Metagenomics is a relatively new field that seeks to understand the
structure and function of the shockingly large number of microorganisms on
our planet.  New technologies permit us to now sequence samples taken from
their environment rather than only those that are cultivated in the lab. For
example, Craig Ventner’s Global Ocean Sampling Expedition has collected water throughout the world’s oceans, captured organisms, and sequenced their DNA. In the initial pilot study alone, nearly 150 new bacteria were discovered through this process.

The science and computing challenges are huge. A single gram of soil
contains approximately one trillion base pairs of DNA. Scientists at the National Institutes of Health recently compared over 100,000 bacterial gene sequences on the human skin and discovered a far larger number of different bacteria living on human skin than had been previously known (Science, May 28, 2009). Sequencing and making sense of these data introduces new computational problems, not merely slight extensions of existing ones.

The potential impacts of understanding these data are huge as well. In the
case of soil, microbial communities have an impact on carbon sequestration
and understanding them may help us with cleaning toxic waste. In our bodies,
microbial cells are estimated to outnumber our human cells by a factor of
ten to one and are important in protecting our skin, digestion, and much
more. Understanding these large microbial communities is therefore likely to
have a positive impact on human health. The NIH has launched the Human
Microbiome Project to support work in this field.

Complete DNA sequences of thousands of organisms are piling up in databases
because of the efficiency of DNA sequencing technologies. Most of this
remains unanalyzed for several reasons. We don’t yet know the right
biological questions to ask. We don’t have all the clever programs that
would actually ask these questions of the computer. And there is now so much
data that many questions totally overwhelm even existing high performance
computers.

Among the computational challenges in this field are the design of new
algorithms and cloud computing technologies. In the National Academies of
Science publication “The New Science of Metagenomics: Revealing the Secrets
of our Microbial Planet”, the authors conclude “What then, will metagenomics
have become, in 20 years? We believe that it too will be a concept-driven
computational science… We can expect, in 20 years, enormous advances on
three fronts – technical, computational, and biological – as well as a host
of specific applications.”

We encourage our community to explore and engage in this and other emerging
fields at the crossroads of biology and computation. This is one of the
exciting areas for 21st century computing.

Contributed by Bill Feiereisen with assistance from Ran Libeskind-Hadas

Informatics Europe was created five years ago as a European version of CRA (which is a North American association by charter). A recurring theme at this meeting was the concern that the European scientific research community still does not fully appreciate computing as an intellectually vibrant research discipline in its own right; instead the field is often viewed as an enabler of research in other disciplines. A number of discussions centered on ways in which the computing community can do a better job in explaining the field to others.

Informatics Europe is working on a series of additional initiatives of value to its members. It is beginning data collection analogous to the CRA’s Taulbee Survey and there are discussions on overseeing the process of departmental evaluation. Evaluation is a major issue in Europe, where departments are regularly and systematically evaluated under national government authority. The fear among computing researchers is that the evaluation process will be defined by a non-computing body using irrelevant or inappropriate standards. Therefore, Informatics Europe hopes to develop its own evaluation process and a list of potential evaluators. There was considerable discussion of the possibility that the end result would be a European-wide ranking of departments, an outcome that is generally not favored.

Overall, it was an interesting meeting and, being held in Paris, the food was quite above the typical conference fare, including wine with lunch.  A further note – ACM chose this conference to announce the creation of ACM in Europe (http://dev.acm.org/test/europe/).

This report was contributed by Andrew Bernat, Executive Director of CRA.

While the technical program plainly illustrates the quality of the conference, what is more difficult to convey to those who did not attend is the positive energy and excitement that permeates the entire event.  Hundreds of students excitedly swarmed the Amazon.com booth to write code, with the hopes of being dubbed a “Ninja Coder.”  Students showcased t-shirts with the slogan “I code like a girl, and I’m proud of it!”  Throughout the halls, colleagues and friends hugged as they reunited.  And who can overlook the not one but two dance parties put on by the conference and its sponsors.  Hundreds of computer scientists on the dance floor, celebrating the fact that they are in a field where they get to do what they love every day.

As its name implies, the Grace Hopper Celebration is not simply a conference, but a celebration of the work that we do as computer scientists, and particularly as women computer scientists.   It’s a wonderful reminder to all of us—men and women, students and faculty, academics and those from industry—that we work in an exciting field at an exciting time.  It is clear from the conference that the women in the field are critical in driving this exciting field forward, both now and in the future.

This report was contributed by Dr. Christine Alvarado, Assistant Professor of Computer Science at Harvey Mudd College.

Could prizes be useful to the broader computing community in advancing research?  The Clay Mathematics Institute established the Millenium Prizes in 2000, offering $1 million for the solutions to each of seven famous open problems, including the question of whether P=NP.  It’s hard to imagine that many researchers have decided to shape their research agendas based on the existence of this prize.  On the other hand, Wolfram Research sponsored a $25,000 prize, with a blue ribbon prize committee, to determine if a specific small (2 states and 3 symbols) Turing Machine is universal. The problem was solved (in the affirmative) in 2007 by a 20-year-old from Birmingham, England.

There is a rich history of prizes for technical innovation.  In the early 18th century, the British Parliament offered the Longitude Prize for a practical method of precisely determining a ship’s longitude, with different monetary amounts depending on the accuracy of the instrument.  The rules were changed during the course of the competition and the prize was never awarded.

More recently, there have been numerous technical prizes such as the $10 million Ansari X PRIZE for carrying three people to 100 kilometers above the earth’s surface.  Following on the success of the Ansari Prize, The X PRIZE Foundation has established several other major prizes for specific achievements that have “the potential to benefit humanity”.

Are there some major problems in computer science that could be incentivized by prizes – financial or otherwise?  What are the potential benefits and risks of this approach?  We’re eager to hear your thoughts.

Some good additional readings include the following:

• Two articles at Slate Magazine, one on the Netflix prize and one on the use of prizes for innovation in the pharmaceutical industry.
• A scholarly paper on the subject by Stephen Maurer and Suzanne Scotchmer.
• A report from the National Research Council on Innovation Inducement Prizes at the National Science Foundation.

The inspiring list in the attached PDF was compiled by the following individuals and their colleagues: Bill Bonvillian (MIT), Susan Graham (Berkeley), Anita Jones (University of Virginia), Ed Lazowska (University of Washington), Pat Lincoln (SRI), Fred Schneider (Cornell), and Victor Zue (MIT).

We solicit your suggestions for additional student contributions of comparable impact – add them as comments below and email them to Ed Lazowska.

Here’s the list!

The third meeting will likely be scheduled for late October, though draft vision/consensus documents are being crafted before this next meeting.  The meeting will held at IBM in Austin, Texas, and will engage leaders from funding agencies as part of the program.

Under the leadership of Beverly Woolf, head of the GROE initiative, the group discussed the results of the April workshop held in Tempe, Arizona, as well as the next steps for research involving the “road map” that came out of that workshop.

This meeting provided enthusiasm, support, and new ideas for the GROE initiative.  Results of the workshop will soon be reported on the CCC’s GROE initiative web site.

Under the CIFellows project – conceived of and implemented by CCC and CRA, and funded by a $15 million award from NSF – 60 extraordinary new Ph.D. graduates have been paired with 60 outstanding mentors for postdoctoral opportunities that advance the computing field.

The CIFellows project was conceived in February as a response to the current economic climate.  The goal is to keep outstanding Ph.D. graduates “in the research and education game” until the climate improves.  It is a huge tribute to NSF, CCC, CRA, the computing research community, and Peter Lee (who directs the project) that we were able to go from conception to sub-award in 5 months!

• Transition Team white papers (see them here)
• Library of Congress symposium (transparencies and videos here)
• Computing Innovation Fellows project (blog post here)
• NetSE Research Agenda (blog post here)

See the presentation here (pdf).

Over the past forty years, computer networks, and especially the Internet, have gone from research curiosity to fundamental infrastructure. However, this is no time to rest on the successes of the past. To meet society’s future requirements and expectations the Internet will need to be better: more secure, more accessible, more predictable and more reliable.

In 2008, the Computing Community Consortium charged the NetSE Council with developing a comprehensive research agenda that would support the development of a better Internet. The NetSE Research Agenda report summarizes the findings and recommendations of the NetSE Council.

The intended audiences for the report include members of the computing research community, funding agencies, and policymakers.  The report provides a framework or context within which various targeted research agendas can be moved forward by their communities.  The report is your document (literally hundreds have contributed to it in various ways), and it is a living document – comments are earnestly solicited, as indicated on CCC’s NetSE activity web page.

Read the full report here!

Many thanks to Ellen Zegura for seeing this activity through to a successful conclusion!

The website for submitting applications was taken down on schedule at midnight on June 9, and the reviewing process commenced two days later. We’ve been very busy reviewing ever since, assigning each application to multiple reviewers, to guarantee a minimum of three reviews for each awardee. We are targeting July 10 for completing the review and decision process.

The 526 applications come from 415 145 distinct colleges and universities and specify a total of 949 different applicant-mentor pairs. The mentors span 198 different universities, companies, and non-profits.

27% of the applicants declare themselves to be female and 62% male. 42% are US citizens and 5% are permanent residents. The two largest international groups are from China (15%) and India (14%). 6% of the applicants are members of an underrepresented racial/ethnic group.

We asked each applicant to specify his or her research subdiscipline. A quick tabulation of the responses is as follows:

• 21%: AI / Machine Learning / Robotics / Vision
• 2%: Communications/Signal Processing
• 3%: Computer Science Education / Educational Technology
• 6%: Databases / Information Retrieval / Data Mining
• 3%: Graphics / Visualization
• 7%: Hardware / Architecture
• 7%: HCI / CSCW
• 7%: Information Assurance / Security / Privacy / Cryptography
• 2%: Information Systems / Information Science
• 5%: Mobile / Ubiquitous / Embedded Computing
• 9%: Networks / Operating Systems
• 3%: Numerical/Scientific Computing / HPC / Data-Intensive Scalable Computing
• 3%: Other (e.g., Quantum Computing, Synthetic Biology, Computational Neuroscience, Technology for the Developing World)
• 3%: Programming Languages / Compilers
• 8%: Scientific/Medical Informatics (includes Bioinformatics, Computational Biology, Clinical Informatics, Public Health Informatics, Chemical Informatics)
• 2%: Social Computing / Social Informatics
• 2%: Software Engineering
• 0% (2): Technology Policy
• 6%: Theory / Algorithms

(It seems clear that a further subdivision of AI/ML/Robotics/Vision into separate areas would provide better information.)

The response by both prospective mentors and applicants far exceeds our expectations! The level of interest has been extremely gratifying, and we truly appreciate the cooperation of almost all of the mentors and recommenders in submitting their endorsements on time. The members of both the Selection Committee and Steering Committee have been working very, very hard on a completely volunteer basis. The CCC’s oversight is working well to ensure broad community input, notification and, ultimately, participation.

We’re all looking forward to making the final decisions, in about two weeks or so …

– Peter Lee and Ed Lazowska

Videos of the presentations (as well as slides) are now available on the symposium website.  See http://www.cra.org/ccc/locsymposium_slides.php for the complete agenda with individual links, or see our YouTube channel, http://www.youtube.com/computingresearch.

Talks at the Symposium included:

• Introductory Session
  • Ed Lazowska (University of Washington), “Changing the World”

• Session 1: The Internet and the World Wide Web
  • Alfred Spector (Google), “Why We’re Able to Google”
  • Eric Brewer (UC Berkeley), “The Magic of the ‘Cloud’: Supercomputers for Everybody, Everywhere”
  • Luis von Ahn (Carnegie Mellon University), “Human Computation”

• Session 2: Evolving Foundations
  • Barbara Liskov (Massachusetts Institute of Technology), “Security of Online Information”
  • Daphne Koller (Stanford University), “Learning to Improve Our Lives”
  • Jon Kleinberg (Cornell University), “Global Information Networks”

• Session 3: The Transformation of the Sciences via Computation
  • Larry Smarr (UC San Diego), “Supercomputers and Supernetworks are Transforming Research”
  • Chris Johnson (University of Utah), “Computing and Visualizing the Future of Medicine”
  • Gene Myers (Howard Hughes Medical Institute), “Zooming In On Life”

• Session 4: Computing Everywhere!
  • Deborah Estrin (UCLA), “Sensing Everywhere!”
  • Pat Hanrahan (Stanford University), “Pixels Everywhere!”
  • Rodney Brooks (Massachusetts Institute of Technology), “Robots Everywhere!”

In the 30+ year history of this award, only one computer scientist has been recognized.  A principal reason is we don’t nominate many people.  Let’s change that!  It’s too early to submit nominations, but it’s not too early to start thinking about who you’d be willing to nominate.

