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REPORT OVERVIEW . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

1. INTRODUCTION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41.1 Charge to Update the HPCC Grand Challenges . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41.2 Why Have New Grand Challenges? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

2. CRITERIA AND TEMPLATE FOR FORMULATING THE NITRD ILLUSTRATIVE GRAND CHALLENGES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52.1 Title. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52.2 Description of the Multi-Decade Grand Challenge . . . . . . . . . . . . . . . . . . . . . . . . . . 52.3 Focus in the Next Ten Years. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52.4 Benefits . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52.5 Relationship to National Priorities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52.6 Relationship to IT Hard Problem Areas and IT Hard Problems. . . . . . . . . . . . . . . . 62.7 Indications of Progress . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

3. FUTURE OF THE NITRD ILLUSTRATIVE GRAND CHALLENGES . . . . . . . . . . . . . . . . . . . . . . . . . 7

Figure 1. Relationships Between the Illustrative Grand Challenges and the National Priorities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

Figure 2. Relationships Between the Illustrative Grand Challenges and the IT Hard Problem Areas. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

4. DETAILED DESCRIPTIONS OF THE NITRD ILLUSTRATIVE GRAND CHALLENGES4.1 Knowledge Environments for Science and Engineering . . . . . . . . . . . . . . . . . . . . . 124.2 Clean Energy Production Through Improved Combustion. . . . . . . . . . . . . . . . . . . 144.3 High Confidence Infrastructure Control Systems . . . . . . . . . . . . . . . . . . . . . . . . . . 164.4 Improved Patient Safety and Health Quality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 184.5 Informed Strategic Planning for Long-Term Regional Climate Change . . . . . . . . 204.6 Nanoscale Science and Technology: Explore and Exploit the

Behavior of Ensembles of Atoms and Molecules . . . . . . . . . . . . . . . . . . . . . . . . . . . 224.7 Predicting Pathways and Health Effects of Pollutants. . . . . . . . . . . . . . . . . . . . . . . . 244.8 Real-Time Detection, Assessment, and Response to Natural

or Man-Made Threats . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 264.9 Safer, More Secure, More Efficient, Higher-Capacity Multi-Modal

Transportation System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 284.10 Anticipate Consequences of Universal Participation in a Digital Society . . . . . . . . 304.11 Collaborative Intelligence: Integrating Humans with Intelligent Technologies . . . 324.12 Generating Insights From Information at Your Fingertips . . . . . . . . . . . . . . . . . . . 344.13 Managing Knowledge-Intensive Organizations in Dynamic Environments . . . . . . 364.14 Rapidly Acquiring Proficiency in Natural Languages . . . . . . . . . . . . . . . . . . . . . . . 384.15 SimUniverse: Learning by Exploring . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 404.16 Virtual Lifetime Tutor for All. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41

APPENDIX 1: ILLUSTRATIVE IT HARD PROBLEMS CATEGORIZED BY IT HARD PROBLEM AREAS . . 44

APPENDIX 2: THE HPCC PROGRAM’S GRAND CHALLENGES . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46

APPENDIX 3: NITRD GRAND CHALLENGES AUTHORS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48

APPENDIX 4: NITRD GRAND CHALLENGES TASK FORCE CONTACT INFORMATION. . . . . . . . . . . 49

APPENDIX 5: ACRONYMS AND GLOSSARY . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54

ACKNOWLEDGEMENTS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56

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TABLE OF CONTENTS

This definition is characteristic of human-ity’s resolve to find solutions to chal-

lenges that go far beyond mere intellectualcuriosity. It is embedded in humanity’s verynature to conquer new frontiers for social,economic, and political advancement. Infor-mation technology is an important elementin conquering these frontiers.

Recognizing this role, the U.S. Govern-ment makes critical decisions aboutappropriate investments in IT R&D to helpsociety forward both socially and eco-nomically. To inform that decision-making, inJuly of 2003, a group of leading Governmenttechnical program managers who participatein the Networking and InformationTechnology Research and Development(NITRD) Program completed their formu-lation of 16 illustrative science, engineering,and societal grand challenges. Addressingthese grand challenges will require inno-vations in IT R&D.

The new NITRD illustrative grand chal-lenges were formulated to stimulate currentand future generations of NITRD andapplications researchers. They are illustrativeand not definitive or exhaustive because thereare easily hundreds if not thousands of grandchallenges that could be identified.

The NITRD illustrative grand challengescan be symbolically depicted as both a journeyand a destination. The journey, which is likelyto take more than a decade, is to a destinationthat lies far beyond current human under-standing and capability. By describing thesechallenges, we intend to explain why IT R&Dis important to society, justify why publicinvestments in IT R&D are necessary anddesirable, and galvanize the NITRD agenciesand the IT R&D community to solve IT hardproblems. Given the rapidly evolving natureof information technology, solutions totoday’s grand challenges will fuel the grandchallenges of tomorrow.

The NITRD Program’s illustrative grandchallenges are:

Knowledge Environments for Science andEngineering

Clean Energy Production Through Improved Combustion

High Confidence Infrastructure Control Systems

Improved Patient Safety and Health Quality

Informed Strategic Planning for Long-TermRegional Climate Change

Nanoscale Science and Technology: Explore andExploit the Behavior of Ensembles of Atomsand Molecules

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“ A Grand Challenge is a long-term science, engineering,

or societal advance, whose realization requires innovative

breakthroughs in information technology research and

development (IT R&D) and which will help address our

country’s priorities.”

—The 2003 NITRD Program Grand Challenge definition

REPORT OVERVIEW

Predicting Pathways and Health Effects of Pollutants

Real-Time Detection, Assessment, and Response toNatural or Man-Made Threats

Safer, More Secure, More Efficient, Higher-Capacity, Multi-Modal Transportation System

Anticipate Consequences of UniversalParticipation in a Digital Society

Collaborative Intelligence: Integrating Humanswith Intelligent Technologies

Generating Insights From Information at Your Fingertips

Managing Knowledge-Intensive Dynamic Systems

Rapidly Acquiring Proficiency in NaturalLanguages

SimUniverse: Learning by ExploringVirtual Lifetime Tutor for All

This booklet provides an elaboration ofeach of the 16 illustrative NITRD grandchallenges (henceforth referred to as grandchallenges). They cover a wide spectrum ofdisciplines and sub-disciplines includingeducation, the environment, health, thephysical sciences, security, and transpor-tation. For each grand challenge, there is abrief description, component challenges thatrequire focus within the next decade, theirrelationship to national priorities, theirpotential benefits, IT hard problems thatneed solving in order to help realize the goalsof the grand challenge, and indicators thatwill show that progress is being made in theintermediate term. These grand challengesare aligned with the missions of the NITRDagencies and address a vast array of broadsocietal goals.

The national priorities and the IT hardproblems are the key pillars on which thegrand challenges are structured:

By describing the relationship between agrand challenge and national priorities,the grand challenge’s significance isconnected to the highest aspirations ofour country.

The IT hard problems, whose solutionthe grand challenge requires, tie thegrand challenge to core elements ofinformation technology research anddevelopment and the NITRD Program.

These two sets of relationships areillustrated on pages 8 and 9.

Chapter 1 describes how and why thesegrand challenges were developed, chapter 2explains the template used to describe thegrand challenges, chapter 3 discusses thefuture of the grand challenges, and chapter 4describes the grand challenges in detail.

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1. INTRODUCTIONIn November 2002, the Interagency Work-

ing Group on Information TechnologyResearch and Development (IWG/IT R&D) ofthe National Science and Technology Council(NSTC), Executive Office of the President,established a Grand Challenges Task Force.The IWG/IT R&D, a body of leadingGovernment experts who provide hands-oncoordination of the multi-agency Networkingand IT R&D (NITRD) Program, charged theTask Force with identifying a set of science,engineering, and societal challenges that willrequire innovations in IT R&D. The TaskForce consisted of expert volunteers from tenNITRD agencies—AHRQ, DARPA, DOE/SC,EPA, NIH, NIST, NOAA, NSA, NSF,ODDR&E—plus FAA, OSTP, and theNCO/IT R&D (these acronyms are spelledout in Appendix 4).

1.1 CHARGE TO UPDATE THE HPCC GRAND CHALLENGES

The charge to formulate a set of NITRDgrand challenges was specifically a call toupdate the list called for in the High-Performance Computing (HPC) Act of 1991(P.L. 102-192) that formally established theHigh Performance Computing andCommunications (HPCC) Program. Throughthe HPCC Program, the U.S. Governmentcoordinated multi-agency investments indeveloping and using high-performancecomputing systems and advanced networkingtechnologies to meet the mission needs of theparticipating agencies and larger nationalgoals. The Act’s objectives included to:

Develop teraops (trillions of operationsper second) computing systems

Develop gigabit (billions of bits persecond) networks

Develop advanced algorithms and software

Demonstrate innovative solutions to“grand challenge” problems using HPCCtechnologies

1.2 WHY HAVE NEW GRAND CHALLENGES?

Seven HPCC Program agencies identified32 grand challenges (listed in Appendix 2).By the time of its formal conclusion in 1996,the HPCC Program had met both itsprogrammatic and grand challenge objec-tives (see the HPCC Program’s FY 1996annual report to Congress known as the BlueBook at http://www.nitrd.gov/pubs/blue96/).

The NITRD Program has succeeded theHPCC Program. The scope has beenexpanded to include all areas of informationtechnology research and development, notjust high-end computing and high-speednetworking. This expansion has enabled theparticipating agencies to address a vastlybroader range of information technologiesand IT application challenges than in theHPCC Program and its grand challenges.

