Mary Baker, Tom Coughlin, Paolo Faraboschi, Eitan Frachtenberg, Kim Keeton, Danny Lange, Phil Laplante, Andrea Matwyshyn, Avi Mendelson, Cecilia ...
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Technology
Predictions 2021
Mary Baker, Tom Coughlin, Paolo Faraboschi,
Eitan Frachtenberg, Kim Keeton, Danny Lange,
Phil Laplante, Andrea Matwyshyn, Avi Mendelson,
Cecilia Metra, Dejan Milojicic, Roberto Saracco
↓
2 In This Report 01 Team 04 Individual 12 Predictions 02 Introduction 05 Comparing Predictions 03 12 Predictions Landscape 06 Overall Summary
3
SECTION 01
Broader
Mary Baker Tom Coughlin Paolo Faraboschi Eitan Frachtenberg Kim Keeton
HP Inc. Coughlin Associates Hewlett Packard Ent. Reed College Entrepreneur
Technology
Predictions
Team
Danny Lange Phil Laplante Katherine Mansfield Andrea Matwyshyn Avi Mendelson
Unity Technologies Penn State IEEE Computer Society Penn State Technion and NTU
Cecilia Metra Dejan Milojicic Roberto Saracco Jeffrey Voas
Bologna University Hewlett Packard Ent. IEEE FDC NIST, IEEE Computer EIC
4
SECTION 02
Introduction
Technology Predictions from Hypothetical
Exercise to Critical Planning
The pandemic data1 • Societal distancing vs depleting individuals’ social
credits
• 28 Trillion $ loss in 2020, unevenly distributed across
• Future of workforce
regions and sectors
• Forced to trusting AI to assist in transportation,
• Recovery time estimated at 2 to 4 years; market and
healthcare, elderly, etc.
priorities reshaped
Acceleration of the Digital Transformation was forced
• Up to 10% of GDP in jobs support, good portion is
upon work, education, and private life
“wasted” money
The pandemic had impact on: human lives, supply chains, Technologies increasingly play crucial role in all of this and
workforce, unpredictability of operations and markets are becoming essential for our survival
Counter-measures: cutting costs; repurposing assets; Predicting technologies helps addressing pandemics, it
eliminating the middle-man; shift to “as-a-Service” models goes well beyond hypothetical exercise
Pandemics have created STRESS on current humankind
existence, values, and daily lives
1
From Roberto Saracco, Industry Advisory Board report to IEEE Future Directions Committee
5
SECTION 03
1. Remote workforce technologies
2. Social distancing technologies
3. Reliability/Safety for Intelligent
Autonomous Systems
4. Synthetic Data for training ML
systems free of bias
5. Disinformation detection
6. HPC as a Service
7. Election security / social media
Technology
controls
8. Trustworthy & Explainable AI/ML
Predictions at the
9. Low latency virtual musical
rehearsal and performance
10. Computational memory
Times of Pandemics 11. AI for additive & subtractive
manufacturing
12. Advanced Cyber Weapons
12 Predictions Landscape
6 SECTION 03 Relationship Between Predicted Technologies
7 SECTION 03 In Reality ...
8
SECTION 04: INDIVIDUAL 12 PREDICTIONS
Remote
Problems/demand Impact
• The pandemic required social • Customized remote work environments
distancing, which in turn created an and Improved accommodation of
Workforce
immediate explosion in remote work, workers with different needs.
especially for white-collar industries
• A more inclusive work environment,
• The vast majority of remote work policy, and culture
uses preexisting technology, such as
• Growth in remote-based services such
video conferencing and virtual private
as tele-health.
networks
Tools, policies, and regulations for remote • But there are many important
• Adoption of remote collaboration, hiring,
and training.
work will evolve rapidly, improving workplace interactions that don’t
currently have a great technological Sustainable solution/business
existing remote roles and expanding solution and are ripe for innovation opportunity
to use cases that don’t currently have Opportunities • A permanent transition to a remote
or hybrid work model in white-collar
• Technology to facilitate proximity-
ideal solutions, such as education, based or spontaneous collaboration,
industries.
substituting for the office environment • Increase in worker mobility and
manufacturing, and healthcare.
01
geographical diversity.
