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Information Technology Innovation: Resurgence, Confluence, and Continuing Impact (2020)

Chapter: Appendix B: Research Developments and Impacts Depicted in Figure 2.1

« Previous: Appendix A: Biographical Information for Committee and Panel Members
Suggested Citation:"Appendix B: Research Developments and Impacts Depicted in Figure 2.1." National Academies of Sciences, Engineering, and Medicine. 2020. Information Technology Innovation: Resurgence, Confluence, and Continuing Impact. Washington, DC: The National Academies Press. doi: 10.17226/25961.
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data science. Dr. Rus is a Class of 2002 MacArthur fellow; a fellow of ACM, AAAI, and IEEE; and a member of the National Academy of Engineering and the American Academy of Arts and Sciences. She earned her Ph.D. in computer science from Cornell University.

BART SELMAN is a professor of computer science at Cornell University. Dr. Selman previously was at AT&T Bell Laboratories. His research interests include computational sustainability, efficient reasoning procedures, planning, knowledge representation, and connections between computer science and statistical physics. Dr. Selman has authored or co-authored over 100 publications, and has received six best paper awards. His papers have appeared in venues spanning Nature, Science, Proceedings of the National Academy of Sciences, and a variety of conferences and journals in AI and computer science. Dr. Selman has received the Cornell Stephen Miles Excellence in Teaching Award, the Cornell Outstanding Educator Award, an NSF Career Award, and an Alfred P. Sloan Research Fellowship. He is a fellow of the AAAI and a fellow of the AAAS.

Suggested Citation:"Appendix B: Research Developments and Impacts Depicted in Figure 2.1." National Academies of Sciences, Engineering, and Medicine. 2020. Information Technology Innovation: Resurgence, Confluence, and Continuing Impact. Washington, DC: The National Academies Press. doi: 10.17226/25961.
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B
Research Developments and Impacts Depicted in
Figure 2.1


Research Area Example Origins Significance/Impact
Networking, Communications Local area networking Wireless packet networks (University of Hawaii, 1971). Ethernet invention at Xerox PARC (1974). 3COM founded 1979 and first product 1981. First ethernet standard 1980. Faster standards and products in the 1990s and 2000s. Successive generations of local area networks connect computers at ever-higher bandwidth.
TCP/IP protocols and algorithms Packet switching (RAND, 1961), ARPANET launch and first deployment (1967-1969). TCP common reliable transport protocol for the Internet (1970s), standard in 1983. van Jacobsen congestion control incorporated 1988 as part of the Berkeley Software Distribution (BSD). Many further advances including fast retransmission (Tahoe and Reno congestion control algorithms,1988-1989), maximum transmission unit discovery (around 1999), random early detection (University of California, Berkeley, 1993), binary increase congestion (added to Linux kernel 2004). Reliable data transport over the Internet. Congestion management.
Suggested Citation:"Appendix B: Research Developments and Impacts Depicted in Figure 2.1." National Academies of Sciences, Engineering, and Medicine. 2020. Information Technology Innovation: Resurgence, Confluence, and Continuing Impact. Washington, DC: The National Academies Press. doi: 10.17226/25961.
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Research Area Example Origins Significance/Impact
  Content delivery networks Early foundations developed in universities, contributing algorithms, systems concepts, and people to companies such as Akamai (1998). A fundamental part of how the Internet works, essential to provision of services such as YouTube, news sites, and many others.
MIMO Academic research in the 1970s. Bell Labs research 1970s-1990s. Stanford research commercialized at Clarity Wireless. Multiple-input, multiple-output orthogonal frequency-division multiplexing 1990s for high bandwidth. IEEE 802.11.n, WiMAX, and 4G LTE standards in the 2000s. Exploiting multipath to send multiple signals. A fundamental enabler in today’s wireless communications, including Wi-Fi, WiMax, 4G, LTE, and others.
Radio-frequency complementary metal-oxide semiconductor integrated circuits Basic research and some serious engineering efforts at Bell Labs and later at universities throughout the 1990s. Widely adopted commercially in the late 1990s. The foundational device technology for Wi-Fi, Global Positioning System (GPS), Bluetooth, and others.
Low-latency switching Origins in academic and industry research of 1980s in massively multiprocessing computers (e.g., BBN, Massachusetts Institute of Technology [MIT], Intel Corporation), single chip CMOS switches with cut-through routing widely used in parallel machines, took on independent function with ATM—asynchronous transfer mode (Digital Equipment Corporation’s [DEC’s] AutoNet, 1990), start-ups (Myrinet, 1994), merged with storage Area networks (Infiniband, 2001) overcoming network interface overheads. A basis for multigigabit ethernet switching widely used in modern cloud data centers.
Suggested Citation:"Appendix B: Research Developments and Impacts Depicted in Figure 2.1." National Academies of Sciences, Engineering, and Medicine. 2020. Information Technology Innovation: Resurgence, Confluence, and Continuing Impact. Washington, DC: The National Academies Press. doi: 10.17226/25961.
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Research Area Example Origins Significance/Impact
  Software-defined networking Origins in academic research. Started with NSF’s Active Networks program 1990s. Continued with NSF’s Future Internet Design program 2000s, PlanetLab (launched by researchers at Princeton University, University of California, Berkeley, and Intel Corp.), the Clean-Slate Security Architecture and its Ethane implementation, the 2008 OpenFlow switch interface and NOX software-defined network controller (2011, Open Networking Foundation). Stanford University and University of California, Berkeley researchers found Nicira (2007). Separation of control plane from data plane and defining control plan abstraction.
Academic research networks leading to commercial Internet ARPANET (1969), CSNET (1981), NSFNET (1986), commercial backbone (1990). The Internet Engineering Task Force working group process started in academia formalized as RFC 2418 (1998). Origin of technologies and standards processes of the Internet.
Systems, Architecture Supercomputers First massively parallel computer designed at Illinois with Advanced Research Projects Agency support starting 1964, built by Burroughs and connected to ARPANET in 1975. C.mmp developed at Carnegie Mellon University (1971-1972). IBM RP3 built in collaboration with New York University (1988-1991). Highly successful Cray-1 launched 1976. First exaflop computer, Summit at ORNL (2014-2018) built by IBM, NVIDIA, and Mellanox, uses Infiniband interconnect. Big machines custom-built for scientific research, engineering, and national security applications.
