#ibmwatson

[cizotech]

🚀 Discover the Best No-Code AI Tools for 2024! 🚀

Dive into our latest blog to explore how no-code AI tools like Akkio, ChatGPT, Canva, and more are revolutionizing the tech world. Perfect for businesses, designers, and hobbyists looking to harness the power of AI without writing a single line of code.

🔍 Find out which tool is right for you! 📈 Boost your productivity and creativity! 📱 Plus, learn how our expert team can create high-quality, user-friendly mobile apps tailored to your unique needs. Offering top-notch iOS and Android app development services.

👉 Read the full blog here: https://cizotech.com/best-no-code-ai-tools-for-2024-a-comprehensive-guide/

[govindhtech]

Complete Guide to AI model risk management Framework

AI Model Risk Management involves detecting, mitigating, and addressing AI threats. It emphasizes formal AI risk management frameworks and includes tools, techniques, and principles.

Artificial intelligence risk management aims to minimize its drawbacks and maximize its benefits.

AI risks management Governance of AI includes risk management. AI governance safeguards AI tools and systems from harm.

Artificial intelligence risk management is part of AI governance. AI model risk management targets vulnerabilities and threats to protect AI systems. To ensure safety, fairness, and human rights, AI governance sets guidelines, regulations, and standards for research, development, and application. IBM Consulting can integrate appropriate AI governance into your business.

The Importance Of AI Risk Management AI adoption has increased across industries in recent years. According to McKinsey, 72% of organizations employ AI, up 17% from 2023. Organizations pursue AI’s benefits innovation, efficiency, and productivity but don’t always address its risks privacy, security, and ethical and legal challenges.

Leaders know this problem. According to an IBM Institute for Business Value (IBM IBV) survey, 96% of CEOs think generative AI increases security risks. Meanwhile, the IBM IBV showed that 24% of generative AI projects are secure.

AI model risk management can help organizations maximize AI systems’ potential without compromising ethics or security.

Artificial Intelligence Risk Management Like other security risks, AI risk measures how likely and damaging an AI related attack is to effect an organisation. Each AI model and use case has various dangers, but four categories exist:

Data threats Simulate dangers The operational hazards Ethics and law dangers AI systems and organisations can suffer financial losses, reputational damage, regulatory penalties, public confidence erosion, and data breaches if these risks are not addressed properly. Data threats

Data sets used by AI systems may be tampered with, breached, biased, or attacked. From AI creation to training and deployment, organizations may reduce these risks by ensuring data integrity, security, and availability.

Common data threats Security: AI systems’ biggest and most important challenge is data security. Threat actors can corrupt AI data sets, causing unauthorized access, data loss, and confidentiality issues for organizations. AI systems manage sensitive personal data, which can lead to privacy breaches and regulatory and legal difficulties for organisation. Training data determines AI model risk management reliability. Data distortion can cause false positives, inaccurate outputs, and poor decision making. Simulate dangers Threat actors may steal, reverse engineer, or manipulate AI models. AI models’ architecture, weights, and parameters the key components that determine their behavior and performance can be tampered with by attackers.

AI Model Risk Management Some frequent model risks Input data manipulation: Adversarial attacks trick AI systems into producing inaccurate predictions or classifications. For instance, attackers may give AI algorithms hostile instances to distort or interfere with decision making. PROMPT injections attack huge language models. Hackers trick generative AI systems into disclosing sensitive data, spreading falsehoods, or worse. Even simple cue injections can cause AI chatbots like ChatGPT break system rules and utter stuff. Analyzing complex AI models can be difficult, making it hard for users to understand their decisions. Lack of transparency hinders bias identification and accountability and erodes AI system and provider trust. Threat actors target AI systems during development, deployment, and maintenance via supply chain attacks. Attackers could exploit weaknesses in third party AI development components to breach data or get unauthorized access. AI And Machine Learning In Risk Management

AI model risk management are not magic, but rather complex code and machine learning algorithms. Like other technology, they have operational hazards. If ignored, these risks can cause system failures and security vulnerabilities that threat actors can exploit.

