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https://killexams.com/exam_list/IBMKillexams : IBM Research Rolls Out A Comprehensive AI And Platform-Based Edge Research Strategy Anchored By Enterprise Use Cases And Partnerships
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 Excellerate 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 Excellerate 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:
Increase automation and scalability across dozens of plants using 100s of AI / ML models. This client has already seen value in applying AI/ML models for manufacturing applications. IBM Research is helping with re-training models and implementing new ones in an edge environment to help scale even more efficiently. Edge offers faster inference and low latency, allowing AI to be deployed in a wider variety of manufacturing operations requiring instant solutions.
Dramatically reduce the time required to onboard new models. This will allow training and inference to be done faster and allow large models to be deployed much more quickly. The quicker an AI model can be deployed in production; the quicker the time-to-value and the return-on-investment (ROI).
Accelerate deployment of new inspections by reducing the labeling effort and iterations needed to produce a production-ready model via data summarization. Selecting small data sets for annotation means manually examining thousands of images, this is a time-consuming process that will result in - labeling of redundant data. Using ML-based automation for data summarization will accelerate the process and produce better model performance.
Enable Day-2 AI operations to help with data lifecycle automation and governance, model creation, reduce production errors, and provide detection of out-of-distribution data to help determine if a model’s inference is accurate. IBM believes this will allow models to be created faster without data scientists.
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.
First, models running in unattended environments must be monitored. From an operational standpoint, detecting when a model’s effectiveness has significantly degraded and if corrective action is needed is critical.
Secondly, in a hub and spoke model, data is being generated and collected in many locations creating a need for data life cycle management. Working with large enterprise clients, IBM is building unique capabilities to manage the data plane across the hub and spoke estate - optimized to meet data lifecycle, regulatory & compliance as well as local resource requirements. Automation determines which input data should be selected and labeled for retraining purposes and used to further Excellerate the model. Identification is also made for atypical data that is judged worthy of human attention.
The third issue relates to AI pipeline compression and adaptation. As mentioned earlier, edge resources are limited and highly heterogeneous. While a cloud-based model might have a few hundred million parameters or more, edge models can’t afford such resource extravagance because of resource limitations. To reduce the edge compute footprint, model compression can reduce the number of parameters. As an example, it could be reduced from several hundred million to a few million.
Lastly, suppose a scenario exists where data is produced at multiple spokes but cannot leave those spokes for compliance reasons. In that case, IBM Federated Learning allows learning across heterogeneous data in multiple spokes. Users can discover, curate, categorize and share data assets, data sets, analytical models, and their relationships with other organization members.
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:
Reduced operating costs
Increased distribution and density
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:
End-to-end 5G network slice management with planning & design, automation & orchestration, and operations & assurance
Network Data and AI Function (NWDAF) that collects data for slice monitoring from 5G Core network functions, performs network analytics, and provides insights to authorized data consumers.
Improved operational efficiency and reduced cost
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:
Multi-modal (RF level + network-level) analytics (AI/ML) for wireless communication with high-speed ingest of 5G data
Capability to learn patterns of metric and log data across CUs and DUs in RF analytics
Utilization of the antenna control plane to optimize throughput
Primitives for forecasting, anomaly detection and root cause analysis using ML
Opportunity of value-added functions for O-RAN
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-Goodsonis Vice President and Principal Analyst for quantum computing, artificial intelligence and space at Moor Insights and Strategy. You can follow him onTwitterfor more current information on quantum, AI, and space.
Note: Moor Insights & Strategy writers and editors may have contributed to this article.
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Mon, 08 Aug 2022 03:51:00 -0500Paul Smith-Goodsonentext/htmlhttps://www.forbes.com/sites/moorinsights/2022/08/08/ibm-research-rolls-out-a-comprehensive-ai-and-ml-edge-research-strategy-anchored-by-enterprise-partnerships-and-use-cases/Killexams : Businesses confess: We pass cyberattack costs onto customersNo result found, try new keyword!Almost 50 percent of the costs of a breach are incurred more than a year after the incident, IBM found. Such numbers show not only that a given organization will likely sustain a data breach, but that ...Thu, 28 Jul 2022 18:35:17 -0500en-ustext/htmlhttps://www.msn.com/en-us/money/other/businesses-confess-we-pass-cyberattack-costs-onto-customers/ar-AA105ESbKillexams : Five hybrid cloud challenges that Indian organizations must solve to thrive in the techade
India’s ‘techade’ will witness several business trends accelerate, from hybrid workplace to contactless delivery. To transform and keep pace with these trends, businesses will need to become more agile and responsive to the market. This business imperative is making hybrid cloud the prevalent IT architecture. A hybrid cloud architecture combines best-of-breed cloud services and functionality from multiple cloud vendors, flexibility in choosing optimal cloud computing environments for each workload and moving those workloads freely between public and private cloud as circumstances change.
