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Machine Learning As A Service In Manufacturing Market Impact Of Covid-19 And Benchmarking – Cole of Duty

June 21, 2020 | Author: admin

Market Overview

Machine learning has become a disruptive trend in the technology industry with computers learning to accomplish tasks without being explicitly programmed. The manufacturing industry is relatively new to the concept of machine learning. Machine learning is well aligned to deal with the complexities of the manufacturing industry.

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Manufacturers can improve their product quality, ensure supply chain efficiency, reduce time to market, fulfil reliability standards, and thus, enhance their customer base through the application of machine learning. Machine learning algorithms offer predictive insights at every stage of the production, which can ensure efficiency and accuracy. Problems that earlier took months to be addressed are now being resolved quickly.

The predictive failure of equipment is the biggest use case of machine learning in manufacturing. The predictions can be utilized to create predictive maintenance to be done by the service technicians. Certain algorithms can even predict the type of failure that may occur so that correct replacement parts and tools can be brought by the technician for the job.

Market Analysis

According to Infoholic Research, Machine Learning as a Service (MLaaS) Market will witness a CAGR of 49% during the forecast period 20172023. The market is propelled by certain growth drivers such as the increased application of advanced analytics in manufacturing, high volume of structured and unstructured data, the integration of machine learning with big data and other technologies, the rising importance of predictive and preventive maintenance, and so on. The market growth is curbed to a certain extent by restraining factors such as implementation challenges, the dearth of skilled data scientists, and data inaccessibility and security concerns to name a few.

Segmentation by Components

The market has been analyzed and segmented by the following components Software Tools, Cloud and Web-based Application Programming Interface (APIs), and Others.

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Segmentation by End-users

The market has been analyzed and segmented by the following end-users, namely process industries and discrete industries. The application of machine learning is much higher in discrete than in process industries.

Segmentation by Deployment Mode

The market has been analyzed and segmented by the following deployment mode, namely public and private.

Regional Analysis

The market has been analyzed by the following regions as Americas, Europe, APAC, and MEA. The Americas holds the largest market share followed by Europe and APAC. The Americas is experiencing a high adoption rate of machine learning in manufacturing processes. The demand for enterprise mobility and cloud-based solutions is high in the Americas. The manufacturing sector is a major contributor to the GDP of the European countries and is witnessing AI driven transformation. Chinas dominant manufacturing industry is extensively applying machine learning techniques. China, India, Japan, and South Korea are investing significantly on AI and machine learning. MEA is also following a high growth trajectory.

Vendor Analysis

Some of the key players in the market are Microsoft, Amazon Web Services, Google, Inc., and IBM Corporation. The report also includes watchlist companies such as BigML Inc., Sight Machine, Eigen Innovations Inc., Seldon Technologies Ltd., and Citrine Informatics Inc.

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Benefits

The study covers and analyzes the Global MLaaS Market in the manufacturing context. Bringing out the complete key insights of the industry, the report aims to provide an opportunity for players to understand the latest trends, current market scenario, government initiatives, and technologies related to the market. In addition, it helps the venture capitalists in understanding the companies better and take informed decisions.

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Machine Learning As A Service In Manufacturing Market Impact Of Covid-19 And Benchmarking - Cole of Duty

Zeroth-Order Optimisation And Its Applications In Deep Learning – Analytics India Magazine

June 21, 2020 | Author: admin

Deep learning applications usually involve complex optimisation problems that are often difficult to solve analytically. Often the objective function itself may not be in analytically closed-form, which means that the objective function only permits function evaluations without any gradient evaluations. This is where Zeroth-Order comes in.

Optimisation corresponding to the above types of problems falls into the category of Zeroth-Order (ZO) optimisation with respect to the black-box models, where explicit expressions of the gradients are hard to estimate or infeasible to obtain.

Researchers from IBM Research and MIT-IBM Watson AI Lab discussed the topic of Zeroth-Order optimisation at the on-going Computer Vision and Pattern Recognition (CVPR) 2020 conference.

In this article, we will take a dive into what Zeroth-Order optimisation is and how this method can be applied in complex deep learning applications.

Zeroth-Order (ZO) optimisation is a subset of gradient-free optimisation that emerges in various signal processing as well as machine learning applications. ZO optimisation methods are basically the gradient-free counterparts of first-order (FO) optimisation techniques. ZO approximates the full gradients or stochastic gradients through function value-based gradient estimates.

