IBM-Internet-of-Things

The VMware® acquisition by Broadcom has changed VMware’s product and partner strategies. In November 2023, Broadcom finalized its acquisition (link resides outside ibm.com) of VMware for USD 69 billion, with an aim to enhance its multicloud strategy. Further to the acquisition, Broadcom decided to discontinue (link resides outside ibm.com) its AWS authorization to resell VMware Cloud on AWS as of 30 April 2024. As a result, AWS will no longer be able to offer new subscriptions or additional services. This might lead to VMware’s network of clients and partners having to prepare for major adjustments.   This blog discusses the impact of VMware-Broadcom acquisition, paths beyond VMware, viable options for AWS clients to move VMware to AWS Cloud and the advantages, critical actions, success stories and the next steps.  Paths beyond VMware   The impact of acquisition: A 2023 Forrester study (link resides outside ibm.com) predicts that 20% of VMware clients will move away from VMware stack due concerns around price increases, limited customer support and a mandatory requirement for subscription to software bundles —all of which are a result of the acquisition. Forrester® further states that clients are looking at alternative options such as virtualization, cloud management, end-user computing and other viable approaches.   The growing need for cloud cost optimization: Cost optimization is essential when exiting VMware because it helps manage and reduce the initial and ongoing expenses associated with migration, ensures efficient use of resources in the new environment, and maximizes the return on investment. By leveraging tools like IBM Turbonomic® for real-time resource management and Apptio® (an IBM company) for cloud cost optimization and financial transparency, organizations can achieve a smooth, cost-effective transition from VMware while positioning themselves for future growth and innovation.  Considering VMware alternatives  As technology evolves and business needs change, it is essential for businesses to ensure that their infrastructure aligns with strategic goals, such as optimizing costs, improving performance or embracing a cloud-first approach. Clients are likely to start evaluating alternatives for VMware, both from cost and functionality perspective for their current IT landscape.  Understanding VMware migration: VMware migration is a strategic effort focused on moving traditional VMware workloads to cloud-native settings. AWS offers an extensive alternative platform for VMware providing a comprehensive suite of cloud services, global infrastructure and robust security features. This approach overcomes the challenges associated with on-premises infrastructure, including hardware reliance, scalability limitations and elevated operational expenses. By transferring VMware workloads to AWS, organizations can take advantage of cloud capabilities such as scalability, pay-as-you-go pricing models and a broad array of AWS services. IBM offers alternative solution options with optimal scalability, from scale-up to scale-out, enabling VMware transition and helping move to AWS.  Exploring data center (DC) exit: Transitioning from VMware to a modern cloud or hybrid environment can significantly expedite clients’ data center consolidation and exit. This migration not only reduces operational costs and complexities associated with maintaining physical data centers but also enhances security, compliance and innovation capabilities. IBM Consulting® offers data center migration (also known as DC exit), a comprehensive solution designed to assist organizations in efficiently and strategically transitioning from their existing data center infrastructure to AWS Cloud. IBM brings the power of generative AI, supported by IBM–AWS joint investments to provide accelerated and automated migration at scale.   Migrating from VMware to AWS: Best case scenarios  In case the client’s VMware license is expiring in the next 6 months, within a year or down the line, businesses can explore various scenarios:  Migration-first approach for VMware to Amazon EC2 migration: Clients can directly migrate existing VMware workloads to AWS instances. This involves rehosting applications on Amazon Elastic Compute Cloud (Amazon EC2) (link resides outside ibm.com) instances, which may require some reconfiguration of the applications to optimize them for the cloud environment. Post migration, businesses can look to modernize their applications to take full advantage of AWS native services such as Amazon Relational Database Service (Amazon RDS) for database management, AWS Lambda for serverless computing and Amazon S3 for scalable storage.    Container-first approach: IBM offers a comprehensive end-to-end stack of products and services designed to meet the complex needs of modern enterprises. It encompasses a broad range of technologies and services, from cloud infrastructure and AI to cybersecurity and data management. Central to this offering are Red Hat® OpenShift® Service on AWS (ROSA) and OpenShift Virtualization. These technologies exemplify IBM’s commitment to providing flexible, scalable and integrated solutions that drive business innovation and efficiency. Mitigate potential vendor lock-in, such as with VMware, by containerizing workloads across the AWS Cloud using services like ROSA, Elastic Kubernetes Service (EKS) and Amazon ECS on AWS Fargate (link resides outside ibm.com). Red Hat OpenShift virtualization allows businesses to run and manage both containerized and virtual machine (VM) workloads on a single platform.  Software as a service (SaaS) approach: Applications running on VMware can be moved to AWS as a SaaS solution. SaaS provides a flexible, cost-effective and efficient way to deliver software applications to users. By leveraging AWS Cloud infrastructure, the SaaS approach eliminates the need for extensive on-premises resources and maintenance, allowing businesses to focus on their core competencies while ensuring they have access to the latest technological advancements.  Managed services approach: As an AWS certified managed service provider (MSP), IBM can help move VMware workloads to a managed services model. Managed services for AWS is designed to simplify and automate cloud management. Our services help enterprises adopt and operate within the AWS Cloud with best practices, security and compliance at the forefront. By leveraging managed services, organizations can focus on their core business activities while IBM handles the heavy lifting of cloud infrastructure management.  Accelerating VMware exit with IBM Migration Factory   Leveraging years of collaboration and experience, IBM brings a deep understanding of AWS technologies, enabling seamless integration and optimized implementations for enterprises. IBM offers a customized approach that is adapted to the current situation, allowing you to meet your clients at their point of need.  IBM Consulting offers AWS Migration Factory, an innovative engagement model that is built on IBM Garage™ Methodology for app modernization. It is a squad-based, workstream-centric method that leverages generative AI (gen AI) and automation to achieve rapid transformation at scale.  AWS Migration Factory offers a structured and efficient framework for migrating large-scale workloads to AWS. By leveraging automated tools, best practices and a phased approach, it helps organizations minimize risks, reduce costs and accelerate their cloud migration journeys. The factory leverages joint incentive programs to accelerate client engagements.  Fig 1: IBM Consulting’s AWS Migration Factory approach    Before embarking on VMware takeout, IBM conducts a thorough assessment of  the client’s existing VMware environment. This involves identifying workload dependencies, performance metrics and migration requirements. Based on the assessment, a detailed migration plan should be developed, outlining timelines, resource allocation and risk mitigation strategies. IBM brings several assets and proprietary tools such as IBM Consulting Cloud Accelerator, IBM Consulting Delivery Curator, IBM PRISM (a platform engineering control plane which is a collection of accelerators that help to quickly provision and manage a client environment). and IBM AIOps (artificial intelligence for IT operations) solutions to plan, design and execute end-to-end migration and modernization journey.   Underpinning these assets is the IBM Delivery Central Platform (IDCP). This end-to-end delivery execution platform transforms how we deliver by digitizing and enhancing delivery workflows and enabling real-time governance. Powered by generative AI, these assets and assistants are made available to key personas, catering to their mode of consumption.  AWS also offers several tools and services to facilitate the migration of VMware workloads, such AWS Migration Hub (link resides outside ibm.com). This service  streamlines and manages the migration of  applications to AWS, providing visibility, tracking and coordination throughout the migration process, including AWS Application Migration Services (link resides outside ibm.com).   Leveraging generative-AI powered migration  IBM has built a suite of migration tools and assets using Amazon Bedrock (link resides outside ibm.com). This innovative approach helps migrate applications and workloads to the cloud using generative AI technologies integrated with Amazon Bedrock.   Generative AI-powered discovery as a service: Helps extracting key data elements from client data repositories and fast-track the application discovery process. Generative AI-powered migration assist: Translates natural language questions to SQL query to obtain the migration status of applications and servers from a centralized data lake (also known as Delivery Curator) during the migration lifecycle.  Generative AI-powered design assistant: Applies models like llama2 on the centralized data repository in Delivery Curator and the design assistant accelerates the design phases bringing in much efficiency.  Fig 2: IBM and AWS accelerators for migrations  Helping a global manufacturing firm migrate VMware workloads to AWS  Companies that have effectively transferred their VMware workloads to AWS can experience substantial advantages such as cost reduction, scalability and innovation. IBM helped a global leader in consumer products manufacturing migrate 400 applications to AWS in two years as part of its efforts to embrace its transition to a new product strategy. The client migrated to AWS to improve business agility and meet new scalability and security requirements. In addition, the client also needed to prepare its employees for the architectural shift and upskill them on new data handling processes. To meet these goals, the client decided to migrate its technology from on premises to AWS, resulting in a 50% performance improvement across their business and up to 50% cost savings using Amazon RDS.  Why IBM and AWS   VMware migration to AWS offers organizations a seamless migration journey to modernize their IT infrastructure and unlock the full potential of cloud computing. By leveraging AWS extensive capabilities and global footprint, businesses can achieve cost optimization, scalability and agility, driving innovation and competitiveness in today’s digital economy.  As a Premier Tier AWS Partner, IBM  has more than 24,000 AWS certifications, 17 validated service delivery programs and mastery in 19 AWS competencies for cloud-native application development including the AWS Migration and Modernization competency(link resides outside ibm.com). IBM continues to strengthen AWS competencies through acquisitions and co-development of solutions with AWS. Read how IBM Consulting augments expertise with AWS competencies.Learn how IBM expertise in security, enterprise scalability and open innovation with Red Hat® OpenShift® can help businesses grow quickly on the AWS Cloud.  help businesses grow quickly on the AWS Cloud.    Find out why IBM is recognized as a global leader in migration and modernization services in the IDC MarketScape: Worldwide Application Modernization Services 2023 Vendor Assessment (link resides outside ibm.com).  Learn more about IBM Consulting Cloud Migration capabilities.   IBM’s deep experience in AI, market-leading AI capabilities and the shared commitment with AWS to accelerate the adoption of generative AI—makes IBM Consulting the ideal AWS partner of choice. With 21,000+ skilled AI practitioners, with 1000 skilled in gen AI, IBM brings watsonx™ capabilities and expertise in Amazon technologies. IBM has experience and expertise with AWS generative AI technologies including Amazon SageMaker, Amazon CodeWhisperer, Amazon Q and Amazon Bedrock, a fully managed service that offers a choice of high-performing foundation models (FMs) from leading AI companies like AI21 Labs, Anthropic, Cohere, Meta, Stability AI and Amazon through a single API. Additionally, we offer  a broad set of capabilities to build generative AI applications with security, privacy and responsible AI.  Exploring VMware alternatives?  If you are considering VMware migration to AWS for your organization, explore the possibilities and discover how IBM and AWS can help accelerate your cloud migration journey. Contact IBM to learn more about IBM Consulting Services for AWS Cloud. Learn more about AWS consulting at IBM The post Approaches to migrating your VMware workloads to AWS    appeared first on IBM Blog.

The central processing unit (CPU) is the computer’s brain, assigning and processing tasks and managing essential operational functions. Computers have been so seamlessly integrated with modern life that sometimes we’re not even aware of how many CPUs are in use around the world. It’s a staggering amount—so many CPUs that a conclusive figure can only be approximated. How many CPUs are now in use? It’s been estimated that there may be as many as 200 billion CPU cores (or more) now running. As an example of what such a monumental number means from a different perspective, chip manufacturer Arm claimed to have shipped 7.3 billion chips within a single quarter of 2020, or roughly 900 CPUs for each second of that entire quarter. (There are approximately 7.8 million seconds in 3 months.) That leads to a stunning comparison. Projections by the US Census Bureau list the 2024 global population at around 8 billion people. If those 200 billion CPUs currently in existence were all distributed equitably by persons—and none were devoted to business, government or scientific applications—there would be exactly 25 CPUs serving as the brain of the computer for each human brain on the planet. This prompts a key question: With so many CPUs in operation, how are they all being used? Seven top CPU use cases With CPUs, we’re talking about a very small processor chip, and yet there’s very little that this very little thing cannot do. A brief survey shows which industries depend the most heavily on CPUs: Consumer electronics Many of the world’s most profitable companies—such as Apple—make devices for the consumer electronics industry. The rampant demand for personal computing platforms (like smartphones, laptops and gaming consoles) has driven a massive and ongoing expansion of CPU use. Beyond that, household devices blessed with Internet of Things (IoT) technology means that CPUs are now being incorporated into refrigerators, thermostats, security systems and more. Data analytics The goal of data analytics is to take raw data and refine it into an understandable narrative that addresses business goals. The first part of that process is assembling and cleaning the data. CPUs are instrumental to these activities, serving as the primary computer processing units. In addition, the high clock speeds achieved by CPUs make them perfectly suited to handle the type of rapid-fire scanning and retrieval of information that data analytics requires. Defense and space The CPU is the true backbone of modern defense systems. Any country that desires to be a global power must have modern computers as part of its security arsenal. Likewise, humankind’s achievements in space exploration could never have occurred without the CPU to handle the awesome computational challenges of calculating the distance and routes of space flights. Space poses a unique challenge for computers, which must be radiation-hardened to withstand powerful solar rays. Financial services Like data analytics, fintech companies depend on CPUs to enable the fast and efficient processing of huge amounts of financial-based information. By running advanced analyses on such data and then applying a range of different scenarios to that data, risk management systems that CPUs enable can help financial institutions reduce losses. CPUs also assist that effort in another key way—by helping flag oddities and detecting cases of fraud. Healthcare Nearly every type of industry benefits because of the fast speeds CPUs achieve, but none as importantly as healthcare, where lives literally hang in the balance and timing is a critical concern. Beyond the ability to quickly shuttle vital patient information between providers, CPUs can be used to help automate the ordering and tracking of prescriptions and other supplies. Computers can also both create pre-surgical 3D models of organs and help pathologists study illnesses. Manufacturing The use of semiconductors has radically changed manufacturing, synching the input of materials and improving quality control. Manufacturing is also being revolutionized by computer-aided manufacturing (CAM), where CPU-driven computer systems help run industrial production operations. CAM uses direct or indirect connections that exist between the CPU and production operations to schedule, control and manage manufacturing activity. Telecom The telecom industry offers its own bread-and-butter products—communication technology devices—but also assists other industries in important ways. Those use cases include enabling digital transactions (for the financial services industry) and assisting healthcare by supporting robotic surgeries with precision capabilities and data updates. In addition, CPUs are essential to operating autonomous vehicles, which rely on telecom signals for navigational guidance. Key parts of the CPU Modern CPUs typically contain the following components: Arithmetic/logic unit (ALU): Executes arithmetic and logical operations, including math equations and logic-based comparisons. Buses: Manages proper data transfer and data flow between components within a computer system. Control unit: Uses intensive circuitry that controls the computer system by issuing a system of electrical pulses and instructs the system to execute high-level computer instructions. Instruction registers and pointer: Shows location of next instruction set to be executed by the CPU. Memory unit: Manages memory usage and data flow between RAM and the CPU. The main memory unit supervises the handling of the cache memory. Registers: Provides built-in permanent memory for constant, repeated data needs that must be administered regularly, without exception. Important CPU concepts To be fully conversant in CPU terminology, it’s helpful to understand the following concepts: Cache: Storage areas whose location allows users to quickly access data that’s been in recent use. Cache memory stores data in areas that are built into a CPU’s processor chip to reach data retrieval speeds even faster than random access memory (RAM). Clock speed: The rate of activity per computer clock cycle. The internal clock built into computers regulates the speed and frequency of computer operations. The clock manages the CPU’s circuitry through the transmittal of electrical pulses. The delivery rate of pulses is called “clock speed.” Core: The processor within the processor. Cores are processing units that read and execute various program instructions. Processors are classified according to how many cores are embedded into them; single-core, dual-core and quad-core processors are some of the examples. (The term “Intel Core” is used commercially to market Intel’s product line of multi-core CPUs.) Threads: The shortest sequences of programmable instructions that an operating system’s scheduler can manage and send to a CPU for processing. Through multithreading, the use of multiple threads running simultaneously, various computer processes can be run concurrently, supporting multitasking. (“Hyper-threading” is Intel’s proprietary term for its form of multithreading.) Top CPU manufacturers and products The two major companies battling for control of this ultra-lucrative marketplace are Intel and Advanced Micro Devices (AMD): Intel Markets processors and microprocessors through four product lines: Intel® Core® (high-end premium line), Intel® Xeon® (office and business use), Intel® Pentium® (personal computers and laptops), and Intel®Celeron® (low-end, low-cost personal computing use). Obviously, different chips are best suited to certain applications. The Intel® Core i5-13400F is a good desktop processor that features 10 cores. But when it comes to a processing-intensive application like video editing, many users opt for the Intel® Core i7 14700KF 20-Core, 28-thread CPU. Advanced Micro Devices (AMD) Sells two types of processors and microprocessors: CPUs and APUs (which stands for accelerated processing units). APUs are CPUs that are equipped with proprietary Radeon® graphics. AMD® makes high-speed, high-performance Ryzen® processors for the video-game market. The AMD® Ryzen® 7 5800X3D, for example, features a 3D V-Cache technology that helps it push game graphics to new heights. Athlon® processors used to be considered AMD’s high-end line, but AMD now uses it as a basic computing alternative. Arm Arm® doesn’t manufacture equipment, but instead leases out its valued processor designs and/or other proprietary technologies to other companies who do make equipment. For general-purpose computing, such as running an operating system like Windows and using multimedia programs, most AMD Ryzen® or Intel® Core® processors can handle the workloads involved. Ongoing CPU trends Several tangential issues will continue to influence CPU development and the use cases for which they are utilized in coming years: Increased use of GPUs: Graphics processing units (GPUs) are an electronic circuit first developed for use in smartphone and video game consoles. Their use is about driving processing speeds, so in addition to accelerating graphics cards, GPUs are being used in processing-intensive pursuits like cryptocurrency mining and the training of neural networks. The drive to miniaturize: The history of computer hardware has been a quest to make computer processors smaller. Early computers required vast floor space and vacuum tubes. Then, CPUs became smaller and more efficient with the introduction of transistors. Later, computer scientists created a CPU called the microprocessor that could be held within a small integrated circuit chip. The drive to make processors smaller will continue unabated as long as there are consumers and businesses who want more processing power and faster speed. Peripheral proliferation: Peripheral devices help optimize and increase the functionality of computing. Peripherals can be attached to the outside of a computer and include devices like keyboards, mice, scanners and printers. Expect to see more peripherals created in response to ongoing customer demand. Sustainability issues: Moving forward, matters of power consumption will become increasingly important. Companies will become more focused on energy-efficient solutions as energy costs rise. When CPU use increases on a grand scale—like in hyperscale data centers, with thousands of linked computers working around the clock, the energy used is often measured in gigahertz (GHz)—which is comparable to the entire energy consumption of villages or small towns. Forecasting future CPU growth In its 2022–2028 processor revenue forecast, analyst group Yole Intelligence calculated that the total processor market in 2022 was worth $154 billion. That total figure included the following processor segments and their respective worths: Central processing units (CPUs): USD 65 billion Application processing units (APUs): USD 61 billion Graphics processing units (GPUs): USD 22 billion SoC FPGA: USD 2.6 billion (SoC FPGA stands for “system-on-chip field programmable gate array,” which are semiconductor-based devices that incorporate programmable logic into processor cores.) AI ASICs: USD 1.5 billion (AI ASICs stands for “application-specific integrated circuits,” specifically those related to artificial intelligence.) Data processing units (DPUs): USD 0.6 billion In its 2028 projections, you can see how Yole’s experts expect certain segments to grow, namely AI and DPUs. Yole anticipates an 8% total yearly growth, leading to an expected 2028 total value of USD 242 billion, based on these figures: CPUs: USD 97 billion APUs: USD 65 billion GPUs: USD 55 billion AI ASICs: USD 11 billion DPUs: USD 8.1 billion SoC FPGAs: USD 5.2 billion In addition to substantial market growth in AI ASICs and DPUs, Yole Intelligence’s forecast shows nearly identical growth for CPUs and GPUs during the same period, with growth predictions of USD 32 billion and USD 33 billion respectively. These projections also demonstrate the ongoing centrality of CPUs, since this category leads all others now and will continue to do so in the future, according to Yole Intelligence. Take the next step With over 200 billion CPUs in current operation, it’s reasonable to conclude that CPUs are here to stay—very probably a permanent part of the human condition moving forward. But it’s also a safe bet that the CPU will continue to be further developed and refined, to keep maxing out its utility for high-performance systems and the new, graphics-rich computer programs they run. That’s why it’s smart to invest wisely when purchasing the associated equipment needed to execute computing objectives. It’s important to have hardware that can keep pace with modern CPUs. IBM servers offer flexibility in addition to strength, so you can get the processing power you need now, along with room to grow in the future. Explore IBM servers The post Seven top central processing unit (CPU) use cases appeared first on IBM Blog.

