July 14, 2023|7 min reading

Artificial intelligence glossary: 70+ terms to know

Artificial intelligence glossary: 70+ terms to know

  1. Agent: An AI system that can perceive its environment and take actions independently.
  2. AI alignment: The process of ensuring that AI systems function as intended and align with desired goals.
  3. Algorithm: Step-by-step instructions that a computer follows to solve a problem or make predictions.
  4. Anthropomorphism: Attributing human qualities to nonhuman entities, like considering a chatbot as having human characteristics.
  5. Artificial general intelligence (AGI): AI systems capable of performing any intellectual task that a human can.
  6. Artificial intelligence (AI): The simulation of human intelligence processes by computer systems.
  7. Bias: Systematic prejudices that may be present in AI algorithms, leading to biased outcomes.
  8. Black box AI: AI systems whose inner workings are not easily understandable or explainable.
  9. ChatGPT: A chatbot developed by OpenAI that generates text responses based on user input.
  10. Chatbot: An AI-powered tool designed to engage in conversation with users.
  11. Constitutional AI: Training AI systems to align with a predefined set of values or principles.
  12. Convolutional neural network (CNN): A type of AI model used for computer vision tasks.
  13. Corpus: A large collection of written or spoken words used to train language models.
  14. Copilot: Microsoft's suite of AI-assisted workplace products.
  15. Cutoff date: The date at which the information used to train an AI model ends.
  16. Data mining: The process of discovering patterns and extracting useful information from large datasets.
  17. Data validation: Checking the quality and accuracy of data before using it to train AI models.
  18. Dall-E: OpenAI's AI-powered image generator that creates images based on textual prompts.
  19. Deepfake: Convincing AI-generated audio, video, or images that can be used to create deceptive content.
  20. Deep learning: A subset of machine learning that mimics the way humans learn and acquire knowledge.
  21. Embodied agents: AI agents with a physical body that perform tasks in the physical environment.
  22. Emergence: Unpredictable capabilities that arise in AI systems as they become more complex.
  23. EU AI Act: A regulatory framework for responsible AI deployment in the European Union.
  24. Expert system: AI systems that simulate the knowledge and behavior of human experts.
  25. Fréchet inception distance (FID): A metric for evaluating the quality of images generated by AI.
  26. Garbage in, garbage out (GIGO): The concept that the quality of an AI system's output depends on the quality of its input data.
  27. Generative adversarial network (GAN): A type of AI model consisting of two neural networks that compete with each other to generate and refine data.
  28. Generative AI: AI technology that creates new content based on learned patterns in training data.
  29. Graphics processing unit (GPU): A specialized processor used to accelerate AI computations.
  30. Generative pre-trained transformer (GPT): A family of AI algorithms, such as GPT-3 and GPT-4, used for natural language processing and generation.
  31. Hallucination: When an AI system presents false information as if it were true.
  32. Knowledge engineering: The field of AI that aims to replicate human expertise in specific domains.
  33. Large language model (LLM): Deep learning algorithms trained on large datasets to understand, summarize, and generate text.
  34. Large Language Model Meta AI (LLaMA): An open-source large language model released by Meta.
  35. Machine learning: A branch of AI that enables computers to learn and improve from data without being explicitly programmed.
  36. Moats: Mechanisms that protect the proprietary aspects of a large language model.
  37. Model: A trained AI algorithm that can make predictions or perform specific tasks.
  38. Multimodal AI: AI systems capable of processing and producing output in multiple forms such as text, images, and sound.
  39. Model collapse: When low-quality generated content contaminates the training set of AI models.
  40. Natural language generation (NLG): Using AI to generate written or spoken language based on data patterns.
  41. Natural language processing (NLP): AI's ability to understand and interpret human language.
  42. Neural network: A network of artificial neurons that process and transmit information, used in deep learning.
  43. Neuromorphic computing: Computing systems designed to mimic the structure and functioning of the human brain.
  44. OpenAI: An AI research organization that develops and releases various AI models and technologies.
  45. Overfitting: When an AI model becomes too specialized in the training data and performs poorly on new, unseen data.
  46. Parameter: Internal settings learned by an AI model during training that affect its behavior and predictions.
  47. Prompt: Input provided to an AI system to generate desired output or responses.
  48. Pathways Language Model (PaLM): Google's transformer-based large language model.
  49. Prompt engineering: The process of refining prompts to elicit desired output from a large language model.
  50. Q-learning: A reinforcement learning technique that enables AI models to learn through trial and error.
  51. Recommendation engine: An AI algorithm that suggests content based on user preferences.
  52. Reinforcement learning: A machine learning approach where an AI agent learns through interactions and feedback from its environment.
  53. Reinforcement learning from human feedback (RLHF): Training AI models directly using feedback from humans.
  54. Sentiment analysis: Analyzing text to determine the underlying sentiment or opinion expressed.
  55. Supervised learning: Training AI models using labeled data, where the desired outcome is known.
  56. Speech recognition: AI technology that converts spoken language into text.
  57. Synthetic data: Computer-generated data used for testing and training AI models.
  58. Technological singularity: A hypothetical future point where AI surpasses human intelligence, leading to rapid and uncontrollable technological advancement.
  59. Training data: Data used to train AI models and teach them patterns and behaviors.
  60. Transformer: A model architecture used for natural language processing, capable of processing context and long-term dependencies in language.
  61. Turing test: A test to determine if a computer can exhibit human-like intelligence.
  62. Token: The basic unit of text that an AI model uses to understand and generate language.
  63. Unsupervised learning: Training AI models on unlabeled data, allowing them to discover patterns and structures on their own.
  64. Variational autoencoder: A generative AI model used for efficient data coding and signal analysis.
  65. Zero-shot learning: AI models predicting classes for samples they were not explicitly trained on, based on related knowledge.
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