What Will 2022 Look Like? the IEEE CS 2022 Report
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PERSPECTIVES What Will 2022 Look Like? The IEEE CS 2022 Report Hasan Alkhatib, SSN Services Paolo Faraboschi, HP Labs Eitan Frachtenberg, Facebook Hironori Kasahara, Waseda University Danny Lange, Amazon.com Phil Laplante, Pennsylvania State University Arif Merchant, Google Dejan Milojicic, HP Labs Karsten Schwan, Georgia Tech Over the last two years, nine IEEE Computer merican baseball person- ality Yogi Berra famously Society tech leaders collaborated to identify opined, “It’s tough to make important industry advances that promise predictions, especially about theA future.” Forecasting is even more to change the world by 2022. The 23 difficult in the computer industry, due to dramatic and swift changes in tech- technologies provide new insights into the nology and the numerous challenges to innovation. Only a small fraction emergence of “seamless intelligence.” of innovations truly disrupt the state of the art.1 Some innovations are not practical or cost-effective, some are ahead of their time, › help laymen understand where technology is and some simply don’t address a market need. Examples evolving and the implications for society, and abound of arguably superior technologies that never › help the IEEE Computer Society organize and achieved wide adoption—Apple’s Newton, IBM’s OS/2,2 prepare for this future. and Sony’s Betamax3 come to mind—because others arrived on time or fared better in the market. After two face-to-face workshops, followed by sev- eral months of email discussions and consultations OUR CHALLENGE with other technologists, we identified 23 disruptive Despite Berra’s admonition, in January 2013 Dejan Milo- technology areas and shared the list with the IEEE CS jicic, then president of the IEEE Computer Society (CS), Industrial Advisory Board. We organized these areas presented a team of scientists and engineers with the around themes including market categories, technolo- following challenge: gies, human capital, and policies, as Figure 1 illustrates. The 23 technology areas are › predict future technologies that will disrupt the state of the art, › life sciences; › help researchers understand the future impact of › computational biology and bioinformatics; these disruptive technologies, › medical robotics; 68 COMPUTER PUBLISHED BY THE IEEE COMPUTER SOCIETY 0018-9162/15/$31.00 © 2015 IEEE Market category Life sciences Computational biology Medical robotics and bioinformatics Computer vision and pattern recognition Machine learning and intelligent systems Natural user interfaces 3D printing Big data and analytics Technologies High-performance computing Cloud computing Internet of Things Networking and interconnectivity Software-dened networks 3D integrated circuits Multicore technology Photonics Universal memory Quantum computing Device and nanotechnology Human capital Massive open online courses computer vision and pattern › Open intellectual property movement Sustainability recognition; Policies › machine learning and intelligent Security cross-cutting issues systems; › natural user interfaces; FIGURE 1. Landscape of the 23 disruptive technology areas. These technology areas are › 3D printing; organized around themes comprising market categories, technologies, human capital, and › big data and analytics; policies. › high-performance computing; › cloud computing; › the Internet of Things (IoT); with the industry and its profession- with an average response of about 31 › networking and als. We recognize that our list is incom- percent.4 So our response rate of about interconnectivity; plete and that several other technology 20 percent seems low, but is likely due › software-defined networks; areas, such as electronic currency and to this continued downward trend of › 3D integrated circuits; autonomous vehicles, may be consid- response rates and the length of the › multicore technology; ered transformational. But we had to paper we asked members to read, as it › photonics; draw a line. We also avoided the phil- was more than 160 pages long. › universal, nonvolatile memory; osophical notion of technology’s con- Respondents’ reported job roles were › quantum computing; tributions to humankind, a question diverse, including engineers (44.97 per- › device and nanotechnology; frequently asked by those who have cent), academics (11.69 percent), scien- › massive open online courses read the report. The report’s premise, tists (7.25 percent), researchers (6.95 (MOOCs); echoed in this article, is that technol- percent), managers (5.77 percent), con- › open intellectual property ogy enables capabilities; what human- sultants (5.62 percent), and others (17.75 movement; ity chooses to do with that technology percent) including technologists and › sustainability; and