Exploring Artificial Intelligence

What is the primary goal of machine learning?

• To store data in a more efficient way.

• To create complex algorithms without any data.

• To replace human intelligence entirely.

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

Define supervised learning in machine learning.

• Unsupervised learning uses labeled data to train models.

• Supervised learning is a method that requires no data for training.

• Supervised learning focuses solely on clustering data without labels.

• Supervised learning is a machine learning approach that uses labeled data to train models to make predictions.

What is the difference between classification and regression?

• Classification predicts categories; regression predicts continuous values.

• Classification predicts numerical values; regression predicts categories.

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

• Classification requires more data than regression.

Explain the concept of natural language processing (NLP).

• Natural Language Processing is a method for processing images.

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

• NLP is a programming language for software development.

• Natural Language Processing focuses solely on speech recognition.

What is tokenization in NLP?

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

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

• 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.

How do sentiment analysis algorithms work?

• They translate text into different languages.

• They classify audio clips based on tone of voice.

• They analyze images to determine facial expressions.

• Sentiment analysis algorithms analyze text to determine the sentiment expressed, classifying it as positive, negative, or neutral.

What are some common ethical concerns in AI?

• Cost reduction, market expansion, user satisfaction

• Bias, privacy, accountability, transparency, job displacement

• Data storage, algorithm complexity, system speed

• Efficiency, scalability, user engagement

What is algorithmic bias and how can it affect AI systems?

• Algorithmic bias is the use of algorithms to enhance AI performance.

• Algorithmic bias is the systematic prejudice in AI systems that leads to unfair outcomes.

• Algorithmic bias is the preference for certain programming languages in AI development.

• Algorithmic bias refers to the random errors in AI calculations.

Why is transparency important in AI decision-making?

• Transparency is irrelevant in AI as it operates purely on data.

• Transparency can lead to slower decision-making processes.

• Transparency is important in AI decision-making to build trust, ensure accountability, and understand decision processes.

• Transparency is only necessary for regulatory compliance.

What are the main components of computer vision?

• Hardware components, power supply, cooling systems

• Image storage, data compression, network transmission

• User interface design, software development, database management

• Image acquisition, image processing, feature extraction, object detection, machine learning.