Intelligent Edge Categories

TABLE OF CONTENTS 2

TABLE OF CONTENTS A

TABLE OF CONTENTS Audience ...... 1 Author’s Note ...... 2 Executive Summary ...... 5 Building An Intelligent Edge Strategy ...... 7 Edge Project Types ...... 7 The Early Adopter’s Problem ...... 9 Considering Life Cycles...... 9 The Strategy Box ...... 11 Intro to The Intelligent Edge ...... 13 What is The Edge? ...... 13 Why Location Matters ...... 15 Edge & Cloud ...... 15 What is the Intelligent Edge? ...... 16 Intelligent Edge Categories ...... 17 01. Edge Computing ...... 18 02. Edge AI ...... 20 03. Smart Devices, Supersensors, Actuators...... 22 04. Edge Data Management ...... 24 05. Edge Infrastructure ...... 26 Edge Markets ...... 29 Horizontal Markets ...... 29 Vertical Markets ...... 30 Complementary Markets ...... 30 Venture Capital & Other Investments ...... 31 Intelligent Edge Models & Terms ...... 32 Clarification of Terms ...... 32 Edge Overview Diagram ...... 34 Intelligent Edge v. Cloud ...... 34 Device Edges v. Infrastructure Edges ...... 35 Device Edges ...... 35 Infrastructure Edges ...... 35 Homogeneous and Heterogeneous Edges ...... 41 Homogeneous Edge ...... 41

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Heterogeneous Edge ...... 42 Autonomy and Local Interactivity ...... 43 Device Edge Characteristics ...... 45 Planning Your Strategic Edge Initiative ...... 47 Intelligent Edge Life Cycles ...... 47 Edge Data Management ...... 53 Strategic Edge Data Design ...... 53 Edge AI ...... 58 A Brief History of AI ...... 58 Categories of AI ...... 59 AI Companies ...... 60 Executive Interviews...... 67 Author ...... 69

STRATEGY AT THE INTELLIGENT EDGE 1

AUDIENCE This report should be read by CIOs, CTOs, CMOs, RedChip Ventures. We have interviewed dozens of strategy executives, enterprise architects, and other executives, salespeople, engineers, designers, and technology executives and managers involved with visionaries at the cutting-edge of this field. Mentions technology planning. It aims to demystify Intelligent of companies in this document are intended as Edge technology and help leaders and companies illustrations of market evolution and are not intended derive strategic value from its expansion. It is the as endorsements or product/ service recommenda- result of over a year of research and reflection within tions.

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AUTHOR’S NOTE Years ago, I knew the chancellor of a major state university. Charlie, a former chemistry professor, was a brilliant guy. He worked on the first atomic bomb and loved to tinker with computers. He wrote and spoke about the Internet a lot. In the mid-1990s, he predicted that the Internet would become something like a worldwide, video-based CB radio. This seemed like an unusual prediction at the time, but watching my sons on Instagram this afternoon I am struck with how insightful it was. Charlie’s work had staying power like that. In the In 1998 I had the chance to talk with 1980s, he wrote custom software for an apartment Steve Jobs after he’d come back and management company. The application, which ran on a Mac, handled rent roll and maintenance. In the turned Apple around. I was there to early 2000s, this same company asked me to help help Telecom Italia try to do a deal them select new software. By that time, the company had grown and managed thousands of units. To my with Apple, but after that business amazement, they still used Charlie's custom software which had not been changed since the 80s. was completed I couldn’t help asking I recommended updating to a new, web-based a question. “Steve,” I said, “this system. As a self-proclaimed emerging technologist, turnaround at Apple has been I had no choice. At first the new software was not popular, but the users did eventually prefer it. In a impressive. But everything we know few weeks, the new system began delivering about the personal-computer measurable benefits. business says that Apple will always I learned through this experience that timing really does matter -- a lot. It turns out this company was have a small niche position. The not late to adopt this software as I had originally network externalities are just too thought. The timing was about right. Earlier efforts would have been much too slow and expensive. Later strong to upset the de facto ‘Wintel’ efforts would have inconvenienced tenants, standard. So what are you trying to increased costs, and threatened the company’s viability. do? What’s the longer-term strategy?" How should a company decide when to adopt a new technology? As a strategist, the theme of “when (or if) to adopt a new technology” recurs frequently. You can't chase every new trend. Most emerging technologies simply are not worth chasing. They can end up making a mess of an otherwise well-run company. But some technologies are worth pursuing. Indeed, some technologies create so many opportunities that you have no choice but to embrace them.

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The Edge is like this. It will be the largest and most influential technology of my lifetime. The lack of Edge technologies will be what later generations notice about our time; just as we notice what’s lacking in pictures of cities in the 1890s with horses and bug- gies, or of families in the 1930s gathered around vacuum-tube radios. Now is a time when computers and Artificial Intelligence (“AI”) don’t yet interact freely with us in all aspects of life. We live in the pre- Edge era where computer input is accomplished tediously with our fingers pecking away at screens and boards. This will all seem quaint and innocent in the future. There is a lot of information available about the Edge. But this information, mostly in the form of reports, articles, and videos, is primarily focused on what Edge is -- trends, technologies, and features. Instead, this report will focus on how Edge technology can be applied to business problems. It aims to help you answer the question, ”How can I use He (Jobs) didn’t agree or disagree Edge to gain advantages and make money for my business?” with my assessment of the market. Trends can be informative, but strategic advantages He just smiled and said, “I am are not derived from trends. Strategic advantages are going to wait for the next big derived from critical uncertainties -- who and what will win out in the market, what will your competitors thing.” --Richard Rumelt do? Anticipating these uncertainties is half the battle. But as Rumelt points out regarding Steve Jobs, the most effective strategies are also well-timed. Embrac- ing Edge too soon will not provide an advantage. It will be a net cost to many companies and create mis- rule that ripples through those organizations for years. Adopting Edge technologies late will cause different problems. It will drag operations down and can cultivate bureaucracy. Companies will make this mistake too. Either mistake can put a company out of business. Getting it right matters. The purpose of this report is to help you think through timing, technologies, and uncertainties so you can build an effective Edge strategy. I hope that by reading it, you will come away with a better idea of how to position your company to harness the full potential of the Edge.

Daniel Sexton, 2019

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STRATEGY AT THE INTELLIGENT EDGE 5

EXECUTIVE SUMMARY Disruption in business is accelerating. Half of the algorithms and data processing closer to points of companies listed on the S&P 500 will be replaced over origin. But it is more than this too. Compelling new the next 10 years. In 1958, the average tenure of a business cases are emerging as advances in sensor listed company was 61 years; today it is 17 years; by technology, improvements to Edge network 2027, it will be 12 years. Unlike the gentle movements infrastructure, and AI come together at a single point. of Smithian hand of old, today’s invisible hand is swift The Edge is certain to disrupt businesses and markets and exacting. Companies that are not prepared for in unexpected ways. Edge market projections already new market swings quickly become irrelevant. far outstrip Cloud. The Internet of Things (“IoT”) The Intelligent Edge (“Edge”) is the next such make- market alone, which is only a subset of the Edge, is or-break development. At a basic level, Edge enables projected to be between $442 billion and $1.2 trillion computer processing within or near a service point by 2022 (depending on how you define IoT) and is rather than in the Cloud. While this may sound like a growing at 29.4% per year— roughly double the size normal development in tech progress, it will alter life and growth of the global Cloud market. Edge AI, 5G, and business as we know it. New Edge capabilities Edge infrastructure, Edge computing, new sensors are already ushering in a historic wave of and devices, and Edge data management will all be technological innovations. Edge is transforming massive, growing markets over the next decade. customer experiences, redefining business and Yet, market projections alone do not capture the revenue models, and streamlining operations. essence of what the Edge will become. Edge will The term “Edge” describes a physical location. It is bridge the physical and digital realms in revolutionary where computing resources are moved as close as ways. Channeling its power will require radical, possible to end users and devices. But, in some ways, strategic thinking. ”Edge” is a misnomer. It is the Edge only with respect Deriving value from Edge will be challenging. When to centralized computing systems such as the cloud cars can drive themselves and factories operate or enterprise data centers. Yet the Edge is autonomously, entire cities will transform. The everywhere. It is where people and machines unite second-order effects of such a historical revolution with the digital world. The Edge landscape includes will change how businesses operate and should not our homes, bodies, apparel, stores, factories, cities, be underestimated. What will cities look like when streets, parks, buildings, hospitals, sports facilities, parking is not needed or can be moved further away? outer space, and unlimited other places and spaces, people and things, living and not. Increasingly, the It was easy to predict mass car ownership Edge will be at the center of our lives. but hard to predict Walmart. - Carl Sagan This report will explain why Edge is so revolutionary, There are definitive steps you can take to better align how it will unfold in technical and social terms, and yourself to the opportunities that lie ahead. I hope how companies can harness its potential. Edge is this report will help you and your company navigate often described as an extension of Cloud, but it is this amazing market revolution and ensure that the much more than that. It is a revolution in AI invisible hand brings you along for the ride. distribution, edge infrastructure, sensor technology, and unstructured data management. For many applications, executing AI in the cloud creates latency problems. Edge solves these problems by moving

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STRATEGY AT THE INTELLIGENT EDGE 7

BUILDING AN INTELLIGENT EDGE STRATEGY The purpose of this report is to answer two questions in detail:

What is the Intelligent Edge How can I use Edge to my (“Edge” or “the Edge”)? company’s advantage? Before we dive into the details of what comprises Edge technologies (Question 1), let’s first consider how we might construct a strategy to take advantage of the Edge.

EDGE PROJECT TYPES To start, consider this question: Which of the follow- innovation, that benefit from a more sophisticated ing best describes your company’s Edge initiative? strategic approach. 1. Improvement of operational efficiencies Since Carr’s article was written, IT has been or reduction of expenses. increasingly relied upon to play a role in strategic 2. Scaling a current line of business. corporate outcomes. Helping to increase revenues is 3. Entering new markets. a qualitatively different type of project than historical, 4. Innovation or development of a new cost-saving IT projects. But most IT projects already product or service. fail today. So as IT projects become more sophisticated and centered on growth, strategy You may notice that these selections become becomes critical to the success of the company. progressively more difficult as you go from 1 to 4. Historically, most IT projects have fallen into category As IT becomes a profit center as well as a cost center, 1. These projects help improve operations, lower identifying which type of initiative you are expenses, and keep the business running smoothly. undertaking becomes a critical part of building a The other 3 types are intended to increase revenues. strategy. Categorizing project types helps determine when and how to undertake projects. Reducing Why do you need an Edge strategy? operational costs with technology is simpler and more Some argue that IT projects never actually provide an straightforward than creating growth. But with Edge advantage. IT, some have claimed, is a cost center; technologies, and in fact any emerging technology, nothing more than a necessary cost of doing reducing costs can also be tricky. Finding ROI-based business. In 2003, Nick Carr wrote a pivotal article in Edge business cases can be difficult for several The Harvard Business Review entitled, “IT Doesn’t reasons. To start, implementation costs and timelines Matter,” where he argued that IT provides only short- are hard to establish upfront. This makes it harder to term advantages: predict how much value a project can provide. Also, early technologies generally haven’t had time to The trap that executives often fall into, however, is commoditize, so critical component costs are often assuming that opportunities for advantage will be still high. available indefinitely. In actuality, the window for gaining advantage from an infrastructural technology Revenue-generating projects (selections 2, 3, and 4) is open only briefly. - Nick Carr are not only more difficult and costly to implement but their success relies more on strategic approach. Certainly, projects of type 1 provide only short-term In cost-reducing projects, what your competitors do advantages, if any. But, in recent years, projects of matters a lot less. But growth initiatives must take types 2, 3, and 4 have been increasing and are aimed into consideration how technologies will change and at top-line growth. It is these latter three, especially how competitors will act and react to your actions. Additionally, the technical implementation is also

www.redchipventures.com BUILDING AN INTELLIGENT EDGE STRATEGY 8 more difficult because, to be strategic, components A good strategy and approach can improve those must be implemented earlier in their life cycles which odds. The following two sections will help set the introduces complexities. stage for developing your overall Edge plan as you learn and explore what The Intelligent Edge can do for your company.

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THE EARLY ADOPTER’S PROBLEM A few years ago, a Fortune 500 company began build- Should this project have been attempted? Or would it ing a proprietary AI application. The new system used have been better to wait until the technologies some cutting-edge features, such as natural language matured? processing (“NLP”). After attending a particularly If you start a tech project too early, you can get compelling conference, an executive was finally sold leapfrogged by competitors who will face lower costs on the benefits of building an internal Data Science and have access to better technologies later. Any team. He hired a small team of engineers and data advantages may be short-lived, and it is not likely that scientists to build custom algorithms and applications any advantage will be gained. When future costs and for targeted business cases. When they began build- success ratios are considered against ROI, it almost ing this system, NLP Application Programming Inter- always appears beneficial to wait, especially for larger faces (“APIs”) on the market were not very good. incumbents. They were too generic, and the results were inaccu- rate. Considering all the variables, building their own If you wait, however, many competitors will attempt solution seemed like the best option at the time. innovative projects, and some will succeed. And some of those who succeed will parlay this success into Fast-forward a few years and the results of the long-term advantages. Out of the initial large pool of project have been mediocre. The system did provide participants, a few will survive to challenge and even value, but it was expensive to implement and it did displace large incumbents. If you wait, you are likely not provide the lasting competitive advantage that to lose the current advantages you have. was sought. Today, a better solution can be built at pennies on the dollar because much of what was This is the puzzle facing thousands of companies right developed internally is now available on the market now regarding Edge projects. Do you dive into your in AI SaaS components. For example, Cloud Service Edge project now or do you wait? And, if you do dive Providers (“CSPs”) now offer competitive NLP APIs. in, what do you attempt? For instance, Google offers Google Natural Language CONSIDERING LIFE CYCLES API. (Try out the tool here.) Amazon offers Comprehend. AWS offers vertical solutions, such as One way to address these concerns is to align Comprehend Medical. You can even customize your technology life cycles with the type of project being own Comprehend algorithm. Microsoft Azure offers attempted. Emerging technologies change quickly. As LUIS. TextRazor is another option. indicated above, they can even change within the timeline of a project. Additionally, not all technologies Further, the market has disaggregated more within the Intelligent Edge arena are emerging. Some generally into discrete components, such as APIs for are mature and some are innovations that can pre- AI, cloud infrastructure, SD-WAN and managed cloud cede emerging markets by years. So, it is important databases. This value-chain disaggregation was not to determine the pace and acceleration of key compo- anticipated while the system was being designed, so nents within their life cycles. Understanding how system components are coupled to outmoded components are likely to evolve goes a long way infrastructure, code, database, and algorithm choices toward producing a common-sense strategic plan. which has created technical debt. To take advantage of new APIs, the code will need to be refactored. Also, Consider the following chart which depicts 4 stages since personnel resources were spread thin into areas of the technology life cycle. Components start as that the market would later address, the proprietary innovations (lower left), and mature into products, data design was less than optimal. So, the approach and eventually become commodities and services to experimental modeling will need to be rethought (upper right). and redesigned.