• Technology to facilitate effective
teaching and learning with rich • Improvements in diversity, equity, and
communication, substituting for the inclusion of the workforce.
classroom environment • Enablers: tech & app innovations; AR/
• Technology to facilitate effective large- VR, regulation
scale meetings, substituting for the • Inhibitors: company policy, lack of
conference environment (short-term) financial support and buy-in
More details at https://doi.org/10.36227/techrxiv.13278092
9
SECTION 04: INDIVIDUAL 12 PREDICTIONS
Problems/demand Closeness provides a sense of safety in
Social
case of perceived danger.
• The pandemic required social
distancing, which in turn created an • The negative aspects of social distancing
immediate explosion in remote work, can be decreased by improving
especially for white-collar industries technologies to overcome separation
Distancing
(e.g. VR/AR).
• The vast majority of remote work
uses preexisting technology, such as • The growing awareness at personal
video conferencing and virtual private & social level can stimulate specific
networks behavior and foster proactive
healthcare. Social Credits growth was
• But there are many important
A host of technology is converging, workplace interactions that don’t
noticed in China.
currently have a great technological
creating streams of data that will be solution and are ripe for innovation
Sustainable solution/business
opportunity
processed locally and globally creating Opportunities • We can expect emergence of new and
improvement of existing wearable
a framework of massively distributed • Societal Distancing Techniques can
increase service quality and decrease
devices. Existing smartphones improve
sustainability (reuse), like the adoption
intelligence with impact on apps, cost by leveraging alternative ways to
turn distance into closeness (by creating
of GPS/Wireless, Bluetooth
wearables, and sensors.
02
a feeling of presence) • The leverage of data is also in line
of sustainability, opening up biz
• On the business side they open up new
opportunity, e.g. adoption of existing
opportunities, as demonstrated by the
data frameworks like Electronic Health
number of tools for managing virtual
Records (EHR)z
meetings and by the rapid evolution of
their features. • The need to create personal data space
goes hand in hand with the evolution
• Social distances techniques, when duly
trends of Digital Twins and Personal
addressed, provide tools that foster the
Digital Twins that in turn is likely to
digital transformation
foster new biz opportunity
Impact • Enablers: VR/AR technologies, social
credits
• The impact of Social Distancing is
substantial. Humans are social animals, • Inhibitors: distancing enforcement,
psychologically against “distancing”. poor technology support
10
SECTION 04: INDIVIDUAL 12 PREDICTIONS
Problems/demand Impact
Reliability • Expected market growth for intelligent
autonomous systems (e.g. mobile robots,
vehicles) with high level of autonomy
• Significant reduction of humans’ work risks
• Improvement of humans’ health
• More efficient healthcare, surveillance, and
and Safety
• To enable high levels of autonomy, stringent
better services
requirements in terms of reliability/safety of
their components have to be met • Technological boost
• The ability to reach a safe state in a fully Sustainable solution/business opportunity
for Intelligent
autonomous way (thanks to reliable
components) has to be guaranteed in case of • Significant research investment (academia
hazardous conditions and industry) in high reliability and safety
solutions for highly autonomous intelligent
• High reliability and safety should be
Autonomous
systems
guaranteed with respect to transient faults
and aging phenomena occurring in the field • Research needed to investigate interaction
among reliability, safety and security and time
Opportunities determinism constraints
Systems
• Intelligent, autonomous systems proved very • Applicability to environmental monitoring,
helpful in facing the pandemic emergency catastrophes’ prediction and avoidance
03
(e.g., robots to disinfect infected areas, • Enablers: Innovative approaches for
autonomous vehicles transporting Covid19- enhanced reliability and safety; new
tests, etc). international standards in the field;
regulations for ethical responsibility
Fueled by the pandemic,
• Moving towards fully autonomous systems
will significant help humans by preventing • Inhibitors: technical challenges; regulations
substantial growth in
exposure to health’s risky conditions
• Applications are pandemic support,
autonomous systems will further environmental monitoring, post-earthquake
management, space exploration, etc.