Recognizing that IT advances will enhanceexisting applications and enable new onesthat can have an even greater impact onscience, engineering, and society, the NITRDGrand Challenges Task Force developed anew definition of a grand challenge (foundon page 2) and identified 16 illustrativegrand challenges. These grand challengesare expected to yield significant break-throughs of practical importance to mankind.As progress is made, these challenges cancontinuously evolve, be updated, and bereplaced by new grand challenges.

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2. CRITERIA AND TEMPLATE FORFORMULATING THE NITRDILLUSTRATIVE GRAND CHALLENGESA set of criteria was established to guide

the development of the grand challenges.These criteria are reflected in the templateused in chapter 4 to describe the grandchallenges and are explained below:

Title

Description of the Multi-Decade Grand Challenge

Focus in the Next Ten Years

Benefits

Relationship to National Priorities

Relationship to IT Hard Problem Areas

Indications of Progress

2.1 TITLE

To stimulate multi-disciplinary thinking,the titles were crafted to reflect the TaskForce’s goal that they challenge theintellectual aspirations of our country’sresearchers beyond their understandingtoday or in the next decade. The list beginswith physical science challenges in honor oftheir HPCC predecessors, followed bychallenges that have strong human aspects.However, the list has not been prioritized bylevel of importance.

2.2 DESCRIPTION OF THE MULTI-DECADEGRAND CHALLENGE

The description articulates the challengethat the Task Force thinks is likely to beaccomplished no sooner than ten years fromnow. However, given today’s normal rapidnature of technological advances andoccasional serendipitous developments (such

as the success of the Internet and the earlysuccess of the World Wide Web during theHPCC Program), these goals might beaccomplished sooner. On the other hand,some of these grand challenges may not beaccomplished even in half a century. Indeed,conceptual ideas similar to some of theNITRD grand challenges have already beensubjects of decades of intensive research(natural languages is an example) and theirdescriptions here reflect advances that havebeen made to date.

2.3 FOCUS IN THE NEXT TEN YEARS

While keeping the longer-term grandchallenge in perspective, certain aspects ofthe challenge have been identified forfocused attention in the next ten years. Someof these focus areas were selected becausethey are particularly difficult and need to betackled right away. For others, the knowledgeand resources needed to address them areavailable today. Focus on these componentchallenges in the near term helps sustainfocus on the longer-term grand challenge.

2.4 BENEFITS

The NITRD grand challenges can gen-erate a vast array of social, economic,political, scientific, and technology benefits astheir solutions are found. Common threadspermeating these benefits include findinganswers to complex questions that have longperplexed humanity, creating new disciplinesof human inquiry and areas of multi-disciplinary collaboration, and developingand using new technologies.

2.5 RELATIONSHIP TO NATIONAL PRIORITIES

Working closely with officials at the WhiteHouse Office of Science and TechnologyPolicy (OSTP), the Task Force defined six

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national priorities that reflect the country’sbroad-based scientific, military, social, eco-nomic, and political values and goals. Each ofthe grand challenges strongly contributes toone or more of these national priorities:

Leadership in Science and Technology

Homeland and National Security

Health and Environment

Economic Prosperity

A Well-Educated Populace

A Vibrant Civil Society

While the NITRD grand challenges werestructured within a national context, inter-national collaborations and partnerships willbe essential to successfully address many ofthe grand challenges, and all nations canbenefit from the advances that are made.

Figure 1 (page 8) depicts relationships be-tween the grand challenges and the nationalpriorities. Each cell colored dark blue reflectsan explicit relationship between a grandchallenge and a national priority.

2.6 RELATIONSHIP TO IT HARD PROBLEMAREAS AND IT HARD PROBLEMS

IT hard problems areas are broad categoriesof topics of interest to the informationtechnology research and development com-munity and the NITRD Program. The TaskForce identified 14 IT hard problem areas:

Algorithms and Applications

Complex Heterogeneous Systems

Hardware Technologies

High Confidence IT

High-End Computing Systems

Human Augmentation IT

Information Management

Intelligent Systems

IT System Design

IT Usability

IT Workforce

Management of IT

Networks

Software Technologies

Each grand challenge requires advances inseveral IT hard problem areas, as illustrated inFigure 2 (page 9). Each light blue cell indicatesan explicit relationship between a grand cha-llenge and an IT hard problem area.

For each IT hard problem area, the TaskForce identified one or more illustrative IThard problems (Appendix 1). Specific IThard problems are identified in the write-upof each grand challenge. Progress towards thegrand challenge will require breakthroughsor solutions to these IT hard problems. TheIT hard problems are beyond our currentunderstanding and capability, but inter-mediate progress toward accomplishing themwill contribute to the grand challenge.

The IT hard problems span the breadth ofthe NITRD Program’s current investments.Given the fast moving nature of informationtechnology R&D, the IT hard problems arelikely to change over time, and the NITRDProgram will evolve in response to thesechanges.

2.7 INDICATIONS OF PROGRESS

For a multi-decade activity such as a grandchallenge, it is helpful to identify entitieswhose change over time indicates thatprogress is being made. These entities can bequalitative or quantitative in nature. Some of

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the quantitative entities can nonetheless bedifficult or impossible to measure (examplesare reduced errors or reduced failures). Oftenthe best, the most significant, or the mostinfluential achievements are qualitative innature, at least according to our currentunderstanding. It could take decades toappreciate the impact of inventions or dis-coveries, for example. The indicators ofprogress for the NITRD grand challengesspan this range.

3. FUTURE OF THE NITRD ILLUSTRATIVEGRAND CHALLENGES The NITRD grand challenges are expected

to change over time. Progress will be made.Goals will change. New challenges will emerge.

Twelve years have elapsed since the HPCCProgram identified its grand challenges, andthe NITRD Program or its successor maysomeday revisit its grand challenges with theintention of revising their definition anddetails.

In the meantime these grand challengescan guide technical program managers inNITRD agencies and policymakers in theCongress and the Executive Branch. Theycan serve as beacons for intellectualendeavors of current and future generationsof students and researchers in universitiesand national and corporate laboratoriesacross the country. Researchers wishing toreach beyond current limitations can build onthe NITRD grand challenges in articulatingtheir own visions.

The NITRD Program has a long history ofcollaboration and coordination across Federalagencies and with universities and corp-orations throughout the country, to which itattributes much of its success. Accomplishingthe grand challenges’ goals is possible only byexpanding these interactions.

Success in these grand challenges alsorequires international collaboration andcooperation, as illustrated by the followingaspects of these grand challenges:

Climate change, energy, human health,natural and man-made disasters,pollution, and transportation spannational boundaries.

Researchers, workers, teachers, andstudents live in different countries and/orspeak different languages yet need to useunique scientific instruments or scientificdata sets and need to communicate witheach other both verbally and in writing.Some (emergency first responders andwar fighters, for example) will also needto talk and listen to IT systems such as robots.

The benefits of IT can be brought toremote areas both in the United Statesand around the world, and IT can beused to improve education, increaseunderstanding of different cultures andsocieties, and build communities.

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DETAILED DESCRIPTIONS OF THE NITRD ILLUSTRATIVE GRAND CHALLENGES

Description of the Multi-DecadeGrand Challenge

Organize and make broadly availabledistributed resources such assupercomputers, data archives, distantexperimental facilities, and domain-specific research tools to enable newscientific discoveries and education acrossdisciplines and geography

Focus in the Next Ten YearsUnderstand the needs of scientists andhow science is changing (for example,data sets are more complex and teams aremore interdisciplinary)

Increase access to computing systems,archives, instruments, and facilities

Build on successful experiments:• Upper Atmospheric Research

Collaboratory (UARC) and SpacePhysics and Aeronomy ResearchCollaboratory (SPARC)

• Network for Earthquake EngineeringSimulations (NEES)

• Biomedical Informatics ResearchNetwork (BIRN)

• National Virtual Observatory (NVO)

BenefitsNew discoveries across disciplines (forexample, discoveries in one field canapply to other fields)

Establish new fields of science andengineering

Relationship to National Priorities Leadership in Science and Technology• Help maintain U.S. leadership in a

wide range of science, engineering,and technology disciplines

National and Homeland Security• Safety and security will become

increasingly dependent on advances inscience and engineering

Health and Environment• Improved water quality and

human health

A Well-Educated Populace• Boon to K-12, undergraduate, and

graduate education• Boon to underdeveloped regions

within the United States and around the world

A Vibrant Civil Society• The social sciences and the humanities

also use these environments (forexample, museums make 3-D imagesof ancient artifacts)

IT Hard Problem AreasAlgorithms and Applications• Modeling and simulation throughout

science and engineering

Complex Heterogeneous Systems• Control of science and engineering

experiments• Embedded systems for science and

engineering data collection andexperiments

Hardware Technologies• Mass storage technologies • Biological and nanoscale technologies

applied to sensors

4.1 KNOWLEDGE ENVIRONMENTS FORSCIENCE AND ENGINEERING

12

High Confidence IT• Security• Reliability• Trust tools embedded in applications• Policy-enabled infrastructures (for

example, protocols, policies, andmechanisms for sharing resources, andthe embedding of scheduling inknowledge environments)