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With only one exception (covered later in the report), cycles. It was produced by considering a number of technological assets can only provide strategic value factors, such as supply chain disaggregation, for a limited time as they move along this curve. The adoption rate variables, and market fragmentation key to a great strategy is to identify when, during the (CR4 ratio). It approximates the maturity and course of its life cycle, a technology can provide a strategic value of a number of Intelligent Edge competitive advantage. Then you must ensure that technologies. The S curve shows the path that this aligns with the type of project you are technologies typically take on their life cycle. The undertaking. further a technology is towards the top right of the chart, the easier it is to both purchase and produce. The chart below depicts one interpretation of where The upper right corresponds to products that are a set of Edge technologies fall on their respective life most like commodities.

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Note: These are estimations based on broad years ago. It is also easier to purchase edge sensors technological categories. When building your own with AI capabilities (and to have them designed) than strategic maps for an initiative, be sure to consider it was just a few years ago. Deep learning algorithms, life cycle stages, adoption curves, rates of adoption, such as NLP, can be accessed through third party disaggregation, componentization, and similar APIs, such as TextRazor. Developing proprietary variables. Finding the appropriate level of abstraction algorithms has also become easier with maturing for technological components based on your project languages (R, Python) and development platforms, takes some effort but can provide much insight. such as MathWorks. Additionally, you can outsource your deep learning development more easily than For example, application APIs are easier both to before. develop in-house and to consume compared with five

THE STRATEGY BOX As you consider Edge technologies that matter to companies or organizations are not able to reproduce your initiatives, you may find it useful to produce a those capabilities. similar chart. A project can be decomposed into Components that fall within a certain range on the life technical components and each component can be cycle S curve are most conducive for providing mapped on its life cycle. Technologies often mature strategic advantages. The green box approximates in predictable ways so this exercise can be the stage at which companies benefit from custom- enlightening. building solutions with these components. A blank chart that you can fill in is located in the Technologies at this stage cannot be easily purchased appendix to this report. Remember, the lifecycle is or outsourced, so a company’s execution capability only an estimate. Techniques for how to estimate life relative to competitors can provide an advantage for cycles are covered in more detail later in this report. some period of time. These solutions are sometimes considered complementary assets. Over time, Technology projects can help play a critical role in however, these advantages become necessary costs corporate strategic initiatives without the technology of doing business as lower-price products become itself providing the advantage. Yet some technologies available on the market. do provide competitive advantages because other

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As noted above, it is possible to start initiatives too in more detail later in the report.) That machine learn- early. Technologies that fall closer to innovations in ing (“ML”) is a rapidly maturing market is evidenced their life cycle are often plagued by inefficiencies that by the existence of Sigopt, a type of meta-API so- add significant costs and time delays to commercial lution, was co-founded by Scott Clark, a pioneer in AI solutions. The disillusionment with Big Data a few research. The company provides an API that helps AI- years ago (and IoT more recently) come to mind. If based SaaS companies tune APIs. It provides a your solution is intended to provide a strategic hyperparameter optimization solution that automates advantage, then starting earlier in the life cycle may model tuning to accelerate the model development be worth the risk. But the ideal time to custom-build process and amplify the impact of machine learning solutions is later than some organizations habitually models in production at scale. This process empowers start. developers to generate more high-performing models in production. Of course, it is also possible to start innovative projects too late. One common mistake I’ve seen In other words, Sigopt enables the development of recently is building AI and data science capabilities sophisticated AI APIs for SaaS companies. NLP, text, which are available as third party APIs. Machine and video sentiment analysis, consumer behavior, learning is a rapidly maturing market. Performance or and pricing algorithms are examples of functionality qualitative advantages gained from custom algorithm that is already rapidly commoditizing within SaaS development can be quickly outpaced by the market. markets. This puts pressure on internal algorithm APIs should be considered first unless there are development to keep pace with maturing components reasons other than functionality and performance in the market. that the effort is being pursued. The following graph considers maturity/prevalence Algorithm development is proving to be a less effec- and the speed of evolution for a set of Edge tive defensible strategic approach than data design. components. The color and projected path indicate In fact, algorithms are commoditizing and algorithm the position of each component. Each dotted line advantages are becoming reliant on specialized chips represents how fast a component is expected to that are produced large incumbents. (This is covered mature over a period of time, in this case one year.

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INTRO TO THE INTELLIGENT EDGE

WHAT IS THE EDGE? Over the past five decades, computing architectures computers, and Java applets were popular in times of have favored either centralized or decentralized decentralized computing. Today, the cloud as a approaches. Mainframes with text terminals, Unix centralized core dominates the computing topology. servers, and thin clients found favor in eras of Cloud has had a big impact on applications, business centralized computing. Fat clients, personal models, and businesses.

Sensors Edge Actuators Device

ED E OUD

The pendulum, however, is about to swing. As  Edge AI Algorithms massive and pervasive as hyperscale cloud has  Edge Data Centers become, the Edge will become far larger and more  Micro Data Centers influential. The Edge does not replace centralized  The Internet of Things cloud computing though; it is a complement to it.  5G Computing resources lie on a spectrum between the  Edge Devices, Sensors, Gateways Edge and core. The Edge will be rapidly expanding for  Blockchain at least the next decade.  Operational Technology (OT) The term “Edge” describes a physical location. It is where computing resources are moved as close as possible to end users and devices. The Edge includes locations with harsh conditions, such as remote or The Edge is the decentralized, physical outdoor areas with poor quality connections. It also location where computing resources are includes pristine environments with high-quality being moved. In general, it refers to equipment. Moving resources to the Edge reduces devices or infrastructure resources. latency and improves local interactivity. There are several advantages to this configuration.

An ecosystem of technologies works together to make this new generation of business cases at the Edge viable. This ecosystem includes (but is not limited to):

 Edge AI  Specialized AI Chips

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STRATEGY AT THE INTELLIGENT EDGE 15

WHY LOCATION MATTERS

Providing interactivity and respon- Consider the Autopilot feature found in all siveness at the endpoint location is new Tesla cars (2019): critical to certain applications. To understand why, consider the case of Eight surround cameras provide 360 degrees of the autonomous vehicle. To function, visibility around the car at up to 250 meters of the vehicle needs to continuously monitor its range. Twelve updated ultrasonic sensors surroundings and react appropriately. A range of complement this vision, allowing for detection of powerful sensors generate data that must be both hard and soft objects at nearly twice the processed immediately. These sensors include in- distance of the prior system. A forward-facing frared cameras, 4-D imaging radar, and LIDAR. radar with enhanced processing provides additional data about the world on a redundant The cloud is far too slow to analyze information from wavelength that is able to see through heavy even a single onboard camera. A cloud approach rain, fog, dust, and even the car ahead. would involve sending the data from the car to a cloud service, such as AWS Rekognition. This will not To make sense of all of this data, a new onboard work because the cloud has a baseline network computer with over 40 times the computing latency of 70+ milliseconds which is restricted, in power of the previous generation runs the new part, by the speed of light in fiber and wire, and is Tesla-developed neural net for vision, sonar, and bound by the distance to the data center. radar processing software. Together, this system provides a view of the world that a driver alone Edge solutions solve this issue by moving the cannot access, seeing in every direction algorithms and processing close to the endpoint. This simultaneously, and on wavelengths that go far virtually eliminates concerns with latency, jitter, and beyond the human senses. - tesla.com/autopilot bandwidth and makes the business case viable. Improving responsiveness by moving computations and algorithms to the Edge is just one benefit of Edge applications. EDGE & CLOUD Edge and cloud technologies are not mutually exclusive. Rather, they are complementary. Cloud computing is the on-demand availability of computer system resources, such as data storage and computing power, without direct active management by the user. As such, cloud technology can be located anywhere including the edge. It just so happens that today cloud computing is primarily centralized, but this may not always be the case. Edge data centers (covered later in this report) are a type of cloud offering. Some cloud services which are centralized today will move to the edge for use cases in the future. This has already begun to happen with AWS Outposts, AWS Greengrass, Azure Stack, Google Anthos, and other technologies.

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WHAT IS THE INTELLIGENT EDGE?

The Edge becomes intelligent when AI is combined with smart devices and powerful Edge computing. Intelligent Edge benefits include:  Improved local interactivity; this includes the number of communications and interactions between devices and other devices, and between devices and users or The Intelligent Edge machines; combines advancements in Edge  Handling large volumes of complex data; computing with emerging  Reduced latency and lower bandwidth requirements; complementary technologies --  Lower cost; AI, machine learning algorithms,  Reduced data duplication, data storage, and transmission redundancy; Edge computing, smart devices,  Improved reliability; Edge data centers and networks  Facilitates compliance with laws regarding data -- to provide value right at the transmission and storage; point of interaction.  Improved privacy and security;  Increasingly autonomous features and systems.

Intelligent Edge business cases rely on an ecosystem of technologies that span a number of markets. Below is an incomplete list of related, overlapping complementary markets:

MARKET CAGR FUTURE MARKET SIZE

Edge Computing 58.00% $28.84B (2022)

Edge Data Centers 13.00% $1.54B (2022)

Cloud 17.50% $331.20B (2022)

AI 37.00% $191.00B (2022)

5G 111.00% $202.00B (2022)

IoT Sensors 33.60% $22.48B (2022)

IoT 13.60% $1,200.00B (2022)

OT 6.70% $40.14B (2022)

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5G WILL COVER

15% OF WORLD POPULATION BY 2025

INTELLIGENT EDGE CATEGORIES CURRENTLY, THE INTELLIGENT EDGE HAS 5 CATEGORIES:

01 02 03 04 05

Smart Devices, Edge Edge Edge Data Edge Supersensors, Computing AI Management Infrastructure Actuators

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01. EDGE COMPUTING In general, edge computing means decentralized computing. It covers a broad range of technologies, including: Peer-to-peer computing: Peers are nodes that  AI chips; make a portion of their resources (such as processing  AI accelerators; power, disk storage, or network bandwidth) directly  Deep learning available to other participants, without the need for algorithms; central coordination.  Edge data centers and networks;  Micro data centers; Grid computing: A computer network in which each  Edge devices, sensors and actuators. computer's resources are shared with every other computer in the system. Edge Computing Benefits Mesh computing: A local network topology in which  Improved local interactivity; the infrastructure nodes connect directly,  Reduced latency; dynamically, and non-hierarchically to as many other  Reduced bandwidth; nodes as possible and cooperate with one another to  Lower costs; efficiently route data from/to clients.  Reduction of data duplication;  Reduced data storage and transmission Fog computing: An architecture that uses Edge redundancy; devices for compute, storage, and communication  Improved reliability; locally which are then routed over the internet.  Improved compliance with laws governing Blockchain: a system in which a record of privacy, security, data transmission, and transactions are maintained across several computers storage; that are linked in a peer-to-peer network.  Increasingly autonomous systems and AI; ontent Delivery Networks (“ DNs”): A geographically distributed network of proxy servers EDGE COMPUTING IS A DECENTRALIZED/ and their associated data centers that provide high DISTRIBUTED COMPUTING MODEL WHERE availability and high performance by distributing the COMPUTING RESOURCES SUCH AS COMPUTE service spatially relative to end-users. AND STORAGE ARE MOVED AWAY FROM CENTRALIZED SYSTEMS AND BROUGHT Edge and Micro Data Centers: A smaller or CLOSER TO PHYSICAL LOCATIONS WHERE containerized data center that is designed for THEY ARE MORE USEFUL. computer workloads that are needed closer to endpoints. FOG COMPUTING REFERS TO THE NETWORK CONNECTIONS BETWEEN THE EDGE DEVICES Edge computing components exist on one half of the Edge-cloud spectrum. Edge devices are on one end AND THE CLOUD. FOG COMPUTING EXTENDS THE CLOUD CLOSER TO THE EDGE OF A of this spectrum and centralized cloud services are on NETWORK. SEE OPENFOG CONSORTIUM the other. The spectrum includes a growing ecosystem of overlapping and often competing technologies. FOR COMMONLY USED TERMS WITHIN THE INDUSTRY, SEE: OPEN GLOSSARY OF EDGE Edge computing components are becoming more COMPUTING. THE STATE OF THE EDGE specialized. Increasingly, they are competing in MAINTAINS AN EDGE LANDSCAPE MAP. narrow verticals to address particular business cases. These components include:

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BLOCKCHAIN Blockchain is a relatively new type of edge computing  Payment processing; system. It may soon offer a range of innovative Edge  Monitoring supply chains; solutions.  Sharing data; As a type of distributed, decentralized computing  Weapon tracking; system, blockchain is a natural fit for the Intelligent  Digital IDs; Edge. It is a powerful complement to other types of  Storing of medical records; Edge solutions because it is designed to be  IoT networks and security; decentralized from the ground up.  Tracking prescription drugs;  Copyright protection; Blockchain is a way of storing a list of records (blocks)  Disaster recovery; such that they cannot be easily altered. It is often  Identity management; described as an open, distributed ledger. It uses  Tax regulation; digital signatures and hash functions to ensure that a  Stock trading; list cannot be changed without majority consent. A  Tracing food supply chain; peer-to-peer network manages a blockchain. And it  Digital voting; defines the common protocol that is used to  Title transfers (real communicate and to create new blocks. estate, autos, land); Blockchain has gained attention as the underlying  Gaming. technology of cryptocurrencies. But there are several other potential use cases which include:

Notably, blockchain technology enables smart contracts. Smart contracts are self-executing contracts with the terms of the agreement between buyer and seller being directly written into lines of code. The code and the agreements contained therein exist across a distributed, decentralized blockchain network. Smart contracts permit trusted transactions and agreements to be carried out among disparate, anonymous parties without the need for a central authority, legal system, or external enforcement mechanism. They render transactions traceable, transparent, and irreversible. - Investopedia

Smart contracts are beginning to gain traction. Blockchain will eventually be a powerful technical and Decentralized finance, or DeFi, applications are social component of applications. It elegantly challenging traditional Fintech solutions (see github disintermediates gatekeepers and obviates list). Digital collectibles, enabled by smart contracts, bureaucracy. However, blockchain is still a nascent and non-fungible tokens, allow users to collect unique technology. It has a long way to go before becoming items that are provably scarce. This is unlike Beanie a viable component of practical business applications. Babies, for instance, which can be counterfeit. As an Non-blockchain businesses should consider the costs infrastructure service, Filecoin allows users to get and benefits of using blockchain. Due to its newness, paid for hosting the storage of files for others. It is an it still carries considerable risks. alternative to cloud service providers, such as Amazon Web Services (AWS) and Azure.