In business and beyond, communication is king. Successful service level agreements (SLAs) operate on this principle, laying the foundation for successful provider-customer relationships. A service level agreement (SLA) is a key component of technology vendor contracts that describes the terms of service between a service provider and a customer. SLAs describe the level of performance to be expected, how performance will be measured and repercussions if levels are not met. SLAs make sure that all stakeholders understand the service agreement and help forge a more seamless working relationship. Types of SLAs There are three main types of SLAs: Customer-level SLAs Customer-level SLAs define the terms of service between a service provider and a customer. A customer can be external, such as a business purchasing cloud storage from a vendor, or internal, as is the case with an SLA between business and IT teams regarding the development of a product. Service-level SLAs Service providers who offer the same service to multiple customers often use service-level SLAs. Service-level SLAs do not change based on the customer, instead outlining a general level of service provided to all customers. Multilevel SLAs When a service provider offers a multitiered pricing plan for the same product, they often offer multilevel SLAs to clearly communicate the service offered each level. Multilevel SLAs are also used when creating agreements between more than two more parties. SLA components SLAs include an overview of the parties involved, services to be provided, stakeholder role breakdowns, performance monitoring and reporting requirements. Other SLA components include security protocols, redressing agreements, review procedures, termination clauses and more. Crucially, they define how performance will be measured. SLAs should precisely define the key metrics—service-level agreement metrics—that will be used to measure service performance. These metrics are often related to organizational service level objectives (SLOs). While SLAs define the agreement between organization and customer, SLOs set internal performance targets. Fulfilling SLAs requires monitoring important metrics related to business operations and service provider performance. The key is monitoring the right metrics. What is a KPI in an SLA? Metrics are specific measures of an aspect of service performance, such as availability or latency. Key performance indicators (KPIs) are linked to business goals and are used to judge a team’s progress toward those goals. KPIs don’t exist without business targets; they are “indicators” of progress toward a stated goal. Let’s use annual sales growth as an example, with an organizational goal of 30% growth year-over-year. KPIs such as subscription renewals to date or leads generated provide a real-time snapshot of business progress toward the annual sales growth goal. Metrics such as application availability and latency help provide context. For example, if the organization is losing customers and not on track to meet the annual goal, an examination of metrics related to customer satisfaction (that is, application availability and latency) might provide some answers as to why customers are leaving. What SLA metrics to monitor SLAs contain different terms depending on the vendor, type of service provided, client requirements, compliance standards and more and metrics vary by industry and use case. However, certain SLA performance metrics such as availability, mean time to recovery, response time, error rates and security and compliance measurements are commonly used across services and industries. These metrics set a baseline for operations and the quality of services provided. Clearly defining which metrics and key performance indicators (KPIs) will be used to measure performance and how this information will be communicated helps IT service management (ITSM) teams identify what data to collect and monitor. With the right data, teams can better maintain SLAs and make sure that customers know exactly what to expect. Ideally, ITSM teams provide input when SLAs are drafted, in addition to monitoring the metrics related to their fulfillment. Involving ITSM teams early in the process helps make sure that business teams don’t make agreements with customers that are not attainable by IT teams. SLA metrics that are important for IT and ITSM leaders to monitor include: 1. Availability Service disruptions, or downtime, are costly, can damage enterprise credibility and can lead to compliance issues. The SLA between an organization and a customer dictates the expected level of service availability or uptime and is an indicator of system functionality. Availability is often measured in “nines on the way to 100%”: 90%, 99%, 99.9% and so on. Many cloud and SaaS providers aim for an industry standard of “five 9s” or 99.999% uptime. For certain businesses, even an hour of downtime can mean significant losses. If an e-commerce website experiences an outage during a high traffic time such as Black Friday, or during a large sale, it can damage the company’s reputation and annual revenue. Service disruptions also negatively impact the customer experience. Services that are not consistently available often lead users to search for alternatives. Business needs vary, but the need to provide users with quick and efficient products and services is universal. Generally, maximum uptime is preferred. However, providers in some industries might find it more cost effective to offer a slightly lower availability rate if it still meets client needs. 2. Mean time to recovery Mean time to recovery measures the average amount of time that it takes to recover a product during an outage or failure. No system or service is immune from an occasional issue or failure, but enterprises that can quickly recover are more likely to maintain business profitability, meet customer needs and uphold SLAs. 3. Response time and resolution time SLAs often state the amount of time in which a service provider must respond after an issue is flagged or logged. When an issue is logged or a service request is made, the response time indicates how long it takes for a provider to respond to and address the issue. Resolution time refers to how long it takes for the issue to be resolved. Minimizing these times is key to maintaining service performance. Organizations should seek to address issues before they become system-wide failures and cause security or compliance issues. Software solutions that offer full-stack observability into business functions can play an important role in maintaining optimized systems and service performance. Many of these platforms use automation and machine learning (ML) tools to automate the process of remediation or identify issues before they arise. For example, AI-powered intrusion detection systems (IDS) constantly monitor network traffic for malicious activity, violations of security protocols or anomalous data. These systems deploy machine learning algorithms to monitor large data sets and use them to identify anomalous data. Anomalies and intrusions trigger alerts that notify IT teams. Without AI and machine learning, manually monitoring these large data sets would not be possible.   4. Error rates Error rates measure service failures and the number of times service performance dips below defined standards. Depending on your enterprise, error rates can relate to any number of issues connected to business functions. For example, in manufacturing, error rates correlate to the number of defects or quality issues on a specific product line, or the total number of errors found during a set time interval. These error rates, or defect rates, help organizations identify the root cause of an error and whether it’s related to the materials used or a broader issue. There is a subset of customer-based metrics that monitor customer service interactions, which also relate to error rates. First call resolution rate: In the realm of customer service, issues related to help desk interactions can factor into error rates. The success of customer services interactions can be difficult to gauge. Not every customer fills out a survey or files a complaint if an issue is not resolved—some will just look for another service. One metric that can help measure customer service interactions is the first call resolution rate. This rate reflects whether a user’s issue was resolved during the first interaction with a help desk, chatbot or representative. Every escalation of a customer service query beyond the initial contact means spending on extra resources. It can also impact the customer experience. Abandonment rate: This rate reflects the frequency in which a customer abandons their inquiry before finding a resolution. Abandonment rate can also add to the overall error rate and helps measure the efficacy of a service desk, chatbot or human workforce. 5. Security and compliance Large volumes of data and the use of on-premises servers, cloud servers and a growing number of applications creates a greater risk of data breaches and security threats. If not monitored appropriately, security breaches and vulnerabilities can expose service providers to legal and financial repercussions. For example, the healthcare industry has specific requirements around how to store, transfer and dispose of a patient’s medical data. Failure to meet these compliance standards can result in fines and indemnification for losses incurred by customers. While there are countless industry-specific metrics defined by the different services provided, many of them fall under larger umbrella categories. To be successful, it is important for business teams and IT service management teams to work together to improve service delivery and meet customer expectations. Benefits of monitoring SLA metrics Monitoring SLA metrics is the most efficient way for enterprises to gauge whether IT services are meeting customer expectations and to pinpoint areas for improvement. By monitoring metrics and KPIs in real time, IT teams can identify system weaknesses and optimize service delivery. The main benefits of monitoring SLA metrics include: Greater observability A clear end-to-end understanding of business operations helps ITSM teams find ways to improve performance. Greater observability enables organizations to gain insights into the operation of systems and workflows, identify errors, balance workloads more efficiently and improve performance standards. Optimized performance By monitoring the right metrics and using the insights gleaned from them, organizations can provide better services and applications, exceed customer expectations and drive business growth. Increased customer satisfaction Similarly, monitoring SLA metrics and KPIs is one of the best ways to make sure services are meeting customer needs. In a crowded business field, customer satisfaction is a key factor in driving customer retention and building a positive  reputation. Greater transparency By clearly outlining the terms of service, SLAs help eliminate confusion and protect all parties. Well-crafted SLAs make it clear what all stakeholders can expect, offer a well-defined timeline of when services will be provided and which stakeholders are responsible for specific actions. When done right, SLAs help set the tone for a smooth partnership. Understand performance and exceed customer expectations The IBM® Instana® Observability platform and IBM Cloud Pak® for AIOps can help teams get stronger insights from their data and improve service delivery. IBM® Instana® Observability offers full-stack observability in real time, combining automation, context and intelligent action into one platform. Instana helps break down operational silos and provides access to data across DevOps, SRE, platform engineering and ITOps teams. IT service management teams benefit from IBM Cloud Pak for AIOps through automated tools that address incident management and remediation. IBM Cloud Pak for AIOps offers tools for innovation and the transformation if IT operations. Meet SLAs and monitor metrics with an advanced visibility solution that offers context into dependencies across environments. IBM Cloud Pak for AIOps is an AIOps platform that delivers visibility into performance data and dependencies across environments. It enables ITOps managers and site reliability engineers (SREs) to use artificial intelligence, machine learning and automation to better address incident management and remediation. With IBM Cloud Pak for AIOps, teams can innovate faster, reduce operational cost and transform IT operations (ITOps). Explore IBM Instana Observability Explore IBM Cloud Pak for AIOps The post 5 SLA metrics you should be monitoring appeared first on IBM Blog.

As organizations strive to harness the power of AI while controlling costs, leveraging anything as a service (XaaS) models emerges as a strategic approach. In this blog, we’ll explore how businesses can use both on-premises and cloud XaaS to control budgets in the age of AI, driving financial sustainability without compromising on technological advancement.  Embracing the power of XaaS  XaaS encompasses a broad spectrum of cloud-based and on-premises service models that offer scalable and cost-effective solutions to businesses. From software as a service (SaaS) to infrastructure as a service (IaaS), platform as a service (PaaS) and beyond, XaaS enables organizations to access cutting-edge technologies and capabilities without the need for upfront investment in hardware or software.  Harnessing flexibility and scalability  One of the key advantages of XaaS models is their inherent flexibility and scalability, whether deployed on premises or in the cloud. Cloud-based XaaS offerings provide organizations with the agility to scale resources up or down based on demand, enabling optimal resource utilization and cost efficiency. Similarly, on-premises XaaS solutions offer the flexibility to scale resources within the organization’s own infrastructure, providing greater control over data and security.  Maintaining cost predictability and transparency  Controlling budgets in the age of AI requires a deep understanding of cost drivers and expenditure patterns. XaaS models offer organizations greater predictability and transparency in cost management by providing detailed billing metrics and usage analytics. With granular insights into resource consumption, businesses can identify opportunities for optimization and allocate budgets more effectively.  Outsourcing infrastructure management  Maintaining and managing on-premises infrastructure for AI workloads can be resource-intensive and costly. By leveraging both cloud-based and on-premises XaaS offerings, organizations can offload the burden of infrastructure management to service providers. Cloud-based XaaS solutions provide scalability, flexibility and access to a wide range of AI tools and services, while on-premises XaaS offerings enable greater control over data governance, compliance and security.  Accessing specialized expertise  Implementing AI initiatives often requires specialized skills and expertise in areas such as data science, machine learning and AI development. XaaS models provide organizations with access to a vast ecosystem of skilled professionals and service providers who can assist in the design, development and deployment of AI solutions. This access to specialized expertise enables businesses to accelerate time-to-market and achieve better outcomes while controlling costs.  Facilitating rapid experimentation and innovation  In the age of AI, rapid experimentation and innovation are essential for staying ahead of the competition. XaaS models facilitate experimentation by providing businesses with access to a wide range of AI tools, platforms and services on demand. This enables organizations to iterate quickly, test hypotheses and refine AI solutions without the need for significant upfront investment. Embracing a culture of experimentation helps businesses drive innovation while minimizing financial risk.  Managing budgets effectively  As organizations navigate the complexities of AI adoption and strive to control budgets, leveraging both on-premises and cloud XaaS models emerges as a strategic imperative. By embracing the flexibility, scalability, cost predictability and access to expertise provided by XaaS offerings, businesses can optimize costs, drive innovation and achieve sustainable growth. Whether deployed on premises or in the cloud, XaaS serves as a catalyst for success, empowering organizations to unlock the full potential of AI while maintaining financial resilience in an ever-evolving business landscape.  IBM solutions  Master your AI budget with IBM Storage as a Service and Flexible Capacity on Demand for IBM® Power®.  Whether on premises in your data center or in the IBM Cloud®, you can provision, budget and get the same customer experience from these IBM offerings.   Learn more about hybrid cloud solutions The post Mastering budget control in the age of AI: Leveraging on-premises and cloud XaaS for success  appeared first on IBM Blog.

The average lost business cost following a data breach was USD 1.3 million in 2023, according to IBM’s Cost of a Data Breach report. With the rapid emergence of real-time payments, any downtime in payments connectivity can be a significant threat. This downtime can harm a business’s reputation, as well as the global financial ecosystem. For this reason, it’s paramount that financial enterprises support their resiliency needs by adopting a robust infrastructure that is integrated across multiple environments, including the cloud, on prem and at the edge. Resiliency helps financial institutions build customer and regulator confidence Retaining customers is crucial to any business strategy, and maintaining customer trust is key to a financial institution’s success. We believe enterprises that prioritize resilience demonstrate their commitment to providing their consumers with a seamless experience in the event of disruption. In addition to maintaining customer trust, financial enterprises must maintain regulator trust as well. Regulations around the world, such as the Digital Operational Resilience Act (DORA), continue to grow. DORA is a European Union regulation that aims to establish technical standards that financial entities and their critical third-party technology service providers must implement in their ICT systems by 17 January 2025. DORA requires financial institutions to define the business recovery process, service levels and recovery times that are acceptable for their business across processes, including payments. Traditionally, this has caused covered institutions to evaluate their cybersecurity protection measures. To meet customer and regulator demands, it is critical that financial institutions are proactive and strategic about creating a cohesive strategy to modernize their payments infrastructure with resiliency and compliance at the forefront. How IBM helps clients address resiliency in payments As the need for operational resilience grows, enterprises increasingly adopt hybrid cloud strategies to store their data across multiple environments including the cloud, on prem and at the edge. By developing a workload placement strategy based on the uniqueness of a financial entity’s business processes and applications, they can optimize the output of these applications to enable the continuation of services 24/7. IBM Cloud® remains committed to providing our clients with an enterprise-grade cloud platform that can help them address resiliency, performance, security and compliance obligations. IBM Cloud also supports mission-critical workloads and addresses evolving regulations around the globe. To accelerate cloud adoption in financial services, we built IBM Cloud for Financial Services®, informed by the industry and for the industry. With security controls built into the platform, we aim to help financial entities minimize risk as they maintain and demonstrate their compliance with their regulators. With approximately 500 industry practitioners across the globe, the expertise of the IBM Payments Center® provides clients with guidance on their end-to-end payments’ modernization journey. Also, clients can use payments as a service, including checks as a service, which can help give them access to the benefits of a managed, secured cloud-based platform that can scale up and down to meet changing electronic payment and check volumes. IBM’s swift connectivity capabilities on IBM Cloud for Financial Services enable resiliency and use IBM Cloud multizone regions to help keep data secured and enable business continuity in case of advanced ransomware or cyberattacks. IBM® can help you navigate the highly interconnected payments ecosystem and build resiliency. Partner with us to reduce downtime, protect your reputation and maintain the trust of your customers and regulators. Learn how IBM can help you on your payments journey The post Prioritizing operational resiliency to reduce downtime in payments appeared first on IBM Blog.

