Computing Leaders Release Scorecard for IEEE Computer Society’s 2022 Tech Predictions
LOS ALAMITOS, Calif., 8 December 2022 – The IEEE Computer Society (IEEE CS) known for reporting top technology trends in computing, today reveals the official scorecard for its 2022 Technology Predictions. The scorecard for the 2022 predictions indicates the level of performance and impacts achieved for currently trending technologies against the projections made in January 2022. The IEEE CS Technology Predictions for 2022 garnered a collective grade of B/C.
The IEEE CS Technology Predictions for 2023 will be revealed in January 2023 – sign up today for free exclusive access: https://www.computer.org/2023-top-technology-predictions
The IEEE CS team evaluated their overall prediction of technology advancement with a score of B/C, and its impact on humanity a B-.
The highest success was attributed to the predictions for the Convergence of HPC, AI, and Data Analytics (tech advancement: B+; impact: B-); Datacentric AI (B+; B+); Remote Medicine (B+; A-); and Digital Twins in Manufacturing (B; B).
- Convergence of HPC, AI, and Data Analytics was driven by all three technologies and equally by governments, industry, and academia.
- Datacentric AI was successful due to the critical importance of data for training AI models.
- Remote medicine has caught on during COVID and extended its importance in post-COVID days.
- Digital twins made the most progress in manufacturing where constrained environments took full advantage of digital twin benefits.
“On average, we ranked the impact of predicted technologies on humanity as B- with the overall confidence in all scorecard elements (success, impact, maturity) also a B-. This is very similar to our previous years, perhaps slightly on the lower side. This is not surprising given the very volatile past year due to COVID,” said Dejan Milojicic, former IEEE CS president (2014) and current Distinguished Technologist at Hewlett Packard Labs.
The team’s continued success in predicting was primarily brought down by NFTs (technology advancement D+; impact D/E), followed by unsuccessful predictions for Disinformation Detection/Correction (C), Low-Code/No-Code (C+), and Commoditization of Space Technologies (C+).
In terms of maturity, two additional classes were introduced for the first time – Unsuccessful and Broad Adoption. The NFTs and Commoditization of Space Technologies were classified as unsuccessful, and Convergence of HPC, AI, and Data Analytics were classified as Broad Adoption.
The following list compares the Top 16 Technology Trends for 2022, and is ranked by the grades for <success in prediction, impact to humanity, measured impact of technology, technology maturity, and confidence in the prediction>:
Top 16 Technology Trends Success, Impact, maturity, confidence
Convergence of HPC, AI, HPDA: <B+, B-, Broad adoption, B+>
Datacentric AI: <B+, B+, Mature, A/B>
Remote Medicine: <B+, A-, Emerging, B>
Digital Twins in Manufacturing: <B, B, Mature, B+>
Health, Safety, Wearable Biomed Tech: <B, B+, Mature, B->
Safety for Autonomous Systems: <B-, B+, Mature, B>
3D Print in Healthcare: <B-, A/B, Emerging, B/C>
AI@Edge, Federated Learning: <B-, B-, Emerging, B>
Trustworthy AI: <B-, A/B, Emerging, B+>
Confidential Computing: <B/C, B/C, Incubating, B/C>
Metaverse: <B/C, C+, Prototype, B>
Cybersecurity of Critical Infrastructure: <C+, A/B, Emerging, B->
Commoditization of Space Tech: <C+, B/C, Unsuccessful, B/C>
Low-Code/No-Code: <C+, C+, Incubating, B/C>
Disinformation Detection/Correction: <C, B/C, Prototype, B->
Non-Fungible Tokens (NFTs): <D+, D/E, Unsuccessful, B/C>
Following the established process from previous years, the authors who originally made the predictions in January 2022 evaluated their predictions individually. The averages and standard deviations were used as a basis for the discussion that eventually resulted in the final rating.
The IEEE CS team of leading technology experts includes Rosa M. Badia, Barcelona Supercomputing Center; Mary Baker, HP Inc.; Tom Coughlin, Coughlin Associates; Paolo Faraboschi, Hewlett Packard Enterprise VP and Fellow; Eitan Frachtenberg, data scientist, Hewlett Packard Labs; Vincent Kaabunga, AKEM Consulting; Hironori Kasahara, Waseda University; Kim Keeton, Google; Danny Lange, VP of AI at Unity Technologies; Phil Laplante, professor, Penn State University; Avi Mendelson, professor, Technion, and NTU Singapore; Cecilia Metra, professor, Bologna University and former IEEE CS president; Dejan Milojicic, Hewlett Packard Enterprise Distinguished Technologist and former IEEE CS president; Nita Patel, L3 Technologies, IEEE CS president-elect; Roberto Saracco, chair of the IEEE-FDC’s Symbiotic Autonomous Systems Initiative; Michelle Tubb, IEEE CS Director of Marketing and Sales; and Irene Pazos Viana, IT consultant.
Note: The statements expressed in this report do not represent the opinions of the authors’ employers.
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About the IEEE Computer Society
Through conferences, publications, and programs, the IEEE Computer Society (IEEE CS) sets the standard for the education and engagement that fuels global technological advancement. By bringing together engineers, scientists, researchers, and practitioners from all areas of computing and at every career phase, the IEEE CS enables new opportunities and empowers not only its members but also the greater industry. Visit computer.org for more information.