With the passage of the CHIPS+ Act by Congress and its imminent signing by the President of the United States, a lot of attention has been paid to the construction of new semiconductor manufacturing megasites by Intel, TSMC, and Samsung. But beyond the manufacturing side of the semiconductor business, there is a significant need to invest in related areas such as research, talent training, small and medium business development, and academic cooperation. I recently had the opportunity to tour a prime example of such a facility that integrates all these other aspects of chip manufacturing into a tight industry, government, and academic partnership. That partnership has been going on for over 20 years in Albany, New York where IBM Research has a nanotechnology center that is located within the State University of New York (SUNY) Poly's Albany NanoTech Complex. With significant investment by New York State through the NY CREATES development agency, IBM in close partnership with several universities and industry partners is developing state-of-the-art semiconductor process technologies in working labs for the next generation of computer chips.
The center provides a unique facility for semiconductor research – its open environment facilitates collaboration between leading equipment and materials suppliers, researchers, engineers, academics, and EDA vendors. Presently, IBM has a manufacturing and research partnership with Samsung Electronics and a research partnership was announced with Intel last year. Key chipmaking suppliers such as ASML, KLA, and Tokyo Electron (TEL) have equipment installed, and are working actively with IBM developing advanced processes and metrology for leading edge technologies.
These facilities do not come cheap. It takes billions of dollars of investment and many years of research to achieve each new breakthrough. For example, the High-k metal gate took 15 years to go into products; the FinFET transistor, essential today, took 13 years; and the next generation transistor, the gate-all-around/nano sheet, which Samsung is putting into production now, was in development for 14 years. In addition, the cost to manufacture chips at each new process node is increasing 20-30% and the R&D costs are doubling for each node’s development. To continue supporting this strategic development, there needs to be a partnership between industry, academia, and government.
IBM Makes The Investment
You might ask why IBM, which sold off its semiconductor manufacturing facilities over the years, is so involved in this deep and expensive research. Well, for one, IBM is very, very good at semiconductor process development. The company pioneered several critical semiconductor technologies over the decades. But being good at a technology does not pay the bills, so IBM’s second motivation is that the company needs the best technology for its own Power and IBM Z computers. To that end, IBM is primarily focused on developments that support high-performance computing and AI processing.
Additional strategic suppliers and partners help to scale these innovations beyond just IBM’s contribution. The best equipment from the world-class equipment suppliers provides a testbed for partners to experiment and advance the state-of-the-art technology. IBM along with its equipment partners have built specialized equipment where needed to experiment beyond the capabilities of standard equipment.
But IBM only succeeds if it can transfer the technology from the labs into production. To do so, IBM and Samsung have been working closely on process developments and the technology transfer.
The Albany NanoTech Complex dovetails with the CHIPS Act in that it will allow the United States to develop leadership in manufacturing technologies. It can also allow smaller companies to test innovative technologies in this facility. The present fab building is running 24/7/365 and is highly utilized, but there’s space to build another building that can double significantly expand the clean room space. There’s also a plan for a building that will be able to support the next generation of ASML EUV equipment called high NA EUV.
The Future is Vertical
The Albany site also is a center for chiplet technology research. As semiconductor scaling slows, unique packaging solutions for multi-die chips will become the norm for high-performance and power-efficient computing. IBM Research has an active program of developing unique 2.5D and 3D die-stacking technologies. Today the preferred substrate for building these multi-die chips is still made from silicon, based on the availability of tools and manufacturing knowledge. There are still unique process steps that must be developed to handle the specialized processing, including laser debonding techniques.
IBM also works with test equipment manufacturers because building 3D structures with chiplets presents some unique testing challenges. Third party EDA vendors also need to be part of the development process, because the ultimate goal of chiplet-based design is to be able to combine chips from different process nodes and different foundries.
Today chiplet technology is embryonic, but the future will absolutely need this technology to build the next generation of data center hardware. The is a situation where the economics and technology are coming together at the right time.
The Albany NanoTech Complex is a model for the semiconductor industry and demonstrates one way to bring researchers from various disciplines and various organizations together to advance the state-of-the-art semiconductor technology. But this model also needs to scale up and be replicated throughout North America. With more funding and more scale, there also needs to be an appropriately skilled workforce. Here is where the US needs to make investments in STEM education on par with the late 1950s Space Race and sites like Albany that offer R&D on leading-edge process development that should inspire more students to go into physics, chemistry, and electrical engineering and not into building the next crypto currency startup.
Tirias Research tracks and consults for companies throughout the electronics ecosystem from semiconductors to systems and sensors to the cloud. Members of the Tirias Research team have consulted for IBM, Intel, GlobalFoundries, Samsung, and other foundries.
I recently met with Dr. Nick Fuller, Vice President, Distributed Cloud, at IBM Research for a discussion about IBM’s long-range plans and strategy for artificial intelligence and machine learning at the edge.
Dr. Fuller is responsible for providing AI and platform–based innovation for enterprise digital transformation spanning edge computing and distributed cloud management. He is an IBM Master Inventor with over 75 patents and co-author of 75 technical publications. Dr. Fuller obtained his Bachelor of Science in Physics and Math from Morehouse College and his PhD in Applied Physics from Columbia University.
Edge In, not Cloud Out
In general, Dr. Fuller told me that IBM is focused on developing an "edge in" position versus a "cloud out" position with data, AI, and Kubernetes-based platform technologies to scale hub and spoke deployments of edge applications.
A hub plays the role of a central control plane used for orchestrating the deployment and management of edge applications in a number of connected spoke locations such as a factory floor or a retail branch, where data is generated or locally aggregated for processing.
“Cloud out” refers to the paradigm where cloud service providers are extending their cloud architecture out to edge locations. In contrast, “edge in” refers to a provider-agnostic architecture that is cloud-independent and treats the data-plane as a first-class citizen.
IBM's overall architectural principle is scalability, repeatability, and full stack solution management that allows everything to be managed using a single unified control plane.
IBM’s Red Hat platform and infrastructure strategy anchors the application stack with a unified, scalable, and managed OpenShift-based control plane equipped with a high-performance storage appliance and self-healing system capabilities (inclusive of semi-autonomous operations).