Some frequent operational risks Model drift, where data or data point associations alter, can affect AI model performance. Over time, a fraud detection model may grow inaccurate and miss fraudulent transactions. Sustainability concerns: AI systems are difficult and need scale and assistance. Sustainability issues can make maintaining and updating these systems difficult, resulting in inconsistent performance, higher operating costs, and energy use. AI system integration with IT infrastructure is difficult and resource intensive. Data silos, system interoperability, and compatibility plague organizations. By increasing cyberthreats’ attack surface, AI systems can develop new vulnerabilities. Absence of accountability: AI systems are new, thus many companies lack suitable corporate governance mechanisms. AI systems are generally unsupervised. Only 18% of organizations have a council or board that can make responsible AI governance decisions, according to McKinsey. AI Risk Management Certification Ethics and law dangers When creating and implementing AI systems, organizations risk privacy violations and biased results if safety and ethics are not prioritized. Biased recruiting data may promote gender or racial stereotypes and develop AI models that favors particular demographic groupings.

Typical ethical and legal risks Lack of transparency: Organizations that refuse to disclose their AI systems risk losing public trust.

Not complying with government regulations: The GDPR and sector specific standards can result in costly fines and punitive consequences.

AI biases: Training data can prejudice AI systems, resulting in biased hiring decisions and unequal financial services access.

Privacy, autonomy, and human rights issues might arise from AI judgements. When handled poorly, these issues can damage an organization’s reputation and public trust. Without explainability, AI systems’ decisions are hard to understand and defend.

Unexplainability can destroy trust, reputation, and legal issues. A CEO not understanding where their LLM receives training data might lead to poor headlines or regulatory problems.

Artificial Intelligence Risk Management Framework Many organizations use AI Model Risk Management frameworks to handle risks across the AI lifecycle.

These guidelines are playbooks that explain an organization’s AI rules, procedures, roles, and duties. Organisations can build, deploy, and operate AI systems using AI risk management frameworks to minimise risks, uphold ethics, and comply with regulations.

AI risk management frameworks A NIST AI Risk Management Framework EU AI ACT ISO/IEC norms AI executive directive from the US AI RMF by NIST AI risk was organized by NIST’s AI Risk Management Framework (AI RMF) in January 2023. NIST AI RMF has created AI Model Risk Management standards since then. AI Risk Management Software AI RMF helps organizations design, develop, implement, and employ AI systems to control risks and encourage trustworthy, responsible AI practices. The voluntary AI RMF, developed with the public and private sectors, applies to any company, industry, or area.

Two sections make up the framework. Part 1 covers trustworthy AI system threats and traits. AI RMF Core Part 2 lists four functions to help organizations manage AI system risks:

Create an AI Model Risk Management culture in your organisation. Map: Business-specific AI threats Evaluate AI risks Handle mapped and assessed risks The EU AI Act AI development and use in the EU are regulated by the EU AI Act. The act regulates AI systems based on their risks to human health, safety, and rights. The act also regulates building, training, and deploying general-purpose AI models like ChatGPT and Google Gemini. AI Risk Management ISO/IEC Norms AI Model Risk Management standards are available from ISO and IEC.

In AI risk management, ISO/IEC standards emphasize openness, accountability, and ethics. They also provide actionable AI model risk management principles from design and development to deployment and operation.

AI executive directive from the US At the end of 2023, the Biden administration issued an executive order on AI security. This comprehensive directive establishes new AI technology risk management criteria, however it is not a risk management framework.

Trustworthy, transparent, explainable, and accountable AI is one of the executive order’s main concerns. The executive order created a precedent for private sector AI risk management.

How AI risk management aids businesses Despite varying from organisation to organisation, AI risk management can give certain common basic benefits when implemented correctly.

Better security Organizations may improve cybersecurity and AI security using AI risk management. Enterprises can discover AI lifecycle risks and weaknesses by undertaking frequent risk assessments and audits. Their risk mitigation techniques can be implemented after these assessments.

This method may incorporate data security and model robustness improvements. Institutional changes like ethical rules and access controls may be needed. Organisation can reduce data breaches and cyberattacks by taking a proactive strategy to threat detection and response.