Derivative-Free methods for black-box optimisation has been studied by the optimisation community for many years now. However, conventional Derivative-Free optimisation methods have two main shortcomings that include difficulties to scale to large-size problems and lack of convergence rate analysis.

ZO optimisation has the following three main advantages over the Derivative-Free optimisation methods:

ZO optimisation has drawn increasing attention due to its success in solving emerging signal processing and deep learning as well as machine learning problems. This optimisation method serves as a powerful and practical tool for evaluating adversarial robustness of deep learning systems.

According to Pin-Yu Chen, a researcher at IBM Research, Zeroth-order (ZO) optimisation achieves gradient-free optimisation by approximating the full gradient via efficient gradient estimators.

Some recent important applications include generation of prediction-evasive, black-box adversarial attacks on deep neural networks, generation of model-agnostic explanation from machine learning systems, and design of gradient or curvature regularised robust ML systems in a computationally-efficient manner. In addition, the use cases span across automated ML and meta-learning, online network management with limited computation capacity, parameter inference of black-box/complex systems, and bandit optimisation in which a player receives partial feedback in terms of loss function values revealed by her adversary.

Talking about the application of ZO optimisation to the generation of prediction-evasive adversarial examples to fool DL models, the researchers stated that most studies on adversarial vulnerability of deep learning had been restricted to the white-box setting where the adversary has complete access and knowledge of the target system, such as deep neural networks.

In most of the cases, the internal states or configurations and the operating mechanism of deep learning systems are not revealed to the practitioners, for instance, Google Cloud Vision API. This in result gives rise to the issues of black-box adversarial attacks where the only mode of interaction of the adversary with the system is through the submission of inputs and receiving the corresponding predicted outputs.

ZO optimisation serves as a powerful and practical tool for evaluating adversarial robustness of deep learning as well as machine learning systems. ZO-based methods for exploring vulnerabilities of deep learning to black-box adversarial attacks are able to reveal the most susceptible features.

Such methods of ZO optimisation can be as effective as state-of-the-art white-box attacks, despite only having access to the inputs and outputs of the targeted deep neural networks. ZO optimisation can also generate explanations and provide interpretations of prediction results in a gradient-free and model-agnostic manner.

The interest in ZO optimisation has grown rapidly over the last few decades. According to the researchers, ZO optimisation has been increasingly embraced for solving big data and machine learning problems when explicit expressions of the gradients are difficult to compute or infeasible to obtain.

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Zeroth-Order Optimisation And Its Applications In Deep Learning - Analytics India Magazine

Researchers use machine learning to build COVID-19 predictons – Binghamton University

June 21, 2020 | Author: admin

By Chris Kocher

June 16, 2020

As parts of the U.S. tentatively reopen amid the COVID-19 pandemic, the nations long-term health continues to depend on tracking the virus and predicting where it might surge next.

Finding the right computer models can be tricky, but two researchers at Binghamton Universitys Thomas J. Watson School of Engineering and Applied Science believe they have an innovative way to solve those problems, and they are sharing their work online.

Using data collected from around the world by Johns Hopkins University, Arti Ramesh and Anand Seetharam both assistant professors in the Department of Computer Science have built several prediction models that take advantage of artificial intelligence. Assisting the research is PhD student Raushan Raj.

Arti Ramesh, assistant professor, computer science

Machine learning allows the algorithms to learn and improve without being explicitly programmed. The models examine trends and patterns from the 50 countries where coronavirus infection rates are highest, including the U.S., and can often predict within a 10% margin of error what will happen for the next three days based on the data for the past 14 days.

We believe that the past data encodes all of the necessary information, Seetharam said. These infections have spread because of measures that have been implemented or not implemented, and also because how some people have been adhering to restrictions or not. Different countries around the world have different levels of restrictions and socio-economic status.

For their initial study, Ramesh and Seetharam inputted global infection numbers through April 30, which allowed them to see how their predictions played out through May.

Certain anomalies can lead to difficulties. For instance, data from China was not included because of concerns about government transparency regarding COVID-19. Also, with health resources often taxed to the limit, tracking the virus spread sometimes wasnt the priority.

Anand Seetharam, assistant professor, computer science

We have seen in many countries that they have counted the infections but not attributed it on the day they were identified, Ramesh said. They will add them all on one day, and suddenly theres a shift in the data that our model is not able to predict.