Organizations are finding great value from the early stages of hybrid cloud adoption for improving product and service delivery while fostering innovation. In fact, a exact IBM Institute for Business Value study estimates the value of hybrid cloud investments multiplies up to 13x on average when combined with other levers of transformation. This is why 99% of organizations in India are now using varied combinations of hybrid cloud architecture.
However, the question to ask is, are we adopting the right strategy to make the most of this opportunity? Here are five major challenges in the way of hybrid cloud mastery, which organizations must pay special attention to leverage its full potential.
Architecture that provides a suite of cloud services
During the pandemic, several companies in India had to adopt new hybrid cloud architectures at speed, assembling public, private and on-premises environments without proper integration. There was no organised structure or platform to bind them. Mastering hybrid cloud will require integrating cloud assets with a clear vision, starting with a hybrid cloud platform architecture that defines a “fabric” of cloud services across multiple environments.
A modern hybrid cloud infrastructure is starting to coalesce around a unified hybrid multi-cloud platform that includes support for cloud native application development, a single operating system and automating the deployment of applications across all cloud environments.
For instance, Bharti Airtel has built a telco network cloud using hybrid cloud and cognitive enterprise capabilities to deliver a better customer experience through enhanced network performance, improved availability, operations automation and scaling the network to the edge.
Breaking the silos
Indian companies are facing a shortage of talent, which makes it difficult to cover all areas of cloud management. Moreover, they are faced with a lack of a single infrastructure for seamless work experience which leads to work getting done in silos. Mastering hybrid cloud requires employees with critical cloud skills to do their work effectively in an integrated way across a common hybrid cloud operating model. To do it right, organizations should design operating models for incorporating cloud native, efficient, and connected working practices across the hybrid environment, addressing gaps in skills, talent, and experience.
Scale with security
Security has always been a key concern for organizations on their digitization journey, but with unintegrated cloud architecture the risk is greater, leading to data breaches, financial impact, reputational damage, regulatory enforcement actions and more. Organizations need to adopt a security-aware and security-first culture, ensuring robust security protocols and capabilities across the hybrid platform in a consistent way. For example, in a hybrid cloud architecture, you can reserve behind-the firewall private cloud resources for sensitive data and highly regulated workloads and use more economical public cloud resources for less-sensitive workloads and data. This allows organizations to foresee any potential threats across operations and mitigate them.
Maximizing returns on cloud investment
Managing cloud investments becomes very difficult when costs rise or are unpredictable. In certain cases, the cost of moving the data could go as high as 50%. In a hybrid cloud environment, organizations can manage their cloud cost through a single window to assess how cloud services are disbursed across the whole enterprise, allowing them to optimise the cloud cost by directly matching it with business priorities.
The Godrej Group, for example, has deployed cloud solutions which are expected to help them save 10% on the total cost of ownership over a period of five years, along with zero security incidents and a 100% increase in disaster recovery coverage.
Unlocking value with partner ecosystem
Deploying hybrid cloud often requires a whole ecosystem of partners, whether external or internal, who come with their own competing interests. Mastering hybrid cloud requires getting these naturally competing interests to embrace open innovation and co-creation through an aligned strategy to deliver a successful program.
To conclude, Indian organizations need to take a closer look at their hybrid cloud journey. Consider the five challenges and determine actions required to course correct. Not every organization will have a templatized approach to adopting hybrid cloud. They need to find a sweet spot between building hybrid cloud capabilities and the roadmap for better business performance in a software-driven world. Once mastered, businesses will create new value propositions and become a lever of innovation in the techade.
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Views expressed above are the author's own.
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Tue, 26 Jul 2022 04:30:00 -0500Sandip Patelen-UStext/htmlhttps://timesofindia.indiatimes.com/blogs/voices/five-hybrid-cloud-challenges-that-indian-organizations-must-solve-to-thrive-in-the-techade/Killexams : Robots And Machines Making Work Inclusive
The DAWN café in central Tokyo, Japan looks like any other modern service establishments in a bustling metropolis with its sleek architecture, open space and plenty of greenery. But there’s a catch. Instead of human staff, the floor is busy with robot staff who greet customers at the door; who help them find their seat and take their orders. The robot staff can even recommend different coffee beans that customers can choose from so that a robot barista can make the perfect coffee.