is beyond the report’s scope. programmers. › security cross-cutting issues. The survey comprised two classes DRIVERS AND DISRUPTORS of questions, asking respondents to We used this information to create To validate the premises and conclu- numerically rank our consolidated lists the comprehensive IEEE CS 2022 Report, sions made by the CS 2022 Report team, of driver and disruptor technologies. For which describes each of the 23 technol- we surveyed more than 5,000 IEEE instance, a rank of 1 meant the technol- ogy areas and summarizes the state of the CS senior members. We posed related ogy is not considered a driver or dis- art, potential challenges, where we think questions, but didn’t share the report rupter, 3 meant it’s a moderate driver or each technology will go, and its poten- itself to avoid influencing members’ disrupter, and 5 meant it’s considered a tial for disruption. Some are already responses. We received 690 complete major driver or disrupter. being adopted today, such as multicore survey responses and 368 partial ones. For the drivers, we asked responders technology, high-performance comput- Of the total 1,058 responses, 784 were to rank the following items: ing, cloud computing, and software- usable, yielding a response rate of 18.3 defined networks. Others are only percent with a margin of error of ±3.2; › increasing average life being explored at this time, such as 3D thus, we could be 95 percent confident expectancy, printing, nonvolatile memory technolo- that the true answer lies within ±3.2 › increasing ratio of retirees to gies, and quantum computing. The full percentage points of the finding. workers, report is available on the IEEE CS web- Kim Bartel Sheehan found in 2006 › public concern about control over site (www.computer.org/cms/Computer that the response rate for email sur- access to and amount of personal .org/ComputingNow/2022Report.pdf). veys is inversely proportional to survey information, In identifying these 23 technology length and that response rates for email › desire for sustainable energy areas, we hope to better align the CS surveys have generally been declining, sources, MARCH 2015 69 PERSPECTIVES 50 45 s 40 s 35 30 25 20 15 who ranked technologie Percentage of respondent 10 5 0 s s y s ) y Climate changeGlobal terrorism Use of big data analytics Reduction in availability of grants Wireless/broadband connectivit Increasing average life expectancy Increasing ratio of retireesDesire to forworker sustainable energy source Quickening pace of knowledge transfer Widening economic inequality worldwide Alternative distributionUse of technology chains for(direct medical sale procedures People’s concern over personal information Long-term availability of certain energy source Reduced job security in a global market economReduction in cost of data collection and retention FIGURE 2. Comparison of major technology drivers as ranked by survey respondents. › reduction in availability of grants by wireless/broadband connectivity and Figure 3 shows the percentage of and philanthropic resources, the desire for sustainable energy sources. responders who believed that individ- › widening economic inequality Also highly ranked were the use of big ual technologies are disrupters. Again, worldwide, data and analytics, long-term availability these results supported our findings › reduced job security in a global of energy resources, and the quickening in the CS 2022 Report; for example, the market economy, pace of knowledge transfer. All of these highest-ranked disrupters were the use › climate change, drivers are discussed in the report. of robots as labor and 3D printing, fol- › global terrorism, For the disruptors, we asked the lowed by cloud computing, MOOCs, and › use of big data and analytics, responders to numerically rank the new user interfaces. The higher per- › reduction in cost of data collec- following: centage of responses identifying driv- tion and retention (for use in ers, compared to disruptors, supports analytics), › crowdsourcing/open sourcing of the idea that only a fraction of technol- › quickening pace of knowledge hardware development; ogies truly become disruptive. transfer such as instantaneous › changes in educational structure/ global communication, design such as MOOCs; SEAMLESS INTELLIGENCE › long-term availability of certain › virtual/alternative currencies In creating the CS 2022 Report, we were energy sources, such as Bitcoin; searching for a meta-innovation that › alternative distribution chains › smartphone use as a device for would tie all these technology areas such as manufacturers selling payment; together. We found a unifying theme directly to consumers, › cloud computing; in seamless intelligence, where every- › use of technology for medical › use of robots as a source of labor; thing is connected