Although infection rates are declining in many parts of the U.S., they are rising in other countries, and U.S. health officials fear a second wave of COVID-19 when people tired of the lockdown fail to follow safely guidelines such as wearing face masks.

The main utility of this study is to prepare hospitals and healthcare workers with proper equipment, Seetharam said. If they know that the next three days are going to see a surge and the beds at their hospitals are all filled up, theyll need to construct temporary beds and things like that.

As the coronavirus sweeps around the world, Ramesh and Seetharam continue to gather data so that their models can become more accurate. Other researchers or healthcare officials who want to utilize their models can find them posted online.

UNIVERSITY JOINS CORONAVIRUS FIGHT

Faculty, staff and students are leading Binghamton Universitys efforts in the coronavirus pandemic. Here are just a few examples:

Each data point is a day, and if it stretches longer, it will produce more interesting patterns in the data, Ramesh said. Then we will use more complex models, because they need more complex data patterns. Right now, those dont exist so were using simpler models, which are also easier to run and understand.

Ramesh and Seetharams paper is called Ensemble Regression Models for Short-term Prediction of Confirmed COVID-19 Cases.

Earlier this year, they launched a different tracking project, gathering data from Twitter to determine how Americans dealt with the early days of the COVID-19 pandemic.

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Researchers use machine learning to build COVID-19 predictons - Binghamton University

The Inter-dependence of Quantum Computing and Robotics – Analytics Insight

June 21, 2020 | Author: admin

Looking at quantum computing-fueled applications of the future, we much of the time look to the innovations capability to take care of computationally-intensive mathematical problems, which could lead to breakthroughs in drug discovery, logistics, cryptography, and finance.

A research paper by Bernhard Dieber and different scholastics entitled Quantum Computation in Robotic Science and Applications, researches how quantum computing could augment numerous operations where robots are confronted with intensive computational assignments, where commonly broadly useful GPUs have been utilized to deal with intensive tasks.

While we may not see the appearance of quantum-fueled robots in the coming decade, the paper refers to how the rise of cloud-based quantum computing services and even quantum co-processors (QPUs) could work coupled with traditional CPUs to propel the improvement of much increasingly powerful and smart robots.

Australian physicists state they have adapted methods from autonomous vehicles and robotics to effectively evaluate the performance of quantum gadgets. A University of Sydney team reports that its new methodology has been indicated tentatively to outflank simplistic characterisation of these situations by a factor of three, with a lot higher outcome for increasingly complex simulated environments. Lead creator Riddhi Gupta says one of the hindrances to creating quantum computing systems to useful scale is beating the blemishes of hardware.

Qubits the fundamental units of quantum technology are exceptionally delicate to disturbances from their environments, for example, electromagnetic noise and show performance varieties that lessen their usefulness.

To address this, Gupta and associates took strategies from old style estimation utilized in robotics and adapted them to improve hardware performance. This is accomplished through the proficient automation of procedures that map both environment of and performance variations across huge quantum gadgets.

Conventional AI, as opposed to current machine learning applications, depends on formal knowledge representations like rules, realities and algorithms so as to improve the robot behavior or copy intelligent behavior.

Artificial intelligence applications are as often as possible utilized in robotics technology, similar to path planning, the derivation of goal-oriented action plans, system diagnosis, the coordination of different specialists, or thinking and reasoning of new knowledge. A significant number of these applications use varieties of ignorant (visually impaired) or informed (heuristic) search algorithms, which depend on crossing trees or diagrams, where every node represents a potential state in the search space, associated with further follow-up states.

Quantum computing can fill in as an option for pretty much every search algorithm utilized in robotics and AI applications and decrease unpredictability. For graph search, for instance, there is a quantum alternative based on quantum random walks.

In robotics, Gupta says, machines depend on simultaneous localisation and mapping (SLAM) algorithms. Gadgets like automated vacuum cleaners are ceaselessly mapping their surroundings and then evaluating their area within that environment so as to move. The trouble with adjusting SLAM algorithms to quantum frameworks is that if you measure, or characterise, the performance of a solitary qubit, you obliterate its quantum data.

Gupta has built up a versatile algorithm that measures the performance of one qubit and utilities that data to assess the capacities of nearby qubits. We have called this Noise Mapping for Quantum Architectures., she says. Instead of gauging the old-style environment for every single qubit, we can automate the procedure, lessening the number of estimations and qubits required, which accelerates the entire procedure.