Information on the Waterman Award is on the NSF web here.

The Computing Innovation Fellows (CIFellows) Project will fund as many as 60 such positions. Applications are due very soon: June 9, 2009. Awards are expected to be announced by July 10. Positions will commence in Autumn 2009.

Go to http://cifellows.org to apply to be a CIFellow.

Also: Go to http://cifellows.org to advertise your interest in hosting a CIFellow at your organization.

Individuals who complete the requirements for their PhD from a U.S. institution between May 1, 2008 and August 31, 2009 in Computer Science, Computer Engineering, Information Science, or a closely related field are eligible to apply. Applicants must obtain commitments from between one and three prospective hosts/mentors. Hosts/mentors must not be at the same institution as the one granting the PhD. The CIFellows website provides resources for both prospective applicants and host/mentors to announce their interests and availability.

Complete information is available at http://cifellows.org. A poster that you may print and post is available at http://www.cs.cmu.edu/poster/.

– Ed Lazowska, Chair of the Computing Community Consortium Council
– Peter Lee, Incoming Chair of the Computing Research Association

The Computing Community Consortium, working with a remarkable coalition of all the major groups involved in cyberinfrastructure for research and education, has been weighing in heavily on broadband strategy.  This week, the Chronicle of Higher Education featured this group’s position paper, “Unleashing Waves of Innovation.”

Our basic messages – consistent with the position advocated by Microsoft and others:

• Use an aggressive definition of broadband – 100 mbps – in order to be competitive with other nations.
• Focus the initiative on K-12 schools, higher education institutions, libraries, community centers, clinics, and hospitals:  reach a broad range of citizens, and the next generation of innovators and consumers.
• Recognize the historic role of the state and regional networks that grew out of the higher education community in reaching unserved and underserved regions.  In general, neither  telecommunications companies nor state agencies have provided effective leadership.

The recommendations in the “Unleashing” document were reached with remarkable unanimity among individuals and organizations representing a broad range of academic and infrastructure organizations.  The objective is to help shape the approach that will be adopted by the Department of Commerce and the Department of Agriculture in allocating nearly $7 billion in broadband stimulus funds.  A similar task must be undertaken in each and every state – state governments will play a significant role in formulating and prioritizing proposals to Commerce and Agriculture for stimulus funds.

Read the “Unleashing” paper here.  Read the Chronicle article here.

- Ed Lazowska

http://www.cra.org/ccc/locsymposium_slides.php

Videos of all talks will be available soon.

Previous posts describing the symposium are available here and here.

Many thanks to our speakers for preparing and delivering such wonderful talks, and for making their materials available to the community at large.

Inspiration for the program came from a large number of responses from the computing research community to two November CCC blog posts — this was your symposium!

Each of the talks was superb.  Honestly, in 35 years in the field, I’ve never before spent a day with such uniformly high quality of content and presentation.  It was remarkable.  The videos of the 20-minute talks will be a great resource for all of us.

My introductory talk (pdf) provided a quick overview of the impact and promise of the field, as well as a peek at the day’s program.  I drew upon a recent New York Times article describing a Wharton School assessment of “the top innovations of the last 30 years” (more than half of which were direct results of computing research!) as well as a recent CSTB study “Assessing the Impacts of Changes in the IT R&D Ecosystem” (which described a day without information technology as “a day the Earth stood still”).

My closing remarks summarized both the content and the messages of the day-long symposium.  I won’t repeat Susan’s earlier summary of the content, but here are a few additional highlights:

• Alfred Spector commented that “Google did not arise through spontaneous generation in a garage in Palo Alto — it drew upon a broad set of computing research advances.”
• A number of the talks — Luis von Ahn’s, Jon Kleinberg’s, Rodney Brooks’s, probably others — alluded to emerging “hybrid systems”:  humans + computers.
• Daphne Koller presented a terrific catalog of the successes of machine learning.
• Gene Myers asserted that “computation is the bottleneck in every [modern molecular biology] project” — a perfect bookend to Larry Smarr’s session-leadoff talk on the transition to data-intensive science.
• Chris Johnson made it clear that in the past decade, modeling and visualization have become valuable tools in advanced surgical practice — M.D.’s are beating down his door to obtain access.
• Pat Hanrahan presented neat timelines of the transformation of all media — publishing, audio, photography, and video — from analog to digital.
• Rodney Brooks ended the technical sessions on a cautionary note:  The future of robotics is robots that operate in unstructured environments.  America has a wide lead now in this field.  But once, we led in manufacturing robotics, and we allowed that lead to slip away.  Will we allow that to happen again?

That’s a good jumping-off point for the messages of the day.  Here’s my list:

• Computing research truly has changed the world.
• A rich and complex ecology — involving government, academia, and industry — has made America the world leader.
• Research has laid the foundation — you can find federally-funded university-based research at the heart of essentially every billion-dollar sector of the IT industry.
• It consistently takes 10 or 15 years from “research breakthrough” to”billion-dollar sector.” So you need patience — there’s no such thing as “just-in-time research.”
• Often, “products” in IT are created by synthesizing multiple advances — unlike biomedicine, where a single patent can yield a blockbuster drug.
• Often, old ideas gain new life.  We’ve had recent breakthroughs in search and in machine learning, but each traces its roots back at least 40 years.
• While computing research often is motivated by a “strategic objective” — we see a practical value if the research succeeds — we’re often not very good at predicting what the greatest impact of our innovations will be.  Serendipity plays a huge role.  Any attempt to decide early-on what research is “important” is likely a losing proposition.
• While much of the exciting computing research today is interdisciplinary and collaborative, it’s important to have a balanced portfolio:  core + interdisciplinary, single-investigator + team, etc.

The bottom line:  We have an extraordinary track record — America has an IT R&D ecosystem that again and again leads to massive transformations.  And the next ten years can be our golden age:  on March 25th we heard about some amazing recent accomplishments, and we heard from some extraordinary young people (as well as some extraordinary not-so-young people) who are driving the field forward.  The opportunities for impact are greater than they have ever been.  Go out and change the world!

– Ed Lazowska

I’ve just returned from the CCC-organized Symposium on “Computing Research that Changed the World.” (http://www.cra.org/ccc/locsymposium.php) It was a marvelous experience. There were 12 wonderful 15-minute talks that highlighted major achievements in computing in the last 10-20 years, the research advances that enabled them, and the opportunities to move forward in the various fields in the years ahead.

In the morning, Al Spector outlined the technologies that enable us to google, Eric Brewer explained the emergence of the cloud, and Luis von Ahn showed us how captchas are being used to build accurate digital archives of corpuses such as the New York Times. Then Barbara Liskov explained the key ideas and challenges of security in distributed systems, Daphne Koller highlighted some of the myriad applications enabled or enhanced by machine learning, and Jon Kleinberg taught us about the science that underlies social networking and the ways in which those concepts are fueling new applications.

As if that weren’t enough, in the afternoon, Larry Smarr showed some of the major achievements (both scientific and technical) fostered by the nation’s investments in supercomputing for the research community, and highlighted the importance of huge amounts of data and ultra-high bandwidth networking for future progress, Chris Johnson showed us the rapid evolution of visualization techniques and the scientific understanding they have facilitated, and Gene Myers gave a fast summary of genome sequencing past and future and the opportunities to drive progress in molecular biology as a data-driven science. Then Deborah Estrin showed the wondrous new applications that are being enabled by the ubiquity of sensors, and the research challenges that must be met, Pat Hanrahan reminded us of the remarkable evolution of digital media from text to audio to video to photography to HDTV, and Rod Brooks gave us a great summary of the stunning advances in robotics.

The day was spellbinding. I never once opened my laptop. I was reluctant to tell speakers their time was running out when I moderated a session. I was reminded over and over how rich our field is and how fast it continues to evolve. Just as it was when I started out as a student, it’s an exciting time to be in computing.

Through the kind auspices of Congressman Bart Gordon, the symposium was held in the Members Room of the Library of Congress.

It’s a beautiful room, but relatively small, so attendance was limited. But it was a great crowd — some senior C.S. faculty, some junior faculty, key former and current NSF people from CISE, from other parts of the Foundation, and from the National Science Board (including current Director Arden Bement and former Director Erich Bloch), Congressional staffers, and a collection of colleagues from other greater Washington organizations. Congressmen Lipinski and Holt were able to join some of us for lunch.

The sessions in the Members Room were followed by a closing session (more like a reception) in the Madison Room. There were some really cool demos there. Ed Lazowska, who had made the opening remarks in the morning, gave a brilliant summary of the day, despite the challenge of talking in a cocktail party setting. Congressman Lipinski also spoke, and gave those not at lunch an opportunity to meet him.

The speakers did an outstanding job in making their talks accessible to that diverse audience. Consequently, these are great talks to share with student audiences, to show them what computing is really about. Those of you that checked might have noticed that there was no webcasting, but the talks and the discussions that followed were videotaped, and will appear on the CCC website soon. I strongly encourage you to take a look!

– Susan Graham

I finished The Mystic Arts of Erasing All Signs of Death by Charlie Huston just before the City Archives collapsed in Cologne, Germany, on March 3. I soon found myself at my 11th disaster, but unlike Webb, the protagonist who must come to grips with the events that led him to a janitorial job cleaning up trauma sites, I was clear on why I was there standing in the rain. I was there in the hope that we could make a difference with technology — that we could enable the fire rescue teams to save a life, prevent a responder’s death, or even bring a family’s agonizing wait to closure. Or to help the structural engineers discover and document What Went Wrong. And, if not that, learn something for the next time.

We accomplished option C at least.

Let me offer this photograph as the spoiler alert for my personal blog (rescuerobotics.blogspot.com) which has details, history of robots for building collapses, and some pictures and video. In the picture, I’m on the right, down at the mid-level of the collapse (there were two more stories of flooded or damaged subway structure below us) with the Cologne Chief of the Fire Department, the head of special operations, and the safety officer (all wearing reflective bunker gear). The photo was snapped a few minutes after we had reached the conclusion that robots could not be used. In the photo we are chatting about the collapse, the dangers, the sequence of events that had led to the catastrophe and resulting challenges before clambering up the scaffolding to the safer street level.

Standing there in Germany, I couldn’t help but be reminded of all the ways computing could be applied. Robots. Cyber-physical systems. Sensors to penetrate the rubble and algorithms to process and mine the data. Reliable and secure high bandwidth wireless networks. Optimal resource allocations and scheduling. Mapping and 3D surface reconstruction. Sketch-based and multi-modal interfaces to tablet PCs to make it easier for the experts to express their knowledge. Social networking to help the displaced residents figure out how to adapt, where their friends had been relocated to, what was really going on.

Looking again at the photograph brings me back to the The Mystic Arts. I was particularly touched as to how at the end of the novel Webb comes to view his work as a sacred duty. The disaster “lifecycle” of prevention, preparedness, response, and recovery is a lot like trauma work — it is infrequent, not well-funded or understood, very challenging, and ultimately inevitable.  Standing there in Germany, I glimpsed the journey ahead for the computing community as we, too, embrace the difficult and necessary, and make the field of emergency informatics our sacred duty as computing, well applied, can help erase all signs (and maybe the causes) of death.

– Robin Murphy is a Professor at Texas A&M University, and one of the newest members of the CCC Council. Previously, Robin’s robots also conducted search-and-rescue operations at the World Trade Center shortly after 9/11.

I think that this symposium is really important because with a new administration in Washington, we have people who appreciate the importance of fundamental research. If we increase the size of the funding pie, all of us will benefit. The best way to increase the size of the computing research part of the pie is to link advances in computing research to advances in other fields and the larger society.

That is what the March 25 symposium is about. With the cast of speakers we have lined up and the number of people from government agencies and Congress who will be attending, we have a great chance to make a difference. Check out the program; we had to keep the invitation list small due to the location, but all the talks will be recorded.

– Greg Andrews

It’s certainly exciting to see core computer science issues featured so prominently in the press! Indeed, this article has generated quite a bit of discussion in the research community. For example, while acknowledging that a new network architecture would certainly play an important role in improving security, Purdue’s Gene Spafford writes on his CERIAS blog, “Do we need a new Internet? Short answer: Almost certainly, no.” (Gene tells me that he will be interviewed on this topic on C-SPAN’s Washington Journal, airing at 9:30am on Saturday, February 21.) UCSD’s Stefan Savage is largely in agreement, saying that “the network is by and large the smallest part of the security problem” and that “at a technical level the security problem is really an end-host issue, coupled with an interface issue — lots of power given to lots of different pieces of software whose couplings present opportunities to bad guys that aren’t anticipated, at a social level its a human factors issue.” The bottom line is that, outside of resource management (that is, controlling DDoS) and attribution/accountability, the main sources of security risk are at the end points — a key point missed in the NY Times article. Peter Freeman perhaps puts it most plainly:

To be succinct, although technical improvements are clearly needed, a large part of the security issue comes back to people, not technology. If we could figure out how to educate people so they don’t respond to pleas from Nigerians who need to transfer money or they don’t leave their passwords on post-its or never install the frequent security patches that are issued, we could make huge improvements immediately.