Suggested Citation:"Appendix B: Research Developments and Impacts Depicted in Figure 2.1." National Academies of Sciences, Engineering, and Medicine. 2020. Information Technology Innovation: Resurgence, Confluence, and Continuing Impact. Washington, DC: The National Academies Press. doi: 10.17226/25961.
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Research Area Example Origins Significance/Impact
  Timesharing Suggested as early as 1954, first realized in CTSS (MIT, 1959), systems to let multiple users interact concurrently with one shared computer. In the 1960s and 1970s, timesharing services were offered with access on terminals via dial-up (telephone modems) including Dartmouth Time Sharing System sold by General Electric Company (1965). Eventually, all computers were designed to support timesharing, and most operating systems (even those run by a single user) were able to run multiple tasks concurrently. Timesharing tracked the decline in computer size from mainframe, to minicomputer, to workstation, to personal computer. But timesharing as a service declined sharply as personal computers took hold (starting in 1982). A key insight from timesharing experience was the value of a shared file system in supporting collaboration among a system’s users.
Unix, Linux, and related operating systems Inspiration from Multics (Project MAC, MIT, 1964). Unix started at Bell Labs (1969), licensed to universities 1973, implemented on Vax 1978. University of California, Berkeley BSD started 1975, first BSD distribution 1978. BSD used for SunOS (1982). Linux development started in 1991. Mach kernel developed at Carnegie Mellon University starting 1985 is a basis for NeXTSTEP (1988), Mac OS X (2000), and iPhone OS (2007). Basis for widely used operating systems today.
Virtual memory Early academic work at Manchester University commercialized by Atlas Computer (1959-1961). Early implementations: Burroughs B5000 (1961), Project Genie (University of California, Berkeley, 1964-1965), Motorola 68010 processor (1982). Creates an illusion of much larger main memory, simplifying software development.
Suggested Citation:"Appendix B: Research Developments and Impacts Depicted in Figure 2.1." National Academies of Sciences, Engineering, and Medicine. 2020. Information Technology Innovation: Resurgence, Confluence, and Continuing Impact. Washington, DC: The National Academies Press. doi: 10.17226/25961.
×
Research Area Example Origins Significance/Impact
  Networked workstations By the mid-1970s, semiconductor memory was sufficiently inexpensive that small yet powerful computers could be constructed. These “workstations” were personal computers, each with a keyboard, a display, disk storage. These were not just minicomputers because they were connected to each other and the ARPANET and Internet via a local area network, such as Ethernet (among others). Operating software for these machines used the network to access services and resources not available in a workstation, e.g., large file stores, printers, programs running on bigger machines, and of course the wide area network. Key milestones include the founding of Apollo Computer (1980), the Xerox Alto (1973), and Xerox Star (1980), the founding of SUN Microsystems by a Stanford student (1982), commercialization of MIT Lisp machines (1980-1981), the IBM PC (1981, not initially networked), and the Macintosh (1984). Networked workstations were a prototype for personal computers of today; costing perhaps $20,000 in 1975, today’s equivalent would be less than $1,000. The systems, applications, and services developed for networked workstations built on and drove the expansion of ubiquitous networking.
VLSI architecture research Initially developed as a way for California Institute of Technology (Caltech) students to design, fabricate, and test small integrated circuits. It was then scaled up to include a textbook (Mead and Conway’s Introduction to VLSI Systems, 1980), a collection of university teachers and courses, and research projects investigating new CAD tools and new digital architectures. A fabrication service, prototyped at Xerox PARC, then scaled up and supported at the University of Southern California Information Sciences Institute’s Metal Oxide Semiconductor Implementation Service (MOSIS), which initially served the ARPA research community, but eventually became broadly available for research. Several ambitious and innovative chips were designed by university teams and formed the basis for commercial products, e.g., reduced instruction set computers (RISC), and the Geometry Engine and enabled academic research exploration of new architectures. Innovations in CAD software tools developed in universities influenced or transferred to commercial CAD products.
Suggested Citation:"Appendix B: Research Developments and Impacts Depicted in Figure 2.1." National Academies of Sciences, Engineering, and Medicine. 2020. Information Technology Innovation: Resurgence, Confluence, and Continuing Impact. Washington, DC: The National Academies Press. doi: 10.17226/25961.
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Research Area Example Origins Significance/Impact
  Reduced instruction set computers (RISC) The major advance in reduced instruction set computers (RISC) was the result of two ARPA-funded research projects inspired by IBM 801 research processor design (1975-1980): Berkeley RISC (University of California, Berkeley, 1980-1984) and MIPS (Stanford University, 1981-1984). The designs were based on analysis of machine code executed by a collection of benchmark applications. Prototype microprocessor chips for these designs were designed by the university teams, fabricated using the MOSIS multiproject chip fab service, then tested and analyzed by the universities. MIPS founded 1984 and MIPS R2000 released 1985. Berkeley RISC influenced design of the SPARC instruction set (1987). Stanford and Berkeley designs influence DEC Alpha processor (1992) and the RISC-V open standard (2010). Both research projects resulted in commercial chips: Berkeley RISC was the basis for the design of the Sun Microsystems SPARC architecture, and the Stanford design transferred, with key leaders, to the founding of MIPS Computer Systems in 1984. Research would ultimately inspire the ARM architecture widely used in smartphones and other applications.
Multiprocessors MIPS processors at Stanford University, early 1990s. SPARC processors from late 1980s-early 1990s Sun Microsystems-Xerox PARC collaboration used in a series of Sun Microsystems multiprocessor products. Early 1990s academic and industry development of message passing interface and research on loosely coupled clusters (MIT, 1990s) influenced Beowulf clusters starting at National Aeronautics and Space Administration (NASA) in 1994. Development of processor-to-processor interconnect protocols and cache coherence algorithms enabled high-performance multicomputer based on commodity components.
Suggested Citation:"Appendix B: Research Developments and Impacts Depicted in Figure 2.1." National Academies of Sciences, Engineering, and Medicine. 2020. Information Technology Innovation: Resurgence, Confluence, and Continuing Impact. Washington, DC: The National Academies Press. doi: 10.17226/25961.