Software as a service (SaaS) applications have become a boon for enterprises looking to maximize network agility while minimizing costs. They offer app developers on-demand scalability and faster time-to-benefit for new features and software updates.  SaaS takes advantage of cloud computing infrastructure and economies of scale to provide clients a more streamlined approach to adopting, using and paying for software. However, SaaS architectures can easily overwhelm DevOps teams with data aggregation, sorting and analysis tasks. Given the volume of SaaS apps on the market (more than 30,000 SaaS developers were operating in 2023) and the volume of data a single app can generate (with each enterprise businesses using roughly 470 SaaS apps), SaaS leaves businesses with loads of structured and unstructured data to parse. That’s why today’s application analytics platforms rely on artificial intelligence (AI) and machine learning (ML) technology to sift through big data, provide valuable business insights and deliver superior data observability. What is application analytics? Broadly speaking, application analytics refers to the process of collecting application data and performing real-time analysis of SaaS, mobile, desktop and web application performance and usage data. App analytics include: App usage analytics, which show app usage patterns (such as daily and monthly active users, most- and least-used features and geographical distribution of downloads). App performance analytics, which show how apps are performing across the network (with metrics such as response times and failure rates) and identify the cause and location of app, server or network problems. App cost and revenue analytics, which track app revenue—such as annual recurring revenue and customer lifetime value (the total profit a business can expect to make from a single customer for the duration the business relationship)—and expenditures such as customer acquisition cost (the costs associated with acquiring a new customer). Using sophisticated data visualization tools, many of which are powered by AI, app analytics services empower businesses to better understand IT operations, helping teams make smarter decisions, faster. AI in SaaS analytics Most industries have had to reckon with AI proliferation and AI-driven business practices to some extent. Roughly 42% of enterprise-scale organizations (more than 1,000 employees) have used AI for business purposes, with nearly 60% of enterprises already using AI to accelerate tech investment. And by 2026, more than 80% of companies will have deployed AI) )AI-enabled apps in their IT environments (up from only 5% in 2023). SaaS app development and management is no different. SaaS offers businesses cloud-native app capabilities, but AI and ML turn the data generated by SaaS apps into actionable insights. Modern SaaS analytics solutions can seamlessly integrate with AI models to predict user behavior and automate data sorting and analysis; and ML algorithms enable SaaS apps to learn and improve over time. Using comprehensive, AI-driven SaaS analytics, businesses can make data-driven decisions about feature enhancements, UI/UX improvements and marketing strategies to maximize user engagement and meet—or exceed—business goals.  SaaS app analytics use cases While effective for some organizations, traditional SaaS data analysis methods (such as relying solely on human data analysts to aggregate data points) sometimes fall short in handling the massive quantities of data SaaS apps produce. They may also struggle to fully leverage the predictive capabilities of app analytics. The introduction of AI and ML technologies, however, can provide more nuanced observability and more effective decision automation. AI- and ML-generated SaaS analytics enhance: 1. Data insights and reporting Application analytics help businesses monitor key performance indicators (KPIs)—such as error rates, response time, resource utilization, user retention and dependency rates, among other key metrics—to identify performance issues and bottlenecks and create a smoother user experience. AI and ML algorithms enhance these features by processing unique app data more efficiently. AI technologies can also reveal and visualize data patterns to help with feature development. If, for instance, a development team wants to understand which app features most significantly impact retention, it might use AI-driven natural language processing (NLP) to analyze unstructured data. NLP protocols will auto-categorize user-generated content (such as customer reviews and support tickets), summarize the data and offer insights into the features that keep customers returning to the app. AI can even use NLP to suggest new tests, algorithms, lines of code or entirely new app functions to increase retention. With AI and ML algorithms, SaaS developers also get granular observability into app analytics. AI-powered analytics programs can create real-time, fully customizable dashboards that provide up-to-the-minute insights into KPIs. And most machine learning tools will automatically generate summaries of complex data, making it easier for executives and other decision-makers to understand reports without needing to review the raw data themselves. 2. Predictive analytics. Predictive analytics forecast future events based on historical data; AI and ML models—such as regression analysis, neural networks and decision trees—enhance the accuracy of these predictions. An e-commerce app, for example, can predict which products will be popular during the holidays by analyzing historical purchase data from previous holiday seasons. Most SaaS analytics tools—including Google Analytics, Microsoft Azure and IBM® Instana®—offer predictive analytics features that enable developers to anticipate both market and user behavior trends  and shift their business strategy accordingly.  Predictive analytics are equally valuable for user insights. AI and ML features enable SaaS analytics software to run complex analyses of user interactions within the app (click patterns, navigation paths, feature usage and session duration, among other metrics), which ultimately helps teams anticipate user behavior. For instance, if a company wants to implement churn prediction protocols to identify at-risk users, they can use AI functions to analyze activity reduction and negative feedback patterns, two user engagement metrics that often precede churn. After the program identifies at-risk users, machine learning algorithms can suggest personalized interventions to re-engage them (a subscription service might offer discounted or exclusive content to users showing signs of disengagement). Diving deeper into user behavior data also helps businesses proactively identify app usability issues. And during unexpected disruptions (such as those caused by a natural disaster), AI and SaaS analytics provide real-time data visibility that keeps businesses running—or even improving—in challenging times.  3. Personalization and user experience optimization. Machine learning technologies are often integral to providing a personalized customer experience in SaaS applications. Using customer preferences (preferred themes, layouts and functions), historical trends and user interaction data, ML models in SaaS can dynamically tailor the content that users see based on real-time data. In other words, AI-powered SaaS apps can automatically implement adaptive interface design to keep users engaged with personalized recommendations and content experiences. News apps, for instance, can highlight articles similar to the ones a user has previously read and liked. An online learning platform can recommend courses or onboarding steps based on a user’s learning history and preferences. And notification systems can send targeted messages to each user at the time they’re likeliest to engage, making the overall experience more relevant and enjoyable. At the application level, AI can analyze user journey data to understand the typical navigation paths users take through the app and streamline navigation for the entire user base. 4. Conversion rate optimization and marketing. AI analytics tools offer businesses the opportunity to optimize conversion rates, whether through form submissions, purchases, sign-ups or subscriptions. AI-based analytics programs can automate funnel analyses (which identify where in the conversion funnel users drop off), A/B tests (where developers test multiple design elements, features or conversion paths to see which performs better) and call-to-action button optimization to increase conversions. Data insights from AI and ML also help improve product marketing and increase overall app profitability, both vital components to maintaining SaaS applications. Companies can use AI to automate tedious marketing tasks (such as lead generation and ad targeting), maximizing both advertising ROI and conversation rates. And with ML features, developers can track user activity to more accurately segment and sell products to the user base (with conversion incentives, for instance).  5. Pricing optimization. Managing IT infrastructure can be an expensive undertaking, especially for an enterprise running a large network of cloud-native applications. AI and ML features help minimize cloud expenditures (and cloud waste) by automating SaaS process responsibilities and streamlining workflows. Using AI-generated predictive analytics and real-time financial observability tools, teams can anticipate resource usage fluctuations and allocate network resources accordingly. SaaS analytics also enable decision-makers to identify underutilized or problematic assets, preventing over- and under-spending and freeing up capital for app innovations and improvements. Maximize the value of SaaS analytics data with IBM Instana Observability AI-powered application analytics give developers an advantage in today’s fast-paced, hyper-dynamic SaaS landscape, and with IBM Instana, businesses can get an industry-leading, real-time, full-stack observability solution. Instana is more than a traditional app performance management (APM) solution. It provides automated, democratized observability with AI, making it accessible to anyone across DevOps, SRE, platform engineering, ITOps and development. Instana gives companies the data that they want—with the context that they need—to take intelligent action and maximize the potential of SaaS app analytics. Explore IBM Instana Observability Book a live demo The post Maximizing SaaS application analytics value with AI appeared first on IBM Blog.

In today’s competitive business environment, firms are confronted with complex, computational issues that demand swift resolution. Such problems might be too intricate for a single system to handle or might require an extended time to resolve. For companies that need quick answers, every minute counts. Allowing problems to linger for weeks or months is not feasible for businesses determined to stay ahead of the competition. To address these challenges, enterprises across various industries, such as those in the semiconductor, life sciences, healthcare, financial services and more, have embraced high-performance computing (HPC). With HPC, enterprises are taking advantage of the speed and performance that comes with powerful computers working together. This can be especially helpful amid a steadily growing push to build AI on a larger and larger scale. While analyzing massive amounts of data might feel impossible, HPC enables the use of high-end computational resources that can perform many computations rapidly and in parallel to help businesses get insights faster. At the same time, HPC is used to help businesses bring new products to market. It is also used to better manage risks and more, which is why an increasing number of enterprises are adopting it. The role of cloud in HPC Most commonly, enterprises that run workloads with surges in activity are finding that they exceed the compute capacity available on-premises. This is an example of where cloud computing can augment on-premises HPC to transform the business’s approach to HPC with cloud resources. Cloud can help address peaks in demand during product development cycles, which might last from a short duration to a longer duration, and enable organizations to get access to the resources and capabilities that they might not have a need for around the clock. Businesses using HPC from the cloud can take advantage of the benefits of greater flexibility, enhanced scalability, better agility, improved cost efficiencies and more. Cadence uses IBM Cloud HPC Cadence is a global innovator in electronic design automation (EDA) with over 30 years of computational software experience. It has helped companies across the world design electronic products that drive today’s emerging technology, including chips. The growing demand for more chips, along with the company’s incorporation of AI and machine learning into its EDA processes means that their need for compute power is at an all-time high. For organizations in the EDA industry like Cadence, solutions that enable workloads to seamlessly shift between on premises and the cloud, while also allowing for differentiation from project to project, are key. Cadence uses IBM Cloud® HPC with IBM Spectrum® LSF as the workload scheduler to support the development of chip and system design software, which requires innovative solutions, powerful compute resources and advanced security support. By using IBM Cloud HPC, Cadence reports improved time-to-solution, performance enhancements, cost reductions and streamlined workload management. Additionally, Cadence understands firsthand that moving to the cloud can require new knowledge and capabilities that not every company possesses. The Cadence Cloud comprehensive portfolio aims to help customers across the world use the possibilities of the cloud with Cadence Managed Cloud Service as a turnkey solution ideal for start-ups and small and medium customers, and with the customer-managed cloud option known as Cloud Passport to enable Cadence tools for large enterprise customers. Cadence is dedicated to giving its customers an easy path to the cloud by connecting them with knowledgeable service providers, such as IBM®, whose platforms can be used to deploy Cadence tools in cloud environments. For enterprises that want to drive innovation at scale, the Cadence Cloud Passport model can deliver access to cloud-ready software tools for use on IBM Cloud. Taking a hybrid cloud approach to HPC Traditionally, HPC systems were built on-premises. However, the large models and large workloads that exist today are often not compatible with the hardware that most companies have on premises. Given the high up-front costs of obtaining GPUs, CPUs and networking, as well as those of building the data center infrastructures needed to efficiently run compute at scale, many companies have used cloud infrastructure providers that have already made massive investments in their hardware. To realize the full value of public cloud and on-premises infrastructures, many organizations are adopting a hybrid cloud architecture that is focused on the mechanics of transforming portions of a company’s on-premises data center into private cloud infrastructure. By adopting a hybrid cloud approach to HPC where cloud and on premises are used together, organizations can use the strengths of both, allowing organizations to achieve the agility, flexibility and security required to meet their demands. For example, IBM Cloud® HPC can help organizations flexibly manage compute-intensive workloads on-premises. With security and controls built into the platform, IBM Cloud HPC also allows organizations to consume HPC as a fully managed service while helping them address third- and fourth-party risks. Looking ahead By using hybrid cloud services through platforms like IBM Cloud HPC, enterprises can solve many of their most difficult challenges. As organizations continue to embrace HPC, they should consider how a hybrid cloud approach can complement traditional on-premises HPC infrastructure deployments. Learn more about how IBM can help you take a hybrid cloud approach to HPC The post Agility, flexibility and security: The value of cloud in HPC appeared first on IBM Blog.