That’s not to say, however, that reinventing some aspects of networking isn’t an important research goal. Peter Freeman, while he was the director of NSF’s computer science (CISE) division, was instrumental in helping to launch the GENI Project in 2004, with the goal of developing an experimental platform for exploring truly reliable and higher capacity networks. For Freeman and others, new approaches to networking were deemed an important area for government investment because of the basic nature of the research problems involved.

Mounting a global-scale effort such as GENI has been a major challenge for the computing research community, perhaps similar to what the astronomy community goes through when it decides to develop large telescopes. But the initiative has already had several ripple effects. Guru Parulkar, who was the NSF program manager for GENI at the start, went to work with Nick McKeown and helped start the Clean Slate Project mentioned in the NY Times article. The GENI effort also put Princeton’s Larry Peterson in the middle of things, as the PlanetLab Consortium was one of the most influential early inspirations for GENI. And now, a much broader visioning effort in Network Science and Engineering, or NetSE, supported by the Computing Community Consortium (CCC), is defining the critical research questions in a wide range of network-related areas.

As for GENI itself, significant progress on development of a prototype has been made, coordinated by a GENI Project Office (GPO) and involving a large number of academic researchers. BBN’s Chip Elliott says that a version of the testbed will be available for early testing in a matter of months, “which will allow researchers to investigate many core networking research questions, some of which are the thorniest questions for Network Science and Engineering, upon the earliest end-to-end prototype of GENI.” Ellen Zegura, Georgia Tech professor and NetSE Council Chair, cites the importance of this development, saying “For me, the deep technical issues of security and privacy are at the heart of the GENI effort, and one of the main reasons for developing it.”

The demand for better security grows with the public’s dependence on computing and networking. As Chip Elliott states:

Would our lives improve if all aspects of the Internet were firmly bound to real-world personal and organizational identities? Might total public transparency reduce crime and misbehavior – in short, might less privacy lead directly to more security? Is privacy already a vanishing concern, fated to disappear in a few years without widespread regret?

Careful thinking will illuminate these issues — particularly if coupled to a vigorous program of experimentation.

This, in a nutshell, is what the NetSE and GENI initiatives aim to address.

– Peter Lee

The United States of America has steadily fallen further and further behind Asian and European nations with respect to broadband penetration and related services. This is impeding the development of new consumer applications (and related new industry and services) and limiting communications in an economy where knowledge exchange is vital in order to be to be a major player of the emerging , seamless and unobstructed global market. Reversing this trend may be of high interest to the incoming administration, but the viability of extending broadband is dependent on the deployment of new high bandwidth and high value applications that (a) will justify the investments required and (b) will contribute digital solutions to many of the key societal problems in this Energy-Climate Era (as recently identified by Thomas L. Friedman in his book Hot, Flat and Crowded) such as growing demand for ever scarcer energy supplies and natural energy, rapid and accelerating biodiversity loss, and disruptive climate change.

In 1997, Jaron Lanier, at the time the chief scientist of Advanced Networks and Services, started the National Tele-Immersion Initiative, as a coalition of research universities studying advanced applications over Internet2. We never capitalized on this initiative in United States. Instead, virtually all major advances in the commercial design and development of 3D multimedia input and output devices such as 3D stereo cameras, 3D displays, integrated solutions for the next generation of home entertainment systems were undertaken abroad. If we look at the corporate landscape of multimedia technology and its integrated multimedia solutions, they come mostly from Asia (e.g., NEC, Panasonic, Sony, FXPal, Samsung) and Europe (e.g., Phillips, Thomson). Swift action is needed to ensure American universities and industries seize the academic and business leadership of the next generation of tele-immersive systems, the 4D Immersive Holographic Spaces.

4D Immersive Holographic Spaces will be joint multi-view multimedia-rich spaces where people can immerse themselves in their physical full body size into a joint cyber-physical space with other people, and execute physical activities (e.g., physiotherapy rehabilitation), walk around people and observe detailed full-body social behaviors and communication cues of people in real-time, as if they were co-located in the same room, even though they are geographically distributed and thousands of miles apart. The impact of such systems would be dramatic, contributing to the increase of innovative economic opportunities, to the “green energy” efforts, and to the decrease of gap between regions of “have” and “have-not” experts. With respect to innovation leadership, venture capitalists will for the first time be able to interact with entrepreneurs located thousands of miles away as if they were next door. Our nation’s ability to find and grow new and emerging high-technology high-quality job-creating companies would extend to all regions of America. In health care, new services based on these cutting edge information systems could be delivered to our rural areas. A physiotherapist based in Washington would be able to provide rehabilitation assistance to multiple remote patients after heart-attack in neighboring regions and a wheelchair basketball coach in Illinois could inspire and train wheelchair children in Montana to play the sport. The children of the men and women of our armed forces would be able to explore the Amazon forest with their parents in a virtual world or simply learn basic values from their parents by bringing them together in their (virtual) home. All of these scenarios are dependent on the ability of 4D Immersive Holographic Spaces to deliver rich visible social cues and multi-view capture of human/group behaviors.

America can lead in the area of broadband information-rich immersive spaces if major investments are made to develop and build national tele-immersive infrastructures. We can then ensure US companies deliver innovative applications and services solutions with our academic institutions as key partners in addressing the research and development challenges. Advances in real-time computer vision, real-time computer graphics, integration of speech, vision and tactile sensory information, dynamic and task-dependent signal compression, and broadband wired and wireless networking with advanced stream-based and multi-view distributed and operating systems and architectures will be needed for the future tele-immersive systems. It is imperative that we move boldly and commit ourselves to this effort.

What is a “better Internet”? The current Internet has been a remarkable success, providing a platform for innovation that far exceeds its original vision as a research instrument. It is well documented that the Internet has transformed the lives of billions of people in areas as diverse as education, healthcare, entertainment and commerce. Yet many of these successes are threatened by the increasing sophistication of security attacks and the organizations that propagate them. A materially more secure Internet would be “better”. Further, billions of people remain untouched by the advantages of the Internet; Internet World Statistics puts worldwide average Internet penetration at about 22% in mid 2008. An Internet that affordably reaches the other 80% of the world population would be “better”.

Beyond security and accessibility, there are other areas where limitations of the current Internet are significant. The Internet usually works pretty well, but every user has experienced inexplicable periods of degraded performance or outright non-function. The current Internet provides no visibility to end-users and shockingly little visibility to network managers and operators to support understanding, adapting to and fixing reliability problems. Such limitations require lay people spend their leisure time as network systems administrators and companies to spend heavily in network operations. Further, the lack of performance reliability prevents the Internet from advancing to become a truly dependable, critical infrastructure. Indeed, current societal reliance on the Internet for critical functions is disproportionate to our ability to deliver a high degree of dependability. A more predictable Internet would be “better”.

The Internet embeds societal values in ways that are often implicit and not well understood. For example, the Internet is “open”, usually intended to mean that anyone can join the network by implementing the public protocol IP. In principle, users can run any application on the Internet, without limitation imposed by the network protocols. Open networks promote organic growth, but suffer from a lack of mechanisms to vet or bar participation. Issues of trust and individual accountability are confusing. As the well-known cartoon says, “On the Internet, no one knows you’re a dog.” An Internet that contains support for identity would be “better”.

The research community is poised to dramatically advance the agenda of building better networks through advances in both empirical design methodology and systematic design methodology. We have an approach to support large-scale and flexible experimentation based on programmability of devices and federation of multiple test-beds. We have a nascent mathematical framework for understanding architectural features and underlying principles. The time is right to advance and link both methodologies to realize better networks.

– Ellen Zegura

Back in early 2008, they began organizing four workshops, one each in four topical areas of robotics: manufacturing and logistics, healthcare and medical robotics, service robotics and emerging technologies.  More than 100 people attended these workshops, representing a mix of industry and academia.  Preliminary workshop reports were made available to the community, and an online discussion board provided a forum for further input.

A sampling of the four workshops’ findings:

• In manufacturing it is evident that the main applications so far have been large-scale production of entities such as cell phones and cars whereas small-scale production has received limited attention.  It is further evident that processes such as logistics and material handling have significant potential for use of robotics, but so far little attention has been devoted to such applications.  There is a need to consider new methods for easy programming of robots, and further integration of sensory information to enable robust and safe operations.  Less than 5% of all industrial robots today use sensors as part of the primary control system.
• Medical robots are today widely used for prostate surgery and are also gaining momentum for cardiac procedures and hip replacement.  The main motivations are faster recovery, improved quality and a reduced risk of any side effects.  The potential for medical robotics is very significant.  Related to healthcare there is also the use of robots for rehabilitation as it enables a higher degree of customization to individual patients and faster initiation of training.  In addition the engagement with robots is sometimes easier than interaction with humans due to privacy and scheduling considerations.  Wider adoption of healthcare robotics calls for new methods in machine learning, human robot interaction and flexible mechanisms for physical interaction with humans.
• Service robotics has two aspects: professional and domestic.  Professional robotics involves for example agriculture, forestry, mining and harbor automation. The number of people involves with agriculture and related industries is decreasing while the demand is increasing and there is a need to further automate the industry to remain competitive.  For domestic services there is a need to provide cleaning, surveillance, life sign monitoring, remote video, etc to assist people in their busy lives, but also to provide key functionality to enable people to remain in their homes as mobility and mental capabilities are reduced with age.  In service robotics it is characteristic that users have no or very limited training and the systems must be intuitive / easy to use.  In addition there is a need for flexible integration with existing technology (a scalable integration strategy). Finally there is a need for navigation and flexible perception to allow deployment in natural environments (e.g. homes).
• In emerging technologies that are several opportunities as sensing become ubiquitous, more flexible mechanisms are designed and new technologies such as nano become available.  The access to complex computing with a limited footprint allows deployment of AI in new settings.  The use of machine learning and new types of interfaces with a high degree of connectivity opens entirely new opportunities for use of robotics. Not to mentioned new actuation methods.

Robotics in general is characterized by a significant economic potential and has research opportunities across the entire spectrum from basic to applied.  There are clear short-term opportunities in areas such as medicine and manufacturing and at the same time there is a potential to create an entirely new industry for cognitively endowed robots with richer interaction with the world.

The workshop reports are nearly in their final form.  You can watch for them and other updates at http://www.us-robotics.us.

–Andrew McCallum

“Congress is now debating the American Recovery and Reinvestment Act of 2009. Included in this package is over 10 billion dollars for science facilities, research, and instrumentation.

“The reason for this inclusion is simple: today’s research is tomorrow’s infrastructure.

“When our nation faces immediate challenges, the feasible solutions depend upon the ideas, resources, and designs that are “on the shelf,” ready to deploy …

“Increasingly, information technology is the cornerstone of America’s infrastructure. Today’s information technology research is a cornerstone of tomorrow’s infrastructure.”

Read the full editorial here.  A set of white papers describing the role of computing research in meeting the challenges of the 21st century is available here.

Today we’re asking members of our Computing Research Advocacy Network (CRAN) — and anyone else with an interest in seeing fundamental research and research infrastructure budgets reflect their critical importance to the long-term health of U.S. economy and quality of life — to contact their representatives in Congress and urge their support for science funding in the nearly $900 billion stimulus bill now making its way through Congress…

…It is important that we generate letters from as many institutions as possible. Because the Senate has come out with sharply reduced numbers in their version of the bill, there will be temptation in the conference process to reduce or trade away big science increases for gains elsewhere in the bill. Significant participation rates in this effort will help keep the pressure on Members to continue to support science in the bill.

The full text of the CRAN Action Alert is available here, along with a sample letter.

This is an incredibly important time right now for our nation and for the future of science research. If ever there was a time to act, it is now.

What questions shape our intellectual future? What attracts the best and brightest minds of a new generation? What are the next big computing ideas – the ones that will define the future of computing, galvanize the very best students, and catalyze research investment and public support?

The Computing Community Consortium (CCC) is charged with mobilizing the computing research community to answer these questions by identifying major research opportunities for the field, and by creating venues for community participation in this process. The CCC supports these efforts through advocacy with federal agencies, through visioning activities such as workshops, through arranging plenary talks on key topics at major venues, and through other community building activities.

The CCC is funded by the National Science Foundation under a cooperative agreement with the Computing Research Association. The work of the CCC is carried out by an active and engaged Council, currently chaired by Ed Lazowska with Susan Graham as vice-chair, which reports to the CRA board. The members of the Council are appointed by CRA in consultation with NSF, with staggered 3 year terms. In the aggregate, the Council must reflect the full breadth of the computing research community – research area, institutional character, etc. Details on the role of CCC, as well as the current composition of the Council, may be found at http://www.cra.org/ccc/.