High-End Computing Systems• HEC systems for applications that

require computationally-intensivemodeling and simulation

Human Augmentation IT• Presence and awareness tools

embedded in applications used inremote collaboration such asteleoperation

Information Management• Creation and management of massive

data and information repositories• Data analysis tools

Intelligent Systems• Knowledge discovery in massive

databases of archived knowledge

IT System Design• Interoperability• Scalability of tools and environments as

the number of users and sites increase

IT Usability• Managing screen real estate to aid

experimenters, data analyzers, and forchat facilities

IT Workforce• Advanced IT for technicians

Management of IT• Copyright restrictions to collecting and

harvesting knowledge• Software and infrastructure standards

Networks• Reliable, secure networks with

differentiated services• Bandwidth for international

collaborations

Software Technologies• Software that recognizes different

individual and group roles in scienceand engineering

Indications of ProgressMore users of distributed science andengineering environments

More distributed science and engineeringcollaborations

More scientists and engineers in remoteparts of the country

New tools and applications for more areasof science and engineering

New science and engineering ideas andinnovations

Scientists and engineers achieve theirgoals more efficiently and effectively

More “hands-on” science education in K-12 and undergraduate school

13


Improve the efficiency of the combustionof fossil fuels, which are the dominantsource of energy in the United States

Make our environment healthier byreducing greenhouse gas emissions

Focus in the Next Ten YearsOptimize the design of combustionengines

Improve catalysis—trapping of pollutinggases produced by combustion engines—to minimize emissions

Understand the impact of emissions onglobal climate

BenefitsImprove the design of engines andturbines

Reduce greenhouse gas build-up andglobal warming, which could result inrising ocean levels

Improve human health by removingcancer-causing agents in by-products ofcombustion

Relationship to National PrioritiesLeadership in Science and Technology• Remain at the forefront in designing

advanced simulation tools• Advance the science of combustion• Simulate an entire internal combustion

engine

National and Homeland Security• Reduce dependence on foreign oil

Health and Environment• Reduce emissions of carbon and

other pollutants

Economic Prosperity• Fuel consumption has smaller demand

on the economy

4.2 CLEAN ENERGY PRODUCTION THROUGHIMPROVED COMBUSTION

14

IT Hard Problem AreasAlgorithms and Applications• Model the interactions of solids and

gases, the moving boundary betweenthem, and the changing geometries ofan internal combustion engine

• Incorporate complex nonlinearinteractions of hundreds of chemicalspecies in order to correctly predictpollutant production

• Move from laboratory scale modeling(for example, table-top size engineburning pre-mixed fuel in a particularway with by-products measured bylasers) to modeling a diesel engine withcomplex geometry under less thanlaboratory-quality conditions, with 10times more species and reactions,multiple time and space scales, andgreater difficulty predicting soot andother by-products

High-End Computing Systems• Research in high-performance

computing systems architectures thatsupport multiphysics applications withirregular memory access patterns

Information Management• Coherent database of kinetic and

thermo-chemical reactions developedfrom a large number of distributedsources of varying quality

Management of IT• Sequester data in the general model

from proprietary data that belong todifferent companies

Indications of ProgressAccuracy of predictive models

Technology transfer

Increased fuel efficiency as tracked inCAFE standards

Reduction in pollutant production15


Ensure the continuous, safe operation ofthe Nation’s infrastructure systems such asthe power grid, water supply, andtransportation systems

Protect against malicious attacks, physicalfailures, and complex cascading failures

Focus for the Next Ten YearsSupervisory Control and Data Acquisition(SCADA) systems

Transformation of legacy systems tocapable, resilient IT-enabledinfrastructures

Coordinated decentralized supervisorycontrol of new forms of distributedinfrastructure such as air traffic controland transportation scheduling

Supervisory control of advanced powergrid technologies (for example,distributed power generation andadvanced devices for controllingalternating current (AC) transmissionsystems)

BenefitsRobust, survivable infrastructures that canprovably withstand broad classes ofmalicious attacks and failures

Higher capacity systems through refinedmanagement of safety margins

Ability to isolate failures more easily,prevent widespread disruption, andreduce impact of failures

Relationship to National PrioritiesNational Security• Information warfare • Military command, control, and

communications

Homeland Security• Critical infrastructure protection

Economic Prosperity• Trustworthy infrastructure and energy

independence

A Vibrant Civil Society• Modern society requires reliable

infrastructure

4.3 HIGH CONFIDENCE INFRASTRUCTURECONTROL SYSTEMS

16

IT Hard Problem AreasComplex Heterogeneous Systems• Understand and balance simultaneous

conflicting interacting requirements:Tolerate failures (known as fault-tolerance)Recover within time constraintsMaintain security while recovering from failures

• Understand and control emergent(hard to predict) behavior in SCADAsystems. Local interactions can lead toglobal-scale instability.

High Confidence IT• Integrate security (authentication,

access control, intrusion detection) intonetworked embedded systems where ithas never existed

• Establish a new paradigm of operatingat acceptable levels through attacks.Shutting down to thwart attacks is notan option.

Networks• Secure and survivable networks

Indications of ProgressDecrease mean time to recovery (MTTR)to increase availability

Fewer and smaller scale failures

17


Improve patient care through saferevidence-based medicine and reducedmedical errors

Link patient information with medicalknowledge to improve the decisions ofhealth care providers and their patients

Focus in the Next Ten YearsIncrease awareness of the magnitude ofthe problem of medical errors andpotential solutions

Establish uniform medical reportingrequirements to enable analysis andfeedback

Collect and analyze medical error data toenable patient safety research

Learn how to reduce errors in the healthindustry by studying other industries. Forexample, the airline industry requirespilots to report near misses.

Educate the population about whatquestions to ask medical professionals

BenefitsImproved health care quality and patientrecovery. For example, workers can goback to work more quickly and the elderlycan live longer in their own homes

A health care IT infrastructure thatsupports continuous improvements.Examples are bar-coded drugs, nursinguse of time stamps, and use of radiofrequency identification devices.

Efficient use of health care resources

Analysis system to monitor improvementswhile protecting confidentiality

Relationship to National PrioritiesHealth and Environment• Improved health of the American

people and better use of resources

Economic Prosperity• Improved quality of life

IT Hard Problem AreasAlgorithms and Applications• Modeling and simulation of

medical errors

High Confidence IT • Security and privacy for health

information including authorization,authentication, biometrics,certification, encryption, and interfaces

Human Augmentation IT• Scalable interoperable use of

prompts, alerts, and reminders bydoctors and patients

4.4 IMPROVED PATIENT SAFETY AND HEALTH QUALITY

18

Information Management• Data, information, and knowledge

management to support evidence-based decision making

• Data mining and data warehousing todevelop and enrich knowledge aboutpatient safety

Intelligent Systems• Human language technology such as

common medical terminology toenable accurate communication andoptimum decision making

IT System Design• Interoperability of health

information systems within hospitals,across providers, and among otherstakeholders such as insurancecompanies, accreditation committees,and governments

IT Usability• User interfaces that provide prompts,

alerts, and reminders at the time andpoint of medical decision making

• Designs that vary for users of differentskill levels and experience

Management of IT• Public support for open source

electronic health records to encourageinnovative applications

• Economic, legal, policy, and socialimplications of the use of IT in patient safety

Networks• Networking management, reliability,

and scalability, to expand successfulpatient safety improvements nationallyand internationally

Software Technologies• Health information software

requirements, engineering, anddevelopment

• Software reliability, performance, andquality assurance

Indications of ProgressReported medical errors first rise asreporting improves then fall in severity as more organizations systematicallyreport errors

Reduced mortality and illness due tomedical errors

New evidence-based tools for healthcareproviders. For example, a clearinghousefor healthcare quality measures.

Industry decisions about purchasinghealth care plans that incorporate patientsafety research results

19


Provide decision makers with timelyknowledge developed throughcomprehensive assessments ofobservations, models, and theories of theimpacts of regional-level climate changeto help them select the best adaptationand mitigation strategies

Focus in the Next Ten YearsProvide science-based information toinform the public debate about the effectsof climate change and what to do about it

Reduce uncertainty in climate forecasts toenable common understanding andimprove the prospects of consensus

Identify possible alternative futures andpaths to those futures (global warmingcould cause ice to melt and ocean levelsto rise and warrant reduced constructionnear ocean beaches)

Improve decision-making in national andinternational arenas

Identify opportunities to manage the risksor mitigate the effects of climate change

BenefitsImproved long-term decision makingbased on predicted regional climatechange

Relationship to National PrioritiesLeadership in Science and Technology • Ability to predict climate change and

evaluate alternative scenarios

Health and Environment• Viable health and climate

Economic Prosperity• Viable agriculture industry

A well-Educated Populace• Inform the public debate

IT Hard Problem AreasAlgorithms and Applications• Modeling of the environment

including atmosphere, oceans, andbiological systems under conditions ofchanging composition

• Scaling of algorithms, efficientparallelization, and communicationamong neighboring nodes

Hardware Technologies• Faster computers, access to results, and

nanoscale data storage

High Confidence IT• Security• Models that run for long periods of

time on computers that do not crash

4.5 INFORMED STRATEGIC PLANNING FORLONG-TERM REGIONAL CLIMATE CHANGE

20

High-End Computing Systems • High-resolution regional

climate models require massivecomputing power

• Access grids, data grids, andvisualization grids for tighter couplingbetween the large distributed climatecommunity and large but lessdistributed computing, storage, andvisualization resources, and to enableregional models to access globalclimate data