The DAWN café, however, is not a story about automation-where machines take over a human task. The café is operated by Ory Laboratory, a tech startup that builds the robot servers that are operated remotely by human pilots who can’t leave their houses, and who are in many cases, bedridden. According to government statistics, Japan has over 34 million people who are house-bound due to physical disability, mental illness or old age.
DAWN which stands for “Diverse Avatar Working Network” began as a social experiment to create inclusive hospitality jobs for those who are housebound. Over 60 participants control the robots through a mouse, ipad, or gaze-controlled remote from their homes, and can see and speak to the customers through the robot. The robots have a screen displaying a photo and introduction of the person operating the robot, which helps to enhance communications between the servers and customers. According to testimonials of participants, the opportunity to work and “to be needed by others is motivating.”
One billion people, or 15 percent of the world’s population experience some form of disability, according to the World Health Population. With the increase of an ageing workforce and rising rates of chronic illness, as well as an overall decline in mental health worldwide, a focus on disabilities of varying physical, sensory, and cognitive abilities is of growing importance for the workplace. With mounting evidence of the various challenges faced by working-age people with disabilities, there is increasing awareness of, and discussion about promoting disability inclusion in the workplace.
In regards to assistive technologies making work more accessible for those with disabilities, using robots is one of several examples. A 2021 study by the World Intellectual Property Organization (WIPO) on the landscape of assistive technologies reveals a sector which has moved beyond its mechanical engineering origins to now include enabling technologies such as AI, IoT, computer/machine interface (BCI/BMI) and advanced sensors. These technologies can augment mobility, cognition, vision, hearing and communications within the workplace.
Take the example of communications, which involves the use of multiple faculties that include speech, hearing, vision, motor abilities and cognition. According to the 2021 WIPO report, special software and services for assistive communication technologies had the highest number of patent filings between 1998-2019, especially in the area of emulation software which transforms the user interface of a device (including hardware input devices) into a customized software interface for easier interaction and accessibility for users. With the rise in emulation software, large consumer electronic goods companies in the mobile and computing industry such as Apple, IBM, Microsoft, Panasonic and Samsung are leading the way.
Disability rights have been fervently supported by Apple CEO Tim Cook who believes that Apple’s commitment to accessibility is so complete that it never looks at the return of investment, but considers it “just and right.” And on April 21, 2021, Microsoft announced their five-year commitment to accelerate accessible technology development, create opportunities for more people with disabilities to enter the workforce and to build a working culture that is more inclusive for people with disabilities This announcement comes on the back of 25 years of work on accessibility at Microsoft, first triggered as a response to the 1990 American Disabilities Act.
More recently, discussion is centering around disability inclusion in non-physical spaces, such as the metaverse, and what features are necessary to ensure that it is accessible and inclusive.
Technologies, however, only are one component to addressing disability inclusion in the workplace. Many people point to the importance of developing a more inclusive corporate culture starting with developing recruitment and retention policies for disabled employees, implementation of training and awareness of disabilities in the workplace, and reframing accessibility as a course that concerns everybody.
Fri, 29 Jul 2022 17:49:00 -0500Tomoko Yokoientext/htmlhttps://www.forbes.com/sites/tomokoyokoi/2022/07/30/some-machines-are-making-work-more-accessible/Killexams : Priming your business for the new age of quantum computing
The majority of UK business leaders (81%) expect industry disruption from quantum computing by 2030, according to EY research. That’s a bold claim for a technology that’s yet to have a proven business case. The UK, though, is betting the near future will see the practical application of quantum computing in a wide range of industries.
Last year also saw the establishment of the National Quantum Computing Centre (NQCC). Commenting on EY’s research when it was published, Dr Simon Plant, deputy director for Innovation at the NQCC, said: "Quantum computing is expected to significantly speed up the time to solution for certain tasks, addressing computational problems that are currently intractable using conventional digital technologies. As a result, the pace of development is accelerating, and the question is how and when – not if – quantum computing can address industrially-relevant use cases.”
