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

Exactpro
8 Sept 202224:09

TLDRThis video introduces the ISTQB Certified Tester AI Testing certification, designed for professionals involved in testing AI systems. It covers AI's evolution, definitions, and categories like Narrow AI, General AI, and Super AI. The video also discusses AI technologies, including machine learning, reasoning techniques, and neural networks, and highlights the importance of AI standards and regulations.

Takeaways

  • 📘 The ISTQB Certified Tester AI Testing Certification is designed for professionals involved in testing AI systems or using AI for testing purposes.
  • 🤖 Artificial Intelligence (AI) was first coined in the 1950s and has significantly evolved since then, with modern AI being about the capability of systems to acquire, process, and apply knowledge and skills.
  • 🔍 AI is categorized into three types: Narrow AI (weak AI), General AI (strong AI), and Super AI, with each having different capabilities and applications.
  • 🧠 Narrow AI, the most common form, is used for specific tasks and can assist in various testing processes, including generating test cases and improving defect reports.
  • 🚀 General AI, also known as strong AI, is the theoretical concept of machines that can perform any intellectual task a human can, but no such system has been realized yet.
  • 🌟 Super AI refers to an intelligence exceeding human cognitive capacity in all fields, a concept often associated with the technological singularity.
  • 🔢 AI systems use a variety of techniques, including reasoning techniques, machine learning methods like supervised, unsupervised, and reinforcement learning, and other algorithms like genetic algorithms and neural networks.
  • 🛠️ AI implementation can be done using different technologies and frameworks, which have made AI more accessible to a broader audience, including through libraries like Keras, Pytorch, and Tensorflow.
  • 💻 Appropriate hardware is essential for AI, with GPUs being more efficient for training and running ML models compared to CPUs, and specialized hardware like TPUs being designed for AI-specific tasks.
  • 🛡️ The use of pre-trained models and transfer learning can reduce costs and resource consumption in AI development, but they come with certain risks and considerations.
  • 🏛️ There are various international and regional standards for AI, including those from the European Parliament, the Joint Technical Committee of IEC and ISO, and industry bodies like IEEE, which aim to ensure quality and ethical use of AI.

Q & A

  • What is the purpose of the ISTQB Certified Tester AI Testing Certification?

    -The ISTQB Certified Tester AI Testing Certification is designed for anyone involved in testing AI-based systems and/or AI for testing, including professionals like testers, test analysts, data analysts, and software developers. It aims to provide a basic understanding of testing AI-based systems and exploring ways to use AI for testing.

  • What does the acronym 'AI' stand for, and who coined the term?

    -AI stands for 'Artificial Intelligence', and the term was coined in the 1950s by John McCarthy.

  • How has the definition of AI evolved since its inception?

    -The definition of AI has evolved significantly from its inception. A modern definition describes AI as the capability of an engineered system to acquire, process, and apply knowledge and skills, reflecting a shift from the initial concept of programming machines to imitate human intelligence.

  • What is the 'AI Effect', and how does it influence the perception of AI?

    -The 'AI Effect' refers to the changing concept of what constitutes AI. As society's perception of AI changes, so does its definition. When a problem is solved using AI, it often loses its 'mysterious' allure and moves from being unattainable to mundane, leading to a shift in the focus area of AI.

  • What are the three categories of AI, and how do they differ?

    -The three categories of AI are Narrow AI (or weak AI), General AI (or strong AI), and Super AI. Narrow AI is programmed for specific tasks with limited context and is widely available today. General AI, which has not been realized yet, would be capable of performing any intellectual task a human can. Super AI refers to an intelligence that vastly exceeds human cognitive capacity in all fields of knowledge.

  • How can AI assist in the testing process?

    -AI can assist in the testing process by generating test cases, planning the overall test process, improving defect report quality, and using natural language processing (NLP) and sentiment analysis for intelligent capturing and text recognition without human intervention.

