ISTQB Certified Tester AI Testing Explained – Chapter 1 – Introduction to AI

This paragraph introduces the speaker, Dmitrii, and his role in the Exactpro research team. It outlines the purpose of the video, which is to provide an overview of the ISTQB Certified Tester AI Testing syllabus. The video is not a replacement for the official syllabus but aims to supplement it. The target audience includes various professionals involved in AI-based systems testing. The paragraph also discusses the historical evolution of AI, its modern definition, and the changing perceptions of what constitutes AI, known as the 'AI Effect'. It concludes with an explanation of strong and weak AI, providing examples of each and their roles in testing.

This section delves into the distinctions between different types of AI, starting with General AI, which is capable of any intellectual task a human can perform but has not yet been realized. It then introduces Super AI, a concept defined by Nick Bostrom as an intelligence exceeding human cognitive capacity in all fields. The paragraph discusses the technological singularity and the potential implications of Super AI's development. It also contrasts conventional computer systems with AI systems, highlighting the latter's observe-and-learn approach and the use of data patterns to inform reactions to new data. The paragraph concludes with an overview of various AI implementation technologies, including reasoning techniques and machine learning methods.

The paragraph provides an in-depth look at machine learning techniques, categorizing them into supervised, unsupervised, and reinforcement learning. It explains the process of supervised learning with examples of algorithms like linear regression, logistic regression, and support vector machines. The section on Bayesian models discusses their probabilistic nature and examples like Naive Bayes. Decision tree algorithms are also explored, including random forests and their advantages over traditional decision trees. Unsupervised learning is highlighted for its ability to find patterns in unlabeled data, with clustering and association algorithms like K-means and DBSCAN. Reinforcement learning is introduced as an interactive learning approach without training data, using examples from robotics and autonomous vehicles. The paragraph also mentions other algorithm types like genetic algorithms and neural networks, and their applications in tasks like fuzzy logic and recommendation systems.

This paragraph discusses the democratization of AI development due to the availability of various libraries and frameworks, making AI accessible beyond a select group of researchers. It mentions popular AI development frameworks tailored for specific programming languages and use cases, such as Keras, Pytorch, and Tensorflow for neural networks, and Scikit-learn for classical machine learning. The paragraph also addresses the hardware requirements for AI, emphasizing the importance of low-precision arithmetic, large data structure handling, and massively parallel processing. It contrasts the efficiency of general-purpose CPUs with GPUs designed for AI tasks and introduces AI-specific hardware like TPUs and SoCs. The discussion on AI components includes their creation, third-party use, and the concept of ML as a service, highlighting the benefits of using pre-trained models and transfer learning to reduce resource consumption and risk.

The paragraph addresses the potential risks associated with using pre-trained models, such as a lack of transparency, insufficient similarity to required functionality, and inherited vulnerabilities. It suggests that thorough documentation can mitigate some of these risks. The paragraph then shifts focus to the regulatory landscape of AI, mentioning the European Parliament's proposal for harmonized AI rules and the establishment of international standards by organizations like ISO/IEC JTC 1/SC42. It also touches on the GDPR's impact on AI systems, emphasizing the need for accurate personal data handling and individuals' rights regarding automated decision-making. The importance of safety-related system regulations, such as ISO 26262 for automotive software, is highlighted, and the voluntary nature of standards is discussed, with an emphasis on their value in creating higher quality products.

The final paragraph wraps up the video by inviting viewers to engage with the content through likes and subscribing to the channel for more informative videos. It also teases the next video in the series, which will cover chapter 2 of the ISTQB Certified Tester AI Testing syllabus, encouraging viewers to stay tuned for further insights into AI testing.