Key Terms

  1. Artificial Intelligence (AI): The simulation of human intelligence processes by machines, especially computer systems. This includes learning to solve problems and make decisions.
  2. Bias: Systematic errors in AI outputs due to unfair or one-sided or prejudiced training data, leading to unfair or inaccurate results.
  3. Chain-of-Thought Prompting: A method that helps AI think better by breaking down complex tasks into smaller, manageable steps.
  4. Context Window: The amount of text (measured in tokens) that an AI model can consider at once when generating a response.
  5. Emergent Behavior: Unexpected capabilities that arise in large AI models, such as performing tasks they were not explicitly trained for.
  6. Ethics in AI: Making sure AI is developed and used in a fair, transparent, and responsible way.
  7. Fuzzy Logic: A way for AI to think more like humans by handling uncertainty and considering all possibilities between “yes” and “no.”
  8. Generative AI: A type of AI that can create new content, such as text, images, or music, by learning patterns from existing data.
  9. Hallucination: When AI makes up information that sounds real but isn’t based on actual data or facts.
  10. Large Language Model (LLM): A type of AI model trained on vast amounts of text data to understand and generate human-like text. Examples include GPT-3 and GPT-4.
  11. Multimodal AI: Multimodal AI, or Multimodal LLM, refers to advanced AI models that can process and interact with various input formats like text, images, audio, and video.
  12. Natural Language Processing (NLP): A field of AI focused on the interaction between computers and humans through natural language. It involves tasks like language translation, sentiment analysis, and speech recognition.
  13. Neural Networks: A series of algorithms that attempt to recognize underlying relationships in a set of data through a process that mimics the way the human brain operate.
  14. Parameters: The settings in an AI model that it learns from training data. These settings help determine how the model makes decisions and predictions.
  15. Prompt: A prompt is a request you give to an AI chatbot to perform a task, like creating content or researching a topic. The chatbot processes your message and responds based on your instructions.
  16. Reinforcement Learning: A type of machine learning where an AI learns by trying different actions and receiving feedback to make better decisions over time.
  17. Retrieval-Augmented Generation (RAG): A technique that enhances the output of large language models by retrieving relevant information from external data sources.
  18. Token: In the context of NLP, a token is a piece of a text string, such as a word or a part of a word, that an AI model reads and processes.
  19. Topic Modeling: A method that helps AI find and group similar words in a text to discover hidden themes or topics.
  20. Transformer Model: A type of AI that processes text more effectively by focusing on important parts of the input, and it’s the foundation for many advanced language models.