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02. EDGE AI

Edge A n rastr ct re Devices

Sensors Edge Actuators Device

ED E OUD

AI is the defining characteristic of Intelligent Edge  Nvidia applications. AI makes split-second decisions at the  IBM point of interaction. Autonomous vehicles, AR/VR,  Xilinx medical devices, and real-time facial identification are  HiSilicon examples of business cases that require high-  AMD powered Edge AI processing.  Groq  Apple In the past, AI was centralized and software-focused.  NuvoMind2 AI algorithms were executed on general-purpose  Thinci chips that ran on a centralized server. But AI solutions  Mythic AI are moving to the Edge. Algorithms running on specialized hardware tuned for specific tasks are As Edge AI matures, both hardware and algorithms quickly becoming the norm. will continue to become specialized. An example of AI specialization is iPhone’s Neural Engine. In As a result, a number of companies are building chips September 2017, Apple released Face ID which specifically designed to run AI at the Edge. This list unlocks the device. The feature uses dedicated neural includes: network hardware that is powered by a custom-built  NVIDIA (Jetson) chip for iPhone 8, 8 plus, and X. The A11 Bionic chip  Intel (Movidius and Myriad) can perform up to 600 billion operations per second.  Alphabet - Google TPU1 The new A12 Bionic can do 5 trillion operations per  Baidu second. There are faster, non-mobile chips on the  Arm market, but the A series is the fastest mobile chip. As  ViaTech a comparison, Deep Blue, the IBM supercomputer  LG that beat Gary Kasparov at chess in 1997, achieved  MediaTek 11.38 gigaflops. The A12 is up to 90 times faster at 1  SambaNova teraflop.  Wave Computing Without this specialized hardware, Face ID may not  Qualcomm have been a success:  Graphcore  Intel The experiences we deliver through the phone are  Imagination Technologies critically dependent on the chip… We couldn't have done  Adapteva that[Face ID] properly without the Neural Engine. --  Samsung Apple VP, Tim Millet

2 1 https://www.aitrends.com/edge-computing/ai-on-the-edge-evolving- https://www.forbes.com/sites/moorinsights/2019/06/03/novumind-an- rapidly-with-specialized-chips/ early-ai-chip-startup/#79056d515b54

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Google also uses its own custom chips for machine AI algorithms are also maturing. For instance, image learning. The new Pixel phones process image data classification using CIFAR datasets began to exceed directly on the device. Nvidia makes high-end average human capabilities in late 2014. For example, graphics hardware and AI software platforms. certain algorithms are geared to ingesting a set of Nvidia’s Drive PX Pegasus is can process 320 million images and describing the contents. For example, instructions per second which helps handle inputs for Imagenet Image Recognition ranks humans against autonomous vehicles (lidar, radar, HD cameras, etc.). algorithms for identifying objects in images (below).

Source: https://www.eff.org/ai/metrics

AI algorithms are beginning to catch up to or exceed human capabilities. Here is a short list of tasks for which algorithms are currently being trained to compete with humans. Humans perform better at most tasks but the margins are decreasing.  Written language;  Scientific and technical capabilities;  Reading comprehension;  Solving constrained, well-specified technical  Language modelling; problems;  Conversation;  Reading technical papers;  Translation;  Solving real-world technical problems;  Spoken language;  Generating computer programs from  Speech recognition; specifications;  Music information retrieval;  Answering science exam questions;  Instrumental tracks recognition;  Learning to learn better.

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03. SMART DEVICES, SUPERSENSORS, ACTUATORS

Device Edge

Sensors Edge Actuators Device

ED E OUD

Since their inception, computers have been able to calculate and perform logic. In order to be useful, however, data had to be formatted and input into a computer. Now computers can not only see, smell, hear, feel, and taste, but also reason (to some extent) from raw sensory inputs. Recent advances in AI enable computers to accept all kinds of unstructured data and make sense of it. These capabilities open up great opportunities to help solve business problems. For example, computer vision and auditory AI can be  Ground-penetrating RADAR (Wavesense) -- used to analyze a human’s: sees 9-10 feet underground at speeds up to 70mph  Facial expressions;  4-D imaging radar (Arbe Robotics) -- can  Vocal affects; create detailed images at distances of over  Body poses; 900 feet.  Interpersonal distance;  Gestures; Other examples of sensors used for edge applications  Heart and respiration rate to infer emotions, include: mood, health, and honesty.  Quantum sensors; Not all sensors are advanced. For decades, a broad  High Resolution cameras; range of commodity sensors has been used in  Ultrasonic sensors; industry. Some examples are:  GNSS (Navigation);  IMU (Inertial Measurement Kit)  Pressure;  LIDAR (Luminar and Blackmore Sensors --  Temperature; acquired by Aurora Innovation and Analytics).  Humidity;  Motion detection; Recent innovations in sensor technology are creating  CO2; fascinating opportunities. Consider the Gravity  Accelerometers; Pioneer project. A consortium of scientific and  Voltage. engineering companies is developing quantum, cold- atom sensors. These sensors can detect and monitor These basic sensors are an important component of objects under the ground with incredible precision. the Intelligent Edge. Yet, powerful new types of The sensor uses rubidium atoms cooled by lasers to sensors are emerging. Consider the following sensors just above absolute zero. The atoms are propelled that are being used in autonomous vehicles: upward in a vacuum and then measured as they fall  Far infrared cameras (FLIR, AdaSky, and Seek back under gravity. Thermal);

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As an example of how sensor technology can be AI algorithms which scan three dimensional applied, consider the startup Doxel. Doxel uses robots representations of job sites to show daily progress. and drones to survey construction sites to inspect Sensors, new and old, are ushering in a new quality and find errors. The robots use sophisticated generation of applications. These deal with HD imaging and lasers to scan sites at all phases of everything from complex machinery to simple, development. This replaces work done by humans everyday tasks. Below are some additional resources which takes days or weeks to complete and lags for further information on the use of sensor construction activity. The reports are generated using technologies.

Drones are a rapidly growing technology with great potential for Edge applications. The number of drones in the United States is large and growing rapidly:

1,499,839 1,079,610 Drones Registered Recreational Drones Registered 416,210 158,554 Commercial Drones Registered Remote Pilots Certified

Autonomous Car Companies

Aptiv Waymo (formerlyGoogle Luminar Aurora (acquired self-driving car) Blackmore Sensors)

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04. EDGE DATA MANAGEMENT Moving new sensing capabilities and cutting-edge AI Data produced at the Edge will into all areas of life and business means that data -- be too large to be transmitted lots of it -- will be generated everywhere. According to the cloud. In fact, recent to the International Data Corporation (“IDC”), the projections exceed the sum of the world’s data will grow from 33 zettabytes capacity of existing in 2018 to 175ZB by 2025.3 The IDC also estimates underground fiber. that there will be 41.6 billion connected IoT devices, Consequently, it will be or "things," generating 79.4ZB of data in 2025. necessary to process most data at the source. Today, 10% of data is created and This data will need to be managed and integrated processed outside of a centralized data center or wisely. Data is quickly becoming the primary technical cloud. By 2022, Gartner predicts this figure will reach strategic asset. Some data generated at the Edge will 75%.4 be sent to a centralized server, but most of it will not.

By way of example, consider the amount of data generated by these two systems

19TB per hour 70TB per hour Autonomous car Commercial Aircraft

As autonomous and semi-autonomous vehicles fill the primary benefits over traditional, cloud analytics roadways in coming years, the amount of data systems and data warehouses. First, it makes it generated from them will be astronomical. But possible to obtain real-time insights from devices for automobiles and aircraft comprise only a small visualizations and insights at the Edge. This means fraction of the overall Edge data that will be that machines and users can benefit from immediate produced. Edge devices of all kinds, including IoT data analysis at the location where the data is being and OT devices, will produce massive amounts of generated. The second benefit is that it reduces the disparate data from billions of endpoints. amount of data which travels to the cloud. EDGE ANALYTICS Edge analytics is a large market with staid industry Edge analytics is an approach to data collection and players such as Microsoft, IBM, and Amazon Web analysis in which automated analytical computations Services. However, smaller, innovative companies are performed on data at the Edge. The purpose of have entered this space as well. As an example, here analytics systems is to derive actionable insights from are three companies which are addressing ancillary data. Analyzing data at the Edge provides two Edge markets in new ways:

3 https://www.seagate.com/our-story/data-age-2025/ 4 https://blog.seagate.com/vision/edge-computing-and-the-future-of- the-data-center/

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 Element AI  Cardlytics

Edge Intelligence: Provides an analytics software platform for mobile Edge computing that processes data in real-time and provides insights into geographically distributed Edge data from devices such as network servers, routers, and threat intelligence platforms.

Phizzle: Builds MEC marketing solutions by using Edge computing to help organizations combine their business intelligence and machine learning to improve everything from SMS marketing to social data visualization. The Charlotte Hornets use the organization’s Edge computing offering to compile and distribute records and data for the team's millions of fans. This helps eliminate duplicate records and provides real-time marketing data.

Foghorn: Develops Edge intelligence software (real- time equipment insights to reduce processing and storage costs) for Industrial IoT (“IioT”) in sectors ranging from oil and gas to smart buildings and manufacturing. The following is a short list of innovative edge analytics companies which are attacking some other interesting verticals.

 Databricks  Aruba Networks  Lookout

 Redis Labs  MapR  SparkCognition

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05. EDGE INFRASTRUCTURE

Edge n rastr ct re Access Aggregation ayer ayer Devices

Sensors Edge Actuators Device

ED E OUD

Edge infrastructure comprises the service provider MODULAR EDGE DATA CENTERS side of the last mile network. This includes Edge data For Edge deployments, many enterprises do not have centers and micro data centers as well as multi- available space. A common solution is to retrofit a access edge computing (“MEC”) and Narrowband IoT small area into a Micro Data Center. However, since (“NB-IoT”). these areas are not designed to house IT equipment, Average network latency to a cloud service from an they can have several concerns including high cost, Edge device is around 70-80 milliseconds. This means power availability, security, cooling and other that a round trip call to a cloud service does not environmental controls. typically execute faster than approximately 200 Modular Edge Data Centers solve this problem by milliseconds. Edge infrastructure, such as edge data offering a prefabricated structure that is designed to centers, can offer round trip speeds under 10 house critical IT equipment. These systems are easy milliseconds. This makes edge infrastructure a critical to deploy and cost between 20-30 percent less than architectural component of many Edge business standard data centers. Cooling, power, security, cases. environmental controls and fire protection are built EDGE DATA CENTERS into the units. Edge data centers (“EDCs”) are a subcategory of CSP ON-PREMISE HYBRID CLOUD (ITAAS) Edge computing. EDCs are similar to content delivery CPSs offer their own variations of edge data centers networks (“CDNs”). EDCs are designed to be a faster, which facilitate hybrid cloud solutions. Hybrid cloud is light-weight alternative to cloud infrastructure. They an approach to enterprise architecture that involves can be used for caching, processing, storage, and running workloads across Edge and Cloud related functions. EDCs are also designed to infrastructures. CSPs are now offering on-premise complement existing cloud or colocation hardware solutions with many of the same service deployments. They can be located on-premise or offerings that are found in the centralized cloud. within 5-10 miles of users and endpoints. Edge data Prominent examples include AWS Outposts, Azure centers are a type of cloud offering that is closer to Stack, and Google Anthos. These offerings vary quite endpoints and the “last mile” of delivery. a bit between providers, but, in theory, hybrid on- MICRO DATA CENTERS premise solutions are excellent for Edge applications that require low latency or have data residency Micro Data Centers (“MDCs”) are smaller than EDCs. concerns. Since hardware is deployed on-premises, it They are about the size of gun storage cabinets. can be customized to provide fast and specialized Typically, they are 3-6 kilowatts in capacity per edge data processing per use case. cabinet. MDCs are deployed in offices, stores, warehouses, factories, within telecommunications MULTI-ACCESS EDGE COMPUTING (MEC) computing rooms, and in other locations where space Multi-access Edge computing (“MEC”) provides cloud is at a premium. computing services in an ultra-low latency, high

STRATEGY AT THE INTELLIGENT EDGE 27 bandwidth environment. MEC is located physically at NARROWBAND IOT (“NB-IOT”) [1][2] the edge of a cellular network . It works in Narrowband IoT is a Low Power Wide Area Network conjunction with mobile base stations but can be used (“LPWAN”) radio technology standard developed by with any network including LTE and 5G. Operators 3GPP to enable a wide range of cellular devices and may authorize third parties to use their Radio Access services. NB-IoT offers improved indoor coverage and Network (“RAN”). This allows third parties to deploy can support a large number of lightweight devices. applications. MEC is an Industry Specification Group These devices are typically quick, low cost, and low (ISG) within the European Telecommunications power. NB-IoT can be deployed within an LTE carrier Standards Institute (“ETSI”). (in-band), within an LTE carrier’s guard-band, or as a standalone in a dedicated spectrum.

