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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 Improve 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.

Industry 4.0

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 Improve 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.

  1. 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.
  2. 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 Improve the model. Identification is also made for atypical data that is judged worthy of human attention.
  3. 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.
  4. 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
  • Improved efficiency
  • Increased distribution and density
  • Lower latency

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).

Wrap up

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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Killexams : 3 Skills Every Programmer Should Consider Learning No result found, try new keyword!As IT evolves, programmers must master risk analysis, machine learning, and Web3/blockchain development to keep their skills optimized. Wed, 27 Jul 2022 03:03:00 -0500 en-us text/html https://www.msn.com/en-us/money/smallbusiness/3-skills-programmers-need-to-increase-employability-and-performance/ar-AA1023VO Killexams : The right and wrong way to use artificial intelligence

For decades, scientists have been giddy and citizens have been fearful of the power of computers. In 1965 Herbert Simon, a Nobel laureate in economics and also a winner of the Turing Award (considered “The Nobel Prize of computing”), predicted that “machines will be capable, within 20 years, of doing any work a man can do.” His misplaced faith in computers is hardly unique. Sixty-seven years later, we are still waiting for computers to become our slaves and masters.

Businesses have spent hundreds of billions of dollars on AI moonshots that have crashed and burned. IBM’s “Dr. Watson” was supposed to revolutionize health care and “eradicate cancer.” Eight years later, after burning through $15 billion with no demonstrable successes, IBM fired Dr. Watson.

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In 2016 Turing Award Winner Geoffrey Hinton advised that “We should stop training radiologists now. It’s just completely obvious that within five years, deep learning is going to do better than radiologists.” Six years later, the number of radiologists has gone up, not down. Researchers have spent billions of dollars working on thousands of radiology image-recognition algorithms that are not as good as human radiologists.

What about those self-driving vehicles, promised by many including Elon Musk in his 2016 boast that “I really consider autonomous driving a solved problem. I think we are probably less than two years away.” Six years later, the most advanced self-driving vehicles are arguably Waymos in San Francisco, which only operate between 10 p.m. and 6 a.m. on the least crowded roads and still have accidents and cause traffic tie-ups. They are a long way from successfully operating in downtown traffic during the middle of the day at a required 99.9999% level of proficiency.

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The list goes on. Zillow’s house-flipping misadventure lost billions of dollars trying to revolutionize home-buying before they shuttered it. Carvana’s car-flipping gambit still loses billions.

We have argued for years that we should be developing AI that makes people more productive instead of trying to replace people. Computers have wondrous memories, make calculations that are lightning-fast and error-free, and are tireless, but humans have the real-world experience, common sense, wisdom and critical thinking skills that computers lack. Together, they can do more than either could do on their own.

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Effective augmentation appears to be finally happening with medical images. A large-scale study just published in Lancet Digital Health is the first to directly compare AI cancer screening when used alone or to assist humans. The software comes from a German startup, Vara, whose AI is already used in more than 25% of Germany’s breast cancer screening centers.

Researchers from Vara, Essen University and the Memorial Sloan Kettering Cancer Center trained the algorithm on more than 367,000 mammograms, and then tested it on 82,851 mammograms that had been held back for that purpose.

In the first strategy, the algorithm was used alone to analyze the 82,851 mammograms. In the second strategy, the algorithm separated the mammograms into three groups: clearly cancer, clearly no cancer, and uncertain. The uncertain mammograms were then sent to board-certified radiologists who were given no information about the AI diagnosis.

Doctors and AI working together turned out to be better than either working alone. The AI pre-screening reduced the number of images the doctors examined by 37% while lowering the false-positive and false-negative rates by about a third compared to AI alone and by 14%-20% compared to doctors alone. Less work and better results!

As machine learning improves, the AI analysis of X-rays will no doubt become more efficient and accurate. There will come a time when AI can be trusted to work alone. However, that time is likely to be decades in the future and attempts to jump directly to that point are dangerous.

We are optimistic that the productivity of many workers can be improved by similar augmentation strategies — not to mention the fact that many of the tasks that computers excel at are dreadful drudgery; e.g., legal research, inventory control and statistical calculations. But far too many attempts to replace humans entirely have not only been an enormous waste of resources but have also undermined the credibility of AI research. The last thing we need is another AI winter where funding dries up, resources are diverted and the tremendous potential of these technologies are put on hold. We are optimistic that the accumulating failures of moonshots and successes of augmentation strategies will change the way that we think about AI.

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Funk is an independent technology consultant who previously taught at National University of Singapore, Hitotsubashi and Kobe Universities in Japan, and Penn State, where he taught courses on the economics of new technologies. Smith is the author of ”The AI Delusion” and co-author (with Jay Cordes) of ”The 9 Pitfalls of Data Science” and ”The Phantom Pattern Problem.”

Fri, 05 Aug 2022 21:00:00 -0500 en-US text/html https://www.nydailynews.com/opinion/ny-oped-the-right-and-wrong-way-to-use-artificial-intelligence-20220806-txybtmlcwfgddnfdozvynz5u64-story.html
Killexams : Machine Learning Artificial intelligence Market Size from 2022 to 2028

The MarketWatch News Department was not involved in the creation of this content.

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The global Machine Learning Artificial intelligence market size is projected to reach multi million by 2028, in comparision to 2021, at unexpected CAGR during 2022-2028 (Ask for demo Report).

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The assembly, revenue, price, market share, and rate of growth of each type are displayed in the Machine Learning Artificial intelligence market industry research report, which is divided into Deep Learning,Natural Language Processing,Machine Vision,Others. The status and outlook for main applications/end users, consumption (sales), market share, and rate of growth for each application, including Automotive & Transportation,Agriculture,Manufacturing,Others, are all included in this Machine Learning Artificial intelligence market research report. North America: United States, Canada, Europe: GermanyFrance, U.K., Italy, Russia,Asia-Pacific: China, Japan, South, India, Australia, China, Indonesia, Thailand, Malaysia, Latin America:Mexico, Brazil, Argentina, Colombia, Middle East & Africa:Turkey, Saudi, Arabia, UAE, Korea is a list of regions covered in the Machine Learning Artificial intelligence Market Report. The report has a page count of 129.

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The top competitors in the Machine Learning Artificial intelligence Market, as highlighted in the report, are:

  • AIBrain
  • Amazon
  • Anki
  • CloudMinds
  • Deepmind
  • Google
  • Facebook
  • IBM
  • Iris AI
  • Apple
  • Luminoso
  • Qualcomm

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Market Segmentation

The worldwide Machine Learning Artificial intelligence Market is categorized on Component, Deployment, Application, and Region.