Field programmable gate arrays (FPGAs) and microcontroller units (MCUs) are two types of commonly compared integrated circuits (ICs) that are typically used in embedded systems and digital design. Both FPGAs and microcontrollers can be thought of as “small computers” that can be integrated into devices and larger systems. As processors, the primary difference between FPGAs and microcontrollers comes down to programmability and processing capabilities. While FPGAs are more powerful and more versatile, they are also more expensive. Microcontrollers are less customizable, but also less costly. In many applications, microcontrollers are exceptionally capable and cost-effective. However, for certain demanding or developing applications, like those requiring parallel processing, FPGAs are necessary. Unlike microcontrollers, FPGAs offer reprogrammability on the hardware level. Their unique design allows users to configure and reconfigure the chip’s architecture depending on the task. FPGA design can also handle parallel inputs simultaneously, whereas microcontrollers can only read one line of code at a time. An FPGA can be programmed to perform the functions of a microcontroller; however, a microcontroller cannot be reprogrammed to perform as an FPGA.  What is a field programmable gate array (FPGA)? First introduced by manufacturer Xilinx in 1985, FPGAs are highly valued for their versatility and processing power. As a result, they are a preferred choice in many high-performance computing (HPC), digital signal processing (DSP) and prototyping applications. Unlike traditional application-specific integrated circuits (ASICs), FPGAs are designed to be configured (and reconfigured) “in the field” after the initial manufacturing process is complete. While customization is the FPGAs greatest value offering, it should be noted that FPGAs not only allow for programmability, they require it. Unlike ASICs, FPGAs are not “out-of-the-box” solutions, and they must be configured prior to use with a hardware description language (HDL), such as verilog or VHDL. Programming an FPGA requires specialized knowledge, which can increase costs and delay deployments. While some FPGAs do offer non-volatile memory that can retain programming instructions when powered off, typically FPGAs must be configured on start-up.   FPGA benefits Despite these challenges, FPGAs remain useful in applications requiring high-performance, low-latency and real-time flexibility. FPGAs are particularly well suited for applications requiring the following: Rapid prototyping: FPGAs can be quickly configured into multiple types of customized digital circuits, allowing for expedited deployments, assessments and modifications without the need for costly and time-consuming fabrication processes.  Hardware acceleration: Demanding applications benefit from the FPGA’s parallel-processing capabilities. FPGAs may offer significant performance improvements for computationally intensive tasks, such as signal processing, cryptography, and machine learning algorithms. Customization: FPGAs are a flexible hardware solution that can be easily optimized to meet specific project requirements.  Longevity: FPGA-based designs may benefit from a longer hardware lifespan as FPGAs can be updated and reconfigured to meet evolving project demands and technology standards.  FPGA components To achieve reconfigurability, FPGAs are composed of an array of programmable logic blocks interconnected by a programmable routing fabric. The main components of a typical FPGA are as follows: Configurable logic blocks (CLBs): CLBs provide compute functionality and may contain a small number of primitive logic elements, such as logic gates, small look-up tables (LUTs), multiplexors and flip-flops for data storage.  Programmable interconnects: Made up of wire segments joined by electrically programmable switches, these linkages provide routing pathways between the various FPGA resources, allowing for different configurations and the creation of custom digital circuits.  I/O Blocks (IOBs): The interface between an FPGA and other external devices is enabled by input output (I/O) blocks, which allow the FPGA to receive data from and control peripherals  FPGA use cases Versatile by nature, FPGAs are common among a wide variety of industries and applications: Aerospace and defense: Offering high-speed parallel processing valuable for data acquisition, FPGAs are a preferred choice for radar systems, image processing and secure communications.  Industrial control systems (ICS): Industrial control systems used to monitor infrastructure—like power grids, oil refineries and water treatment plants—use FPGAs that can be easily optimized to meet the unique needs of various industries. In these critical industries, FPGAs can be used to implement various automations and hardware-based encryption features for efficient cybersecurity. ASIC development: FPGAs are often used in the prototyping of new ASIC chips.  Automotive: Advanced signal processing also makes FPGAs well-suited for automotive applications, including advanced driver assistance systems (ADAS), sensor fusion and GPS. Data centers: FPGAs add value to data centers by optimizing high-bandwidth, low-latency servers, networking and storage infrastructure. FPGA features Processing core: Configurable logic blocks Memory: External memory interface  Peripherals: Configurable I/O blocks Programming: Hardware description language (VHDL, Verilog)  Reconfigurability: Highly reconfigurable, reprogrammable logic What is a microcontroller? Microcontrollers are a type of compact, ready-made ASIC containing a processor core (or cores), memory (RAM), and erasable programmable read-only memory (EPROM) for storing the custom programs that run on the microcontroller. Known as a “system-on-a-chip (SoC)” solution, microcontrollers are essentially small computers integrated into a single piece of hardware that can be used independently or in larger embedded systems.  Consumer-grade microcontrollers, such as the Arduino Starter Kit or Microchip Technology PIC, can be configured using assembly language or common programming languages (C, C++), and they are favored by hobbyists and educators for their cost-effective accessibility. Microcontrollers are also capable of handling more complex and critical tasks and are common in industrial applications. However, decreased processing power and memory resources can limit the microcontroller’s efficacy in more demanding applications.  Microcontroller benefits Despite their limitations, microcontrollers offer many advantages, including the following: Compact design: Microcontrollers integrate all necessary components onto a small, single chip offering a small footprint valuable in applications where size and weight are a priority.  Energy efficiency: Designed to operate on low power, microcontrollers are well suited for battery-powered devices and other applications where power consumption is a concern. Cost-effective: Microcontrollers offer a complete SoC solution that reduces the need for additional peripherals and components. Low-cost, general-purpose microcontrollers can greatly reduce overall project expenses.  Flexibility: Although not as versatile as FPGAs, microcontrollers are programmable for a wide range of various applications. While they cannot be reprogrammed on the hardware level, microcontrollers can be easily reconfigured, updated and optimized on a software level.  Microcontroller components When reprogrammability is not a priority, self-contained microcontrollers offer a compact and capable alternative. The following are the key components of a microcontroller:  Central processing unit (CPU): Colloquially referred to as the “brain,” the central processing unit (CPU) serves as the core component responsible for executing instructions and controlling operations.   Memory: Microcontrollers contain both volatile memory (RAM), which stores temporary data that may be lost if the system loses power, and non-volatile memory (ROM, FLASH) for storing the microcontroller’s programming code. Peripherals: Depending on the intended application, a microcontroller may contain various peripheral components, such as input/output (I/O) interfaces like timers, counters, analog-to-digital converters (ADCs) and communication protocols (UART, SPI, I2C). Microcontroller use cases Unlike FPGAs, small, affordable, and non-volatile microcontrollers are ubiquitous in modern electronics, frequently deployed for specific tasks, including the following: Automotive systems: Microcontrollers are used in engine control, airbag deployment and in-car infotainment systems.   Consumer electronics: Microcontrollers are critical to smartphones, smart TVs and other home appliances, especially devices that integrate into the Internet of Things (IoT). Industrial automation: Microcontrollers are well-suited to industrial applications, such as controlling machinery, monitoring systems and process automation.  Medical devices: Microcontrollers are often deployed in life-saving devices, such as pacemakers, blood glucose monitors and diagnostic tools.  Microcontroller features Processing core: Fixed CPU Memory: Integrated RAM and ROM/Flash  Peripherals: Built-in I/O interfaces for Programming: Software (C, Assembly)  Reconfigurability: Limited, firmware updates Key differences between FPGAs and microcontrollers When comparing FPGAs and microcontrollers, it is important to consider a number of key differences, including hardware architecture, processing capabilities, power consumption, and developer requirements.  Hardware structure FPGA: Highly configurable programmable logic blocks and interconnects, allowing for reprogrammable and custom digital circuits.  Microcontroller: Fixed architecture with predefined components (CPU, memory, peripherals) integrated into a single chip.  Processing capabilities FPGA: Advanced parallel processing enables multiple simultaneous operations. Microcontroller: Designed for sequential processing, microcontrollers can only execute instructions one at a time. Power consumption FPGA: Typically consumes more power than microcontrollers. Microcontroller: Optimized for low power consumption, suitable for battery-powered applications. Programming FPGA: Require specialized knowledge in hardware description languages to configure and debug. Microcontroller: Can be programmed using software development languages including Javascript, Python, C, C++ and assembly languages.  Cost FPGA: Offering increased power, but requiring advanced skills, FPGA hardware is often more expensive with the additional cost of higher power consumption and specialized programmer talent.  Microcontroller: Generally, a more cost-effective solution with off-the-shelf availability, lower power consumption and support for more accessible programming languages. Versatility FPGA: The FPGA is far more flexible than the microcontroller, allowing for customization on the hardware level. Microcontroller: While suitable for a broad range of applications, microcontrollers offer only superficial customization compared to FPGAs. Explore IBM infrastructure solutions  Whether looking for a versatile and powerful FPGA processor or a compact and cost-effective microcontroller, consider how IBM can help take your business to the next level with cutting-edge infrastructure solutions. New IBM FlashSystem 5300 provides improved performance and cyber-resilience. New IBM Storage Assurance simplifies storage ownership and helps you address IT lifecycle challenges. Explore IBM Storage FlashSystem The post Field programmable gate arrays (FPGAs) vs. microcontrollers: What’s the difference? appeared first on IBM Blog.

What is a CPU? The central processing unit (CPU) is the computer’s brain. It handles the assignment and processing of tasks and manages operational functions that all types of computers use. CPU types are designated according to the kind of chip that they use for processing data. There’s a wide variety of processors and microprocessors available, with new powerhouse processors always in development. The processing power CPUs provide enables computers to engage in multitasking activities. Before discussing the types of CPUs available, we should clarify some basic terms that are essential to our understanding of CPU types. Key CPU terms There are numerous components within a CPU, but these aspects are especially critical to CPU operation and our understanding of how they operate: Cache: When it comes to information retrieval, memory caches are indispensable. Caches are storage areas whose location allows users to quickly access data that’s been in recent use. Caches store data in areas of memory built into a CPU’s processor chip to reach data retrieval speeds even faster than random access memory (RAM) can achieve. Caches can be created through software development or hardware components. Clock speed: All computers are equipped with an internal clock, which regulates the speed and frequency of computer operations. The clock manages the CPU’s circuitry through the transmittal of electrical pulses. The delivery rate of those pulses is termed clock speed, which is measured in Hertz (Hz) or megahertz (MHz). Traditionally, one way to increase processing speed has been to set the clock to run faster than normal. Core: Cores act as the processor within the processor. Cores are processing units that read and carry out various program instructions. Processors are classified according to how many cores are embedded into them. CPUs with multiple cores can process instructions considerably faster than single-core processors. (Note: The term “Intel® Core™” is used commercially to market Intel’s product line of multi-core CPUs.) Threads: Threads are the shortest sequences of programmable instructions that an operating system’s scheduler can independently administer and send to the CPU for processing. Through multithreading—the use of multiple threads running simultaneously—a computer process can be run concurrently. Hyper-threading refers to Intel’s proprietary form of multithreading for the parallelization of computations. Other components of the CPU In addition to the above components, modern CPUs typically contain the following: Arithmetic logic unit (ALU): Carries out all arithmetic operations and logical operations, including math equations and logic-based comparisons. Both types are tied to specific computer actions. Buses: Ensures proper data transfer and data flow between components of a computer system. Control unit: Contains intensive circuitry that controls the computer system by issuing a system of electrical pulses and instructs the system to carry out high-level computer instructions. Instruction register and pointer: Displays location of the next instruction set to be executed by the CPU. Memory unit: Manages memory usage and the flow of data between RAM and the CPU. Also, the memory unit supervises the handling of cache memory. Registers: Provides built-in permanent memory for constant, repeated data needs that must be handled regularly and immediately. How do CPUs work? CPUs use a type of repeated command cycle that’s administered by the control unit in association with the computer clock, which provides synchronization assistance. The work a CPU does occurs according to an established cycle (called the CPU instruction cycle). The CPU instruction cycle designates a certain number of repetitions, and this is the number of times the basic computing instructions will be repeated, as enabled by that computer’s processing power. The three basic computing instructions are as follows: Fetch: Fetches occur anytime data is retrieved from memory. Decode: The decoder within the CPU translates binary instructions into electrical signals, which engage with other parts of the CPU. Execute: Execution occurs when computers interpret and carry out a computer program’s set of instructions. Basic attempts to generate faster processing speeds have led some computer owners to forego the usual steps involved in creating high-speed performance, which normally require the application of more memory cores. Instead, these users adjust the computer clock so it runs faster on their machine(s). The “overclocking” process is analogous to “jailbreaking” smartphones so their performance can be altered. Unfortunately, like jailbreaking a smartphone, such tinkering is potentially harmful to the device and is roundly disapproved by computer manufacturers. Types of central processing units CPUs are defined by the processor or microprocessor driving them: Single-core processor: A single-core processor is a microprocessor with one CPU on its die (the silicon-based material to which chips and microchips are attached). Single-core processors typically run slower than multi-core processors, operate on a single thread and perform the instruction cycle sequence only once at a time. They are best suited to general-purpose computing. Multi-core processor: A multi-core processor is split into two or more sections of activity, with each core carrying out instructions as if they were completely distinct computers, although the sections are technically located together on a single chip. For many computer programs, a multi-core processor provides superior, high-performance output. Embedded processor: An embedded processor is a microprocessor expressly engineered for use in embedded systems. Embedded systems are small and designed to consume less power and be contained within the processor for immediate access to data. Embedded processors include microprocessors and microcontrollers. Dual-core processor: A dual-core processor is a multi-core processor containing two microprocessors that act independently from each other. Quad-core processor: A quad-core processor is a multi-core processor that has four microprocessors functioning independently. Octa-core: An octa-core processor is a multi-core processor that has eight microprocessors functioning independently. Deca-core processor: A deca-core processor is an integrated circuit that has 10 cores on one die or per package. Leading CPU manufacturers and the CPUs they make Although several companies manufacture products or develop software that supports CPUs, that number has dwindled down to just a few major players in recent years. The two major companies in this area are Intel and Advanced Micro Devices (AMD). Each uses a different type of instruction set architecture (ISA). Intel processors use a complex instruction set computer (CISC) architecture. AMD processors follow a reduced instruction set computer (RISC) architecture. Intel: Intel markets processors and microprocessors through four product lines. Its premium, high-end line is Intel Core. Intel’s Xeon® processors are targeted toward offices and businesses. Intel’s Celeron® and Intel Pentium® lines are considered slower and less powerful than the Core line. Advanced Micro Devices (AMD): AMD sells processors and microprocessors through two product types: CPUs and APUs (which stands for accelerated processing units). APUs are CPUs that have been equipped with proprietary Radeon™ graphics. AMD’s Ryzen™ processors are high-speed, high-performance microprocessors intended for the video game market. Athlon™ processors was formerly considered AMD’s high-end line, but AMD now uses it as a basic computing alternative. Arm: Although Arm doesn’t actually manufacture equipment, it does lease out its valued, high-end processor designs and/or other proprietary technologies to other companies who do make equipment. Apple, for example, no longer uses Intel chips in Mac® CPUs but makes its own customized processors based on Arm designs. Other companies are following this example. Related CPU and processor concepts Graphics processing unit (GPUs) While the term “graphics processing unit” includes the word “graphics,” this phrasing does not truly capture what GPUs are about, which is speed. In this instance, its increased speed is the cause of accelerating computer graphics. The GPU is a type of electronic circuit with immediate applications for PCs, smartphones and video game consoles, which was their original use. Now GPUs also serve purposes unrelated to graphics acceleration, like cryptocurrency mining and the training of neural networks. Microprocessors The quest for computer miniaturization continued when computer science created a CPU so small that it could be contained within a small integrated circuit chip, called the microprocessor. Microprocessors are designated by the number of cores they support. A CPU core is “the brain within the brain,” serving as the physical processing unit within a CPU. Microprocessors can contain multiple processors. Meanwhile, a physical core is a CPU built right into a chip, but which only occupies one socket, thus enabling other physical cores to tap into the same computing environment. Output devices Computing would be a vastly limited activity without the presence of output devices to execute the CPU’s sets of instruction. Such devices include peripherals, which attach to the outside of a computer and vastly increase its functionality. Peripherals provide the means for the computer user to interact with the computer and get it to process instructions according to the computer user’s wishes. They include desktop essentials like keyboards, mice, scanners and printers. Peripherals are not the only attachments common to the modern computer. There are also input/output devices in wide use and they both receive information and transmit information, like video cameras and microphones. Power consumption Several issues are impacted by power consumption. One of them is the amount of heat produced by multi-core processors and how to dissipate excess heat from that device so the computer processor remains thermally protected. For this reason, hyperscale data centers (which house and use thousands of servers) are designed with extensive air-conditioning and cooling systems. There are also questions of sustainability, even if we’re talking about a few computers instead of a few thousand. The more powerful the computer and its CPUs, the more energy will be required to support its operation—and in some macro-sized cases, that can mean gigahertz (GHz) of computing power. Specialized chips The most profound development in computing since its origins, artificial intelligence (AI) is now impacting most if not all computing environments. One development we’re seeing in the CPU space is the creation of specialty processors that have been built specifically to handle the large and complex workloads associated with AI (or other specialty purposes): Such equipment includes the Tensor Streaming Processor (TSP), which handles machine learning (ML) tasks in addition to AI applications. Other products equally suited to AI work are the AMD Ryzen Threadripper™ 3990X 64-Core processor and the Intel Core i9-13900KS Desktop Processor, which uses 24 cores. For an application like video editing, many users opt for the Intel Core i7 14700KF 20-Core, 28-thread CPU. Still others select the Ryzen 9 7900X, which is considered AMD’s best CPU for video editing purposes. In terms of video game processors, the AMD Ryzen 7 5800X3D features a 3D V-Cache technology that helps it elevate and accelerate game graphics. For general-purpose computing, such as running an OS like Windows or browsing multimedia websites, any recent-model AMD or Intel processor should easily handle routine tasks. Transistors Transistors are hugely important to electronics in general and to computing in particular. The term is a mix of “transfer resistance” and typically refers to a component made of semiconductors used to limit and/or control the amount of electrical current flowing through a circuit. In computing, transistors are just as elemental. The transistor is the basic building unit behind the creation of all microchips. Transistors help comprise the CPU, and they’re what makes the binary language of 0s and 1s that computers use to interpret Boolean logic. The next wave of CPUs Computer scientists are always working to increase the output and functionality of CPUs. Here are some projections about future CPUs: New chip materials: The silicon chip has long been the mainstay of the computing industry and other electronics. The new wave of processors (link resides outside ibm.com) will take advantage of new chip materials that offer increased performance. These include carbon nanotubes (which display excellent thermal conductivity through carbon-based tubes approximately 100,000 times smaller than the width of a human hair), graphene (a substance that possesses outstanding thermal and electrical properties) and spintronic components (which rely on the study of the way electrons spin, and which could eventually produce a spinning transistor). Quantum over binary: Although current CPUs depend on the use of a binary language, quantum computing will eventually change that. Instead of binary language, quantum computing derives its core principles from quantum mechanics, a discipline that has revolutionized the study of physics. In quantum computing, binary digits (1s and 0s) can exist in multiple environments (instead of in two environments currently). And because this data will live in more than one location, fetches will become easier and faster. The upshot of this for the user will be a marked increase in computing speed and an overall boost in processing power. AI everywhere: As artificial intelligence continues to make its profound presence felt—both in the computing industry and in our daily lives—it will have a direct influence on CPU design. As the future unfolds, expect to see an increasing integration of AI functionality directly into computer hardware. When this happens, we’ll experience AI processing that’s significantly more efficient. Further, users will notice an increase in processing speed and devices that will be able to make decisions independently in real time. While we wait for that hardware implementation to occur, chip manufacturer Cerebras has already unveiled a processor its makers claim to be the “fastest AI chip in the world” (link resides outside ibm.com). Its WSE-3 chip can train AI models with as many as 24 trillion parameters. This mega-chip contains four trillion transistors, in addition to 900,000 cores. CPUs that offer strength and flexibility Companies expect a lot from the computers they invest in. In turn, those computers rely upon having a CPUs with enough processing power to handle the challenging workloads found in today’s data-intensive business environment. Organizations need workable solutions that can change as they change. Smart computing depends upon having equipment that capably supports your mission, even as that work evolves. IBM servers offer strength and flexibility, so you can concentrate on the job at hand. Find the IBM servers you need to get the results your organization relies upon—both today and tomorrow. Explore IBM servers The post Types of central processing units (CPUs) appeared first on IBM Blog.