Better judgement AI risk management can also improve group decision making. Organizations can assess their risks using qualitative and quantitative studies, statistical approaches, and expert opinions. This holistic view helps organizations prioritize high-risk threats and make better AI adoption decisions, balancing innovation and risk mitigation.

Regulation compliance The GDPR, CCPA, and EU AI Act protect sensitive data worldwide.

Those who break these laws face severe fines and punishments. AI model risk management can help organizations comply and stay in good standing as AI rules grow almost as quickly as the technology.

Resilience operations Artificial intelligence risk management helps companies minimize disruption and maintain business continuity by addressing AI system issues in real time. By helping organizations build defined AI management practices and processes, AI Model Risk Management may improve accountability and sustainability.

Trust and openness increased AI Model Risk Management prioritizes trust and transparency to promote AI ethics.

Executives, AI developers, data scientists, users, policymakers, and ethicists are involved in most AI model risk management processes. AI systems are designed and deployed ethically with all stakeholders in mind with this inclusive approach.

Constant testing, verification, and monitoring Testing and monitoring an AI system’s effectiveness helps organizations spot new dangers faster. Monitoring helps organizations comply with regulations and mitigate AI hazards earlier, lowering threats.

AI Risk Management AI technologies can improve labour efficiency, but they can pose risks. Nearly every enterprise IT can be misused.

Organizations can use generative AI. It should be treated like any other technical tool. That involves knowing the risks and taking precautions to prevent an assault.

IBM watsonx.governance lets organizations administer, manage, and track AI initiatives in one place. Watsonx.governance can control any vendor’s generative AI models, assess model health and accuracy, and automate compliance operations.

Read more on Govindhtech.com

[bigsuit11]

Top 10 IBM Development Service Providers Around the World

In our tech-driven world, business cannot afford to get left behind. Whether cloud or AI or enterprise systems — IBM has powerful solutions that can help companies scale smartly. But here’s the hitch: receiving the full benefits of IBM technologies relies on selecting the proper developer associate.

Here are the Top 10 Global IBM Development Service Providers As of October 2023 They are known for providing tailored solutions using the IBM ecosystem — including IBM Cloud, Watson, AI, data analytics and enterprise automation.

What Makes These Companies Stand Out?

Excellent domain knowledge of IBM technology and toolsets

Tailored solutions that are scalable and secure

Industry experience in diverse industries including finance, healthcare, retail, and more

Comprehensive support — from consultation through implementation and beyond

With up to October 2023 data, Do you imagine Picking the right IBM development company not only get you faster innovation or smoother processes but also a competitive advantage in IBM.

These companies don’t just build tech — they serve as strategic growth partners. Their goal? To leverage the power of IBM’s ecosystem for your benefit.

Final Thoughts

IBM technologies are powerful, but they need the right hands to make an impact. The companies on this list have a proven track record of delivering exactly that. If you're planning to take your business to the next level with IBM solutions, partnering with one of these top service providers is a smart move.

For more detail: https://www.linkedin.com/pulse/top-10-ibm-development-services-companies-world-izhar-ali-qiwpf/

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[ardinate]

Top Innovations of 2025!

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[lavishangle]