Sparking quantum disruption
We must first quantify the claim that quantum computing will be disruptive, and define this within the context of business processes. The pharmaceutical, finance and automotive sectors, for instance, are likely to be the first adopters. Moving away from classical computing architectures could revolutionise the development of new drugs, for example, radically cutting the typical ten-year development cycle and slashing the $2 billion average cost of bringing a new drug to market.
Quantum advantage, however, is fast approaching for all businesses, according to managing director of Accenture Technology in the UK and Ireland, Maynard Williams. “The power that this new generation of machines will create will start to make these core challenges achievable, with quantum at the pinnacle of next-generation problem solving. The single biggest watershed moment for computing will be when quantum computers solve the problems that were considered quite literally intractable – making the impossible possible.”
The key to harnessing quantum computers will be to place them into context and use these machines alongside binary computers. Classic computers, indeed, have their own place in the computing landscape in such a way that all enterprises, no matter their size, can take full commercial advantage of the next technological wave.
“I hope the detailed quantum strategy will put a strong focus on industry skills in addition to research and development,” says Richard Hopkins, distinguished engineer and fellow of the Institute of Engineering and Technology from IBM. His company is one of many striving to achieve quantum supremacy alongside the likes of D-Wave and Google. "There's a lot of value the UK could derive by being the first to apply quantum computing to real-world industrial problems, but that will need us to invest in the associated skills.
The business case for quantum
The popularity behind the potential of adopting quantum commuting is vast, as research by Fujitsu and the Tecknowlodgy Group – looking closely at how quantum computing could be applied across a diverse business landscape – found. The study saw 81% of business leaders across sectors including manufacturing, life sciences, retail, transport, and utilities saying optimising business processes can help them to tackle digital transformation challenges. It’ll also allow such businesses to remain competitive in a fast-changing market.
It’s telling that there's been a profound shift in the quantum computing discourse, with the conversation moving from theory to how to apply the technology in practice. Now the worst of COVID-19 has passed, businesses must also be more agile and innovate at speed, which includes establishing how to adopt emerging and once-gimmicky technologies like quantum computing. Business leaders now see this new frontier for computing as a tool in the arsenal towards boosting innovation, including the likes of BASF and Volkswagen, which are already using quantum algorithms within their businesses.
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In March this year, HSBC detailed its intention to join the IBM Quantum Accelerator programme, which would supply the bank access to the company's 127-qubit processor known as Eagle. “This technology has the potential to transform how we run areas of the bank by addressing challenges which classical computers may never be able to solve alone,” commented HSBC Bank plc and HSBC Europe chief executive officer (CEO), Colin Bell, on the announcement.
Accenture’s Maynard Williams also advises that businesses should begin their strategic planning to shape their quantum future. "The quickest action business leaders can take on the journey to quantum readiness is to begin evaluating how these technologies will shape the operations of their enterprise,” he says. “This will allow them to identify their knowledge and operational gaps, and begin filling them in before it's too late.”
Striving for a quantum advantage
The cost of investment has, until today, positioned quantum computing almost exclusively within the corporate realm. Smaller and medium-sized businesses (SMBs) and startups, however, will also be hurry to tap into its potential. Is the quantum door closed to them?
Digital Annealer, produced by Fujitsu, is another quantum-inspired technology architecture designed to help businesses solve complex combinatorial challenges beyond the capabilities of today's computers. Although not an real deployment of quantum computing, this technology does show the way to the quantum future many businesses can see as part of their development over the next decade.
In some sectors in which there are many smaller businesses – notably financial services and fintech especially – quantum computing is seen as a potential business differentiator, and certainly a technology that could deliver commercial advantages. Despite the progress made in exact years, however, the real-world practical applications of quantum computing won’t arrive until 2030, according to McKinsey. Until then, quantum-inspired algorithms will continue to be developed and deployed in association with high performance computing (HPC) models.
Do all businesses have a quantum future? “Given the difference in the nature of this technology, organisations may wish to ‘phone a friend’ to help them develop their quantum strategy and start their initial investigations,” Deloitte’s Scott Buchholz, national emerging technology research director and CTO for government and public services tells IT Pro. “Done with the right partner, that approach will save time and speed up the learning process.”
Jan Beitner, lead data scientist at Boston Consulting Group, also advises businesses should take practical action today: "Businesses should start by examining their value chain and identifying the use cases most relevant to them. From there, they need to quantify them and, with the support of experts, work out when computers can address them. From that starting point companies can then build a roadmap to prepare for the application and integration of quantum as its potential gets unlocked by advances in the technology.”