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EDGE MARKETS The term Intelligent Edge describes how a technical solution is designed. It is not a single market or specific application. There are numerous Edges, Edge components, and markets. To help describe Edge initiatives, the following sections explore market categories.

HORIZONTAL MARKETS Some large providers serve horizontal markets. These include cloud service providers, telecoms, and data  Dell IoTech center providers. Many of these providers have begun  Dell & Intel: Foghorn adding Edge products and services to their offerings.  SAP Edge Services For instance, Amazon Web Services offers AWS Greengrass, AWS IoT Core, and AWS Outposts. Microsoft, on the other hand, offers Azure IoT, Azure  NVidia EGX IoT Edge, and Azure Stack.  Edge and Micro Data Centers The following list includes a number of large  Cisco providers/ solutions addressing horizontal markets:  Juniper Networks  Arista Networks  AWS IoT  Huawei  AWS IoT Core  EdgeConneX  AWS Greengrass  Flexential  Microsoft Azure IoT  Equinix  Azure IoT Edge  Vxchng  IBM IoT  365 Data Centers  Google Cloud IoT  Google Edge TPU *Note: Cisco, Dell, and Microsoft formed OpenFog  Cisco Edge Computing Infrastructure Consortium.  General Electric Edge Computing Note: Also consider the other horizontal markets for  Hewlett Packard Enterprise Edge Computing components within the edge ecosystem. HPE: The Edgeline Converged Edge  Microcontrollers, microprocessors, sensors, Systems (Model EL 1000) gateways, and similar parts are relatively mature  AT&T Edge horizontal markets which affect Intelligent Edge  Intel Edge solutions.

Example Horizontal Specialty Companies: Rigado: IoT data solutions for smart, connected environments. The Cascade-500 IoT Gateway product provides Edge connectivity to sensors, devices, and the cloud and was one of the first to include AWS Greengrass. The company’s gateway products help reduce latency and also allow a range of endpoint connectivity options including Bluetooth and LTE. Clear Blade: IoT Platform. Edge computing software lets businesses securely run and scale IoT devices in real-time.

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VERTICAL MARKETS Consumer products and end-to-end solutions tend to address market verticals. Examples are too numerous to list but include:  Energy & utilities  Olli  Healthcare  Autonomous Trucks (McKinsey Report)  Agriculture  Uber Advanced Technologies Group  Manufacturing (defunct)  Transportation & logistics  TuSimple  Railway Cargo  Retail  Hitachi  Data Centers  GE  Wearables  Autonomous Buses  Smart Cities, Smart Home, Smart Buildings

COMPLEMENTARY MARKETS Intelligent Edge business cases rely on an ecosystem of technologies. These technologies span several markets. Below is an incomplete list of related, overlapping complementary markets:

MARKET CAGR FUTURE MARKET SIZE

Edge Computing 58.00% $28.84B (2022)

Edge Data Centers 13.00% $1.54B (2022)

Cloud 17.50% $331.20B (2022)

AI 37.00% $191.00B (2022)

5G 111.00% $202.00B (2022)

IoT Sensors 33.60% $22.48B (2022)

IoT 13.60% $1,200.00B (2022)

OT 6.70% $40.14B (2022)

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VENTURE CAPITAL & OTHER INVESTMENTS Intelligent Edge investments comprise several As of this writing, early-to-medium stage investments markets. A non-comprehensive list includes: were highest in AI. In fact, in 2018, almost $100B flowed into AI with $9.9B going to early-stage AI  Edge AI (Artificial Intelligence) startups. Contrast that with hardware-heavy areas  AI chips (such as IoT devices, OT devices, and networking  Edge AI algorithms hardware) which are experiencing a steady decline in  Edge Data Centers early and later-stage investments.  Micro Data Centers  Multi-Access Edge Computing Meanwhile, larger, established companies are  IoT investing in the “Intelligent Edge.” HPE and Microsoft  Platforms announced $4B and $5B investments respectively.  Analytics Likewise, the telecom industry is investing in 5G.  Data Management Massive MIMO and small cell/dense deployment  Security infrastructure is reaping the majority of the effort, but  5G software-defined (“SD”) solutions are also being  Edge Devices, Sensors, Gateways explored as they tend to reduce generation cycles  Blockchain from years to weeks.

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INTELLIGENT EDGE MODELS & TERMS The Intelligent Edge is a broad term. It can help to have descriptive models that can guide conversations and planning efforts. The following sections provide a basic framework to help guide edge planning efforts.

CLARIFICATION OF TERMS EDGE & FOG COMPUTING INTELLIGENT EDGE V. INTELLIGENT MESH “Intelligent Mesh” is a subtype of edge computing. It Fog computing is a system-level horizontal is included within the scope of the Intelligent Edge. architecture that distributes resources and services It uses a less structured architecture than does of computing, storage, control, and networking traditional edge computing (or it can even use an anywhere along the continuum from Cloud to unstructured architecture). Typical Edge Things. - OpenFog Consortium architectures have structured, defined layers with

clear responsibilities. Dynamic mesh architectures The difference between Edge and fog depends partly enable flexible and responsive Edge systems in a less on whom you ask. The terms are often used organized way. interchangeably. In general, Edge refers to a location. Fog, a play on the term “cloud,” refers to a broader UBIQUITOUS COMPUTING, PERVASIVE IT architecture. COMPUTING, INTERNET OF THINGS, AND AMBIENT INTELLIGENCE The fog architecture is growing outward from For the last 25 years, technologists have been centralized cloud services. Fog mirrors the predicting the “third wave of computing.”5 The first standardized, scalable model of cloud computing. It wave was mainframe or centralized computing. extends the standardized services of cloud computing Mainframes facilitated a centralized “many people to to the Edge. Fog includes the space towards the Edge one machine” model. The second wave was personal that is not cloud. computers. PCs brought about the “one person to one The OpenFog Consortium includes Cisco Systems, machine” model. The third wave, demarcated by Intel, Microsoft, Princeton University, Dell, and ARM pervasive, tiny computers and sensors, was intended Holdings. According to OpenFog, fog computing to be a “many people to many machines” model. always uses Edge computing. Edge computing may The terminology used to describe the third wave of or may not use fog computing. Additionally, fog computing came out of funded research labs. These includes the cloud whereas Edge does not. terms include:  Ubiquitous computing  Pervasive computing  Internet of Things In February of 2017, OpenFog released its  Ambient intelligence reference architecture for fog computing. Mark Weiser, the chief technologist of Xerox Parc, coined the term “ubiquitous computing” in 1988. He used it to describe a paradigm that would overtake PCs. His vision consisted of tiny devices embedded in things in the physical world. These devices communicate and inter-operate via new wireless

5https://www.sciencedirect.com/science/article/pii/S0267364915001144

STRATEGY AT THE INTELLIGENT EDGE 33 communications technologies. 6 Notably, he defined For the purposes of this report, “third wave of this type of computing as “distributed, unobtrusive, computing” terms overlap with and are contained and context-aware.7 within the Edge. They define different ways of describing Edge systems. Ubiquitous and pervasive The more recent term, “ambient intelligence,” was computing describe Edge applications that are coined in the late 1990s by Eli Zelkha and his team at anywhere and everywhere. Ambient intelligence Palo Alto Ventures. It has been defined as “...an describes systems that are both sensitive to and emerging discipline that brings intelligence to our responsive to human beings. IoT systems can be everyday environments and makes those either or both. The defining characteristic of an IoT environments sensitive to us. Ambient intelligence system is asset tagging. Some sort of asset or thing (AmI) research builds upon advances in sensors and is monitored and analyzed. sensor networks, pervasive computing, and artificial intelligence.”8 EDGE INCLUDES IOT, OT, IT The term “Internet of Things” is thought to have Other popular terms related to Edge include the been coined by Kevin Ashton at Proctor & Gamble’s following technologies. Auto-ID center. Early IoT definitions centered around  The Internet of Things (IoT) radio-frequency identification (“RFID”) and tagging  Operational Technology (OT) “things” so that computers could manage them. It  Information Technology (IT) has since evolved into a broader definition. The global IoT market, as it is currently defined, is projected to The scope and definition of these terms overlap quite be valued over one trillion dollars by 2022. a bit, but each one is a subset of Edge.

6 https://dl.acm.org/citation.cfm?id=329126 8https://www.sciencedirect.com/science/article/abs/pii/S1574119209000 25X 7 https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2613198

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EDGE OVERVIEW DIAGRAM

Heterogeneous

Edge Device Sensors/ Edge Gateway Actuators Device Edge Data Center Homogeneous

Sensors/ Edge Actuators Device Edge Device Gateway Micro Data Center

DEVICE INFRASTRUCTURE CLOUD / CORE EDGE EDGE

INTELLIGENT EDGE V. CLOUD

INTELLIGENT EDGE

CENTRALIZED CLOUD

Edge Data Center Edge Device Gateway Sensors/ Edge Actuators Device Micro Data Center

“Edge” and “cloud” are not opposing terms.  Cost constraints vary widely and compel Nevertheless, Edge systems do have different thoughtful designs. attributes than cloud systems. Here are some of the  Power management can be a concern. key differentiators of Edge systems when compared  Data tends to be more complex and with traditional, centralized cloud systems: heterogeneous.  Data generated at the Edge can be massive -  Scale and diversity of devices, data, and - most of it will not be stored or transmitted. protocols is far larger.  AI/ML-specific hardware is a design  Architectural implementations tend to be differentiator. idiosyncratic, highly specific, and coupled to specific use cases.

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 New tools, development environments, and  Devices can run on an OS (Linux), VM, or ecosystems add to application development bare metal. costs.  Common protocols (HTTP, TCP/IP) may or  Risk management and security are more may not be used. complex. For example:  Solutions are highly specific at the device  Locations sometimes share legal domain level. and precedents have yet to be established.

DEVICE EDGES V. INFRASTRUCTURE EDGES There are two Edges-- Device Edges and Infrastructure Edges.

DEVICE EDGE INFRASTRUCTURE EDGE CLOUD / CORE

Edge Data Center Edge Device Gateway Sensors/ Edge Actuators Device Micro Data Center

DEVICE EDGES Device Edges interact with users and machines.  Battery or electric line powered; Devices include not only mobile phones, laptops, and  Run operating systems (Linux), VMs, tablets, but also less conventional devices, such as containers, or bare metal; connected automobiles, airplanes, refrigerators, and  Send data via application level or lower OSI industrial motor sensors. The device edge includes levels; IoT, OT and IT devices.  Several protocols; HTTP, XMPP, CoAP, MQTT, AMQP, VSCP, DDS, STOMP; Edge devices can perform several functions. These  Produce various types of data, including: include sensing, sending data to other devices and  Standardized text data, such as JSON; gateways, running algorithms, caching and analyzing  Digital video, image and audio data; data, actuating and controlling machines, and  SCADA data; running operating systems, containers, and VMs. In  Analog data; practice, Edge devices can be managed by cloud  Binary data. services using Amazon Web Services (Greengrass, IoT), Azure (IoT), and other providers. Examples of Edge devices include simple sensors, such as these temperature sensors. Characteristics of Edge devices:

 Single-function (temperature sensor) or multi- function (mobile phone);  Send data via radio frequencies or wire;

INFRASTRUCTURE EDGES

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Infrastructure Edges are large-scale facilities and provide significantly more computing power than operated by service providers and network operators. networks made up of Edge devices. These facilities are located within the “last mile EDCs are evolving to handle complex, distributed network.” They are centered between Device Edges architectures by supporting specialized accelerators. and centralized clouds or data centers. For instance, Microsoft is investing at FPGA AI Physically, the Infrastructure Edge is located within a accelerators at the Edge.9 The NVIDIA EGX platform few miles of devices. It primarily serves to reduce “enables companies to perform low-latency AI at the latency and improve bandwidth. Near-zero edge — to perceive, understand and act in real time millisecond latency can be handled at the Device on continuous streaming data between 5G base Edge. But medium-range latency requirements can stations, warehouses, retail stores, factories and be handled at the Infrastructure Edge. The beyond.”10 Cisco, Juniper Networks, Arista Networks, Infrastructure Edge provides a balance between and Huawei are also making strides in Edge-based latency and resource density. It is a middle ground data center and software-defined networking. between Edge devices and a centralized cloud. Azion operates more than 30 Edge network data Open standards projects are helping to create an centers worldwide with 70 slated to open over the Edge supply chain for deploying and managing next few years. The company helps businesses build infrastructure. Examples include: scalable and secure serverless applications at the Edge. The Edge products connect to cloud service  Open Compute providers and facilitate app-building which decreases  Open 19 latency times for content downloading, personalized EDGE DATA CENTERS security building, basic connectivity, and other Edge Data Centers (EDCs) act like mini-clouds (fog) activities. and provide substantial latency improvements over centralized cloud services with similar availability (modified ANSI/TIA-942). Designed to complement existing cloud or colocation deployments, EDCs can be located on-premise or within a few miles of users. On-prem Edge data centers are often connected directly through a private, high-bandwidth network and can also be wired directly to a LAN through fiber optic cabling. Off-prem EDCs are spread across cities and suburbs at 5-10 mile intervals.EDCs are much smaller than cloud data centers (between 50 to 100 square feet) and are 50-150 kilowatts in capacity. Cloud data centers, on the other hand, are 25-35 megawatts, have up to 80,000 servers and can take up hundreds of thousands of square feet. Sub-10 millisecond latency will be common for EDCs located in tier-1 markets such as New York and Atlanta as well as tier-2 markets like Pittsburgh or St. Louis. Primarily, this is because EDCs are much larger