IBM’s strategy also includes several in-progress platform-level technologies for scalable data, AI/ML runtimes, accelerator libraries for Day-2 AI operations, and scalability for the enterprise.
It is an important to mention that IBM is designing its edge platforms with labor cost and technical workforce in mind. Data scientists with PhDs are in high demand, making them difficult to find and expensive to hire once you find them. IBM is designing its edge system capabilities and processes so that domain experts rather than PhDs can deploy new AI models and manage Day-2 operations.
Why edge is important
Advances in computing and storage have made it possible for AI to process mountains of accumulated data to provide solutions. By bringing AI closer to the source of data, edge computing is faster and more efficient than cloud. While Cloud data accounts for 60% of the world’s data today, vast amounts of new data is being created at the edge, including industrial applications, traffic cameras, and order management systems, all of which can be processed at the edge in a fast and timely manner.
Public cloud and edge computing differ in capacity, technology, and management. An advantage of edge is that data is processed and analyzed at / near its collection point at the edge. In the case of cloud, data must be transferred from a local device and into the cloud for analytics and then transferred back to the edge again. Moving data through the network consumes capacity and adds latency to the process. It’s easy to see why executing a transaction at the edge reduces latency and eliminates unnecessary load on the network.
Increased privacy is another benefit of processing data at the edge. Analyzing data where it originates limits the risk of a security breach. Most of the communications between the edge and the cloud is then confined to such things as reporting, data summaries, and AI models, without ever exposing the raw data.
IBM at the Edge
In our discussion, Dr. Fuller provided a few examples to illustrate how IBM plans to provide new and seamless edge solutions for existing enterprise problems.
Example #1 – McDonald’s drive-thru
Dr. Fuller’s first example centered around Quick Service Restaurant’s (QSR) problem of drive-thru order taking. Last year, IBM acquired an automated order-taking system from McDonald's. As part of the acquisition, IBM and McDonald's established a partnership to perfect voice ordering methods using AI. Drive-thru orders are a significant percentage of total QSR orders for McDonald's and other QSR chains.
McDonald's and other QSR restaurants would like every order to be processed as quickly and accurately as possible. For that reason, McDonald's conducted trials at ten Chicago restaurants using an edge-based AI ordering system with NLP (Natural Language Processing) to convert spoken orders into a digital format. It was found that AI had the potential to reduce ordering errors and processing time significantly. Since McDonald's sells almost 7 million hamburgers daily, shaving a minute or two off each order represents a significant opportunity to address labor shortages and increase customer satisfaction.
Example #2 – Boston Dynamics and Spot the agile mobile robot
According to an earlier IBM survey, many manufacturers have already implemented AI-driven robotics with autonomous decision-making capability. The study also indicated that over 80 percent of companies believe AI can help Boost future business operations. However, some companies expressed concern about the limited mobility of edge devices and sensors.
To develop a mobile edge solution, IBM teamed up with Boston Dynamics. The partnership created an agile mobile robot using IBM Research and IBM Sustainability Software AI technology. The device can analyze visual sensor readings in hazardous and challenging industrial environments such as manufacturing plants, warehouses, electrical grids, waste treatment plants and other hazardous environments. The value proposition that Boston Dynamics brought to the partnership was Spot the agile mobile robot, a walking, sensing, and actuation platform. Like all edge applications, the robot’s wireless mobility uses self-contained AI/ML that doesn’t require access to cloud data. It uses cameras to read analog devices, visually monitor fire extinguishers, and conduct a visual inspection of human workers to determine if required safety equipment is being worn.
IBM was able to show up to a 10X speedup by automating some manual tasks, such as converting the detection of a problem into an immediate work order in IBM Maximo to correct it. A fast automated response was not only more efficient, but it also improved the safety posture and risk management for these facilities. Similarly, some factories need to thermally monitor equipment to identify any unexpected hot spots that may show up over time, indicative of a potential failure.
IBM is working with National Grid, an energy company, to develop a mobile solution using Spot, the agile mobile robot, for image analysis of transformers and thermal connectors. As shown in the above graphic, Spot also monitored connectors on both flat surfaces and 3D surfaces. IBM was able to show that Spot could detect excessive heat build-up in small connectors, potentially avoiding unsafe conditions or costly outages. This AI/ML edge application can produce faster response times when an issue is detected, which is why IBM believes significant gains are possible by automating the entire process.
IBM market opportunities
Drive-thru orders and mobile robots are just a few examples of the millions of potential AI applications that exist at the edge and are driven by several billion connected devices.
Edge computing is an essential part of enterprise digital transformation. Enterprises seek ways to demonstrate the feasibility of solving business problems using AI/ML and analytics at the edge. However, once a proof of concept has been successfully demonstrated, it is a common problem for a company to struggle with scalability, data governance, and full-stack solution management.
Challenges with scaling
“Determining entry points for AI at the edge is not the difficult part,” Dr. Fuller said. “Scale is the real issue.”
Scaling edge models is complicated because there are so many edge locations with large amounts of diverse content and a high device density. Because large amounts of data are required for training, data gravity is a potential problem. Further, in many scenarios, vast amounts of data are generated quickly, leading to potential data storage and orchestration challenges. AI Models are also rarely "finished." Monitoring and retraining of models are necessary to keep up with changes the environment.
Through IBM Research, IBM is addressing the many challenges of building an all-encompassing edge architecture and horizontally scalable data and AI technologies. IBM has a wealth of edge capabilities and an architecture to create the appropriate platform for each application.
IBM AI entry points at the edge
IBM sees Edge Computing as a $200 billion market by 2025. Dr. Fuller and his organization have identified four key market entry points for developing and expanding IBM’s edge compute strategy. In order of size, IBM believes its priority edge markets to be intelligent factories (Industry 4.0), telcos, retail automation, and connected vehicles.
IBM and its Red Hat portfolio already have an established presence in each market segment, particularly in intelligent operations and telco. Red Hat is also active in the connected vehicles space.
There have been three prior industrial revolutions, beginning in the 1700s up to our current in-progress fourth revolution, Industry 4.0, that promotes a digital transformation.
Manufacturing is the fastest growing and the largest of IBM’s four entry markets. In this segment, AI at the edge can Boost quality control, production optimization, asset management, and supply chain logistics. IBM believes there are opportunities to achieve a 4x speed up in implementing edge-based AI solutions for manufacturing operations.