In the dynamic landscape of digital commerce, seamless integration and efficient communication drive the success of buyers, sellers and logistics providers. The Open Network for Digital Commerce (ONDC) platform stands as a revolutionary initiative to streamline the digital commerce ecosystem in India. When coupled with the robust capabilities of IBM API Connect®, this integration presents a game-changing opportunity for buyers, sellers and logistics partners to thrive in the digital marketplace. Let’s delve into its benefits and potential impact on business. Introduction to ONDC and IBM API Connect  The ONDC platform, envisioned by the Government of India, aims to create an inclusive and interoperable digital commerce ecosystem. It facilitates seamless integration among various stakeholders—including buyers, sellers, logistics providers and financial institutions —fostering transparency, efficiency and accessibility in digital commerce.  IBM API Connect is a comprehensive API management solution that enables organizations to create, secure, manage and analyze APIs throughout their lifecycle. It provides capabilities for designing, deploying and consuming APIs, thereby putting up secure and efficient communication between different applications and systems.  Benefits for buyers and sellers apps  Enhanced integration: Integration with IBM API Connect allows buyers and sellers apps to seamlessly connect with the ONDC platform, enabling real-time data exchange and transaction processing. This makes for smoother operations and improved user experience for buyers and sellers alike.  Expanded services: Buyers and sellers apps can leverage the ONDC platform’s wide range of services, including inventory management, order processing and payment solutions. Integration with IBM API Connect enables easy access to these services, empowering apps to offer comprehensive solutions to their users.  Improved efficiency: By automating processes and streamlining communication, the integration enhances the overall efficiency of buyers and sellers apps. Tasks such as inventory updates, order tracking and payment reconciliation can be performed seamlessly, reducing manual effort and minimizing errors.  Better data insights: IBM API Connect provides advanced analytics capabilities that enable buyers and sellers apps to gain valuable insights into customer behavior, market trends and inventory management. By leveraging these insights, apps can optimize their operations, personalize user experiences and drive business growth. Impact on business and logistics  Operational efficiency: The integration of IBM API Connect with the ONDC platform streamlines operations for buyers, sellers and logistics partners, reducing costs and improving productivity. Automated processes and real-time data exchange enable faster order fulfilment and smoother logistics operations.  Customer experience boost: Seamless communication between buyers and sellers apps and the ONDC platform translates into a better customer experience. From faster order processing to accurate inventory information, customers benefit from a more efficient and transparent shopping experience.  Business growth: By leveraging the integrated capabilities of IBM API Connect and the ONDC platform, buyers and sellers apps can expand their reach, attract more customers and increase sales. The ability to offer a seamless and comprehensive shopping experience gives apps a competitive edge in the market.  Logistics optimization: Logistics providers can also benefit from this integration by gaining access to real-time shipment data, optimizing delivery routes and improving inventory management. This leads to faster delivery times, reduced transportation costs and enhanced customer satisfaction.  Enhanced integration with IBM API Connect  The integration of IBM API Connect with the ONDC network platform represents a significant advancement in the digital commerce ecosystem. Buyers, sellers and logistics partners stand to benefit from enhanced integration, expanded services, improved efficiency and valuable data insights.   As businesses embrace this integration, they can expect to see tangible impacts on operational efficiency, customer experience and overall business growth. By leveraging the combined capabilities of IBM API Connect and the ONDC platform, stakeholders can navigate the complexities of digital commerce with confidence and unlock new opportunities for success.  Explore IBM API Connect Try it for free The post Streamlining digital commerce: Integrating IBM API Connect with ONDC  appeared first on IBM Blog.

The new era of generative AI has spurred the exploration of AI use cases to enhance productivity, improve customer service, increase efficiency and scale IT modernization. Recent research commissioned by IBM® indicates that as many as 42% of surveyed enterprise-scale businesses have actively deployed AI, while an additional 40% are actively exploring the use of AI technology. But the rates of exploration of AI use cases and deployment of new AI-powered tools have been slower in the public sector because of potential risks. However, the latest CEO Study by the IBM Institute for the Business Value found that 72% of the surveyed government leaders say that the potential productivity gains from AI and automation are so great that they must accept significant risk to stay competitive. Driving innovation for tax agencies with trust in mind Tax or revenue management agencies are a part of the public sector that might likely benefit from the use of responsible AI tools. Generative AI can revolutionize tax administration and drive toward a more personalized and ethical future. But tax agencies must adopt AI tools with adequate oversight and governance to mitigate risks and build public trust. These agencies have a myriad of complex challenges unique to each country, but most of them share the goal of increasing efficiency and providing the transparency that taxpayers demand. In the world of government agencies, risks associated to the deployment of AI present themselves in many ways, often with higher stakes than in the private sector. Mitigating data bias, unethical use of data, lack of transparency or privacy breaches is essential. Governments can help manage and mitigate these risks by relying on IBM’s five fundamental properties for trustworthy AI: explainability, fairness, transparency, robustness and privacy. Governments can also create and execute AI design and deployment strategies that keep humans at the hearth of the decision-making process. Exploring the views of global tax agency leaders To explore the point of view of global tax agency leaders, the IBM Center for The Business of Government, in collaboration with the American University Kogod School of Business Tax Policy Center, organized a series of roundtables with key stakeholders and released a report exploring AI and taxes in the modern age. Drawing on insights from academics and tax experts from around the world, the report helps us understand how these agencies can harness technology to improve efficiencies and create a better experience for taxpayers. The report details the potential benefits of scaling the use of AI by tax agencies, including enhancing customer service, detecting threats faster, identifying and tackling tax scams effectively and allowing citizens to access benefits faster. Since the release of the report, a subsequent roundtable allowed global tax leaders to explore what is next in their journey to bring tax agencies around the globe closer to the future. At both gatherings, participants emphasized the importance of effective governance and risk management. Responsible AI services improve taxpayer experiences According to the FTA’s Tax Administration 2023 report, 85% of individual taxpayers and 90% of businesses now file taxes digitally. And 80% of tax agencies around the world are implementing leading-edge techniques to capture taxpayer data, with over 60% using virtual assistants. The FTA research indicates that this represents a 30% increase from 2018. For tax agencies, virtual assistants can be a powerful way to reduce waiting time to answer citizen inquiries; 24/7 assistants, such as watsonx™’s advanced AI chatbots, can help tax agencies by decentralizing tax support and reducing errors to prevent incorrect processing of tax filings. The use of these AI assistants also helps streamline fast, accurate answers that deliver elevated experiences with measurable cost savings. It also allows for compliance-by-design tax systems, providing early warnings of incidental errors made by taxpayers that can contribute to significant tax losses for governments if left unresolved. However, these advanced AI and generative AI applications come with risks, and agencies must address concerns around data privacy and protection, reliability, tax rights and hallucinations from generative models. Furthermore, biases against marginalized groups remain a risk. Current risk mitigation strategies (including having human-in-system roles and robust training data) are not necessarily enough. Every country needs to independently determine appropriate risk management strategies for AI, accounting for the complexity of their tax policies and public trust. What’s next? Whether using existing large language models or creating their own, global tax leaders should prioritize AI governance frameworks to manage risks, mitigate damage to their reputation and support their compliance programs. This is possible by training generative AI models using their own quality data and by having transparent processes with safeguards that identify and alert for risk mitigation and for instances of drift and toxic language. Tax agencies should make sure that technology delivers benefits and produces results that are transparent, unbiased and appropriate. As leaders of these agencies continue to scale the use of generative AI, IBM can help global tax agency leaders deliver a personalized and supportive experience for taxpayers. IBM’s decades of work with the largest tax agencies around the world, paired with leading AI technology with watsonx™ and watsonx.governance™, can help scale and accelerate the responsible and tailored deployment of governed AI in tax agencies. Learn more about how watsonx can help usher in governments into the future. The post Responsible AI can revolutionize tax agencies to improve citizen services appeared first on IBM Blog.

In the realm of software development, efficiency and innovation are of paramount importance. As businesses strive to deliver cutting-edge solutions at an unprecedented pace, generative AI is poised to transform every stage of the software development lifecycle (SDLC). A McKinsey study shows that software developers can complete coding tasks up to twice as fast with generative AI. From use case creation to test script generation, generative AI offers a streamlined approach that accelerates development, while maintaining quality. This ground-breaking technology is revolutionizing software development and offering tangible benefits for businesses and enterprises. Bottlenecks in the software development lifecycle Traditionally, software development involves a series of time-consuming and resource-intensive tasks. For instance, creating use cases require meticulous planning and documentation, often involving multiple stakeholders and iterations. Designing data models and generating Entity-Relationship Diagrams (ERDs) demand significant effort and expertise. Moreover, techno-functional consultants with specialized expertise need to be onboarded to translate the business requirements (for example, converting use cases into process interactions in the form of sequence diagrams). Once the architecture is defined, translating it into backend Java Spring Boot code adds another layer of complexity. Developers must write and debug code, a process that is prone to errors and delays. Crafting frontend UI mock-ups involves extensive design work, often requiring specialized skills and tools. Testing further compounds these challenges. Writing test cases and scripts manually is laborious and maintaining test coverage across evolving codebases is a persistent challenge. As a result, software development cycles can be prolonged, hindering time-to-market and increasing costs. In summary, traditional SDLC can be riddled with inefficiencies. Here are some common pain points: Time-consuming Tasks: Creating use cases, data models, Entity Relationship Diagrams (ERDs), sequence diagrams and test scenarios and test cases creation often involve repetitive, manual work. Inconsistent documentation: Documentation can be scattered and outdated, leading to confusion and rework. Limited developer resources: Highly skilled developers are in high demand and repetitive tasks can drain their time and focus. The new approach: IBM watsonx to the rescue Tata Consultancy Services, in partnership with IBM®, developed a point of view that incorporates IBM watsonx™. It can automate many tedious tasks and empower developers to focus on innovation. Features include: Use case creation: Users can describe a desired feature in natural language, then watsonx analyses the input and drafts comprehensive use cases to save valuable time. Data model creation: Based on use cases and user stories, watsonx can generate robust data models representing the software’s data structure. ERD generation: The data model can be automatically translated into a visual ERD, providing a clear picture of the relationships between entities. DDL script generation: Once the ERD is defined, watsonx can generate the DDL scripts for creating the database. Sequence diagram generation: watsonx can automatically generate the visual representation of the process interactions of a use case and data models, providing a clear understanding of the business process. Back-end code generation: watsonx can translate data models and use cases into functional back-end code, like Java Springboot. This doesn’t eliminate developers, but allows them to focus on complex logic and optimization. Front-end UI mock-up generation: watsonx can analyze user stories and data models to generate mock-ups of the software’s user interface (UI). These mock-ups help visualize the application and gather early feedback. Test case and script generation: watsonx can analyse code and use cases to create automated test cases and scripts, thereby boosting software quality. Efficiency, speed, and cost savings All of these watsonx automations lead to benefits, such as: Increased developer productivity: By automating repetitive tasks, watsonx frees up developers’ time for creative problem-solving and innovation. Accelerated time-to-market: With streamlined processes and automated tasks, businesses can get their software to market quicker, capitalizing on new opportunities. Reduced costs: Less manual work translates to lower development costs. Additionally, catching bugs early with watsonx-powered testing saves time and resources. Embracing the future of software development TCS and IBM believe that generative AI is not here to replace developers, but to empower them. By automating the mundane tasks  and generating artifacts throughout the SDLC, watsonx paves the way for faster, more efficient and more cost-effective software development. Embracing platforms like IBM watsonx is not just about adopting new technology, it’s about unlocking the full potential of efficient software development in a digital age. Learn more about TCS – IBM partnership The post Empower developers to focus on innovation with IBM watsonx appeared first on IBM Blog.

Virtually every organization recognizes the power of data to enhance customer and employee experiences and drive better business decisions. Yet, as data becomes more valuable, it’s also becoming harder to protect. Companies continue to create more attack surfaces with hybrid models, scattering critical data across cloud, third-party and on-premises locations, while threat actors constantly devise new and creative ways to exploit vulnerabilities. In response, many organizations are focusing more on data protection, only to find a lack of formal guidelines and advice. While every data protection strategy is unique, below are several key components and best practices to consider when building one for your organization. What is a data protection strategy? A data protection strategy is a set of measures and processes to safeguard an organization’s sensitive information from data loss and corruption. Its principles are the same as those of data protection—to protect data and support data availability. To fulfill these principles, data protection strategies generally focus on the following three areas: Data security—protecting digital information from unauthorized access, corruption or theft throughout its entire lifecycle. Data availability—ensuring critical data is available for business operations even during a data breach, malware or ransomware attack. Access control—making critical data accessible only to employees who need it and not to those who don’t. Data protection’s emphasis on accessibility and availability is one of the main reasons it differs from data security. While data security focuses on protecting digital information from threat actors and unauthorized access, data protection does all that and more. It supports the same security measures as data security but also covers authentication, data backup, data storage and achieving regulatory compliance, as in the European Union’s General Data Protection Regulation (GDPR). Most data protection strategies now have traditional data protection measures, like data backups and restore functions, as well as business continuity and disaster recovery (BCDR) plans, such as disaster recovery as a service (DRaaS). Together, these comprehensive approaches not only deter threat actors but also standardize the management of sensitive data and corporate information security and limit any business operations lost to downtime. Why it’s important for your security strategy Data powers much of the world economy—and unfortunately, cybercriminals know its value. Cyberattacks that aim to steal sensitive information continue to rise. According to IBM’s Cost of a Data Breach, the global average cost to remediate a data breach in 2023 was USD 4.45 million, a 15 percent increase over three years. These data breaches can cost their victims in many ways. Unexpected downtime can lead to lost business, a company can lose customers and suffer significant reputational damage, and stolen intellectual property can hurt a company’s profitability, eroding its competitive edge. Data breach victims also frequently face steep regulatory fines or legal penalties. Government regulations, such as the General Data Protection Regulation (GDPR), and industry regulations, such as the Health Insurance Portability and Accounting Act (HIPAA), oblige companies to protect their customers’ personal data. Failure to comply with these data protection laws can result in hefty fines. In May 2023, Ireland’s data protection authority imposed a USD 1.3 billion fine on the California-based Meta for GDPR violations. Unsurprisingly, companies are increasingly prioritizing data protection within their cybersecurity initiatives, realizing that a robust data protection strategy not only defends against potential data breaches but also ensures ongoing compliance with regulatory laws and standards. Even more, a good data protection strategy can improve business operations and minimize downtime in a cyberattack, saving critical time and money. Key components of data protection strategies  While every data protection strategy is different (and should be tailored to the specific needs of your organization), there are several solutions you should cover. Some of these key components include: Data lifecycle management  Data lifecycle management (DLM) is an approach that helps manage an organization’s data throughout its lifecycle—from data entry to data destruction. It separates data into phases based on different criteria and moves through these stages as it completes different tasks or requirements. The phases of DLM include data creation, data storage, data sharing and usage, data archiving, and data deletion. A good DLM process can help organize and structure critical data, particularly when organizations rely on diverse types of data storage. It can also help them reduce vulnerabilities and ensure data is efficiently managed, compliant with regulations, and not at risk of misuse or loss. Data access management controls Access controls help prevent unauthorized access, use or transfer of sensitive data by ensuring that only authorized users can access certain types of data. They keep out threat actors while still allowing every employee to do their jobs by having the exact permissions they need and nothing more. Organizations can use role-based access controls (RBAC), multi-factor authentication (MFA) or regular reviews of user permissions. Identity and access management (IAM) initiatives are especially helpful for streamlining access controls and protecting assets without disrupting legitimate business processes. They assign all users a distinct digital identity with permissions tailored to their role, compliance needs and other factors. Data encryption Data encryption involves converting data from its original, readable form (plaintext) into an encoded version (ciphertext) using encryption algorithms. This process helps ensure that even if unauthorized individuals access encrypted data, they won’t be able to understand or use it without a decryption key. Encryption is critical to data security. It helps protect sensitive information from unauthorized access both when it’s being transmitted over networks (in transit) and when it’s being stored on devices or servers (at rest). Typically, authorized users only perform decryption when necessary to ensure that sensitive data is almost always secure and unreadable. Data risk management To protect their data, organizations first need to know their risks. Data risk management involves conducting a full audit/risk assessment of an organization’s data to understand what types of data it has, where it’s stored and who has access to it. Companies then use this assessment to identify threats and vulnerabilities and implement risk mitigation strategies. These strategies help fill security gaps and strengthen an organization’s data security and cybersecurity posture. Some include adding security measures, updating data protection policies, conducting employee training or investing in new technologies. Additionally, ongoing risk assessments can help organizations catch emerging data risks early, allowing them to adapt their security measures accordingly. Data backup and recovery Data backup and disaster recovery involves periodically creating or updating more copies of files, storing them in one or more remote locations, and using the copies to continue or resume business operations in the event of data loss due to file damage, data corruption, cyberattack or natural disaster. The subprocesses ‘backup’ and ‘disaster recovery’ are sometimes mistaken for each other or the entire process. However, backup is the process of making file copies, and disaster recovery is the plan and process for using the copies to quickly reestablish access to applications, data and IT resources after an outage. That plan might involve switching over to a redundant set of servers and storage systems until your primary data center is functional again. Disaster recovery as a service (DRaaS) is a managed approach to disaster recovery. A third-party provider hosts and manages the infrastructure used for disaster recovery. Some DRaaS offerings might provide tools to manage the disaster recovery processes or enable organizations to have those processes managed for them. Data storage management Whenever organizations move their data, they need strong security. Otherwise, they risk exposing themselves to data loss, cyber threats and potential data breaches.  Data storage management helps simplify this process by reducing vulnerabilities, particularly for hybrid and cloud storage. It oversees all tasks related to securely transferring production data to data stores, whether on-premises or in external cloud environments. These stores cater to either frequent, high-performance access or serve as archival storage for infrequent retrieval. Incident response Incident response (IR) refers to an organization’s processes and technologies for detecting and responding to cyber threats, security breaches and cyberattacks. Its goal is to prevent cyberattacks before they happen and minimize the cost and business disruption resulting from any that do occur. Incorporating incident response into a broader data protection strategy can help organizations take a more proactive approach to cybersecurity and improve the fight against cybercriminals. According to the Cost of a Data Breach 2023, organizations with high levels of IR countermeasures in place incurred USD 1.49 million lower data breach costs compared to organizations with low levels or none, and they resolved incidents 54 days faster. Data protection policies and procedures Data protection policies help organizations outline their approach to data security and data privacy. These policies can cover a range of topics, including data classification, access controls, encryption standards, data retention and disposal practices, incident response protocols, and technical controls such as firewalls, intrusion detection systems and antivirus and data loss prevention (DLP) software. A major benefit of data protection policies is that they set clear standards. Employees know their responsibilities for safeguarding sensitive information and often have training on data security policies, such as identifying phishing attempts, handling sensitive information securely and promptly reporting security incidents. Additionally, data protection policies can enhance operational efficiency by offering clear processes for data-related activities such as access requests, user provisioning, incident reporting and conducting security audits. Standards and regulatory compliance Governments and other authorities increasingly recognize the importance of data protection and have established standards and data protection laws that companies must meet to do business with customers. Failure to comply with these regulations can result in hefty fines, including legal fees. However, a robust data protection strategy can help ensure ongoing regulatory compliance by laying out strict internal policies and procedures. The most notable regulation is the General Data Protection Regulation (GDPR), enacted by the European Union (EU) to safeguard individuals’ personal data. GDPR focuses on personally identifiable information and imposes stringent compliance requirements on data providers. It mandates transparency in data collection practices and imposes substantial fines for non-compliance, up to 4 percent of an organization’s annual global turnover or EUR 20 million. Another significant data privacy law is the California Consumer Privacy Act (CCPA), which, like GDPR, emphasizes transparency and empowers individuals to control their personal information. Under CCPA, California residents can request details about their data, opt out of sales, and request deletion. Additionally, the Health Insurance Portability and Accountability Act (HIPAA) mandates data security and compliance standards for “covered entities” like healthcare providers handling patients’ personal health information (PHI). Related: Learn more about GDPR compliance Best practices for every data protection strategy Inventory all available data Having secure data starts with knowing what types of data you have, where it’s stored and who has access to it. Conduct a comprehensive data inventory to identify and categorize all information held by your organization. Determine the sensitivity and criticality of each data type to prioritize protection efforts, then regularly update the inventory with any changes in data usage or storage. Keep stakeholders informed Maintain strong communications with key stakeholders, such as executives, vendors, suppliers, customers and PR and marketing personnel, so they know your data protection strategy and approach. This open line of communication will create greater trust, transparency and awareness of data security policies and empower employees and others to make better cybersecurity decisions. Conduct security awareness training Conduct security awareness training across your entire workforce on your data protection strategy. Cyberattacks often exploit human weakness, making insider threats a significant concern and employees the first line of defense against cybercriminals. With presentations, webinars, classes and more, employees can learn to recognize security threats and better protect critical data and other sensitive information. Run regular risk assessments Running ongoing risk assessments and analyses helps identify potential threats and avoid data breaches. Risk assessments allow you to take stock of your data footprint and security measures and isolate vulnerabilities while maintaining updated data protection policies. Additionally, some data protection laws and regulations require them. Maintain strict documentation Documenting sensitive data in a hybrid IT environment is challenging but necessary for any good data protection strategy. Maintain strict records for regulators, executives, vendors and others in case of audits, investigations or other cybersecurity events. Updated documentation creates operational efficiency and ensures transparency, accountability and compliance with data protection laws. Additionally, data protection policies and procedures should always be up-to-date to combat emerging cyber threats. Perform ongoing monitoring  Monitoring offers real-time visibility into data activities, allowing for the swift detection and remediation of potential vulnerabilities. Certain data protection laws may even require it. And even when it’s not required, monitoring can help keep data activities compliant with data protection policies (as with compliance monitoring). Organizations can also use it to test the effectiveness of proposed security measures. While strategies will differ across industries, geographies, customer needs and a range of other factors, nailing down these essentials will help set your organization on the right path forward when it comes to fortifying its data protection. Explore IBM’s data protection solution The post Data protection strategy: Key components and best practices appeared first on IBM Blog.