improve reliability and safety of
such autonomous systems. For more details, please join the IEEE Computer Society Special Technical Community
(STC) on Reliable, Safe, Secure and Time Deterministic Intelligent Systems at
https://www.computer.org/communities/special-technical-communities/rsstdis11
SECTION 04: INDIVIDUAL 12 PREDICTIONS
Problems/demand Impact
• Real-world data often embed strong biases • More and better training data at order of
found throughout society thus allowing AI magnitude lower cost
Synthetic Data
algorithms to amplify those undesirable biases
• Creating training data reflecting the world as
• Real-world data is difficult to gather and very we want to see it rather than the biased world
expensive to manually label hence limiting the we live in
for Training ML
use of ML
• Eliminate the risk of model overfitting
• Real-world data often raise privacy concerns
• Disrupting model accuracy through active
which may severely limit its application
learning and dynamic data generation in a
• Read-world data can sometimes not be virtuous feedback loop
collected in a certain environment due to
We shall see a substantial safety reasons
• Democratization of AI/ML by lowering barriers
of entry for ML practitioners
increase in the adoption of Opportunities
Sustainable solution/business opportunity
synthetic data for training • Synthetic data can be generated in infinite
amounts at extremely low cost
• Low-cost smart cameras with computer vision
ML in the coming year. • Improved AI functionality in Augmented Reality
04
• Synthetic data is automatically perfectly
labelled • ML Models as-a-Service
• Synthetic can be engineered to be free of bias • Testing ML systems with anonymized data
and promote inclusion • Improve limited real-world datasets with
• Synthetic data can easily be shared and used synthetic data
in collaborative environments • Enablers: Push for Responsible AI; privacy
• Generating behavioral data such as in robotics concerns; bias prevention; inclusion; low cost;
simulations at speeds much higher the wall scalability; and flexibility
clock • Inhibitors: Limited technology offerings;
• Greater variation in data including black swan awareness; insufficient skills & knowledge
events of complex data theories; and deep rooted
skepticism to data not originating from real-
world events; legal uncertainty12
SECTION 04: INDIVIDUAL 12 PREDICTIONS
Disinformation Problems/demand
• ‘Post-truth’ declared word of the year by
Impact
• More reliable detection of “fake” people
Detection
Oxford Dictionaries, 2016 and information can positively impact
social governance
• Twitter Has Flagged 200 of Trump’s
posts as ‘Disputed’ or Misleading since • It will improve the quality of products
election day and the CNN refused to and services we are getting
broadcast some of his statements.
Critical importance of having Based on what tools/information they Sustainable solution/business
opportunity
are making their decision?
accurate information will • People are making judgments about • We need better regulations and laws
trigger techniques to determine trustworthiness based on reviews and
critics of claiming to be “objectives”.
that will mitigate the use and the spread
of false data and false information
disinformation in politics, Apparently, many of them are being
paid directly or indirectly to vote for or
• We need to develop more reliable tools
to detect false data – tools that people
business, and social media. against the product/service. can trust
05
Opportunities • Enablers: regulation; advanced
machine learning algorithms; tech & app
• The current technology of profiling
innovations
and understanding market demands
and needs are focusing on commercial • Inhibitors: legislators, politicians,
purposes, but the same technology can perceived commercial interests of
be used to detect disinformation existing players
• Recently, quite a new researchers are
using advanced machine learning and
data mining techniques to achieve that
goal13
SECTION 04: INDIVIDUAL 12 PREDICTIONS
Problems/demand • HPC platforms will modernize and new
programming models beyond MPI &
HPC as a Service
• Not everyone can afford
multi-threading will evolve
Supercomputer, delivering high end
HPC hardware on as needed basis • Entirely new applications may evolve
eliminates CAPEX from tighter convergence of HPC and AI
fueled by HPCaaS
(HPCaaS)
• HPC is converging towards AI1 and most
of AI is executed in the Cloud or on aaS
Sustainable solution/business
platforms
opportunity
• However, HPC applications still require
• International governments embracing
high parallelism and specialized, noise-
HPC as a Service
During 2021, we will see free interconnects, as well as HPC-
specific tools (schedulers, libraries, etc.) • HPCaaS Adoption for Engineering
increasing progress towards Opportunities
solutions in oil and gas, finance, etc.
(mid- to low-end HPC
delivering medium HPC systems • Applications in the Cloud have evolve
their worfklow and tool chains in a more
• We need to develop more reliable tools
to detect false data – tools that people
as a Service. superior fashion than HPC can trust.