Inside IBM’s AI Revolution: From the 1950s to Now, Explore IBM’s groundbreaking AI evolution—from the earliest 1950s experiments to the transformative power of Watson—and see how they’re shaping the future of technology. https://www.youtube.com/channel/UC3o4B5eoAcewBjxvaeC5Rxg?sub_confirmation=1 IBM’s role in the development of artificial intelligence traces back to the 1950s, when the company began experimenting with early AI programs that would lay the groundwork for modern computing. Known for its long history of technological milestones, IBM accelerated its AI research for decades, culminating in the unveiling of IBM Watson in 2011. This landmark system demonstrated the potential of AI to interpret vast amounts of data and generate insights in real-time—an achievement that caught the world’s attention. Watson’s victory on the game show Jeopardy! was more than just a pop-culture moment; it showcased the real capability of AI to process natural language, learn from large data sets, and respond with accuracy previously thought impossible. Since then, IBM has continued to expand the boundaries of AI, focusing on solutions that serve a broad range of industries—from personalized medicine in healthcare to risk analysis in finance. By partnering with multiple organizations, IBM aims to democratize AI applications, making them both accessible and impactful on a global scale. Throughout this journey, IBM’s commitment to research and innovation has never wavered. Its strategy involves deep collaboration between scientists, developers, and industry experts who together push the limits of AI technology. As AI continues to evolve, IBM’s focus on ethical and responsible implementation remains a guiding principle, underlining the company’s vision to leverage AI for solving some of humanity’s most complex problems. Today, IBM’s legacy is a testament not only to how far AI has come, but also to the transformative potential it holds for the future. 📂 For The Latest Stories on luxury travel, getaways goods, the rich, companies, Top 10’s, biographies, Lavish History, news, and more 📂 https://www.youtube.com/@Lavishangle 🎉 For business enquires contact us at full4sog (@) gmail dot com 💬 Don't forget to leave your thoughts in the comments below. We love hearing from you! 😍 and hit that bell to stay updated on all new videos we release. #lavishgetaways #thelavishandaffluentangle #thelavish&affluentangle #tlaa #shorts #shorts #shortvideo #shortsvideo #shortsviral #shortvideos #shortsyoutube #shortsbeta #viralshorts #viralshort #viral #viralreels #youtubeshorts #viralyoutubeshorts #viralshorts #viralshort #viralshorts2024 #IBMHistory #ArtificialIntelligence #AIRevolution #IBMWatson #MachineLearning #TechInnovation #DataAnalytics #HealthcareAI #FinanceSolutions #ResearchAndDevelopment #TechnologyLeaders #HistoricMilestones #FutureOfComputing #EarlyAI #GlobalInnovation #ScienceAndTech #ResponsibilityInAI #IBMResearch #TechPioneers #ComputingLegacy #AIJourney #NextGenTech #IndustryCollaboration #TransformativePower #50sAIOrigins via The Lavish & Affluent Angle https://www.youtube.com/channel/UC3o4B5eoAcewBjxvaeC5Rxg January 06, 2025 at 06:30PM

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[alphonic-in]

Know the Details About the IBM Watson Architecture

The IBM Watson architecture is a Linux program based on the IBM power system. Since Watson is a user-friendly, human-generated tool to compute in natural language, the architecture is simple and unique as well.

Details about the IBM Watson architecture

IBM Watson architecture is a parallel tool based on the IBM Power 750 that racks the standard configuration. It runs on the Linux based on the Novell’s Linux Enterprise Server and IBM power combined. It is made up of 16 Terabytes of memory along with 4 terabytes of the clustered storage system.

The Watson is racked with 10 random racks that include the server, networking system, data analytics, and shared disk system, and cluster storage, controller. The IBM Watson has a total power of 2880 power banks.

The deep insights of the Watson

The IBM Watson architecture is divided into different racks and steps. Here is the basic architecture of the Watson:

Service management

To deliver, plan, and operate the cloud services that are offered to the customers. Alphonic and other web development services use this facility to manage the clouds of the customers.

Cognitive

The IBM Watson architecture takes care of computing and unlocking the unstructured and structured data with intelligence and understanding by developing the insights of the data.

Private cloud

It manages all the private cloud of the individual organization.

Microservices

The cloud-native approach towards building the mobile and web application with microservices architecture.

Data and analytics

This IBM Watson architecture helps in developing solutions and analyze the data including web and social platform. It reports on the data and understands the diverse structure to drive insights and visualization.

Internet of things

It connects the various IoT devices and builds apps to gain insight of the IoT data and the clouds with cognitive services.

Virtualization

Virtually creates and uses the software using the IBM cloud software and the platform to store the data for the client.

API transformation

Exposes the business assets according to the new digital ecosystem through the API.

Blockchain

It is an integral part of the IBM Watson architecture since it records the history of the transaction and accounts them according to different categories.

