Computer Experts Release Scoreboard for IEEE Computer Society’s 2021 Technical Predictions

LOS ALAMITOS, California, December 16 – The IEEE Computer Society (IEEE CS) revealed the scorecard for its 2021 Technology Forecasts, which measures the performance of trending technologies against the projections made in December 2020. The IEEE CS Technology Predictions for 2021 have earned a collective grade of B-.

IEEE CS Technology Forecasts for 2022 to be Revealed in January – register for free exclusive access.

“Last year was as unpredictable as 2020, and not surprisingly our predictions were graded the same as in the previous year, averaging a B-score,” said Dejan Milojicic, former IEEE CS president (2014) and current Chief Technician at Hewlett Packard Labs . . “The advancement of technologies requiring remote manpower due to the pandemic has certainly been accelerated, while most other technologies have not been so accelerated. We look forward to ending the 2022 forecasts and tracking the paths of the fastest moving developments in the coming year.”

Remote manpower technologies (graded A) were strongly driven by the need for continuity of work; they have been critical for many industries and professions in which physical presence was not required. HPC as a Service (graded B +) enabled remote access to high-performance resources critical in pandemic assessments. In-memory computing (graduated B) was considered advanced in the last year.

In addition to measuring success in forecasting, this year the team tracked technology maturity, its confidence and the impact the technologies have made, presented in the “Scorecard of the Predictions” chart. All values ​​for these four categories were assigned by averaging their individual classification by each technology predicted last year from A to F. For maturity, they were prompted by the technology readiness level that was adjusted for that purpose. They mapped AF respectively as mature, emerging, incubating, prototype, conceptual, and unsuccessful. In the graph, bubble color corresponds to maturity level, bubble diameter corresponds to impact, and success in prediction and our confidence are represented on Y and X axes respectively. If some predictions are aligned to the same alphabetical grade, numerically they may differ, which is reflected in their ranking. This is presented in the rank-ordered list that follows the graph. The team will continue to experiment with scorecards in the coming year including simplifying summary charts, introducing trends, and removing perceived correlation across metrics.

The 2021 technologies predicted last year are listed, ranked according to the success of the predictions. The current grade is included, followed by a comparison of how they were ranked last year (→ means the same rank; ↑ means improved rank; ↓ means decreased rank).

1 Remote manpower technologies (graded A, ). In hindsight, it was obvious that remote technologies, such as collaborative tools and remote presence would be very successful. This applies in a variety of use cases, such as education, manufacturing, and healthcare, none of which were considered easily conducted far before the pandemic. The team was very confident about this technology, the impact was high, and the technologies evolved to be more mature. This technology was exactly where it was intended to be, ranking 1st.

2 HPC as a Service (graduated B +, ). The team predicted the delivery of High Performance Computing as a Service (HPCaaS) as it is increasingly accepted among HPC users. The convergence of AI, high-performance data analytics, and HPC has further fueled the growth and adoption of HPCaaS. Similar to remote manpower technologies, the team’s confidence was high, the impact was high and the technology reached maturity level. This technology has outperformed last year’s forecast (rank 6).

3 Memory computing (B +, ). Although in-memory computing is not yet a mature technology, several emerging implementations have reached beyond prototypes and resulted in a relatively high ranking, albeit with a slightly lower confidence. This is reinforced by the potentially huge impact this technology could have on the market, especially in edge and endpoint systems requiring low power. This technology has significantly outperformed last year’s forecast (rank 10).

4-5 Machine learning (ML) for additive and subtraction manufacturing (B, ) and advanced cyber weapons (B, ) share positions 4-5, with exactly the same ranking. The advances of both technologies have been accurately predicted. The impact of ML for additional manufacturing is slightly amplified by the pandemic and need for remote work, but maturity is still lower than advanced weapons. High-level cyber weapons have also been strengthened by the pandemic and have unfortunately reached a higher degree of maturity, as evidenced by a number of alleged or suspected cyber attacks that took place last year. (They were ranked 11th and 12th last year.)

6 Social distancing technologies (B, ). While much higher success of these technologies has been expected, their impact is still high but slightly moderated due to conflict associated with privacy regulations. Social distancing technologies have not progressed as they may have done largely due to concerns about (possible) privacy breaches, not due to internal technology gaps. All of this also resulted in lower confidence in the scoring. Last year, these technologies were ranked in the same place as remote workforce (1), so these technologies were significantly lower than expected.

7 Reliability / security challenges for smart autonomous systems (B-, ). The team continues to believe in the importance of these technologies but has expected a slightly higher rate of progress over the past year. Automation is one of the enablers in many other technologies, including remote workforce technologies and social distancing as discussed above, but perhaps its time has not yet come. Last year, these technologies shared the top spot with remote workforce and social distance, so they also dropped significantly.

8-9 Synthetic data for training of bias-free ML systems (B / C, ) and misinformation detection (B / C, ) divide positions 8-9 with exactly the same score. This is another pair of related technologies with a similar level of maturity (incubation) and a similar level of impact. The overall confidence in assessing misinformation detection was much higher than for synthetic data for unbiased ML training. Synthetic data for training ML systems free of prejudice were ranked 4th last year and misinformation detection fifth, so they were both lower.

10 Low latency virtual music rehearsal and performance (B / C, ). This is a relatively narrow market but a nice success story that made possible musical performances and rehearsals last year. There are already some early products on the market and new ones coming. Last year, this technology was the 9th.

11 Reliable and explainable AI / ML (C +, ). These technologies continue to be of enormous importance, but unfortunately little progress has been made over the past year, in the direction of reliability and explicability, which are closely linked. New approaches to hardware and software offer some potential but have not been realized. This technology has the lowest maturity. Last year, these technologies were classified 8.

12 Social security / social media controls (C, ). Similar to social distancing and detection of misinformation, election security and control of social media have not been successful this year. Another reason for the lack of progress is their close connection with governance, which is extremely complex to introduce. Also similar to previous technologies, these have less maturity. Last year, these technologies were classified 7.

Following the established process of previous years, the authors, who originally made the predictions in November 2020, assessed their predictions individually. The averages and standard deviations were used as a basis for the discussion which subsequently resulted in the final assessment.

The 2021 Scoreboard was prepared by Mary Baker (HP Inc.), Thomas Coughlin (Coughlin Associates), Paolo Faraboschi (Hewlett Packard Labs), Eitan Frachtenberg (Reed College), Hironori Kasahara (Waseda University), Kim Keeton (Google), Danny Lange (Unity Technologies), Phil Laplante (Penn State), Andrea Matwyshyn (Penn State Law), Avi Mendelson (Technion), Dejan Milojicic (Hewlett Packard Labs), Cecilia Metra (Bologna University), and Roberto Saracco (IEEE FDC).

Sign up to receive the 2022 Technical Forecasts to be announced in January.

About the IEEE Computer Society

The IEEE Computer Society is the world home of computer science, engineering and technology. A global leader in providing access to computer research, analysis and information, the IEEE Computer Society offers a comprehensive range of unparalleled products, services and opportunities for individuals at all stages of their professional careers. Known as the premier organization that empowers technology-driven people, the IEEE Computer Society offers international conferences, peer-reviewed publications, a unique digital library, and training programs. Visit for more information.

Source: IEEE Computer Society

Leave a Reply

Your email address will not be published.