Exploring Artificial Intelligence

What is the primary goal of machine learning?

• To replace human intelligence entirely.

• To enable computers to learn from data and make predictions or decisions.

• To store data in a more efficient way.

• To create complex algorithms without any data.

Define supervised learning in machine learning.

• Supervised learning is only applicable to image recognition tasks.

• Unsupervised learning involves training models without labeled data.

• Supervised learning is a machine learning approach where models are trained on labeled data to predict outcomes.

• Supervised learning requires no data preprocessing before training.

What is the difference between classification and regression?

• Classification requires more data than regression.

• Classification predicts numerical values; regression predicts categories.

• Classification predicts categories; regression predicts continuous values.

• Classification is used for time series; regression is for image analysis.

Explain the concept of overfitting in machine learning.

• Overfitting happens when a model is trained on too little data, leading to generalization issues.

• Overfitting is when a model performs equally well on both training and new data.

• Overfitting occurs when a model is too simple and cannot capture the underlying patterns.

• Overfitting is when a model learns the training data too well, leading to poor performance on new data.

What is natural language processing (NLP)?

• Natural Language Processing (NLP) is a branch of artificial intelligence that enables computers to understand, interpret, and generate human language.

• A method for teaching computers to play games.

• A technique for improving computer hardware performance.

• A system for managing databases efficiently.

Name two common applications of NLP.

• Chatbots, Sentiment Analysis

• Image Recognition

• Data Visualization

• Machine Learning Models

What is tokenization in the context of NLP?

• Tokenization refers to the analysis of the sentiment of a text.

• Tokenization is the method of translating text into different languages.

• Tokenization is the process of summarizing text into a single sentence.

• Tokenization is the process of dividing text into individual tokens, such as words or phrases.

Describe the role of sentiment analysis in NLP.

• Sentiment analysis is a method for detecting spam in emails.

• Sentiment analysis is used to translate text into different languages.

• Sentiment analysis focuses solely on grammatical structure of sentences.

• Sentiment analysis helps in understanding and categorizing emotions in text, enabling insights into opinions and attitudes.

What are some ethical concerns related to AI?

• Speed, scalability, user engagement, market growth

• Cost reduction, data storage, algorithm complexity

• Bias, privacy, job displacement, accountability, misuse.

• Efficiency, transparency, user satisfaction, innovation

How can bias be introduced in machine learning models?

• Bias is eliminated by using more features in the model.

• Bias is only a concern in supervised learning.

• Bias can be introduced through biased training data, algorithmic bias, and human biases in feature selection.

• Bias can be introduced through excessive model complexity.