We invite nominations (including self-nominations) for members to serve on the CCC Council for the next three years. Please send suggestions, together with the information below, to Eric Grimson, Sarita Adve, or Andrew Chien by January 14th. This committee’s recommendations will serve as input to CRA and NSF, who are responsible for making the final selection.

1. Name, affiliation, and email address of the nominee.
2. Research interests.
3. Previous significant service to the research community and other relevant experience, with years it occurred (no more than *five* items).
4. A brief biography or curriculum vitae of the nominee.
5. A statement from the nominator or nominee of less than 1 page, supporting the nomination by describing the nominee’s ideas for, commitment to, accomplishments in, and potential future contributions to the missions of the CCC in engaging broader communities, finding wider funding sources, and encouraging new research directions. What the CCC Council needs is not famous people with lots of awards, but people with ideas, judgment, and the willingness to work.

Now, the full story:

The CCC’s mission is “to foster exciting new research visions in the computing community which attract support.” Looking back at what has transpired over the past year, community participation has been tremendous. Many dozens of people have stepped up to propose workshops, make presentations, write articles for this blog, and chip in with thoughtful feedback and ideas. It’s been productive and, well, fun.

Of course, the name of the game is to turn research visions into reality, and one of the core strategies for doing this is to “improve public and policymaker understanding of the importance of computing and computing research in our society”. This seems particularly important right now, as our nation makes a historic transition, hopefully ushering in a new era in the government’s approach to research support.

Without really knowing what kinds of results we would get, we put out a challenge to a small number of people to write very briefly (we asked for two pages) on “computing research initiatives for the 21st century.” What does the new government need to know about the value of computing research? What are some of the most promising and exciting research opportunities in the field? What computing capabilities are critical for the nation today and into the future?

Well, the response has been tremendous. A sample of what we received is now posted on the CCC web site at http://www.cra.org/ccc/initiatives. There are essays on the central role that computing research has in our economy, ideas for research/education infrastructure, “re-envisioning DARPA”, and proposals for research initiatives in personalized medicine, transportation, “big data” computing, computer architecture, networking, cyber-physical systems, and more. WIth this treasure trove of thoughtful inputs, we are now using available channels and the CRA and CCC’s resources to get these noticed by as many policymakers as possible.

We have been so heartened by the response that we are now talking about having a more organized process for soliciting and publishing these sorts of idea-pieces. Stay tuned here and on the CCC web site for more details, some time early in 2009. We’ll also be asking some of the authors of these writeups to post followup discussion pieces on this blog.

Again, thanks for all of your support and participation. 2009 is looking like a truly exciting year.

– Peter Lee and Ed Lazowska

http://www.youtube.com/watch?v=_lLDNosedHk

It’s inspirational.

– Ed Lazowska

• The Internet and the World Wide Web as we know them today
• Search technology – Where once we filed, today we search
• Cluster computing
• The transformation of science via computation

In this post, we summarize just a sample of your additions (we have grabbed text from your posted comments, without a lot of editing, so this will be loose – “it’s the thoughts that count”) and invite your further comments – cleaning up these additions, or providing others.  Please let us hear from you!

Secure communication – the foundation of e-commerce

All of e-commerce relies on the results of computing research:  the Internet, the World Wide Web, cluster computing, parallel relational database systems, cryptography and algorithms for secure credit card transactions.  Here, we focus on the latter.  Without secure communication – for example, the ability to conduct a credit card transaction with an online merchant – there would be no e-commerce.  The complex of events (both theoretical advances and deployment of practical, useful software) that allow a user to type a credit card number into a web browser and be reasonably assured of its safety is a game-changer, making secure communication and secure commerce a reality for (potentially) all users of the Internet.  Without these artifacts, we would have no Amazon.com, no eBay, no thriving online pornography industry, …

Mobile computing and communication

Twenty years ago, computing was a desktop experience.  “Portable computers” were the size of a briefcase.  Communication was via 9600 baud telephone modem.  Contrast that to today:  2 pound laptops that fit in a mailing envelope, mobile phones with Web browsers that fit in a shirt pocket, and ubiquitous WiFi and 3G cellular at many millions of bits per second.   Clearly, mobile computing and communication – the untethered lifestyle – is a game-changer.

Expert systems become ubiquitous

Thousands of routine decisions daily are made by computer systems that have specialized knowledge of a problem area. In the past, rule changes at a central office – e.g., the IRS, or the headquarters for a corporation – were incorporated slowly into practice. With expert systems, the people making the decisions have the benefit of codified knowledge bases that reflect current policy and practices.

Research on expert systems began in the 1970’s with support from DARPA, the National Institutes of Heath, and NSF. Expert systems have subsequently become an essential part of the IT toolkit for every major company. Help desks, credit checking and equipment troubleshooting are examples of systems that have been replicated many times over and are routinely saving money for business and public institutions.

Expert systems technology is a game-changer.

Robotics in everyday life

Twenty years ago, robots appeared in artificial intelligence laboratories, automated assembly lines, and science fiction movies.  In recent years, iRobot Corporation has sold roughly 1,000,000 Roomba robotic home vacuum cleaners annually, and multiple robotic automobiles have completed the DARPA Grand Challenge and Urban Challenge, autonomously navigating a 150-mile desert course and a 60-mile urban course.  Robots have entered the mainstream of society, integrating a wide variety of Artificial Intelligence technologies such as computer vision, sensing, and planning.  This is a game-changer, and the best clearly is yet to come.

Digital media

Today, almost no one thinks of photography in any form other than digital.  The means by which we capture, edit, and share digital images are the result of multiple breakthroughs in computer science.

Similarly, digital compact disc audio – a breakthrough when it entered the mainstream only two dozen years ago – is going the way of the dinosaur, replaced by MP3 audio on personal devices such as iPods.

Our video entertainment is in digital form too – whether on a DVD, a personal video device, streaming media, or a video game.

Digital media is revolutionizing entertainment and the entertainment industry – a game-changer.

GPS, mapping, and navigation

GPS – the ability to pinpoint your position nearly anywhere on earth – is a marvel.  But even more amazing are the algorithms that provide navigation – available on the Web, and in $200 self-contained portable devices from Garmin, TomTom, and others.  GPS, mapping, and navigation are game-changers.

Collaborative filtering and recommender systems

Collaborative filtering and recommender systems dramatically altered how we think about computing applications by introducing the idea that the actions and preferences of other people could be a useful resource in computations intended to support someone else’s activities.  This is easily appreciated by a broad audience – anyone who has used Amazon.com’s “people who bought this also bought…” or other social features; a somewhat narrower audience will also appreciate that a major improvement in search engine performance occurred when they started taking into account link structures and then click behaviors.

There’s a clear  tie to computing research, both in work on algorithms for using data from other people, and in interfaces for collecting it and presenting predictions or recommendations.  The idea was first articulated in CACM and in the ACM CSCW and CHI conferences, and there are now thousands of papers about it.

A few additional ideas that were suggested

These need fleshing out or weeding out!  Our comments in [blue brackets] …

• Something related to applications of machine learning – the applications within computing (e.g., NLP, vision, graphics), to other sciences (with big data), to finance (credit card fraud, and dare I say Wall Street) abound [for sure - needs fleshing out]
• Something related to advances in software engineering, and the application of logic to analyzing both hardware and software designs and artifacts [the application of logic might work; we still have a "software crisis," though, and "there (still) is no silver bullet," so need to be careful with claims]
• Something related to scientific computing and large-scale computational science, simulations, etc. [we meant this to be covered by one of our original topics - "the transformation of science via computation"]
• Virtualization [can someone say "1960s"?]
• Network coding [would need to be painted larger]
• Compressed sampling/sensing [would need to be painted larger]
• Quantum computing [premature]
• Elliptic curve crypto [covered crypto under secure communication]
• Molecular computing [come see us in 10 years!]
• Randomized algorithms [would need to be painted larger - colored with applications]
• Theory of distributed computing: impossibility results, Byzantine generals [we meant to feature this under our "cluster computing" topic, which relies integrally on these algorithms; cluster computing is not a hardware breakthrough, it's a distributed algorithms breakthrough!]
• Wearable/ubiquitous/mobile computing [covered under mobile computing and communication, a new topic above]
• Sensor networks [tell me more]
• Human computation (Captchas, the ESP game, etc.) [maybe ...]
• Computational microeconomics: ad placement, automated mechanism design [sounds good - say more!]

Again, we invite your comments!  Let us hear from you!

– Ed Lazowska and Peter Lee

–

The CCC blog has published a couple of articles on the multi-core challenge, all emphasizing the difficulty of making parallel programming prevalent and, hence, the difficulty of leveraging multi-core systems in mass markets. The challenge is, indeed, significant and requires important investments in research and development; but, at UPCRC Illinois, we do not believe that the sky is falling.

Parallel programming, as currently practiced, is hard: Programs, especially shared memory programs, are prone to subtle, hard-to-find synchronization bugs and parallel performance is elusive. One can reach two possible conclusions from this situation: It is possible that parallel programming is inherently hard, in which case, indeed the sky is falling. An alternative view is that, intrinsically, parallel programming is not significantly harder than sequential programming; rather, it is hampered by the lack of adequate languages, tools and architectures.  In this alternative view, different practices, supported by the right infrastructure, can make parallel programming prevalent.

This alternative, optimistic view is based on many years of experience with parallel programming. While some concurrent code, e.g., OS code, is often hard to write and debug, there are many forms of parallelism that are relatively easy to master: Many parallel scientific codes are written by scientists with limited CS education; the time spent handling parallelism is a small fraction of the time spent developing a large parallel scientific code. Parallelism can be hidden behind an SQL interface and exploited by programmers with little difficulty. Many programmers develop GUI’s that are, in effect, parallel programs, using specialized frameworks. Parallelism can be exposed using scripted object systems such as Squeak Etoys in ways that enable young children to write parallel programs. These examples seem to indicate that it is not parallelism per se that is hard to handle; rather it is the unstructured, unconstrained interaction between concurrent threads that result in code that is hard to understand both from a correctness and performance view, hence hard to debug and tune.

The state-of-the-art in parallel programming is what sequential computing was several decades ago. A major reason for this situation is that parallel programming has been an art exercised by a group of experts whose small population did not justify major investments in programming environments aimed at making their life easier. This reason disappears as parallelism becomes available on all platforms. Furthermore, we can make faster progress now because we understand well the principles it takes to make programming easier — principles such as safety, encapsulation, modularity, or separation of concerns; we also have more experience in developing sophisticated IDE’s.

What will it take to bring these principles of computer science to parallel programming? It will require a broad based attack across the system stack. As has been said in these blogs, we need research in languages, compilers, runtime, libraries, tools, hardware … What has not been said explicitly is that none of these areas are likely to produce the silver bullet on their own. The solution that will work eventually will be one that brings together technologies from all these areas to bear on each other. However, we do not have the luxury of doing this via incremental and reactive changes over decades. The research truly needs to be interdisciplinary and the idea of co-design needs to be internalized. Unfortunately, the mainstream systems community has all but abandoned this mode of research in the last several years. Language researchers are locked into mechanisms that will only be supported by commodity hardware and hardware researchers are locked into a mode that requires supporting the lowest common denominator software. It is imperative that we break out of these shells and get the research community into a mindset that we are truly looking to define a new age of computing — a mindset that nurtures research where a clean system slate is an acceptable starting point.

The sky is not falling, but the ground is shifting rapidly. The multi-core challenge requires a concerted effort of academia and industry to generate new capabilities. We are confident that in the future, as in the past, new capabilities will breed new applications. Multi-core parallelism can be leveraged to develop human-centered consumer products that provide more intelligent and more intuitive interfaces through better graphics and vision, better speech and text processing and better modeling of the user and the environment.

The task of providing better performance is shifting from the hardware to the software. This is an exciting time for Computer Science.

Marc Snir
4323 Siebel Center, 201 N Goodwin, IL 61801
Tel (217) 244 6568
Web http://www.cs.uiuc.edu/homes/snir

The workshop vastly exceeded my expectations – 8 hours of brainstorming about strategies and best practices, in four areas:

·     “Go Outside Your Box” – what strategies can we adopt to increase collaboration across subfields and with other fields?

·     “The World Needs Us” – how to contribute to the solution of societal “Grand Challenge” problems while simultaneously driving computing research forward.

·     “Breaking the Cycle” – can we change the reward structure to decrease incrementalism, encouraging long-range thinking?

·     “Serving the Community” – how can we further increase the culture of service to the research community and to the nation?