As businesses look to re-draw their digital transformation roadmaps in the wake of COVID-19, quantum computing should be a component of that journey. As development continues, we’re not far from a breakthrough that will see quantum computers enter perhaps not the mainstream, per se, but become a transformative technology all businesses can access. The key is having a clear deployment strategy and defined business cases to harness quantum computing's unique properties.
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Sun, 31 Jul 2022 23:41:00 -0500entext/htmlhttps://www.itpro.co.uk/technology/368578/priming-your-business-for-quantum-computingKillexams : The Rule of Logistics: Walmart and the Architecture of Fulfillment
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Tue, 30 Mar 2021 07:51:00 -0500entext/htmlhttps://www.jstor.org/stable/10.5749/j.ctt1f2qr7p.2Killexams : Comcast Business, Fortinet Partner to Deliver Enterprises New SASE & SSE Solutions
Comcast Business announced a strategic partnership with Fortinet, a global leader in broad, integrated and automated cybersecurity solutions, to deliver enterprises a new set of secure access service edge (SASE), and security service edge (SSE) solutions to help enterprises protect their distributed workforces using a cloud-delivered approach to security policy enforcement.
This collaboration expands Comcast Business’s managed services expertise, while giving enterprises greater flexibility to choose the cloud architecture and vendor mix that is right for them.
In today’s work-from-anywhere world, IT leaders are challenged with balancing employee convenience with securing their networks. New security architectures, such as SASE, which converges networking and security via SD-WAN and cloud-delivered security, and SSE, the security foundation of SASE, enable enterprises to strengthen their security posture while enhancing employees’ experience, regardless of their location. In fact, according to a exact Foundry CIO research study sponsored by Masergy (a Comcast Business company) and Fortinet, the overwhelming majority (98%) of enterprise IT leaders surveyed cited the convergence of network and security as critical or very important, while 94% said their adoption of SASE solutions has accelerated.
The new Comcast Business offerings — delivered through Comcast Business Secure Gateways —
give enterprises the option to choose from either SASE or SSE solutions backed by Fortinet’s security-driven networking technology and Equinix’s flexible cloud connection Equinix FabricÔ for a complete secure network service. Enterprises wanting to enable safe access to cloud and web services can take advantage of the Comcast Business SSE solution, which brings together multiple cloud-delivered network security technologies in a fully-hosted environment. The Comcast Business SASE solution provides this hosted SSE security architecture combined with zero trust capabilities and any of Comcast Business’s SD-WAN solutions.
Comcast Business Secure Gateways provide a fully-hosted set of SASE or SSE services covering a broad range of security networking solutions for Firewall-as-a-Service (FWaaS), Intrusion Prevention (IPS), Data Loss Prevention (DLP), Cloud Access Security Brokers (CASB), and Zero Trust Network Access (ZTNA). Comcast Business Secure Gateways are hosted across the United States at Equinix data centers, offering up to 10 gigabits per second (Gbps) of cloud connectivity for public, private, or hybrid cloud deployments. The Equinix FabricÔ enables support of Amazon Web Services, Microsoft Azure, Google Cloud, IBM and more than 200 SaaS providers.
Amit Verma, Chief Technology Officer, Enterprise Solutions, Comcast Business By expanding our relationship with Fortinet, we are offering our clients more choice and the flexibility to choose a solution that works for them – while providing some of the latest security solutions to help keep them ready for the day – today and tomorrow.
John Maddison, EVP of products and CMO, Fortinet In order to enhance user experience, reduce complexity, and Excellerate their security posture against today’s most advanced and persistent threats, organizations must adopt solutions that converge networking and security. We’re pleased to work closely with Comcast Business to build SASE services that support customers at any stage of digital innovation with Fortinet’s industry leading security-driven networking technology.
Tue, 02 Aug 2022 13:05:00 -0500entext/htmlhttps://www.thefastmode.com/technology-solutions/26621-comcast-business-fortinet-partner-to-deliver-enterprises-new-sase-sse-solutionsKillexams : IBM unveils chip simulating brain functions
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Over 130 artists are continuing to call attention to the working conditions at the site of the Guggenheim Abu Dhabi, despite the exact intervention by the Guggenheim Foundation and Museum and changes from the Tourism Development & Investment Company (TDIC). The $800 million museum designed by Frank Gehry is just beginning construction on Saadiyat Island.
