Artificial Intelligence I ISO/IEC 22989:2022 Training – Part 6 | Clause 5.11.9 to 5.12.1.2 Explained скачать в хорошем качестве

Artificial Intelligence I ISO/IEC 22989:2022 Training – Part 6 | Clause 5.11.9 to 5.12.1.2 Explained

Edicent Quality Registrar (EQR) Services: Certification, Training and Advising Contact Details: +91-8802650960; info@edicentcertification.org In this Part 6 of the ISO/IEC 22989:2022 training series, we continue exploring advanced Artificial Intelligence concepts, focusing on machine learning lifecycle management and neural network algorithms as defined in the AI terminology and conceptual framework of the standard. This session begins with the concept of retraining in machine learning systems. Retraining is essential for maintaining the performance and reliability of AI models over time. As real-world environments evolve, AI systems can experience data drift, where the statistical properties of input data change, or concept drift, where the relationship between inputs and outputs shifts. Without retraining, these changes can degrade model accuracy and reliability. The training also discusses catastrophic forgetting, a challenge in which previously learned knowledge may be lost when models are updated with new data. The video further explores continuous learning, a more advanced paradigm where machine learning systems continuously update their knowledge through incremental training. Continuous learning enables AI systems to operate without interruption while adapting to new information and evolving conditions. It often involves incorporating human actions and feedback, allowing the system to improve performance over time. However, continuous learning introduces several challenges, including dynamic environments, data augmentation requirements, and the integration of new knowledge domains, making the design and governance of such systems particularly important. The training then moves to examples of machine learning algorithms, focusing on neural networks, one of the most influential AI technologies. Neural networks are computational models inspired by biological neural systems and consist of interconnected processing elements known as neurons. These neurons are organized into multiple layers, where each neuron processes inputs using weighted connections and passes the results forward through the network. Through training processes, neural networks learn patterns and relationships within data, enabling them to perform complex tasks such as classification, prediction, recognition, and decision support. In modern AI applications, deep learning techniques extend neural networks into deeper architectures capable of creating hierarchical representations of data, reducing the need for manual feature engineering. However, deep learning models often require significant computational power and large datasets for effective training. The session also introduces different neural network architectures, including feed-forward neural networks (FFNN), recurrent neural networks (RNN), and convolutional neural networks (CNN). Special attention is given to the feed-forward neural network, the most straightforward neural architecture in which information flows in a single direction from input to output without feedback connections. In fully connected FFNN structures, each neuron in one layer is connected to neurons in the next layer, enabling structured learning across the network. By understanding retraining strategies, continuous learning mechanisms, and neural network architectures, viewers gain deeper insight into how modern AI systems evolve and maintain performance within dynamic environments. These concepts are fundamental to the AI terminology and conceptual framework described in ISO/IEC 22989:2022, supporting better communication, governance, and implementation of Artificial Intelligence technologies across organizations. This training is particularly valuable for AI professionals, data scientists, cybersecurity experts, auditors, researchers, and organizations implementing AI governance frameworks aligned with international standards. Stay tuned for the next parts of the series as we continue exploring the global standard for Artificial Intelligence concepts and terminology under ISO/IEC 22989:2022. 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.