For its Industry 4.0 use case development, IBM, through product, development, research and consulting teams, is working with a major automotive OEM. The partnership has established the following joint objectives:
Maximo Application Suite
IBM’s Maximo Application Suite plays an important part in implementing large manufacturers' current and future IBM edge solutions. Maximo is an integrated public or private cloud platform that uses AI, IoT, and analytics to optimize performance, extend asset lifecycles and reduce operational downtime and costs. IBM is working with several large manufacturing clients currently using Maximo to develop edge use cases, and even uses it within its own Manufacturing.
IBM has research underway to develop a more efficient method of handling life cycle management of large models that require immense amounts of data. Day 2 AI operations tasks can sometimes be more complex than initial model training, deployment, and scaling. Retraining at the edge is difficult because resources are typically limited.
Once a model is trained and deployed, it is important to monitor it for drift caused by changes in data distributions or anything that might cause a model to deviate from original requirements. Inaccuracies can adversely affect model ROI.
Day-2 AI Operations (retraining and scaling)
Day-2 AI operations consist of continual updates to AI models and applications to keep up with changes in data distributions, changes in the environment, a drop in model performance, availability of new data, and/or new regulations.
IBM recognizes the advantages of performing Day-2 AI Operations, which includes scaling and retraining at the edge. It appears that IBM is the only company with an architecture equipped to effectively handle Day-2 AI operations. That is a significant competitive advantage for IBM.
A company using an architecture that requires data to be moved from the edge back into the cloud for Day-2 related work will be unable to support many factory AI/ML applications because of the sheer number of AI/ML models to support (100s to 1000s).
“There is a huge proliferation of data at the edge that exists in multiple spokes,” Dr. Fuller said. "However, all that data isn’t needed to retrain a model. It is possible to cluster data into groups and then use sampling techniques to retrain the model. There is much value in federated learning from our point of view.”
Federated learning is a promising training solution being researched by IBM and others. It preserves privacy by using a collaboration of edge devices to train models without sharing the data with other entities. It is a good framework to use when resources are limited.
Dealing with limited resources at the edge is a challenge. IBM’s edge architecture accommodates the need to ensure resource budgets for AI applications are met, especially when deploying multiple applications and multiple models across edge locations. For that reason, IBM developed a method to deploy data and AI applications to scale Day-2 AI operations utilizing hub and spokes.
The graphic above shows the current status quo methods of performing Day-2 operations using centralized applications and a centralized data plane compared to the more efficient managed hub and spoke method with distributed applications and a distributed data plane. The hub allows it all to be managed from a single pane of glass.
Data Fabric Extensions to Hub and Spokes
IBM uses hub and spoke as a model to extend its data fabric. The model should not be thought of in the context of a traditional hub and spoke. IBM’s hub provides centralized capabilities to manage clusters and create multiples hubs that can be aggregated to a higher level. This architecture has four important data management capabilities.
In addition to AI deployments, the hub and spoke architecture and the previously mentioned capabilities can be employed more generally to tackle challenges faced by many enterprises in consistently managing an abundance of devices within and across their enterprise locations. Management of the software delivery lifecycle or addressing security vulnerabilities across a vast estate are a case in point.
Multicloud and Edge platform
In the context of its strategy, IBM sees edge and distributed cloud as an extension of its hybrid cloud platform built around Red Hat OpenShift. One of the newer and more useful options created by the Red Hat development team is the Single Node OpenShift (SNO), a compact version of OpenShift that fits on a single server. It is suitable for addressing locations that are still servers but come in a single node, not clustered, deployment type.
For smaller footprints such as industrial PCs or computer vision boards (for example NVidia Jetson Xavier), Red Hat is working on a project which builds an even smaller version of OpenShift, called MicroShift, that provides full application deployment and Kubernetes management capabilities. It is packaged so that it can be used for edge device type deployments.
Overall, IBM and Red Hat have developed a full complement of options to address a large spectrum of deployments across different edge locations and footprints, ranging from containers to management of full-blown Kubernetes applications from MicroShift to OpenShift and IBM Edge Application Manager.
Much is still in the research stage. IBM's objective is to achieve greater consistency in terms of how locations and application lifecycle is managed.
First, Red Hat plans to introduce hierarchical layers of management with Red Hat Advanced Cluster Management (RHACM), to scale by two to three orders of magnitude the number of edge locations managed by this product. Additionally, securing edge locations is a major focus. Red Hat is continuously expanding platform security features, for example by recently including Integrity Measurement Architecture in Red Hat Enterprise Linux, or by adding Integrity Shield to protect policies in Red Hat Advanced Cluster Management (RHACM).
Red Hat is partnering with IBM Research to advance technologies that will permit it to protect platform integrity and the integrity of client workloads through the entire software supply chains. In addition, IBM Research is working with Red Hat on analytic capabilities to identify and remediate vulnerabilities and other security risks in code and configurations.
Telco network intelligence and slice management with AL/ML
Communication service providers (CSPs) such as telcos are key enablers of 5G at the edge. 5G benefits for these providers include:
The end-to-end 5G network comprises the Radio Access Network (RAN), transport, and core domains. Network slicing in 5G is an architecture that enables multiple virtual and independent end-to-end logical networks with different characteristics such as low latency or high bandwidth, to be supported on the same physical network. This is implemented using cloud-native technology enablers such as software defined networking (SDN), virtualization, and multi-access edge computing. Slicing offers necessary flexibility by allowing the creation of specific applications, unique services, and defined user groups or networks.
An important aspect of enabling AI at the edge requires IBM to provide CSPs with the capability to deploy and manage applications across various enterprise locations, possibly spanning multiple end-to-end network slices, using a single pane of glass.
5G network slicing and slice management
Network slices are an essential part of IBM's edge infrastructure that must be automated, orchestrated and optimized according to 5G standards. IBM’s strategy is to leverage AI/ML to efficiently manage, scale, and optimize the slice quality of service, measured in terms of bandwidth, latency, or other metrics.
5G and AI/ML at the edge also represent a significant opportunity for CSPs to move beyond traditional cellular services and capture new sources of revenue with new services.
Communications service providers need management and control of 5G network slicing enabled with AI-powered automation.