• Ensembles may be run over grids

Human Augmentation IT• Help humans “get their heads around”

huge data sets

Information Management• Access to and data warehousing, data

mining, and knowledge managementof multi-decade multi-disciplinary data sets

Intelligent Systems• Automated ways of diagnosing and

organizing data• Assess data across multiple disciplines• Multiple language access for the

international arena

IT System Design• Interoperability across diverse

communities and diverse platforms• Earth systems modeling framework

portable to major architectures

IT Usability• Interfaces that let users ranging from

climate scientists to decision makersinteract in ways that are natural to each group

IT Workforce• Decision makers need to use advanced

IT without becoming IT experts

Management of IT• Database intellectual property issues• Community moving toward open

source due to small size of high-endcomputing market

Networks• Sensor networks

Software Technologies• Enable diverse community of

researchers ranging from modelers todata managers to work together

Indications of ProgressAccuracy of predictive models

Technology transfer

Broad agreement in national andinternational communities

A viable economy in the 22nd centuryand beyond

21


Predict what ensembles of atoms andmolecules will do at the nanoscale

Assemble ensembles of atoms andmolecules into new devices

Focus in the Next Ten YearsSimulate from first principles thefundamental behavior of ensembles ofatoms and molecules

Design and manufacture molecular scale devices• Apply physical properties to grow

large quantities of self-organizingnanoscale materials by chemicalreactions (examples are nanotubes and nanowires)

• Fabricate these materials into useful devices

• Detect and correct faults in nanoscale materials

Nanoscale computers• Distributed control of processing

elements in nanoscale computers—themany small unsophisticated elementswill need to synchronize with eachother directly rather than have acentral clock

• Understand and apply nanoscale signaltransport to nanoscale computer I/O

BenefitsA second Industrial Revolution

Relationship to National Priorities Leadership in Science and Technology• Ability to predict nanoscale

behavior and manufacture reliablenanoscale devices

National and Homeland Security• “Smart dust”—simple nanoscale

sensors that blanket a battlefield tomonitor movement of troops orpathogens and enable commanders tobetter plan attacks

Health and Environment• New pharmaceutical drugs, new

microbes for environmentalremediation, etc.

Economic Prosperity• New materials and devices with

new magnetic properties, greaterstrength, etc.

4.6 NANOSCALE SCIENCE AND TECHNOLOGY: EXPLORE AND EXPLOIT THE BEHAVIOR OF ENSEMBLESOF ATOMS AND MOLECULES

22

IT Hard Problem AreasAlgorithms and Applications• Modeling and simulation to

understand the transition betweennanoscale and microscale behavior,which will enable the application ofnovel emergent nanoscale behavior todevices with 1,000 to one million atoms

Hardware Technologies• Nanoscale technologies• Denser storage

High-End Computing Systems• Nanoscale technologies applied to

high-end computing systems

Human Augmentation IT• Materials to augment human

capabilities (an example is clothingthat camouflages by changing color)

IT System Design• Continue to decrease component size

Networks• Development of sensor networks

Indications of ProgressNumber of atoms in nano-object whoseproperties can be predicted

Length of time that a nanoscale devicecan be simulated

Density of storage media

Development of designer materials andpharmaceuticals

23


Better understand how pollutants aretransported and transformed in theenvironment

Predict how pollutants reach people andhow they affect human health

Focus in the Next Ten YearsBetter understand the movement ofpollutants across boundaries (forexample, transfer through skin) and howpollutants are transformed in the body

Better predict the response of genes, cells,organs, and people to pollutants

BenefitsMore efficiently and effectively identifyand reduce health risks

Quicker approval for use of safe new compounds

Reduced need for laboratory animal testing

Relationship to National PrioritiesLeadership in Science and Technology• Advance scientific learning about

health risks and develop technologiesto address them

• Help maintain U.S. leadership ingenomics, toxicology, andenvironmental science

National and Homeland Security• Better respond to disasters such as

mass releases of dangerous chemicals

Health and Environment• Reduced environmental health risks

Economic Prosperity• Faster approval for materials

determined to be of low rather thanhigh risk (for example, a low-riskpesticide)

IT Hard Problem AreasAlgorithms and Applications• Modeling and simulation of the

environment, people, and theirinteractions

• Communication between simulations ofdifferent scales such as pollutant effectsin cells vs. organs

Complex Heterogeneous Systems• Environmental sensors need IT

advancements

High Confidence IT• Protecting the privacy of human

health data

4.7 PREDICTING PATHWAYS AND HEALTHEFFECTS OF POLLUTANTS

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High-End Computing• Grid computing• Make HEC systems more usable by

making codes run more efficiently ondifferent architectures

Human Augmentation IT• Visualize modeling and simulation

results to see patterns in complexdatasets

• Better collaboration among people ofdifferent disciplines and in differentlocations

Information Management• Holistic approach to different

disciplines and their data (alternative iscustom programming)

• Manage and store large data sets forlong periods of time so that the datacan be read decades from now

• Data mining to find new patterns ornew information (for example,examine health and environmentaldata to identify risks of exposure topollutants)

Intelligent Systems• Generate and represent new

knowledge to be shared and integratedacross disciplines

IT Usability• Human computer interfaces and

interactions with data collection tools(such as gene chip arrays), the datathey produce, and data analysis ofcomplex data sets

IT Workforce• Environmental scientists such as

biologists need to use advanced ITwithout becoming IT experts

• Interdisciplinary interactions betweenIT specialists such as computerscientists and non-IT specialists whodepend on IT such as biologists,chemists, ecologists, economists, andmeteorologists

Networks• Grid R&D to make distributed IT

resources easily usable by non-specialists

Software Technologies• Build systems of models (for example,

chemical, environmental, skin, andinside the body) that interact in well-defined manners

• Model development by non-softwareengineers

Indications of ProgressReduced health risks

Reduced cost and time to screenchemicals for harmful effects

Better predictions of sub-populations thatare sensitive to pollutants

25


Locate and assess the source and level ofnatural (earthquakes, hurricanes, etc.) orman-made (chemical, biological, andradiological hazards) threats, and respondrapidly to minimize loss of life andproperty

Focus in the Next Ten YearsHeating, ventilation, and air conditioning(HVAC) and water systems able to identifyand respond to meteorological, weather,chemical, biological, and radiologicalhazards

Networks of semi-autonomous robots forhazard removal

More accurate predictions of micro effectsof natural and man-made threats

BenefitsIncreased safety and security of theenvironment with reduced susceptibilityto threats

Safety and security of publicinfrastructure and physical systems suchas water supply, communications lines, airterminals, office buildings, etc.

Safer technologies for hazard removal

Relationship to National PrioritiesNational and Homeland Security• Minimize the impact of terrorists

threats or attacks

Health and the Environment• Safer environment

Economic Prosperity• Better quality of life• Fewer disruptions with less impact on

the economy

4.8 REAL-TIME DETECTION, ASSESSMENT, ANDRESPONSE TO NATURAL OR MAN-MADE THREATS

26

IT Hard Problem AreasAlgorithms and Applications• Modeling and simulation for:

Threat assessment, location, andresponsePredicting earthquakes, floods, tornados, etc.Complex physical systems in realtime (for example, airflow in anairport terminal)

Complex Heterogeneous Systems• Heterogeneous sensors, networks, and

computing systems• Distributed control of networks of

autonomous and semi-autonomousrobotic responders

High-End Computing• Faster architectures for demanding

modeling and simulation

Human Augmentation IT• Collaboration and visualization

technologies for responders

Information Management• Asynchronous collecting and

processing of large numbers ofindependent data streams

Intelligent Systems• Reasoning, cooperating robotic

responders

Networks• Deploy, manage, and monitor large-

scale dynamically reconfigurablenetworks of heterogeneous detectors

• Fault-tolerant sensors and robots,enabling systems to survive and recover

Indications of ProgressNetwork size and richness of topology

Mean time to detection, location, andresponse

Ability to model smaller-scale naturaldisasters (wind shear, for example)

27


Analyze long-term transportation needsand alternative solutions and their costs

Design, construct, operate, and maintainan integrated multi-modal transportationsystem that is safer, more secure, moreefficient, and has higher capacity than today

Focus in the Next Ten YearsFacilitate commuter and traveler time,cost, and safety

Analysis of long-term needs and costs (forexample, compare the cost of buildingand maintaining subway systems to thecost of building and maintaining highwaysystems and highway vehicles)

Intelligent vehicles that maintain safe distances

Automated highway systems that increase capacity

Intelligent passenger screening systems

City-wide timing of traffic lights to enablehigher capacity and fast, effective reactionto accidents

Synchronized scheduling of publictransportation systems (airplanes, trains,subways, and buses) so more people canget to their destinations faster

Airplanes controlled by pilots rather thanair traffic control centers, to increaseairspace and airport capacity

Faster trains (bullet and magneticlevitation)

Innovative transportation systems such as SegWay™, unmanned air vehicles(UAVs), and highly-automated personalair vehicles

Integrate modes (for example, subwaysthat go to train stations and airports withon-demand service)

Standards for software-centrictransportation systems to enable fastersafety and security certification

BenefitsTravelers save time and have increasedflexibility in scheduling travel

Governments expend fewer resources onbuilding, maintaining, and securing ourtransportation systems