06
• Security models are better in the Cloud • Enablers: growth of AI accelerators;
with shared infrastructure concerted efforts by US DoE; push by
top tier Cloud providers
• Scale-out models in the Cloud are
proved to be more cost-efficient than • Inhibitors: legacy HPC applications
on-premise are still dominated by MPI and multi-
threaded models; very high end systems
Impact and highly parallel applications will
• Making HPC more broadly available can demand on-premise
democratize many high end applications
and also foster innovation
1
R. Stevens, J. Nichols, K. Yellick, “AI for Science,” DoE Report.
https://publications.anl.gov/anlpubs/2020/03/158802.pdf14
SECTION 04: INDIVIDUAL 12 PREDICTIONS
Problems/demand • Balancing diverse perspectives with
Election
responsible propagation of content
• The 2020 US presidential election
challenges the continued fit of traditional
shone a light on the oversized role of
legal approaches to intermediary
technology, in particular social media, on
protection
perceptions of election security
Security and
• Debate continues about the appropriate Impact
scope of intermediary responsibility and
• Building trust in the democratic process
liability for disinformation and its viral
and election results
spread
Disinformation
• Potentially overlapping interests across
• Concerns about interference from
the political aisle
foreign adversaries are real, and social
media demonstrated continued influence • Minimizing provably false information in
operations the public discourse
Technological tools and new Opportunities Sustainable solution/business
opportunity
• Social media technologies and
laws will develop to safeguard companies influence the views of • Industry investment in advanced AI/
07
millions. This influence can be harnessed ML technology to improve content
election security, increase for voter education and communication,
rather than furthering viral spread of
monitoring, classification and filtering
trust and confidence in the disinformation, distrust, and ”echo
chambers”.
• A new, possibly more restrictive
regulatory climate for these companies
democratic process, and curb the • The commercial incentives of social
• An increase (or possibly decrease) of
trust in traditional social media products;
spread of disinformation. media companies should align with the
democratic interests and values of each
room for arrival of new entrants
country • Enablers: Better content classification
tech, regulation
• Opportunities exist to merge the
conversation from election security with • Inhibitors: conflicting perceptions of
WHO’s inquiries into “infodemiology” and commercial and political interests
disinformation as a threat to public well-
being15
SECTION 04: INDIVIDUAL 12 PREDICTIONS
Problems/demand Impact
Trustworthy &
• Today’s AI practices and tools are • Applicability of AI/ML to mission-critical
designed for performance, but lack processes
transparency and accountability
• Mitigation of biased decisions caused by
• They introduce or amplify bias due to AI technology
Explainable AI/ML
training data quality
• Human-in-the-loop decision making
• They are not capable of explaining the process with validation and compliance
decision process
Sustainable solution/business
• They can’t be used in compliance-
opportunity
In addition to performance,
sensitive or mission-critical applications
• Trustworthy AI toolkits and standard
Opportunities practices to design, analyze and
the AI/ML developers will start • Design for explainability: systems should
measure AI solutions and technologies
focusing on explainable and be interpretable and observable • AI/ML technology that is “designed
for trust” by construction, with built-in
• Design for reproducibility: systems
trustworthy tools and code. should be designed to act upon traits
support to measure and analyze the
important explainability metrics
that are invariant
08
• Enablers: several world-wide initiatives
• Design for robustness: systems should
towards a converged set of AI Ethics
be stable during training and inference,
principles (e.g., Secure, Private, Inclusive,
and robust against adversarial attacks
Human, Responsible, Robust)1
• Design for fairness: systems should be
• Inhibitors: AI/ML center of gravity
able to measure and mitigate bias
in consumer applications (e.g.,
recommenders) that are less
sensitive to trust issues; emphasis on
performance rather than trust.
1
Example (IEEE): https://bit.ly/IEEE_AI_Ethics_Principles16
SECTION 04: INDIVIDUAL 12 PREDICTIONS
Problems/demand Impact
Low Latency • The pandemic has made it unsafe for singers
and wind instrumentalists to rehearse
together indoors. Outdoor rehearsals aren’t
• Enablement of collaborative music making
for instruction, ensemble rehearsals, and
performances
Virtual Music
always possible, due to weather.