Dr. Fuller sees a variety of opportunities in this area. "When it comes to applying AI and ML on the network, you can detect things like intrusion detection and malicious actors," he said. "You can also determine the best way to route traffic to an end user. Automating 5G functions that run on the network using IBM network automation software also serves as an entry point.”
In IBM’s current telecom trial, IBM Research is spearheading the development of a range of capabilities targeted for the IBM Cloud Pak for Network Automation product using AI and automation to orchestrate, operate and optimize multivendor network functions and services that include:
Future leverage of these capabilities by existing IBM Clients that use the Cloud Pak for Network Automation (e.g., DISH) can offer further differentiation for CSPs.
5G radio access
Open radio access networks (O-RANs) are expected to significantly impact telco 5G wireless edge applications by allowing a greater variety of units to access the system. The O-RAN concept separates the DU (Distributed Units) and CU (Centralized Unit) from a Baseband Unit in 4G and connects them with open interfaces.
O-RAN system is more flexible. It uses AI to establish connections made via open interfaces that optimize the category of a device by analyzing information about its prior use. Like other edge models, the O-RAN architecture provides an opportunity for continuous monitoring, verification, analysis, and optimization of AI models.
The IBM-telco collaboration is expected to advance O-RAN interfaces and workflows. Areas currently under development are:
IBM Cloud and Infrastructure
The cornerstone for the delivery of IBM's edge solutions as a service is IBM Cloud Satellite. It presents a consistent cloud-ready, cloud-native operational view with OpenShift and IBM Cloud PaaS services at the edge. In addition, IBM integrated hardware and software Edge systems will provide RHACM - based management of the platform when clients or third parties have existing managed as a service models. It is essential to note that in either case this is done within a single control plane for hubs and spokes that helps optimize execution and management from any cloud to the edge in the hub and spoke model.
IBM's focus on “edge in” means it can provide the infrastructure through things like the example shown above for software defined storage for federated namespace data lake that surrounds other hyperscaler clouds. Additionally, IBM is exploring integrated full stack edge storage appliances based on hyperconverged infrastructure (HCI), such as the Spectrum Fusion HCI, for enterprise edge deployments.
As mentioned earlier, data gravity is one of the main driving factors of edge deployments. IBM has designed its infrastructure to meet those data gravity requirements, not just for the existing hub and spoke topology but also for a future spoke-to-spoke topology where peer-to-peer data sharing becomes imperative (as illustrated with the wealth of examples provided in this article).
Edge is a distributed computing model. One of its main advantages is that computing, and data storage and processing is close to where data is created. Without the need to move data to the cloud for processing, real-time application of analytics and AI capabilities provides immediate solutions and drives business value.
IBM’s goal is not to move the entirety of its cloud infrastructure to the edge. That has little value and would simply function as a hub to spoke model operating on actions and configurations dictated by the hub.
IBM’s architecture will provide the edge with autonomy to determine where data should reside and from where the control plane should be exercised.
Equally important, IBM foresees this architecture evolving into a decentralized model capable of edge-to-edge interactions. IBM has no firm designs for this as yet. However, the plan is to make the edge infrastructure and platform a first-class citizen instead of relying on the cloud to drive what happens at the edge.
Developing a complete and comprehensive AI/ML edge architecture - and in fact, an entire ecosystem - is a massive undertaking. IBM faces many known and unknown challenges that must be solved before it can achieve success.
However, IBM is one of the few companies with the necessary partners and the technical and financial resources to undertake and successfully implement a project of this magnitude and complexity.
It is reassuring that IBM has a plan and that its plan is sound.
Paul Smith-Goodson is Vice President and Principal Analyst for quantum computing, artificial intelligence and space at Moor Insights and Strategy. You can follow him on Twitter for more current information on quantum, AI, and space.
Note: Moor Insights & Strategy writers and editors may have contributed to this article.
Moor Insights & Strategy, like all research and tech industry analyst firms, provides or has provided paid services to technology companies. These services include research, analysis, advising, consulting, benchmarking, acquisition matchmaking, and speaking sponsorships. The company has had or currently has paid business relationships with 8×8, Accenture, A10 Networks, Advanced Micro Devices, Amazon, Amazon Web Services, Ambient Scientific, Anuta Networks, Applied Brain Research, Applied Micro, Apstra, Arm, Aruba Networks (now HPE), Atom Computing, AT&T, Aura, Automation Anywhere, AWS, A-10 Strategies, Bitfusion, Blaize, Box, Broadcom, C3.AI, Calix, Campfire, Cisco Systems, Clear Software, Cloudera, Clumio, Cognitive Systems, CompuCom, Cradlepoint, CyberArk, Dell, Dell EMC, Dell Technologies, Diablo Technologies, Dialogue Group, Digital Optics, Dreamium Labs, D-Wave, Echelon, Ericsson, Extreme Networks, Five9, Flex, Foundries.io, Foxconn, Frame (now VMware), Fujitsu, Gen Z Consortium, Glue Networks, GlobalFoundries, Revolve (now Google), Google Cloud, Graphcore, Groq, Hiregenics, Hotwire Global, HP Inc., Hewlett Packard Enterprise, Honeywell, Huawei Technologies, IBM, Infinidat, Infosys, Inseego, IonQ, IonVR, Inseego, Infosys, Infiot, Intel, Interdigital, Jabil Circuit, Keysight, Konica Minolta, Lattice Semiconductor, Lenovo, Linux Foundation, Lightbits Labs, LogicMonitor, Luminar, MapBox, Marvell Technology, Mavenir, Marseille Inc, Mayfair Equity, Meraki (Cisco), Merck KGaA, Mesophere, Micron Technology, Microsoft, MiTEL, Mojo Networks, MongoDB, MulteFire Alliance, National Instruments, Neat, NetApp, Nightwatch, NOKIA (Alcatel-Lucent), Nortek, Novumind, NVIDIA, Nutanix, Nuvia (now Qualcomm), onsemi, ONUG, OpenStack Foundation, Oracle, Palo Alto Networks, Panasas, Peraso, Pexip, Pixelworks, Plume Design, PlusAI, Poly (formerly Plantronics), Portworx, Pure Storage, Qualcomm, Quantinuum, Rackspace, Rambus, Rayvolt E-Bikes, Red Hat, Renesas, Residio, Samsung Electronics, Samsung Semi, SAP, SAS, Scale Computing, Schneider Electric, SiFive, Silver Peak (now Aruba-HPE), SkyWorks, SONY Optical Storage, Splunk, Springpath (now Cisco), Spirent, Splunk, Sprint (now T-Mobile), Stratus Technologies, Symantec, Synaptics, Syniverse, Synopsys, Tanium, Telesign,TE Connectivity, TensTorrent, Tobii Technology, Teradata,T-Mobile, Treasure Data, Twitter, Unity Technologies, UiPath, Verizon Communications, VAST Data, Ventana Micro Systems, Vidyo, VMware, Wave Computing, Wellsmith, Xilinx, Zayo, Zebra, Zededa, Zendesk, Zoho, Zoom, and Zscaler. Moor Insights & Strategy founder, CEO, and Chief Analyst Patrick Moorhead is an investor in dMY Technology Group Inc. VI, Dreamium Labs, Groq, Luminar Technologies, MemryX, and Movandi.