×
Research Area Example Origins Significance/Impact
  Virtualization Virtualization started with IBM work in the late 1960s. It was reborn with Stanford University research in the late 1990s and the founding of VMware (1998). Academic hypervisor research in the late 1990s is also realized in commercial products in the late 1990s and early 2000s. Academic research on hypervisors in the early 2000s that was later commercialized. Virtualization originally developed to share an expensive computer among many users is reintroduced to allow multiple commodity operating systems to run on a multiprocessor. Numerous uses of virtual machines (VMs) in replay, resource containment, information flow control, migration. Linux kernel-based virtualization (paravirtualization). Intel Corporation revises instruction set architecture to virtualize. VM-based resource containers become the basis for Infrastructure-as-a-service cloud. Docker containers become primary units of multiapplication software distribution.
World Wide Web World Wide Web developed at European Organization for Nuclear Research (CERN) by Tim Berners-Lee (1990). The National Center for Supercomputing Applications (NCSA) at the University of Illinois, Urbana-Champaign develops Mosaic, the first widely used graphical browser (1993), which inspires Netscape’s browser (1994). Alta Vista search engine developed by DEC (1995). Several other search engine companies spin off from universities including the University of California, Berkeley, Carnegie Mellon University, and Stanford University in the mid-1990s. Research in Stanford Integrated Digital Library Project (supported by NSF, DARPA, the National Security Agency, and others) developed the PageRank algorithm. Members of the team left to found Google in 1998. Hard to overestimate.
Suggested Citation:"Appendix B: Research Developments and Impacts Depicted in Figure 2.1." National Academies of Sciences, Engineering, and Medicine. 2020. Information Technology Innovation: Resurgence, Confluence, and Continuing Impact. Washington, DC: The National Academies Press. doi: 10.17226/25961.
×
Research Area Example Origins Significance/Impact
  File storage systems Industry designs on magnetic disks and tapes from 1960s and academic research (Multics). BSD Unix introduces Fast File System with multilevel index and directory, NTFS, Log-structured file system (LFS), Copy-on-write (COW), 2010s journaling file systems on Solid State Disks. Linux Ext. Growth of storage from megabytes to terabytes. Price per bit drops billion-fold. Bandwidth increases million-fold. Critical to digitalization.
Cloud computing Origins in the early 1990s with clusters (Berkeley Network of Workstations, NSF, DARPA, and Caltech Beowulf DoE), with early commercial developments of VAX Clusters at DEC and IBM System S in the 1980s. Adopted for rendering farms and web search in the 1990s. Major deployment: Amazon Elastic Compute Cloud (2006) and S3 (storage). Allows assignment of compute and storage on demand. Major deployments by Amazon, Google, Microsoft, IBM, and others.
All-flash storage arrays Pure Storage co-founded by University of California, Santa Cruz alumni and faculty in 2009, first products in 2011. High-performance storage for data centers.
Scalable storage clusters Panasas founded in 1993 to commercialize work at Carnegie Mellon University. First products in 2004. Data storage and management for high-performance applications.
Theory, Programming Languages Structured programming Expressing programs using structure such as nested lexical structure, if-then-else, loops, functions rather than “Goto.” Expressed in part in language designs, such as Algol 60, and also in reaction to languages such as COBOL (1960) and FORTRAN with explicit Goto. Famous note by E.W. Dijkstra, “Goto statement considered harmful” (1968), which spiked progress as well as controversy. Broad impact on most programming languages, leading to greater programmer productivity, programming tools (such as compilers, interactive debuggers).
Suggested Citation:"Appendix B: Research Developments and Impacts Depicted in Figure 2.1." National Academies of Sciences, Engineering, and Medicine. 2020. Information Technology Innovation: Resurgence, Confluence, and Continuing Impact. Washington, DC: The National Academies Press. doi: 10.17226/25961.
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Research Area Example Origins Significance/Impact
  System programming languages High-level languages were developed for writing system software, especially operating systems. These emerged cautiously, because programmers favored the precision and performance available writing in assembly language (machine code). Streamlined structured programming languages, often able to include assembly language snippets, were developed for system programming. Examples include PL/I (Multics, 1964-1984), C (Unix, 1969), PL360 (Algol-W, 1968), Bliss, C, C++, Ada, Swift (2014), and Rust (2015). As these languages co-evolved with other programming languages, they benefited from optimizing compilers, debuggers, expressive type systems, programming environments, etc. Portability to different computer architectures became a key feature of most of these languages. In 2020, system programming is done with these languages, except for tiny parts of operating systems or language runtimes.
Object-oriented programming Origins in 1960s: Simula 67, Sketchpad, and AED-0. The Smalltalk language (1972) used objects ubiquitously, provided a persuasive demonstration of an interactive programming environment, and helped stimulate research. In the 1980s, languages such as CLU, Ada, Oberon, Objective-C, Common Lisp Object System, Self, C++ emerged in 1985, as well as research conferences such as OOPSLA. Java was developed by Sun Microsystems starting in 1991, released in 1995. Other languages to emerge in the early to mid-1990s include Visual Basic, Python, Ruby, and JavaScript. Since the late 1990s, object-oriented programming has been widely taught to undergraduates. Several languages are broadly supported and have become widely used for production programming, such as Python, Ruby, Java, C++, C#, and Visual Basic. JavaScript has become the dominant client-side (browser) programming environment.
Suggested Citation:"Appendix B: Research Developments and Impacts Depicted in Figure 2.1." National Academies of Sciences, Engineering, and Medicine. 2020. Information Technology Innovation: Resurgence, Confluence, and Continuing Impact. Washington, DC: The National Academies Press. doi: 10.17226/25961.
×
Research Area Example Origins Significance/Impact
  Compiler toolchains Projects started with the goal of developing an open source Unix-like operating system. Key developments include GCC compiler and tool chain (MIT, mid-1980s), LLVM (University of Illinois, Urbana-Champaign, early 2000s). Both tools are widely adopted in industry. GCC compiler, designed to be portable and target different instruction sets, widely used in research and products. Associated compiler tools, and other operating-system utilities, also valuable parts of the GNU Compiler Collection toolchain. LLVM greatly simplifies creating portable implementations of new languages. Intermediate form uses Static Single Assignment (SSA) form, which simplifies optimization, transformation, and verification.
Formal verification Model checking was invented in the early 1980s and incorporated into computer-aided design tools. Academic research throughout the 1980s and 1990s. Further work at Microsoft Research led to Static Driver Verifier. Although testing is widely used to identify errors in systems, it cannot verify the absence of errors. Formal methods are increasingly used to verify the correctness of computer hardware and software. Static Driver Verifier significantly reduced “blue screen” crashes.
