Fundamentals of Artificial Intelligence
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What is Artificial Intelligence (AI)?

The technology that enables computers or machines to simulate human-like intelligence, such as seeing, hearing, speaking, interacting, thinking, predicting, making decisions, etc.

Which of the following are common abilities of AI technologies? (Select all that apply)

  • Form concepts (correct)
  • Self-learn and self-improve (correct)
  • Communicate using human language (correct)
  • Simulate human logic to solve problems (correct)

    • What is Facial Recognition?

  • A technology that uses computer vision techniques to detect faces in images (correct)
  • A method of identifying people based on their unique facial features (correct)
  • A security measure used in various applications, including access control and surveillance (correct)
  • All of the above (correct)

    • What is the primary function of Self-driving cars?

    <p>To use computer vision technology to see the road and make corresponding decisions</p> Signup and view all the answers

    What is the function of Chatbots in AI?

    <p>Natural language processing technologies are used to understand user inputs and generate responses (communicate using human language).</p> Signup and view all the answers

    Explain the working of Song Recommendation systems.

    <p>Deep learning is used to recommend songs based on user history (decision making).</p> Signup and view all the answers

    How do Voice-controlled robots work?

    <p>Speech recognition is also applied to process voice statements (hearing) and Natural Language Processing is applied to understand the commands.</p> Signup and view all the answers

    Which of the following applications/technologies do NOT use AI?

    <p>3D printer</p> Signup and view all the answers

    What is an AI Model?

    <p>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.</p> Signup and view all the answers

    AI models can only learn from an initial set of input data and cannot further adapt themselves.

    <p>False</p> Signup and view all the answers

    What is the purpose of Model Training in AI?

    <p>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.</p> Signup and view all the answers

    The accuracy of an AI model is independent of the quantity and quality of training data.

    <p>False</p> Signup and view all the answers

    What is the impact of insufficient training data on an AI model's performance?

    <p>Insufficient data, including insufficient data coverage, leads to incorrect results, e.g., a lack of data for Cantonese in Text-to-Speech</p> Signup and view all the answers

    What is biased data in AI training?

    <p>Imbalance between different classes of training data (the model will pay more attention to the majority class)</p> Signup and view all the answers

    Once trained, an AI model can only make predictions on the data it was trained on.

    <p>False</p> Signup and view all the answers

    What are Perceptrons in AI?

    <p>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.</p> Signup and view all the answers

    Perceptrons can only process a single input at a time.

    <p>False</p> Signup and view all the answers

    Explain how multiple perceptrons are connected in layers to form a neural network.

    <p>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.</p> Signup and view all the answers

    The learning rate in a neural network determines how quickly the model learns.

    <p>True</p> Signup and view all the answers

    What are the consequences of setting the learning rate too high in a neural network?

    <p>The model may learn faster, however, it may produce a less-than-optimal model.</p> Signup and view all the answers

    What is Computer Vision (CV)?

    <p>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.</p> Signup and view all the answers

    How does computer vision allow computers to ‘see’ and understand the world?

    <p>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.</p> Signup and view all the answers

    Computer vision applications do not raise any ethical concerns.

    <p>False</p> Signup and view all the answers

    What is Automatic Speech Recognition (ASR)?

    <p>Automatic speech recognition (ASR) is a technology that enables computers to recognize and translate human spoken speech into text, i.e., speech to text.</p> Signup and view all the answers

    Explain the process of ASR technology.

    <p>ASR technology involves training and testing an AI model.</p> Signup and view all the answers

    The accuracy of ASR is not affected by any factors.

    <p>False</p> Signup and view all the answers

    What is Natural Language Processing (NLP)?

    <p>Natural language processing (NLP) is a branch of AI that helps computers understand, interpret, and manipulate human language, either in text or speech form.</p> Signup and view all the answers

    What is Sentiment Analysis in NLP?

    <p>Sentiment Analysis is used to recognize subtle nuances in emotions and opinions – and determine how positive or negative they are.</p> Signup and view all the answers

    What is Machine Translation in NLP?

    <p>Machine translation is the process of using artificial intelligence to automatically translate text from one language to another without human involvement.</p> Signup and view all the answers

    What is the function of Chatbots and Virtual Assistants?

    <p>Chatbots and Virtual Assistants are used for automatic question answering, designed to understand natural language and deliver an appropriate response through natural language generation.</p> Signup and view all the answers

    What are Reasoning Systems in AI?

    <p>A reasoning system is a system that uses logical techniques, such as induction and deduction, to generate conclusions from a knowledge dataset.</p> Signup and view all the answers

    Which of the following are applications of Reasoning Systems? (Select all that apply)

    <p>Intrusion Detection</p> Signup and view all the answers

    What are the three levels of reasoning?

    <p>Knowledge-based reasoning</p> Signup and view all the answers

    What is Skills-based reasoning?

    <p>Actions taken based on intuitive sensory-motor behavior, without conscious control of straightforward tasks with mechanical movements.</p> Signup and view all the answers

    What is Knowledge-based reasoning?

