AI Project Life Cycle Quiz
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Questions and Answers

What is the primary goal of data visualization?

  • To present graphical representation for easy understanding (correct)
  • To store data securely
  • To clean and preprocess data
  • To solve mathematical problems

    • A decision tree does not allow for multiple directions or branches.

    False (B)

    What are the two main AI modeling approaches mentioned?

    Rule Based Approach and Learning Based Approach

    Data visualization represents data and information in a ______ context to identify patterns and trends.

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

    Match the stages of an AI model process with their primary focus:

    <p>Problem Scoping = Defining the project goals and objectives Data Acquisition = Gathering relevant data for analysis Modeling = Building predictive models based on data Evaluation = Testing the model for efficiency and performance</p> Signup and view all the answers

    Which of the following best describes the learning-based approach in AI modeling?

    <p>The machine learns patterns from random data (C)</p> Signup and view all the answers

    Decision trees can be used for both classification and prediction tasks.

    <p>True (A)</p> Signup and view all the answers

    Name one benefit of spending time exploring data.

    <p>Identifying trends and patterns</p> Signup and view all the answers

    What is the primary purpose of deployment in AI?

    <p>To integrate the AI model into a production environment (B)</p> Signup and view all the answers

    What is the first stage of the AI project cycle?

    <p>Problem Scoping (D)</p> Signup and view all the answers

    Bias in AI systems can originate from the training data used to develop the model.

    <p>True (A)</p> Signup and view all the answers

    Name one ethical principle that should be considered when developing AI solutions.

    <p>Human Rights</p> Signup and view all the answers

    Training data typically accounts for 20% of the total data used in an AI project.

    <p>False (B)</p> Signup and view all the answers

    The integration of AI into the workplace can lead to job ________.

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

    What does the 4 W’s Canvas help to identify in problem scoping?

    <p>Who, What, When/Where, Why</p> Signup and view all the answers

    Match the following ethical principles with their descriptions:

    <p>Human Rights = Ensuring freedom and non-discrimination Bias = Partiality in decision-making Privacy = Protection of personal information Inclusion = Representation of diverse groups</p> Signup and view all the answers

    ________ is the lifeblood of Artificial Intelligence.

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

    Match the following data collection methods with their descriptions:

    <p>Sensors = Data collected from various biometric devices Surveys = Customer feedback and review data Cameras = Data captured from webcams or CCTV API = Data obtained from applications generated on servers</p> Signup and view all the answers

    Which of the following is NOT a concern when deploying AI?

    <p>Enhancing user engagement (D)</p> Signup and view all the answers

    Which of the following is NOT a method of data acquisition?

    <p>Model Training (D)</p> Signup and view all the answers

    In AI, sufficient dataset size always guarantees unbiased results.

    <p>False (B)</p> Signup and view all the answers

    What can be implemented to mitigate the negative effects of AI on the workforce?

    <p>Retraining programs</p> Signup and view all the answers

    Data exploration is the process of identifying trends and patterns in data.

    <p>True (A)</p> Signup and view all the answers

    What is the purpose of data exploration in the AI project cycle?

    <p>To find patterns and trends in the data.</p> Signup and view all the answers

    Flashcards

    AI Project Cycle - Stage 1

    The initial phase of an AI project, focusing on defining the problem and its scope.

    4 W's Problem Canvas

    A framework for analyzing a problem, considering who, what, when/where, and why.

    Data (AI)

    Information, facts, figures, and statistics used to train and test AI models.

    Training Data

    The input data used to train an AI model.

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    Testing Data

    The data used to evaluate the performance of a trained AI model.

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    Data Acquisition Methods

    Ways to collect data for AI projects, including sensors, surveys, observations, cameras, APIs, and web scraping.

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    Data Exploration

    Analyzing data to identify patterns, trends, and insights.

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    Problem Scoping (AI)

    Identifying a problem and developing a vision to solve it thoroughly.

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    Data Visualization

    A graphical representation of data and information, making trends and patterns easier to understand using charts and graphs.

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    Rule-Based Approach (AI Modeling)

    AI modeling where the relationships and patterns in data are defined by a developer; the machine follows these rules.