Improved safety due to reliable andintelligent transportation system

Lower insurance rates as the systembecomes safer

Relationship to National PrioritiesHealth and Environment• Minimize the impact of vehicle

pollution on human health and theenvironment

• Decrease consumption of gas and oilEconomic Prosperity• Lower commercial and government

cost of moving people and goods• More productive economy due to less

time spent commuting and traveling

4.9 SAFER, MORE SECURE, MORE EFFICIENT, HIGHER-CAPACITY, MULTI-MODAL TRANSPORTATION SYSTEM

28

IT Hard Problem AreasAlgorithms and Applications• Modeling and simulation of current

and evolving transportation systems• Better optimization of transportation

Complex Heterogeneous Systems• Sensors and actuators embedded in

highway systems, vehicles, etc., tomaintain safe distances betweenvehicles of different sizes and undervarious weather conditions

High Confidence IT• Available, reliable, safe, secure

air traffic, highway, railway, andshipping systems

• Smart cards to authorize andauthenticate transportation systempersonnel

Information Management• Data mining of transportation

system information for increased safetyand security

Intelligent Systems• Intelligent vehicles

IT System Design• Integration of diverse transportation

systems

IT Usability• Address a vast range of operator and

user needs

Management of IT• Standards• Certification of systems and procedures

Networks• Reliable, secure mobile networks• Faster, smaller, lighter-weight sensor

networks

Software Technologies• Software for developing and operating

integrated transportation systems

Indications of ProgressReduced time and cost to validate andverify IT components of transportationsystems

Reduced time and cost to certify newtechnologies

Reduced time and cost for system-levelcertification and accreditation

29


Conduct scientific experiments tounderstand the broad politico-socio-economic-technical impact of increasedhuman use of constantly changing digitaltechnologies. These experiments canaddress questions such as:• What is the impact on people who

are left out?• Should digital participation be

universal?• What kind of technologies should

be deployed?• Does digital information work

everywhere?• What are the public policy

implications?

Focus in the Next Ten YearsLongitudinal studies of socio-technicaltransformation such as at Blacksburg(Virginia) Village

Map global social transformation (such asin homes, educational institutions,communities, and from e-business)

Understand intended and unintendedconsequences of a digital society

BenefitsPotential for all to participate

Better predict human behavior in adigital society and intended andunintended consequences

Maximize intended consequences such as enhanced economic productivity,and better, faster innovation andknowledge creation

Minimize unintended consequences such as the digital divide and personalidentity theft

Optimize societal transformations

Relationship to National PrioritiesLeadership in Science and Technology• Broader participation in science and

technology

National and Homeland Security• Strong social networks to enhance trust

and security

Health and Environment• New knowledge about healthcare

Economic Prosperity• Productive industries, e-commerce,

skilled workforce

A Well-Educated Populace• More people can use IT in

their learning• More on-line learning institutions

A Vibrant Civil Society• Tight communities with strong

social networks• On-line voting

4.10 ANTICIPATE CONSEQUENCES OF UNIVERSALPARTICIPATION IN A DIGITAL SOCIETY

30

IT Hard Problem AreasAlgorithms and Applications• Model digital societies and

transformations

Complex Heterogeneous Systems• Complexity and emergent behavior

may be similar in IT and social systems

High Confidence IT• Security, privacy, and trust

High-End Computing Systems• Universal participation will be over

the grid

Human Augmentation IT• Increase human sensory bandwidth, for

example by equipping into the ears ofthe blind to take in what eyes do

Information Management• Research in domains such as digital

libraries and museums is central

Intelligent Systems• Better care for more of the elderly

through a wide variety of remoteintelligent technologies (for example,smart homes and computers that talk)

IT System Design• Society will in part drive IT evolution

and the IT evolution will changesociety

IT Usability• Technologies to enable universal

participation

IT Workforce• Workforce skilled in analyzing

sociological impact of the transition toa digital society

Management of IT• Open source to help fulfill needs• Intellectual property and copyright

issues

Networks• Mobile networks to enhance

universal participation• Last mile problem• Planning that acknowledges that the

fabric of civil society and the fabric ofthe IT infrastructure are intertwined

Software Technologies• New programming methods that let

programmers design for propertiessuch as privacy or surveillance

• A science of software that obeysMoore’s Law, which states thatcomputing performance roughlydoubles every 18 months while chipsize, power, etc., remain constant

• Composability of software modulesdeveloped by different organizations

Indications of ProgressBetter anticipate consequences

Better understand how unintendedconsequences emerge and developremediation strategies

31


Understand how people, (software)agents, robots, and sensors (PARS)contribute to a collaboration

Understand the structural complexity ofPARS collaborations (for example, teams,networks, or hierarchies into which thePARS components can self-organize)

Design architectures in which PARScomponents self-organize for optimalconcerted social/physical/technologicalactions useful to society (for example,manage a crisis, perform surgery, or teachchildren)

Focus in the Next Ten YearsDistributed intelligence

Knowledge representation, management,fusion, and synthesis

Science of coordination (for example,centralized versus decentralizedorganization) and division of labor

Science of collaboration

Mixed human-computer initiative withadjustable autonomy. (For example, eithera person or a robot can start an action,but who is the decision maker? How doesone override the other? If a robot detectslife-threatening information, how does italert the human to flee?)

Understand and interpret implicit signals.(Computers have been explicit but humanshave affects (emotions) that they reveal infacial expressions or tone of voice, forexample. How do agents and robots readthese affects, and how do they responddifferently when conveying commands,suggestions, hints, or urgency?)

Empirical experiments (for example, how humans and robots interact in a crisisor disaster)

BenefitsAbility to rapidly convene coalitions torespond to crises or massive failures ofsystems such as the electrical power grid

Smart homes, hospitals, highways,classrooms, schools, etc.

Remote health care monitoring and care delivery

Dramatic increase in productivity of theservice economy

Manufacturing at the intersection of massproduction (making it cheaper, therebybenefiting the manufacturer) andcustomization (benefiting the consumer)

Relationship to National PrioritiesLeadership in Science and Technology• Distributed entities on the Internet or

the grid organized to collaborate (forexample, robots in Antarctica)

National Security• War fighters and information

technologies coordinate andcollaborate

Health and Environment• Health infrastructure (for example, fill

prescriptions on line)• Telemedicine (sensors in homes,

hospitals, diagnostic sensors, etc.)• Smart aids for the elderly

Economic Prosperity• Increase productivity along value chain

of manufacturers and service providers

4.11 COLLABORATIVE INTELLIGENCE: INTEGRATINGHUMANS WITH INTELLIGENT TECHNOLOGIES

32

A Well-Educated Populace• Pedagogical agents for individual

students or student teams• Collaborative learning environments• Lifelong learning• Integrated research and education• Teachers and students remotely control

instruments such as advancedtelescopes and microscopes

A Vibrant Civil Society• Distributed communities with

common interests

IT Hard Problem AreasAlgorithms and Applications• Model interactions between humans

and intelligent technologies

Complex Heterogeneous Systems• Implement interactions and decision

making among people, agents, robots,and sensors

• Control of interactions with physicalsystems

High Confidence IT• Data and information security

Human Augmentation IT• Augment human cognition and

augment reality with input fromagents, robots, and sensors

• Collaboration environments and tools

Information Management• Knowledge management for

distributed intelligence• Natural languages for communication

between humans and intelligenttechnologies

Intelligent Systems• Cognitive systems aware of context and

human affects

IT System Design• Self-organizing architectures• Interoperability

IT Usability• User interfaces developed from

knowledge of human behavior andhuman interaction with agents, robots,and sensors

IT Workforce• Train non-IT specialists to work with

agents, robots, and sensors

Management of IT• May need open source if humans

and robots write different softwarecomponents

Networks• Mobile networks (such as for crises

or disasters)• Reconfigurable networking to support

ad-hoc alliances• Reliability and scalability

Software Technologies• May need new programming

languages

Indications of ProgressTime savings

Improved outcomes

Better, faster scientific discovery

Achieve goals of larger scale in selecteddomains than are possible today

33


Rapidly and spontaneously retrieveaccurate insights:• Locate information from multiple text

sources, archived databases, imagearchives, and sensor streams for aperson or team solving a problem

• Identify and organize connectionsbetween disparate pieces ofinformation

• Validate or refute hypotheses andovercome human biases abouthypotheses

Focus in the Next Ten YearsAutomate the collection of metadata,which are data about data (for example,description of fields in a database andhow the data were collected andprocessed) as the data are collected

Develop taxonomies (classification in anordered system to indicate relationships)1

for information in different forms (text,images, video, time series, etc.)