I’m sure others will blog on various aspects, and teams have formed to write up specific strategies and best practices.  But here are a few things that really struck me:

Computer science:  the ever-expanding sphere

A model of how our field evolves can help us make smart decisions.  Think of computer science as an ever-expanding sphere (this analogy is due to Alfred Spector; all graphics are due to Peter Lee).  We transform other fields and we change the world.  We do this not just through the application of computation, but through the introduction of computational thinking.  When we transform these fields, we make new discoveries about our own field that enlarge our “bag of tricks” – our ability to transform other fields.  So we constantly reinvent ourselves by reinventing others.

We’ve transformed circuit design, publishing, photography, communication, mechanical CAD, certain fields of science, …  We’re in the process of transforming biology, transportation, …  And we’re always transforming ourselves.  Computer science truly is an endless frontier.

What this means is that even when working inside the sphere, we’ve got to be looking outward.  And at the edges of the sphere, we’ve got to be embracing others, because that’s how we reinvent ourselves.

Computer science lives in Pasteur’s Quadrant

The vast majority of work in our field is motivated both by concerns of use and by a desire to evolve principles of enduring value.  If anything, we may be too much in Pasteur’s Quadrant – we may place too little value on research without obvious utility, and we may be too reluctant to reject as “not computer science” work that’s focused on applications where it may not be obvious that our own field will be advanced.  (Jim Gray had the stature and courage to pioneer our move into data-intensive eScience – today, the transformations this has stimulated “within the sphere” are obvious.)

Lots of the action is at the interfaces

This is true fractally – it’s true of the interfaces between computer science and other fields (the edges of the sphere), and it’s true of the interfaces between subfields of computer science.

We’ve got to produce students who are comfortable at these interfaces.  It’s increasingly difficult.  Stuart Russell observed that “Bohr drives Pasteur” – we need strength at the core, and the core is ever-expanding.  At the same time, students need to be able to make connections.  I’m concerned we’re making the wrong tradeoffs these days.  Students enter graduate school with records that look like promotion cases a decade ago!  We decrease course requirements to get students engaged in our own research as quickly as possible.  Our colloquia are half-empty because everyone’s too busy beavering away to attend.  These factors decrease breadth and agility within the sphere.  We don’t require minors, which would expose students to other fields – this decreases the ability to work at the edge of the sphere.  As a field, we should tackle these issues head-on.

Visions, incremental progress, and random walks

A research project needs to be hard enough to be interesting, and easy enough to be doable.   There needs to be a vision – a sense of where you and your colleagues are headed over a five-year or ten-year period.  And it needs to be tackled in what Alfred Spector calls “factorizable pieces.”

If there’s a vision, then a “factorizable piece” may appear incremental, but it’s headed somewhere important.  Without a vision, it’s part of a random walk.  It’s important to differentiate these!  A goal of the CCC “visioning workshop” process is to articulate some of these visions for our field.

Bob Sproull pointed us to a wonderful paper by Ivan Sutherland on the conduct of research – “Technology and Courage.”  Read it!

Onward

I’m sure others will blog on various aspects of the workshop.  Look in the mirror – is there a field you’d rather be part of?

– Ed Lazowska

• The advance needs to be “game changing,” in the sense of dramatically altering how we think about computing and its applications.
• The importance of the advance needs to be obvious and easily appreciated by a wide audience.
• There needs to be a clear tie to computing research (or to infrastructure initiatives that build upon research and were sponsored by computing research organizations).
• We’re particularly interested in highlighting the impact of federally-funded university-based research.

We’re focusing on work carried out in the past 20 years or so, in part because of the upcoming 20-year celebrations for the CISE directorate at NSF. Of course, lots of great fundamental research can take more than 20 years before the impact becomes obvious, but even in such cases there is usually continuing influences on more recent research that can be cited here.

To get your juices flowing, here are four game-changers that we definitely think belong on the list. Use these to think about others that belong on the list, or feel free to argue with our choices.

The Internet and the World Wide Web as we know them today

In 1988 — 20 years ago — ARPANET became NSFNET. At the time, there were only about 50,000 hosts spread across only about 150 networks. In 1989, CNRI connected MCImail to the Internet — the first “commercial use.” In 1992, NCSA Mosaic triggered the explosive growth of the World Wide Web. In 1995, full commercialization of the Internet was achieved, with roughly 6,000,000 hosts spread across roughly 50,000 networks. Today, there are more than half a billion Internet hosts, and an estimated 1.5 billion Internet users.

While many of the underlying technologies (digital packet switching, ARPANET, TCP/IP) predate the 20-year window, the transition from the relatively closed ARPANET to the totally open Internet and World Wide Web as we know them today falls squarely within that window. NSF-supported contributions included CSnet, NSFNET, and NCSA Mosaic.

The Internet and the World Wide Web are game-changers.

Where once we filed, today we search

The vast majority of the world’s information is available online today, and we find what we need — whether across the continent or on our own personal computer — by searching, rather than by organizing the information for later retrieval.

Research on the retrieval of unstructured information is based on decades of fundamental research in both computer science theory and AI. But the paradigm shift that is web crawling and indexing and desktop search is much more recent. It traces its roots to university projects such as WebCrawler, MetaCrawler, Lycos, Excite, Inktomi, and the NSF Digital Libraries Initiative research which begat Google.

Search is a game-changer.

Cluster computing

At the risk of offending our many computer architect friends, we’re going to assert that cluster computing is the most significant advance in computer architecture in the past 20 years.

A decade ago, Jeff Bezos was featured in magazine advertisements for the DEC AlphaServer, because that’s what Amazon.com ran on — the biggest shared-memory multiprocessor that could be built. Similarly, the AltaVista search engine was designed to showcase the capabilities of big SMP’s with 64-bit addressing.

Today, this seems laughable. Companies such as Google and Amazon.com replicate and partition applications across clusters of tens of thousands of cheap commodity single-board computers, using a variety of software techniques to achieve reliability, availability, and scalability.

The notion of hardware “bricks” probably can be traced to Inktomi, a byproduct of the Berkeley Networks of Workstations project. The software techniques are drawn from several decades of research on distributed algorithms.

Cluster computing is a game-changer.

The transformation of science via computation

The traditional three legs of the scientific stool are theory, experimentation, and observation. In the past 20 years, computer simulation has joined these as a fundamental approach to science, driven largely by the NSF Supercomputer Centers and PACI programs. Entire branches of physics, chemistry, astronomy, and other fields have been transformed.

Today, a second transformation is underway — a transformation to data-centered eScience, which requires semi-automated discovery in enormous volumes of data using techniques such as data mining and machine learning, much of which is based on years of basic research in statistics, optimization theory, and algorithms.

Computational science is a game-changer.

Some non-inclusions

Quantum computing. There is huge potential here, but the impact hasn’t been felt yet.

Simultaneous multithreading. We claim that this, and many other important advances in computer architecture, are dominated by cluster computing. (Remember, we’re trying to be provocative here! Blame Dave Ditzel, who put this idea into Ed’s head.)

Your part goes here!

What’s your reaction to the four game-changers that we’ve identified? Do you agree that they belong on the list? If not, why not? If so, what do you think were the principal components of each — the key contributing research results?

Even more importantly, give us eight more! What are your nominees for game-changing advances from computing research conducted in the past 20 years?

Give us your thoughts!

– Ed Lazowska and Peter Lee

These highlights are designed to provide easily digestible, compelling nuggets of computing research work. Members of Congress, the Administration, and funding agency managers and directors are some of the main audiences for these web pages. We believe the highlights should also prove to be useful for the entire research community. The highlights can be accessed directly, received by email, RSS feed, or even embedded in your own web page.

The current Computing Research Highlight of the Week describes a new algorithm from UCSD researchers that performs route computation in a way that may lead to major improvements in network efficiency. Check it out — it is punchy, informative, and makes good use of some simple graphics while at the same time providing links to the scientific publication and full press release.

So, please submit your own highlights! The response thus far has been very good, and we expect that many people outside of our community, including key decision makers, will make good use of the information.

• The Industrial Revolution of Data. Today’s world-wide web remains a staggering tribute to the typing abilities of the human race. But even with a growing global population, typists are not a scalable source of bit-production going forward. We are entering an era where the overwhelming majority of information will not be hand-crafted. It will be stamped out by machines: software logs, cameras, microphones, GPS transceivers, sensor networks, RFID readers, and so on. This is inevitable. It has already begun to change the computing marketplace: most organizations of size now realize they can afford to save and mine all their logs, and are looking for inexpensive ways to do so. The startup world has responded with a flurry of parallel database and data analytics companies.
• The Crisis of the Three C’s: Coders, Clouds and Cores. Meanwhile, it’s no news that software development is far, far too difficult. In his Turing Award talk a decade ago, Jim Gray identified radical improvements in programming among his 13 remaining long-term challenges for computing — alongside passing the Turing test and building Vannevar Bush’s Memex. What’s changed on this front since 1998 is the rapid rise of parallelism that my colleagues have been blogging about here. Cloud computing infrastructure, with its “shared-nothing” clusters of machines, demands parallel and distributed programs today. Manycore architectures will demand parallelism at a finer grain in the next few years. The pressing need for parallel software — and armies of fluent software developers to build it — raises both the difficulty and the stakes of the Grand Challenge that Jim Gray highlighted in 1998.

Given this background, what excites me these days is that the trend may bring some new solutions to the crisis, in a surprisingly organic way. 

For over twenty years, “Big Data” has been a sustained bright spot in parallel computing. SQL has been a successful, massively parallel programming language since the late 1980’s, when Teradata (a survivor that evaded Dave Patterson’s Rolls of the Dead) first commercialized parallel database research from projects like Gamma and Bubba. In recent years, SQL has been joined by Google’s MapReduce framework, which is bringing algorithmicists into massive data processing in a way that SQL never did. Both SQL and MapReduce will likely thrive, and may well converge: two parallel database startup companies recently announcedintegrated implementations of SQL and MapReduce. (Full disclosure: I advise Greenplum, one of those companies.)

SQL and MapReduce programmers do not think much about parallelism. Rather than trying to unravel an algorithm into separate threads, they focus on chopping up sets of input data into pieces, which get pumped through copies of a single sequential program running asynchronously on each processor. In parallel programming jargon, this kind of code is sometimes dismissively referred to as being “embarrassingly parallel”. But very often, the simplest ideas are the most fertile. Programmers “get” these approaches to parallelism. And remember: the Coders are part of the Crisis of the Three C’s, and the key is to make lots of them happy and productive.

But can those programmers lead us anywhere interesting? The most intriguing part of this story is that in the last 5-10 years, the set-oriented, data-centric approach has been gaining footholds well outside of batch-oriented data parallelism. There has been a groundswell of work on “declarative”, data-centric languages for a variety of domain-specific tasks, mostly using extensions of Datalog. These languages have been popping up in networking and distributed systems, natural language processing, compiler analysis, modular robotics, security, and video games, among other applications. And they are being proposed for tasks that are not embarrassingly parallel. It turns out that focusing on the data can make a broad class of programs simpler — much simpler! — to express.

Networking and distributed coordination protocols are one good example. They run in parallel, and are themselves a key to cloud services. In our work on Declarative Networking over the last years, we showed that a wide range of network and distributed coordination protocols are remarkably easy to express in a data-centric language. For example, our version of the Chord Distributed Hash Table (DHT) protocol is 47 lines of our Overlog language; the reference implementation is over 10,000 lines of C++. (DHTs are a key component of cloud services like Amazon’s Dynamo.) Meanwhile, graduate students at Harvard prototyped a simple version of the tricky Paxos consensus protocol in an alpha edition of Overlog in 44 rules. (Paxos is a key component in cloud infrastructure like Google’s GFS file system and Chubby lock manager.) We have other examples where our declarative programs are line-for-line translations of pseudo-code from research papers. These are the kinds of scenarios where the quantitative differences are best captured qualitatively. You can print out our Chord implementation on one sheet of paper, take it down to the coffee shop, and figure it out. Doing that with 10,000 lines of C++ would be a superhuman feat of Programmer-Fu, and a big waste of paper.

Machine Learning is another area where data-centric declarative programming seems to help with parallelism and distribution. A group at Stanford pointed to a range of standard machine learning tasks that can be expressed almost trivially as MapReduce programs, without any requirement for parallel programming expertise. More deeply, a number of the fundamental algorithms driving Machine Learning center on “message-passing” algorithms like Belief Propagation and Junction Trees that work on a computational model of explicit dataflow, rather than shared memory. Research in ML over sensornets showed how to overlay that logical communication onto a physical network. And these inference networks — much like DHTs — turn out to be a good fit to Overlog. (Distributed Junction Trees in 39 rules, anyone?) The Dyna language is another good example, with a focus on (currently single-node) Natural Language Processing.