Efforts have been made as of late to illuminate old-style automated tasks utilizing AI as another option. In the quantum domain, quantum neural networks could help take care of issues related with kinematics, or the mechanical movement of robots.

There are reports that state how the two degrees of control in robotics, abstract task-planning, and specific movement-planning which are presently illuminated independently, can be explained in an increasingly integrative way with quantum computing.

Quantum computing could play an important job in enhancing the development of machines, including identifying moments of inertia and joint friction. Such difficulties could be addressed with quantum reinforcement learning, with models that can develop themselves, and with hybrid quantum-classical algorithms.

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The Inter-dependence of Quantum Computing and Robotics - Analytics Insight

2 thoughts on Learn Quantum Computing With Spaced Repetition – Hackaday

June 21, 2020 | Author: admin

Everyone learns differently, but cognitive research shows that you tend to remember things better if you use spaced repetition. That is, you learn something, then after a period, you are tested. If you still remember, you get tested again later with a longer interval between tests. If you get it wrong, you get tested earlier. Thats the idea behind [Andy Matuschak s]and [Michael Nielsens] quantum computing tutorial. You answer questions embedded in the text. You answer to yourself, so theres no scoring. However, once you click to reveal the answer, you report if you got the answer correct or not, and the system schedules you for retest based on your report.

Does it work? We dont know, but we have heard that spaced repetition is good for learning languages, among other things. We suspect that like most learning methods, it works better for some people than others.

The series of essays are reasonably technical and assume you understand linear algebra, complex numbers, and Boolean logic. Of course, there are links to help you pick up any of those you lack. Honestly, those topics will help you in lots of other areas, too, so if you dont already have those in your tool belt, it wouldnt hurt to follow some of the links.

If you want to play with quantum computing, we like Quirk. There are also quantum computers you can use for real from IBM, although youll run out of gates pretty quickly.

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2 thoughts on Learn Quantum Computing With Spaced Repetition - Hackaday

New Way to Assess the Performance of Quantum Devices – AZoQuantum

June 21, 2020 | Author: admin

Written by AZoQuantumJun 17 2020

University of Sydney scientists have adapted methods from robotics and autonomous vehicles to efficiently evaluate the performance of quantum devicesa crucial process to help balance the emerging technologies.

The new method has been experimentally demonstrated to supersede the simplistic characterization of these settings by a factor of three, with a relatively higher outcome for more complicated simulated settings.

Using this approach, we can map the noise causing performance variations across quantum devices at least three times as quickly as a brute-force approach. Rapidly assessing the noise environment can help us improve the overall stability of quantum devices.

Riddhi Gupta, Study Lead Author and PhD Student, School of Physics, University of Sydney

The study has been published in Quantum Informationa Nature partner journal.

While quantum computing is still in its preliminary stages of development, it holds implications to redefine technologies by solving issues beyond the context of traditional computing.

One of the obstacles to produce these systems to practical scale is resolving the hardware imperfections. The rudimentary units of quantum technologythat is, quantum bits or qubitsare extremely responsive to disturbance from their settings such as electromagnetic noise and display performance changes that decrease their usefulness.

Ms Gupta, who is also a part of the ARC Centre of Excellence for Engineered Quantum Systems, has used methods from traditional estimation employed in robotics and modified them to enhance the performance of hardware. This was accomplished via the efficient automation of procedures that map both the performance changes and environment over massive quantum devices.

Our idea was to adapt algorithms used in robotics that map the environment and place an object relative to other objects in their estimated terrain. We effectively use some qubits in the device as sensors to help understand the classical terrain in which other qubits are processing information.

Riddhi Gupta, Study Lead Author and PhD Student, School of Physics, University of Sydney

In the field of robotics, machines depend on simultaneous localization and mapping, or SLAM for short, algorithms. Robotic vacuum cleaners are devices that constantly map their settings and then estimate their location inside that setting to move.

The problem with adapting SLAM algorithms to quantum systems is that if individuals define, or quantify, the performance of one qubit, they would damage its quantum data.

As such, Ms Gupta developed an adaptive algorithm that quantifies the performance of a single qubit and applies that data to predict the capabilities of neighboring qubits.

We have called this Noise Mapping for Quantum Architectures. Rather than estimate the classical environment for each and every qubit, we are able to automate the process, reducing the number of measurements and qubits required, which speeds up the whole process.