Static code analysis for security Coverity was founded by Stanford University researchers, drawing on ARPA and NSF-funded research (early 2000s). Acquired by Synopsys and released software security tools in 2014. Automated tools for analyzing security and other properties of software.
Databases, Analytics Relational databases Relational model emerges at IBM (1968). Multiple interactions and interchanges between IBM System R and University of California, Berkeley Ingres research groups (mid 1970s). Project Ingres had multiple commercial spinoffs in the late 1970s and 1980s. This grew into the object/relational model, largely based on late 1980s work at Berkeley (Postgres). Simple but powerful model widely used to represent, store, and query data in myriad applications.
Suggested Citation:"Appendix B: Research Developments and Impacts Depicted in Figure 2.1." National Academies of Sciences, Engineering, and Medicine. 2020. Information Technology Innovation: Resurgence, Confluence, and Continuing Impact. Washington, DC: The National Academies Press. doi: 10.17226/25961.
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Research Area Example Origins Significance/Impact
  Parallel processing for relational databases Teradata formed as a collaboration between Caltech and Citibank researchers (late 1970s), first products shipped early 1980s. Improved database performance.
Embedded key/value database Developed for BSD in the late 1980s. Commercial development by Sleepycat in the mid-1990s. Evolved to large-scale distributed, replicated sharded Key-Value stores that underlie all web services. This is all based on work on Distributed Hash Tables (DHTs) out of MIT and Rice University. Open source high-performance database embedded in many operating systems.
Cluster-computing framework University of California, Berkeley AMPlab research in early 2010s results in widely used open-source Apache Spark framework. Efficient distributed/parallel handling of large-scale data.
Graph-based computation framework Turi founded to continue development of GraphLab developed at Carnegie Mellon University and University of Washington, Seattle, in late 2000s-early 2010s. Commercial products in the mid-2010s. High-performance computing framework for data mining.
Scalable distributed file systems Information Technology Center, a partnership of Carnegie Mellon University and IBM, developed Andrew File System (AFS) in the early 1980s. Kerberos distributed security introduced from MIT research and Transarc commercialized AFS in the late 1980s. AFS heavily influenced Sun Microsystem’s Network File System (NFS) v4. Distributed files systems are widely used in computing clusters, high-performance computers, and cloud systems.
Network attached storage Several technologies from systems research come together—networked workstations (NFS), file storage (fast file system and log-structure file system), and Redundant Array of Inexpensive/Independent Disks (RAID) technology—to enable first net-attached storage companies in the late 1980s. Academic research on log-structured file systems (1990-1993) implemented in BSD (1993). Provides a convenient way to share files among multiple computers. Foundation for today’s large object storage systems.
Suggested Citation:"Appendix B: Research Developments and Impacts Depicted in Figure 2.1." National Academies of Sciences, Engineering, and Medicine. 2020. Information Technology Innovation: Resurgence, Confluence, and Continuing Impact. Washington, DC: The National Academies Press. doi: 10.17226/25961.
×
Research Area Example Origins Significance/Impact
  Object-oriented databases Adds intrinsic database management support for graph-structured objects and persistence to object programming languages. Early 1980s academic (e.g., Brown University and University of Wisconsin, Madison) and industry (e.g., Hewlett-Packard, Bell Labs, and Microelectronics and Computer Consortium [MCC]) research and products (e.g., Gemstone). Early commercial products in the late 1980s to mid-1990s (Objectivity, Object Design, ONTOS). Technology has grown today into document stores and graph databases.
Security, Privacy Protection mechanisms Academic work including research at MIT, 1964-1967. Industry deployments in 1967: System Development Corporation’s ADEPT and IBM VM/370. Hardware and software implementation of access control list system in Multics, a MIT-Honeywell collaboration. Hardware and software mechanisms used to protect information stored in computer systems. Standard feature in modern microprocessors and operating systems.
Public key cryptography Research at Stanford University and MIT (1976-77). Public key cryptography commercialized by RSA Security Inc. (1982), introduced into Lotus Notes (1986), adopted for Netscape SSL/TLS (1995) and used in the Web’s HTTPS secure transmission protocol. Underpinning of modern cryptosystems including confidential Internet communication.
Allows sharing of aggregate statistical information while withholding information about individuals Privacy from perturbation Purdue University (1980), IBM (2000), differential privacy, Microsoft Research (2006), used in Apple iOS 10 (2016), and the 2020 U.S. Census. Allows sharing of aggregate statistical information while withholding information about individuals.
Computer-network authentication Kerberos protocol developed by MIT Project Athena (1988), used in Andrew File System (1989), default authentication in Windows 2000. Allows protection of network services.
Suggested Citation:"Appendix B: Research Developments and Impacts Depicted in Figure 2.1." National Academies of Sciences, Engineering, and Medicine. 2020. Information Technology Innovation: Resurgence, Confluence, and Continuing Impact. Washington, DC: The National Academies Press. doi: 10.17226/25961.
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Research Area Example Origins Significance/Impact
  Blockchain Hash functions proposed in 1976 Diffie-Hellman paper. Hash functions used in Merkle trees (1979), digital time stamps (1991). Pricing using processing (Hashcash, 1977). Digital cash from academic research (1992) and 1995 DigiCash start-up. Academic and industry research results brought together in 2009 Bitcoin proposal. Distributed secure data storage. Technology used to implement bitcoin and other virtual currencies.
Symmetric-key encryption Early civilian research on block ciphers took place at IBM in the early 1970s, resulting in the development of the Data Encryption Standard (1975). Precursor to the Advanced Encryption Standard, which is widely used to encrypt data transmitted over the Internet, generally in combination with public-key cryptography to establish session keys.
Robotics, Cyber-Physical Systems Probabilistic robotics/perception, planning and state estimation Original research from 1980s. Work from SRI International, NASA, and the University of Pennsylvania defined the simultaneous localization and mapping (SLAM) problem for robot perception, leading to the EKF-SLAM and FastSLAM algorithms in the 1990s and 2000s. Various SLAM algorithms are implemented in the open-source robot operating system libraries, often used together with the Point Cloud Library for 3D maps or visual features from OpenCV. Mass-market SLAM implementations can now be found in consumer robot vacuum cleaners. Probabilistic robotics also addresses uncertainty in robot planning, modeled generically as a Partially Observable Markov Decision Process (POMDP), and state estimation, such as the widely employed Monte Carlo Localization (MCL) algorithm. Early work in probabilistic robotics led to the advancement of robot algorithms for control, perception, planning, and state estimation. Mapping and localization algorithms including SLAM-based approaches are now scalable and deployable at city-scale, enabling autonomous navigation
Suggested Citation:"Appendix B: Research Developments and Impacts Depicted in Figure 2.1." National Academies of Sciences, Engineering, and Medicine. 2020. Information Technology Innovation: Resurgence, Confluence, and Continuing Impact. Washington, DC: The National Academies Press. doi: 10.17226/25961.