The Machine Learning Artificial intelligence Market Analysis by types is segmented into:

  • Deep Learning
  • Natural Language Processing
  • Machine Vision
  • Others

The Machine Learning Artificial intelligence Market Industry Research by Application is segmented into:

  • Automotive & Transportation
  • Agriculture
  • Manufacturing
  • Others

In terms of Region, the Machine Learning Artificial intelligence Market Players available by Region are:

  • North America:
  • Europe:
    • Germany
    • France
    • U.K.
    • Italy
    • Russia
  • Asia-Pacific:
    • China
    • Japan
    • South Korea
    • India
    • Australia
    • China Taiwan
    • Indonesia
    • Thailand
    • Malaysia
  • Latin America:
    • Mexico
    • Brazil
    • Argentina Korea
    • Colombia
  • Middle East & Africa:
    • Turkey
    • Saudi
    • Arabia
    • UAE
    • Korea

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Key Benefits for Industry Participants & Stakeholders:

Players, stakeholders, and other stakeholders in the Machine Learning Artificial intelligence market industry research will be able to gain an advantage by utilizing the Machine Learning Artificial intelligence market research report as a resource. The global Machine Learning Artificial intelligence market research report's value chain analysis, sales breakdown, and competitive situation are combined with regional-level forecasts.

The Machine Learning Artificial intelligence market research report contains the following TOC:

  • Report Overview
  • Global Growth Trends
  • Competition Landscape by Key Players
  • Data by Type
  • Data by Application
  • North America Market Analysis
  • Europe Market Analysis
  • Asia-Pacific Market Analysis
  • Latin America Market Analysis
  • Middle East & Africa Market Analysis
  • Key Players Profiles Market Analysis
  • Analysts Viewpoints/Conclusions
  • Appendix

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Highlights of The Machine Learning Artificial intelligence Market Report

The Machine Learning Artificial intelligence Market Industry Research Report contains:

  • International and domestic market segmentation
  • Major changes in the Machine Learning Artificial intelligence market research structure
  • Regional and country-level competitive analysis
  • The Machine Learning Artificial intelligence market share, size, and growth comprehension analysis
  • The most common growth strategies employed by business owners

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COVID 19 Impact Analysis:

The Machine Learning Artificial intelligence market industry trend segmentation is based on various characteristics such as kinds, applications, and regions. In addition, the Machine Learning Artificial intelligence market research report evaluates the impact of the new COVID-19 pandemic on the Machine Learning Artificial intelligence market research and provides a unique assessment of the predicted market changes during the forecasted time frame. Machine Learning Artificial intelligence Market Share, Stock Determinations and Figures, contact information, Sales, Capacity, Production, Price, Cost, Revenue, and Business Profiles are all encased within the Machine Learning Artificial intelligence market report. Over the forecast period, the Machine Learning Artificial intelligence market research report comprises market segmentation, competitive analysis, and regional study.

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Machine Learning Artificial intelligence Market Size and Industry Challenges:

The Machine Learning Artificial intelligence market research report includes Company Profile, Product Specifications, Revenue, Price, and Gross Margin Sales, as well as a comprehensive analysis of the market competitive landscape and detailed information on vendors, as well as comprehensive details of factors that will challenge the growth of major market vendors.

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  • develop effective counter-strategies to acquire a competitive edge.
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Killexams : Strong Dollar is Crushing These 10 Stocks

In this article, we discuss 10 stocks that the strong dollar is crushing. If you want to see more stocks in this selection, check out Strong Dollar is Crushing These 5 Stocks

One of the biggest challenges presented to corporate America this earnings season was a strong dollar. Many prominent market leaders and multinationals with operations worldwide lamented the fact that foreign currency exchange rates cut into their profits and revenues. Some of the significant victims of a strong dollar included Amazon.com, Inc. (NASDAQ:AMZN), Microsoft Corporation (NASDAQ:MSFT), and Alphabet Inc. (NASDAQ:GOOG). 

Companies slashed their guidance for the second half of 2022 and beyond as the strengthening dollar wiped billions from US corporate earnings, and the situation does not seem to be improving moving ahead. Jim Paulsen, chief investment strategist at Leuthold Weeden Capital Management, observed in late-June that the biggest market leaders were under scrutiny since they have global operations and a sizeable chunk of their revenues is attributed to international sales. Roughly 35% of US companies have large enough international exposure that a stronger dollar significantly impacts their earnings per share, noted Gina Martin Adams, director of equity strategy at Bloomberg Intelligence. 

The US currency has climbed to its highest level in 20 years, and paired with high inflation and rising rates, the impact on business and consumer demand is starkly visible. Max Kettner, a strategist at HSBC, told Financial Times on July 25 that even if the dollar stopped rising further now, the currency’s strength in the last 12 months will still lead to slashed earnings estimates solely due to foreign exchange headwinds.

Our Methodology

We reviewed the Q2 2022 earnings reports for companies that have sizeable operations overseas and looked for management’s comments regarding foreign exchange headwinds impacting the business. We selected the 10 most prominent companies that were affected by a strong dollar for this list. The companies are ranked according to the hedge fund sentiment as of Q1 2022, which was gauged from Insider Monkey’s database that tracks more than 900 elite hedge funds. 

10. Digital Realty Trust, Inc. (NYSE:DLR)

Number of Hedge Fund Holders: 31

Digital Realty Trust, Inc. (NYSE:DLR) is an American real estate investment trust that invests in data centers, colocation, and interconnection solutions. The REIT rents out its properties to top businesses and service providers. On July 28, Digital Realty Trust, Inc. (NYSE:DLR) reported its Q2 results, announcing an FFO of $1.72, exceeding market estimates by $0.07. The revenue of $1.1 billion gained only 0.90% year over year and missed Street consensus by $50 million. The REIT also slashed its 2022 guidance to factor in the strength of the U.S. dollar. 

The new 2022 guidance for core FFO per share now stands at $6.75 to $6.85, compared to a consensus estimate of $6.82 and the earlier range of $6.80 to $6.90. Digital Realty Trust, Inc. (NYSE:DLR) expects a full-year revenue of $4.65 billion to $4.75 billion, versus a Street consensus of $4.73 billion and the prior guidance of $4.70 billion to $4.80 billion. 

On August 2, Deutsche Bank analyst Matthew Niknam raised the price target on Digital Realty Trust, Inc. (NYSE:DLR) to $150 from $144 and maintained a Buy rating on the shares after the Q2 results.

Among the hedge funds tracked by Insider Monkey, Mark Wolfson and Jamie Alexander’s Jasper Ridge Partners is one of the leading stakeholders of Digital Realty Trust, Inc. (NYSE:DLR) as of Q1 2022, with 652,448 shares worth $92.5 million. Overall, 31 hedge funds were bullish on the stock at the end of Q1 2022, up from 26 funds in the preceding quarter. 

Digital Realty Trust, Inc. (NYSE:DLR) has lately been impacted by a strong dollar, just like Amazon.com, Inc. (NASDAQ:AMZN), Microsoft Corporation (NASDAQ:MSFT), and Alphabet Inc. (NASDAQ:GOOG). 

