Ex Machina

 

AI Terminologies Explained

1. Artificial General Intelligence (AGI)

AGI refers to a type of AI that possesses the ability to understand, learn, and apply knowledge across a wide range of tasks at a level comparable to human intelligence.

2. Machine Learning (ML)

ML is a subfield of AI focused on developing algorithms that allow computers to learn from and make predictions based on data without being explicitly programmed for each task.

3. Deep Learning

Deep learning is a subset of ML that uses neural networks with multiple layers to analyze various forms of data, such as images and text, enabling complex pattern recognition.

4. Neural Network

A neural network is a computational model inspired by the human brain’s structure, consisting of interconnected nodes (neurons) that process information in layers.

5. Natural Language Processing (NLP)

NLP is an AI subfield that enables computers to understand, interpret, and generate human language, facilitating interactions between humans and machines.

6. Large Language Model (LLM)

An LLM is an advanced AI model trained on vast amounts of text data to perform various language-related tasks, such as translation and summarization.

7. Generative AI

Generative AI refers to techniques that create new content—such as text, images, or music—by learning patterns from existing data.

8. Hallucination

In the context of AI, hallucination refers to instances where an AI generates outputs that are nonsensical or factually incorrect due to limitations in its understanding or training data.

9. Reinforcement Learning

Reinforcement learning is a type of ML where an agent learns how to make decisions by receiving feedback through rewards or penalties based on its actions in an environment.

10. Bias

Bias in AI refers to systematic errors in predictions or outputs caused by imbalanced training data or flawed algorithm design, leading to unfair outcomes.

11. Data Augmentation

Data augmentation involves creating modified versions of existing data points to artificially expand training datasets and improve model performance.

12. Transfer Learning

Transfer learning is a technique where a pre-trained model is adapted for a new but related task by fine-tuning it with additional specific data.

13. Prompt Engineering

Prompt engineering is the practice of designing input prompts for generative models to optimize their output quality and relevance for specific tasks.

14. Knowledge Graph

A knowledge graph is a structured representation of information that illustrates relationships between entities, enhancing search capabilities and contextual understanding.

15. Explainability

Explainability refers to methods used in AI systems that make their decision-making processes understandable and transparent to users.

This list provides concise definitions for key terms commonly encountered in discussions about artificial intelligence technologies and methodologies.

Answer Provided by www.iAsk.ai – Ask AI.

Scroll to Top