ISO 42001/22989: Fundamentals of Artificial Intelligence -AI

You will master the key concepts and fundamental definitions of the ISO 42001 standard.

You will analyze the terminology bases to understand management systems.

Essential concepts of ISO/IEC 22989 In this first learning block we will delve into the fundamental concepts that govern the universe of artificial intelligence (AI).

In this first project, we'll focus on TensorFlow (Keras) because it offers a very gentle learning curve within Colab: it only takes a few lines to define and train the network, and TensorBoard is already integrated to audit it. The goal is to learn how to verify, explain, and document the performance of a Feed-Forward Neural Network (FFNN). Later, in a second exercise, we'll reproduce part of GPT-2 with PyTorch to compare approaches and repeat the audit in a generative model context, but the conceptual foundation is laid here.

Tools and concepts you'll master:

• Google Colab – free GPU-powered cloud notebook: run everything without installing anything.

• TensorFlow / Keras – high-level framework for building and training the FFNN.

• TensorBoard – interactive dashboard for inspecting loss/accuracy curves, weight histograms, and the computational graph.

• Feed-Forward Neural Network (FFNN) – basic AI architecture for binary classification.

• AI audit process – input data validation, overfitting detection, architecture review, and metrics traceability (aligned with ISO 22989 / ISO 42001).

• Model governance – library versions, log recording, and translation of technical findings into business decisions.

• PyTorch + GPT-2 (later phase) – will be used to simulate a language model and repeat the audit, comparing results with the TensorFlow-based workflow.

With this progressive path, students will gain a complete understanding of how to audit neural networks, first in a classical supervised environment and then in a generative AI scenario.

In this section of the course, students will continue to acquire a structured and up-to-date understanding of the technical, functional, and methodological concepts that define artificial intelligence according to the ISO/IEC 22989 standard.

List of key concepts that students will master:

• Data mining vs. machine learning

• Machine learning algorithms and their training

• Autonomous and adaptive decision-making

• Lifelong learning

• Explainable models and algorithmic traceability

• Differences between symbolic and subsymbolic AI

• Artificial neural networks (FFNN, CNN, RNN, LSTM)

• Data quality assessment and validation

• Big data

• Semantic computing and ontological structures

• IoT, cyber-physical systems, and AI infrastructure (cloud and edge)

• Functional lifecycle of AI systems

• AI ecosystems, layered architecture, and functions in intelligent systems

Learn the main fields of application of AI and how to understand how it works:

Main application domains of AI:

Computer vision (We will do a practical exercise to understand it).

• Natural language processing

• Natural language processing (NLP)

• Natural Language Generation (NLG)

• Optical Character Recognition (OCR)

• Text-to-Image/Video Generation (T2I)

• Conversational agents

• Recommendation and personalization systems

• Applications in healthcare, education, industry, finance, and transportation

Learn from the most popular AI tools to understand concepts such as ChatGPT, Eleven Labs, Heygen, Sora, Explotion, among others.