The group of artists talking boycott, including key figures within the Middle Eastern art world, are threatening to withhold their work as well as refusing participation in museum events, which could be detrimental to the museum as they are essentially building a collection from the ground up. In response to the artists proposed boycott, Foundation Director Richard Armstrong issued the following statement, ”While we share the artists’ concern for the workers, we believe that, in light of the steady progress that has been made with respect to recruitment fees, the prompt payment of wages, the ability to retain passports, the provision of health insurance, good living accommodations, and the imminent appointment of an independent monitor in May, their statement is misinformed. We believe that the Guggenheim Foundation’s work with TDIC has been instrumental in bringing about this progress. We will continue to remain focused on this critical priority.”
Wed, 22 Jun 2022 01:09:00 -0500en-UStext/htmlhttps://www.archdaily.com/architecture-news/page/973?text=eKillexams : Diabetes in the Digital Age
Illustration: Jordon Cheung
A day in the life of a diabetes patient is an exercise in micromanagement. Blood sugar metering. Insulin injections. Meal plans. Exercise diaries. Logs filled with heart rate, blood pressure, and even pain measurements. It’s a lot to keep up with, for doctors as well as patients, but it’s also absolutely crucial. Proper diabetes management is key to a patient’s quality of life and to keeping blood sugar levels in line.
For the 387 million people in the world living with diabetes, this is reality. But in many ways, their experience is not so different from that of the rest of us, who struggle to stay on top of our health and to effectively communicate to our health providers what’s going on with our bodies. In America, about half of all adults suffer from chronic illnesses, and according to a 2013 Pew Research Center study, among those who do track their health, about half of them keep up with progress “in their heads.”
Mobile app development, fueled by cloud technology, is going to change these habits dramatically. Already, wearable technologies, such as Fitbit, and food trackers, are helping us log our meals, movement, and weight. These seemingly simple devices have transformative powers. They have been proved to keep people motivated to exercise and lose weight. According to the Pew study, tracking also leads 40 percent of trackers to ask a doctor new questions or to get a second opinion.
But digital tools are pushing healthcare to the cusp of a much larger transformation than that. Apps designed to Excellerate our health are going far beyond simple one-feature tracking and peer support. The new apps are capable of sharing, analyzing, and visualizing real-time health data across different platforms and populations, inspiring a host of possibilities.
These “wellness 2.0” apps will provide ways for people to take control of their own health and promise to transform our healthcare system by giving doctors valuable new insights and researchers clues to the prevention and management of chronic illness.
“The opportunity to put powerful but simple tools in the hands of an individual...as they try to manage their condition is so compelling,” says Sean M. Hogan, VP and General Manager of IBM Healthcare. “It is absolutely going to change the face of medicine.”
Robin Hrassnigg was diagnosed with Type 1 diabetes in 1991. He filled out the usual logs and diaries until he created Diabetizer, a Web-based diabetes-management portal that will launch this fall as a mobile app for iOS and Android named myDIABETIZER. “In the past, you had the diary and, with pen and paper, you put the information in the book. That’s the normal, old way,” says Hrassnigg. “What we supply the diabetic is a complete overview about his or her health status, and this information can also be used to discuss the condition with their doctor.”
Diabetizer works by pulling in information from a number of digital tools, including Fitbit, Runkeeper, and nutrition apps. It meshes that data with glucose readings and insulin information and presents an overall picture of health. Among its most useful features: A risk-index screen shows whether blood sugar, cholesterol, BMI, or any number of other indicators is in the danger zone. Graphs allow users to look back over the past week or even the past year to see how weight, heart rate, or blood sugar has fluctuated or to pick up on patterns—maybe blood sugar is spiking on a day after too little exercise, for example.
“The app makes it all easy to handle,” explains Hrassnigg. “You get more motivated in using it because it makes it fun and you get accurate information that you can look back over for yourself. You can look at your information on your mobile in your free time. That is a big advantage [if you want to discuss] your condition with your doctor and for getting a feel for your own health indicators.” Doctors are able to receive a PDF of a patient's latest health stats from the app, via email.
“The opportunity to put powerful, but simple tools in the hands of an individual and be able to maintain a connection with them as they try to manage their condition is so compelling. It is absolutely going to change the face of medicine. Diabetizer is just one example.”