Riddhi Gupta, Study Lead Author and PhD Student, School of Physics, University of Sydney

Dr Cornelius Hempel, whose experimental group offered Ms Gupta data from experiments performed on a one-dimensional (1D) string of trapped ions, stated that he was happy to observe threefold enhancement even in the mapping of such a tiny quantum system.

However, when Riddhi modelled this process in a larger and more complex system, the improvement in speed was as high as twentyfold. This is a great result given the future of quantum processing is in larger devices, Dr Hempel added.

Professor Michael J. Biercuk, founder of quantum technology company Q-CTRL and director of the University of Sydney Quantum Control Laboratory in the Sydney Nanoscience Hub, is the supervisor of Ms Gupta.

This work is an exciting demonstration that state-of-the-art knowledge in robotics can directly shape the future of quantum computing. This was a first step to unify concepts from these two fields, and we see a very bright future for the continued development of quantumcontrol engineering, Professor Biercuk concluded.

The study was partly funded by the US Army Research Office, the ARC Centre of Excellence for Engineered Quantum Systems, and a private grant from H. & A. Harley.

Gupta, R. S., et al. (2020) Adaptive characterization of spatially inhomogeneous fields and errors in qubit registers. npj Quantum Information. doi.org/10.1038/s41534-020-0286-0.

Source: https://www.sydney.edu.au/

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New Way to Assess the Performance of Quantum Devices - AZoQuantum

Quantum Computing Market 2020 Key Players, Share, Trend, Segmentation and Forecast to 2026 – Cole of Duty

June 21, 2020 | Author: admin

New Jersey, United States,- The report is a must-have for business strategists, participants, consultants, researchers, investors, entrepreneurs, and other interested parties associated with the Quantum Computing Market. It is also a highly useful resource for those looking to foray into the Quantum Computing market. Besides Porters Five Forces and SWOT analysis, it offers detailed value chain assessment, comprehensive study on market dynamics including drivers, restraints, and opportunities, recent trends, and industry performance analysis. Furthermore, it digs deep into critical aspects of key subjects such as market competition, regional growth, and market segmentation so that readers could gain sound understanding of the Quantum Computing market.

The research study is a brilliant account of macroeconomic and microeconomic factors influencing the growth of the Quantum Computing market. This will help market players to make appropriate changes in their approach toward attaining growth and sustaining their position in the industry. The Quantum Computing market is segmented as per type of product, application, and geography. Each segment is evaluated in great detail so that players can focus on high-growth areas of the Quantum Computing market and increase their sales growth. Even the competitive landscape is shed light upon for players to build powerful strategies and give a tough competition to other participants in the Quantum Computing market.

The competitive analysis included in the report helps readers to become aware of unique characteristics of the vendor landscape and crucial factors impacting the market competition. It is a very important tool that players need to have in their arsenal for cementing a position of strength in the Quantum Computing market. Using this report, players can use effective business tactics to attract customers and improve their growth in the Quantum Computing market. The study provides significant details about the competitive landscape and allows players to prepare for future challenges beforehand.

Quantum Computing Market Segmentation

This market has been divided into types, applications and regions. The growth of each segment provides a precise calculation and forecast of sales by type and application, in terms of volume and value for the period between 2020 and 2026. This analysis can help you develop your business by targeting qualified niche markets. . Market share data are available at global and regional levels. The regions covered by the report are North America, Europe, Asia-Pacific, the Middle East and Africa and Latin America. Research analysts understand competitive forces and provide competitive analysis for each competitor separately.

Quantum Computing Market by Type:

YYYY

Quantum Computing Market by Application:

ZZZZ

Quantum Computing Market by Region:

North America (The USA, Canada, and Mexico)Europe (Germany, France, the UK, and Rest of Europe)Asia Pacific (China, Japan, India, and Rest of Asia Pacific)Latin America (Brazil and Rest of Latin America.)Middle East &Africa (Saudi Arabia, the UAE, South Africa, and Rest of Middle East & Africa)

The report answers important questions that companies may have when operating in the Quantum Computing market. Some of the questions are given below:

What will be the size of the Quantum Computing market in 2026?

What is the current CAGR of the Quantum Computing market?

What products have the highest growth rates?

Which application is projected to gain a lions share of the Quantum Computing market?

Which region is foretold to create the most number of opportunities in the Quantum Computing market?

Which are the top players currently operating in the Quantum Computing market?

How will the market situation change over the next few years?

What are the common business tactics adopted by players?

What is the growth outlook of the Quantum Computing market?

