Cats Vs Dogs? Let's make an AI to settle this (LAB)

The video explores using AI to decide between adopting a cat or a dog. It covers building a simple AI model using Python and Google Collaboratory, collecting data through surveys, and testing the model's accuracy. The video highlights issues of bias in AI, particularly how data collection can skew results, and discusses methods to identify and correct these biases to improve model accuracy.

What was the main reason for using AI in the pet adoption decision?

• To save time in the decision-making process

• To avoid personal bias and make a data-driven decision

• To find the cheapest pet available

• To ensure the pet is healthy

What was the primary method used to collect data for the AI model?

• Online research

• Interviews with pet owners

• A survey with yes or no questions

• Observations at a pet shelter

Which programming language and tool were used to build the AI model?

• Python and Google Collaboratory

• JavaScript and Node.js

• Java and Eclipse

• C++ and Visual Studio

What type of neural network was used in the AI model?

• Generative Adversarial Network

• Multilayer Perceptron

• Recurrent Neural Network

• Convolutional Neural Network

What was the unexpected result of the AI model's predictions?

• It showed no preference between cats and dogs

• It could not make any predictions

• It favored dogs over cats

• It favored cats over dogs

What was identified as a potential cause for the AI model's bias?

• Incorrect algorithm choice

• Imbalanced data collection

• Too many features in the model

• Lack of training data

What was the first step taken to address the bias in the AI model?

• Increasing the number of hidden layers

• Removing all features

• Collecting more balanced data

• Changing the algorithm

What is a correlated feature in the context of this AI model?

• A feature that unintentionally predicts a hidden category

• A feature that is unrelated to the output

• A feature that is directly related to the output

• A feature that is not used in the model

What was the main lesson learned from the AI project?

• AI is not useful for personal decisions

• AI requires careful design and iteration to avoid bias

• AI models are always unbiased

• AI can solve all problems without human intervention

What is the importance of human oversight in AI projects?

• To reduce the cost of AI development

• To replace the need for data collection

• To verify the AI's decisions and avoid bias

• To ensure the AI works faster