9 https://www.datacenterknowledge.com/edge-computing/why- 10 https://nvidianews.nvidia.com/news/nvidia-launches-edge-computing- microsoft-betting-fpgas-machine-learning-edge platform-to-bring-real-time-ai-to-global-industries

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Edge Micro, for its part, provides Edge colocation  Juniper Networks data centers. Data centers are carrier neutral,  Azion modular data centers. They use a patent-pending  Edge Micro technology that the company calls Edge Traffic  Arista Networks Exchange (“ETX”). ETX offers cloud-like services (like  Huawei compute, storage, and network resources) at the  Nokia Edge. The data centers offer a single source for  EdgeConneX MNOs, ISPs, and content providers.  Flexential  Equinix Companies in the Edge Data Center market include:  Vxchng  Hewlett-Packard  365 Data Centers  Eaton Corporation EDCs are deployed in a range of close-to-endpoint  IBM Corporation locations and can be custom-built at a specific  Hitachi Vantara location. They can be deployed within buildings and  Rittal rooms not originally designed to support data center  Vertiv equipment. They can even be deployed outdoors.  Flexential Corporation Examples include:  Schneider Electric  365 Operating Company  Modified cell tower shelters  Vapor IO  Modified cabinets  Panduit Corporation  Drop and plug shelters  Fujitsu  Drop and plug cabinets  Cisco  IDF closets in a building  ACI Anywhere  Co-located in an office or data center  Data Center Anywhere  In box on a light pole.  Hyperflex Anywhere

Edge Data Center Google Hyperscale Cloud Data Center

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MULTI-ACCESS EDGE COMPUTING (MEC) streaming, or online games. Edge compute will Multi-access Edge computing (“MEC”) provides cloud become more important as 5G matures, but there are computing services in an ultra-low latency, high near-term challenges to MEC payoff. bandwidth environment. MEC is located physically at If telco’s see themselves as the next generation [1][2] the edge of a cellular network. It works in providers of Edge computing, then they should conjunction with mobile base stations but can be used consider these core issues. with any network including LTE and 5G. Operators may authorize third parties to use their Radio Access  The payoff is uncertain for software-defined Network (“RAN”). This allows third parties to deploy data centers based on a future 5G standard. applications. MEC is an Industry Specification Group  Can applications work natively (like they do on (ISG) within the European Telecommunications cloud today) on a new MEC architecture? Standards Institute (“ETSI”).  The real user need -- if faster and less latency are measures – how much better is the In 2013, Nokia Siemens introduced MEC, originally experience? called Mobile Edge Computing. MEC is a proprietary  How does aggregation of mobile data technology that facilitated Intelligent Edge baseband units (“BBU) managed into one applications called “Liquid Applications.” This master controller matter? technology combined cloud and cellular network  Endpoint security will be key for data capabilities into a platform which provided APIs and protection -- is that a greater threat and at compute resources to developers. Since that time, what cost? Nokia has opened MEC to a larger audience by  Will device microcontrollers drive Edge promoting it as an open standard to be supported by computing as opposed to the MEC at the other manufacturers. In 2015, IBM, NEC, Vodafone, network? NTT Docomo, Orange, and Nokia partnered to create an industry specification group within ETSI to broaden the use of MEC. In 2017, ETSI renamed Mobile Edge Computing to Multi-Access Edge Computing. This reflects a wider definition as Edge computing includes wired networks as well. MECs provide functionality that is similar to traditional CDNs but with the added capability of accelerating data transfer. Some common use cases include:  Data caching;  Augmented reality (“AR”);  Mixed reality (“MR”);  IoT;  Analytics;  CDN-type functionality. MEC implementations are in the early stages, moving from concept architectures into proof-of-concept trials. The primary value appears to be latency improvement for devices or applications that could use increased response times and less jitter, such as Autonomous Vehicles , Over-The-Top (OTT) video

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Example MEC companies: Typically, they are 3-6 kilowatts in capacity per cabinet. MDCs are deployed in offices, stores, Saguna is a company that provides MEC for mobile warehouses, factories, within telecommunications operations and enterprises. The Saguna Edge Cloud computing rooms, and in other locations where space creates edge-cloud-computing environments inside is at a premium. the access network that let 5G features function over existing 4G settings. Additionally, the company’s  Canovate Group Multi-Access Edge Computing for enterprises helps a  Dataracks variety of businesses implement edge data  Hewlett-Packard Enterprise Company processing tools for the growing number of IoT  Delta Power Solutions devices in use.  Attom Technology  Eaton Corporation Vapor IO has existing data centers located at the  Advanced Facilities, Inc. edge of cell phone towers which reduces endpoint  Dell Inc., Dataracks latency. The company uses colocation facilities to  Huawei Technologies Co provide cloud services at the edge of wireless networks. In February 2019, Vapor IO and bare metal MODULAR EDGE DATA CENTERS automation platform Packet unveiled the first two live Conversely, the modular edge data centers contain Kinetic Edge sites in Chicago. multiple MDCs with built-in infrastructure in an all- NARROWBAND IOT (“NB-IOT”) weather container that are pre-equipped with communication and backup power. These ready-to- Narrowband IoT is a Low Power Wide Area deploy modular data centers are installed at remote Network (“LPWAN”) radio technology standard sites/locations. They address the bandwidth and developed by 3GPP to enable a wide range of cellular latency issues by connecting to nearby regional data devices and services. NB-IoT offers improved indoor centers where needed, thereby enhancing the user coverage and can support a large number of experience. Also, such installations are at times lightweight devices. These devices are typically quick, deployed to mission-critical operations. low cost, and low power. NB-IoT can be deployed within an LTE carrier (in-band), within an LTE carrier’s Edge technologies, such as wireless medical and guard-band, or as a standalone in a dedicated health devices, large industrial motor and pump spectrum. sensors, and smart farming equipment, lack the necessary compute and power capabilities to handle

large and complex data. Modular edge data centers are being deployed close to these facilities to boost hyper-local processing power at the edge.

Typically the size of a truck trailer or shipping

container, modular edge data centers are being used to help commercial facilities, mobile network operators (“MNOs”), and service providers augment

capabilities near the edge.

MICRO DATA CENTERS Micro Data Centers (“MDCs”) are smaller than EDCs. They are about the size of gun storage cabinets.

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CSP ON-PREMISE HYBRID CLOUD (ITAAS) CPSs offer their own variations of edge data centers which facilitate hybrid cloud solutions. Hybrid cloud is an approach to enterprise architecture that involves running workloads across Edge and Cloud infrastructures. CSPs are now offering on-premise hardware solutions with the many of the same service offerings that are found in the centralized cloud. Prominent examples include AWS Outposts, Azure Stack, and Google Anthos. These offerings vary quite a bit between providers, but, in theory, hybrid on- premise solutions are excellent for Edge applications that require low latency and have data residency concerns. Since hardware is deployed on-premises, it can be customized to provide fast and specialized edge data processing per use case.

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In the following section, we will discuss multiple models that will help the discovery process for strategic analysis and discussions.

HOMOGENEOUS AND HETEROGENEOUS EDGES Edge landscapes fall into two primary categories -- homogeneous and heterogeneous.

Heterogeneous Edge Homogeneous Edge

 Manufacture SKU-based IoT  Mobile Devices Homogeneous

Solutions  Autonomous Vehicles

 Large Industrial Asset Heterogeneous  IIoT Specific Business Case

Mapping ENVIRONMENT Low High

NUMBER OF ENDPOINTS

HOMOGENEOUS EDGE Homogeneous Edges are associated with widely for both hardware and software. As of October 2019, adopted products and platforms. Mobile devices and there were 5.13 billion mobile devices worldwide.11 smart automobiles are examples of pervasive, The tremendous popularity of Homogenous Edges homogeneous Intelligent Edge platforms. has spillover effects. For instance, manufacturing AI- optimized chips, such as the A12, requires significant Homogeneous Edges provide highly scalable delivery new fab manufacturing capabilities that can be used platforms. As an example, the mobile device is the to create chips for other products. world’s largest, Homogeneous Edge delivery platform

11 https://www.bankmycell.com/blog/how-many-phones-are-in-the- world

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Automobiles are another great example of common many Edge solutions will make this difficult to Homogenous Edge devices, with over one billion replicate. However, there will be Edge markets that currently in existence. Smart cars, in particular, have commoditize both in vertical and horizontal markets. numerous sensors (LiDAR, Radar, Sonar, GPS, Cloud service providers are offering easy-to-use cameras)12 and produce significant amounts of data. cloud-like Edge services. These are called Fog. AWS Estimates range from 1.4-19 terabytes per hour. Outposts is one example. Vertical markets with These figures will grow exponentially as smart cars tailored solutions built for specific use cases will replace older, lower-tech models. expand. IoT and OT, which are both parts of the edge, will merge with enterprise architectures. These autonomous automobiles are nothing short of mobile data centers. They require large compute and HETEROGENEOUS EDGE storage capacity. In fact, in order to handle all this Heterogeneous Edges are associated with highly data and processing, Tesla developed its own diverse environments as they support a wide range purpose-built, self-driving chip. It began shipping in of device types. They are also defined by complex or the Models S and X in March 2019. irregular network and infrastructure services, Any widely-available, uniform product or thing has complex or uneven physical environments, diverse or the potential to become the primary delivery platform difficult to manage data, unpredictable mobile or for a Homogeneous Edge use case. Examples include: stationary deployments, and other variables which discourage in-scope scalability and smoothness.  Houses Industrial plants and manufacturing facilities are  Shoes common examples where complex, heterogeneous  Gaming consoles environments can be deployed.  Medical devices  Mattresses For instance, IoT business cases within a factory or  Refrigerators manufacturing plant are often heterogeneous.  Boats Mapping the assets within a large industrial factory is just one application for this technology. Large Automobiles, for example, are excellent factories have hundreds of thousands of SKUs in homogeneous Edge platforms for a new generation operation. Bringing even a tiny fraction of these of applications, such as insurance apps that gather online can be a complex and unpredictable endeavor. real-time driving information and provide emergency Heterogenous Edge projects often do not scale well response. in this environment. CONSISTENCY CAN BE SCALED A number of business cases within large factories are The incredible growth of cloud homogeneous, however. For instance, motor computing can be attributed both to manufacturers, such as Nidek and ABB, have begun its consistency of service offerings building intelligent sensors and algorithms into their and the economies of scale in motors. Since these add-ons are tied to a specific SKU building out large data centers. The or categories of SKUs, the deployment is complexity and heterogeneity of homogeneous.

12 https://info.microsoft.com/rs/157-GQE-382/images/K24A- 2018%20Frost%20%26%20Sullivan%20- %20Global%20Autonomous%20Driving%20Outlook.pdf

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AUTONOMY AND LOCAL INTERACTIVITY Other important distinguishing factors for Intelligent Edge business cases are levels of autonomy and local interactivity.

Multi-Drone Video Surveillance Complex Large Factory IIoT Solution Technology

Simple IoT Sensors Virtual Assistant

Low High LOCAL INTERACTIVITY LOCAL

AUTONOMY

Autonomy refers to the capability Local interactivity refers to the of the system for independent number of and level of computation and decision making at communication and interaction the edge. Systems with high between: autonomy depend less on a

centralized server. They require less real time interaction. Decision-making behavior (e.g.,  devices and other devices at the edge; or machine learning, narrow AI, algorithms) is placed at  devices and other things or humans at the the edge. edge Lower levels of autonomy require interaction with a Complex local interactivity consists of numerous, central server. They may have an intermittent or sequential, or concurrent steps and/or a higher infrequent internet connection. And they follow a number of sensors. High interactivity requires stateful simpler set of rules (like ants). (usually concurrent) processing between multiple inputs and/or endpoints and devices. Simple levels of

local interactivity require fewer or slower steps.

Autonomous cars and networked drone cameras are examples of products with high levels of autonomy and local interactivity. A car, for instance, must make continuous millisecond decisions which require narrow AI and sophisticated sensors such as LIDAR and cameras.

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The Princeton Edge Lab is conducting interesting research into networked drone technology.13,14 The solution has high autonomy and complex local interactivity: An example of simple local interactivity and low autonomy is an IoT solution with temperature sensors. The sensor measures the temperature at intervals and sends text data to the cloud. This data is often aggregated later into a dashboard which can be viewed on a mobile phone or PC. A network of drone cameras can be deployed to An example of complex local interactivity and low cover live events, such as high-action sports autonomy is an industrial IoT solution in a large game played on a large field, but managing factory. Such a solution may have thousands of networked drone cameras in real-time is sensors that monitor machines. It has low autonomy challenging. Distributed approaches yield because the sensors are not processing much data. suboptimal solutions from lack of coordination Nor are they making decisions at the edge. The local but coordination with a centralized controller interactivity is complex because sensors and devices incurs round-trip latencies of several hundreds of on the manufacturing line need to communicate and milliseconds over a wireless channel. We propose a fog-networking based system architecture to organize in real time. automatically coordinate a network of drones Virtual assistant smart speakers, such as Amazon equipped with cameras to capture and broadcast

Echo, Google Home, and Apple HomePod are the dynamically changing scenes of interest in a examples of products with low levels of local sports game. We design both optimal and practical algorithms to balance the tradeoff interactivity and low autonomy. The user asks one (or between two metrics: coverage of the most at most a few) question that the device responds to important scenes and streamed video bitrate. To in short order. This is indicative of low interactivity. compensate for network round-trip latencies, the Autonomy is low because most or all of the compute centralized controller uses a predictive approach and algorithms are done on a centralized server and to predict which locations the drones should sent back to the device. cover next. The controller maximizes video bitrate by associating each drone to an optimally matched server and dynamically re-assigns drones as relay nodes to boost the throughput in low-throughput scenarios. This dynamic assignment at centralized controller occurs at slower time-scale permitted by round-trip latencies, while the predictive approach and drones’ local decision ensures that the system works in real-time.” - Princeton Edge Lab

13 http://edge.whitegoosetech.com/research/fog-computing/networked- 14 https://ieeexplore.ieee.org/document/8396351 drone-cameras

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DEVICE EDGE CHARACTERISTICS The following chart provides a checklist for categorizing device Edge systems.