E-commerce

The Watson architecture deploys, develops and e-commerce solutions. The allophonic web development services use this feature to develop an e-commerce platform to securely connect the client data to the cloud.

Web application

Like Alphonic web application uses this architecture to develop integrated runtime services and deploy the apps.

For more information visit www.alphonic.in or call at +91 9887133338 , drop an email at info@alphonic.in

[swagpeanutmaker-blog]

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[chivian-technology]

Scenes from the digital transformation conference with @ibm , @cyberspacenaija delegates from different parastatals in the federal capital. #ibmcloud #ibmwatson #ibmcloudpakfordata #digitaltransformation #machinelearningengineer #machinelearning #datageek #pythonprogrammer #analytics #instaleadership #instamemories #instatech #instatechnology https://www.instagram.com/p/CmCyOC1tosb/?igshid=NGJjMDIxMWI=

[govindhtech]

The Future of Work: How BPR Can Help Your Business Change

BPR News

By redesigning key business processes, business process reengineering (BPR) greatly enhances performance, speed, and effectiveness. Continuous innovation and change that improves processes from start to finish and gets rid of waste are examples of BPR. By removing steps and streamlining processes, BPR makes the best use of resources.

BPR challenges organizational conventions and procedures by redesigning business processes. It usually seeks radical process transformation. It should not be confused with business process management (BPM), a more incremental approach to optimizing processes, or business process improvement (BPI), which comprises any systematic effort to enhance processes. This blog shows BPR cases that benefit from BPM.

What is business process reengineering

BPR was a management method developed in the early 1990s to alter business operations by drastically restructuring processes. The 1990 Harvard Business Review article “Reengineering Work: Don’t Automate, Obliterate,” by Michael Hammer, and the 1993 book Reengineering the Corporation by Hammer and James Champy popularised the concept. Ford Motor Company, a 1990s BPR pioneer, streamlined its manufacturing processes and increased competitiveness.

Organizations of all sizes and industries reengineer business processes. First, determine BPR goals, then review the existing condition, identify gaps and opportunities, and process map.

Business Process Reengineering BPR definition

Effective leadership, change management, and continuous improvement are needed to implement business process reengineering. To enable new procedures and significant change, leaders, senior management, team members, and stakeholders must advocate the BPR project and give resources, support, and direction.

Examples of BPR in Companies

Streamlining supply chain management

BPR for supply chain optimisation requires rigorous review and redesign of logistics, inventory management, and procurement. Rethinking procurement, introducing just-in-time inventory, optimising production schedules, and restructuring transportation and distribution networks are all possible supply chain overhauls. To automate and improve things, you can use SCM, ERP, and advanced analytics tools. Predictive analytics helps figure out what people will want and how much to stock, and blockchain technology can make the supply chain more open and easy to track.

Benefits:

Improved efficiency

Reduced cost

Increased transparency

BPR is a crucial technique for organisations seeking to revamp their CRM operations. Business process reengineering for CRM involves integrating customer data from several sources, employing advanced analytics for insights, and optimising service workflows for personalised experiences and lower wait times.

CRM BPR uses may include:

Integrating CRM software to centralise customer data and enable real-time analytics Using multichannel communication to give consistent experiences across touchpoints Equipping frontline staff with training and resources to provide excellent service BPR helps firms anticipate consumer demands, personalise interactions, and resolve issues quickly.

Benefits:

360-degree consumer view

Enhanced sales and retention

Faster problem resolution

Digitising administrative processes

To eliminate human errors, organisations are digitising and automating administrative operations with BPR. This transition involves replacing manual, paper-based procedures with digital systems that use RPA for everyday tasks.

This could involve automated invoicing, payroll, or HR. This can boost efficiency, accuracy, and scalability, helping the company run better.

Benefits:

Shorter processing

Fewer mistakes

Increased adaptability

Process improvement for product development

BPR optimizes product development from inspiration to launch. This revamp evaluates and redesigns workflows, promotes cross-functional collaboration, and innovates employing new technology. This can include cross-functional teams to promote communication and knowledge sharing, agile methodologies to promote iterative development and rapid prototyping, and PLM software to streamline documentation and version control. These BPR activities help companies cut product development cycle times, respond faster to market demands, and offer creative, customer-focused goods.