×
Research Area Example Origins Significance/Impact
  Mobile robotics Autonomous vehicles and planetary rovers, for example, are descendants of research projects in mobile robotics such as SRI’s Shakey the Robot (1960-1970s). Industry led with products such as iRobot’s now ubiquitous Roomba autonomous vacuum cleaner and its ruggedized PackBot platform. The PackBot and similar field robots have been used for searching in the debris of Ground Zero after 9/11, assessing the damage after the Fukushima nuclear incident, assisting service people around the world.
Visual perception (ultrasonic, laser range-finding and structured light cameras) Usage emerged in early 1990s, primarily with SICK Germany’s laser; advanced significantly by Velodyne as a product of the DARPA Grand Challenge. Major advancements enable autonomous vehicles (catalyzed by DARPA Challenges) to consumer products (e.g., Microsoft’s Kinect).
Mobile manipulation and supply chain management Building on mobile manipulation research from back to the early 1970s, early 2000s work at Cornell University leads to founding of Kiva Systems. Kiva develops robots for fulfillment centers, purchased by Amazon in 2008. Carnegie Mellon University students started Bossa Nova Robotics, which worked with Walmart to create mobile inventory robots rolled out in late 2010s. Robots support retail store inventory management and e-commerce order fulfillment.
Open source robot middleware/operating systems Early academic work from the mid-1990s (e.g., Player/Stage from USC, MOOS and YARP from MIT, JAUS by the Department of Defense) to late 2000 start-ups (Robot Operating System [ROS] at Willow Garage and LCM at May Mobility). ROS has become an industry standard across robotics and is maintained by the Open Source Robotics Foundation. Has enabled widespread interoperability across robots to provide ecosystems for code reuse across robotics.
Suggested Citation:"Appendix B: Research Developments and Impacts Depicted in Figure 2.1." National Academies of Sciences, Engineering, and Medicine. 2020. Information Technology Innovation: Resurgence, Confluence, and Continuing Impact. Washington, DC: The National Academies Press. doi: 10.17226/25961.
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Research Area Example Origins Significance/Impact
  Robot assisted surgery Actual use began in the late 1980s with Robodoc (Integrated Surgical Systems, Sacramento, CA), the orthopedic image-guided system developed by Hap Paul and William Bargar (University of California, Davis) for use in prosthetic hip replacement. SRI International/DARPA robotic telepresence surgery research from the mid-1980s leads to teleoperated robotic surgical systems. The research that eventually led to the development of the da Vinci Surgical System was performed in the late 1980s at SRI with NIH support. The SRI prototype garnered DARPA support. Robots improve surgical outcomes and reduce costs.
Assisted-GPS and WiFi-based asset tracking Early work at Xerox PARC and Bell Labs picked up in Intel’s PlaceLab research with academic collaborators (Georgia Tech, UCSD, Washington) research efforts in early 2000. Led to companies like Ekahau and other WiFi-based real-time location solutions that are used today.
Activity trackers BodyMedia, a Carnegie Mellon University spinout, was acquired by Jawbone in 2014. Jawbone founded in 2004. Fitbit launched in 2007. FitBit leads market in activity trackers as more capabilities are built into other wearables (e.g., Apple Watch, Garmin).
Home sensing and automation A series of inventions (x10, infrastructure mediated sensing) for home sensing and automation in academia, then start-ups (e.g., Zensi, Nest) to major industry (Belkin, Google). A growing market of Smart Home commercial technologies (WeMo, SmartThings—IoT-Home Automation, Google Nest).
Self-driving technology 1980s DARPA-funded research on self-driving cars at Carnegie Mellon University. Laser range finding and SLAM used in the mid-2000s DARPA Grand Challenge. Deep learning for computer vision and other aspects in the mid-2010s. Google X and other industry research efforts started in the late 2000s. Multiple industry testing programs late 2010s. Significant technical progress but many technical and nontechnical barriers to widespread deployment.
Suggested Citation:"Appendix B: Research Developments and Impacts Depicted in Figure 2.1." National Academies of Sciences, Engineering, and Medicine. 2020. Information Technology Innovation: Resurgence, Confluence, and Continuing Impact. Washington, DC: The National Academies Press. doi: 10.17226/25961.
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Research Area Example Origins Significance/Impact
Artificial Intelligence, Machine Learning Neural networks Perceptrons (Cornell University, 1958) enhanced with back propagation (1975) and multilayer networks with gradient descent training (1985). HNC Software spin off from the University of California, San Diego (1986) provides commercial fraud detection software (1990). University of Buffalo research starting 1992 used for U.S. Postal Service Handwritten Address Interpretation (1997). Resurgence of neural networks and early commercial applications.
Bayesian filters Early 1990s academic research on Bayesian networks learning from data, mid-1990s academic and industry work on probabilistic text classification, and late 1990s use in commercial spam-filtering products. Effective email spam filtering.
Convolutional neural networks (CNNs) Time delay neural network (Carnegie Mellon University, 1987). Image recognition with convolutional neural network trained by gradient descent (Bell Labs, 1989). Allows image analysis that is invariant of scale and translation. Applied to recognition of handwritten numbers on checks (1998). Google team beats state of art for speech recognition (2009). CNNs win computer vision competitions (2011-2012). Industry R&D leads to public release of widely used TensorFlow and PyTorch machine learning frameworks that underpin many products and services. Both are widely used in industry and academic research. Resurgence of neural networks and application to computer vision and speech recognition.
Suggested Citation:"Appendix B: Research Developments and Impacts Depicted in Figure 2.1." National Academies of Sciences, Engineering, and Medicine. 2020. Information Technology Innovation: Resurgence, Confluence, and Continuing Impact. Washington, DC: The National Academies Press. doi: 10.17226/25961.
