Artificial Intelligence I ISO/IEC 22989:2022 Training – Part 3 | Clause 5.1 to 5.4 Explained скачать в хорошем качестве

Artificial Intelligence I ISO/IEC 22989:2022 Training – Part 3 | Clause 5.1 to 5.4 Explained

Edicent Quality Registrar (EQR) Services: Certification, Training and Advising Contact Details: +91-8802650960; info@edicentcertification.org 🤖 ISO/IEC 22989:2022 Explained – Part 3 AI Concepts: From Narrow AI to Intelligent Agents & Knowledge Framework Welcome to Part 3 of our comprehensive training on ISO/IEC 22989:2022 – Artificial Intelligence: Concepts and Terminology. In this session, we explore Clause 5.1 to 5.4, focusing on foundational AI concepts, agent paradigms, and the structured understanding of knowledge within AI systems. 📘 AI Concepts – General Foundations Artificial Intelligence is an interdisciplinary field involving the development of AI systems within computer systems that perform intelligent tasks by perceiving and interacting with their environment. AI integrates techniques from: Computer Science Mathematics Philosophy Linguistics Economics Psychology Cognitive Science Modern AI systems exhibit interesting and practical features such as: Interactive behavior Contextual understanding Oversight mechanisms Adaptive learning capabilities AI systems perceive their environment, process information, and produce intelligent outputs aligned with defined objectives. 📘 From Strong & Weak AI to Narrow & General AI The distinction between strong AI and weak AI originates from philosophical discussions: Strong AI: Machines possessing genuine cognitive abilities Weak AI: Systems simulating intelligence without consciousness However, in practice, researchers and practitioners focus on: Narrow AI – Task-specific systems (current dominant AI form) General AI (AGI) – Hypothetical systems with broad cognitive capabilities From an operational perspective, the narrow vs. general AI classification is more suitable for technical and governance discussions. 📘 AI Agent Paradigm AI systems are often conceptualized as agents. An AI agent: Interacts with its environment through sensors Acts upon the environment using actuators Takes actions to achieve predefined goals Exhibits rational behavior within environmental constraints Agent behavior depends on: Environment characteristics Available information Defined performance measures Several AI agent types include: Reflex Agents – Act based on current perception Model-Based Agents – Maintain internal state models Goal-Based Agents – Act to achieve defined objectives Utility-Based Agents – Optimize performance measures Learning Agents – Improve through experience More sophisticated and high-level architectures combine these paradigms to achieve complex intelligent behavior. 📘 Knowledge in Artificial Intelligence Knowledge in AI is a structured and technical concept. Key aspects include: The Data–Information–Knowledge hierarchy Differentiation between information and knowledge Representation of knowledge in various forms Multiple representations conveying the same meaning Technical implications of knowledge modeling Unlike raw data, knowledge involves structured interpretation and contextual meaning. AI systems may incorporate: Domain-specific cognitive capabilities Symbolic or subsymbolic knowledge representations Mechanisms for inference and reasoning It is important to distinguish: Knowledge vs. information Cognitive vs. non-cognitive processes Different representations with equivalent semantic meaning The way knowledge is structured directly impacts AI system performance, interpretability, and technical robustness. 🎯 Why Clause 5.1–5.4 Matters Understanding AI concepts at this level is essential for: ✔ AI governance frameworks ✔ Risk management ✔ System design & architecture ✔ Regulatory alignment ✔ Responsible AI deployment Without structured conceptual clarity, AI development risks inconsistency and misinterpretation. ISO/IEC 22989 provides a standardized foundation to ensure AI discussions are technically accurate, globally aligned, and conceptually robust. 📌 In the next part, we will continue building structured understanding around AI systems and their implementation frameworks. Compliance of any international standard has three pillars management team, audit, and training only, it adds more valuable than marketing in short and long term run, compliance importance and usefulness is all belong to a business internally itself not on external dependence. You may connect for our service at www.edicentcertification.org, please like, subscribe and share. Bank account details for your support EQR Account Detail: Bank Name: HDFC Bank Current Account Name: Edicent Quality Registrar Current Account Number: 50200086783433 IFSC Code: HDFC0005269 SWIFT Code: HDFCINBBDEL UPI ID: 8882814173@hdfcbank Paypal ID: https://paypal.me/EQRQuality Thank you.