IEEE CS Grades 2018 Technology Predictions

Industrial IoT, Blockchain, Deep Learning, and Accelerators Earn Top Scores
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LOS ALAMITOS, Calif., 27 November 2018 – In December 2017, the IEEE Computer Society (IEEE-CS) released its technology adoption predictions for 2018, with a focus on deep learning, digital currencies, blockchain, industrial IoT, robotics, assisted transportation, and others on the front line of computing breakthroughs. Today, the IEEE-CS graded its 2018 predictions while providing insights on the advancements of the trends over the past year and analyses of their future potential. The overall score for the 2018 technology predictions is B.
“The past year has seen mature deployment of industrial IoT, continued adoption of deep learning, and expanded uses of blockchain technologies,” said Dejan Milojicic, IEEE-CS past president (2014). “We have seen continuation of innovative technologies from last year that are setting the stage for a promising 2019.”
Following are the IEEE-CS predictions, grades, and analyses:
1. Deep learning: A-
Undoubtedly, deep learning (DL) made huge strides toward broad adoption during the past year. The only reason it did not get an A is the extensive nature of DL that leads it to be used for everything and anything. We are confident that this will be filtered out next year and DL will become ubiquitous in the very near future.
2. Digital currencies: C-
The promise of the wide adoption of digital currencies has not seen fruition, primarily due to the volatility of the cryptocurrency markets.
3. Blockchain: A
There are extended opportunities for use of blockchain technologies independent of its initially popular use for digital currencies. This resulted in a high mark for our predictions.
4. Industrial IoT: A+
Industrial IoT scored a B+ last year due to insufficient adoption. Its adoption surpassed expectations this year, and was therefore the highest rated. It was ultimately the usefulness of the approach that swayed us in its ranking. This is also the technology where there is the most agreement among those who made the prediction (standard deviation was zero).
5. Robotics: B+
Robotics continued to have increased adoption, fueled by the adoption of accelerators and DL. However, it still has not reached its full potential, even though it continues to have a bright future.
6. Assisted transportation: A-
Adoption of assisted transportation (or fully autonomous vehicles) has continued at a steady pace. In addition, electrical vehicles in constrained environments (airports, factories, etc.) have increasing autonomous deployment.
7. Augmented reality and virtual reality (AR/VR): B-
We expected a higher degree of AR and VR, beyond limited gadgets. It appears that its time has not quite come. We were too early in predicting its adoption.
8. Ethics, laws, and policies for privacy, security, and liability: C+
Even though the use of AI is emerging and ethics have been addressed sporadically, more general approaches to policies, privacy, security, and liability have not yet been addressed at the required scope.
9. Accelerators and 3D: A
AI and DL have forcefully driven the economy of accelerator use for a broad range of applications. This is true for traditional companies as well as a plethora of startups focusing on new accelerator hardware and software.
10. Cybersecurity and AI: C
The time for cybersecurity to benefit from AI (and vice versa) has not quite come. We are still confident that the two technologies will mutually benefit each other, but our prognosis was overly optimistic.
The process followed for ranking the predictions was simple and straightforward. The authors who originally made the predictions evaluated their predictions individually. Averages and standard deviations were used as a basis for the discussion, which eventually resulted in the final rating.
Authors’ overall rating for all 2018 predictions resulted in a score of B, a close comparison to the past 2017 technology predictions score of A-.
Predictions and the scorecard were delivered by Fred Douglis (Perspecta Labs), Paolo Faraboschi (Hewlett Packard Labs), Eitan Frachtenberg (data scientist), Erik DeBenedictis (Sandia National Labs), Phil Laplante (Penn State), and Dejan Milojicic (Hewlett Packard Labs).
IEEE-CS will announce its 2019 Technology Predictions in December 2018. The complete list will be available in the IEEE-CS Press Room.


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