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Aug 05, 2022 (Alliance News via COMTEX) -- The global AI development service market size was US$ 31.1 billion in 2021. The global AI development service market is forecast to grow to US$ 705.1 billion by 2030, registering a compound annual growth rate (CAGR) of 36.5 during the forecast period from 2022 to 2030.
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Factors Influencing the Market
The trending use of multi-cloud functioning is driving the growth of the industry. In addition, the rising demand for cloud-based intelligence services will propel the market growth during the forecast period. IBM estimates that around 98% of the organization’s plan will adopt multi-cloud architectures by 2021.
Companies are focusing on integrating artificial intelligence (AI) technology into their applications, businesses, analytics, and services in order to expand the business and make the task easy for employees. Thus, it will benefit the AI development service market. Furthermore, AI development services will also help businesses cut operating costs in order to enhance profit margins. All of this will contribute to the growth of the global AI development service market.
Many government bodies, particularly in emerging economies, are investing highly due to the benefits of AI. In India, the Digital India initiative is expected to offer ample growth opportunities for the market. In addition, the firms are creating services for testing AI-based applications. In April 2019, Google Cloud Platform launched the AI Platform, a new end-to-end environment for teams to test, train, and deploy models. As a result, it will offer ample growth opportunities for the global AI development service market.
Impact of COVID-19 on AI Development Service Market
The sudden onset of COVID-19 has surged the growth of AI development services in the healthcare sector. AI development services are used to Boost treatment and enhance accuracy and efficiency. AI can predict patient outcomes using a significant quantity of data available in electronic healthcare records. All of these factors are significantly contributing to the growth of the global AI development service industry.
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The Asia-Pacific AI development service market is forecast to hold dominance during the study period. The growth of the region is attributed to the rising number of investments in artificial intelligence. In addition, the presence of top tech giants in the region is forecast to benefit the Asia-Pacific AI development service market. Furthermore, emerging countries, such as India and Taiwan, are witnessing an increasing adoption of new AI-based services or models. As a result, it will further expand the potential scope of the studied market.
Competitors in the Market
The global AI development service market segmentation focuses on Deployment, Organization, End-User, and Region.
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Based on region
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1.The market report presents the estimated size of the market at the end of the forecast period. The report also examines historical and current market sizes.
2.During the forecast period, the report analyzes the growth rate, market size, and market valuation.
The report presents current trends in the industry and the future potential of the North America, Asia Pacific, Europe, Latin America, and the Middle East and Africa markets.
3.The report offers a comprehensive view of the market based on geographic scope, market segmentation, and key player financial performance.
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A data breach within a healthcare system could cost in excess of $10 million—more than in any other sector—according to a new report.
The cost is on the rise, up about $1 million from last year. The uptick is partially due to increasingly integrated technology systems.
The report, released by IBM at the end of last month, collected national data from more than 550 organizations across industries from March 2021 to March 2022, analyzing how cybersecurity attacks impact organizations. Breaches within the healthcare sector have cost companies $10.1 million per breach, a nearly 10% increase from last year and a 42% increase from 2020. The average cost of a critical infrastructure data breach globally in any industry was just under $4.5 million.
Financial organizations experience the second-most-expensive breaches, at nearly $6 million per breach, IBM reports.
Cyberattacks can happen in many different ways, said Limor Kessem, a principal consultant in cyber crisis management for IBM’s Security X-Force. Destructive attacks and ransomware attacks—wherein hackers disrupt a hospital’s technologies, for example, and ask the hospital to pay a ransom in order to get access back—are disruptive as well as costly.
“Attacks that take place in real time cause direct losses to hospitals, which have to reroute patients, deny care, lose access to electronic health records and see the risk to human lives rise as a result of the attack,” Kessem told Crain’s. “That’s on top of staff distress and having to revert to manual procedures and paperwork.”
The stakes are particularly high for New York hospitals. According to industry standards, on average every bed in a hospital uses 15 devices that are often interconnected, including monitors and IV pumps, according to Chad Holmes, a product specialist at Cynerio, a cybersecurity company on the Upper West Side. A 1,000-bed hospital could have 15,000 devices that could all be impacted by an attack, he said.
“If a city like New York lost access, that would be really bad for ERs and could have a really bad cascading effect,” Holmes said. If patients had to be diverted from a city health system location but all sites were impacted by a breach, it could have a domino effect, he said.
Healthcare organizations are more vulnerable to cybersecurity attacks than other systems are because hackers know they are impacted more when technologies aren’t working, Kessem said. Such downtime costs organizations financially, but it also can cost lives if medical systems are disrupted.
The complexity of the technology infrastructure healthcare systems tend to use also makes them more vulnerable to attacks, Kessem said, and many organizations run outdated programs on devices they use every day, exacerbating the issue.
According to IBM’s report, highly regulated environments such as healthcare systems wind up paying for data breaches for longer compared with less-regulated industries. Typically a healthcare organization can take more than 10 months to recover from a data breach.
Cynerio released a report last week that shows hospitals typically have to pay $250,000 to $500,000 to recover access to their technology after a ransomware attack, and there is no real way to recoup those costs, Holmes said. The firm asked 517 hospital leaders about the frequency of attacks; leaders reported that once their system was hit, they got hit many more times afterward. Overall, 11% of the time, healthcare systems were attacked 25 or more times.