DEVICE EDGE CONSIDERATIONS Edge Description Homogeneous Heterogeneous Local Interactivity Simple Complex Autonomy High Low Deployment Area Populated Unpopulated Deployment Environment Open, line of sight, few barriers Closed, walls, buildings, objects Deployment Space Public Private Environment Friendly Hostile Online - Status Always-on Intermittent Number of Endpoints High Low Endpoint Type Living Inanimate Endpoint Movement Mobile Stationary Endpoint Data Producer Consumer Endpoint Data Type Text, descriptive Binary Bandwidth Requirements High Low Reliance on Low Latency High Low Data Processing at Edge High Low Data Processing at Core High Low Data Passed to Central Server High Low Data Transfer Transactional Best Effort Data Transmission Redundancy High Low Intelligence - Algorithms Narrow AI Conditional (if/then) Intelligence - Situation Changing Fixed Endpoint Duration Permanent Temporary Person, Animal, Plant Machine, Building, Device, Examples Street, Bridge Security Simple Complex Integrated Risk Management Simple Complex

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PLANNING YOUR STRATEGIC EDGE INITIATIVE The purpose of this section is to help you plan your Generally speaking, to be most effective, project strategic Edge initiatives. Because emerging tech types with higher numbers require components that projects are built with rapidly maturing technologies, fall earlier on their life cycles. Once you have an idea it is most effective to begin with a vision for how your of what types of Intelligent Edge projects you are key components are likely to mature over time. This considering for your organization, it can be useful to is especially true if you intend to gain a competitive decompose the solutions into technical components. advantage from your efforts. These components can then be mapped on their life cycles. Next, a plan can be developed that anticipates As mentioned in an earlier section, the type of project maturity timelines. you are undertaking is an important consideration. The four project types are: The following section explains how to use life cycles to enhance your strategic plan. In the second section,  Improvement of operational efficiencies or since Edge projects often produce large amounts of reduction of expenses. data, we explore data management at the Edge. In  Scaling a current line of business. the third section, we explore AI, specifically Deep  Entering new markets. Learning, and look at business cases in order to  Innovation or development of a new product develop a better understanding of what types of AI or service. can be applied to business problems at the Edge.

INTELLIGENT EDGE LIFE CYCLES Often, the key components in Intelligent Edge value fall approximately where Moore has defined this systems are emerging technologies. Yet, sometimes chasm. this is not the case. For example, mobile devices are mature Edge products. Intelligent Edge features of the iPhone, such as Face ID, have high adoption rates and a sophisticated supply chain. This is a new feature differentiation, but it is not an emerging technology. An important step in developing a strategy is to distinguish emerging Edge technologies from other types of technologies. To begin, let’s consider the life cycle S-curve graph below which approximates the current stage of Intelligent Edge systems. Of course, real consumer adoption is never smooth. As an example, consider the number of iPhones sold The technology life cycle curve correlates to its per quarter in the four years following its original consumer adoption curve. In fact, it is the integral of release in 2007. it. The adoption curve was developed through research done by Everett Rogers in 1962. Geoffrey Moore popularized it in his 1991 book Crossing The Chasm. Moore argues there is a chasm between visionaries and pragmatists. The Intelligent Edge solutions that have the most potential for strategic

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In contrast to commodity products, emerging technologies have:  Relatively high production costs  Low number of users  Incomplete and/or inconsistent technology which is not robust  Complex manufacturing processes -- often they must be custom-built from start to finish  Higher margins Innovations Innovations precede emerging technologies by some period of time, sometimes years, decades, or iPhone units sold per quarter centuries. A current example of an innovation is the biotechnology that produces high-fidelity stem cells. source: wikipedia commons This process involves removing human stem cells, One critical step in the planning process, therefore, is sorting them by fidelity, and then injecting a to assess whether the key components required to percentage of the cells back into the body. This build your Edge system are commodities, emerging treatment can cure certain diseases, such as sickle technologies, or innovations. Each is discussed below. cell anemia, and may reverse aspects of aging. Commodity Products This stem cell therapy approach has been proven scientifically in separate stages. It will likely be Technologies in commodity markets that are effective as disease treatment in time. But the nearing or past 80-90% adoption rates (upper right manufacturing process is complicated. It has yet to of technology life cycle curve) have the following be solved even for low production numbers. features: Engineering a solution that keeps the cells alive long  Relatively low production costs enough to complete the process has proven difficult.  High number of users Currently, the process is conducted by single teams  Robust, feature-rich technology offerings of researchers. These scientists use generic lab  Efficient, streamlined, and disaggregated equipment and manually sort individual cells. manufacturing processes All one needs to do to understand the difference  Low margins between an innovation and an emerging Current examples of commodity products include technology is to consider the difference between HDTVs, toasters, washing machines, automobiles, Magic Leap and sorting stem cells. These may appear and microwaves. next to each other on the life cycle curve, but they require qualitatively different approaches to bring to Emerging Technologies market. An example of an emerging technology is the An example of a technology that has run its course augmented reality (AR) product developed by Magic from innovation to commodity product is the gas- Leap. Magic Leap One is a wearable that enables powered automobile. Over time, incremental users to interact with digital devices in a visually improvements were made not only to the product but cinematic way. The product superimposes 3D, also the manufacturing process. These improvements computer-generated imagery over real world objects. allowed production to scale and helped democratize Current revenues are in the hundreds of millions, but access to automobiles worldwide. valuations for the company exceed $6B.

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Considering Maturity & Adoption extensive effort and a broader level of expertise than building one in a mature market. On average, consumer adoption occurs faster today than it has in the past. The following chart Consider shipbuilding. Early shipyards, which date depicts how long in years certain technologies took to back at least to the 4th millennium B.C., started the reach 90% adoption: process with wood and other raw materials. It required an enormous amount of effort, planning, and expertise to turn trees into ships. By contrast, modern shipbuilding employs almost entirely prefabricated sections. Known as “block construction,” completed multi-deck segments of the hull or superstructure are built elsewhere and then transported to the building dock and lifted into place. Equipment, pipes, electrical cables, computers, electronics, and other components within the blocks are preinstalled. Most of the components used to assemble large construction blocks are purchased in massive, secondary markets. Those markets supply Speed of adoption has become extremely fast in hundreds of thousands of SKUs that go into a recent years. This is especially true for software and completed ship. Modern production is also difficult, software-defined replacements for hardware. APIs, but the effort goes into scale and pace. for example, are being rapidly developed and adopted. This breaking up of the process of manufacturing into smaller components or elements for which new Research indicates that adoption rates are influenced markets then evolve is called disaggregation. The by five factors. 15, 16: web and mobile application supply chain has  Perceived relative advantage: Does the undergone a disaggregation similar to the technology provide some sort of clear shipbuilding industry although the change happened advantage? in 10 years rather than 7,000.  Ease of compatibility: How easy is it to In the early 2000s, the manufacturing process of assimilate the technology into current developing an application primarily consisted of processes? custom-building an entire app from start to finish.  Low complexity: How easy is it to configure Application deployments contained mostly custom the technology and get working? code sometimes built on top of web application  Trialability: Can the technology be tested frameworks, such as Spring MVC, JSF and Struts. easily? Ancillary files that provided third-party code called  Observability: Can others easily observe the “archived files” (e.g., .jar and .war files) were technology? included with deployments to complement custom CONSIDERING SUPPLY CHAIN code. Unlike APIs, however, they were coupled to DISAGGREGATION application versions and were clunky to deploy and An inefficient and unsophisticated supply chain is one maintain. These ancillary files are similar to of the demarcating factors of early markets. Building embedded Edge APIs which are discussed later in this a product in an early market requires a far more section.

15 https://www.amazon.com/Diffusion-Innovations-5th-Everett- 16 http://itidjournal.org/index.php/itid/article/dow nload/142 3/524 Rogers/dp/0743222091

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Today, by contrast, web and mobile software can interact with to build intelligent and complex products rely on an average of 10-20 external APIs communications systems. per application. There are thousands of APIs Since application development has matured around a available. A few popular examples include: centralized computing approach, this secondary Stripe: Online payment processing. market for APIs has emerged to outsource activities that go into developing applications such as Amazon Rekognition: Uses AI to identify the registration, SMS messaging, credit-card processing, objects, people, text, scenes, and activities in photos and algorithm processing. The application market and videos. does this in the same way that automobile and ship SendGrid: Delivering your transactional and manufacturing companies outsource pre-assembled marketing emails through the world's largest cloud- components. based email delivery platform. The disaggregation of traditional application Full Contact: From an email address or social media development is evidenced not only by the existence identifier, provides a full user profile including name, of the growing API market but also next generation age, location, gender, and social network accounts. API management companies, such as Google Apigee, MuleSoft, and Kong. These companies act as API Okta: Adds authentication, authorization, and user gateways, manage numerous APIs, and handle management to your web or mobile app within common issues such as logging, authentication and minutes. setting rate limits. Twitter API: THE EDGE SUPPLY CHAIN HAS NOT YET Facebook API: DISAGGREGATED IBM Watson: Mostly NLP AI for business -- itbuilds As the current API and SaaS market has grown models and develops applications to make more around traditional, centralized software and mobile accurate predictions, automate processes, interact application development, it makes sense that a new with users and customers, and augment expertise. model, Edge computing, would require a different type of outsourced components. Indeed, it does. Newscred: Provides brands and publishers with access to fully-licensed articles, images, and video Traditional APIs do not work for Edge use cases. from more than 2,500 world-class sources. These APIs are deployed on centralized servers and accessed from applications running on PCs, mobile Uipath: Robotic software quickly automates devices, and other nodes. This approach is repetitive processes. antithetical to the core benefits of Edge computing. Algorithmia: Deploy AI at scale; API that exposes the collective knowledge of algorithm developers across the globe. Blockcypher: Enables companies to easily build blockchain applications with web APIs and callbacks. Import.io: Web Data Integration solution extracts, prepares, and integrates high-quality comprehensive web data into customers' analytics platforms and business applications. Twilio: Handles messy telecom hardware and exposes a globally available cloud API that developers

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INTELLIGENT EDGE BENEFITS CENTRALIZED API ACCESS

Handles large volumes of complex data Too much data to transmit

Reduces latency Increases latency

Lowers bandwidth requirements Increases bandwidth requirements

Increases autonomy Reduces autonomy

As an example, consider the Edge business case of a sub-10-millisecond response time, they may be a camera that is conducting real-time license plate viable deployment model to make traditional APIs identification on an interstate. Testing indicates that available to Edge applications. if a per-image response time of less than 10 milliseconds is achieved, the system can correctly identify up to 98% of the plates. A cloud API, such as AWS Rekognition, has a baseline network latency of 70 milliseconds. Additional time to process and send the payload is also required. Clearly, in this scenario, a cloud-based API solution will not work. In order to meet the engineering requirements of this application, the AI algorithm that reads the license plate needs to be moved closer to the camera. In other words, Edge applications need a different infrastructure model for disaggregated edge components which we will call ”Edge APIs” for lack of a better term. Edge APIs can be deployed:  In an Edge or micro data center (Edge data centers can have sub-10 millisecond response times and micro data centers are typically even faster);  On a processor within local proximity -- AWS Greengrass on a nearby server, for instance;  On the camera via an embedded API. Currently, most commercial license plate cameras use embedded APIs or embedded custom code. This means that a local copy of the API is kept on the camera which, of course, means that you no longer have the benefits of a centralized API; namely, a single, managed access point for transacted, predefined units of work. Embedded APIs are similar to archive files in that they are coupled, one-to-one, per deployment. Since Edge data centers can offer

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In addition, markets consolidate and prices become lower. These factors can be used to help map where components fall on the curve and where they will be in the future. This helps keep your solution set current and to provide future strategic advantages. The previous sections have discussed what the technology life cycle and adoption curves look like as well as how supply chain disaggregation and market forces can affect your Edge solution. To start,

technologies fall into four primary life cycle stages Intelligent Edge applications, such as industrial IoT shown below: applications running AI at the edge, are typically mostly custom-built, much like the web applications from twenty years ago. Other components of Intelligent Edge applications, however, do have sophisticated supply chains. AI chips borrow manufacturing techniques from fab facilities that have been honed over decades. Custom-built AI chips are relatively new, but AI has been augmented by coprocessors since the early 1990s. Apple’s A12 and new A13 are manufactured by TMSC (Taiwan

Semiconductor Manufacturing Company, Limited) and are produced in high-tech manufacturing Many proposed Edge systems have components that facilities. The chips are designed to work with state- fall into each one of the last three stages: strategic of-the-art AI and deep learning algorithms which also advantages, products, and commodities. It is makes the output new and powerful. Similarly, important to differentiate components by stage and microcontrollers, sensors, and actuators used in IoT create a roadmap and budget that reflects how the devices are mature, decades-old markets with staid components are likely to evolve. As components manufacturing processes. mature, costs become lower, the supply chain disaggregates, and markets consolidate. Planning to Mapping Your Edge Components adopt new technologies as they move from The goal of mapping Edge components is to innovations to custom-built systems and early determine approximately where they fall on their life products can maintain a competitive advantage for cycles and how fast they are maturing and being strategic initiatives. adopted. This allows you to anticipate and plan for As an example, consider a simple IoT business case how technologies are likely to change over time, where a company that manages forklifts would like to which components are strategic assets, and how to measure how its fleet is loading pallets. The company build long-term roadmaps that provide strategic would like to have sensors on its forklifts that identify advantages. pallets via RFID and also provide a centralized As technologies mature, associated supply chains dashboard that reports real-time loading information. tend to disaggregate and become more sophisticated.