BenefitsMore rapid decision making

Greater accuracy due to using multiplesources and points of view

Faster progress in science throughunderstanding the implications ofindividual findings and linking findings together

More national and internationalinterdisciplinary cooperation anddiscovery (for example, globally connectpeople who generated related results,identify researchers in similar fields)

Relationship to National PrioritiesLeadership in Science and Technology• More rapid scientific discovery across

disciplines

National and Homeland Security• Better insights into crises as

they happen• Assessment of intelligence data

Health and the Environment• Integrate public health data• Understand the ecosystem

Economic Prosperity• New products and processes and more

efficient supply chains due tounderstanding and analyzinginformation more efficiently

• Making good information more readilyavailable is essential to today’sInformation Economy

4.12 GENERATING INSIGHTS FROM INFORMATIONAT YOUR FINGERTIPS

34

__________________________________________________________________________________________

1 American Heritage Dictionary of the EnglishLanguage, Houghton Mifflin, 2000.

IT Hard Problem AreasComplex Heterogeneous Systems• Rapidly collect, analyze, and draw

conclusions about information frommultiple sources such as SCADAsystems and networks of sensors

High Confidence IT• Confidentiality of proprietary

information

Information Management• Comprehensive ability to find and

analyze information on disparatetopics, from disparate sources, and ofvarying quality

• Preservation of metadata

Intelligent Systems• Automated tools that either start with

an initial hypothesis and look forsupporting evidence or look foralternative hypotheses and find dataand information to support or refutesuch hypotheses

• Automated tools to analyzeinformation and identify causalrelationships

• Analyze and present information inmultiple languages

IT Usability• Interact with a wide variety of users

who have a wide variety of inquiriesand presentation preferences

Networks• Ad-hoc networking• Sensornets

Indications of ProgressStandard information retrieval metricssuch as precision (the proportion ofretrieved items that are relevant) andrecall (the proportion of relevant itemsthat are retrieved)

Time to perform a task

Ability to understand something better

Industry interest in adopting thesetechnologies to improve productivity

35


Establish management practices thatenable knowledge-intensive organizationsto use structured global collections ofknowledge to make complex decisionsthat result in rapid reconfiguration ofprocesses, and rescheduling andredeployment of resources, to respondquickly to changing circumstances

Maintain stability and achieve peakperformance in knowledge-intensiveorganizations characterized by uncertaintyand constant change

Focus in the Next Ten YearsSimulate knowledge-intensiveenvironments involving hundreds ofcomplex interacting agents

Validate simulations with realorganizational data (for example,instrument parts of organizations to see iftheory underlying simulations holds true)

Develop and evaluate new real-timeinformation systems for knowledge-intensive environments

Catalogue lessons learned to identify bestpractices for managing dynamicenvironments

BenefitsOrganizations function more smoothly

Best use of resources

Peak performance during times ofconstant change

Relationship to National PrioritiesNational and Homeland Security• Reengineer intelligence agencies to

handle uncertainty and change

Health and Environment• Help hospitals better respond to

emergencies

Economic Prosperity• Increase organizational productivity

IT Hard Problem AreasAlgorithms and Applications• Model change in knowledge-intensive

environments

Complex Heterogeneous Systems• Rapid reconfiguration and

rescheduling of human and machineresources

• Information sharing and distributeddecision-making in organizationalhierarchies

4.13 MANAGING KNOWLEDGE-INTENSIVEORGANIZATIONS IN DYNAMIC ENVIRONMENTS

36

High Confidence IT• Intelligence agencies need data and

information to be stored in a securefashion, retrieved in a timely manner,and transported safely and correctly.

High-End Computing Systems• Intelligence agencies have some of the

most demanding knowledge-management needs, which requirehigh-end computing.

Human Augmentation IT• Improve complex decision making

through augmented cognition andaugmented reality, collaboration, andvisualization of large knowledgecollections

Information Management• Scalable distributed processing

and storage

Intelligent Systems• Collaborative knowledge discovery,

retrieval, representation, andintegration to make inferences

• How do we best gather, represent, andshare knowledge about sources,designs, scheduling, customer profiles,process status, energy, and geo-politics?

IT System Design• Maintain system stability and

predictability when everything is in flux• Best mechanisms for negotiating

protocols (plug-and-play is still a dream)

IT Usability• User interfaces for displaying complex

structured knowledge

IT Workforce• Understand workflow rules (which may

be hidden)

Management of IT• Intellectual property issues• Open source issues

Networks• Reconfigure networks (people

everywhere need access to networkeddevices on the fly)

Software Technologies• This new area may require new

software languages, etc.

Indications of ProgressImproved decision making

Meet deadlines

Integrate and balance change and stability

Quicker reaction times

37


Develop computational models of howpeople acquire language • Use acquisition modeling, along with

empirical data, to understand firstlanguage acquisition by children andlater acquisition of other languages

• Use acquisition modeling to advanceunderstanding of the structure oflanguages and how they relate tohuman cognitive processes

Use best models of acquisition to develop tools• Use acquisition models to develop

courses and computerized tutoringsystems for language learning

• Apply knowledge from acquisitionmodels to develop advanced machinetranslation and automated languageinformation extraction

Focus for the Next Ten YearsDevelop and test partial models oflanguage acquisition phenomena

Merge partial models into a unifiedmodel of language acquisition

Experiment with different learningmodels (such as reinforcement,evolutionary, clustering, and supervised)to develop an overall natural languagelearning model

BenefitsBetter understanding of how people learnlanguages and similar skills

Better ways of teaching—bothautomatically and via individualizedhuman instruction

Systems to help immigrants acquireEnglish proficiency

Relationship to National PrioritiesLeadership in Science and Technology• Maintain U.S. leadership in linguistics,

cognitive psychology, and artificialintelligence

National and Homeland Security• Translation devices, language data

mining, language training, intelligence gathering

A Well-Educated Populace • Use both a first language and later

languages more effectively

A Vibrant Civil Society• Better human language

communication

4.14 RAPIDLY ACQUIRING PROFICIENCY INNATURAL LANGUAGES

38

IT Hard Problem AreasAlgorithms and Applications• Model how languages link to

knowledge and model learning of languages

Complex Heterogeneous Systems• Robots respond to natural

language input• Multi-language systems

Human Augmentation IT• Assist people who have learning

disabilities or linguistic handicaps• Assist people with problems in

acquiring non-native languages

Information Management• Create and manage large corpora of

latitudinal language learning data fortesting acquisition models

Intelligent Systems• Sharpen knowledge of machine

learning and how different types ofmachine learning can be combined tolearn languages

• Convert large unstructured humanlanguage databases to structureddatabases (for example, to enablesuccessful searching)

IT Usability• Computer interfaces that adapt

to user input

Indications of ProgressModels that show how the human worldview is structured by language and howlanguage structures world view

Better model-based systems for teaching languages

Improvements in information extractionfrom natural language data

Databases that track facts (what, why, how,when, where, who?)

Industry uptake of language acquisition models and the languagemodels they imply

39


Learn about our world and beyond byexploring what happens in interoperableplug-and-play learning modules thatsimulate various aspects of the universe• Modules have various levels of

expertise and assume various levels ofuser knowledge

• Can be used by people of all ages andexpertise

Focus in the Next Ten YearsInitial modules for biological systems (forexample, blood or digestive system),weather, and planetary system

Modules for grade school throughgraduate school and for lifelong learning

BenefitsMore effective learning environment

Inexpensive resources available to alllearning institutions

Evens out the education system across the country

Relationship to National PrioritiesLeadership in Science and Technology• Train the next generation of

researchers and workforce

Health and Environment• Simulation helps people better

understand the effects of disease andthe effects of pollutants in theenvironment

Economic Prosperity• A better educated citizenry contributes

more to society

A Well-Educated Populace• All of society benefits from educated

citizens

IT Hard Problem AreasAlgorithms and Applications• Simulation of all systems in the

universe

Human Augmentation IT• Visualizations of simulations

IT System Design• Robust self-evolving, self-maintaining,

interoperable modules

IT Usability• Interfaces for different levels of

expertise• Maximize the time spent interacting

with content, not with the interface

Software Technologies• End user programming so that non-

professional users can create their ownsimulations and modules

Indications of ProgressNumber of modules contributed toSimUniverse

Number of SimUniverse users

Improved standardized test scores

Increased number of masters degrees andPhDs in science and technology

New scientific discoveries

Increased quality in scholarly papers

4.15 SIMUNIVERSE: LEARNING BY EXPLORING

40

41

4.16 VIRTUAL LIFETIME TUTOR FOR ALL


A personal tutor that understands what auser knows and does not know, providesjust-in-time tutoring as needed, adapts toa user’s learning style and knowledgelevel, and is initiated by either the user orthe tutor

Focus in the Next Ten YearsEmploy a model of the user (strengths,weaknesses, preferred learning methods)and machine learning to tailor a general-purpose tutor for a variety of knowledgedomains and expertise that can adapt tothe user’s capabilities and learning needsfrom birth

Identify and begin work on topics such as:• Practical how-to training for the

workforce. For example, computerskills for novices to experienced users,learning new software languages bybuilding on languages one alreadyknows, or a refresher to maintain skills.

• Foreign languages. For example,develop a tutor that can teach Spanishto people ranging in age from 3 to 103.

BenefitsA better educated populace

Customized learning environments at lower cost

Relationship to National PrioritiesEconomic Prosperity • Just-in-time training

A Well-Educated Populace• Better educated workforce

A Vibrant Civil Society • All of society benefits from

educated citizens

IT Hard Problem AreasAlgorithms and Applications• User modeling

Human Augmentation IT• Context-aware information delivery

Information Management• Adding in new content modules

Intelligent Systems• Understand human cognition• Human language technology

IT System Design • Evolve the tutor to new platforms with

little user involvement

IT Usability• Adaptable user interfaces for different

levels of user expertise

Indications of ProgressImproved productivity

Increased participation in communityactivities

Increased understanding of other culturesand societies

Increased return on investment foreducation funding

APPENDICES

For each IT hard problem area, the GrandChallenges Task Force identified one or moreillustrative IT hard problems. Progresstoward the grand challenges will requirebreakthroughs or solutions to these IT hardproblems. Specific examples are given in thedescription of the grand challenges inchapter 4.