I’ll be the first to admit that Datalog syntax is horrible, and it is not a reasonable language for developers. There is much to be done before adoption of complex data-centric languages can occur. But what excites me here is that the main positive trend in parallel programming — the one driven by the Industrial Revolution of Data, the one with programmer feet on the street — that trend feeds into this promising new generation of much richer data-centric languages. If MapReduce is the boot camp for a next round of parallel languages, those languages are likely to be data-centric. And there’s growing reason to believe that the data-centric approach will suit a wide range of tasks.

– Joe Hellerstein is a Professor of Computer Science at the University of California, Berkeley. His research focuses on data management and networking.

Thanks for the opportunity to update the community on what has been happening recently with the Network Science and Engineering (NetSE) effort, from my perspective as chair of the NetSE Council.

Let me explain my take on NetSE with an anecdote from my Georgia Tech colleague Mike Best based on a recent trip he made to Africa. Mike and his group met with a group of chiefs of the Acholi people in Northern Uganda. This is an area that has suffered through profound conflict and lacks for essentially any communication technology. Mike and his team wanted to engage in participatory design to understand the existing communication needs, unmet needs and requirements, and latent requirements.

They were very cautious not to influence the conversation towards modern communication technologies so they did not mention specific systems. But after about thirty minutes of this exercise one of the chiefs finally stated, “We want the internet. Unless you have something better.”

To me, NetSE is about the potential for something better. That isn’t to take away from how incredible the Internet is, but that success has led to a dependence on an infrastructure that we understand surprisingly little about. Figuring out what “better” means and how we might get there is a challenge that is intellectual, economic, political and social. In other words, hard, but incredibly important.

The last couple of months have been busy for the NetSE community. Five workshops and meetings have taken place since mid-June covering Network Design and X, where X has been Network Science, Societal Values, Theoretical Computer Science, Behavioral Economics, and Network Engineering. The goal of these activities has been to add to all the good work on research opportunities done under the auspices of GENI, but without the yoke of justifying a large facility.

NetSE is shaping up to be strongly disciplinary AND interdisciplinary. There remain major challenges and opportunities in the core disciplines of networking and distributed systems, as well as across disciplines in and out of CISE. For example, technology advances are producing the ability to program all the way down to the photon or RF wavelength. How can and should future networks take advantage of programmability at this extreme? In the interdisciplinary vein, there are important and exciting opportunities at the intersection of human behavior and network behavior. How should home networks be structured so that mere mortals can deploy and manage them?

Over the next couple of months, we will be synthesizing the output of the various activities into a NetSE research agenda that will include recommendations to funding agencies about what is needed to advance the agenda. You can watch for updates on the NetSE page hosted by the CCC at www.cra.org/ccc/netse.php.

– Ellen Zegura is Professor and Chair of Computer Science, School of Computer Science, College of Computing, at the Georgia Institute of Technology.

–

For over thirty years, we have watched the great cycle of innovation defined by the commodity hardware/software ecosystem — faster processors enable software with new features and capabilities that in turn require faster processors, which beget new software. The great wheel has turned, but it no more, as power constraints and device physics now limit the performance achievable with single microprocessors. Multicore chips — those with multiple, lower power processors per chip — are now the norm. Moreover, current multicore chips (those with 4-8 cores/chip) are but the beginning. We can expect hundreds of cores per chip in the future, with diverse functionality (graphics, packet protocol processing, DSP, cryptography and other features).

The software research challenge is clear — developing effective programming abstractions and tools that hide the diversity of multicore chips and features while exploiting their performance for important applications. Hence, we need a vibrant community of researchers exploring diverse approaches to parallel programming — languages, libraries, compilers, tools — and their applicability to multiple application domains.

Microsoft researchers are investigating all of these approaches, from coordination languages for robots and distributed systems to mobile phones to desktops and data center clouds. To engage the academic community, Microsoft funds multicore research projects and many sites, and we have partnered with Intel to fund the Universal Parallel Computing Research Centers (UPCRCs) at the University of California at Berkeley and the University of Illinois at Urbana-Champaign.

As Richard Hamming famously noted, “The purpose of computing is insight, not numbers.” In that spirit, I believe our research challenge is to break free from the limitations of the desktop metaphor and exploit the ever greater performance of multicore chips to create new human-computer interaction metaphors that are more natural and intuitive. This will require new approaches to parallel computing education and increased collaboration with researchers in application domains.

As an example, consider one possible future — “spatial computing” — where real-time vision and speech processing, coupled with knowledge bases, distributed sensors and responsive objects, enhance human activities in contextually relevant ways while remaining otherwise unobtrusive. Such an infosphere would adapt to its user’s needs and behavior and move seamlessly across home, work and play.

Multicore brings enormously interesting intellectual challenges and the opportunity to rethink much of how we approach computing.  Let’s embrace the opportunity!

– Daniel Reed is Microsoft’s Scalable and Multicore Computing Strategist and a member of the President’s Council of Advisors on Science and Technology (PCAST). Contact him at reed@microsoft.com or his blog at www.hpcdan.org

–

Multicore (parallelism) represents a fundamental challenge and change for all of computing and computer science. It represents the fundamental constraints of physics — nature loves parallelism — surfacing and interacting with some fundamental tenets of computing. We have formulated our theory of computation and complexity primarily on sequence — in control and state. Fundamental physics (and consequently circuits and architecture) which makes parallelism fundamentally cheaper is now challenging us to broaden the foundation of computing with parallelism as a first class element. I believe that as a research community, this is a first-order challenge to respond — in nearly all disciplines of computer science. First and foremost, this is a major intellectual challenge to the computer science community to “reinvent” or at least broaden computer science in this way. Second, this is a major educational challenge, where students we are training today to think about “Computer Science founded on sequence” are being launched into a world of parallelism. For their benefit, we must mount a rapid response in pedagogy and curriculum to ensure these students emerge armed to deal with the future of computing in their careers.

Now, let me turn to research funding in parallelism — which is a critical need in all areas from architecture, runtimes, compilers, programming languages, algorithms, and theory. We need major increases in funding and research activity in all of these areas. Governments must take the primary role in funding research in information technology for the long term economic development and societal well-being. We would like to see aggressive large-scale funding of long-range research in parallelism, and that the fruits of that research be made broadly available for commercialization. In the U.S., DARPA has a long track record of funding such research in IT, but such investment has decreased in recent years. We would like to see it increase both in DARPA, as well as other parts of the US government, and yes around the world. History has proven that only governments are able to invest in this type long-term general economic development, and it is critical that the research outputs be generally available for society at large to benefit — not just a small population of gatekeepers. It is great to see this vision being pursued in many regions around the world.

At Intel, we depend heavily on a broad range of science and engineering research pursued by the global academic community. Many of the innovations we commercialize were first conceived in universities — often many years before their practicality — and we have contributed additional innovations and refinements to bring them to the broadest swath of society possible. We strongly support (and contribute our time, money, and leadership to) the health of the research and innovation community globally. We have made significant investments in education (multicore curriculum and training) and research (research grants, Universal Parallel Computing Research Center with Microsoft) for parallelism, and continue to encourage others to join us in doing so.

– Andrew Chien

– To see the first article in this series, click here.

As I mention on my personal blog (at http://csdiary.org), this points out a somewhat strange aspect of computing research, namely that there isn’t much computing research in the major core-science publications. I’m thinking specifically of Science magazine, Nature, and PNAS. In fact, I took a quick scan over the past 5 issues of Science and Nature.  Over those issues, in Science one sees 35 research articles and reports in the biology and medical science areas, 14 in chemistry/materials, 10 in earth and atmosphereic sciences, 5 in astronomy and astrophysics, and several in physics, psychology, and archeology. Only one article in computer science!

In Nature, the situation is even more stark. In the last 5 issues we see 11 research articles in biology, 2 in chemistry, 1 in astrophysics, and 1 in psychology. None in computer science.

Why should we care about this? Well, lately the computing research community has become very concerned about its “image”, particularly in the lay public (including, notably, the US Congress). Yes, we want people to know the full impact of computing, the range of jobs and activities the computing professionals are involved in, and the great economic benefits the come from our research. But we also need, in the interests of public education and our image, to explain computing research to the world’s science scholars. Doing so not only puts our research to a good test, but it also helps to cast an aura of intellectual respectability that would undoubtedly contribute positively to the image of the field.

There could be important consequences within the federal government, too. I asked Peter Harsha, the director of government affairs for the CRA, what he thought about this. Here is what he said:

I think all three [Science, Nature, PNAS] generate news in the more mainstream press that gets noticed by Members of Congress and Administration folks. So while most policymakers and their staff generally don’t read the periodicals directly, the noteworthy stuff they publish finds its way into the NY Times, WSJ, or Washington Post, which quickly gets policymaker attention.

I think all three publications have a good track record of generating that buzz in the mainstream press (Science and Nature, especially).

As we as a community work on getting our government to step up its support of basic science research, to what extent will our representatives include computer science and engineering? While computing will be hard to forget in any serious discussion about funding priorities, putting ourselves “front and center” in these sorts of publications should help not only the cause of computing research but also the large cause of scientific research.

– Peter Lee

–

Since the first commercial computer in 1950, the information technology industry has improved cost-performance of computing by about 100 billion overall. For most of the last 20 years, architects used the rapidly increasing transistor speed and budget made possible by silicon technology advances to double performance every 18 months. The implicit hardware/software contract was that increases in transistor count and power dissipation were OK as long as architects maintained the existing programming model. This contract led to innovations that were inefficient in transistors and power but which increased performance. This contract worked fine until we hit the power limit that a chip could dissipate.

Computer architects were forced to find a new paradigm to sustain ever-increasing performance. The industry decided that only viable option was to replace the single power-inefficient processor by several more efficient processors on the same chip. The whole microprocessor industry thus declared that its future was in parallel computing, with a doubling of the number of processors or cores each technology generation, which occur every two years. This style of chip was labeled a multicore microprocessor. Hence, the leap to multicore is not based on a breakthrough in programming or architecture; it’s actually a retreat from the even harder task of building power-efficient, high-clock-rate, single-core chips.

Many startups tried commercializing multiple core hardware over the years. They all failed, as programmers accustomed to continuous improvements in sequential performance saw little need to explore parallelism. Convex, Encore, Floating Point Systems, INMOS, Kendall Square Research, MasPar, nCUBE, Sequent, and Thinking Machines are just the best-known members of the Dead Parallel Computer Society, whose ranks are legion. Given this sad history, there is plenty of reason for pessimism about the future of multicore. Quoting computing pioneer and Stanford President John Hennessy:

“…when we start talking about parallelism and ease of use of truly parallel computers, we’re talking about a problem that’s as hard as any that computer science has faced. … I would be panicked if I were in industry.”

Jeopardy for the IT industry means opportunity for the research community. If researchers meet the parallel challenge, the future of IT is rosy. If they don’t, it’s not. Failure could jeopardize both the IT field and the portions of the economy that depend upon rapidly improving information technology. It is also an opportunity for the leadership in IT to move from the US to wherever in the world someone invents the solution to make it easy to write efficient parallel software.

Given this current crisis, its ironic that since 2001 DARPA chose to decrease funding of academic research in computer systems research. Knowing what we know today, if we could go back in time we would have launched a Manhattan Project to bring together the best minds in applications, software architecture, programming languages and compilers, libraries, testing and correctness, operating systems, hardware architecture, and chip design to tackle this parallel challenge.

Since we don’t have time travel, there is an even greater sense of urgency to get such an effort underway. Indeed, industry has recently stepped in to fund three universities to get underway–Berkeley, Illinois, and Stanford–but its unrealistic to expect industry to fund many more. Its also clear given the urgency and importance to the industry and the nation, we can’t depend on just three academic projects to preserve the future of the US IT industry. We need the US Government to return to its historic role to bring the many more minds on these important problem. To make real progress, we would need a long-term, multi-hundred million dollar per year program.

The consequences of not funding aren’t a drop in Nobel prizes or research breakthroughs; its a decline in the US-led IT industry, a slowdown in portions of the US economy, and possibly ceding the leadership in IT to another part of the world were governments understand the potential economic impact of funding academic IT research on parallelism.

– David Patterson

Interest was strong — more than 125 department chairs and lab directors attended the 90-minute session, more than 3X as many as have stuck around for any previous final session at Snowbird. Ed Lazowska, Susan Graham, Richard Ladner, Randy Bryant, and Chip Elliott presented. All presentation materials are on the web here. A 20-minute Q&A session followed the presentations.

