Here’s a breakdown of some common terms and concepts, organized by topic:
General AI Concepts
- Artificial Intelligence (AI): The simulation of human intelligence in machines.
- Machine Learning (ML): A subset of AI that enables machines to learn from data.
- Deep Learning: A type of ML that uses neural networks with many layers.
- Natural Language Processing (NLP): A field focusing on the interaction between computers and human language.
- Reinforcement Learning: A type of ML where an agent learns to make decisions by receiving rewards or penalties.
Algorithms and Models
- Algorithm: A set of rules or steps that a computer follows to perform a task.
- Model: A mathematical representation of a real-world process based on data.
- Neural Network: A model inspired by the human brain, used in deep learning.
- Decision Tree: A flowchart-like model used for decision-making.
- Support Vector Machine (SVM): A classification algorithm that finds the hyperplane that best divides a dataset into classes.
- Dataset: A collection of data used to train or test a model.
- Training Data: The portion of the dataset used to train a model.
- Testing Data: The portion of the dataset used to evaluate a model.
- Feature: An individual measurable property of the data.
- Label: The output variable in supervised learning.
- Accuracy: The ratio of correctly predicted instances to the total instances.
- Precision: The ratio of true positives to the sum of true positives and false positives.
- Recall: The ratio of true positives to the sum of true positives and false negatives.
- F1 Score: A balanced measure of precision and recall.
- Confusion Matrix: A table used to evaluate the performance of a classification model.
Hardware and Software
- CPU (Central Processing Unit): The main processor of a computer, generally less specialized but more versatile than GPUs for AI tasks.
- GPU (Graphics Processing Unit): A specialized processor efficient for the matrix and vector operations commonly used in AI.
- TPU (Tensor Processing Unit): A specialized chip designed specifically for machine learning tasks.
- TensorFlow: An open-source ML library developed by Google.
- PyTorch: An open-source ML library developed by Facebook.
Ethics and Bias
- Ethical AI: The study of ensuring that AI systems operate in a manner that is ethical and just.
- Bias: Prejudice in favor of or against one thing, often in a way considered to be unfair.
- Explainability: The ability to explain the decisions made by AI systems.
- Transparency: The openness about the functioning and decision-making of AI systems.
- Data Privacy: Protecting the information used in and generated by AI systems.