9. Aflac Incorporated (NYSE:AFL)

Number of Hedge Fund Holders: 32

Aflac Incorporated (NYSE:AFL) is a Georgia-based provider of supplemental health and life insurance products. The company  operates through two segments – Aflac Japan and Aflac U.S. On August 1, Aflac Incorporated (NYSE:AFL) posted a Q2 non-GAAP EPS of $1.46, beating market estimates by $0.18. The revenue of $5.4 billion dropped about 3% on a year over year basis but exceeded Wall Street consensus by $610 million. The company also declared a $0.40 per share quarterly dividend, which is payable on September 1 to shareholders of record as of August 24. The forward yield was 2.82%. The company reiterated on August 1 that sales in its Japan division were under pressure due to a softer yen. The weaker yen/dollar conversion rate chipped away at the EPS by $0.09 in Q2 2022. 

On July 11, JPMorgan analyst Jimmy Bhullar raised the price target on Aflac Incorporated (NYSE:AFL) to $62 from $61 and maintained a Neutral rating on the shares. The analyst sees the risk/reward in the life insurance sector as “less compelling than previously”. The sector is a primary beneficiary of rising rates and falling COVID cases, but short-term results will be soft, said the analyst, who is worried about downside risk to market estimates. The weak stock market will likely challenge earnings and slash capital ratios for some insurers, and this will limit flexibility, the analyst wrote.

According to Insider Monkey’s data, 32 hedge funds were bullish on Aflac Incorporated (NYSE:AFL) at the end of the first quarter of 2022, compared to 31 funds in the prior quarter. John W. Rogers’ Ariel Investments is the leading stakeholder of the company, with 1.26 million shares worth $81.60 million. 

Here is what Madison Funds has to say about Aflac Incorporated (NYSE:AFL) in its Q2 2021 investor letter:

“This quarter we are highlighting Aflac (AFL) as a relative yield example in the Financial sector. AFL is a leading provider of life and supplemental medical insurance in Japan and the U.S. AFL products offer financial protection against loss of income for policyholders based on qualifying health events. Aflac Japan generates approximately 70% of total revenues, and the company has dominant market share in Japan. In the U.S., AFL provides voluntary insurance for policyholders at businesses with products sold through payroll deduction by its large sales force which sells primarily through face-to-face interactions. We believe AFL’s dominant market position in Japan and its large U.S. sales force create a sustainable competitive advantage for the company.

Our thesis on AFL is that its sales will recover from the impact of the COVID pandemic, and it will return a significant amount of capital to shareholders. Sales were negatively impacted in both Japan and the U.S. but appear to be in early stages of recovering. We believe sales will Improve further as economies open and new products are introduced in Japan. In the U.S., agents will be able to return to face-to-face interactions as people get vaccinated, something that was restricted last year.

In terms of capital returns, AFL committed to returning $8-9 billion between 2020-2022, which is expected to be 75% of operating earnings. The company returns capital via share buybacks and dividend increases. AFL is a Dividend Aristocrat that has increased its dividend 39 years in a row including 10% annually over the last five years; it also recently announced an 18% dividend increase. Other favorable attributes include an A- rated balance sheet by Standard and Poor’s and an attractive valuation with a relative yield near the high end of its historical range.

We believe its valuation is cheap with its forward expected Price/Earnings (P/E) ratio just 9x and a relative P/E of 0.4x versus the S&P 500 despite an industry leading return on equity. At the time of purchase, AFL had a dividend yield of 2.5% and its relative dividend yield vs. the S&P 500 was 1.8x, as shown. Some risks to the thesis include a prolonged economic downturn, loss of market share due to unsuccessful new product rollouts and potential losses in its investment portfolio.”

8. International Business Machines Corporation (NYSE:IBM)

Number of Hedge Fund Holders: 43

International Business Machines Corporation (NYSE:IBM) is an American multinational provider of integrated technology solutions worldwide. The company operates through four business segments – Software, Consulting, Infrastructure, and Financing. On July 18, International Business Machines Corporation (NYSE:IBM) stock plunged by 4% as the company reiterated its outlook for the rest of 2022, saying it is going to be affected by the strengthening U.S. dollar and the company concluding its business in Russia. The company’s CFO said that the “significant movement” in the U.S. dollar led to a “6-point headwind to revenue growth”. However, for the third quarter and the entire fiscal year, the company forecasts constant currency revenue growth to be at the high end of the mid-single digit estimates. 

On July 20, Credit Suisse analyst Sami Badri lowered the price target on International Business Machines Corporation (NYSE:IBM) to $156 from $166 and kept an Outperform rating on the shares after the Q2 results. The analyst still views International Business Machines Corporation (NYSE:IBM) as a notable facilitator of hybrid cloud architectures and one of the primary strategic enablers of a digital transformation.

According to Insider Monkey’s data, 43 hedge funds were bullish on International Business Machines Corporation (NYSE:IBM) at the end of Q1 2022, compared to 44 funds in the last quarter. Peter Rathjens, Bruce Clarke, and John Campbell’s Arrowstreet Capital is the leading stakeholder of the company, with 4.46 million shares worth about $580 million. 

St. James Investment Company mentioned International Business Machines Corporation (NYSE:IBM) in its Q4 2021 investor letter. Here is what the firm had to say:

“IBM was not the first company to build computers. The distinction belongs to Sperry-Rand’s subsidiary UNIVAC, which introduced the first commercially successful computers in the early 1950s. In this era, IBM did possess the largest research and development department of the business machines industry and quickly caught up, introducing cost-competitive computers a few years after UNIVAC. By the late 1950s, IBM held the dominant market share in computers. IBM also touted a vastly superior sales organization, which used a sales tactic called “paper machines” (the equivalent of today’s “vaporware”). If a competitor’s product was selling well in a market segment that IBM had yet to penetrate, the company would announce a competing product and start taking orders for the “paper machine” long before it was available.

One cannot overstate how powerful IBM was in the computer industry in the 1950s and 1960s. Every competitor rightly worried that if their product worked too well for too long, it was only a matter of time before an army of IBM salesforce representatives mobilized. In their easily recognizable uniforms of starched white shirts, red ties and blue suits, IBM marketers marched on their customers and offered a more expensive, but much more defensible, choice. “Nobody gets fired for buying IBM” was a common phrase. Even competitors acknowledged that the company excelled at sales. As a UNIVAC executive once complained, ‘It doesn’t do much good to build a better mousetrap if the other guy selling mousetraps has five times as many salesmen.’” (Click here to see the full text)

7. NIKE, Inc. (NYSE:NKE)

Number of Hedge Fund Holders: 67

NIKE, Inc. (NYSE:NKE), the American multinational retailer of athletic footwear and apparel, is another company that was crushed by a strong dollar. Rising inflation and foreign exchange headwinds impacted results negatively as gross margin declined by 80 basis points to 45% amid increasing freight and logistics costs, while a stronger dollar led to a 4% gap between constant currency sales and the numbers reported. The rampant costs were largely problematic in the greater China region since they added to “higher inventory obsolescence” in the second quarter.

