Podcast
Questions and Answers
What is the primary goal of data visualization?
What is the primary goal of data visualization?
A decision tree does not allow for multiple directions or branches.
A decision tree does not allow for multiple directions or branches.
False
What are the two main AI modeling approaches mentioned?
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.
Data visualization represents data and information in a ______ context to identify patterns and trends.
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Match the stages of an AI model process with their primary focus:
Match the stages of an AI model process with their primary focus:
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Which of the following best describes the learning-based approach in AI modeling?
Which of the following best describes the learning-based approach in AI modeling?
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Decision trees can be used for both classification and prediction tasks.
Decision trees can be used for both classification and prediction tasks.
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Name one benefit of spending time exploring data.
Name one benefit of spending time exploring data.
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What is the primary purpose of deployment in AI?
What is the primary purpose of deployment in AI?
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What is the first stage of the AI project cycle?
What is the first stage of the AI project cycle?
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Bias in AI systems can originate from the training data used to develop the model.
Bias in AI systems can originate from the training data used to develop the model.
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Name one ethical principle that should be considered when developing AI solutions.
Name one ethical principle that should be considered when developing AI solutions.
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Training data typically accounts for 20% of the total data used in an AI project.
Training data typically accounts for 20% of the total data used in an AI project.
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The integration of AI into the workplace can lead to job ________.
The integration of AI into the workplace can lead to job ________.
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What does the 4 W’s Canvas help to identify in problem scoping?
What does the 4 W’s Canvas help to identify in problem scoping?
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Match the following ethical principles with their descriptions:
Match the following ethical principles with their descriptions:
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________ is the lifeblood of Artificial Intelligence.
________ is the lifeblood of Artificial Intelligence.
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Match the following data collection methods with their descriptions:
Match the following data collection methods with their descriptions:
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Which of the following is NOT a concern when deploying AI?
Which of the following is NOT a concern when deploying AI?
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Which of the following is NOT a method of data acquisition?
Which of the following is NOT a method of data acquisition?
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In AI, sufficient dataset size always guarantees unbiased results.
In AI, sufficient dataset size always guarantees unbiased results.
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What can be implemented to mitigate the negative effects of AI on the workforce?
What can be implemented to mitigate the negative effects of AI on the workforce?
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Data exploration is the process of identifying trends and patterns in data.
Data exploration is the process of identifying trends and patterns in data.
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What is the purpose of data exploration in the AI project cycle?
What is the purpose of data exploration in the AI project cycle?
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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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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.