Algorithms and ApplicationsModeling and simulation

Complex Heterogeneous SystemsAdaptive scheduling and control

Complex systems/emergent behavior

Control of physical systems includingscientific experiments and SCADAsystems

Distributed decision making

Embedded systems including actuators,sensors, and MEMS

Robotics

Hardware TechnologiesBiological technologies

Nanoscale technologies

New mass storage technologies

Quantum technologies

High Confidence ITData and information security

High confidence middleware

High confidence open source

Reliability

Safety

Security including authorization,authentication, biometrics, certification,encryption, interfaces, and protocols

Software assurance

High-End Computing SystemsGrid computing

High-end computing architectures,systems software, and applicationssoftware

Use of biological, nanoscale, andquantum technologies in high-endcomputing systems

Human Augmentation ITAugmented cognition and augmentedreality

Collaboration technologies

Visualization

APPENDIX 1: ILLUSTRATIVE IT HARD PROBLEMS CATEGORIZEDBY IT HARD PROBLEM AREAS

44

Information ManagementAsynchronous collection and processingof independent data streams

Coherent databases developed fromdistributed data of varying quality

Data and information management

Data mining and data warehousing

Distributed processing and storage

Metadata creation and use

Preservation

Intelligent SystemsCognitive systems

Context-aware computing and autonomicnetworks to add more intelligence to ITsystems

Human language technology

Knowledge discovery, representation, and integration

IT System DesignArchitecture

Graceful evolution

Hardware/software co-design

Interoperability

Preservation

IT UsabilityHuman/computer interaction includinguser interfaces

Universal accessibility

IT WorkforceAdvanced IT for non-IT specialists

Interdisciplinary interaction

IT workforce issues

Management of ITIntellectual property issues

Open source issues

Standards

Technology transfer

NetworksAd-hoc networking/reconfigurablenetworking

Grid

Mobility

Network middleware

Networking management, reliability, andscalability

Sensor networks

Software TechnologiesProgramming environments

Programming languages

Software requirements engineering,software development methods and tools,and software engineering

Systems software and middleware

45

NSFAerospace• Coupled field problems

Computer Science• Machine learning• Parallel input/output (I/O) methods for

I/O-intensive grand challenges

Environmental Modeling and Prediction• Large-scale environmental modeling• Adaptive coordination of results of

predictive models with experimentalobservations

• Earthquake ground motion modelingin large basins

• High-performance computing for landcover dynamics

• Massively parallel simulation of large-scale, high-resolution ecosystem

Molecular Biology and BiomedicalImaging• Biomolecular design• Imaging in biological research• Advanced computational approaches to

biomolecular modeling and structuredetermination

• Understanding human joint mechanics through advancedcomputational models

Product Design and Process Optimization• High-capacity atomic-level simulations

for the design of materials

Space Science• Black hole binaries: coalescence and

gravitational radiation• Formation of galaxies and large-

scale structure• Radio synthesis imaging

DOE/SCEnergy• Mathematical combustion modeling• Quantum chromodynamics calculations• Oil reservoir modeling• Numeral Tokamak project

Environmental Monitoring andPrediction• Computational chemistry• Global climate modeling• Groundwater transport and

remediation

Molecular Biology and Biomedical Imaging• Computational structural biology

Product Design and Process Optimization• First-principles simulation of

materials properties

APPENDIX 2: THE HPCC PROGRAM’S GRAND CHALLENGES2

46

__________________________________________________________________________________________

2 The list of the HPCC Program’s grandchallenges appears in “Evolving the HighPerformance Computing and CommunicationsInitiative to Support the Nation’s InformationInfrastructure,” Computer Science andTelecommunications Board, National ResearchCouncil, National Academy Press, Washington,D.C., 1995.

47

NASALarge-scale structure and galaxyformation

Cosmology and accretion astrophysics

Convective turbulence and mixing inastrophysics

Solar activity and heliospheric dynamics

NIHMolecular biology

Biomedical imaging

NISTProduct design and process optimization

EPALinked air and water quality modeling

NOAAClimate change prediction and weather forecasting

48

Knowledge Environments for Science and Engineering—C.S. Iacono (NSF), W. Bainbridge (NSF)

Clean Energy Production Through Improved Combustion—C. Romine (DOE/SC)

High Confidence Infrastructure ControlSystems—G. Koob (DARPA), H. Gill (NSF)

Improved Patient Safety and Health Quality—J.M. Fitzmaurice (AHRQ)

Informed Strategic Planning for Long-Term Regional Climate Change—W. Turnbull (NOAA)

Nanoscale Science and Technology:Explore and Exploit the Behavior ofEnsembles of Molecules and Atoms—C. Romine (DOE/SC)

Predicting Pathways and Health Effects of Pollutants—S. Fine (EPA)

Real-Time Detection, Assessment, andResponse to Natural and Man-MadeThreats—C. Romine (DOE/SC)

APPENDIX 3: NITRD GRAND CHALLENGES AUTHORS ANDCONTRIBUTORS

Safer, More Secure, More Efficient, Higher Capacity Multi-ModalTransportation System—E. Lucier (FAA)

Anticipate the Consequences of UniversalParticipation in a Digital Society—C.S. Iacono (NSF), W. Bainbridge (NSF)

Collaborative Intelligence: IntegratingHumans with Intelligent Technologies—C.S. Iacono (NSF), W. Bainbridge (NSF)

Generating Insights From Information atYour Fingertips—M. Pazzani (NSF)

Managing Knowledge Intensive Dynamic Systems—C.S. Iacono (NSF), W. Bainbridge (NSF)

Rapidly Acquiring Proficiency In NaturalLanguages—L. Reeker (NIST)

SimUniverse: Learning by Exploring—J. Scholtz (NIST)

Virtual Lifetime Tutor for All—J. Scholtz (NIST)

IT Hard Problems Sub-Task Group Chair—B. Wheatley (NSA)

49

AHRQJ. Michael Fitzmaurice, Ph.D., FACMISenior Science Advisor for InformationTechnology, Immediate Office of the Director,Agency for Healthcare Research and Quality540 Gaither Road, Suite 3026Rockville, MD 20850(301) 417-1227FAX: (301) 427-1210

DARPAGary M. Koob, Ph.D.Program Manager, Information ProcessingTechnology Office, Defense Advanced ResearchProjects Agency3701 North Fairfax DriveArlington, VA 22203-1714(703) 696-7463FAX: (703) 696-4534

DOE/SCDaniel A. Hitchcock, Ph.D.Senior Technical Advisor for Advanced ScientificComputing Research, Office of AdvancedScientific Computing Research (OASCR),Department of Energy/Office of ScienceOASCR, SC-30Germantown Building1000 Independence Ave., S.W.Washington, D.C. 20585-1290(301) 903-6767FAX: (301) 903-4846

Charles Romine, Ph.D.Program Manager, Mathematical, Information,and Computational Sciences, (MICS) Division,Office of Advanced Scientific Computing Research(OASCR), Department of Energy/Office of ScienceOASCR/MICS, SC-31Germantown Building1000 Independence Avenue, S.W.Washington, D.C. 20585-1290(301) 903-5152FAX: (301) 903-7774

EPASteven Fine, Ph.D.Networking and Information Technology R&DProgram Manager, Environmental ProtectionAgency MD 243-01Research Triangle Park, NC 27711(919) 541-0757FAX: (919) 541-1379

FAAErnest R. LucierAdvisor on High Confidence Systems, FederalAviation AdministrationFAA/AIO-4, 800 Independence Ave., S.W.Washington, D.C. 20591(202) 366-0633FAX: (202) 366-3064

APPENDIX 4: NITRD GRAND CHALLENGES TASK FORCECONTACT INFORMATION

NOTE: The contact information provided in this appendix was current as of the document’s initial publication in November 2003 and may have changed.

Marshall Potter, Ph.D.Chief Scientist for IT, Federal AviationAdministrationFAA/AIO-4, 800 Independence Ave., S.W.Washington, D.C. 20591(202) 267-9878FAX: (202) 267-5080

NCO/IT R&DEdward L. GarciaContract Program Manager, NationalCoordination Office for Information TechnologyResearch and DevelopmentSuite II-405, 4201 Wilson BoulevardArlington, VA 22230(703) 292-4873FAX: (202) 292-9097

Helen Gigley, Ph.D.HCI&IM, HCSS, and SDP Liaison, NationalCoordination Office for Information TechnologyResearch and DevelopmentSuite II-405, 4201 Wilson BoulevardArlington, VA 22230(703) 292-4504FAX: (202) 292-9097

Sally E. Howe, Ph.D.Associate Director, National Coordination Officefor Information Technology Research andDevelopmentSuite II-405, 4201 Wilson BoulevardArlington, VA 22230(703) 292-7923FAX: (202) 292-9097

Frankie D. KingSpecial Projects Liaison, National CoordinationOffice for Information Technology Research andDevelopmentSuite II-405, 4201 Wilson BoulevardArlington, VA 22230(703) 292-7920FAX: (202) 292-9097

Martha MatzkeSenior Technical Writer, National CoordinationOffice for Information Technology Research andDevelopmentSuite II-405, 4201 Wilson BoulevardArlington, VA 22230(703) 292-7926FAX: (202) 292-9097

50

51

Grant Miller, Ph.D.LSN Liaison, National Coordination Office forInformation Technology Research andDevelopmentSuite II-405, 4201 Wilson BoulevardArlington, VA 22230(703) 292-7928FAX: (202) 292-9097

David B. Nelson, Ph.D.Director, National Coordination Office forInformation Technology Research andDevelopmentSuite II-405, 4201 Wilson BoulevardArlington, VA 22230(703) 292-4873FAX: (202) 292-9097

NIHMichael Marron, Ph.D.Director, Biomedical Technology, National Centerfor Research Resources, National Institutes ofHealthOne Rockledge Center6705 Rockledge Drive, Room 6160Bethesda, MD 20892-7965(301) 435-0753FAX: (301) 480-3659