Piper Sandler analyst Abbie Zvejnieks on July 25 initiated coverage of NIKE, Inc. (NYSE:NKE) with a Neutral rating and a $115 price target. NIKE, Inc. (NYSE:NKE)’s gross margins have improved but headwinds in China could be an issue, since it is NIKE, Inc. (NYSE:NKE)’s highest margin region, the analyst told investors. Macro challenges in China, such as pandemic-driven lockdowns and nationalism trends, and uncertain consumer backdrops in the United States and Europe “leave us sidelined,” added the analyst.

Among the hedge funds tracked by Insider Monkey, 67 funds were long NIKE, Inc. (NYSE:NKE) at the end of Q1 2022, compared to 68 funds in the last quarter. Ken Fisher’s Fisher Asset Management is the biggest stakeholder of the company, with 8.2 million shares worth $1.11 billion. 

Here is what ClearBridge All Cap Growth Strategy has to say about NIKE, Inc. (NYSE:NKE) in its Q4 2021 investor letter:

“Nike is another play on e-commerce as well as the anticipated growth in consumer spending as we learn to live with COVID-19. After selling out of the stock in 2016 due to competitive concerns, we were motivated to repurchase shares because of optimism around a new management team’s focus on accelerating Nike’s shift toward e-commerce and direct-to-consumer (DTC) distribution. Near-term supply chain issues in Vietnam and retail weakness in China that we see as ephemeral provided a good buying opportunity. We do not believe the market is giving proper credit to Nike’s potential to deliver attractive, high-single-digit revenue growth while delivering operating margin expansion as more merchandise is sold directly. Nike is also still under indexed to the women’s category, which we see as a significant ongoing catalyst.”

6. Johnson & Johnson (NYSE:JNJ)

Number of Hedge Fund Holders: 83

Johnson & Johnson (NYSE:JNJ) is an American multinational healthcare firm. On July 19, the company slashed guidance despite its above consensus Q2 results. Johnson & Johnson (NYSE:JNJ) cited the strong dollar and unfeasible foreign exchange movements lowering the company’s margins. The company’s CFO observed that the U.S. dollar has reached the same level as the euro, which is “something we haven’t seen in 20 years”. He reiterated that if the dollar were to pull back, Johnson & Johnson (NYSE:JNJ) would revise its guidance accordingly.

On July 21, UBS analyst Kevin Caliendo lowered the price target on Johnson & Johnson (NYSE:JNJ) to $180 from $185 and reaffirmed a Neutral rating on the shares. The company’s Q2 results were indicative of the turbulent macro environment, which includes the significant swing in currency, rampant inflation, and slow elective procedure recovery, the analyst informed investors. 

According to Insider Monkey’s database, Johnson & Johnson (NYSE:JNJ) was part of 83 hedge fund portfolios at the end of Q1 2022, with collective stakes worth $7.40 billion. Rajiv Jain’s GQG Partners is the largest stakeholder of the company, with 6.50 million shares valued at $1.15 billion. 

In addition to Amazon.com, Inc. (NASDAQ:AMZN), Microsoft Corporation (NASDAQ:MSFT), and Alphabet Inc. (NASDAQ:GOOG), a strong dollar hurt Johnson & Johnson (NYSE:JNJ)’s Q2 results.

Click to continue studying and see Strong Dollar is Crushing These 5 Stocks

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Disclosure: None. Strong Dollar is Crushing These 10 Stocks is originally published on Insider Monkey.

Wed, 03 Aug 2022 01:59:00 -0500 en-US text/html https://www.insidermonkey.com/blog/strong-dollar-is-crushing-these-10-stocks-1055533/
Killexams : Colorado’s P-TECH Students Graduate Ready for Tech Careers (TNS) — Abraham Tinajero was an eighth grader when he saw a poster in his Longmont middle school’s library advertising a new program offering free college with a technology focus.

Interested, he talked to a counselor to learn more about P-TECH, an early college program where he could earn an associate’s degree along with his high school diploma. Liking the sound of the program, he enrolled in the inaugural P-TECH class as a freshman at Longmont’s Skyline High School.

“I really loved working on computers, even before P-TECH,” he said. “I was a hobbyist. P-TECH gave me a pathway.”


He worked with an IBM mentor and interned at the company for six weeks as a junior. After graduating in 2020 with his high school diploma and the promised associate’s degree in computer science from Front Range Community College, he was accepted to IBM’s yearlong, paid apprenticeship program.

IBM hired him as a cybersecurity analyst once he completed the apprenticeship.

“P-TECH has given me a great advantage,” he said. “Without it, I would have been questioning whether to go into college. Having a college degree at 18 is great to put on a resume.”


Stanley Litow, a former vice president of IBM, developed the P-TECH, or Pathways in Technology Early College High Schools, model. The first P-TECH school opened 11 years ago in Brooklyn, New York, in partnership with IBM.

Litow’s idea was to get more underrepresented young people into tech careers by giving them a direct path to college while in high school — and in turn create a pipeline of employees with the job skills businesses were starting to value over four-year college degrees.

The program, which includes mentors and internships provided by business partners, gives high school students up to six years to earn an associate's degree at no cost.

SKYLINE HIGH A PIONEER IN PROGRAM

In Colorado, St. Vrain Valley was among the first school districts chosen by the state to offer a P-TECH program after the Legislature passed a bill to provide funding — and the school district has embraced the program.

Colorado’s first P-TECH programs started in the fall of 2016 at three high schools, including Skyline High. Over the last six years, 17 more Colorado high schools have adopted P-TECH, for at total of 20. Three of those are in St. Vrain Valley, with a fourth planned to open in the fall of 2023 at Longmont High School.

Each St. Vrain Valley high school offers a different focus supported by different industry partners.

Skyline partners with IBM, with students earning an associate’s degree in Computer Information Systems from Front Range. Along with being the first, Skyline’s program is the largest, enrolling up to 55 new freshmen each year.

Programs at the other schools are capped at 35 students per grade.

Frederick High’s program, which started in the fall of 2019, has a bioscience focus, partners with Aims Community College and works with industry partners Agilent Technologies, Tolmar, KBI Biopharma, AGC Biologics and Corden Pharma.

Silver Creek High’s program started a year ago with a cybersecurity focus. The Longmont school partners with Front Range and works with industry partners Seagate, Cisco, PEAK Resources and Comcast.

The new program coming to Longmont High will focus on business.

District leaders point to Skyline High’s graduation statistics to illustrate the program’s success. At Skyline, 100 percent of students in the first three P-TECH graduating classes earned a high school diploma in four years.

For the 2020 Skyline P-TECH graduates, 24 of the 33, or about 70 percent, also earned associate’s degrees. For the 2021 graduating class, 30 of the 47 have associate’s degrees — with one year left for those students to complete the college requirements.

For the most latest 2022 graduates, who have two years left to complete the college requirements, 19 of 59 have associate’s degrees and another six are on track to earn their degrees by the end of the summer.

JUMPING AT AN OPPORTUNITY

Louise March, Skyline High’s P-TECH counselor, keeps in touch with the graduates, saying 27 are working part time or full time at IBM. About a third are continuing their education at a four year college. Of the 19 who graduated in 2022 with an associate’s degree, 17 are enrolling at a four year college, she said.