• Increased sense of community beyond
• A large audience congregating for a live individual artistic pursuits
performance is dangerous
• Potential for larger, more inclusive audiences
• Remote workforce technology adds significant than possible with in-person events
Collaboration
latency and jitter, making it unusable for
• Post-pandemic: reduced need for commuting
collaborative musical rehearsals and live
to rehearsals and events
performances
• New business models around remote, rather
Opportunities than in-person, delivery
New technologies will enable • Technologies to facilitate virtual rehearsals
with low enough latency (< 25ms) to support
Sustainable solution/business opportunity
vocal and instrumental collaborative music making for:
• Musical instruction (e.g., individual or small
• Hardware and software support for low-
latency, DVD-quality Internet audio and video
ensembles to make
09
processing
group lessons)
• Easy-to-use devices, applications, and
collaborative music in real-time • Small ensemble rehearsals (e.g., chamber
groups, jazz combos)
cloud services to simplify configuration and
management for non-technical users
for instruction, rehearsal, and • Larger ensemble rehearsals (e.g.,
choruses, concert bands)
• Enablers: several initiatives are exploring
performance. • Technologies to facilitate live virtual
solutions for Internet music making. E.g:
JackTrip Foundation and Jamkazam
performances of musical ensembles, with
• Inhibitors: physics (speed-of-light delays limit
audience interactivity
geographic spread of musical collaborators),
insufficient financial support for new arts-
related technologies
1
https://www.jacktrip.org/
2
https://jamkazam.com17
SECTION 04: INDIVIDUAL 12 PREDICTIONS
Computational Storage and
In-Memory Processing
Increased deployment Problems/demand Impact
• Creating value and enabling quick decisions from growing data • Local data processing makes IoT data more useful and reduces
of IoT sensors with stores while reducing energy consumption and cost will enable the impact of this data on baseband network bandwidth
wide-spread applications that enhance public safety, improve
minimal power health, and enable greater understanding
• Local data processing with embedded devices using non-
volatile memories can make power constrained applications
consumption • It will allow us to experience the world and others in new ways
and help create a true partnership between humans and
possible, enabling new medical/health, AR/VR and many other
apps
will accelerate machines
• In addition to saving power, greater use of non-volatile memory,
domain specific processing and disaggregated and composable
development and
10
Opportunities
infrastructure could increase the overall data center efficiency
• Many IoT/AI real time applications (e.g. autonomous vehicles)
deployment of benefit from processing at the edge Sustainable solution/business opportunity
computational • Processing at endpoints or at the edge, often with embedded
SoC devices, can improve latency, reduce network bandwidth
• Processing data locally saves energy, provides higher
performance, saves network bandwidth and reduces latency
storage and in- and also reduce energy consumption
• New networking architectures and memory/storage strategies
• Replacing volatile with non-volatile memory, emerging memory will create new opportunities for computing resources
memory processing fabrics and domain specific processors to implement in-
• Low power embedded devices enable more apps and new
memory processors or computational storage to reduce
devices, software movement of data
packaging options—such as smart glasses with voice control
• Enablers: improving AI and nonvolatile memory enable local
stacks, and products. • Replacing volatile with non-volatile memory in industrial,
civic and consumer embedded devices can reduce energy
processing using less energy
consumption and often provide more memory for AI and other • Inhibitors: Need higher volume lower cost non-volatile
applications, increasing their usefulnes memories and implementation of new AI applications in
embedded devices and data centers18
SECTION 04: INDIVIDUAL 12 PREDICTIONS
AI for Digital
Problems/demand Impact
• Desire for faster ideation-to- • Shorter time from need to
production time for parts created production
through additive and subtractive
Manufacturing
• Higher quality solutions/parts
manufacturing
• Increased customization
• Need for higher confidence that
parts meet functional constraints • Designs created and produced by
and quality requirements those who want them
We will see further uptake of additive and • Need for easier and faster
customization of designs
Sustainable solution/business
opportunity
subtractive manufacturing with lower • Desire to open up design ideation
and creation to any user
• Data science for industrial
design-to-production times, higher part
manufacturing across and inside of
factory workflows steps
Opportunities
quality, and increased customization • Feedback/learning loops
• Data science for functional proof of
produced parts
opportunities provided via new applications in manufacturing/testing
equipment for higher quality and • AI for modeling and design by
of data science and ML. faster achievement of desired untrained users
11
functionality • Enablers: advances in AI and
• Feedback/learning loops in design physical calibration
through consumer usage • Inhibitors: lack of communication
• Learn from tagged design libraries between different workflow
and images of products to achieve components and consumer
desired aesthetics or functionality feedback
of new parts
• Human-centered, even verbal, input
into design process19
SECTION 04: INDIVIDUAL 12 PREDICTIONS
Advanced Cyber Defense
New autonomous and semi-autonomous Problems/demand • Theft or corruption of vast amounts
of data; massive financial harm
• New and more innovative attack tools
security tools will emerge to defend being developed
to corporations, public entities,
governments
against increasingly sophisticated • Increased interconnectedness and
ubiquity of computing enlarges attack
• Widespread destruction, failure or
malfunctioning of critical infrastructure
attacks that are capable of causing surface.