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Research Area Example Origins Significance/Impact
  Expert systems Stanford University spinoff Intellicorp (1980) developed an expert system environment to run on Lisp machines (1979 and 1980 MIT spinoffs). Production-rule-based system R1 (Carnegie Mellon University, 1978) used for configuring DEC Vax computers (1980). Systems that solve complex knowledge by reasoning through bodies of knowledge expressed as rules. Early applications in medical diagnosis and computer ordering configuration. Lisp machines were an early inspiration for networked workstations.
Speech recognition Hidden Markov models (1966) used in late 1970s and early 1980s research at Carnegie Mellon University and IBM. Dragon Systems founded 1982 by former Carnegie Mellon and IBM researcher. Commercial products emerged in 1987 (Kurzweil) and 1990 (Dragon). Technology used by SRI International and spun off as Siri and incorporated into the iPhone (2011).
Computer vision Work at Carnegie Mellon University and University of Munich in the late 1980s shows the feasibility of self-driving cars. Neural network and machine learning boosting methods (2001) led to fast and practical face detection algorithms. Convolutional neural networks for specific applications (e.g., skin cancer detection) surpass human performance (2017). Practical application of computer vision to driver assistance, face recognition for photography, vision-based assistance for physicians.
Graphics, Simulation Point-plotting displays and early computer games Several early research computers used point-plotting displays—essentially an X-Y oscilloscope connected to computer-driven digital-to-analog converters for the X and Y position. MIT researchers wrote Spacewar!, a space combat video game, for the point-plotting console of the PDP-1, a commercialized version of the TX-0. A pioneering networked multiuser game, Trek, was written at the University of Rochester for Xerox PARC’s Alto. There were earlier games on unique one-off computers, but the PDP-1 was a product with much greater exposure. Spacewar! influenced the arcade video games and Trek presaged the explosion of online multiplayer games that would follow.
Suggested Citation:"Appendix B: Research Developments and Impacts Depicted in Figure 2.1." National Academies of Sciences, Engineering, and Medicine. 2020. Information Technology Innovation: Resurgence, Confluence, and Continuing Impact. Washington, DC: The National Academies Press. doi: 10.17226/25961.
×
Research Area Example Origins Significance/Impact
  Computer drafting Ivan Sutherland’s 1963 MIT Ph.D. thesis was a compelling and inspirational demonstration of innovative graphical interaction techniques and programming methods. A light pen directly manipulated graphical objects: rubber-band lines and circles; “snapping” points to coincide with others; positioning, sizing, and rotating sub-pictures. Structural relations among objects were recognized and retained, so that four lines terminating at a point all changed when the point moved. Constraints could be placed on objects to ensure parallelism, or equal distances, or alignment to vertical or horizontal axes. This “drafting” application, together with emerging graphics hardware products, inspired the birth of commercial CAD offerings. The direct feedback and smooth interaction set a standard for all human-computer interaction that continued, e.g., with Engelbart’s NLS system, with the Xerox Alto, with workstation applications, and with personal computers.
Hidden-surface elimination, smooth shading The goal of a major ARPA-funded research project at the University of Utah, started in the late 1960s, was to produce realistic computer-generated images. In the early 1970s, the group invented several algorithms for hidden-surface elimination, an essential first step. And several “smooth shading” techniques were developed to counter the unrealistic look of uniformly colored surfaces. All of these algorithms faced performance challenges: The calculation of the color of each was a tradeoff between fidelity and speed. The first commercial products were “continuous tone” (CT) imaging systems by Evans and Sutherland. Silicon Graphics Inc. (SGI) and others would further develop the techniques starting in the early 1980s, and they would be found in 3D workstations in the late 1980s. The techniques from Utah, especially the Z buffer for hidden-surface elimination and Henri Gouraud’s smooth shading, are now universally available in graphics hardware and software.
Suggested Citation:"Appendix B: Research Developments and Impacts Depicted in Figure 2.1." National Academies of Sciences, Engineering, and Medicine. 2020. Information Technology Innovation: Resurgence, Confluence, and Continuing Impact. Washington, DC: The National Academies Press. doi: 10.17226/25961.
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Research Area Example Origins Significance/Impact
  Shading Academic research, often drawing on results from materials science and optics, to improve models of how light reflects and scatters off surfaces. Representative examples: radiosity (Cornell University, 1984), texture mapping (University of Utah, 1974), bump mapping (Caltech, 1978), noise functions (Perlin, 1985), shadows (Cornell, 1978), adapting the Torrance-Sparrow model (Cornell, 1982), stochastic sampling to avoid aliasing, estimation or measurement of the bidirectional reflectance distribution function (BRDF) of different materials (Cornell, 1991). A variety of shading models and approximations form a “library” from which to choose, based on accuracy (not always important, e.g., when making animated movies), computational requirements, and unintended artifacts. Many of these are supported by present-day graphics software (e.g., OpenGL, Renderman) and hardware.
Shaders High-performance rendering hardware was limited in the shades (color, intensity) it could compute for a pixel. But as programmable elements arrived, especially graphics processing units (GPUs), it became possible to insert small bits of code in the rendering pipeline to achieve special lighting or other effects: these bits of code are called shaders. Shaders can achieve effects such as bump mapping, shadows, specular highlights, translucency, blurring, or texture mapping. The OpenGL, Direct3D, and Renderman libraries all allow shader code to be specified for rendering; manufacturers of graphics hardware (especially GPUs) include processing units for executing shader code. Shaders are a powerful, though idiosyncratic, way to augment the normal rendering pipeline. They are especially useful for special effects in video games.
Suggested Citation:"Appendix B: Research Developments and Impacts Depicted in Figure 2.1." National Academies of Sciences, Engineering, and Medicine. 2020. Information Technology Innovation: Resurgence, Confluence, and Continuing Impact. Washington, DC: The National Academies Press. doi: 10.17226/25961.
×
Research Area Example Origins Significance/Impact
  Ray tracing Ray tracing renders an image by tracing rays corresponding to each pixel from the eye, through the pixel, to the first intersection with an object in the scene. Recursive ray tracing then examines rays from the point of intersection in the direction of reflection, refraction (if a transparent object) or light source to determine the color for the pixel. Ray tracing was used for early hidden-line elimination in 1968, but recursive ray tracing (Bell Labs,1979) was the innovation that made it useful for high-quality rendering. Research has investigated many ways to improve the quality (sampling rays with sub-pixel resolution) or speed (efficient data structures for intersecting rays and objects or collections of objects). Ray tracing is used for the highest quality rendering, though computationally expensive: the first use for all of a full-length movie was in 2013. Research systems have demonstrated speeds adequate for interaction, based on restricted scenes and/or complex multi-computing systems. But today’s GPUs provide both the raw computational power and specialized features that speed up ray tracing.
