OS1 Day 7 Exploring AI Concepts and Applications

What is the primary goal of supervised machine learning?

• To find patterns in data without any labels

• To predict outcomes based on labeled data

• To cluster data into groups

• To reduce the dimensionality of data

Answer explanation

The primary goal of supervised machine learning is to predict outcomes based on labeled data. This involves training a model on input-output pairs to make accurate predictions on new, unseen data.

Which of the following is a common ethical concern in AI?

• High computational cost

• Data privacy and security

• Lack of scalability

• Limited data storage

Answer explanation

Data privacy and security is a major ethical concern in AI, as it involves protecting sensitive information from unauthorized access and ensuring that data is used responsibly.

In Natural Language Processing, what does the term "tokenization" refer to?

• Converting text into numerical data

• Splitting text into individual words or phrases

• Translating text from one language to another

• Summarizing large texts

Answer explanation

Tokenization is the process of splitting text into individual words or phrases, which is essential for various NLP tasks. This makes 'Splitting text into individual words or phrases' the correct choice.

Which of the following is a common task in computer vision?

• Speech recognition

• Image classification

• Text summarization

• Sentiment analysis

Answer explanation

Image classification is a key task in computer vision, where algorithms are trained to identify and categorize images. The other options, like speech recognition and sentiment analysis, are related to different fields such as audio processing and natural language processing.

What is a feature of a common AI workload?

• High latency

• Large data throughput

• Low computational power

• Minimal data storage

Answer explanation

A common AI workload typically involves processing large volumes of data, which requires large data throughput to handle the input and output efficiently. High latency, low computational power, and minimal data storage are not characteristic features.

Which algorithm is typically used for classification tasks in machine learning?

• K-Means

• Decision Trees

• Principal Component Analysis

• Apriori

Answer explanation

Decision Trees are commonly used for classification tasks as they create a model based on decision rules derived from the data. In contrast, K-Means is for clustering, PCA for dimensionality reduction, and Apriori for association rule learning.

What is the purpose of a confusion matrix in machine learning?

• To visualize the performance of a classification model

• To reduce the dimensionality of data

• To cluster data into groups

• To optimize hyperparameters

Answer explanation

A confusion matrix is used to visualize the performance of a classification model by showing the true vs. predicted classifications, helping to identify errors and understand model accuracy.

Which of the following is an example of AI bias?

• A model that performs equally well across all demographics

• A model that favors one demographic over others

• A model that has high accuracy

• A model that uses a large dataset

Answer explanation

AI bias occurs when a model favors one demographic over others, leading to unequal performance. The correct choice highlights this issue, while the other options indicate fairness or performance without bias.

In Natural Language Processing, what is "stemming"?

• Removing stop words from text

• Reducing words to their root form

• Translating text into another language

• Converting text into numerical data

Answer explanation

Stemming is the process of reducing words to their root form, which helps in normalizing variations of a word for better text analysis. For example, 'running' becomes 'run'. This distinguishes it from other options like stop word removal.

What is the primary function of convolutional layers in a Convolutional Neural Network (CNN)?

• To perform linear regression

• To detect and learn spatial hierarchies in images

• To cluster data into groups

• To reduce the dimensionality of data

Answer explanation

The primary function of convolutional layers in a CNN is to detect and learn spatial hierarchies in images. They achieve this by applying filters that capture patterns and features at various levels of abstraction.