In November 2023, the California Privacy Protection Agency (CPPA) released a set of draft regulations on the use of artificial intelligence (AI) and automated decision-making technology (ADMT). The proposed rules are still in development, but organizations may want to pay close attention to their evolution. Because the state is home to many of the world’s biggest technology companies, any AI regulations that California adopts could have an impact far beyond its borders.  Furthermore, a California appeals court recently ruled that the CPPA can immediately enforce rules as soon as they are finalized. By following how the ADMT rules progress, organizations can better position themselves to comply as soon as the regulations take effect. The CPPA is still accepting public comments and reviewing the rules, so the regulations are liable to change before they are officially adopted. This post is based on the most current draft as of 9 April 2024. Why is California developing new rules for ADMT and AI? The California Consumer Privacy Act (CCPA), California’s landmark data privacy law, did not originally address the use of ADMT directly. That changed with the passage of the California Privacy Rights Act (CPRA) in 2020, which amended the CCPA in several important ways. The CPRA created the CPPA, a regulatory agency that implements and enforces CCPA rules. The CPRA also gave the CPPA the authority to issue regulations concerning California consumers’ rights to access information about, and opt out of, automated decisions. The CPPA is working on ADMT rules under this authority. Who must comply with California’s ADMT and AI rules? As with the rest of the CCPA, the draft rules would apply to for-profit organizations that do business in California and meet at least one of the following criteria: The business has a total annual revenue of more than USD 25 million. The business buys, sells, or shares the personal data of 100,000+ California residents. The business makes at least half of its total annual revenue from selling the data of California residents. Furthermore, the proposed regulations would only apply to certain uses of AI and ADMT: making significant decisions, extensively profiling consumers, and training ADMT tools.  How does the CPPA define ADMT? The current draft (PDF, 827 KB) defines automated decision-making technology as any software or program that processes personal data and uses computation to execute a decision, replace human decision-making, or substantially facilitate human decision-making. The draft specifically notes that this definition includes software and programs “derived from machine learning, statistics, other data-processing techniques or artificial intelligence.” The draft rules explicitly name some tools that do not count as ADMT, including spam filters, spreadsheets, and firewalls. However, if an organization attempts to use these exempt tools to make automated decisions in a way that circumvents regulations, the rules will apply to that use. Covered uses of ADMT Making significant decisions The draft rules would apply to any use of ADMT to make decisions that have significant effects on consumers. Generally speaking, a significant decision is one that affects a person’s rights or access to critical goods, services, and opportunities. For example, the draft rules would cover automated decisions that impact a person’s ability to get a job, go to school, receive healthcare, or obtain a loan. Extensive profiling Profiling is the act of automatically processing someone’s personal information to evaluate, analyze, or predict their traits and characteristics, such as job performance, product interests, or behavior.  “Extensive profiling” refers to particular kinds of profiling: Systematically profiling consumers in the context of work or school, such as by using a keystroke logger to track employee performance. Systematically profiling consumers in publicly accessible places, such as using facial recognition to analyze shoppers’ emotions in a store. Profiling consumers for behavioral advertising. Behavioral advertising is the act of using someone’s personal data to display targeted ads to them. Training ADMT The draft rules would apply to businesses’ use of consumer personal data to train certain ADMT tools. Specifically, the rules would cover training an ADMT that can be used to make significant decisions, identify people, generate deepfakes, or perform physical or biological identification and profiling. Who would be protected under the AI and ADMT rules? As a California law, the CCPA’s consumer protections extend only to consumers who reside in California. The same holds true for the protections that the draft ADMT rules grant. That said, these rules define “consumer” more broadly than many other data privacy regulations. In addition to people who interact with a business, the rules cover employees, students, independent contractors, and school and job applicants. What are the draft CCPA rules on AI and automated decision-making technology? The draft CCPA AI regulations have three key requirements. Organizations that use covered ADMT must issue pre-use notices to consumers, offer ways to opt out of ADMT, and explain how the business’s use of ADMT affects the consumer. While the CPPA has revised the regulations once and is likely to do so again before the rules are formally adopted, these core requirements appear in each draft so far. The fact that these requirements persist suggests they will remain in the final rules, even if the details of their implementation change. Learn how IBM Security® Guardium® Insights helps organizations meet their cybersecurity and data compliance regulations. Pre-use notices Before using ADMT for one of the covered purposes, organizations must clearly and conspicuously serve consumers a pre-use notice. The notice must detail in plain language how the company uses ADMT and explain consumers’ rights to access more information about ADMT and opt out of the process. The company cannot fall back on generic language to describe how it uses ADMT, like “We use automated tools to improve our services.” Instead, the organization must describe the specific use. The notice must direct consumers to additional information about how the ADMT works, including the tool’s logic and how the business uses its outputs. This information does not have to be in the body of the notice. The organization can give consumers a hyperlink or other way to access it. If the business allows consumers to appeal automated decisions, the pre-use notice must explain the appeals process. Opt-out rights Consumers have a right to opt out of most covered uses of ADMT. Businesses must facilitate this right by giving consumers at least two ways to submit opt-out requests.  At least one of the opt-out methods must use the same channel through which the business primarily interacts with consumers. For example, a digital retailer can have a web form for users to complete. Opt-out methods must be simple and cannot have extraneous steps, like requiring users to create accounts. Upon receiving an opt-out request, a business must stop processing a consumer’s personal information using that automated decision-making technology within 15 days. The business can no longer use any of the consumer’s data that it previously processed. The business must also notify any service providers or third parties with whom it shared the user’s data. Exemptions Organizations do not need to let consumers opt out of ADMT used for safety, security, and fraud prevention. The draft rules specifically mention using ADMT to detect and respond to data security incidents, prevent and prosecute fraudulent and illegal acts, and ensure the physical safety of a natural person. Under the human appeal exception, an organization need not enable opt-outs if it allows people to appeal automated decisions to a qualified human reviewer with the authority to overturn those decisions.  Organizations can also forgo opt-outs for certain narrow uses of ADMT in work and school contexts. These uses include: Evaluating a person’s performance to make admission, acceptance, and hiring decisions. Allocating tasks and determining compensation at work. Profiling used solely to assess a person’s performance as a student or employee. However, these work and school uses are only exempt from opt-outs if they meet the following criteria:  The ADMT in question must be necessary to achieve the business’s specific purpose and used only for that purpose.  The business must formally evaluate the ADMT to ensure that it is accurate and does not discriminate. The business must put safeguards in place to ensure that the ADMT remains accurate and unbiased.  None of these exemptions apply to behavioral advertising or training ADMT. Consumers can always opt out of these uses. Learn how IBM data security solutions protect data across hybrid clouds and help simplify compliance requirements. The right to access information about ADMT use  Consumers have a right to access information about how a business uses ADMT on them. Organizations must give consumers an easy way to request this information.  When responding to access requests, organizations must provide details like the reason for using ADMT, the output of the ADMT regarding the consumer, and a description of how the business used the output to make a decision. Access request responses should also include information on how the consumer can exercise their CCPA rights, such as filing complaints or requesting the deletion of their data. Notification of adverse significant decisions If a business uses ADMT to make a significant decision that negatively affects a consumer—for example, by leading to job termination—the business must send a special notice to the consumer about their access rights regarding this decision. The notice must include: An explanation that the business used ADMT to make an adverse decision. Notification that the business cannot retaliate against the consumer for exercising their CCPA rights. A description of how the consumer can access additional information about how ADMT was used. Information on how to appeal the decision, if applicable.  Risk assessments for AI and ADMT The CPPA is developing draft regulations on risk assessments alongside the proposed rules on AI and ADMT. While these are technically two separate sets of rules, the risk assessment regulations would affect how organizations use AI and ADMT. The risk assessment rules would require organizations to conduct assessments before they use ADMT to make significant decisions or carry out extensive profiling. Organizations would also need to conduct risk assessments before they use personal information to train certain ADMT or AI models. Risk assessments must identify the risks that the ADMT poses to consumers, the potential benefits to the organization or other stakeholders, and safeguards to mitigate or remove the risk. Organizations must refrain from using AI and ADMT where the risk outweighs the benefits.  How do the draft CCPA regulations relate to other AI laws? California’s draft rules on ADMT are far from the first attempt at regulating the use of AI and automated decisions. The European Union’s AI Act imposes strict requirements on the development and use of AI in Europe.  In the US, the Colorado Privacy Act and the Virginia Consumer Data Protection Act both give consumers the right to opt out of having their personal information processed to make significant decisions. At the national level, President Biden signed an executive order in October 2023 directing federal agencies and departments to create standards for developing, using, and overseeing AI in their respective jurisdictions.  But California’s proposed ADMT regulations attract more attention than other state laws because they can potentially affect how companies behave beyond the state’s borders. Much of the global technology industry is headquartered in California, so many of the organizations that make the most advanced automated decision-making tools will have to comply with these rules. The consumer protections extend only to California residents, but organizations might give consumers outside of California the same options for simplicity’s sake. The original CCPA is often considered the US version of the General Data Protection Regulation (GDPR) because it raised the bar for data privacy practices nationwide. These new AI and ADMT rules might produce similar results. When do the CCPA AI and ADMT regulations take effect? The rules are not finalized yet, so it’s impossible to say with certainty. That said, many observers estimate that the rules won’t take effect until mid-2025 at the earliest. The CPPA is expected to hold another board meeting in July 2024 to discuss the rules further. Many believe that the CPPA Board is likely to begin the formal rulemaking process at this meeting. If so, the agency would have a year to finalize the rules, hence the estimated effective date of mid-2025. How will the rules be enforced? As with other parts of the CCPA, the CPPA will be empowered to investigate violations and fine organizations. The California attorney general can also levy civil penalties for noncompliance. Organizations can be fined USD 2,500 for unintentional violations and USD 7,500 for intentional ones. These amounts are per violation, and each affected consumer counts as one violation. Penalties can quickly escalate when violations involve multiple consumers, as they often do. What is the status of the CCPA AI and ADMT regulations? The draft rules are still in flux. The CPPA continues to solicit public comments and hold board discussions, and the rules are likely to change further before they are adopted. The CPPA has already made significant revisions to the rules based on prior feedback. For example, following the December 2023 board meeting, the agency added new exemptions from the right to opt out and placed restrictions on physical and biological profiling. The agency also adjusted the definition of ADMT to limit the number of tools the rules would apply to. While the original draft included any technology that facilitated human decision-making, the most current draft applies only to ADMT that substantially facilitates human decision-making.  Many industry groups feel the updated definition better reflects the practical realities of ADMT use, while privacy advocates worry it creates exploitable loopholes. Even the CPPA Board itself is split on how the final rules should look. At a March 2024 meeting, two board members expressed concerns that the current draft exceeds the board’s authority.   Given how the rules have evolved so far, the core requirements for pre-use notices, opt-out rights, and access rights have a strong chance to remain intact. However, organizations may have lingering questions like: What kinds of AI and automated decision-making technology will the final rules cover? How will consumer protections be implemented on a practical level? What kind of exemptions, if any, will organizations be granted? Whatever the outcome, these rules will have significant implications for how AI and automation are regulated nationwide—and how consumers are protected in the wake of this booming technology. Explore data compliance solutions Disclaimer: The client is responsible for ensuring compliance with all applicable laws and regulations. IBM does not provide legal advice nor represent or warrant that its services or products will ensure that the client is compliant with any law or regulation. The post What you need to know about the CCPA draft rules on AI and automated decision-making technology appeared first on IBM Blog.

The exchange of securities between parties is a critical aspect of the financial industry that demands high levels of security and efficiency. Triparty repo dealing systems, central to these exchanges, require seamless and secure communication across different platforms. The Clearing Corporation of India Limited (CCIL) recently recommended (link resides outside ibm.com) IBM® MQ as the messaging software requirement for all its members to manage the triparty repo dealing system. Read on to learn more about the impact of IBM MQ on triparty repo dealing systems and how you can use IBM MQ effectively for smooth and safe transactions. IBM MQ and its effect on triparty repo dealing system IBM MQ is a messaging system that allows parties to communicate with each other in a protected and reliable manner. In a triparty repo dealing system, IBM MQ acts as the backbone of communication, enabling the parties to exchange information and instructions related to the transaction. IBM MQ enhances the efficiency of a triparty repo dealing system across various factors: Efficient communication: IBM MQ enables efficient communication between parties, allowing them to exchange information and instructions in real-time. This reduces the risk of errors and miscommunications, which can lead to significant losses in the financial industry. With IBM MQ, parties can make sure that transactions are executed accurately and efficiently. IBM MQ makes sure that the messages are delivered exactly once, and this aspect is particularly important in the financial industry. Scalable and can handle more messages: IBM MQ is designed to handle a large volume of messages, making it an ideal solution for triparty repo dealing systems. As the system grows, IBM MQ can scale up to meet the increasing demands of communication, helping the system remain efficient and reliable. Robust security: IBM MQ provides a safe communication channel between parties, protecting sensitive information from unauthorized access. This is critical in the financial industry, where security is paramount. IBM MQ uses encryption and other security measures to protect data, so that transactions are conducted safely and securely. Flexible and easy to integrate: IBM MQ is a flexible messaging system that can be seamlessly integrated with other systems and applications. This makes it easy to incorporate new features and functionalities into the triparty repo dealing system, allowing it to adapt to changing market conditions and customer needs. How to  use IBM MQ effectively in triparty repo dealing systems Follow these guidelines to use IBM MQ effectively in a triparty repo dealing system and make a difference: Define clear message formats for different types of communications, such as trade capture, confirmation and settlement. This will make sure that parties understand the structure and content of messages, reducing errors and miscommunications. Implement strong security measures to protect sensitive information, such as encryption and access controls. This will protect the data  from unauthorized access and tampering. Monitor message queues to verify that messages are being processed efficiently and that there are no errors or bottlenecks. This will help identify issues early, reducing the risk of disruptions to the system. Use message queue management tools to manage and monitor message queues. These tools can help optimize message processing, reduce latency and improve system performance. Test and validate messages regularly to ensure that they are formatted correctly and that the information is accurate. This will help reduce errors and miscommunications, enabling transactions to be executed correctly. CCIL as triparty repo dealing system and IBM MQ The Clearing Corporation of India Ltd. (CCIL) is a central counterparty (CCP) that was set up in April 2001 to provide clearing and settlement for transactions in government securities, foreign exchange and money markets in the country. CCIL acts as a central counterparty in various segments of the financial markets regulated by the Reserve Bank of India (RBI), namely., the government securities segment, that is, outright, market repo and triparty repo, USD-INR and forex forward segments. As recommended by CCIL, all members are required to use IBM MQ as the messaging software for the triparty repo dealing system. IBM MQ v9.3 Long Term Support (LTS) release and above is the recommended software to have in the members’ software environment. IBM MQ plays a critical role in triparty repo dealing systems, enabling efficient, secure, and reliable communication between parties. By following the guidelines outlined above, parties can effectively use IBM MQ to facilitate smooth and secure transactions. As the financial industry continues to evolve, the importance of IBM MQ in triparty repo dealing systems will only continue to grow, making it an essential component of the system. Ready to enhance your triparty repo transactions? Join us for a webinar on 6 June  to learn more about the CCIL’s notification and discover how IBM MQ can streamline your operations and ensure secure, reliable communication. Visit the IBM MQ page to learn more The post Enhancing triparty repo transactions with IBM MQ for efficiency, security and scalability appeared first on IBM Blog.