Raster display architecture; GPUs Generating 60 full-frame images of a complex 3D scene each second requires more and specialized hardware. Early academic research explored using custom VLSI “smart memory” chips to combine pixel storage with appropriate processing (University of North Carolina, 1981). Most architecture evolution has occurred since in industry, using custom chips in more conventional parallel structures that achieve very high memory bandwidths in graphics accelerators. Several workstation companies offered 3D graphics accelerators: SGI, Sun Microsystems, and Hewlett-Packard. Today, personal computers can host graphics cards with high-performance rendering, often using one or more GPUs These are chips customized to operate in parallel on large frame buffers and offer some programmable elements to use as “shaders.” GPUs are also used in supercomputer systems to accelerate scientific computing and machine learning.
Suggested Citation:"Appendix B: Research Developments and Impacts Depicted in Figure 2.1." National Academies of Sciences, Engineering, and Medicine. 2020. Information Technology Innovation: Resurgence, Confluence, and Continuing Impact. Washington, DC: The National Academies Press. doi: 10.17226/25961.
×
Research Area Example Origins Significance/Impact
  Media: games, special effects, movies Even before graphics were interactive, artists were exposing film on computer-driven scanners to make art and animations. As the technologies have become cheaper and more capable, the applications to “media” have blossomed. At first, single “shots” in movies featured the latest rendering developments. The capability and investment to make a full-length computer-generated movie took years to develop: Toy Story (1995) is generally credited as the first. Insatiable demand for cost-effective performance: large affordable markets for games; enough computing to create dramatic effects that suspend viewers’ disbelief for movies. Research and development continuously advances the state of the art in modeling, animation, rendering, and other areas, with contributions from academics, but mostly performed by many industrial groups, some of them quite small, such as Lucasfilm, Pixar, Softimage, Pacific Data Images, Alias, Autodesk, Avid, Industrial Light and Magic, Disney.
Virtual environments A “head mounted display” (HMD) research at Harvard University in 1968 with hardware 3D graphics pipeline but only wire-frame images. Awaited raster displays, realistic shaded images, lighter HMDs, inexpensive sensors and declining costs. Academic research to reduce latency, increase working volume, study various applications (e.g., desensitization, PTSD therapy). Used for games, which led to development of Unity, a software platform for VR applications and games. Since about 2015, rapid industry innovation in HMDs: Oculus Rift and Quest; Microsoft HoloLens (untethered, wide field of view). Significant application in technical markets, e.g., for training, guiding maintenance, surgery. Military applications such as visualizing and rehearsing missions in unfamiliar settings. Retail markets are projected to expand rapidly with recent HMD advances. Smartphones, with built-in inertial sensors and cameras, are a widely available device for AR.
Suggested Citation:"Appendix B: Research Developments and Impacts Depicted in Figure 2.1." National Academies of Sciences, Engineering, and Medicine. 2020. Information Technology Innovation: Resurgence, Confluence, and Continuing Impact. Washington, DC: The National Academies Press. doi: 10.17226/25961.
×
Research Area Example Origins Significance/Impact
  Computer vision in computer graphics The resurgence in AI, especially in vision, is enabling new or improved graphics methods. Examples of techniques adopted by industry include generative scene construction (Stanford University, 2013), and extracting and refining object motions from videos (various universities, 2016). Generally, computer vision analyzes images to extract models, while computer graphics synthesizes images from models. The two are linked by models of many kinds: geometric, structural, motion, and hybrids. Although simple modeling is not hard, complex models are a continuing challenge, and pace many advances in vision and graphics.
Interactive data visualization Stanford University research (1999-2002) on visualization techniques for exploring and analyzing relational databases commercialized by founding of Tableau Software (2003). Later acquired by Salesforce (2019). Tools that allow non-database experts to explore large data sets.
Human-Computer Interaction Direct manipulation interfaces The now ubiquitous direct manipulation interface, where visible objects on the screen are directly manipulated with a pointing device, was first demonstrated by Ivan Sutherland in Sketchpad, which was his 1963 MIT Ph.D. thesis. SketchPad supported the manipulation of objects using a light-pen, including grabbing objects, moving them, changing size, and using constraints. It contained the seeds of myriad important interface ideas and would spawn many products. MIT Project Whirlwind (mid-1950s to 1960) developed the light pen, later overtaken by tablets (RAND tablet, 1963) and SRI International’s mouse (1965) later used in the Xerox Alto. Overlapping windows were first demonstrated in Smalltalk (University of Utah, early 1970s). Components and psychological foundations for direct manipulation identified at Maryland in the early 1980s. With many innovative improvements, the mouse and windows became the standard input device for WIMP (window, icon, menu, pointer) user interfaces, Pervasive and industry wide impact as the default interaction metaphor for personal computing and the foundation for interaction on mobile devices.
Suggested Citation:"Appendix B: Research Developments and Impacts Depicted in Figure 2.1." National Academies of Sciences, Engineering, and Medicine. 2020. Information Technology Innovation: Resurgence, Confluence, and Continuing Impact. Washington, DC: The National Academies Press. doi: 10.17226/25961.
×
Research Area Example Origins Significance/Impact
  Desktop computing Work at Stanford University and Xerox PARC paved the path for a series of innovations in the 1970s and early 1980s (mouse; WIMP; what you see is what you get (WYSIWYG), and cut/copy/paste) With many innovative improvements, became the pervasive experience of desktop computing (WIMP) and graphical user interfaces.
Data gloves and 3D user input Early academic and industry research from late 1970s through 1990 in natural interfaces included using full hand gestures and position. Commonly used in gaming and virtual reality.
Collaboration tools Early industry (GROVE at MCC) and academic research around 1989 in tools for collaborative writing and information sharing led to key inventions for supported distributed and asynchronous activities. Used in Google Docs (2009) and other products. Enterprise and web-based technologies for collaboration.
Collaborative filtering, personalized recommendations GroupLens project at University of Minnesota leads to Net Perceptions start-up, licensed to Amazon 1996. Collaborative filtering research led to the invention of recommendation systems. Now a core element of today’s e-commerce systems.
Accessibility Accessibility research in academia and industry starting alongside the invention of the PC and into Web and mobile platforms in the mid-1990s. Computing technologies aspire to be accessible for people with persistent visual, hearing, motor, and cognitive impairments and adaptable for situational impairments and eyes-busy situations.