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EDGE DATA MANAGEMENT

According to the IDC, the sum of the world’s data will strategy. Edge data strategies differ from centralized grow from 33 zettabytes in 2018 to 175ZB by 202517. data strategies in tactics and engineering, but the The IDC also estimates that there will be 41.6 billion goal is the same -- to produce a long-term asset that connected IoT devices, or "things," generating appreciates over time and provides a high net return 79.4ZB of data in 2025. on investment.

The amount of data generated at the edge will be far The goal of a Data Strategy is to produce a long- too large to handle centrally. So it is imperative to term asset that appreciates over time and provides make good data management decisions at the edge. a high net return on investment. As data becomes the most valuable technology asset in organizations, deciding which data to store and There are many ways to measure the ROI of a data how to store it will require a solid methodology. strategy. One emerging discipline is Infonomics. Integrating this methodology with the organization's Infonomics is the practice of applying accounting overall strategy requires thoughtful planning. principles to data and managing it as a company STRATEGIC EDGE DATA DESIGN asset. Less formal methods can be useful too. For most organizations, how you measure the value of Proprietary data assets provide the best long-term data does not matter as much as creating a plan that strategic advantage. Emerging technologies, such as you can execute and then sticking to it. IoT, AI, and ML can provide short-term advantages. Over time, however, technologies mature and To help with this process of building an Edge data become necessary costs of doing business. strategy, I developed a quick, useful model to get Intellectual property can provide defensibility, but it efforts pointed in the right direction. The model has is often ineffective for software. 3 stages. Data is the one technical asset with which your First, the organization’s strategy is considered. The business can form a mutually beneficial symbiotic data and the design of the data should align with the relationship. When properly designed, data becomes goals of the company. This may sound simple, but is more useful and valuable over time. What other too often an afterthought. assets have this characteristic? Second, the data should be collected in a way that Consequently, building defensible data assets is more is fit for experimental modeling. This allows a range important than ever. It has become easier to collect of stakeholders to probe the data for insights. data to train AI and ML models. Growing sources of Third, for strategic data that is being used to build a free data sets are readily accessible and well company asset (not all data is strategic), the data organized. Other data is also available through APIs should be defensible/excludable. This means that it is and data brokers. difficult or impossible for competitors to replicate the Edge applications can produce massive amounts of data or gather similar data that provides the same data -- far too much to store or process -- so it is insights. critical at the outset to have a categorical Edge data

17 https://www.seagate.com/our-story/data-age-2025/

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STRATEGIC DATA DESIGN

Aligned to Fit for Defensible/ Organizational Experimental Excludable Strategy Modeling Data

The data and design is The data and design Data collected/produced is aligned to organization’s allows for data scientists difficult or impossible for strategy and possible and statisticians to test others to copy or future scenarios valid hypotheses reproduce

1. DATA IS ALIGNED TO ORGANIZATIONAL STRATEGY Defining data collection and design that aligns with can be analyzed to derive site intelligence. The and provides insights into the formation of software is collaboration-focused and allows maps to organizational strategy is the first step. Data should be shared across team members. be robust to an organization’s strategy and possible The software, however, can also be used in a variety future scenarios. If the data/design allow of industries such as solar energy, agriculture, stakeholders to test and explore a range of viable insurance inspection, construction, and more. For strategic options at the business and corporate example, in the agriculture industry, DroneDeploy strategy level, then it is more than just a closed-loop can be used to efficiently examine crop yield and function of IT that is specific to some operational or minimize crop loss. For insurance claims, similar process. DroneDeploy can be used to take overhead shots of An example of an Intelligent Edge company that the site, which can be used in claims analysis. collects data providing numerous strategic options is This allows Drone Deploy to branch into a number of DroneDeploy. DroneDeploy is a drone and UAV data verticals and provides strategic business mapping platform primarily used to capture job sites. opportunities to test markets, provide APIs, partner, Users are able to automate flights using a mobile app, or license. capture geotagged imagery, and generate maps that

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2. DATA FIT FOR EXPERIMENTAL MODELING When data is poorly designed or siloed, extracting The key is to design the data in such a way that value from it is difficult. This has less to do with stakeholders — programmers, data scientists, technical design than with experimental modeling. statisticians, analysts, salespeople, managers, and Data design can be technically pristine, follow all the executives — can freely create and test a range of best practices, and still be coupled to its primary valid hypotheses across datasets. Ideally, data is purpose and inflexible for use in other ways. designed to allow for meaningful experimental modeling. Consider the following from Data Scientist Brian Caffo at Johns Hopkins:

BAD DATA SCIENCE SITUATION IDEAL DATA SCIENCE SITUATION18

Weak or no hypothesis Clearly defined data hypothesis specified a priori

Same data to form hypothesis is used to interrogate Access to rich and varied data sets in various hypothesis phases of design

Limited access to experimental design Control of experimental design

Retrospective data or only observational data (not Data is robust, complete. This includes A/B testing, random) randomization, and stratification can interrogate Hypothesis.

Population is wrong Clean data.

Sparse or proxy data Random sample data

Fragile conclusions Clear conclusions

Unclear decisions Decision is obvious

Opaque knowledge Parsimonious knowledge

An example of a company that collects data fit for distance because skin reddens slightly during experimental modeling is Neurodata Lab. Neurodata heartbeats. Lab provides AI-based solutions for real-time emotion Because computer vision data is so voluminous and analytics and analysis of consumer behavior in retail, could be used in a variety of ways, NeuroData Lab banking, insurance industries, HoReCa (i.e., service must carefully consider how its data is collected, industries), and service robotics. The solution stored, and analyzed. The data that NeuroData Lab analyzes facial expressions, vocal affects, body poses, collects undergoes careful experimental design that is interpersonal distance, gestures, respiration rate, and being continuously improved to ensure that the other variables to analyze human behavior. Notably, algorithms are accurate, not biased, and handle the the camera can detect skin redness subtly enough to normal range of human expression. determine a person’s heart rate at a reasonable

18 Source: Brian Caffo, PhD, Professor, Johns Hopkins University

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3. DATA DEFENSIBILITY/EXCLUDABILITY If anyone can obtain or reproduce a dataset, it is  Is unique to proprietary situations; difficult to create value from it. Defensible data is  Is unique to predictable, future situations that difficult or impossible for others to copy or reproduce. are difficult for others to reproduce; There are a number of dimensions to defensible data.  Has high dimensionality (many attributes) and These include data that: breadth (a range of possible values for attributes);  Requires unique, proprietary knowledge to  Is not perishable (remains valuable and produce or interpret; pertinent for a long time);  Requires unique knowledge and expertise to  Is highly perishable but has a consistent scrub and prep; future stream;  Is unique to a brand;  Has a positive feedback loop -- in other words,  Is time and/or location sensitive and is being the data becomes more robust and valuable collected by only one party; as more of it is gathered.

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As an example of defensible data, consider the complications. It can also be used by those with a company CardioSecur. CardioSecur is a mobile ECG known family history of heart issues. Results can that works by placing your mobile device on your easily be shared with a physician. chest. It is able to determine if your current Because the app is convenient when an ECG is not symptoms require immediate medical attention. This readily available, it provides historical data that other software is used by individuals as a preventative companies, even physicians and hospitals, cannot measure to keep track of their heart health. It can easily reproduce. It can also be analyzed later to also be used by users who have suffered a heart predict medical outcomes and for other purposes. attack and want to stay in-the-know, in order to avoid further heart damage. The software is also able to Only one company had access to John Smith’s ECG track recurring symptoms after treatment for data on March 10, 2019 at 2:45pm E.T. Every data arrhythmias and atrial fibrillation which can help point gained adds to a defensible data set for prevent stroke. CardioSecur. Patients, physicians, hospitals, pharmaceutical companies, and other entities could Users can employ the CardioSecur mobile app as a all benefit from more consistent ECG history for a part of their daily routine to keep control over their broader range of patients. heart symptoms and potentially prevent further

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EDGE AI It is no secret that AI, specifically, deep learning, is  1988: The Strategic Computing Initiative the enabling layer for a new generation of Intelligent canceled AI spending. Edge applications. For many years, AI in the  1990s: Fifth Generation computer project’s commercial sphere did not work that well. But since goals ended. 2010, one particular subset of AI— deep learning— More recently, starting around 2010, another AI has matured into a set of useful, commercial products boom occurred with Deep Learning (“DL”). DL, a type that address practical business problems. of machine learning that is a subcategory of AI, is In this section, we are going to briefly explore the AI perhaps the biggest AI breakthrough in history Most story and examine how AI can be used at the edge. of the commercial AI applications today are DL We will explore what types of algorithms and use systems. cases are working, and what is not. You are already using DL although you may not A BRIEF HISTORY OF AI realize it since it’s not usually advertised as such. Deep learning examples include: In the summer of 1956, a group of researchers came together at Dartmouth University to brainstorm about  Google photo app (DL) how computers could be programmed to behave like  Buzzfeed headlines (tuned with DL) humans. This meeting, called The Dartmouth  Airbnb pricing (DL) Workshop, included Marvin Minsky, Nathaniel  Pinterest visual search (DL) Rochester, and Claude Shannon, among other  Facebook chat M app (NLP is DL) famous scientists. The event lasted eight weeks and serves as the foundation of what we currently think of as AI. In from three to eight years we will have a machine with the general intelligence of an

It is interesting to note that what we call Artificial average human being. I mean a machine Intelligence might be called Cybernetics today, but the that will be able to read Shakespeare, grease organizer of The Dartmouth Workshop, John McCarthy, a car, play office politics, tell a joke, have a

did not wish to get into debates with the argumentative fight. At that point the machine will begin to Nobert Wiener who was the leading expert in educate itself with fantastic speed. In a few Cybernetics at the time so McCarthy chose the more months it will be at genius level and a few generic term “artificial intelligence.” months after that its powers will be incalculable. -- Marvin Minsky, 1970 Since the 1950s, AI research has grown in fits and starts. There have been times when AI seemed to be making excellent progress. During these times, super The software ecosystem supporting deep intelligence seemed imminent: learning research has been evolving quickly,

At other times, called AI Winters, research languished and has now reached a healthy state: open- and little progress was made. The two largest AI source software is the norm; a variety of winters were 1974–1980 and 1987–1993, but there frameworks are available, satisfying needs have been a number of others. Some examples spanning from exploring novel ideas to include: deploying them into production; and strong industrial players are backing different  1967 to 1976: The quiet decade of machine software stacks in a stimulating competition. translation. - Pascal Lamblin on behalf of Yoshua  1969-1970: Fall of connectionism. Bengio

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CATEGORIES OF AI  Components in smart cars(e.g., Tesla Model X); There are three basic categories of AI:  Drone delivery (e.g., Walmart and Amazon).  Narrow AI or Weak AI. Narrow AI is pro- When building business cases, it is important to grammed/trained to perform a specific task. understand what Narrow AI does well and where it  Strong AI or Artificial General Intelli- has limitations. There are specialized tasks where AI gence (“A ”)): Theoretical application of performs better than humans and there are tasks AI that equals/emulates human intellectual where AI struggles to compete with humans or which activity by being able to address a broad array are not yet ready for commercial applications. of problems.  Super Intelligence or Super AI: Theoreti- The Electronics Frontier Foundation (“EFF”) maintains cal AI that exceeds human abilities and other an up-to-date list that provides the status of AI smaller episodes. research for a range of tasks. This can be an excellent resource for verifying the feasibility of projects as it DL is a type of Narrow AI. Contrary to popular media gives specifics on how AI algorithms currently reports, Narrow AI is the only AI that exists today. perform on certain tasks. You can also get a sense of Narrow AI can do amazing things, but in many ways the progress that is being made over time on a it is still fairly limited. There is not a definitive particular task. As chips get faster and algorithms approach even within research for AGI, so it is not improve, you can expect steady improvements in this likely that it will emerge any time soon arena. notwithstanding commercial efforts to make it happen. Microsoft recently announced that it has As an example, image classification that provides a invested $1B into OpenAI in an effort to produce set of images of various objects using CIFAR began Strong AI applications within Azure. 19 To date, exceeding average human capabilities in late 2014. 20 however, there are no AGI models or functional Algorithm performance for this task is mapped below: Strong AI applications. Narrow AI does a number of specialized tasks well. Here are common examples where AI is currently (or soon to be) commercially available.  Apple’s Siri;  Amazon Alexa;  Amazon’s product recommendation engine;  Image recognition, such as facial recognition(e.g., AWS Rekognition);  Sentiment analysis,i.e., determining emotions from text;(e.g., CloudFactory’s NLP engine); Source: https://www.eff.org/ai/metrics

19 https://www.tractica.com/artificial-intelligence/microsofts-openai- 20 https://en.m.wikipedia.org/wiki/CIFAR-10 deal-puts-spotlight-on-access-to-high-end-ai-compute/

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However, algorithms performing Visual Question For Edge applications, AI has two critical Answering (which involves recognizing events, components-- algorithms and chips (see earlier relationships, and context from an image) do not yet sections for companies related to these markets). AI perform at human levels. The best algorithms accelerators are specially designed as hardware perform at 70% accuracy, whereas humans perform acceleration for artificial intelligence applications, at 95%. The image below depicts the type of problem especially artificial neural networks, machine vision that is tested for this task. and machine learning. As this emerging hardware market matures, chips will be developed for specific tasks, notably DL tasks and algorithms. The viability of Edge business cases that require AI depends in no small part on both the cost and functionality of chips and algorithms as they pertain to particular tasks. Understanding how these two markets are developing can be useful when developing strategic roadmaps. Since AI tasks can be abstract, it can be useful to look at a range of practical applications to get an intuitive feel for which types of AI business cases are working and which are not. The following section contains a list of AI companies and applications with a practical explanation of what each one does. AI COMPANIES This section covers several companies that are using AI to tackle business problems today. The name of Here are a number of AI tasks for which the EFF each company is followed by a brief description of compares algorithm and human performance: how AI is being applied to real-world problems.  Written language; Affectiva  Reading comprehension;

 Language modelling; “Developer of an emotion-recognition software  Conversation; designed to analyze subtle facial and vocal  Translation; expressions to identify human emotions. The  Spoken language; company's software uses computer vision,  Speech recognition machine learning, and deep learning  Music information retrieval; methodologies to train algorithms that classify  Instrumentals tracks recognition; emotions and analyzes complex and nuanced  Scientific and technical capabilities; human emotions and cognitive states from face  Solving constrained, well-specified technical and voice, enabling creators of digital experiences problems; to build stronger connections with their users in an  Reading technical papers; engaging, interactive and effective manner.” -  Solving real-world technical problems; Pitchbook  Generating computer programs from specifications;

 Answering science exam questions  Learning to learn better.

