Exploring AI Concepts

What does AI stand for?

• Automated Integration

• Artificial Interaction

• Artificial Intelligence

• Advanced Interface

What is an LLM in the context of AI?

• Limited Language Model

• Linguistic Logic Model

• Large Learning Model

• Large Language Model

What are tokens in natural language processing?

• Tokens are the programming languages used in NLP applications.

• Tokens are the individual units of text, such as words or phrases, used in natural language processing.

• Tokens are the final output of a natural language processing model.

• Tokens are the algorithms used for text analysis.

What is prompt engineering?

• Prompt engineering is the process of creating and optimizing input prompts for AI models to achieve specific outputs.

• The study of AI algorithms and their efficiency.

• The design of user interfaces for AI applications.

• A method for training AI models on large datasets.

How does fine-tuning improve AI models?

• Fine-tuning only increases the model's training time without benefits.

• Fine-tuning reduces the model's ability to generalize.

• Fine-tuning improves AI models by allowing them to adapt to specific tasks or datasets, enhancing their accuracy and performance.

• Fine-tuning is only useful for large datasets.

What is inference in AI?

• Inference in AI is the act of storing data for future use.

• Inference in AI refers to the training of models using raw data only.

• Inference in AI is the process of collecting data without analysis.

• Inference in AI is the process of making predictions or decisions based on a trained model and new input data.

What are AI agents?

• AI agents are exclusively hardware devices that do not use software.

• AI agents are autonomous or semi-autonomous software programs that use AI to perform tasks.

• AI agents are only used for gaming purposes.

• AI agents are human-like robots that can think independently.

What is the purpose of training data in AI?

• To store data for future use

• To enable models to learn patterns and make accurate predictions.

• To generate random outputs

• To reduce the size of the model

How do neural networks relate to AI?

• Neural networks are unrelated to AI and serve no purpose.

• Neural networks are a key component of AI, enabling machines to learn and make decisions.

• Neural networks are only used in robotics, not AI.

• Neural networks are a type of hardware used in computers.

What is the difference between supervised and unsupervised learning?

• Supervised learning can only be applied to images, while unsupervised learning can be applied to text.

• Supervised learning uses labeled data for training, while unsupervised learning uses unlabeled data to find patterns.

• Supervised learning is used for clustering, while unsupervised learning is used for classification.

• Supervised learning requires no data for training, while unsupervised learning requires labeled data.