NIST Judith Devaney, Ph.D.Group Leader, Scientific Applications andVisualization Group, Mathematical andComputational Sciences Division, InformationTechnology Laboratory, National Institute ofStandards and Technology 100 Bureau Drive, Stop 8911Gaithersburg, MD 20899-8911(301) 975-2882FAX: (301) 975-3218

Larry Reeker, Ph.D.Senior Computer Scientist, InformationTechnology Laboratory, National Institute ofStandards and Technology100 Bureau Drive, Stop 8911Gaithersburg, MD 20899-8911(301) 975-5147FAX: (301) 946-1784

Jean Scholtz, Ph.D.Information Access Division, InformationTechnology Laboratory, National Institute ofStandards and Technology100 Bureau Drive, Stop 8911Gaithersburg, MD 20899-8911(301) 975-2520FAX: (301) 975-5287


NOAAWilliam T. TurnbullDeputy Chief Information Officer and Director,HPCC Office, National Oceanic and AtmosphericAdministrationRoom 96361325 East-West HighwaySilver Spring, MD 20910(301) 713-9600 x133FAX: (301) 713-4040

NSAGeorge CotterOffice of Corporate Assessments, National Security Agency9800 Savage Road, Suite 6217Fort George G. Meade, MD 20755-6217(301) 688-6434FAX: (301) 688-4980

Barbara Wheatley, Ph.D.Office of Corporate Assessments, National Security Agency9800 Savage Road, Suite 6217Fort George G. Meade, MD 20755-6217(301) 688-8448FAX: (301) 688-4980

NSFFrank Anger, Ph.D.Deputy Division Director and Program Director,Software Engineering and Languages, Computer-Communications Research Division, Directoratefor Computer and Information Science andEngineering, National Science Foundation 4201 Wilson Boulevard, Suite 1145Arlington, VA 22230(703) 292-8911FAX: (703) 292-9059

William S. Bainbridge, Ph.D.Deputy Division Director and Program Director,Information and Intelligent Systems Division,Directorate for Computer and Information Scienceand Engineering, National Science Foundation4201 Wilson Boulevard, Suite 1115Arlington, VA 22230(703) 292-8930FAX: (703) 292-9073

Helen Gill, Ph.D.Program Director, Computer-CommunicationsResearch Division, Directorate for Computer andInformation Science and Engineering, NationalScience Foundation4201 Wilson Boulevard, Suite 1145Arlington, VA 22230(703) 292-8910FAX: (703) 292-9059

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53

C. Suzanne Iacono, Ph.D.Program Director, Information TechnologyResearch (ITR), Directorate for Computer andInformation Science and Engineering, NationalScience Foundation4201 Wilson Boulevard, Suite 1145Arlington, VA 22230(703) 292-8930FAX: (703) 292-9073

Stephen R. MahaneySenior Advisor for Budget, Management, andPlanning, Directorate for Computer andInformation Science and Engineering, NationalScience Foundation4201 Wilson Boulevard, Suite 1145Arlington, VA 22230(703) 292-8900FAX: (703) 292-9074

Michael Pazzani, Ph.D.Division Director, Information and IntelligentSystems Division, Directorate for Computer andInformation Science and Engineering4201 Wilson Boulevard, Suite 1145Arlington, VA 22230(703) 292-8930FAX: (703) 292-9073

George O. Strawn, Ph.D. (GrandChallenges Task Force Chair)Chief Information Officer, National ScienceFoundation4201 Wilson Boulevard, Suite 1145Arlington, VA 22230(703) 292-8102FAX: (703) 292-9084

ODDR&ESteven E. King, Ph.D.Special Advisor for Critical InfrastructureProtection, Information Systems Directorate,Office of the Deputy Undersecretary of Defense(Science and Technology), Department of Defense1777 North Kent Street, Suite 9030Rosslyn, VA 22209(703) 588-7414FA: (703) 588-7560

OSTPSimon Szykman, Ph.D.National Science and Technology CouncilEisenhower Executive Office Building1650 Pennsylvania Avenue, N.W.Washington, D.C. 20502-0001(202) 456-6054FAX: (202) 456-6021


AHRQ. . . . . . . . . . . . . . . . . . . . . . Agency for Healthcare Research and Quality

BIRN. . . . . . . . . . . . . . . . . . . . . . . Biomedical Informatics Research Network

CAFE . . . . . . . . . . . . . . . . . . . . . . Corporate Average Fuel Economy

DARPA . . . . . . . . . . . . . . . . . . . . . Defense Advanced Research Projects Agency

DoD . . . . . . . . . . . . . . . . . . . . . . . Department of Defense

DOE/SC . . . . . . . . . . . . . . . . . . . . Department of Energy/Office of Science

EPA . . . . . . . . . . . . . . . . . . . . . . . . Environmental Protection Agency

FAA . . . . . . . . . . . . . . . . . . . . . . . . Federal Aviation Administration

FACMI . . . . . . . . . . . . . . . . . . . . . Fellow of the American College of Medical Informatics

Gigabits. . . . . . . . . . . . . . . . . . . . . billions of bits

HCI&IM . . . . . . . . . . . . . . . . . . . Human-Computer Interaction and Information Management

HEC . . . . . . . . . . . . . . . . . . . . . . . High End Computing

HPCC . . . . . . . . . . . . . . . . . . . . . . High Performance Computing andCommunications

HVAC . . . . . . . . . . . . . . . . . . . . . . heating, ventilation, and air conditioning

IT . . . . . . . . . . . . . . . . . . . . . . . . . Information Technology

ITR . . . . . . . . . . . . . . . . . . . . . . . . Information Technology Research

IWG/IT R&D . . . . . . . . . . . . . . . . Interagency Working Group onInformation Technology Research and Development

LSN. . . . . . . . . . . . . . . . . . . . . . . . Large Scale Networking

Moore’s Law . . . . . . . . . . . . . . . . . computing performance roughly doubles every 18 months while chip size, power, etc., remain constant

MTTR. . . . . . . . . . . . . . . . . . . . . . mean time to recovery

NCO/IT R&D. . . . . . . . . . . . . . . . National Coordination Office forInformation Technology Research and Development

NEES . . . . . . . . . . . . . . . . . . . . . . Network for Earthquake Engineering Simulation

APPENDIX 5: ACRONYMS AND GLOSSARY

54

NIH. . . . . . . . . . . . . . . . . . . . . . . . National Institutes of Health

NIST . . . . . . . . . . . . . . . . . . . . . . . National Institute of Standards and Technology

NITRD . . . . . . . . . . . . . . . . . . . . . Networking and Information TechnologyResearch and Development

NOAA . . . . . . . . . . . . . . . . . . . . . . National Oceanic and AtmosphericAdministration

NOx . . . . . . . . . . . . . . . . . . . . . . . Ozone

NSA. . . . . . . . . . . . . . . . . . . . . . . . National Security Agency

NSF . . . . . . . . . . . . . . . . . . . . . . . National Science Foundation

NSTC . . . . . . . . . . . . . . . . . . . . . . National Science and Technology Council

NVO . . . . . . . . . . . . . . . . . . . . . . . National Virtual Observatory

ODDR&E . . . . . . . . . . . . . . . . . . . DoD’s Office of the Director, DefenseResearch and Engineering

OSTP . . . . . . . . . . . . . . . . . . . . . . White House Office of Science and Technology Policy

PARS . . . . . . . . . . . . . . . . . . . . . . people, agents, robots, and sensors

R&D . . . . . . . . . . . . . . . . . . . . . . . Research and Development

SCADA . . . . . . . . . . . . . . . . . . . . . Supervisory Control and Data Acquisition

SDP . . . . . . . . . . . . . . . . . . . . . . . Software Design and Productivity

SPARC . . . . . . . . . . . . . . . . . . . . . Space Physics and Aeronomy ResearchCollaboratory

Teraops . . . . . . . . . . . . . . . . . . . . trillions of operations per second

UARC . . . . . . . . . . . . . . . . . . . . . Upper Atmospheric Research Collaboratory

UAV. . . . . . . . . . . . . . . . . . . . . . . . Unmanned Air Vehicle

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GRAND CHALLENGES TASK FORCE CHAIRGeorge O. Strawn, NSF

EXECUTIVE EDITORSally E. Howe, NCO/IT R&D

EDITORFrankie D. King, NCO/IT R&D

A C K N O W L E D G E M E N T SThis booklet is designed to share with the broader

scientific community and the general public insightsinto the formulation of the Networking and Infor-mation Technology Research and Development’s(NITRD) grand challenges. Many people over manymonths contributed to the broader grand challengesactivity that made possible this booklet’s printing.

First, the Task Force Chair and Editors extend aspecial thanks to the members of the 2003 GrandChallenges Task Force who dedicated countless hoursabove and beyond their normal workload. Their tirelesscontributions are reflective of their commitment, notonly to this booklet, but also to the advancement ofscience and technology.

Thanks also goes to Nicole Ausherman of Noesis Inc.for creating the booklet’s design, structuring its layout,and overseeing the administration of its printing.

Finally, the Editors thank their NCO colleagues,particularly Helen Gigley, LaShante Jenkins, MarthaMatzke, and Grant Miller, for their contributions tocritiquing, editing, and proofreading the booklet.

C O P Y R I G H TThis is a work of the U.S. Government and is in the

public domain. It may be freely distributed and copied,but it is requested that the National Coordination Officefor Networking and Information Technology Researchand Development (NCO/NITRD) be acknowledged.

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