that the conversations will flow more freely once there are specific technologies to which to react. Cavoukian, for her part, holds that Sidewalk’s only failing thus far is not pushing back aggressively enough against complaints about the company’s seriousness when it comes to privacy. The company is giving every sign so far, she says, of making the correct moves.


The tech industry famously moves quickly, municipal governments less so. And as tech companies grow more interested in building cities of the future, some urban-development experts are drawing attention to what they say is a power imbalance. On the one hand, there’s a well-funded industry comfortable with navigating the cutting edge of innovation. On the other, there are local governments eager to get their hands on tech’s benefits quickly—but which often lack the time, money and expertise to properly assess what, exactly, they stand to gain from it.

Cities can find themselves at the mercy of companies pitching everything from shiny new apps to, in the case of Amazon, a shiny second headquarters. “Toronto got excited, people in the city are demanding this new city, and the city has, a little bit, lost control of the conversation,” says Simone Brody, executive director of Bloomberg Philanthropies’ What Works Cities. “This isn’t just a Google-Toronto problem. It’s an Uber problem. It’s an autonomous vehicle problem. Cities haven’t figured out how to take the power back.”

Sidewalk says it appreciates the balance and takes pride in not nudging cities for handouts. Even Townsend, who can be critical of Sidewalk’s work on Toronto, says it compares well on that point to Amazon, whose high-profile headquarters search has seen cities competing to offer the giant the sweetest deal. They should be careful what they wish for, he says: “I think Amazon is always going to be an occupying force wherever they end up.”

But Brody argues that even where companies attempt to carefully navigate that power imbalance, it exists. The cold hard fact is that while cities have struggled, Silicon Valley has companies that have produced astonishingly large heaps of cash. And in many cases, they need places to put it. One way of looking at the Sidewalk Toronto project, argues Townsend, is as a bid for a hugely valuable piece of real estate dressed up with a bit of what he calls “smartwashing.” Google has been in the market for attractive properties in knowledge centers lately. It recently bought Manhattan’s Chelsea Market for a whopping $2.4 billion. For companies with overflowing coffers, cities are an attractive place to put that cash.

Municipal governments, naturally, welcome those deep-pocketed corporate citizens, who often come bearing ideas for urban improvements. But officials might soon find that those companies end up owning not just slices of real estate but also, as they take on more local responsibility, huge chunks of information about how cities themselves function. “When it comes to future negotiations, it’s frightening that Google will have that data and cities won’t,” Brody says. Sidewalk has pointedly engaged in a series of public meetings and online consultations meant to give Torontonians a voice in the process. “We are aware that we can’t do anything without government,” Doctoroff has said, “and we are extraordinarily respectful of it.” An on-site pavilion is meant to open up in Quayside in late summer. Some in the urban affairs world see that back-and-forth as a huge improvement over how developers tend to work, and give Sidewalk credit for rethinking the often hopelessly adversarial process that often marks real-estate projects.

“This is really a grand experiment, in many respects, that is going to teach not just Toronto but really cities all across the world what is the future city going to look like,” says Bruce Katz, the author and former Brookings Institution official. “And I can’t imagine a better group of people to be entrusted with it.”

“Of course,” he adds with a laugh, “if you’re Toronto, you’re the lab.”