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
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/rsstdis
11 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 uncertainty
12 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 goal
13 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.pdf
14 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- being
15 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_Principles
16 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.com
17 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 centers
18 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 process
19 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 defense
20 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) above
22 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 centers
25 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.
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