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AI: Decoded! Mastering the Language of Artificial Intelligence

by Lilt  Han Mai, Associate Director, Demand Generation  ·  Localization, Translation, Cultural Nuances, Localization Tools, Quality Assurance, Compliance

Let's talk AI. Understanding some key terms is like cracking the code to this whole field. Words like algorithms, neural networks, and machine learning (both supervised and unsupervised) will help you see how AI works, what it can do, and what it can't. Whether you're an AI pro or just curious, this knowledge can help you make informed decisions about AI in business, education, and even your daily life.

  1. Artificial Intelligence (AI): AI is a field of computer science focused on creating systems capable of performing tasks that typically require human intelligence.

  2. Machine Learning (ML): The process by which machines learn from data, identify patterns, and improve their accuracy without being explicitly programmed. 

  3. Deep Learning (DL): A type of machine learning that uses neural networks to analyze and interpret data. 

  4. Neural Network (NN): A model inspired by the human brain, composed of interconnected nodes (neurons) that is capable of recognizing complex patterns in data.

  5. Algorithm: A set of rules or instructions that a machine follows to perform a specific task.

  6. Training Data: The data used to train a machine learning model to learn and improve its performance.

  7. Model: A model is a mathematical representation or program that is trained using data to recognize patterns, make predictions, or perform specific tasks based on the learned information.

  8. Supervised Learning: A type of machine learning where the model is trained on a labeled dataset (each input comes with a corresponding correct output). The model learns to make predictions or decisions by finding patterns in the training data and is then evaluated on its ability to predict the correct outputs for new, unseen data.

  9. Unsupervised Learning: A type of machine learning where the model is trained on a dataset without labeled outputs. Instead of being guided by correct answers, the model tries to identify patterns, structures, or relationships within the data on its own.

  10. Reinforcement Learning: A type of machine learning where the model learns by interacting with its environment and receiving rewards or penalties based on this behavior.

  11. Natural Language Processing (NLP): A subfield of AI that focuses on the interaction between computers and humans using natural language, including speech and text.

  12. Chatbot: A computer program that simulates human-like conversations with users.

  13. Robotics: The branch of AI that deals with the design, construction, and use of robots to perform tasks that are either too dangerous, too difficult, or too tedious for humans to do.

  14. Computer Vision: A subfield of AI that enables computers to interpret and understand visual data from images and videos. 

  15. Edge AI: A type of AI that is deployed on devices at the edge of the network, closer to the source of the data.

  16. Explainability: The ability to understand and interpret the decisions made by AI models.

  17. Bias: The unintended consequences or unfair outcomes that can occur when AI models are trained on biased data.

  18. Fairness: The principle of ensuring that AI models are unbiased and treat all individuals equally.

  19. Transfer Learning: The ability of a machine learning model to apply knowledge learned in one context to another related context.

  20. Big Data: Large, complex data sets that require computing power and advanced analytical tools to extract meaningful insights.

These vocabulary words should give you a solid foundation in AI-related terminology.