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It offers recommendations and advice for new entrants of the Quantum Computing market and carefully guides established players for further market growth

Apart from hottest technological advances in the Quantum Computing market, it brings to light the future plans of dominant players in the industry

Table of Contents

Market Overview: This section comes under executive summary and is divided into four sub-sections. It basically introduces the Quantum Computing market while focusing on market size by revenue and production, market segments by type, application, and region, and product scope.

Competition by Manufacturers: It includes five sub-sections, viz. market competitive situation and trends, manufacturers products, areas served, and production sites, average price by manufacturers, revenue share by manufacturers, and production share by manufacturers.

Market Share by Region: It provides regional market shares by production and revenue besides giving details about gross margin, price, and other factors related to the growth of regional markets studied in the report. The review period considered here is 2015-2019.

Company Profiles: Each player is assessed for its market growth in terms of different factors such as markets served, gross margin, price, revenue, production, product specification, and areas served.

Manufacturing Cost Analysis: It is sub-divided into four chapters, viz. industrial chain analysis, manufacturing process analysis, manufacturing cost structure, and key raw materials analysis.

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Quantum Computing Market 2020 Key Players, Share, Trend, Segmentation and Forecast to 2026 - Cole of Duty

Learn Quantum Computing With Spaced Repetition – Hackaday

June 21, 2020 | Author: admin

Continue reading Learn Quantum Computing With Spaced Repetition

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Learn Quantum Computing With Spaced Repetition - Hackaday

GlobalQuantum Software Market Report 2020 Sales Forecast to Grow Negatively in Western Regio post COVID 19 Impact Analysis Updated Edition Top Players…

June 21, 2020 | Author: admin

Global Quantum Software Market analysis 2015-2027, is a research report that has been compiled by studying and understanding all the factors that impact the market in a positive as well as negative manner. Some of the prime factors taken into consideration are: various rudiments driving the market, future opportunities, restraints, regional analysis, various types & applications, Covid-19 impact analysis and key market players of the Quantum Software market. nicolas.shaw@cognitivemarketresearch.com or call us on +1-312-376-8303.

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Global Quantum Software Market: Product analysis: System Software, Application Software

Global Quantum Software Market: Application analysis: Big Data Analysis, Biochemical Manufacturing, Machine Learning

Major Market Players with an in-depth analysis: Origin Quantum Computing Technology, D Wave, IBM, Microsoft, Intel, Google, Ion Q

The research is presented in such a way that it consists of all the graphical representations, pie charts and various other diagrammatic representations of all the factors that are used for the research. Quantum Software market research report also provides information on how the industry is anticipated to provide a highly competitive analysis globally, revenues generated by the industry and increased competitiveness and expansions among various market players/companies.

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The Quantum Software industry is projected in assembling information regarding dynamic approximations and also listings of a profitable progression rate annually in the expected duration according to a recent & latest study. The latest Coronavirus pandemic impact along with graphical presentations and recovery analysis is included in the Quantum Software research report. The research report also consists of all the latest innovations, technologies and systems implemented in the Quantum Software industries.

Various factors with all the necessary limitations, expenditure/cost figures, consumer behaviour, supply chain, government policies and all the information related to the market have been included in the Quantum Software Market report. The research report also provides light on various companies & their competitors, market size & share, revenue, forecast analysis and all the information regarding the Quantum Software Market.

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GlobalQuantum Software Market Report 2020 Sales Forecast to Grow Negatively in Western Regio post COVID 19 Impact Analysis Updated Edition Top Players...

Is China Threatening Americas Dominance In The Digital Space? – Forbes

June 21, 2020 | Author: admin

The Digital Silk Road is one of the flagship projects in China's Belt & Road Initiative

The Digital Silk Road

As one of Chinas grand proposals within the Belt and Road Initiative (BRI), the Digital Silk Road (DSR) project was introduced in March 2015.The DSR incorporates four interrelated, tech-focused components. First, China is investing resources into digital frameworks abroad, including fiber optic cable lines and data hubs. Secondly, the project focuses on creating cutting edge innovations - like satellite navigation systems and smart city projects - which will be fundamental in giving China an edge as a global economic and military power. China is working on rapidly expanding its area of DSR projects. In 2019, the Chinese government launched a new project called the "Belt and Road Digital Economy International Cooperation Initiative'' with a number of nations including the United Arab Emirates, Thailand, Turkey, Laos, Serbia, Kingdom of Saudi Arabia, and Egypt. The Chinese government also established cooperation agreements with 16 other countries to develop technology focused projects under the umbrella of the New Silk Road Initiative.