Several highlights for me:

• CCC’s “Data-Intensive Scalable Computing” initiative, led by Randy Bryant and Thomas Kwan, has really taken off:  two new NSF programs, multiple workshops and conferences, significant educational penetration. There is a ton of opportunity here for our field — great computing research challenges, and great chances to partner with other fields that are transitioning from data-poor to data-rich. (There is a “new computational science” here whose breadth and impact will totally dwarf the breadth and impact of first-generation simulation-oriented computational science.)
• The theory community really has its act together — I’m excited at the prospect of the “nuggets” that will emerge from the recent workshop led by Richard Ladner and others.
• GENI is alive and well, although its shape has changed. GENI is no longer envisioned as necessarily being a single huge uber-instrument. Rather, a collection of research instrumentation needs are likely to emerge from this summer’s formulation of a broad Network Science and Engineering research agenda — needs that might, perhaps, be met by several focused instruments.The GENI Project Office is about to announce a number of awards to explore technologies for constructing such instruments; there have been major support commitments by the private sector, complementing those of NSF.

That’s the scoop.  It’s a great time to be engaged in computing research!

– Ed Lazowska

The Taulbee Survey “headline” this year was (roughly) “computer science bachelors degrees drop again.” In my view, this is not news — it was entirely predictable from the legitimate headline four years ago: (roughly) “freshman interest and new enrollments drop again.” The actual news right now in the CRA data is that freshman interest and new enrollments seem to be stabilizing and turning the corner — starting to trend upward. “Degrees granted” is a lagging indicator — it lags freshman interest by 4 years. The fact that the number of bachelors degrees granted this past year decreased is not news — anyone could have looked at the freshman interest data from 4 years ago and told you it was going to happen.

The natural question is “What’s responsible for these oscillations?” There are a number of factors. I want to be really clear that I am not in denial about various substantive areas in which we need to continue to work to improve our field and its attractiveness. But by far the most important factors are (a) the job market (or people’s sense of the job market), and (b) the level of “buzz” associated with the field.

Let’s start by considering graduate enrollment, rather than undergraduate enrollment. For the past 15 years, the number of Ph.D.s granted annually in computer science has been in the 900-1100 range. Suddenly, though, in the past 2 years, it has climbed to 1800. Why is this? The answer is totally obvious:

• In 2001, lots of startup companies went bust.
• This dumped onto the job market a number of the best bachelors graduates from a few years before, who now had two or three years of experience under their belts.
• This made it hard for some excellent new bachelors graduates of 2001 and 2002 to get the super-exciting jobs they had anticipated — they were competing with people whose academic records were every bit as good as theirs, but who also had 2 or 3 years of experience working at a hot startup.
• Because these great new bachelors graduates couldn’t get exciting jobs, they went to graduate school instead.
• And, mirabile dictu, 6 years later, they’re emerging with Ph.D.s.

This is not a news flash — it didn’t take a genius to predict, a few years ago, that it was going to happen, and it doesn’t take a genius to explain it, either.

Similarly for bachelors degrees. Starting in about 2002, there was lots of news about the tech bust. Tech was no longer sexy. Jobs were no longer plentiful. Subsequently, there was a lot of misleading information about the impact of offshoring. And the newspapers never bothered to report that by late 2004, US IT employment was back to the 2000-2001 level — we had fully recovered from the bust — somehow that wasn’t considered newsworthy. So it’s not surprising that interest in bachelors programs decreased sharply, and that 4 and 5 years later, the number of degrees granted precisely mirrored this decline.

Also, it’s not surprising that things are turning around. Google is hot. Tech in general is hot. There are startups everywhere. It’s clear to anyone that there are plenty of jobs. (By the way, given the incredible state of today’s bachelors job market, it doesn’t take a genius to predict that the number of Ph.D. graduates in 2014 will show a decline. When you read the scary headlines 6 years from now, remember that you heard it here first!)

So, what about the enrollment at my university — the University of Washington — and enrollments vs. projected need nationally?

At UW, our enrollments are stable, because they are limited. We take 160 new bachelors students every year. This is far lower than the demand. So changes in the level of interest result in changes in the proportion of applicants that we must reluctantly turn away.

One place where we can easily measure changes in student interest is in the enrollment in our first introductory course, which serves the entire university. Between 2003-04 and 2007-08 (a 4-year period), enrollment in this course is up by 27%. Enrollment by women is up by 45%. (Annual enrollment of women into the major is up by 64% over that same interval.)

Our national peers see this same sort of upturn in interest — not surprisingly, as explained above.

Here’s one more comment about enrollment. There is lots of discussion about computer science enrollment, because it actually matters to the nation! But take a look at this NSF data for bachelors degrees granted in 2004 by all US institutions:

• Computer science: 57,405
• Mathematics: 13,755
• All of the physical sciences combined (astronomy, physics, chemistry, …): 14,240
• All of the earth, atmospheric, and ocean sciences combined: 3,903
• All of engineering combined (aero/astro, chemical, civil, electrical, industrial, materials, mechanical, …): 64,675

Now, we know that computer science degrees have decreased since then, but they’re heading back up, and it’s important to keep things in perspective relative to other fields. Nobody is crying in their beer about the death of the physical sciences, yet computer science produced 4x as many degrees as all of the physical sciences together!

The bad news is that psychology and the social sciences generated 220,067 degrees. And the biological and agricultural sciences generated 80,933 degrees. So the physical and mathematical and earth sciences and engineering are not faring well. But compared to the other physical and mathematical and earth sciences and engineering, computer science is not looking like the problem child! (Skill testing question: What was the fastest-growing bachelors major in America the last time I checked? Answer here.)

What about workforce demand? There are several things to say about this:

• Computer science, increasingly, is great preparation for all sorts of careers. That is, lots of people get computer science degrees to go to law school, business school, medical school, biotech labs, etc.
• And even “Information Technology” is much broader than the software industry. 70% of all IT jobs are with “IT consumers” (companies that use it) rather than with “IT producers” (companies that invent it).
• And the software industry is really hot right now, and also it is really cool — it’s creative, interactive, vibrant.

Here is a spreadsheet with charts showing Bureau of Labor Statistics projections for employment between 2006 and 2016 for all fields in the sciences and engineering (including the social sciences). What it shows is that of all of these fields, between now and 2016:

• 70% of all newly-created jobs will be in computer science.
• 62% of all job openings (both newly-created jobs and jobs available due to retirements) will be in computer science.

(The latter is a smaller % than the former because other fields are “older” and thus will have a greater number of retirements.)

There is a huge gap between “people” and “jobs” in computer science — there is plenty of opportunity!

What do we do at UW to attract students? Many many things. As one example, starting tomorrow at UW we’re running an annual 3-day workshop for high school teachers of math and science, sponsored by Google. The goal is to show these teachers that computer science is important to their fields, and is a great field to send their smartest students into. Information is available at http://cs4hs.cs.washington.edu/. (We do this jointly with Carnegie Mellon and UCLA.)

We have a set of terrific videos that illustrate several important points:

1. People enter the field of computer science for all sorts of aspirational reasons.
2. People do all sorts of things with their computer science degrees in addition to working in the software industry.
3. Working in the software industry is highly exciting and creative and interactive.

You can take a look at these videos at http://www.cs.washington.edu/WhyCSE/.

Most importantly, we really invest in our students. Word gets out. At the University of Washington, we have the strongest undergraduates, because students know they can get a great education here.

How do we “calibrate” our program — make sure our students are ready for careers? Here is a Word document I prepared recently for another purpose. Every year we are a top-5 supplier to Microsoft, Google, and Amazon.com — our students are fantastic.

You asked what US companies can do. They can demand greater federal investment in K-12 science and math education, and greater federal investment in research in the physical sciences and engineering (which drives education at research-intensive universities, who are the producers of most of the students that high-tech employers seek). And they can invest themselves — they can be good corporate citizens. Obviously they can run internship programs and things like that — there’s lots of mutual benefit in that.

But most importantly, companies and individual citizens can insist on federal policies that support education and research. Stop pissing money away in Iraq. Stop pissing money away on “security theater” in airports and along our borders. Stop being hostile to talented immigrants. Stop destroying K-12 by obsessing over high-stakes tests that can be gamed. Stop denigrating science. Stop substituting ideology for objective analysis in designing federal policies. Stop all of the disastrous policies of the past 8 years. Return sanity and accountability to our national leadership. Institute policies such as those outlined in the Executive Summary of the National Academies’ Rising Above the Gathering Storm.

Please note that these are not partisan political statements. Historically, support for education, support for research, and belief in a reasonable degree of intellectual integrity have not been partisan issues.

I do want to emphasize again that I am not in denial about various substantive areas in which we need to continue to work to improve our field and its attractiveness — and I don’t think our community is, either. But it’s important to keep things in perspective! Public lamentations are not likely to improve the situation.

– Ed Lazowska

Workshop Notes (excerpts)

There were 35 people in attendance, including Joe Bordogna (former COO NSF), Clint Kelly (formerly DARPA), Elena Messina (NIST), William Joyner (Semiconductor Research Corporation), people from industry (General Motors, General Electric, ABB, C&S Whole Grocers, Willow Garage,…), plus academics (GATech, CMU, Berkeley, Utah, Colorado, UPenn,…).

This workshop was unlike those that typically happen at research conferences.  The discussion was not about pure science, but the intersection of science, national needs, public policy and funding.  There were almost no prepared talks. Instead, the program consisted mostly of group discussions, break-out sessions, and consolidation discussions with all attendees together.

Henrik Christensen gave a brief initial presentation to set stage. This was then followed a series of presentation by non-academics:

• Joe Bordogna: On how big science gets funded.
• William Joyner: On collaboration between industry and research in the semi-conductor industry.
• A series of talks by industry folks (GM, GE, food industry…). What is the state of the art, and what is needed? These presentations made clear the large impact of robotics on national infrastructure and economy, what past/current techniques are, and what new technology is needed for progress.

After the presentation, the workshop divided into breakout groups:

• Societal/Business drivers for robotics. Why does the country need robotics?
  • external drivers: inflation, human-resource costs, energy, environment
  • demographics: aging workforce, different skills/job expectations
  • manufacturing as a critical technology for economy and security
  • maintenance/management of national infrastructure: bridge painting…
  • successful design (our current strength) requires manuf. know-how
  • trend toward personal manufacturing: lot-size=1, customized products
  • traceability: salmonella, tomato processing…
• Obstacles to progress in these drivers. What research/technical progress needed.
  • Henrik: “Only at level that could be understood by Congressmen.”
• If given $$$ for robotics, what would you invest it in? Included technical discussion in both application and theory.
• If you had to write a roadmap now, how would you tell the story. How would investing in robotics make a difference?

By bringing together academic researchers, industry experts and public-policy experts, there were great exchanges and discussions that don’t happen at typical research conferences.

After the workshop, a number of documents were produced. That night, immediately after the workshop, a “DRAFT DRAFT” version of workshop report was produced. Later, a draft outline of of “roadmap” for robotics in a manufacturing and automation was also generated. This was presented to a Robotics Congressional Caucus, which involved congresspersons from Pennsylvania and Tennessee. (The documents will be made available on the web at www.us-robotics.us/blog. (Register to be able to comment on the report and participate in community discussion.)

The plan is to have a revision and synthesis of comments by September, in time for a possible review meeting at IROS’08, and then a synthesis workshop around November 2008. The goal, then, is to have a first complete document in November 2008, and presentation to university presidents and others in December. (There was a suggestion to organize an open meeting at IROS to get more feedback from the robotics community. However, IROS organizers said that it would be difficult to make room for such a discussion. Other possible conference venues are being explored.)

At one point during the workshop, I asked Henrik Christensen several questions:

Andrew McCallum: How did Robotics Congressional Caucus start?

Henrik Christensen: The President of CMU started it on his own initiative. The CMU Robotics Institute is important to CMU, and he wants to keep it strong.  (Side comment:  University Presidents are a great, highly-connected resource, and we should think about ways to leverage them more often than we have historically.)

MC: What is the end game for your CCC robotics initiative?

HC: I’d like to make menu of research opportunities, in the form of a 2×2 matrix. On one axis we have long-term vs short-term, and on the other axis we would have applied research vs basic research. We could then take this to many congresspeople, pitch lots of ideas, with the hope that some of them get excited. Then, we would ask for their help in enhancing robotics research opportunities. The entries in the matrix could also be targeted to specific funding agencies:

• short-term applied: go to NIH
• long-term basic-research: go to NSF
• long-term applied: go DARPA,…

Concluding thoughts

I think the organizers of this effort are on the right track. They have their sights focused on things that aren’t already provided by existing workshop venues.  The set of participants and the break-out sessions were all very high quality.  To his credit, Henrik runs a tight ship.

The CCC should encourage and find ways to provide even more support for this effort. In particular, Henrik and others are trying to make important governmental connections on their own. The CCC Council should be well equipped to help him. Even more importantly, the CCC should provide services, pointers, and contacts to other CCC groups that aren’t already as savvy as Henrik.