systems with associated major societal
• “Payoff” for success very large for low damage
significant physical harms and failures of investment of effort
• A massive disruption of computing
critical infrastructure. • Single individuals and small rogue
groups can cause material losses due
service triggering second and third
order failures of computing and non-
to information security harms computing systems worldwide
12
Opportunities Sustainable solution/business
• Cross platform, application and device opportunity
malware emerging • Cross government-industry solutions
• AI technologies allow for evolving, • International alliances and information
adaptive and mutating malware sharing
• State actors, rogue nations, terrorists, • Enablers: low barriers to entry,
protest groups can easily create and rogue governments and groups,
deploy advanced attack tools and enlarged attack surface due to
malware ubiquity of computing elements and
interconnectedness
Impact
• Inhibitors: prevention activities,
• Physical harm (including loss of life) on preparedness, appropriate response/
massive scale defense20 SECTION 04: INDIVIDUAL 12 PREDICTIONS 2021 Technologies Ordered by Predictions Team’s Votes
21
SECTION 05
Comparing
Predictions
Impact, Likelihood,
Confidence
1) Likelihood was determined by the overall number of votes and consensus (average) among the committee, it
defines probability of prediction happening
2) Impact is determined relative among different predictions
3) Prediction confidence describes our own confidence in prediction, and consensus (standard deviation), so it is
different from 1) above22 SECTION 05: COMPARING PREDICTIONS #Predictions Categorized by Primary Focus
23 SECTION 05: COMPARING PREDICTIONS #Predictions Categorized by Primary and Secondary Focus
24
SECTION 05: COMPARING PREDICTIONS
Some
General
Observations
Some of the industries are severely impacted by Some of the technologies and approaches are applicable
pandemics, dramatically reducing demand. Technology across many fields
can help them too, but nothing can substantially reverse
lack of demand, e.g. • AI/ML/DL techniques can be applied across all other
technologies
• Transportation, especially air, cruises, taxis/lyft/uber,
• Cybersecurity is essential to protect against malicious
etc.
behavior which is especially concerning in critical times
• Oil and gas, lack of travel drove down demand for oil
• Digital transformation is taking place in general and
(Oil future contract went negative)
broadest sense
• Tourism, hospitality, accommodation industry (hotels,
• Disaster recovery of manufacturing and supply chain
AirBnB)
very similar to data centers25
SECTION 06
Overall Summary
The Pandemics increasingly useful
• Technology is critical in times of a • We continue to experiment with
pandemic, it helps overcome some of the approaches and delivery models
negatives • We are becoming more and more
• Pandemics also enable aggressive systematic and rigorous in our predictions
technology evolution. Necessity is mother
of invention Seeking Feedback
• What do you think of our predictions? What
General have we missed, what is wrong, different?
• Technology Predictions (or Trends) were • Feel free to approach us with feedback,
always popular, now they are becoming questions at k.mansfield@computer.org.About the IEEE Computer Society The IEEE Computer Society is the premier source for information, inspiration, and collaboration in computer science and engineering. Connecting members worldwide, the IEEE Computer Society empowers the people who advance technology by delivering tools for individuals at all stages of their professional careers. Our trusted resources include international conferences, peer-reviewed publications, a robust digital library, globally recognized standards, and continuous learning opportunities. The society hosts and co-hosts 210+ conferences every year featuring academic, technical, and industry leaders. Learn more about the IEEE Computer Society at computer.org. Twitter: @computersociety Facebook: @ieeecomputersociety LinkedIn: @ieee computer society Instagram: @ieee_computer_society YouTube: @ieeeComputerSociety
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