Data is the lifeblood of every organization. As your organization’s data footprint expands across the clouds and between your own business lines to drive value, it is essential to secure data at all stages of the cloud adoption and throughout the data lifecycle. While there are different mechanisms available to encrypt data throughout its lifecycle (in transit, at rest and in use), application-level encryption (ALE) provides an additional layer of protection by encrypting data at its source. ALE can enhance your data security, privacy and sovereignty posture. Why should you consider application-level encryption? Figure 1 illustrates a typical three-tier application deployment, where the application back end is writing data to a managed Postgres instance. Figure 1: Three-tier application and its trust boundary If you look at the high-level data flow, data originates from the end user and is encrypted in transit to the application, between application microservices (UI and back end), and from the application to the database. Finally, the database encrypts the data at rest using either bring your own key ( or keep your own key ( strategy. In this deployment, both runtime and database admins are inside the trust boundary. This means you’re assuming no harm from these personas. However, as analysts and industry experts point out, there is a human element at the root of most cybersecurity breaches. These breaches happen through error, privilege misuse or stolen credentials and this risk can be mitigated by placing these personas outside the trust boundary. So, how can we enhance the security posture by efficiently placing privileged users outside the trust boundary? The answer lies in application-level encryption. How does application-level encryption protect from data breaches? Application-level encryption is an approach to data security where we encrypt the data within an application before it is stored or transmitted through different parts of the system. This approach significantly reduces the various potential attack points by shrinking the data security controls right down to the data. By introducing ALE to the application, as shown in figure 2, we help ensure that data is encrypted within the application. It remains encrypted for its lifecycle thereon, until it is read back by the same application in question. Figure 2: Protecting sensitive data with application-level encryption This helps make sure that privileged users on the database front (such as database administrators and operators) are outside the trust boundary and cannot access sensitive data in clear text. However, this approach requires changes to the application back end, which places another set of privileged users (ALE service admin and security focal) inside the trust boundary. It can be difficult to confirm how the encryption keys are managed in the ALE service. So, how are we going to bring the value of ALE without such compromises? The answer is through a data security broker. Why should you consider Data Security Broker? IBM Cloud® Security and Compliance Center (SCC) Data Security Broker (DSB) provides an application-level encryption software with a no-code change approach to seamlessly mask, encrypt and tokenize data. It enforces a role-based access control (RBAC) with field and column level granularity. DSB has two components: a control plane component called DSB Manager and a data plane component called DSB Shield, as shown in Figure 3. Figure 3: Protecting sensitive data with Data Security Broker DSB Manager (the control plane) is not in the data path and is now running outside the trust boundary. DSB Shield (the data plane component) seamlessly retrieves the policies such as encryption, masking, RBAC and uses the customer-owned keys to enforce the policy with no-code changes to the application! Data Security Broker offers these benefits: Security: Personally identifiable information (PII) is anonymized before ingestion to the database and is protected even from database and cloud admins. Ease: The data is protected where it flows, without code changes to the application. Efficiency: DSB supports scaling and to the end user of the application, this results in no perceived impact on application performance. Control: DSB offers customer-controlled key management access to data. Help to avoid the risk of data breaches Data breaches come with the high cost of time-to-address, the risk of industry and regulatory compliance violations and associated penalties, and the risk of loss of reputation. Mitigating these risks is often time-consuming and expensive due to the application changes required to secure sensitive data, as well as the oversight required to meet compliance requirements. Making sure your data protection posture is strong  helps avoid the risk of breaches. IBM Cloud Security and Compliance Center Data Security Broker provides the IBM Cloud and hybrid-multicloud with IBM Cloud Satellite® no-code application-level encryption  to protect your application data and enhance your security posture toward zero trust guidelines. Get started with IBM Cloud® Data Security Broker today The post Enhance your data security posture with a no-code approach to application-level encryption appeared first on IBM Blog.

Phrases like “striking the post” and “direct free kick outside the 18” may seem foreign if you’re not a fan of football (for Americans, see: soccer). But for a football scout, it’s the daily lexicon of the job, representing crucial language that helps assess a player’s value to a team. And now, it’s also the language spoken and understood by Scout Advisor—an innovative tool using natural language processing (NLP) and built on the IBM® watsonx™ platform especially for Spain’s Sevilla Fútbol Club.  On any given day, a scout has several responsibilities: observing practices, talking to families of young players, taking notes on games and recording lots of follow-up paperwork. In fact, paperwork is a much more significant part of the job than one might imagine.  As Victor Orta, Sevilla FC Sporting Director, explained at his conference during the World Football Summit in 2023: “We are never going to sign a player with data alone, but we will never do it without resorting to data either. In the end, the good player will always have good data, but then there is always the human eye, which is the one that must evaluate everything and decide.”  Read on to learn more about IBM and Sevilla FC’s high-scoring partnership.  Benched by paperwork  Back in 2021, an avalanche of paperwork plagued Sevilla FC, a top-flight team based in Andalusia, Spain. With an elite scouting team featuring 20-to-25 scouts, a single player can accumulate up to 40 scout reports, requiring 200-to-300 hours of review. Overall, Sevilla FC was tasked with organizing more than 200,000 total reports on potential players—an immensely time-consuming job.  Combining expert observation alongside the value of data remained key for the club. Scout reports look at the quantitative data of game-time minutiae, like scoring attempts, accurate pass percentages, assists, as well as qualitative data like a player’s attitude and alignment with team philosophy. At the time, Sevilla FC could efficiently access and use quantitative player data in a matter of seconds, but the process of extracting qualitative information from the database was much slower in comparison.   In the case of Sevilla FC, using big data to recruit players had the potential to change the core business. Instead of scouts choosing players based on intuition and bias alone, they could also use statistics, and confidently make better business decisions on multi-million-dollar investments (that is, players). Not to mention, when, where and how to use said players. But harnessing that data was no easy task.  Getting the IBM assist Sevilla FC takes data almost as seriously as scoring goals. In 2021, the club created a dedicated data department specifically to help management make better business decisions. It has now grown to be the largest data department in European football, developing its own AI tool to help track player movements through news coverage, as well as internal ticketing solutions.   But when it came to the massive amount of data collected by scouters, the department knew it had a challenge that would take a reliable partner. Initially, the department consulted with data scientists at the University of Sevilla to develop models to organize all their data. But soon, the club realized it would need more advanced technology. A cold call from an IBM representative was fortuitous.  “I was contacted by [IBM Client Engineering Manager] Arturo Guerrero to know more about us and our data projects,” says Elias Zamora, Sevilla FC chief data officer. “We quickly understood there were ways to cooperate. Sevilla FC has one of the biggest scouting databases in the professional football, ready to be used in the framework of generative AI technologies. IBM had just released watsonx, its commercial generative AI and scientific data platform based on cloud. Therefore, a partnership to extract the most value from our scouting reports using AI was the right initiative.”   Coordinating the play  Sevilla FC connected with the IBM Client Engineering team to talk through its challenges and a plan was devised.   Because Sevilla FC was able to clearly explain its challenges and goals—and IBM asked the right questions—the technology soon followed. The partnership determined that IBM watsonx.ai™ would be the best solution to quickly and easily sift through a massive player database using foundation models and generative AI to process prompts in natural language. Using semantic language for search provided richer results: for instance, a search for “talented winger” translated to “a talented winger is capable of taking on defenders with dribbling to create space and penetrate the opposition’s defense.”   The solution—titled Scout Advisor—presents a curated list of players matching search criteria in a well-designed, user-friendly interface. Its technology helps unlock the entire potential of the Sevilla FC’s database, from the intangible impressions of a scout to specific data assets.  Sevilla FC Scout Advisor UI  Scoring the goal  Scout Advisor’s pilot program went into production in January 2024, and is currently training with 200,000 existing reports. The club’s plan is to use the tool during the summer 2024 recruiting season and see results in September. So far, the reviews have been positive.      “Scout Advisor has the capability to revolutionize the way we approach player recruitment,” Zamora says. “It permits the identification of players based on the opinion of football experts embedded in the scouting reports and expressed in natural language. That is, we use the technology to fully extract the value and knowledge of our scouting department.”   And with the time saved, scouts can now concentrate on human tasks: connecting with recruits, watching games and making decisions backed by data.  When considering the high functionality of Scout Advisor’s NLP technology, it’s natural to think about how the same technology can be applied to other sports recruiting and other functions. But one thing is certain: making better decisions about who, when and why to play a footballer has transformed the way Sevilla FC recruits.   Says Zamora: “This is the most revolutionary technology I have seen in football.”     Want to learn how watsonx technology can score goals for your team?  See what watsonx can do The post Shooting to score with Scout Advisor’s NLP appeared first on IBM Blog.

Machine learning (ML) has become a critical component of many organizations’ digital transformation strategy. From predicting customer behavior to optimizing business processes, ML algorithms are increasingly being used to make decisions that impact business outcomes. Have you ever wondered how these algorithms arrive at their conclusions? The answer lies in the data used to train these models and how that data is derived. In this blog post, we will explore the importance of lineage transparency for machine learning data sets and how it can help establish and ensure, trust and reliability in ML conclusions. Trust in data is a critical factor for the success of any machine learning initiative. Executives evaluating decisions made by ML algorithms need to have faith in the conclusions they produce. After all, these decisions can have a significant impact on business operations, customer satisfaction and revenue. But trust isn’t important only for executives; before executive trust can be established, data scientists and citizen data scientists who create and work with ML models must have faith in the data they’re using. Understanding the meaning, quality and origins of data are the key factors in establishing trust. In this discussion we are focused on data origins and lineage.   Lineage describes the ability to track the origin, history, movement and transformation of data throughout its lifecycle. In the context of ML, lineage transparency means tracing the source of the data used to train any model understanding how that data is being transformed and identifying any potential biases or errors that may have been introduced along the way.  The benefits of lineage transparency There are several benefits to implementing lineage transparency in ML data sets. Here are a few: Improved model performance: By understanding the origin and history of the data used to train ML models, data scientists can identify potential biases or errors that may impact model performance. This can lead to more accurate predictions and better decision-making. Increased trust: Lineage transparency can help establish trust in ML conclusions by providing a clear understanding of how the data was sourced, transformed and used to train models. This can be particularly important in industries where data privacy and security are paramount, such as healthcare and finance. Lineage details are also required for meeting regulatory guidelines. Faster troubleshooting: When issues arise with ML models, lineage transparency can help data scientists quickly identify the source of the problem. This can save time and resources by reducing the need for extensive testing and debugging. Improved collaboration: Lineage transparency facilitates collaboration and cooperation between data scientists and other stakeholders by providing a clear understanding of how data is being utilized. This leads to better communication, improved model performance and increased trust in the overall ML process.  So how can organizations implement lineage transparency for their ML data sets? Let’s look at several strategies: Take advantage of data catalogs: Data catalogs are centralized repositories that provide a list of available data assets and their associated metadata. This can help data scientists understand the origin, format and structure of the data used to train ML models. Equally important is the fact that catalogs are also designed to identify data stewards—subject matter experts on particular data items—and also enable enterprises to define data in ways that everyone in the business can understand. Employ solid code management strategies: Version control systems like Git can help track changes to data and code over time. This code is often the true source of record for how data has been transformed as it weaves its way into ML training data sets. Make it a required practice to document all data sources: Documenting data sources and providing clear descriptions of how data has been transformed can help establish trust in ML conclusions. This can also make it easier for data scientists to understand how data is being used and identify potential biases or errors. This is critical for source data that is provided ad hoc or is managed by nonstandard or customized systems. Implement data lineage tooling and methodologies: Tools are available that help organizations track the lineage of their data sets from ultimate source to target by parsing code, ETL (extract, transform, load) solutions and more. These tools provide a visual representation of how data has been transformed and used to train models and also facilitate deep inspection of data pipelines. In conclusion, lineage transparency is a critical component of successful machine learning initiatives. By providing a clear understanding of how data is sourced, transformed and used to train models, organizations can establish trust in their ML results and ensure the performance of their models. Implementing lineage transparency can seem daunting, but there are several strategies and tools available to help organizations achieve this goal. By leveraging code management, data catalogs, data documentation and lineage tools, organizations can create a transparent and trustworthy data environment that supports their ML initiatives. With lineage transparency in place, data scientists can collaborate more effectively, troubleshoot issues more efficiently and improve model performance.  Ultimately, lineage transparency is not just a nice-to-have, it’s a must-have for organizations that want to realize the full potential of their ML initiatives. If you are looking to take your ML initiatives to the next level, start by implementing data lineage for all your data pipelines. Your data scientists, executives and customers will thank you! Explore IBM Manta Data Lineage today The post How to establish lineage transparency for your machine learning initiatives appeared first on IBM Blog.