    <p>Actions derived from adapting previous knowledge and planning, or analyzing the tasks to achieve a goal for complex tasks in uncertain or unfamiliar environments.</p> Signup and view all the answers

    What is Simulation in AI?

    <p>Simulation imitates real-world systems or processes by including real-world components and real-world rules (the inner mechanisms of the real-world system).</p> Signup and view all the answers

    Simulation is always real-time and cannot be adjusted for speed.

    <p>False</p> Signup and view all the answers

    What is Supervised Learning in AI?

    <p>Supervised learning is mainly about using data that is well labeled, i.e., data that is already tagged with the correct answer.</p> Signup and view all the answers

    What is Reinforcement Learning in AI?

    <p>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.</p> Signup and view all the answers

    What are the five ethical principles of AI?

    <p>Transparency, Justice and Fairness, Beneficence, Responsibility, Privacy</p> Signup and view all the answers

    Ethics in AI are not important because AI systems are incapable of causing harm.

    <p>False</p> Signup and view all the answers

    Study Notes

    Artificial Intelligence (AI)

    • AI is the technology enabling computers/machines to simulate human-like intelligence (seeing, hearing, speaking, interacting, thinking, predicting, making decisions).
    • AI technologies can communicate using human language.
    • AI technologies can identify and form concepts.
    • AI technologies can self-learn and improve.
    • AI technologies can simulate human logic to solve problems.
    • Examples include facial recognition (computer vision detecting faces in images) and self-driving cars (computer vision for road-based decisions).
    • Chatbots use natural language processing to understand user input and respond accordingly.
    • Song recommendation systems use deep learning based on user history to suggest music.
    • Voice-controlled robots use speech recognition and natural language processing.
    • Some applications do not use AI (e.g., 3D printers, ATMs, web browsing).

    Fundamentals of AI

    • AI models rely on mathematical algorithms.
    • Models use a substantial input dataset for training.
    • After training, models simulate expert decisions.
    • Example: classifying cats and dogs from image data.
    • Model training requires a large amount of labeled data.
    • During training, the AI model learns key features from the data.

    Model Performance and Training Data

    • Training data significantly influences AI systems.
    • The quantity and quality of training data affect model performance.
    • More training data generally leads to higher accuracy.
    • Insufficient training data or lack of data coverage can lead to inaccurate results (e.g., language-specific data).
    • High-quality data leads to high-performing models; low-quality data leads to underperforming models.
    • Biased data can cause the model to favor specific classes in the training data (the majority class).
    • Noisy data (incorrectly labeled data) will negatively affect the model.

    Perceptrons and Neural Networks

    • Artificial neural networks are modeled after the human brain.
    • Basic units in neural networks are called perceptrons.
    • Perceptrons generate output based on input and internal parameters.
    • Multiple perceptrons connected in layers form a neural network.
    • Layer outputs are used as inputs for the next layer.
    • During training, internal perceptron parameters are updated based on the learning rate.
    • High learning rate leads to faster but potentially suboptimal learning.
    • Low learning rate leads to slower training.

    Computer Vision (CV)

    • Computer Vision (CV) is an AI field enabling computers to derive meaningful information from visual data (images, videos).
    • CV allows computers to "see" the world through sensors and camera inputs.
    • CV analyzes and performs tasks like Object detection, classification, and tracking.
    • Privacy and ethical concerns are important considerations when using CV solutions.

    Automatic Speech Recognition (ASR)

    • Automatic Speech Recognition (ASR) converts spoken language into text.
    • ASR technology involves training and testing AI models.
    • Training processes include input preparation, training, and model refinement.
    • Model output is the speech transcription.
    • ASR accuracy can be influenced by speaker characteristics (accent, style, etc.), environmental factors, and the quality of recording devices.
    • Challenges include low-resource languages or a lack of data.

    Natural Language Processing (NLP)

    • NLP is an AI branch enabling computers to understand, interpret, and manipulate human language (text or speech).
    • NLP applications are used in daily tasks like sentiment analysis and machine translation.
    • Sentiment analysis determines the emotional tone of text (positive, negative, neutral).
    • Machine translation automatically translates text between languages.
    • Chatbots and Virtual Assistants use NLP for automatic question answering.

    AI Reasoning Systems

    • Reasoning systems utilize logical techniques to generate conclusions based on knowledge sets.
    • Reasoning comes in multiple forms including Skills-based, Rules-based, and Knowledge-based.

    AI Simulation

    • Simulation mimics real-world systems/processes with real-world components and rules.
    • Simulations allow for experimentation impractical in the real world.
    • Simulations improve decision-making through understanding possible events.
    • Examples include driving, flight, and game simulations.

    AI and Ethics

    • Ethical principles are essential in AI development.
    • Key AI ethical principles include, transparency, fairness, beneficence, accountability, and privacy.

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    Description

    This quiz explores the foundational concepts of Artificial Intelligence (AI), including how machines simulate human-like intelligence through technologies like natural language processing, computer vision, and deep learning. It covers various applications, such as chatbots and self-driving cars, and addresses common misconceptions about AI. Test your knowledge on AI fundamentals and its impact on technology.

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