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AI - Artificial Intelligence at the University

A guide to policies and tools related to Artificial Intelligence

Glossary Terms

Here is a list of terms provided by Google Gemini:

This glossary provides definitions for key terms commonly encountered in AI research. It's designed to be a starting point for your research and understanding, and further exploration of specific terms might be necessary depending on your needs.

General Terms:

  • Artificial Intelligence (AI): The field of computer science concerned with creating intelligent machines capable of performing tasks that typically require human intelligence.
  • Machine Learning: A subfield of AI where algorithms learn from data without being explicitly programmed.
  • Deep Learning: A type of machine learning inspired by the structure and function of the brain, using artificial neural networks.
  • Natural Language Processing (NLP): The field concerned with computers understanding and generating human language.
  • Computer Vision: The field concerned with enabling computers to "see" and process visual information.
  • Robotics: The field concerned with designing, building, and operating robots.

Machine Learning Concepts:

  • Algorithm: A set of instructions for solving a specific problem.
  • Data: Information used to train and test machine learning models.
  • Training Data: Data used to train a model to make predictions.
  • Testing Data: Data used to evaluate the performance of a model on unseen data.
  • Model: A representation of the learned knowledge used to make predictions.
  • Prediction: An output of a model based on new input data.
  • Overfitting: When a model performs well on training data but poorly on unseen data.
  • Underfitting: When a model fails to capture the underlying patterns in the data.

Deep Learning Concepts:

  • Neural Network: A series of interconnected nodes inspired by the structure of the brain.
  • Activation function: A function applied to the output of each node in a neural network.
  • Loss function: A function that measures the difference between the predicted output and the actual output.
  • Optimization: The process of adjusting the parameters of a model to minimize the loss function.
  • Backpropagation: An algorithm used to train neural networks by propagating the error back through the network.

Other Important Terms:

  • AI Ethics: The study of the ethical implications of AI development and use.
  • Bias: Unintended prejudice in a model that can lead to unfair outcomes.
  • Explainability: The ability to understand how a model makes its decisions.
  • Generative AI: AI systems that can create new content, such as text, images, or music.
  • Reinforcement Learning: A type of machine learning where an agent learns through trial and error.
  • Supervised Learning: Machine learning where the data is labeled with the desired output.
  • Unsupervised Learning: Machine learning where the data is not labeled.
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