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    Learning-Based Approach (AI Modeling)

    AI modeling where relationships and patterns are not defined by the developer; the machine figures them out from the data.

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    Decision Tree

    A flowchart-like tree structure used for classification and prediction; it's a powerful tool containing nodes and branches that represent tests and outcomes.

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    Data Modeling

    Techniques for creating models of data to understand relationships and trends. Includes rule-based and learning-based.

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    AI Model Evaluation

    Testing AI models to assess efficiency and performance based on parameters like reliability.

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    Data Acquisition

    The stage of gathering data from various sources in the AI model building process.

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    AI Model Improvement

    Using testing data to refine AI models continuously to achieve project goals.

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    AI Deployment

    Integrating a new AI model into a production environment to use it with real data and get desired outputs.

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    AI Ethics

    Ethical principles and considerations related to the development and use of AI systems.

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    AI Ethics Principles

    Factors like human rights, bias, privacy, and inclusion that impact the quality of AI solutions.

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    AI Bias

    Partiality or preference for one group over another learned from data, causing unfair outcomes.

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    Data Bias in AI

    Bias from data used to train AI models, potentially leading to discriminatory outcomes.

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    Human Rights in AI

    Ensuring AI solutions do not violate or take away fundamental human rights.

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    Job Loss and AI

    Potential for AI to automate tasks previously done by humans, raising concerns about unemployment.

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    AI Data Quality

    Importance of realistic and large datasets to avoid bias and ensure fair AI models.

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    Study Notes

    AI Project Life Cycle

    • The project management life cycle is a framework for managing any project, progressing from initiation to deployment.
    • The AI project cycle has various stages, including problem scoping, data acquisition, data exploration, modelling, and evaluation.

    Stage 1: Problem Scoping

    • This involves identifying a problem and creating a vision to solve it.
    • It uses a series of steps to define a problem statement using AI knowledge.
    • The 4Ws problem canvas helps in identifying crucial parameters for solving a problem. These include:
      • Who: Stakeholders facing the problem.
      • What: The nature of the problem and how it is known.
      • Where/When: Context, situation, or location of the problem.
      • Why: The need to solve the problem, and benefits for stakeholders.

    Stage 2: Data Acquisition

    • Data acquisition is the method of collecting reliable data.
    • Data can be in various forms (text, video, photos, audio).
    • It can be gathered from sources like websites, journals, and newspapers.
    • Data is crucial for training AI models.
    • Two main types of data:
      • Training data (80%): Input data used to train the AI model.
      • Testing data (20%): Data used to evaluate the trained model.
    • Data can be collected through surveys, cameras, web scraping, sensors, and APIs.

    Stage 3: Data Exploration

    • Data exploration involves identifying patterns and trends in data.
    • It helps in better understanding the data and determining relationships.
    • Data visualization techniques (charts, graphs) are used to understand trends and patterns.

    Stage 4: Modeling

    • An AI model is a program trained to recognize patterns using a dataset.
    • AI modeling creates algorithms (models) to produce intelligent results, trained through programming code.
    • There are various types of modeling approaches.
      • Rule-based approach: Relationships/patterns are defined by the developer.
      • Learning-based approach: Data patterns and trends are identified by the AI itself.

    Stage 5: Evaluation

    • This stage assesses the efficiency and performance of the AI model.
    • Parameters used for evaluation include accuracy, precision, recall, and F1 score.
    • The evaluation ensures the model meets project goals and continuously improves.

    Deployment

    • Integrating a newly developed AI model into a production environment.
    • It involves preparing the necessary hardware and software settings for efficient use by end-users.

    AI Ethical Issues and Concerns

    • Human rights, bias, privacy and inclusion are important considerations in AI development.
    • Bias in training data may lead to biased results.
    • AI access and data availability are important factors for ensuring inclusive, ethical, and effective AI systems.

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    Related Documents

    AI Project Life Cycle PDF

    Description

    Test your knowledge on the AI project life cycle with this quiz! It covers essential stages such as problem scoping and data acquisition. Understand how to effectively manage AI projects from initiation to deployment.

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