AI Explainer: Glossary of Artificial Intelligence Terms

AI Explainer: Glossary of Artificial Intelligence TermsI speak with customers and partners pretty much every week about artificial intelligence. The knowledge levels can differ quite dramatically — some are quite AI savvy while others find the jargon bewildering. This is quite understandable as AI is a rapidly evolving field with its own set of specialized terminology.

This blog post is purely meant to provide a beginner-friendly reference for some essential AI terms to make it easier to navigate conversations and articles on the topic. Here are some of the more commonly used terms and acronyms:

  • Artificial Intelligence (AI): AI refers to the simulation of human intelligence in machines, allowing them to perform tasks that typically require human intelligence, like learning, reasoning, problem-solving and understanding natural language.
  • Generative AI: Generative AI refers to artificial intelligence systems that are designed to generate new content, whether it's text, images, music or other creative outputs. These systems often use techniques like generative adversarial networks (GANs) or autoregressive models to create content that is original and, in some cases, indistinguishable from content created by humans.
  • Machine Learning (ML): A subset of artificial intelligence, machine learning is the process of training a computer to learn from data and improve its performance over time without being explicitly programmed.
  • Deep Learning (DL): A subfield of machine learning that uses neural networks with multiple layers to model and solve complex problems, inspired by the human brain.
  • Natural Language Processing (NLP): A field that focuses on enabling machines to understand, generate and interact with human language.
  • Large Language Model (LLM): LLMs are a class of AI models that excel at natural language understanding and generation, like ChatGPT. They have numerous applications in text generation, language translation and more.
  • Neural Networks: Computational models inspired by the human brain's structure, consisting of interconnected layers of artificial neurons to process and analyze data.
  • Supervised Learning: A type of machine learning where the model is trained using labeled data to make predictions or classifications.
  • Unsupervised Learning: This is when the model learns patterns and relationships in data without labeled examples, typically used for clustering and dimensionality reduction.
  • Reinforcement Learning: A type of machine learning where an agent learns to make decisions through trial and error, receiving rewards (numerical values) for taking the right actions.
  • Data Preprocessing: The process of cleaning, transforming and organizing data to make it suitable for analysis or machine learning.
  • Feature Extraction: Selecting or creating relevant attributes (features) from data to improve the performance of machine learning models.
  • Overfitting and Underfitting: These are issues in machine learning where a model either learns noise in the data (overfitting) or is too simple to capture underlying patterns (underfitting).
  • Bias and Fairness: AI systems can exhibit bias due to biased training data. Addressing fairness concerns in AI involves mitigating such bias and ensuring equitable outcomes.
  • Computer Vision: The study of enabling machines to interpret and understand visual information from the world, such as images and videos.
  • Chatbots: AI applications designed for simulating humanlike conversations, often used in customer service or virtual assistants.
  • IoT (Internet of Things): The connection of everyday devices to the internet, enabling data collection, analysis and control via AI algorithms. (To be clear, IoT and artificial intelligence are two distinct concepts that complement each other.)
  • Big Data: The enormous volume of digital data generated daily, which AI systems analyze to extract insights and patterns.
  • AI Ethics: The ethical considerations surrounding the development and use of artificial intelligence, including issues related to privacy, bias and accountability.
  • AI Model: A specific instance of an AI system that has been trained on data and can be used for making predictions or solving specific tasks.
  • AI Application: A practical use case of AI technology, like autonomous vehicles, health care diagnostics or recommendation systems.
  • AI Research: The ongoing scientific exploration and development of AI techniques, models and technologies.
  • AI Hardware: Specialized hardware, like GPUs and TPUs, designed to accelerate artificial intelligence and deep learning workloads.

This beginner's guide should help you grasp the basics of AI terminology and serve as a handy reference as you dive deeper into the world of artificial intelligence. AI is a dynamic field, so staying updated on new terms and technologies is part of the journey. In future blog posts, I’ll dive deeper on some of the terms listed here.

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