Benefits:

Faster market entry

Increased innovation

Superior product quality

Technology infrastructure update In this age of rapid technological innovation, BPR is essential for companies updating their technology infrastructure. Migration to cloud-based solutions, adoption of AI and ML, and integration of heterogeneous systems improves data management and analysis, enabling more informed decision-making. New technologies boost performance, cybersecurity, and scalability, setting companies for long-term success.

Benefits:

Improved performance

Enhanced security

Increased innovation

Reduce staff redundancies

Many companies use BPR to reorganize and decrease redundancies in response to changing market dynamics and organizational needs. Strategic initiatives can streamline hierarchies, consolidate divisions, and outsource non-core services. By optimising labour allocation and eliminating duplicate tasks, organisations can cut costs, boost efficiency, and prioritise resources.

Benefits:

Cost-saving

Increased efficiency

Core competencies first

Operational cost reduction

Businesses may efficiently detect inefficiencies, redundancies, and waste via BPR. Organizations can streamline and cut costs.

BPR redesigns processes to optimize resource allocation, decrease non-value-added work, and increase efficiency. Automating tedious operations, reorganising workflows to decrease bottlenecks, renegotiating supplier contracts for better terms, or using technology to improve collaboration and communication may be needed.

Benefits:

Improved efficiency

Lower costs

Increased competitiveness

Improving output quality

From production to service delivery, BPR improves output quality. BPR usually improves KPIs.

Implementing quality control, promoting continuous improvement, and leveraging customer feedback and other metrics to drive innovation can increase output quality.

Technology can automate operations. Eliminating distractions helps staff focus on providing high-quality products and services. It promotes client trust and loyalty and helps the company succeed.

Benefits:

Better client satisfaction

Fewer mistakes

Better brand image

Optimizing HR processes

BPR is essential for HR optimisation. Automating onboarding with simple portals, optimising workflows, establishing self-service portals and applications, employing AI for talent acquisition, and data-driven performance management are examples.

Engaging employees helps attract, develop, and retain outstanding talent. HR methods aligned with company values can boost employee productivity, satisfaction, and business performance.

Benefits:

Recruiting faster

Enhanced employee engagement

Allocating talent strategically

Examples of BPR: Case studies

BPR methodology and use cases work together to assist clients in the following case studies.

Bouygues leads French telecom AI

Bouygues Telecom, a major French communications provider, suffered with ageing systems that couldn’t handle many support calls. The result? Bouygues risked being displaced by competition as frustrated consumers waited in call lines. Bouygues’ pre-IBM Watsonx AI implementation with IBM was a blessing. Phase 1 preparations were ideal for phase 2’s AI integration into the telecom call centre.

Bouygues receives over 800,000 calls a month using IBM watsonx Assistant, and IBM watsonx Orchestrate frees agents from monotonous duties to focus on higher-value jobs. Pre- and post-call workloads dropped 30% for agents. In addition, 8 million customer-agent exchanges, previously partially analysed, have been accurately summarised for actionable insights.

These innovations have made Bouygues a customer care disruptor, reducing operational expenses by USD 5 million and putting them at the forefront of AI technology.

Customer-centric transformation at Finance of America fosters lifetime loyalty. Finance of America co-created with IBM to tailor their operations to consumers, delivering value for them and prospective homebuyers.

FOA expects to increase their customer base in three years after this transition. Revenue and income should rise by 50% and 80%, respectively, in the same timeframe. Finance of America is ready to offer debt advisory and other services that will build customer loyalty.

Business Process Reengineering

IBM business process reengineering analyses fundamental processes to identify and redesign areas for improvement. By stepping back, strategists can examine supply chain, customer experience, and finance. BPR services specialists can integrate new technologies and rework procedures to improve the business. They can help you create intelligent workflows that boost profits, eliminate redundancies, and cut costs.

Read more on govindhtech.com

[allsoft12]

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[globalknowledgetechnologies]

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