Sean M. Hogan, VP and General Manager of IBM Healthcare
“It’s very important that you have that information with you and not only in the ten minutes that you are discussing with the doctor.” -- the doctor may be with you for ten minutes. “You’ve got more concrete information with you and from all over the world you can get access to your data. And it’s also very really easy for a patient to see what to change to Excellerate my health.”
Robin Hrassnigg, founder of Diabetizer
“The structural shift that we’re wrestling with is the existing systems of care that are all built around acute-care models, but a societal need that is chronic and to be really effective needs to be supported with an ongoing basis outside the hospital environment.”
Sean M. Hogan, VP and General Manager of IBM Healthcare
“What we supply the diabetic is a complete overview about his health status with a risk index. And this information can then be used to discuss his condition with his or her doctor or they can send it via PDF to the doctor.”
Robin Hrassnigg, founder of Diabetizer
“If you have information that might come from a fitness tracker to show your physical activity level, plus information from your glucose monitor, and you’re connected to your insulin pump, it's now possible that an app on your phone could tie all this information together to make an updated calculation in real time.”
Sean M. Hogan, VP and General Manager of IBM Healthcare
A day in the life of doctors who treat diabetes can be tedious. They are tasked with evaluating months of glucose readings and many other health indicators, sometimes in as little as 10 minutes while the patient is in the examining room. As for the patients, we all know what it's like to have a few minutes to explain symptoms during a doctor's appointment.
Wellness apps back up these experiences with data that we can easily share with our physicians, which leads to better recommendations and better outcomes. “The doctor is doing the same job,” says Hrassnigg, “but the patient has more concrete and more detailed information, which can now be easily provided to the doctor for an updated review of a case. That makes it easier to discuss with a doctor.”
What the world could use is more effective digital healthcare tools like Diabetizer, and fast. This is where advances in technology and cloud computing are finally cutting development times and making the process more democratic. Start-ups like Diabetizer, which moved its development over to IBM’s Bluemix cloud-based development platform earlier this year, can access a variety of cloud services instead of investing in expensive architecture. “As a start-up, it was very good for us because the costs were low,” says Hrassnigg. For one thing, he adds, “we didn’t have to invest in a big server infrastructure.”
Developers are also able to use the latest IBM Cloud services, such as IBM Bluemix, which offers a multitude of features and capabilities, to quickly add new functions into their apps. For example, Diabetizer used Bluemix to integrate fitness trackers as well as push notifications, email alerts, and photo storage into the app so that users can upload profile pics or even photos of a physical symptom they may want to share with their doctor.
“What’s so exciting about an application built on IBM Cloud’s development platform is you can put it out to a community and rapidly understand if it’s working,” says Hogan. “That’s vastly preferred to having to create an app and put it out on the market, wait multiple months, and go back into the lab.”
Beyond drastically shorter development times, it also helps make apps effective right out of the gate. Developers are ultimately looking to create apps that people use and return to again and again, which in the case of wellness apps is how the real benefit occurs. In addition to rapid application development and response, IBM Cloud also provides analytics and data-insight services that help developers understand how they can continue to refine their apps to meet patient needs.
Chronic diseases like diabetes are by definition daily and ongoing health issues. They’re caused, treated, and may ultimately be prevented by healthier choices and environmental factors—a fact that’s backed up by hard statistics. “Studies show that only about 10% to 25% of your health status is correlated to the clinical care that you receive and up to 30% associated with your unique genetic makeup,” says Hogan. “The predominant influence, up to 60%, is associated with your health behaviors, social and economic factors, and physical environments.”
Yet our healthcare system is currently set up in opposition to these facts. It deals with our well-being intermittently, through occasional doctor’s visits—or worse, when our health spirals out of control and we are hospitalized. This method of care drives up healthcare costs for everyone, and beyond that, it fails miserably at keeping us well.
“The structural shift that we’re wrestling with is the existing systems of care that are all built around acute-care models,” says Hogan. “But a societal need that is chronic and to be really effective needs to be supported with an ongoing basis outside the hospital environment.” Hogan adds that there's a real need to better understand risk factors for disease so we can Excellerate intervention and prevent disease altogether.
It’s not that we need to cut out doctors while building a new health paradigm. Doctors benefit as much as patients do when we fill in the gaps and keep ourselves well between visits. But digital tools and the data that they collect are proving to be very effective drivers toward 24/7, engaged, empowered, and consumer-driven healthcare.
And these tools are making doctors' lives easier too. As Hogan puts it, “they provide a connection back to the physician without it being overwhelming.”∎