Almost a quarter of cyberattacks Cynerio studied led to increased patient mortality, Holmes said, because attacks disrupted lifesaving medical treatment.
Sher Baig, who works in global cyber commercialization at GE Healthcare, said big hospitals can see losses of up to $50 million in a single quarter because of cyberattacks. The losses are so large they could force hospitals out of business, Baig said, punctuating the need for hospital leaders to have a defense plan in place.
“I highly recommend having an incident response plan, a team in place to carry out the response, and drilling that plan to Boost over time,” Kessem said. “A special playbook for ransomware cases can not only save costs for the hospital—about 58% of the breach’s cost—but it can also save lives.”
IBM has released annual reports on the cost of data breaches for nearly two decades.
This story first appeared in our sister publication, Crain's New York Business.
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A month after the National Institute of Standards and Technology (NIST) revealed the first quantum-safe algorithms, Amazon Web Services (AWS) and IBM have swiftly moved forward. Google was also quick to outline an aggressive implementation plan for its cloud service that it started a decade ago.
It helps that IBM researchers contributed to three of the four algorithms, while AWS had a hand in one. Google is also among those who contributed to SPHINCS+.
A long process that started in 2016 with 69 original candidates ends with the selection of four algorithms that will become NIST standards, which will play a critical role in protecting encrypted data from the vast power of quantum computers.
NIST's four choices include CRYSTALS-Kyber, a public-private key-encapsulation mechanism (KEM) for general asymmetric encryption, such as when connecting websites. For digital signatures, NIST selected CRYSTALS-Dilithium, FALCON, and SPHINCS+. NIST will add a few more algorithms to the mix in two years.
Vadim Lyubashevsky, a cryptographer who works in IBM's Zurich Research Laboratories, contributed to the development of CRYSTALS-Kyber, CRYSTALS-Dilithium, and Falcon. Lyubashevsky was predictably pleased by the algorithms selected, but he had only anticipated NIST would pick two digital signature candidates rather than three.
Ideally, NIST would have chosen a second key establishment algorithm, according to Lyubashevsky. "They could have chosen one more right away just to be safe," he told Dark Reading. "I think some people expected McEliece to be chosen, but maybe NIST decided to hold off for two years to see what the backup should be to Kyber."
After NIST identified the algorithms, IBM moved forward by specifying them into its recently launched z16 mainframe. IBM introduced the z16 in April, calling it the "first quantum-safe system," enabled by its new Crypto Express 8S card and APIs that provide access to the NIST APIs.
IBM was championing three of the algorithms that NIST selected, so IBM had already included them in the z16. Since IBM had unveiled the z16 before the NIST decision, the company implemented the algorithms into the new system. IBM last week made it official that the z16 supports the algorithms.
Anne Dames, an IBM distinguished engineer who works on the company's z Systems team, explained that the Crypto Express 8S card could implement various cryptographic algorithms. Nevertheless, IBM was betting on CRYSTAL-Kyber and Dilithium, according to Dames.
"We are very fortunate in that it went in the direction we hoped it would go," she told Dark Reading. "And because we chose to implement CRYSTALS-Kyber and CRYSTALS-Dilithium in the hardware security module, which allows clients to get access to it, the firmware in that hardware security module can be updated. So, if other algorithms were selected, then we would add them to our roadmap for inclusion of those algorithms for the future."
A software library on the system allows application and infrastructure developers to incorporate APIs so that clients can generate quantum-safe digital signatures for both classic computing systems and quantum computers.
"We also have a CRYSTALS-Kyber interface in place so that we can generate a key and provide it wrapped by a Kyber key so that could be used in a potential key exchange scheme," Dames said. "And we've also incorporated some APIs that allow clients to have a key exchange scheme between two parties."
Dames noted that clients might use Dilithium to generate digital signatures on documents. "Think about code signing servers, things like that, or documents signing services, where people would like to actually use the digital signature capability to ensure the authenticity of the document or of the code that's being used," she said.
During Amazon's AWS re:Inforce security conference last week in Boston, the cloud provider emphasized its post-quantum cryptography (PQC) efforts. According to Margaret Salter, director of applied cryptography at AWS, Amazon is already engineering the NIST standards into its services.
During a breakout session on AWS' cryptography efforts at the conference, Salter said AWS had implemented an open source, hybrid post-quantum key exchange based on a specification called s2n-tls, which implements the Transport Layer Security (TLS) protocol across different AWS services. AWS has contributed it as a draft standard to the Internet Engineering Task Force (IETF).
Salter explained that the hybrid key exchange brings together its traditional key exchanges while enabling post-quantum security. "We have regular key exchanges that we've been using for years and years to protect data," she said. "We don't want to get rid of those; we're just going to enhance them by adding a public key exchange on top of it. And using both of those, you have traditional security, plus post quantum security."
Last week, Amazon announced that it deployed s2n-tls, the hybrid post-quantum TLS with CRYSTALS-Kyber, which connects to the AWS Key Management Service (AWS KMS) and AWS Certificate Manager (ACM). In an update this week, Amazon documented its stated support for AWS Secrets Manager, a service for managing, rotating, and retrieving database credentials and API keys.
While Google didn't make implementation announcements like AWS in the immediate aftermath of NIST's selection, VP and CISO Phil Venables said Google has been focused on PQC algorithms "beyond theoretical implementations" for over a decade. Venables was among several prominent researchers who co-authored a technical paper outlining the urgency of adopting PQC strategies. The peer-reviewed paper was published in May by Nature, a respected journal for the science and technology communities.
"At Google, we're well into a multi-year effort to migrate to post-quantum cryptography that is designed to address both immediate and long-term risks to protect sensitive information," Venables wrote in a blog post published following the NIST announcement. "We have one goal: ensure that Google is PQC ready."
Venables recalled an experiment in 2016 with Chrome where a minimal number of connections from the Web browser to Google servers used a post-quantum key-exchange algorithm alongside the existing elliptic-curve key-exchange algorithm. "By adding a post-quantum algorithm in a hybrid mode with the existing key exchange, we were able to test its implementation without affecting user security," Venables noted.
