AI Project Cycle

What is the first step in the AI project cycle?

• Building the model

• Collecting data

• Deploying the solution

• Defining the problem statement and objectives

What is the process of defining the problem to be solved in the AI project cycle?

• Ignore context and constraints when defining the problem

• Skip data gathering and jump straight to problem articulation

• Identify the specific issue, understand context and constraints, gather data, articulate problem statement.

• Define multiple unrelated problems simultaneously

Which step involves gathering raw data for the AI project?

• Creating visualizations for the data

• Identifying data sources and collecting relevant information

• Running machine learning algorithms

• Cleaning the data before analysis

Which step involves building and training the AI model?

• Modelling

• Evaluating and fine-tuning the model

• Collecting and preparing data

• Step 1: Defining the problem

How is the performance of the AI model evaluated?

• By using metrics such as accuracy, precision, recall, F1 score, ROC-AUC, etc.

• By flipping a coin

• By using metrics such as speed, cost, and color

• By asking random people on the street

What is the last step in the AI project cycle?

• Model training

• Deployment and monitoring

• Data collection

• Testing and validation

What are some common AI models used in projects?

• LSTMs

• Naive Bayes

• Linear Regression

• CNNs, RNNs, SVMs, Decision Trees, K-Means Clustering

Which approach in AI involves the system improving its performance based on experience?

• Supervised Learning

• Unsupervised Learning

• Deep Learning

• Reinforcement Learning

Why is data collection important in AI projects?

• Data collection in AI projects is primarily for entertainment purposes.

• AI models can function effectively without data collection.

• Data collection is crucial for training AI models and enabling them to make accurate predictions.

• Data collection is only necessary for AI projects with small datasets.

How does problem scoping help in the AI project cycle?

• Problem scoping limits the project's flexibility

• Problem scoping is irrelevant in AI projects

• Problem scoping leads to increased project costs

• Problem scoping helps in defining the boundaries, objectives, and constraints of the project.