I thought there was great value from inviting some people outside robotics, in neighboring fields. The CCC should encourage other workshops to do this. We should also encourage other CCC initiatives to structure their workshops like this.

– Andrew McCallum

For example, LIGO (Laser Interferometer Gravitational-wave Observatory) is designed to detect ripples in space-time caused by changes in very large masses (e.g., a star exploding). Such observations, if made successfully, would finally confirm Einstein’s prediction of the existence of gravitational waves. LIGO has a construction cost of about $300M and annual operating costs of more than $30M. Justifying the construction of such an instrument requires an exceptionally compelling science case and a disciplined approach to construction management. Just as important is the need for the scientific research community (astrophysics, in the case of LIGO) to show deep understanding and broad support for the investment.

Is the computing research community in need of such large-scale instrumentation? For the CCC, this question has been a major topic of discussion, instigated initially by the development of the GENI (Global Environment for Network Innovations) concept. The current concept of GENI involves a global experimental network that would support Internet-scale experimentation with new transport technologies, networking protocols, and security mechanisms. GENI, if successful, would not only answer fundamental scientific questions about the behavior of global-scale networks, but also provide design guidance for the future Internet.

Time will tell whether GENI or other computing research concepts will develop into viable MREFC candidate projects. But in the mean time, it is instructive to study how other research communities develop the broad support that is needed in order to make a case to the NSF and the National Science Board.

One of the cornerstones of the whole process is a document that lays out the science case, or science requirements, for the instrument. It is instructive, then, to take some time to study such documents. One of the most recent successful MREFC proposals is for the LSST (Large Synoptic Survey Telescope), a new telescope projected to have a construction cost between $250M and $350M and scheduled to become operational in 2014. In my view, for anyone in the computing research community, it is well worth the time to study the LSST Science Requirements Document. It provides a window into the kind of audacious yet focused investigation that is used to justify such huge science investments. LSST also involves significant data management and computing problems which may be of strong relevance to computing research.

A brief excerpt from one of the founding papers on LSST explains the goal of the instrument as follows:

We describe the most ambitious survey currently planned in the visible band, the Large Synoptic Survey Telescope (LSST). The LSST design is driven by four main science themes: probing dark energy and dark matter, taking an inventory of the Solar System, exploring the transient optical sky, and mapping the Milky Way. LSST will be a large, wide-field ground-based system designed to obtain multiple images covering the sky that is visible from Cerro Pachon in Northern Chile. The current baseline design, with an 8.4m (6.5m effective) primary mirror, a 9.6 sq. deg. field of view, and a 3.2 Gigapixel camera, will allow about 10,000 sq.deg. of sky to be covered using pairs of 15-second exposures in two photometric bands every three nights on average, with typical 5-sigma depth for point sources of r=24.5. The system is designed to yield high image quality as well as superb astrometric and photometric accuracy. … These data will result in databases including 10 billion galaxies and a similar number of stars, and will serve the majority of science programs.

The document lays out the science case and from this derives the requirements on the instrument. The science case is built around four themes. The first theme, on dark matter and dark energy, directly addresses what the National Academies recently identified as one of the “most important scientific questions of our time.” Two other themes, to “explore the transient optical sky” and to “map the Milky Way”, speak to general facilities needs of the astronomy research community. And the fourth theme, on “taking an inventory of the Solar System”, addresses the practical problem of keeping track of asteroids that might “ultimately strike the Earth’s surface.”

The small number of themes and their clear, concise explanation (each one is described in about a single page of text) makes it possible to derive a clear set of requirements on the telescope. Importantly, it also gives a clear basis for disseminating the concepts to the research community, thereby encouraging more informed debate and consensus-building.

In the LSST Science Requirements Document, the high-level requirements are given in terms of a series of design specifications, with both minimum and stretch goals in each case. The level of specificity, particularly in a rather short (about 30 pages) document, is impressive.

Interestingly, one area where the document falls short is in the final section on “Data Processing and Management Requirements”. This is left essentially as a stub, for a yet-to-be-published separate document. The LSST is projected to produce about 100TB/week of image data, and the design requirement is for “snapshots” of the data to be fixed and published annually, to support repeatability of experiments. Yet to be specified is the manner in which up-to-the-minute data is disseminated, organized, and accessed. Certainly these are issues that computer scientists will be interested in and are likely to be well-equipped to answer. We should all look forward to contributing to this part of the LSST effort.

So what does this all say about efforts such as GENI or other future computing-related instrumentation proposals? For one thing, it is probably important to have a set of crisply stated science questions. What is GENI’s analogue to “constraining dark energy and dark matter”? Writing the science case with a level of focus and simplicity that the entire computing research community can understand and accept is also crucial. And, finally, it must be possible to derive, fairly directly, at least a high-level set of design requirements from the statement of the science case.

Computing research is both important and wonderful because it combines fundamental science, hard-core engineering, and practically useful technology all together to an extent that is unique in academic research today. Whether we will find compelling needs for MREFC-scale instrumentation is still an open question, but I have no doubt that if/when we do, that a successful case can be made to fund it.

Robotics research and development have already transformed our lives in many ways: they perform nearly all the welding and painting on the cars we drive; they enable telerobotic surgery resulting in more reliable outcomes and faster recovery times; they perform millions of scientific experiments and observations in chemistry, biology and medical labs.  Increasingly robotics is also providing improved control and functionality in people’s daily lives: some new model cars can park themselves or provide advanced distance-keeping cruise control and collision warnings; millions of autonomous vacuum cleaners are in use in homes across the country; educational toys that move and sense are becoming increasingly popular.

With even larger future impact on the way in elder-care, environmental work and professional services, the international competition for leadership in robotics is significant.  Already, Europe, Japan, and other countries have organized strong, coherent, and well-funded robotics research and development initiatives.  The U.S. must also develop strategic and coordinated support for our robotics research community.

In addition to organizing a set of workshops, Henrik Christensen and his colleagues are leading the creation of a National Robotics Leadership Council to provide an ongoing organization for robotics advocacy and leadership.  The two early meetings will coincide with a caucus on robotics being organized by Congress for June 18.

This attention to connecting with governmental and industrial sponsors is exemplary of what the CCC should be enabling.  The physics research community is well known for its effective governmental lobbying; computer science needs to do a better job of building such connections.  While good scientific ideas must of course form the bedrock of our work, in these times, the focus of our argument must be economic—return on investment—a basis on which I think CS can make a very good case.

In my view, no one in the CS research community will see the CCC as a hero for “defining a research agenda”—these things are usually best developed bottom-up, (and besides there already exist many venues for workshops that are research-focused).  The CCC will be a hero if we can help increase funding for CS.  This will involve creative CS researchers coming together with government and industrial leaders, and developing the right scientific and economic arguments to make the case.

I applaud the National Robotics Leadership Council.  I think they are a good example to us all.

Their workshops, topics and dates are:

• Robotics in Manufacturing and Automation (June 17, DC)
  Organizers: K. Goldberg, V. Kumar, J. Trinkle, H. I. Christensen
• Healthcare and Medical Robotics (June 19-20, DC)
  Organizers: A. Okamura, M. Mataric & H. I. Christensen
• Domestic and Professional Service Robotics (August 7-8, San Francisco)
  Organizers: B. Thomasmeyer, O. Brock, H. Christensen
• Emerging Technologies and Trends (August 14-15, Snowbird, UT)
  Organizers: M. Mason, J. Hollerbach, H. I. Christensen

For more information about these efforts and how to participate see http://www.us-robotics.us/.

The Hadoop Summit was an open event, hosted by Yahoo! Research. Its goal was to build a community among users of the open-source Hadoop software suite for distributed programming in the map-reduce style. About 350 people attended, a much larger crowd than originally expected. The DISC Symposium was an invitation-only event (~125 attendees) whose goal was to build a community among DISC researchers.

The presentations at the Hadoop Summit were fascinating. While they varied greatly in technical depth, in total they gave a sense of rapid growth in the amount of ingenuity being directed towards solving large-scale data-intensive problems on scalable computing clusters. As one might expect, academic researchers were among the speakers, as well as people from industry research labs at Yahoo!, IBM, and Microsoft. But there were also technical talks by developers at places like Google, Amazon, Rapleaf, Facebook, and Autodesk, each one essentially doing a “show-and-tell” on interesting data-intensive problems being tackled in their companies. This gave the attendees a glimpse into the growing industry interest in Hadoop.

The DISC Symposium had attendees from a broad range of companies and research institutions. By design, the program was broad and shallow — the idea was to bring together researchers from all aspects of DISC. Among the highlights:

• In the “DISC systems” arena, Randy Bryant laid out a broad range of research challenges, Jeff Dean gave a lightening-fast overview of Google’s tools (clusters, GFS, MapReduce, BigTable, Chubby), and Garth Gibson talked about challenges in large-scale data systems.
• In the “middleware” arena, ChengXiang Zhai discussed text information management, and Joe Hellerstein promoted declarative programming as a universal elixer.
• In the “applications” arena, Jill Mesirov described computational paradigms for genomic medicine, Jon Kleinberg talked about algorithms for analyzing large-scale social network data, and Alex Szalay described applications in the physical sciences.
• Jeannette Wing and Christophe Bisciglia announced NSF’s new program supporting DISC research utilizing a large-scale cluster provided by Google and IBM.

Slides from all presentations at both the Hadoop Summit and the DISC Symposium, as well as videos of most presentations, are available here.

So what can we conclude about all of this? Well, at the Hadoop Summit, the speakers (especially the ones from industry) were not the “usual suspects”, especially considering the fairly hard-score technical nature of research in large-scale distributed systems. However, there is an overwhelming sense that a major wave is starting, and overall we the excitement level at the meeting was extremely high.

Regarding the concept of “DISC”, here is our unabashed opinion about all of this: Ubiquitous cheap sensors (in gene sequencers, in telescopes, in buildings, on the sea floor, in the form of point-of-sale terminals or the readable web, etc.) are transforming many fields from data-poor to data-rich. The enormous volume of data makes “automated discovery” (machine learning, data mining, visualization) essential, requiring innovation throughout the stack. The traditional “high performance computing” crowd has missed the boat on this one. (The focus must be on the data.) Web companies such as Google, Yahoo!, and Microsoft have made significant strides. But there remains lots of room — and lots of need — for additional breakthroughs. Bluntly, a university that lacks this “big data” capability is not going to be competitive.

The job of the Computing Community Consortium is to facilitate the computing research community in envisioning, articulating, and pursuing longer-range, more audacious research challenges. “Visioning workshops” such as these are one route that the CCC is pursuing. This was the first CCC-sponsored meeting. While there’s room for improvement (more time for discussion, more younger attendees, …), most participants viewed this workshop as a success — there was a real buzz.

Let us know your thoughts!

– Ed Lazowska and Peter Lee

In its founding documents, the CCC describes its goals as follows:

The challenge for the Computing Community Consortium (CCC) is to catalyze the computing research community to debate longer range, more audacious research challenges; to build consensus around research visions; to evolve the most promising visions toward clearly defined initiatives; and to work with the funding organizations to move challenges and visions toward funding initiatives.

All great goals. But how do we set about accomplishing them? While it is still early days for the CCC, a number of very interesting proposals for “visioning” have already been generated by the research community, including proposals in robotics, CS theory, “big data” computing, and a wide variety of other areas. These promise to give us all something to think about, debate, work on, and leverage to enlarge the CS research funding pie. We can all hope that this kind of community involvement not only continues, but accelerates.

Brainstorming, discussing and driving “longer range, audacious research challenges” is the job of the entire community, and the CCC is always looking for ways to encourage and facilitate broad community participation.  The CCC will support and help to organize many venues for this: at workshops and conferences, in publications, and on its own web site at http://www.cra.org/ccc.

The purpose of this blog is to try a more immediate, online mechanism for dissemination of visioning concepts and community discussion/debate about them.

So, this blog is an experiment — being run initially by the CCC Council, but ultimately we would hope that the research community as a whole might somehow take over the most effective online forums. We want to get started, though, and so here we are. To get us off the ground, we’ve assembled a group of researchers to provide the initial set of articles for this blog. Each member of this group blog is being asked to write their own (possibly opinionated) analyses or commentaries on CS visioning concepts, including those funded by the CCC. In addition, there will be reports of interesting events, discoveries, funding news, and even perhaps various scuttlebutt of relevance to the computing research community.

We hope that you will all subscribe! Expect roughly two to three articles per month.  You may post comments, and in fact doing so is very highly encouraged!  (They may even be anonymous if you prefer.)  This will be at least one online mechanism for community debate and discussion. If this works well, it is conceivable that we could muster up the courage to try something more in the vein of collaboration-encouraging social networking.

Welcome!

– Peter Lee and Andrew McCallum