Google and Cloudflare announced a "wide-scale post-quantum experiment" in 2019 implementing two post-quantum key exchanges, "integrated into Cloudflare's TLS stack, and deployed the implementation on edge servers and in Chrome Canary clients." The experiment helped Google understand the implications of deploying two post-quantum key agreements with TLS.
Venables noted that last year Google tested post-quantum confidentiality in TLS and found that various network products were not compatible with post-quantum TLS. "We were able to work with the vendor so that the issue was fixed in future firmware updates," he said. "By experimenting early, we resolved this issue for future deployments."
The four algorithms NIST announced are an important milestone in advancing PQC, but there's other work to be done besides quantum-safe encryption. The AWS TLS submission to the IETF is one example; others include such efforts as Hybrid PQ VPN.
"What you will see happening is those organizations that work on TLS protocols, or SSH, or VPN type protocols, will now come together and put together proposals which they will evaluate in their communities to determine what's best and which protocols should be updated, how the certificates should be defined, and things like things like that," IBM's Dames said.
Dustin Moody, a mathematician at NIST who leads its PQC project, shared a similar view during a panel discussion at the RSA Conference in June. "There's been a lot of global cooperation with our NIST process, rather than fracturing of the effort and coming up with a lot of different algorithms," Moody said. "We've seen most countries and standards organizations waiting to see what comes out of our nice progress on this process, as well as participating in that. And we see that as a very good sign."
Hiten Chadha, former global senior practice leader for Amazon Web Services’ federal financial practice, has joined Cherokee Federal as vice president and technology practice leader.
He will oversee the development and implementation of information technology offerings for the federal government in his new position, the Cherokee Nation Businesses division said Monday.
“With experience in building complex technology solutions and leading large teams, Hiten is a natural fit for Cherokee Federal. We look forward to working alongside him to find new and innovative ways to forecast and solve customer needs,” said Steven Bilby, president of Cherokee Federal.
At AWS, Chadha helped develop a long-term plan to support customers’ cloud adoption and digital transformation initiatives.
Before he joined Amazon’s cloud business, Chadha served as a partner and managing director at IBM (NYSE: IBM) for more than 17 years, according to his LinkedIn profile.
He also worked for professional services firm PwC and enterprise IT provider Optimos.
The MarketWatch News Department was not involved in the creation of this content.
Aug 03, 2022 (The Expresswire) -- A cost-effective market report that deliver top-notch insights into the Industrial Telecom Service Assurance Market. The report also applies both primary and secondary research methodologies to identify new opportunities for development for the Industrial Telecom Service Assurance market during the estimated forecast period, the research further analyses data and screens data on the market growth rate, share and size to enable product owners, stakeholders and field marketing executives identify the challenges in the market. Moreover, the research study also highlights an industry-wide summary of the Industrial Telecom Service Assurance market including drivers, constraints, technological advancements, limitations, growth strategies, growth prospects and others.
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Telecom Service Assurance Market Report learn about offers the scope of exclusive segments and functions that can probably have an effect on the enterprise in the future. Pricing evaluation is blanketed in this record in accordance to every type, manufacturer, regional analysis, price.
List of TOP KEY PLAYERS in Telecom Service Assurance Market Report are: -
● Ericsson Inc.
● Cisco Systems Inc.
● JDS Corporation
● CA Technologies
● Nokia Corporation
● Accenture PLC
● NEC Corporation
● Tata Consultancy Services Limited
● IBM Corporation
● Hewlett-Packard Company
The latest study on the Telecom Service Assurance market published by research offers a deep understanding of the various market dynamics such as the challenges, drivers, trends, and opportunities. The report further elaborates on the micro and macro-economic factors that are expected to shape the growth of the Telecom Service Assurance market during the forecast period (2022-2028).
Market Analysis and Insights:
The global Constipation market size is projected to reach USD million by 2029, from USD million in 2022, at a CAGR during 2022-2029.
On the basis of product type, the Telecom Service Assurance market is primarily split into● System Integration ● Operations Management ● Maintenance ● Consulting and Planning ● Others
On the basis of end-users/application, this report covers the following segments● Small and Medium-sized Enterprises ● Large Enterprises
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The objective of the study is to define market sizes of different segments and countries in accurate years and to forecast the values to the coming years. The report is designed to incorporate both qualitative and quantitative aspects of the industry within each of the regions and countries involved in the study.
There are several manufacturers of Telecom Service Assurance in Europe and North America. In North America, the demand for Telecom Service Assurance is primarily driven by the Healthcare sector. Among the countries in Asia Pacific, the demand was substantially high in developing countries such as China and India. These countries have been witnessing rapid increase in its population along with expansion of their overall economies, which has led to increase in disposable income.