  • What are the main types of machine learning techniques, and how do they differ?

    -The main types of machine learning techniques are Supervised Learning, Unsupervised Learning, and Reinforcement Learning. Supervised learning creates models from labeled data, unsupervised learning deduces patterns from unlabeled data and assigns inputs to different classes, while reinforcement learning involves an agent learning by interacting with the environment and receiving rewards or penalties based on its decisions.

  • What is the role of hardware in AI and machine learning applications?

    -Hardware plays a crucial role in AI and machine learning applications by providing the necessary processing power and capabilities. Specialized hardware like GPUs and Tensor Processing Units (TPUs) are designed for massively parallel processing and are more efficient for training and running ML models compared to general-purpose CPUs.

  • What is transfer learning, and how is it applied in AI?

    -Transfer learning is a method in AI where a pre-trained model is modified to perform a different task or requirement. It leverages the early layers of a neural network that perform basic tasks, while the later layers are retrained to handle more specialized assignments. This approach saves resources by eliminating the need to train the early layers from scratch.

  • What are the potential risks associated with using pre-trained models in AI?

    -The potential risks of using pre-trained models include a lack of transparency, insufficient similarity between the pre-trained model's function and the required functionality, differences in data preparation steps that may impact performance, and inheriting the shortcomings and vulnerabilities of the original model.

  • What is the significance of standards in AI development, and how do they impact testing?

    -Standards in AI development, such as those set by the European Parliament, ISO/IEC, and industry bodies like IEEE, provide guidelines and best practices for creating AI systems. They ensure functional performance, mitigate potential discrimination, and protect individual rights. From a testing perspective, these standards emphasize the need for accuracy and precision in AI systems, especially when dealing with personal data.

Outlines

00:00

📘 ISTQB Certified Tester AI Testing Certification Overview

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.

05:00

🤖 Understanding AI: From General to Super AI

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.

10:02

📚 Exploring Machine Learning Techniques and Algorithms

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.

15:02

🛠️ AI Development Frameworks and Hardware Requirements

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.

20:04

⚠️ Risks of Pre-trained Models and AI Regulations

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.

📢 Conclusion and Invitation to Further ISTQB AI Testing Content

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.

Mindmap

Keywords

💡ISTQB

ISTQB stands for the International Software Testing Qualifications Board. It is an organization that certifies the competence of software testers. In the context of the video, ISTQB offers a Certified Tester AI Testing Certification, which is the main theme of the video. The certification is designed for professionals involved in testing AI-based systems or using AI for testing purposes.

💡AI Testing

AI Testing refers to the process of verifying and validating AI-based systems to ensure they perform as expected. The video discusses the ISTQB's Certified Tester AI Testing syllabus, which is aimed at professionals who want to gain expertise in this field. AI Testing is a key aspect of the certification program.

💡Artificial Intelligence (AI)

Artificial Intelligence, or AI, is the simulation of human intelligence in machines that are programmed to think like humans and mimic their actions. The video provides a historical perspective on AI, starting from its coinage in the 1950s to its modern definition as a system's capability to acquire, process, and apply knowledge and skills.

💡Certified Tester Foundation Level

The Certified Tester Foundation Level is a prerequisite for obtaining the AI Testing Certification. It signifies that the candidate has a foundational understanding of software testing. The video mentions that candidates for the AI Testing Certification must already hold this certificate.

💡Narrow AI (Weak AI)

Narrow AI, also known as weak AI, refers to AI systems designed to perform specific tasks within a limited context. The video explains that these systems can analyze and interpret data with high accuracy and are widely used in applications like game-playing systems, spam filters, and self-driving cars.

💡General AI (Strong AI)

General AI, or strong AI, is the concept of AI systems that possess the ability to perform any intellectual task that a human can do. The video describes it as the 'holy grail' for AI researchers, but clarifies that no such systems have been realized to date.