From a mid to long-term perspective, China plans to strategically fortify this digital initiative. In April of last year, a report published by the Office of the Leading Group for Promoting the Belt and Road Initiative highlighted that the Chinese Communist Party (CCP) has actively focused on the DSR Initiative with innovation-focused industrial frontier areas. These include cloud computing, nanotechnology, quantum computing, big data, artificial intelligence, and smart cities.

China has made significant telecom investments globally under its flagship Belt & Road Initiative

Impact of Covid-19

Covid-19 has significantly impacted the Chinese economy, particularly the small-medium enterprises. Thus, the governments fiscal and monetary policies have shifted focus on stabilizing the domestic economy. This has strongly impacted the BRI. However, the Information Communication Technology (ICT) infrastructure projects are more cost-effective and can be completed at a faster pace compared to transportation and energy projects. Therefore, the DSR projects are more likely to attract significant investment and garner more interest from investors as compared to other BRI projects after Covid-19.

Is the Digital Silk Road a challenge and a national security threat to the US?

Fitch Solutions believes that the race in technological advancements will continue to be a key focal point of tension between the US and China. The Digital Silk Road is an essential pillar of China's global infrastructure strategy to manage the flow of data worldwide. Most of the infrastructure that is being used within the DSR projects involves little if any US technology, which is a cause of major concern for many Western policymakers and business executives. This is compounded by the fact that data traffic monitoring within China has greatly increased and thus the potential interference with sensitive monetary and security-related data could also be significantly compromised. Chinese tech firms have become highly proficient at using artificial intelligence (AI) to strengthen both their local businesses and relationships with consumers. The data and information accumulated from end-users will give Chinese companies considerably greater insights which will ultimately help them in gaining substantial market share within the BRI countries.

The Belt and Road Initiative supported by the DSR projects do not come without criticism and controversy. Critics have claimed that many Southeast Asian countries involved in the BRI have been drawn in by China's rigorous laws on the localization of data. However, some analysts argue that such changes are mostly being made to protect the information of citizens and incentivize foreign investments into local data hubs, rather than an inclination for ascribing to China's model of cyber administration. Nonetheless, the potential for these structures to be utilized by local governments to clamp down on social discord and political opponents cannot be ignored.

Smart City, Guangzhou Urban Skyline

The BRI has advanced Chinas technological prowess to the point where it now poses as the quintessential challenger to the US in terms of global technological partnerships. Earlier this year in February, the CCP made a concerted effort to focus on digital infrastructure projects including 5G expansions as one of the critical solutions to bolster their economy after the Covid-19 pandemic. Chinese companies such as Huawei Marine Networks have already laid down about 36,964 miles of undersea fibre optic cables in more than 95 different projects spanning the Indo-Pacific, South Pacific, and the Atlantic oceans. Chinese firms' global share in such transnational undersea cable projects have skyrocketed from a mere 7% in 2012 to 20% in 2019.

Expected market growth of 5G subscribers globally.

What choice will the US make?

American business leaders and the Trump administration are clearly intent on protecting US companies and supply chains from heavy reliance on China. For example, the Semiconductor Industry Association (SIA)s recent $32B proposal aims to sharpen the edge of the US semiconductor industry. Semiconductor chips underpin some of the critical commercial and defense technologies of the future which include 5G networks and artificial intelligence. Furthermore, US Justice Department officials tacit pressure on a high-capacity undersea data cable system to bypass Hong Kong is a clear example that reflects Americas hardening stance on China.

In the Covid-19 era and especially given the acrimonious sentiment in Washington and Beijing with respect to Sino-US relations, it is incumbent to not forget that competition and cooperation have always defined US-China relations. The United States is currently losing the race against China in the digital space. However, that does not mean the race is over. Public-private partnerships are needed now more than ever. The US government and the private sector must invest significantly more resources into developing technology and advancing innovation at home. Failing to do so could risk the US permanently losing its global technological leadership to China.

A special thanks to Jeeho Bae and Yaser Faheem for contributing to this article.

Authors:

Mr. Earl Carr is the Vice President of International Research at Momentum Advisors, a New York based SEC-registered international wealth management firm. Jeeho Bae and Yaser Faheem are Research Consultants at Momentum Advisors.

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