Telecom Service Assurance Market Forecast:
Historical Years: 2017-2022
Base Year: 2022
Estimated Year: 2022
Forecast Period: 2022-2029
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Table of Contents:
Major Points from Table of Contents:
1 Study Coverage
1.1 Telecom Service Assurance Product Introduction
1.2 Market by Type
1.2.1 Global Telecom Service Assurance Market Size Growth Rate by Type
1.3 Market by Application
1.3.1 Global Telecom Service Assurance Market Size Growth Rate by Application
1.4 Study Objectives
1.5 Years Considered
2 Global Telecom Service Assurance Production
2.1 Global Telecom Service Assurance Production Capacity (2016-2029)
2.2 Global Telecom Service Assurance Production by Region: 2016 VS 2022 VS 2029
2.3 Global Telecom Service Assurance Production by Region
2.3.1 Global Telecom Service Assurance Historic Production by Region (2016-2022)
2.3.2 Global Telecom Service Assurance Forecasted Production by Region (2022-2029)
3 Global Telecom Service Assurance Sales in Volume and Value Estimates and Forecasts
3.1 Global Telecom Service Assurance Sales Estimates and Forecasts 2016-2029
3.2 Global Telecom Service Assurance Revenue Estimates and Forecasts 2016-2029
3.3 Global Telecom Service Assurance Revenue by Region: 2016 VS 2022 VS 2029
3.4 Global Top Telecom Service Assurance Regions by Sales
3.4.1 Global Top Telecom Service Assurance Regions by Sales (2016-2022)
3.4.2 Global Top Telecom Service Assurance Regions by Sales (2022-2029)
3.5 Global Top Telecom Service Assurance Regions by Revenue
3.5.1 Global Top Telecom Service Assurance Regions by Revenue (2016-2022)
3.5.2 Global Top Telecom Service Assurance Regions by Revenue (2022-2029)
3.6 North America
3.9 Latin America
3.10 Middle East and Africa
4 Competition by Manufactures
4.1 Global Telecom Service Assurance Supply by Manufacturers
4.1.1 Global Top Telecom Service Assurance Manufacturers by Production Capacity (2021 VS 2022)
4.1.2 Global Top Telecom Service Assurance Manufacturers by Production (2016-2022)
4.2 Global Telecom Service Assurance Sales by Manufacturers
4.2.1 Global Top Telecom Service Assurance Manufacturers by Sales (2016-2022)
4.2.2 Global Top Telecom Service Assurance Manufacturers Market Share by Sales (2016-2022)
4.2.3 Global Top 10 and Top 5 Companies by Telecom Service Assurance Sales in 2021
4.3 Global Telecom Service Assurance Revenue by Manufacturers
4.3.1 Global Top Telecom Service Assurance Manufacturers by Revenue (2016-2022)
4.3.2 Global Top Telecom Service Assurance Manufacturers Market Share by Revenue (2016-2022)
4.3.3 Global Top 10 and Top 5 Companies by Telecom Service Assurance Revenue in 2021
4.4 Global Telecom Service Assurance Sales Price by Manufacturers
4.5 Analysis of Competitive Landscape
4.5.1 Manufacturers Market Concentration Ratio (CR5 and HHI)
4.5.2 Global Telecom Service Assurance Market Share by Company Type (Tier 1, Tier 2, and Tier 3)
4.5.3 Global Telecom Service Assurance Manufacturers Geographical Distribution
4.6 Mergers and Acquisitions, Expansion Plans
5 Market Size by Type
5.1 Global Telecom Service Assurance Sales by Type
5.1.1 Global Telecom Service Assurance Historical Sales by Type (2016-2022)
5.1.2 Global Telecom Service Assurance Forecasted Sales by Type (2022-2029)
5.1.3 Global Telecom Service Assurance Sales Market Share by Type (2016-2029)
5.2 Global Telecom Service Assurance Revenue by Type
5.2.1 Global Telecom Service Assurance Historical Revenue by Type (2016-2022)
5.2.2 Global Telecom Service Assurance Forecasted Revenue by Type (2022-2029)
5.2.3 Global Telecom Service Assurance Revenue Market Share by Type (2016-2029)
5.3 Global Telecom Service Assurance Price by Type
5.3.1 Global Telecom Service Assurance Price by Type (2016-2022)
5.3.2 Global Telecom Service Assurance Price Forecast by Type (2022-2029)
6 Market Size by Application
6.1 Global Telecom Service Assurance Sales by Application
6.1.1 Global Telecom Service Assurance Historical Sales by Application (2016-2022)
6.1.2 Global Telecom Service Assurance Forecasted Sales by Application (2022-2029)
6.1.3 Global Telecom Service Assurance Sales Market Share by Application (2016-2029)
6.2 Global Telecom Service Assurance Revenue by Application
6.2.1 Global Telecom Service Assurance Historical Revenue by Application (2016-2022)
6.2.2 Global Telecom Service Assurance Forecasted Revenue by Application (2022-2029)
6.2.3 Global Telecom Service Assurance Revenue Market Share by Application (2016-2029)
6.3 Global Telecom Service Assurance Price by Application
6.3.1 Global Telecom Service Assurance Price by Application (2016-2022)
6.3.2 Global Telecom Service Assurance Price Forecast by Application (2022-2029)
7 Telecom Service Assurance Consumption by Regions
7.1 Global Telecom Service Assurance Consumption by Regions
7.1.1 Global Telecom Service Assurance Consumption by Regions
7.1.2 Global Telecom Service Assurance Consumption Market Share by Regions
7.2 North America
7.2.1 North America Telecom Service Assurance Consumption by Application
7.2.2 North America Telecom Service Assurance Consumption by Countries
7.2.3 United States
7.3.1 Europe Telecom Service Assurance Consumption by Application
7.3.2 Europe Telecom Service Assurance Consumption by Countries
7.4 Asia Pacific
7.4.1 Asia Pacific Telecom Service Assurance Consumption by Application
7.4.2 Asia Pacific Telecom Service Assurance Consumption by Countries
7.4.5 South Korea
7.7 Central and South America
7.7.1 Central and South America Telecom Service Assurance Consumption by Application
7.7.2 Central and South America Telecom Service Assurance Consumption by Countries
7.7 Middle East and Africa
7.7.1 Middle East and Africa Telecom Service Assurance Consumption by Application
7.7.2 Middle East and Africa Telecom Service Assurance Consumption by Countries
7.7.4 GCC Countries
7.7.6 South Africa
12 Corporate Profiles
12.1.1 Company Corporation Information
12.1.2 Company Overview
12.1.3 Company Telecom Service Assurance Sales, Price, Revenue and Gross Margin (2016-2022)
12.1.4 Company Telecom Service Assurance Product Description
12.1.5 Company Related Developments
13 Industry Chain and Sales Channels Analysis
13.1 Telecom Service Assurance Industry Chain Analysis
13.2 Telecom Service Assurance Key Raw Materials
13.2.1 Key Raw Materials
13.2.2 Raw Materials Key Suppliers
13.3 Telecom Service Assurance Production Mode and Process
13.4 Telecom Service Assurance Sales and Marketing
13.4.1 Telecom Service Assurance Sales Channels
13.4.2 Telecom Service Assurance Distributors
13.5 Telecom Service Assurance Customers
14 Market Drivers, Opportunities, Challenges and Risks Factors Analysis
14.1 Telecom Service Assurance Industry Trends
14.2 Telecom Service Assurance Market Drivers
14.3 Telecom Service Assurance Market Challenges
14.4 Telecom Service Assurance Market Restraints
15 Key Finding in The Global Telecom Service Assurance Study
16.1 Research Methodology
16.1.1 Methodology/Research Approach
16.1.2 Data Source
16.2 Author Details
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