News aggregation Initial work late 1990s (Georgia Institution of Technology’s [Georgia Tech’s] Krakatoa Chronicle) is refreshed in 2002 as Google News. Early research lays the foundation for Google News and other aggregation algorithms.
World Wide Web (WWW) Mosaic browser (NCSA, 1993). Netscape Navigator was inspired by Mosaic and co-written by Marc Andreessen, a part-time employee of NCSA. Eventually is the basis of Microsoft’s Internet Explorer starting in 1995. Widespread adoption and authoring of WWW content. Many more complex research strategies for hypertext fell away as ease of use and simplicity won in the end.
Suggested Citation:"Appendix B: Research Developments and Impacts Depicted in Figure 2.1." National Academies of Sciences, Engineering, and Medicine. 2020. Information Technology Innovation: Resurgence, Confluence, and Continuing Impact. Washington, DC: The National Academies Press. doi: 10.17226/25961.
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Suggested Citation:"Appendix B: Research Developments and Impacts Depicted in Figure 2.1." National Academies of Sciences, Engineering, and Medicine. 2020. Information Technology Innovation: Resurgence, Confluence, and Continuing Impact. Washington, DC: The National Academies Press. doi: 10.17226/25961.
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Suggested Citation:"Appendix B: Research Developments and Impacts Depicted in Figure 2.1." National Academies of Sciences, Engineering, and Medicine. 2020. Information Technology Innovation: Resurgence, Confluence, and Continuing Impact. Washington, DC: The National Academies Press. doi: 10.17226/25961.
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Suggested Citation:"Appendix B: Research Developments and Impacts Depicted in Figure 2.1." National Academies of Sciences, Engineering, and Medicine. 2020. Information Technology Innovation: Resurgence, Confluence, and Continuing Impact. Washington, DC: The National Academies Press. doi: 10.17226/25961.
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Suggested Citation:"Appendix B: Research Developments and Impacts Depicted in Figure 2.1." National Academies of Sciences, Engineering, and Medicine. 2020. Information Technology Innovation: Resurgence, Confluence, and Continuing Impact. Washington, DC: The National Academies Press. doi: 10.17226/25961.
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Suggested Citation:"Appendix B: Research Developments and Impacts Depicted in Figure 2.1." National Academies of Sciences, Engineering, and Medicine. 2020. Information Technology Innovation: Resurgence, Confluence, and Continuing Impact. Washington, DC: The National Academies Press. doi: 10.17226/25961.
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Suggested Citation:"Appendix B: Research Developments and Impacts Depicted in Figure 2.1." National Academies of Sciences, Engineering, and Medicine. 2020. Information Technology Innovation: Resurgence, Confluence, and Continuing Impact. Washington, DC: The National Academies Press. doi: 10.17226/25961.
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Suggested Citation:"Appendix B: Research Developments and Impacts Depicted in Figure 2.1." National Academies of Sciences, Engineering, and Medicine. 2020. Information Technology Innovation: Resurgence, Confluence, and Continuing Impact. Washington, DC: The National Academies Press. doi: 10.17226/25961.
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Suggested Citation:"Appendix B: Research Developments and Impacts Depicted in Figure 2.1." National Academies of Sciences, Engineering, and Medicine. 2020. Information Technology Innovation: Resurgence, Confluence, and Continuing Impact. Washington, DC: The National Academies Press. doi: 10.17226/25961.
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Suggested Citation:"Appendix B: Research Developments and Impacts Depicted in Figure 2.1." National Academies of Sciences, Engineering, and Medicine. 2020. Information Technology Innovation: Resurgence, Confluence, and Continuing Impact. Washington, DC: The National Academies Press. doi: 10.17226/25961.
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Suggested Citation:"Appendix B: Research Developments and Impacts Depicted in Figure 2.1." National Academies of Sciences, Engineering, and Medicine. 2020. Information Technology Innovation: Resurgence, Confluence, and Continuing Impact. Washington, DC: The National Academies Press. doi: 10.17226/25961.
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Suggested Citation:"Appendix B: Research Developments and Impacts Depicted in Figure 2.1." National Academies of Sciences, Engineering, and Medicine. 2020. Information Technology Innovation: Resurgence, Confluence, and Continuing Impact. Washington, DC: The National Academies Press. doi: 10.17226/25961.
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Suggested Citation:"Appendix B: Research Developments and Impacts Depicted in Figure 2.1." National Academies of Sciences, Engineering, and Medicine. 2020. Information Technology Innovation: Resurgence, Confluence, and Continuing Impact. Washington, DC: The National Academies Press. doi: 10.17226/25961.
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Suggested Citation:"Appendix B: Research Developments and Impacts Depicted in Figure 2.1." National Academies of Sciences, Engineering, and Medicine. 2020. Information Technology Innovation: Resurgence, Confluence, and Continuing Impact. Washington, DC: The National Academies Press. doi: 10.17226/25961.
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Suggested Citation:"Appendix B: Research Developments and Impacts Depicted in Figure 2.1." National Academies of Sciences, Engineering, and Medicine. 2020. Information Technology Innovation: Resurgence, Confluence, and Continuing Impact. Washington, DC: The National Academies Press. doi: 10.17226/25961.
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Next: Appendix C: Presentations to the Study Committee »
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Information technology (IT) is widely understood to be the enabling technology of the 21st century. IT has transformed, and continues to transform, all aspects of our lives: commerce and finance, education, energy, health care, manufacturing, government, national security, transportation, communications, entertainment, science, and engineering. IT and its impact on the U.S. economy—both directly (the IT sector itself) and indirectly (other sectors that are powered by advances in IT)—continue to grow in size and importance.

IT’s impacts on the U.S. economy—both directly (the IT sector itself) and indirectly (other sectors that are powered by advances in IT)—continue to grow. IT enabled innovation and advances in IT products and services draw on a deep tradition of research and rely on sustained investment and a uniquely strong partnership in the United States among government, industry, and universities. Past returns on federal investments in IT research have been extraordinary for both U.S. society and the U.S. economy. This IT innovation ecosystem fuels a virtuous cycle of innovation with growing economic impact.

Building on previous National Academies work, this report describes key features of the IT research ecosystem that fuel IT innovation and foster widespread and longstanding impact across the U.S. economy. In addition to presenting established computing research areas and industry sectors, it also considers emerging candidates in both categories.

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