Two of those 2022 graduates are Anahi Sarmiento, who is headed to the University of Colorado Boulder’s Leeds School of Business, and Jose Ivarra, who will study computer science at Colorado State University.

“I’m the oldest out of three siblings,” Ivarra said. “When you hear that someone wants to give you free college in high school, you take it. I jumped at the opportunity.”

Sarmiento added that her parents, who are immigrants, are already working two jobs and don’t have extra money for college costs.

“P-TECH is pushing me forward,” she said. “I know my parents want me to have a better life, but I want them to have a better life, too. Going into high school, I kept that mentality that I would push myself to my full potential. It kept me motivated.”

While the program requires hard work, the two graduates said, they still enjoyed high school and had outside interests. Ivarra was a varsity football player who was named player of the year. Sarmiento took advantage of multiple opportunities, from helping elementary students learn robotics to working at the district’s Innovation Center.

Ivarra said he likes that P-TECH has the same high expectations for all students, no matter their backgrounds, and gives them support in any areas where they need help. Spanish is his first language and, while math came naturally, language arts was more challenging.

“It was tough for me to see all these classmates use all these big words, and I didn’t know them,” he said. “I just felt less. When I went into P-TECH, the teachers focus on you so much, checking on every single student.”

They said it’s OK to struggle or even fail. Ivarra said he failed a tough class during the pandemic, but was able to retake it and passed. Both credited March, their counselor, with providing unending support as they navigated high school and college classes.

“She’s always there for you,” Sarmiento said. “It’s hard to be on top of everything. You have someone to go to.”

Students also supported each other.

“You build bonds,” Ivarra said. “You’re all trying to figure out these classes. You grow together. It’s a bunch of people who want to succeed. The people that surround you in P-TECH, they push you to be better.”

SUPPORT SYSTEMS ARE KEY

P-TECH has no entrance requirements or prerequisite classes. You don’t need to be a top student, have taken advanced math or have a background in technology.

With students starting the rigorous program with a wide range of skills, teachers and counselors said, they quickly figured out the program needed stronger support systems.

March said freshmen in the first P-TECH class struggled that first semester, prompting the creation of a guided study class. The every other day, hour-and-a-half class includes both study time and time to learn workplace skills, including writing a resume and interviewing. Teachers also offer tutoring twice a week after school.

“The guided study has become crucial to the success of the program,” March said.

Another way P-TECH provides extra support is through summer orientation programs for incoming freshmen.

At Skyline, ninth graders take a three-week bridge class — worth half a credit — that includes learning good study habits. They also meet IBM mentors and take a field trip to Front Range Community College.

“They get their college ID before they get their high school ID,” March said.

During a session in June, 15 IBM mentors helped the students program a Sphero robot to travel along different track configurations. Kathleen Schuster, who has volunteered as an IBM mentor since the P-TECH program started here, said she wants to “return some of the favors I got when I was younger.”

“Even this play stuff with the Spheros, it’s teaching them teamwork and a little computing,” she said. “Hopefully, through P-TECH, they will learn what it takes to work in a tech job.”

Incoming Skyline freshman Blake Baker said he found a passion for programming at Trail Ridge Middle and saw P-TECH as a way to capitalize on that passion.

“I really love that they give you options and a path,” he said.

Trail Ridge classmate Itzel Pereyra, another programming enthusiast, heard about P-TECH from her older brother.

“It’s really good for my future,” she said. “It’s an exciting moment, starting the program. It will just help you with everything.”

While some of the incoming ninth graders shared dreams of technology careers, others see P-TECH as a good foundation to pursue other dreams.

Skyline incoming ninth grader Marisol Sanchez wants to become a traveling nurse, demonstrating technology and new skills to other nurses. She added that the summer orientation sessions are a good introduction, helping calm the nerves that accompany combining high school and college.

“There’s a lot of team building,” she said. “It’s getting us all stronger together as a group and introducing everyone.”

THE SPARK OF MOTIVATION

Silver Creek’s June camp for incoming ninth graders included field trips to visit Cisco, Seagate, PEAK Resources, Comcast and Front Range Community College.

During the Front Range Community College field trip, the students heard from Front Range staff members before going on a scavenger hunt. Groups took photos to prove they completed tasks, snapping pictures of ceramic pieces near the art rooms, the most expensive tech product for sale in the bookstore and administrative offices across the street from the main building.

Emma Horton, an incoming freshman, took a cybersecurity class as a Flagstaff Academy eighth grader that hooked her on the idea of technology as a career.

“I’m really excited about the experience I will be getting in P-TECH,’ she said. “I’ve never been super motivated in school, but with something I’m really interested in, it becomes easier.”

Deb Craven, dean of instruction at Front Range’s Boulder County campus, promised the Silver Creek students that the college would support them. She also gave them some advice.

“You need to advocate and ask for help,” she said. “These two things are going to help you the most. Be present, be engaged, work together and lean on each other.”

Craven, who oversees Front Range’s P-TECH program partnership, said Front Range leaders toured the original P-TECH program in New York along with St. Vrain and IBM leaders in preparation for bringing P-TECH here.

“Having IBM as a partner as we started the program was really helpful,” she said.

When the program began, she said, freshmen took a more advanced technology class as their first college class. Now, she said, they start with a more fundamental class in the spring of their freshman year, learning how to build a computer.

“These guys have a chance to grow into the high school environment before we stick them in a college class,” she said.

Summer opportunities aren’t just for P-TECH’s freshmen. Along with summer internships, the schools and community colleges offer summer classes.

Silver Creek incoming 10th graders, for example, could take a personal financial literacy class at Silver Creek in the mornings and an introduction to cybersecurity class at the Innovation Center in the afternoons in June.

Over at Skyline, incoming 10th graders in P-TECH are getting paid to teach STEM lessons to elementary students while earning high school credit. Students in the fifth or sixth year of the program also had the option of taking computer science and algebra classes at Front Range.

EMBRACING THE CHALLENGE

And at Frederick, incoming juniors are taking an introduction to manufacturing class at the district's Career Elevation and Technology Center this month in preparation for an advanced manufacturing class they’re taking in the fall.

“This will give them a head start for the fall,” said instructor Chester Clark.

Incoming Frederick junior Destini Johnson said she’s not sure what she wants to do after high school, but believes the opportunities offered by P-TECH will prepare her for the future.

“I wanted to try something challenging, and getting a head start on college can only help,” she said. “It’s really incredible that I’m already halfway done with an associate’s degree and high school.”

IBM P-TECH program manager Tracy Knick, who has worked with the Skyline High program for three years, said it takes a strong commitment from all the partners — the school district, IBM and Front Range — to make the program work.

“It’s not an easy model,” she said. “When you say there are no entrance requirements, we all have to be OK with that and support the students to be successful.”

IBM hosted 60 St. Vrain interns this summer, while two Skyline students work as IBM “co-ops” — a national program — to assist with the P-TECH program.