💡Super AI

Super AI, as defined by Nick Bostrom, is an intelligence that vastly exceeds human cognitive capacity in all fields of knowledge. The video discusses this concept and its potential implications, suggesting that the transition from general AI to super AI is known as the technological singularity.

💡Machine Learning

Machine Learning is a subset of AI that allows systems to learn from and make decisions based on data. The video outlines three types of machine learning: supervised, unsupervised, and reinforcement learning, and provides examples of algorithms used in each category.

💡Neural Networks

Neural Networks are computing systems inspired by the human brain that consist of interconnected units or nodes, known as artificial neurons. The video mentions that they can be applied to various tasks in both supervised and unsupervised learning, as well as reinforcement learning.

💡Transfer Learning

Transfer Learning is a method where a pre-trained model is adapted to a new, related problem. The video explains that this approach saves resources by reusing parts of the model that have already been trained, and only retraining the parts necessary for the new task.

💡Pre-trained Models

Pre-trained Models are AI models that have already been trained on large datasets and can be fine-tuned for specific tasks. The video discusses the benefits and risks associated with using pre-trained models, such as reduced resource consumption and potential lack of transparency.

💡AI Frameworks

AI Frameworks are tools and libraries that facilitate the development of AI applications. The video mentions several frameworks like Keras, PyTorch, and TensorFlow, which are used for implementing neural networks and other machine learning algorithms.

💡Hardware Requirements for AI

The video discusses the specific hardware requirements for AI, such as the need for low-precision arithmetic, the ability to work with large data structures, and massively parallel processing capabilities. It mentions that GPUs are typically more efficient for training and running ML models compared to CPUs.

💡AI Standards and Regulations

AI Standards and Regulations refer to the rules and guidelines established to govern the development and use of AI technologies. The video highlights the work of organizations like the European Parliament, ISO/IEC, and IEEE in setting these standards, as well as the importance of GDPR in ensuring the ethical use of AI.

Highlights

The ISTQB Certified Tester AI Testing Certification is designed for professionals involved in testing AI-based systems or using AI for testing.

Candidates must hold the Certified Tester Foundation Level certificate to gain the AI Testing certification.

Artificial Intelligence (AI) was first coined in the 1950s by John McCarthy, aiming to create 'intelligent' machines.

AI has evolved to mean the capability of an engineered system to acquire, process, and apply knowledge and skills.

The perception of AI changes over time, a phenomenon known as the 'AI Effect'.

AI is categorized into Narrow AI (weak AI), General AI (strong AI), and Super AI.

Narrow AI systems perform specific tasks with limited context and are widely available today.

General AI, also known as strong AI, is capable of performing any intellectual task a human can, but has not yet been realized.

Super AI refers to an intelligence that vastly exceeds human cognitive capacity in all fields of knowledge.

AI systems use an observe-and-learn approach to determine reactions to new data based on patterns.

AI can be implemented using reasoning techniques, machine learning, and other technologies.

Supervised learning in AI involves creating models from labeled data to predict outcomes.

Unsupervised learning allows AI to find patterns in unlabeled data and group similar data points.

Reinforcement learning is an iterative process where an agent learns through rewards and penalties.

Genetic algorithms and neural networks are other types of algorithms used in AI.

Fuzzy logic in AI handles the notion of partial truth, similar to human reasoning.

AI development frameworks have made AI more accessible, with support for various activities from data preparation to model deployment.

AI requires appropriate hardware, with GPUs being more efficient for training and running ML models compared to CPUs.

Pre-trained models can be a cost-effective alternative to creating new models from scratch.

Transfer learning allows for the modification of pre-trained models to perform different tasks.

The European Parliament and Council propose harmonized rules for AI to ensure economic and societal benefits.

International standards for AI are set by organizations like ISO/IEC JTC 1/SC42 and address testing of AI-based systems.

The GDPR sets rules for AI systems regarding personal data and automated decision-making to ensure accuracy and fairness.