AI Literacy Test Notes PDF

Chapter 1 - Introduction to Artificial Intelligence (AI) What is Artificial Intelligence (AI)? AI is the technology that enables computers or machines to simulate human-like intelligence, such as seeing, hearing, speaking, interacting, thinking, predicting, making decisions, etc). Some common abi...

Chapter 1 - Introduction to Artificial Intelligence (AI) What is Artificial Intelligence (AI)? AI is the technology that enables computers or machines to simulate human-like intelligence, such as seeing, hearing, speaking, interacting, thinking, predicting, making decisions, etc). Some common abilities of AI technologies AI enables computers or machines to: Communicate using human language Form concepts Self learn and self improve Simulate human logic to solve problems Examples of AI technologies Self-driving cars: Computer vision Facial recognition: Computer vision technologies are used in self-driving techniques are used to detect (see) cars to see the road and make faces in images. corresponding decisions. 1 Chatbots: Natural language processing Song recommendation systems: Deep technologies are used to understand learning is used to recommend songs user inputs and generate responses based on user history (decision making). (communicate using human language). Voice-controlled robots: Speech recognition is also applied to process voice statements (hearing) and Natural Language Processing is applied to understand the commands. Examples of Applications/Technologies that do not use AI Automated Teller 3D printer Web browsing Machine (ATM) 2 Chapter 2 - Fundamentals of AI What is an AI model? AI models are based on mathematical algorithms that use a large amount of input data for training and once trained, simulate the decisions made by experts. For example, given a set of input data such as images of cats and dogs, the trained AI model is able to classify the images as “cats” and “dogs”. Model Training In order to train an AI model for a specific task, we need a large amount of labeled data. During training, the AI model learns to extract key features from the data. 3 Once trained, the model can make predictions on new data. Impact of Training Data on Model Performance Training data has a critical influence on an AI system. The quantity and quality of the training data affects the performance of the AI model. Quantity ○ More training data leads to higher accuracy ○ Insufficient data, including insufficient data coverage, leads to incorrect results, e.g., a lack of data for Cantonese in Text-to-Speech Quality ○ High quality data leads to a high performing model ○ Low quality data leads to a low performing model Biased data – imbalance between different classes of training data (the model will pay more attention to the majority class) Noisy data – training data is incorrectly labeled 4 Perceptrons and Neural Networks Artificial Neural Networks are modeled after the human brain. Similar to how neurons are the basic units of the brain and the nervous system, an artificial neural network is made up of neurons called perceptrons. A perceptron is able to generate output based on the given input(s) and its associated internal parameter(s). For complex tasks, multiple perceptrons are connected in layers to form a neural network, where the output of a previous layer is passed on as the input to the next layer. During training, the internal parameters of each perceptron in the neural network are updated at the end of each epoch according to the learning rate. If the learning rate is set too high, the model may learn faster, however, it may produce a less-than-optimal model. If the learning rate is set too low, training will take too long. 5 Chapter 3 - See What is Computer Vision (CV)? Computer Vision (CV) is a field of AI that enables computers and systems to derive meaningful information from digital images, videos, and other visual inputs – and take actions or make recommendations based on that information. In other words, computer vision allows computers to see, observe, and understand the world. Computer vision ‘sees’ the world through sensors and cameras, and analyzes it with the use of algorithms to perform tasks such as object detection, classification, tracking, etc. Sense Analyze When adopting computer vision solutions, it is important to pay attention to privacy and ethical issues that may arise. 6 Chapter 4 - Hear What is Automatic Speech Recognition (ASR)? Automatic speech recognition (ASR) is a technology that enables computers to recognize and translate human spoken speech into text, i.e., speech to text. ASR technology involves training and testing an AI model. Once model training is completed, the model will interpret the input speech and output the transcription. The input for an ASR system, i.e., spoken audio, is captured using acoustic devices such as microphones. 7  While ASR technology is quite mature now, the accuracy of recognition and transcription is still affected by various factors: ○ Speaker characteristics – speaking style, age, accent, vocabulary, phrasing, pauses ○ Accent – regional trends cause the same word to sounds different (e.g., American and Australian accent for ‘data’) ○ Environmental factors – background noises ○ Recording device – available quality ○ Low resource languages – lack of training data for a given language 8 Chapter 6 - Read What is Natural Language Processing (NLP)? Natural language processing (NLP) is a branch of AI that helps computers understand, interpret, and manipulate human language, either in text or speech form. NLP Applications NLP applications are widely used in our daily lives. The following are some examples of NLP technologies: Sentiment Analysis is used to recognize subtle nuances in emotions and opinions ‒ and determine how positive or negative they are. Machine translation is the process of using artificial intelligence to automatically translate text from one language to another without human involvement. 9 Chatbots and Virtual Assistants are used for automatic question answering, designed to understand natural language and deliver an appropriate response through natural language generation. 10 Chapter 7 - AI Reasoning What are reasoning systems? A reasoning system is a system that uses logical techniques, such as induction and deduction, to generate conclusions from a knowledge dataset. They have a wide range of applications, such as scheduling, rule processing, problem solving, complex event processing, intrusion detection, robotics, computer vision and natural language processing. Levels of reasoning Reasoning can be loosely grouped into 3 levels: 1. Skills-based reasoning: actions taken based on intuitive sensory-motor behavior, without conscious control of straightforward tasks with mechanical movements. 2. Rules-based reasoning: actions based on stored rules or procedures derived from prior occasions or other’s experiences for well-defined and structured tasks with a small degree of adaptation and planning. 3. Knowledge-based reasoning: actions derived from adapting previous knowledge and planning, or analyzing the tasks to achieve a goal for complex tasks in uncertain or unfamiliar environments. 11 Chapter 8 - Simulation What is Simulation? Simulation imitates real-world systems or processes by including real-world components and real-world rules (the inner mechanisms of the real-world system). Simulation is continuous in time, and the speed of simulation can be tuned faster, slower, or paused. Examples of simulations include driving simulation, flight simulation, and even games. Simulation allows us to explore situations and do experiments that would otherwise be difficult or impossible to do so that we can better understand the situation. And thus, simulation can also help humans make better decisions. Building a simulation environment Step 1: Preparing the world model To create a driving simulation, we first need to build and set parameters for objects such as roads, traffic lights, driving rules, speed limits and pedestrian crossings. Step 2: Analyzing and constructing the physics After building the world model, we need to work on modeling the physics of some objects in the simulation environment, such as car engines, transmissions, tires, grips, steering and aerodynamics. Step 3: Enable controls and interaction The last step would be to allow users to control or interact with objects in the simulation environment, such as steering wheels, throttle pedals, brake pedals, gearboxes and dashboards. 12 We also need to set the responses of some objects in the world model, such as collision simulations, traffic light interactions and speed limits. How AI learns from simulation Similar to how humans learn something new, AI can learn from two approaches – supervised learning (SL) and reinforcement learning (RL). Supervised learning is mainly about using data that is well labeled, i.e., data that is already tagged with the correct answer. For example, in the images of cats and dogs that we went through above, each image in the training dataset was already labeled as “cat” or “dog”. Reinforcement learning is based on the simple principle of trial-and-error, where the AI model learns by itself by performing different actions and distinguishing between good and bad actions. 13 Chapter 11 - AI and Ethics AI Ethical Principles 1. Transparency – AI technologies must be understood and explained to users. 2. Justice and Fairness – AI must respect equality, inclusion, and diversity. 3. Beneficence – AI must do no harm. 4. Responsibility – AI must be accountable and honest. 5. Privacy – AI must protect and secure sensitive data. 14