The company hosts two to four formal events for the students each year to work on professional and technical skills, while IBM mentors provide tutoring in algebra. During the pandemic, IBM also paid for subscriptions to tutor.com so students could get immediate help while taking online classes.

“We want to get them truly workforce ready,” Knick said. “They’re not IBM-only skills we’re teaching. Even though they choose a pathway, they can really do anything.”

As the program continues to expand in the district, she said, her wish is for more businesses to recognize the value of P-TECH.

“These students have had intensive training on professional skills,” she said. “They have taken college classes enhanced with the same digital credentials that an IBM employee can learn. There should be a waiting list of employers for these really talented and skilled young professionals.”

©2022 the Daily Camera (Boulder, Colo.). Distributed by Tribune Content Agency, LLC.

Thu, 04 Aug 2022 02:41:00 -0500 en text/html https://www.govtech.com/education/k-12/colorados-p-tech-students-graduate-ready-for-tech-careers
Killexams : IBM Annual Cost of Data Breach Report 2022: Record Costs Usually Passed On to Consumers, “Long Breach” Expenses Make Up Half of Total Damage

IBM’s annual Cost of Data Breach Report for 2022 is packed with revelations, and as usual none of them are good news. Headlining the report is the record-setting cost of data breaches, with the global average now at $4.35 million. The report also reveals that much of that expense comes with the data breach version of “long Covid,” expenses that are realized more than a year after the attack.

Most organizations (60%) are passing these added costs on to consumers in the form of higher prices. And while 83% of organizations now report experiencing at least one data breach, only a small minority are adopting zero trust strategies.

Security AI and automation greatly reduces expected damage

The IBM report draws on input from 550 global organizations surveyed about the period between March 2021 and March 2022, in partnership with the Ponemon Institute.

Though the average cost of a data breach is up, it is only by about 2.6%; the average in 2021 was $4.24 million. This represents a total climb of 13% since 2020, however, reflecting the general spike in cyber crime seen during the pandemic years.

Organizations are also increasingly not opting to absorb the cost of data breaches, with the majority (60%) compensating by raising consumer prices separate from any other latest increases due to inflation or supply chain issues. The report indicates that this may be an underreported upward influence on prices of consumer goods, as 83% of organizations now say that they have been breached at least once.

Brad Hong, Customer Success Manager for Horizon3.ai, sees a potential consumer backlash on the horizon once public awareness of this practice grows: “It’s already a breach of confidence to lose the confidential data of customers, and sure there’s bound to be an organization across those surveyed who genuinely did put in the effort to protect against and curb attacks, but for those who did nothing, those who, instead of creating a disaster recovery plan, just bought cyber insurance to cover the org’s operational losses, and those who simply didn’t care enough to heed the warnings, it’s the coup de grâce to then pass the cost of breaches to the same customers who are now the victims of a data breach. I’d be curious to know what percent of the 60% of organizations who increased the price of their products and services are using the extra revenue for a war chest or to actually reinforce their security—realistically, it’s most likely just being used to fill a gap in lost revenue for shareholders’ sake post-breach. Without government regulations outlining restrictions on passing cost of breach to consumer, at the least, not without the honest & measurable efforts of a corporation as their custodian, what accountability do we all have against that one executive who didn’t want to change his/her password?”

Breach costs also have an increasingly long tail, as nearly half now come over a year after the date of the attack. The largest of these are generally fines that are levied after an investigation, and decisions or settlements in class action lawsuits. While the popular new “double extortion” approach of ransomware attacks can drive long-term costs in this way, the study finds that companies paying ransom demands to settle the problem quickly aren’t necessarily seeing a large amount of overall savings: their average breach cost drops by just $610,000.

Sanjay Raja, VP of Product with Gurucul, expands on how knock-on data breach damage can continue for years: “The follow-up attack effect, as described, is a significant problem as the playbooks and solutions provided to security operations teams are overly broad and lack the necessary context and response actions for proper remediation. For example, shutting down a user or application or adding a firewall block rule or quarantining a network segment to negate an attack is not a sustainable remediation step to protect an organization on an ongoing basis. It starts with a proper threat detection, investigation and response solution. Current SIEMs and XDR solutions lack the variety of data, telemetry and combined analytics to not only identify an attack campaign and even detect variants on previously successful attacks, but also provide the necessary context, accuracy and validation of the attack to build both a precise and complete response that can be trusted. This is an even greater challenge when current solutions cannot handle complex hybrid multi-cloud architectures leading to significant blind spots and false positives at the very start of the security analyst journey.”

Rising cost of data breach not necessarily prompting dramatic security action

In spite of over four out of five organizations now having experienced some sort of data breach, only slightly over 20% of critical infrastructure companies have moved to zero trust strategies to secure their networks. Cloud security is also lagging as well, with a little under half (43%) of all respondents saying that their security practices in this area are either “early stage” or do not yet exist.

Those that have onboarded security automation and AI elements are the only group seeing massive savings: their average cost of data breach is $3.05 million lower. This particular study does not track average ransom demands, but refers to Sophos research that puts the most latest number at $812,000 globally.

The study also notes serious problems with incident response plans, especially troubling in an environment in which the average ransomware attack is now carried out in four days or less and the “time to ransom” has dropped to a matter of hours in some cases. 37% of respondents say that they do not test their incident response plans regularly. 62% say that they are understaffed to meet their cybersecurity needs, and these organizations tend to suffer over half a million more dollars in damages when they are breached.

Of course, cost of data breaches is not distributed evenly by geography or by industry type. Some are taking much bigger hits than others, reflecting trends established in prior reports. The health care industry is now absorbing a little over $10 million in damage per breach, with the average cost of data breach rising by $1 million from 2021. And companies in the United States face greater data breach costs than their counterparts around the world, at over $8 million per incident.

Shawn Surber, VP of Solutions Architecture and Strategy with Tanium, provides some insight into the unique struggles that the health care industry faces in implementing effective cybersecurity: “Healthcare continues to suffer the greatest cost of breaches but has among the lowest spend on cybersecurity of any industry, despite being deemed ‘critical infrastructure.’ The increased vulnerability of healthcare organizations to cyber threats can be traced to outdated IT systems, the lack of robust security controls, and insufficient IT staff, while valuable medical and health data— and the need to pay ransoms quickly to maintain access to that data— make healthcare targets popular and relatively easy to breach. Unlike other industries that can migrate data and sunset old systems, limited IT and security budgets at healthcare orgs make migration difficult and potentially expensive, particularly when an older system provides a small but unique function or houses data necessary for compliance or research, but still doesn’t make the cut to transition to a newer system. Hackers know these weaknesses and exploit them. Additionally, healthcare orgs haven’t sufficiently updated their security strategies and the tools that manufacturers, IT software vendors, and the FDA have made haven’t been robust enough to thwart the more sophisticated techniques of threat actors.”

Familiar incident types also lead the list of the causes of data breaches: compromised credentials (19%), followed by phishing (16%). Breaches initiated by these methods also tended to be a little more costly, at an average of $4.91 million per incident.