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AI Automated Insights intake calculation. Another use case is building a heart rate and stress tracking app on the Azumio Automated Insights specializes in natural language platform. generation (NLG) software that creates readable narratives from data. The company’s platform, Bowery Wordsmith, ingests enormous amounts of data and Agriculture. Plant yields. Uses vision recognition, uses algorithms and rules to produce human- multivariate optimization, robotic controls readable prose. Wordsmith has been described as a "a sort of personal data scientist, sifting through Bowery Farming is applying AI to agriculture in reams of data that might otherwise go un-analyzed innovative ways. It uses AI, lights, robotics, cameras, and creating custom reports that often have an and its own operating system (BoweryOS) to enhance audience of one."1 The service works by ingesting indoor farming. Bowery farming maintains high-tech structured data, analyzing it for insights, and then warehouses to help control exactly how plants grow. writing out those insights in human-friendly prose. The software collects data from cameras and other sensors and uses AI to alter and control the Applied Intuition environment to help plants grow. Applied Intuition allows you to simulate autonomous Cardiogram vehicle software. It brings real-world maps into the equation so that testing is practical. Objects in the Cardiogram is a health and fitness application that environment are also included, making testing provides hospital-quality ECG on your mobile phone. accurate and realistic. In addition to testing your Cardiogram develops a mobile application that software against typical examples, you’re able to provides heart rate data to predict and prevent heart create more complex scenarios, which can account disease. Cardiogram is using data science to detect for Edge cases. Software failures and improvements the heart conditions and atrial fibrillation, all with a can also be easily tracked. smart watch. Read how the technology works here. A company that is developing autonomous vehicle CardioSecur software may use Applied Intuition for testing prior to CardioSecur is a mobile ECG that works by placing investing in expensive test vehicles and time- your mobile device on your chest. It is able to consuming trials. The software can also be used to determine if your current symptoms require track the validity of a new, proposed company immediate medical attention. This software is used by expansion into the field of autonomous vehicles. individuals as a preventative measure to keep track Azumio of their heart. Alternatively, it can be used by patients who have suffered a previous heart attack and want Axumio is a mobile health and fitness AI that offers to stay in-the-know, in order to avoid further heart data tracking for caloric intake, diabetes damage. The software is also able to track recurring management, and vitals checking (sleep, body symptoms after treatment for arrhythmias, and help weight, heart rate). Their food recognition software prevent stroke. works by analyzing a photo of a food and returning nutritional information. The diabetes management Users can employ the CardioSecur mobile app as a software enables users to log food and track their part of their daily routine to keep control over their blood glucose level. In addition to tracking, they offer heart symptoms and potentially prevent further the LifeCoin SDK -- a blockchain-based system that complications. It can also be used by those with a rewards users for their healthy habits. known family history of heart issues. CardioSecur results can easily be shared with a physician. The AI has practical use cases such as building mobile applications targeted towards a fitness-conscious user base and including workout tracking and caloric

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Cloudtag Dialpad Cloudtag has created the product Onitor, which offers Dialpad is an automatic speech recognition software Onitor Track. Onitor Track is a wearable technology that analyzes conversations and returns analytics. that tracks activity and heart rate. It provides custom The data is used to increase productivity and offer weight loss timelines so that people can take charge applicable decision-making insight. Dialpad works of their weight and health. The technology uses through the company’s business phone system contactless sensors to monitor health information, (UberConference), inbound call center, and intelligent and provides accurate data that can be interpreted sales dialer products. and used.Onitor is usable by a mass audience The software can be used to improve customer concerned with personal health and wellness. Onitor service for sales teams by empowering them with an Tracker will offer a dashboard, which users can log AI-enabled VoIP system. They can build rapport and into to see their specialized data. save conversations to the CRM. Conversation Cresta analytics can help sales agents identity patterns and improve scripts. They offer specialized software for Cresta helps sales agents double their sales start-ups, small businesses, and enterprise level conversions by providing data relevant to their clients. Businesses looking to move away from a PBX conversations. It enables them to also provide system and work globally or non-centrally will benefit. responses in quicker time, so that they can hold multiple conversations at once. The software turns Doxel sales agents into experts and allows new team Doxel is an AI that scans construction sites daily using members to ramp up quickly. autonomous robots to track progress and inspect This software can be used by a company to give its quality, so that potential issues can be addressed sales team the leverage it needs to spend more time immediately. They are able to provide figures for the making conversions and less time researching. It can percentage of work completed, earned value also be used to manage and ensure that best statistics, and timeline progress reports. The software practices are being used across the team. Cresta is able to detect errors in construction that the human makes it so that salespeople do not rely on cookie eye cannot detect. cutter responses that make conversations Construction sites can leverage Doxel during the build impersonal. process to track real-time progress. This can be used Cygnon as a tool to reduce potential liability due to poor construction. Doxel can also alert site and project Self-driving software managers to construction problems as they arise so Databricks that they can be addressed before any further development interferes with their ability to fix the DeepMap problem. 3D maps for self-driving algorithms Drive.ai Deserve Drive.ai is a self-driving vehicle AI that uses vision Deserve provides access to fair credit to underserved, recognition, SLAM, and motion planning. The AI is but deserving populations. It creates more equitable two-fold. First, it is learning how to drive and how to credit products for young adults using deep learning. communicate with pedestrians. It prioritizes human Deserve has recently partnered with H20.ai, an open- safety. Consequently, their vehicles have panels that source AI company, to scale and enable faster display their intention to pedestrians like “waiting for deployment of Deserve’s proprietary algorithms. you to cross.” While the vehicles are autonomous,

STRATEGY AT THE INTELLIGENT EDGE 63 they can also be controlled by a remote operator as cuts out the need for third-party services to perform a last resort. landfill biomass monitoring and other surveys. Drive.ai can be used as an on-demand ride hailing Moov system, a new form of public transportation. This sort Moov is an audio fitness coach that guides you of transportation can be beneficial in areas of high through a workout based on your movement and traffic, like around well-known destinations. Users are heart rate. The software encourages proper form and able to select where they will be picked up and where decreases the potential for injuries. It can be used to they will go from a mobile app. track personal bests. Moov can be used for a variety DroneDeploy of movements and exercises like running, swimming, cardio boxing, and circuit training. DroneDeploy is a drone and UAV mapping platform for businesses to capture job sites. Users are able to The Moov software is usable through a hardware automate flights using a mobile app, capture device that can be worn on the wrist or ankle while geotagged imagery, and generate maps that can be exercising. Users can improve their athleticism by analyzed to derive site intelligence. The software is tracking their progress through Moov. The software collaboration-focused and allows maps to be shared analyzes form in 3D, so is able to provide real-time across team members. feedback on form, all which provide audio motivation. The software can be used in a variety of industries OrCam such as solar energy, agriculture, insurance OrCam is an AI with a wearable device that helps inspection, construction, and more. For example, in those with difficulty reading, vision problems, and the agriculture industry, DroneDeploy can be used to blindness. The device sees what the user is seeing, efficiently examine crop yield and minimize crop loss. and is able to discern text, faces, and products. It For insurance claims, DroneDeploy can be used to connects to Bluetooth to enable hands-free usability. take overhead shots of the site, which can be used in OrCam recognizes gestures and voice commands. claims analysis. Best of all, it does not require an Internet connection. Instacart The device can be used by visually impaired Using gradient boosted decision trees, deep learning individuals to aid them in daily life activities like with TensorFlow and Keras. VentureBeat Article recognizing and announcing faces and colors, distinguishing bill money, and identifying grocery Kespry store products by barcode. This allows individuals to Kespry is a drone software for industry to gain aerial live more mobile and independent lives. intelligence about job sites in a short amount of time. PinDrop This can be leveraged in a number of industries for processes like inventory management, claims data PinDrop uses its Phoneprinting™ technology based on accumulation, construction project planning, and machine learning to analyze more than 1,300 audio operations management. Kespry does not rely on features on call center phone calls to reduce fraud. users knowing how to operate a drone. Flights can be PinDrop creates a distinct telephony profile, while planned, and data is returned as automated analysis. also revealing true geo-location, device type, etc. This technology also works from IoT devices to In the pulp and paper industry, Kespry can be provide security and identity voice protection. leveraged for inventory taking and maximizing site operations. The software is able to detect inventory Primer volume at great accuracy more safely, and while Primer is a machine learning software product that sparing production team member time. Kespry also takes data, gathers insight about it, and automatically

www.redchipventures.com PLANNING YOUR STRATEGIC EDGE INITIATIVE 64 produces reports. The software is meant to meet the Suki demands of a world increasingly filled with Suki is a digital assistant for doctors that’s voice- information that requires analysis. The AI is able to enabled and alleviates administrative duties. It allows look at different components of the text including doctors to create accurate notes at a faster speed so structure, ensemble, event, context, differences, and they can focus on other tasks. The software is data- story. secure and HIPPA compliant. Suki is also EHR Primer can be used for language automation where integrated, so notes can be seamlessly added, and there is a need for quick and efficient processing of patient information can be pulled easily. It also syncs documents for meaning. Some companies use across devices. software like Primer to consistently examine product The software is used by doctors to increase time with markets and look for trends that can help streamline patients and decrease time spent on administration. and reduce risk in their supply chain. Suki addresses the needs of the 70% of doctors who Shield AI reported experiencing burnout by reducing their administrative burden. As a result, doctors are able to Shield AI is a drone security Artificial intelligence that cut hours off their work each week. enables robots to see, reason about, and search the world. Shield AI develops AI systems for indoor and Skycatch outdoor intelligence, surveillance, and Skycatch is a drone technology that offers precise reconnaissance defense operations. Shield AI offers imaging. It’s able to export a multitude of data types Hivemind, an AI framework that enables machines, for accurate analysis. Automated routes can be including unmanned ground vehicles , unmanned started from a mobile application. Skycatch uses a aerial vehicles , unmanned aircraft systems , and GPS base station so that data can be analyzed in the unmanned underwater vehicles to learn from their field and in cases where there is no Internet real and synthetic experiences. Likewise, Nova is an connection. autonomous quadcopter that enables access and exploration of buildings, dense urban environments, The software can be used in industries that require and areas lacking in global positioning system (“GPS”) imaging of a large area of land. With the app, you can ) availability. Shield AI has developed a drone that plan and launch your flight, process the flight data, can fly inside buildings using simultaneous and distribute the results. Data files can also be localization and mapping, detect faces, map interior exported to other software for further analysis. spaces, and identify good and bad agents so that Textio soldiers can be better prepared to move into buildings. Textio is an augmented writing software that illuminates what your writing reveals about your Sigopt company culture, measures the impact of your words, This hyperparameter optimization solution automates and increases productivity. Textio Hire uses scoring model tuning to accelerate the model development and suggestions to improve job advertisements to process and amplify the impact of models in attract the best candidates for open positions. It uses production at scale. This process empowers data about a company’s culture to align job postings customers to generate more high-performing models with core values. It also calculates hiring scores that in production. With more models in production, they take into account geographic hiring data. earn a higher return on their modeling investment. Yseop

Yseop uses AI for augmented analysis and sales. It reads text and distills it into actionable, easy-to-read information. Yseop automates report writing to

STRATEGY AT THE INTELLIGENT EDGE 65 produce document summaries and recommend- work. Yseop builds out text on dashboards from ations. The software also processes CRM data and charts, and can measure up performance with goals. gives suggestions to sales people about product Zipline matching for clients. The AI is also able to add narrative descriptions to dashboards. Drone blood delivery. Zipline is an American medical product delivery company headquartered in Half The software can be used by sales teams to increase Moon Bay, California. Zipline designs, builds, and sales performance with intelligent analysis and operates small drone aircraft for delivery of medical suggested timing. It can also take strenuous and products, with a focus on providing services in Africa. repetitive tasks off the table for sales team members, The company operates two distribution centers in so they have more time performing more meaningful Rwanda and four in Ghana.

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EXECUTIVE INTERVIEWS

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AUTHOR

Daniel Sexton, Principal

Daniel Sexton is a Founding Principal at RedChip Ventures. Daniel has over 15 years of experience leading large-scale, technology solutions for Fortune 500 companies, such as Genuine Parts Company, CitiGroup, and Blue Cross Blue Shield. In addition, he has worked with a number of tech startups both as a founder and advisor. Daniel has several software certifications in Java and Cloud technologies. Prior to founding RedChip Ventures, Dan was a Managing Partner at a private investment fund for 6 years where he helped lead and manage investments in technology and product companies.