The physics of atoms and the technology behind treating disease might sound like disparate fields. However, in the past few decades, advances in artificial intelligence, sensing, simulation and more have driven enormous impacts within the biotech industry. Quantum computing provides an opportunity to extend these advancements with computational speedups and/or accuracy in each of those areas. Now is the time for enterprises, commercial organizations and research institutions to begin exploring how to use quantum to solve problems in their respective domains. As a Partner in IBM’s Quantum practice, I’ve had the pleasure of working alongside Wade Davis, Vice President of Computational Science & Head of Digital for Research at Moderna, to drive quantum innovation in healthcare. Below, you’ll find some of the perspectives we share on the future in quantum compute in biotech. What is quantum computing? Quantum computing is a new kind of computer processing technology that relies on the science that governs the behavior of atoms to solve problems that are too complex or not practical for today’s fastest supercomputers. We don’t expect quantum to replace classical computing. Rather, quantum computers will serve as a highly specialized and complementary computing resource for running specific tasks. A classical computer is how you’re reading this blog. These computers represent information in strings of zeros and ones and manipulate these strings by using a set of logical operations. The result is a computer that behaves deterministically—these operations have well-defined effects, and a sequence of operations resulting in a single outcome. Quantum computers, however, are probabilistic—the same sequence of operations can have different outcomes, allowing these computers to explore and calculate multiple scenarios simultaneously. But this alone does not explain the full power of quantum computing. Quantum mechanics offers us access to a tweaked and counterintuitive version of probability that allows us to run computations inaccessible to classical computers.  Therefore, quantum computers enable us to evaluate new dimensions for existing problems and explore entirely new frontiers that are not accessible today. And they perform computations in a way that more closely mirrors nature itself. As mentioned, we don’t expect quantum computers to replace classical computers. Each one has its strengths and weaknesses: while quantum will excel at running certain algorithms or simulating nature, classical will still take on much of the work. We anticipate a future wherein programs weave quantum and classical computation together, relying on each one where they’re more appropriate. Quantum will extend the power of classical.  Unlocking new potential A set of core enterprise applications has crystallized from an environment of rapidly maturing quantum hardware and software. What the following problems share are many variables, a structure that seems to map well to the rules of quantum mechanics, and difficulty solving them with today’s HPC resources. They broadly fall into three buckets: Advanced mathematics and complex data structures. The multidimensional nature of quantum mechanics offers a new way to approach problems with many moving parts, enabling better analytic performance for computationally complex problems. Even with recent and transformative advancements in AI and generative AI, quantum compute promises the ability to identify and recognize patterns that are not detectable for classical-trained AI, especially where data is sparse and imbalanced. For biotech, this might be beneficial for combing through datasets to find trends that might identify and personalize interventions that target disease at the cellular level. Search and optimization. Enterprises have a large appetite for tackling complex combinatorial and black-box problems to generate more robust insights for strategic planning and investments. Though further on the horizon, quantum systems are being intensely studied for their ability to consider a broad set of computations concurrently, by generating statistical distributions, unlocking a host of promising opportunities including the ability to rapidly identify protein folding structures and optimize sequencing to advance mRNA-based therapeutics. Simulating nature. Quantum computers naturally re-create the behavior of atoms and even subatomic particles—making them valuable for simulating how matter interacts with its environment. This opens up new possibilities to design new drugs to fight emerging diseases within the biotech industry—and more broadly, to discover new materials that can enable carbon capture and optimize energy storage to help industries fight climate change. At IBM, we recognize that our role is not only to provide world-leading hardware and software, but also to connect quantum experts with nonquantum domain experts across these areas to bring useful quantum computing sooner. To that end, we convened five working groups covering healthcare/life sciences, materials science, high-energy physics, optimization and sustainability. Each of these working groups gathers in person to generate ideas and foster collaborations—and then these collaborations work together to produce new research and domain-specific implementations of quantum algorithms. As algorithm discovery and development matures and we expand our focus to real-world applications, commercial entities, too, are shifting from experimental proof-of-concepts toward utility-scale prototypes that will be integrated into their workflows. Over the next few years, enterprises across the world will be investing to upskill talent and prepare their organizations for the arrival of quantum computing. Among industries that are making the pivot to useful quantum computing, the biotech industry is moving rapidly to explore how quantum compute can help reduce the cost and speed up the time required to discover, create, and distribute therapeutic treatments that will improve the health, the well being and the quality of life for individuals suffering from chronic disease. According to BCG’s Quantum Computing Is Becoming Business Ready report: “eight of the top ten biopharma companies are piloting quantum computing, and five have partnered with quantum providers.” Partnering with IBM Recent advancements in quantum computing have opened new avenues for tackling complex combinatorial problems that are intractable for classical computers. Among these challenges, the prediction of mRNA secondary structure is a critical task in molecular biology, impacting our understanding of gene expression, regulation and the design of RNA-based therapeutics. For example, Moderna has been pioneering the development of quantum for biotechnology. Emerging from the pandemic, Moderna established itself as a game-changing innovator in biotech when a decade of extensive R&D allowed them to use their technology platform to deliver a COVID-19 vaccine with record speed.  Learn more: How Moderna uses lipid nanoparticles (LNPs) to deliver mRNA and help fight disease Given the value of their platform approach, perhaps quantum might further push their ability to perform mRNA research, providing a host of novel mRNA vaccines more efficiently than ever before. This is where IBM can help.  As an initial step, Moderna is working with IBM to benchmark the application of quantum computing against a classical CPlex protein analysis solver. They’re evaluating the performance of a quantum algorithm called CVaR VQE on randomly generated mRNA nucleotide sequences to accurately predict stable mRNA structures as compared to current state of the art. Their findings demonstrate the potential of quantum computing to provide insights into mRNA dynamics and offer a promising direction for advancing computational biology through quantum algorithms. As a next step, they hope to push quantum to sequence lengths beyond what CPLEX can handle. This is just one of many collaborations that are transforming biotech processes with the help of quantum computation. Biotech enterprises are using IBM Quantum Systems to run their workloads on real utility-scale quantum hardware, while leveraging the IBM Quantum Network to share expertise across domains. And with our updated IBM Quantum Accelerator program, enterprises can now prepare their organizations with hands-on guidance to identify use cases, design workflows and develop utility-scale prototypes that use quantum computation for business impact.  The time has never been better to begin your quantum journey—get started today. Bringing useful quantum computing The post How will quantum impact the biotech industry? appeared first on IBM Blog.

The central processing unit (CPU) is the computer’s brain. It handles the assignment and processing of tasks, in addition to functions that make a computer run. There’s no way to overstate the importance of the CPU to computing. Virtually all computer systems contain, at the least, some type of basic CPU. Regardless of whether they’re used in personal computers (PCs), laptops, tablets, smartphones or even in supercomputers whose output is so strong it must be measured in floating-point operations per second, CPUs are the one piece of equipment on computers that can’t be sacrificed. No matter what technological advancements occur, the truth remains—if you remove the CPU, you simply no longer have a computer. In addition to managing computer activity, CPUs help enable and stabilize the push-and-pull relationship that exists between data storage and memory. The CPU serves as the intermediary, interacting with the primary storage (or main memory) when it needs to access data from the operating system’s random-access memory (RAM). On the other hand, read-only memory (ROM) is built for permanent and typically long-term data storage. CPU components Modern CPUs in electronic computers usually contain the following components: Control unit: Contains intensive circuitry that leads the computer system by issuing a system of electrical pulses and instructs the system to carry out high-level computer instructions. Arithmetic/logic unit (ALU): Executes all arithmetic and logical operations, including math equations and logic-based comparisons that are tied to specific computer actions. Memory unit: Manages memory usage and flow of data between RAM and the CPU. Also supervises the handling of the cache memory. Cache: Contains areas of memory built into a CPU’s processor chip to reach data retrieval speeds even faster than RAM can achieve. Registers: Provides built-in permanent memory for constant, repeated data needs that must be handled regularly and immediately. Clock: Manages the CPU’s circuitry by transmitting electrical pulses. The delivery rate of those pulses is referred to as clock speed, measured in Hertz (Hz) or megahertz (MHz). Instruction register and pointer: Displays location of the next instruction set to be executed by the CPU. Buses: Ensures proper data transfer and data flow between the components of a computer system. How do CPUs work? CPUs function by using a type of repeated command cycle that is administered by the control unit in association with the computer clock, which provides synchronization assistance. The work a CPU does occurs according to an established cycle (called the CPU instruction cycle). The CPU instruction cycle designates a certain number of repetitions, and this is the number of times the basic computing instructions will be repeated, as permitted by that computer’s processing power. The basic computing instructions include the following: Fetch: Fetches occur anytime data is retrieved from memory. Decode: The decoder within the CPU translates binary instructions into electrical signals that engage with other parts of the CPU. Execute: Execution occurs when computers interpret and carry out a computer program’s set of instructions. With some basic tinkering, the computer clock within a CPU can be manipulated to keep time faster than it normally elapses. Some users do this to run their computer at higher speeds. However, this practice (“overclocking”) is not advisable since it can cause computer parts to wear out earlier than normal and can even violate CPU manufacturer warranties. Processing styles are also subject to tweaking. One way to manipulate those is by implementing instruction pipelining, which seeks to instill instruction-level parallelism in a single processor. The goal of pipelining is to keep each part of the processor engaged by splitting up incoming computer instructions and spreading them out evenly among processor units. Instructions are broken down into smaller sets of instructions or steps. Another method for achieving instruction-level parallelism inside a single processor is to use a CPU called a superscalar processor. Whereas scalar processors can execute a maximum of one instruction per clock cycle, there’s really no limit to how many instructions can be dispatched by a superscalar processor. It sends multiple instructions to various of the processor’s execution units, thereby boosting throughput. Who invented the CPU? Breakthrough technologies often have more than one parent. The more complex and earth-shaking that technology, the more individuals who are usually responsible for that birth. In the case of the CPU—one of history’s most important inventions—we’re really talking about who discovered the computer itself. Anthropologists use the term “independent invention” to describe situations where different individuals, who may be located countries away from each other and in relative isolation, each come up with what are similar or complementary ideas or inventions without knowing about similar experiments taking place. In the case of the CPU (or computer), independent invention has occurred repeatedly, leading to different evolutionary shifts during CPU history. Twin giants of computing While this article can’t honor all the early pioneers of computing, there are two people whose lives and work need to be illuminated. Both had a direct connection to computing and the CPU: Grace Hopper: Saluting “Grandma COBOL” American Grace Brewster Hopper (1906-1992) weighed a mere 105 pounds when she enlisted in the US Navy—15 pounds under the required weight limit. And in one of US maritime history’s wisest decisions, the Navy gave an exemption and took her anyway. What Grace Hopper lacked in physical size, she made up for with energy and versatile brilliance. She was a polymath of the first order: a gifted mathematician armed with twin Ph.D. degrees from Yale University in both mathematics and mathematical physics, a noted professor of mathematics at Vassar College, a pioneering computer scientist credited with writing a computer language and authoring the first computer manual, and a naval commander (at a time when women rarely rose above administrative roles in the military). Because of her work on leading computer projects of her time, such as the development of the UNIVAC supercomputer after WWII, Hopper always seemed in the thick of the action, always at the right place at the right time. She had personally witnessed much of modern computing history. She was the person who originally coined the term “computer bug,” describing an actual moth that had become caught within a piece of computing equipment. (The original moth remains on display at the Smithsonian Institution’s National Museum of American History in Washington, DC.) During her experience working on the UNIVAC project (and later running the UNIVAC project for the Remington Rand Corporation), Hopper became frustrated that there was not a simpler programming language that could be used. So, she set about writing her own programming language, which famously came to be known as COBOL (an acronym for COmmon Business-Oriented Language). Robert Noyce: The Mayor of Silicon Valley Robert Noyce was a mover and shaker in the classic business sense—a person who could make amazing activity start happening just by showing up. American Robert Noyce (1927-1990) was a whiz-kid boy inventor. He later channeled his intellectual curiosity into his undergrad collegiate work, especially after being shown two of the original transistors created by Bell Laboratories. By age 26, Noyce earned a Ph.D. in Physics from the Massachusetts Institute of Technology (MIT). In 1959, he followed up on Jack Kilby’s 1958 invention of the first hybrid integrated circuit by making substantial tweaks to the original design. Noyce’s improvements led to a new kind of integrated circuits: the monolithic integrated circuit (also called the microchip), which was formulated using silicon. Soon the silicon chip became a revelation, changing industries and shaping society in new ways. Noyce co-founded two hugely successful corporations during his business career: Fairchild Semiconductor Corporation (1957) and Intel (1968). He was the first CEO of Intel, which is still known globally for manufacturing processing chips. His partner in both endeavors was Gordon Moore, who became famous for a prediction about the semiconductor industry that proved so reliable it has seemed almost like an algorithm. Called “Moore’s Law,” it posited that the number of transistors to be used within an integrated circuit reliably doubles about every two years. While Noyce oversaw Intel, the company produced the Intel 4004, now recognized as the chip that launched the microprocessor revolution of the 1970s. The creation of the Intel 4004 involved a three-way collaboration between Intel’s Ted Hoff, Stanley Mazor and Federico Faggin, and it became the first microprocessor ever offered commercially. Late in his tenure, the company also produced the Intel 8080—the company’s second 8-bit microprocessor, which first appeared in April 1974. Within a couple of years of that, the manufacturer was rolling out the Intel 8086, a 16-bit microprocessor. During his illustrious career, Robert Noyce amassed 12 patents for various creations and was honored by three different US presidents for his work on integrated circuits and the massive global impact they had. ENIAC: Marching off to war It seems overly dramatic, but in 1943, the fate of the world truly was hanging in the balance. The outcome of World War II (1939-1945) was still very much undecided, and both Allies forces and Axis forces were eagerly scouting any kind of technological advantage to gain leverage over the enemy. Computer devices were still in their infancy when a project as monumental in its way as the Manhattan Project was created. The US government hired a group of engineers from the Moore School of Electrical Engineering at the University of Pennsylvania. The mission called upon them to build an electronic computer capable of calculating yardage amounts for artillery-range tables. The project was led by John Mauchly and J. Presper Eckert, Jr. at the military’s request. Work began on the project in early 1943 and didn’t end until 3 years later. The creation produced by the project—dubbed ENIAC, which stood for “Electronic Numerical Integrator and Computer”—was a massive installation requiring 1,500 sq. ft. of floor space, not to mention 17,000 glass vacuum tubes, 70,000 resistors, 10,000 capacitors, 6,000 switches and 1,500 relays. In 2024 currency, the project would have cost USD 6.7 million. It could process up to 5,000 equations per second (depending on the equation), an amazing quantity as seen from that historical vantage point. Due to its generous size, the ENIAC was so large that people could stand within the CPU and program the machine by rewiring connections between functional units in the machine.   ENIAC was used by the US Army during the rest of WWII. But when that conflict ended, the Cold War began and ENIAC was given new marching orders. This time it would perform calculations that would help enable the building of a bomb with more than a thousand times the explosive force of the atomic weapons that ended WWII: the hydrogen bomb. UNIVAC: Getting back to business Following WWII, the two leaders of the ENIAC project decided to set up shop and bring computing to American business. The newly dubbed Eckert-Mauchly Computer Corporation (EMCC) set out to prepare its flagship product—a smaller and cheaper version of the ENIAC, with various improvements like added tape drives, a keyboard and a converter device that accepted punch-card use. Though sleeker than the ENIAC, the UNIVAC that was unveiled to the public in 1951 was still mammoth, weighing over 8 tons and using 125 kW of energy. And it was still expensive: around USD 11.6 million in today’s money. For its CPU, it contained the first CPU—the UNIVAC 1103—which was developed at the same time as the rest of the project. The UNIVAC 1103 used glass vacuum tubes, making the CPU large, unwieldy and slow. The original batch of UNIVAC 1s was limited to a run of 11 machines, meaning that only the biggest, best-funded and best-connected companies or government agencies could gain access to a UNIVAC. Nearly half of those were US defense agencies, like the US Air Force and the Central Intelligence Agency (CIA). The very first model was purchased by the U.S. Census Bureau. CBS News had one of the machines and famously used it to correctly predict the outcome of the 1952 US Presidential election, against long-shot odds. It was a bold publicity stunt that introduced the American public to the wonders that computers could do. Transistors: Going big by going small As computing increasingly became realized and celebrated, its main weakness was clear. CPUs had an ongoing issue with the vacuum tubes being used. It was really a mechanical issue: Glass vacuum tubes were extremely delicate and prone to routine breakage. The problem was so pronounced that the manufacturer went to great lengths to provide a workaround solution for its many agitated customers, whose computers stopped dead without working tubes. The manufacturer of the tubes regularly tested tubes at the factory, subjecting tubes to different amounts of factory use and abuse, before selecting the “toughest” tubes out of those batches to be held in reserve and at the ready for emergency customer requests. The other problem with the vacuum tubes in CPUs involved the size of the computing machine itself. The tubes were bulky and designers were craving a way to get the processing power of the tube from a much smaller device. By 1953, a research student at the University of Manchester showed you could construct a completely transistor-based computer. Original transistors were hard to work with, in large part because they were crafted from germanium, a substance which was tricky to purify and had to be kept within a precise temperature range. Bell Laboratory scientists started experimenting with other substances in 1954, including silicon. The Bell scientists (Mohamed Italia and Dawn Kahng) kept refining their use of silicon and by 1960 had hit upon a formula for the metal-oxide-semiconductor field-effect transistor (or MOSFET, or MOS transistor) modern transistor, which has been celebrated as the “most widely manufactured device in history,” by the Computer History Museum. In 2018 it was estimated that 13 sextillion MOS transistors had been manufactured. The advent of the microprocessor The quest for miniaturization continued until computer scientists created a CPU so small that it could be contained within a small integrated circuit chip, called the microprocessor. Microprocessors are designated by the number of cores they support. A CPU core is the “brain within the brain,” serving as the physical processing unit within a CPU. Microprocessors can contain multiple processors. Meanwhile, a physical core is a CPU built into a chip, but which only occupies one socket, thus enabling other physical cores to tap into the same computing environment. Here are some of the other main terms used in relation to microprocessors: Single-core processors: Single-core processors contain a single processing unit. They are typically marked by slower performance, run on a single thread and perform the CPU instruction cycle one at a time. Dual-core processors: Dual-core processors are equipped with two processing units contained within one integrated circuit. Both cores run at the same time, effectively doubling performance rates. Quad-core processors: Quad-core processors contain four processing units within a single integrated circuit. All cores run simultaneously, quadrupling performance rates. Multi-core processors: Multi-core processors are integrated circuits equipped with at least two processor cores, so they can deliver supreme performance and optimized power consumption. Leading CPU manufacturers Several companies now create products that support CPUs through different brand lines. However, this market niche has changed dramatically, given that it formerly attracted numerous players, including plenty of mainstream manufacturers (e.g., Motorola). Now there’s really just a couple of main players: Intel and AMD. They use differing instruction set architectures (ISAs). So, while AMD processors take their cues from Reduced Instruction Set Computer (RISC) architecture, Intel processors follow a Complex Instruction Set Computer (CISC) architecture. Advanced Micro Devices (AMD): AMD sells processors and microprocessors through two product types: CPUs and APUs (which stands for accelerated processing units). In this case, APUs are simply CPUs that have been equipped with proprietary Radeon graphics. AMD’s Ryzen processors are high-speed, high-performance microprocessors intended for the video-game market. Athlon processors was formerly considered AMD’s high-end line, but AMD now uses it as a general-purpose alternative. Arm: Arm doesn’t actually manufacture equipment, but does lease out its valued processor designs and/or other proprietary technologies to other companies who make equipment. Apple, for example, no longer uses Intel chips in Mac CPUs, but makes its own customized processors based on Arm designs. Other companies are following suit. Intel: Intel sells processors and microprocessors through four product lines. Its premium line is Intel Core, including processor models like the Core i3. Intel’s Xeon processors are marketed toward offices and businesses. Intel’s Celeron and Intel Pentium lines (represented by models like the Pentium 4 single-core CPUs) are considered slower and less powerful than the Core line. Understanding the dependable role of CPUs When considering CPUs, we can think about the various components that CPUs contain and use. We can also contemplate how CPU design has moved from its early super-sized experiments to its modern period of miniaturization. But despite any transformations to its dimensions or appearance, the CPU remains steadfastly itself, still on the job—because it’s so good at its particular job. You know you can trust it to work correctly, each time out. Smart computing depends upon having proper equipment you can rely upon. 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