Global average cost of #databreach is now $4.35M, up 13% since 2020. Much of that are realized more than a year after the attack, and 60% of organizations are passing the costs on to consumers in the form of higher prices. #cybersecurity #respectdataClick to Tweet

Cutting the cost of data breach

Though the numbers are never as neat and clean as averages would indicate, it would appear that the cost of data breaches is cut dramatically for companies that implement solid automated “deep learning” cybersecurity tools, zero trust systems and regularly tested incident response plans. Mature cloud security programs are also a substantial cost saver.

Mon, 01 Aug 2022 10:00:00 -0500 Scott Ikeda en-US text/html https://www.cpomagazine.com/cyber-security/ibm-annual-cost-of-data-breach-report-2022-record-costs-usually-passed-on-to-consumers-long-breach-expenses-make-up-half-of-total-damage/
Killexams : CIOReview Names Cobalt Iron Among 10 Most Promising IBM Solution Providers 2022

LAWRENCE, Kan.--(BUSINESS WIRE)--Jul 28, 2022--

Cobalt Iron Inc., a leading provider of SaaS-based enterprise data protection, today announced that the company has been deemed one of the 10 Most Promising IBM Solution Providers 2022 by CIOReview Magazine. The annual list of companies is selected by a panel of experts and members of CIOReview Magazine’s editorial board to recognize and promote innovation and entrepreneurship. A technology partner for IBM, Cobalt Iron earned the distinction based on its Compass ® enterprise SaaS backup platform for monitoring, managing, provisioning, and securing the entire enterprise backup landscape.

This press release features multimedia. View the full release here: https://www.businesswire.com/news/home/20220728005043/en/

Cobalt Iron Compass® is a SaaS-based data protection platform leveraging strong IBM technologies for delivering a secure, modernized approach to data protection. (Graphic: Business Wire)

According to CIOReview, “Cobalt Iron has built a patented cyber-resilience technology in a SaaS model to alleviate the complexities of managing large, multivendor setups, providing an effectual humanless backup experience. This SaaS-based data protection platform, called Compass, leverages strong IBM technologies. For example, IBM Spectrum Protect is embedded into the platform from a data backup and recovery perspective. ... By combining IBM’s technologies and the intellectual property built by Cobalt Iron, the company delivers a secure, modernized approach to data protection, providing a ‘true’ software as a service.”

Through proprietary technology, the Compass data protection platform integrates with, automates, and optimizes best-of-breed technologies, including IBM Spectrum Protect, IBM FlashSystem, IBM Red Hat Linux, IBM Cloud, and IBM Cloud Object Storage. Compass enhances and extends IBM technologies by automating more than 80% of backup infrastructure operations, optimizing the backup landscape through analytics, and securing backup data, making it a valuable addition to IBM’s data protection offerings.

CIOReview also praised Compass for its simple and intuitive interface to display a consolidated view of data backups across an entire organization without logging in to every backup product instance to extract data. The machine learning-enabled platform also automates backup processes and infrastructure, and it uses open APIs to connect with ticket management systems to generate tickets automatically about any backups that need immediate attention.

To ensure the security of data backups, Cobalt Iron has developed an architecture and security feature set called Cyber Shield for 24/7 threat protection, detection, and analysis that improves ransomware responsiveness. Compass is also being enhanced to use several patented techniques that are specific to analytics and ransomware. For example, analytics-based cloud brokering of data protection operations helps enterprises make secure, efficient, and cost-effective use of their cloud infrastructures. Another patented technique — dynamic IT infrastructure optimization in response to cyberthreats — offers unique ransomware analytics and automated optimization that will enable Compass to reconfigure IT infrastructure automatically when it detects cyberthreats, such as a ransomware attack, and dynamically adjust access to backup infrastructure and data to reduce exposure.

Compass is part of IBM’s product portfolio through the IBM Passport Advantage program. Through Passport Advantage, IBM sellers, partners, and distributors around the world can sell Compass under IBM part numbers to any organizations, particularly complex enterprises, that greatly benefit from the automated data protection and anti-ransomware solutions Compass delivers.

CIOReview’s report concludes, “With such innovations, all eyes will be on Cobalt Iron for further advancements in humanless, secure data backup solutions. Cobalt Iron currently focuses on IP protection and continuous R&D to bring about additional cybersecurity-related innovations, promising a more secure future for an enterprise’s data.”

About Cobalt Iron

Cobalt Iron was founded in 2013 to bring about fundamental changes in the world’s approach to secure data protection, and today the company’s Compass ® is the world’s leading SaaS-based enterprise data protection system. Through analytics and automation, Compass enables enterprises to transform and optimize legacy backup solutions into a simple cloud-based architecture with built-in cybersecurity. Processing more than 8 million jobs a month for customers in 44 countries, Compass delivers modern data protection for enterprise customers around the world. www.cobaltiron.com

Product or service names mentioned herein are the trademarks of their respective owners.

Link to Word Doc:www.wallstcom.com/CobaltIron/220728-Cobalt_Iron-CIOReview_Top_IBM_Provider_2022.docx

Photo Link:www.wallstcom.com/CobaltIron/Cobalt_Iron_CIO_Review_Top_IBM_Solution_Provider_Award_Logo.pdf

Photo Caption: Cobalt Iron Compass ® is a SaaS-based data protection platform leveraging strong IBM technologies for delivering a secure, modernized approach to data protection.

Follow Cobalt Iron

https://twitter.com/cobaltiron

https://www.linkedin.com/company/cobalt-iron/

https://www.youtube.com/user/CobaltIronLLC

View source version on businesswire.com:https://www.businesswire.com/news/home/20220728005043/en/

CONTACT: Agency Contact:

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Wall Street Communications

Tel: +1 801 326 9946

Email:sunny@wallstcom.com

Web:www.wallstcom.comCobalt Iron Contact:

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VP of Marketing

Tel: +1 785 979 9461

Email:maspurlock@cobaltiron.com

Web:www.cobaltiron.com

KEYWORD: EUROPE UNITED STATES NORTH AMERICA KANSAS

INDUSTRY KEYWORD: DATA MANAGEMENT SECURITY TECHNOLOGY SOFTWARE NETWORKS INTERNET

SOURCE: Cobalt Iron

Copyright Business Wire 2022.

PUB: 07/28/2022 09:00 AM/DISC: 07/28/2022 09:03 AM

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Thu, 28 Jul 2022 01:03:00 -0500 en text/html https://www.eagletribune.com/region/cioreview-names-cobalt-iron-among-10-most-promising-ibm-solution-providers-2022/article_56f7dda7-cbd5-586a-9d5f-f882022100da.html
Killexams : Online Adaptive Learning Platform Market Key Player, Competition Weakness and Strengths from 2022 to 2028

The MarketWatch News Department was not involved in the creation of this content.

Aug 02, 2022 (Reportmines via Comtex) -- Pre and Post Covid is covered and Report Customization is available.

The analysis of the "Online Adaptive Learning Platform market research report" is designed to help clients Improve their market position, and is in line with this. The report on the Online Adaptive Learning Platform market is a comprehensive study and presentation of drivers, restraints, opportunities, demand factors, market size, forecasts, and trends in the Online Adaptive Learning Platform market.

