Natural Language Processing & Understanding Quiz

Study Notes

Natural Language Processing (NLP)

NLP enables computers to understand and generate human language.
Key NLP Tasks:
- Language translation
- Text summarization
- Speech recognition
NLP Process in Action:
- Tokenization: Sentence is broken into individual words or tokens.
- Part-of-Speech Tagging: Assigns a grammatical role to each word, clarifying sentence structure.
- Named Entity Recognition: Identifies and classifies entities in the text, like "New York City."
- Intent Recognition: Determines the user's intent from the structure and entities recognized.
- Response Generation: Crafts a response aligned with the recognized intent.

Natural Language Understanding (NLU)

A subset of NLP focused on comprehending the meaning and context of human language.
Key NLU Tasks:
- Entity Recognition: Identifying and classifying key elements in text.
- Intent detection: Understanding the purpose behind a user's input.
- Contextual Relevance: Assessing the significance of language in context.
NLU Process in Action:
- Contextual Analysis: Understanding the sentence's overall meaning.
- Entity Recognition: Identifying important entities in the text, like "Italian restaurant."
- Intent Detection: Recognizing the user's intent (e.g., find a restaurant).
- Sentiment Analysis: Assessing the user's feelings towards the request (e.g., “good” indicates preference for quality).
- Contextual Relevance: Understanding "nearby" refers to the user’s current location for accurate suggestions.
- Relevant Response Generation: Provides suggestions based on the analysis.

Natural Language Generation (NLG)

A branch of NLP that transforms structured information into human-like language.
Key NLG Tasks:
- Report generation: Produces written summaries from data sets.
- Automated journalism: Writing news articles from factual inputs.
- Conversational agents: Generating dialogues for chatbots or virtual assistants.
NLG Process in Action:
- Data Input & Analysis: Receives and interprets raw data.
- Content Structuring: Organizes data points into a logical sequence for reporting.
- Narrative Creation: Builds a story around the data, establishing context.
- Language Synthesis: Transforms structured content into readable text.
- Text Refinement: Polishes the language for clarity and coherence.

Agents and Environment

An AI system comprises an agent and its environment.
Agent: An entity that can perceive its environment through sensors and act on that environment through actuators.
Types of Agents:
- Human: Senses using organs like eyes and ears, acts using hands, legs, mouth, etc.
- Robotic: Sensors like cameras and infrared range finders, actuators like servos and motors.
- Software: Sensors are file contents, keystrokes, network packages; actuators operate on that input, displaying output on a screen.
Environment:
- Fully Observable: AI has complete information about its surroundings. (Ex. Chess-playing AI)
- Partially Observable: AI has limited information about its surroundings. (Ex. Self-driving car)
- Deterministic: Outcome of an action is always the same, given the same starting conditions. (Ex. Chess-playing AI)
- Stochastic: Outcome of an action is uncertain, determined by probability. (Ex. Self-driving car)
- Episodic: Each decision is made independently, without considering past experiences. (Ex. Facial recognition system)
- Sequential: Each decision is influenced by past experiences. (Ex. Recommendation system)
- Single Agent: Only one AI agent operates independently. (Ex. Spam filter AI)
- Multi-Agent: Multiple AI agents interact and make decisions within the same space. (Ex. Autonomous drones)
- Complete: AI has full access to all relevant information. (Ex. Factory robot)
- Incomplete: AI lacks some critical information. (Ex. Robot vacuum cleaner)
- Static: Environment's state doesn't change unless the AI acts. (Ex. Puzzle-solving AI)
- Dynamic: Environment changes with or without the AI’s actions. (Ex. Self-driving car)
- Known: AI has prior experience or knowledge. (Ex. Self-driving car on a familiar route)
- Unknown: AI has no prior knowledge or experience. (Ex. Self-driving car on a new route)