The global Online Adaptive Learning Platform market size is projected to reach multi million by 2028, in comparision to 2021, at unexpected CAGR during 2022-2028 (Ask for demo Report).

All of the segments in this Online Adaptive Learning Platform market research study have been studied based on current and forecasted 2022 - 2028 trends. Geographic breakdown and analysis of each of the previously mentioned segments include regions comprising the North America: United States, Canada, Europe: GermanyFrance, U.K., Italy, Russia,Asia-Pacific: China, Japan, South, India, Australia, China, Indonesia, Thailand, Malaysia, Latin America:Mexico, Brazil, Argentina, Colombia, Middle East & Africa:Turkey, Saudi, Arabia, UAE, Korea. The report is of 123 pages.

Get demo PDF of Online Adaptive Learning Platform Market Analysis https://www.reportmines.com/enquiry/request-sample/1690524

The top competitors in the Online Adaptive Learning Platform Market, as highlighted in the report, are:

  • DreamBox Learning
  • McGraw-Hill
  • Wiley
  • SAS
  • Docebo
  • D2L
  • IBM
  • Cogbooks
  • Smart Sparrow
  • Paradiso
  • ALEKS
  • ScootPad
  • Knewton
  • EdApp Microlearning
  • Imagine Learning
  • Fishtree

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Market Segmentation

The worldwide Online Adaptive Learning Platform Market is categorized on Component, Deployment, Application, and Region.

The Online Adaptive Learning Platform Market Analysis by types is segmented into:

The Online Adaptive Learning Platform Market Industry Research by Application is segmented into:

  • Corporate Training
  • Higher Education
  • Other

In terms of Region, the Online Adaptive Learning Platform Market Players available by Region are:

  • North America:
  • Europe:
    • Germany
    • France
    • U.K.
    • Italy
    • Russia
  • Asia-Pacific:
    • China
    • Japan
    • South Korea
    • India
    • Australia
    • China Taiwan
    • Indonesia
    • Thailand
    • Malaysia
  • Latin America:
    • Mexico
    • Brazil
    • Argentina Korea
    • Colombia
  • Middle East & Africa:
    • Turkey
    • Saudi
    • Arabia
    • UAE
    • Korea

Inquire or Share Your Questions If Any Before the Purchasing This Report https://www.reportmines.com/enquiry/pre-order-enquiry/1690524

Online Adaptive Learning Platform Industry Challenges and Market Size:

The Online Adaptive Learning Platform market research report features a dashboard overview of leading companies' history and current performance, as well as an assessment of successful marketing tactics, market contributions, and latest breakthroughs. The study report uses a variety of approaches and analyses to provide in-depth and comprehensive information about the Online Adaptive Learning Platform business. The Online Adaptive Learning Platform market applications include Corporate Training,Higher Education,Other.

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What's in the Online Adaptive Learning Platform Market Industry Research Report:

  • The market growth rate, growth momentum, or acceleration market carry during the forecast period
  • The key factors driving the Online Adaptive Learning Platform market
  • The size of the Online Adaptive Learning Platform market by value
  • The trends of this market
  • The main factors responsible for a new product launch

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Principal Gains for Industry Players & Stakeholders:

The Online Adaptive Learning Platform market is segmented by Type and by Application, Players, stakeholders, and other participants in the Online Adaptive Learning Platform market will be able to gain the upper hand as they use the report as a powerful resource. The segmental analysis focuses on production capacity, revenue, and forecast. This Online Adaptive Learning Platform market report provides a detailed analysis of several leading Online Adaptive Learning Platform market vendors, including a DreamBox Learning,McGraw-Hill,Wiley,SAS,Docebo,D2L,IBM,Cogbooks,Smart Sparrow,Paradiso,ALEKS,ScootPad,Knewton,EdApp Microlearning,Imagine Learning,Fishtree.

The Online Adaptive Learning Platform market research report contains the following TOC:

  • Report Overview
  • Global Growth Trends
  • Competition Landscape by Key Players
  • Data by Type
  • Data by Application
  • North America Market Analysis
  • Europe Market Analysis
  • Asia-Pacific Market Analysis
  • Latin America Market Analysis
  • Middle East & Africa Market Analysis
  • Key Players Profiles Market Analysis
  • Analysts Viewpoints/Conclusions
  • Appendix

Get a demo of TOC https://www.reportmines.com/toc/1690524#tableofcontents

Impact Analysis of COVID 19:

This Online Adaptive Learning Platform market study especially analyses the impact of the Covid-19 outbreak on the Online Adaptive Learning Platform industry, covering the supply chain analysis, impact assessment of the market size growth rate in several scenarios, and the measures to be undertaken by companies in response to the COVID-19 epidemic. The Online Adaptive Learning Platform market is segmented into Free to Use,Pay to Use based on type.

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Reasons for purchasing the Online Adaptive Learning Platform Market Report:

  • The Online Adaptive Learning Platform market report covers in-depth historical and forecasts analysis.
  • The Online Adaptive Learning Platform market research report provides detailed information about Market Introduction, Market Summary, Global market Revenue, Market Drivers, Market Restraints, Market Opportunities, Competitive Analysis, and Regional and Country levels.
  • The Online Adaptive Learning Platform market report helps to identify opportunities in the marketplace.

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To view the original version on Lemon PR Wire visit Online Adaptive Learning Platform Market Key Player, Competition Weakness and Strengths from 2022 to 2028

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Mon, 01 Aug 2022 22:01:00 -0500 en-US text/html https://www.marketwatch.com/press-release/online-adaptive-learning-platform-market-key-player-competition-weakness-and-strengths-from-2022-to-2028-2022-08-02
Killexams : Keep Vigilant and Carry On: How Law Firms Can Learn from Cybersecurity Cautionary Tales

According to the Ponemon Institute and IBM’s Cost of a Data Breach Report 2021, the average total cost of a data breach increased from $3.86 million to $4.24 million in 2021. The report indicates a 10% year-over-year increase in the average total cost of a breach. Personally identifiable information (PII) breaches now have a cost of $161 per record for organizations. The average number of days it takes to identify a breach increased from 287 days to 316 days as the pandemic forced many into remote workforce situations. If peace of mind is what you seek, these numbers are headed in the wrong direction.

If businesses, including law firms, could identify and contain a breach within 200 days rather than the aforementioned 316 days, they could save up to 30% on costs associated with the breach. However, just when you think you have security under control, another threat emerges, forcing you to change course. Being in a state of constant vigilance is not easy, but legal IT professionals need to approach security as a daily 24/7 battle they must continuously fight. This is the only way to avoid becoming the next cybersecurity cautionary tale.

Sun, 31 Jul 2022 23:03:00 -0500 en text/html https://www.law.com/legaltechnews/2022/08/01/keep-vigilant-and-carry-on-how-law-firms-can-learn-from-cybersecurity-cautionary-tales/?slreturn=20220709014700
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