A basic set of commonly used terms in AI.
Algorithm: A set of rules or instructions, that can be followed by a computer to solve a problem or make a decision. Think of it like a recipe for a computer.
Artificial Intelligence (AI): A broad field aiming to create machines capable of performing tasks that typically require human intelligence, like learning, problem-solving, and decision-making.
Machine Learning (ML): A type of AI where computers learn from data without being explicitly programmed. They identify patterns and make predictions.
Model: A mathematical representation of the patterns learned by a machine learning algorithm. It’s what’s used to make predictions on new data.
Natural Language Processing (NLP): A branch of AI focused on enabling computers to understand, interpret, and generate human language.
Computer Vision: A field of AI that deals with enabling computers to “see” and interpret images and videos, similar to human vision.
Deep Learning: A subfield of machine learning that uses artificial neural networks with many layers to analyze data and learn complex patterns.
Neural Network: A computational model inspired by the human brain, composed of interconnected nodes (neurons) organized in layers. Used extensively in deep learning.
Convolutional Neural Network (CNN): A specialized type of neural network particularly effective for image analysis.
Supervised Learning: A type of machine learning where the training data includes labelled examples (ground truth). The model learns to map inputs to outputs based on these labels.
Unsupervised Learning: A type of machine learning where the training data is unlabelled. The model tries to find patterns and structure in the data on its own.
Training Data: The data used to teach a machine learning model.
Validation Data: A subset of the data used to evaluate the model’s performance during training and tune hyperparameters. Helps prevent overfitting.
Test Data: A separate subset of the data held back from training, used to evaluate the final performance of the trained model.
Bias (in data): Systematic errors in data that can lead to unfair or inaccurate results. For example, if your training data has mostly images of one type of disease, the AI might perform poorly on other types.
Feature: A measurable characteristic or attribute of a data point. In medical imaging, features could be things like the size of a lesion, its shape, or its intensity.
Ground Truth: The actual, verified correct answer or label in a dataset. For example, a radiologist’s confirmed diagnosis would be the ground truth for an image.
Parameter (of a model): Values that the model learns during training, which determine how it makes predictions. Different from hyperparameters.
Hyperparameter: Parameters that are set before training a machine learning model, controlling aspects of the learning process itself (e.g., learning rate). These are different from the parameters the model learns during training.
Overfitting: When a model learns the training data too well, including noise, and performs poorly on new, unseen data. Like memorizing answers for a test instead of understanding the material.
Fine-tuning: Adjusting the parameters of a pre-trained model on a new, smaller dataset to adapt it to a specific task.
Prediction: The output of a machine learning model when given new input data. For example, the model’s guess about whether a nodule is cancerous.
Accuracy: A measure of how often a model’s predictions are correct. Often expressed as a percentage.
Area Under the ROC Curve (AUC): A single value summarizing a model’s overall performance in distinguishing between two classes (e.g., cancerous vs. benign). An AUC of 1.0 is perfect, 0.5 is random.
Precision: Out of all the positive predictions the model made, what proportion were actually correct?
Recall (Sensitivity): Out of all the actual positive cases, what proportion did the model correctly identify?
Backpropagation: The algorithm used to train neural networks by adjusting the model’s parameters based on the errors in its predictions.
Classification: A type of machine learning task where the model predicts which category a data point belongs to (e.g., benign, malignant).
Regression: A type of machine learning task where the model predicts a continuous value (e.g., tumor size).
Segmentation: A task where the model identifies and outlines specific regions or objects within an image (e.g., segmenting a tumor).
Clustering: A type of unsupervised learning where the model groups similar data points together.
Data Augmentation: Techniques used to artificially increase the size of the training data by creating modified versions of existing images (e.g., rotations, flips). Helps prevent overfitting.
Epoch: One complete pass through the entire training dataset during training.
Gradient Descent: An optimization algorithm used to find the best parameters for a model by iteratively minimizing the error.
Inference: The process of using a trained model to make predictions on new data.
Loss Function: A function that measures the error between the model’s predictions and the ground truth. The goal of training is to minimize this loss.
Regularization: Techniques used to prevent overfitting by adding penalties to the loss function.
Transfer Learning: Using a pre-trained model on a large dataset and fine-tuning it on a smaller, task-specific dataset. This can save time and improve performance.
Workflow: A sequence of steps or processes involved in developing and deploying a machine learning model, from data collection to evaluation.
Generative Model: A type of AI model that learns the underlying patterns and structure of input data and then generates new data that has similar characteristics.
Transformer: A type of neural network architecture that uses the “attention mechanism” to weigh the importance of different parts of the input data. This model is revolutionizing natural language processing.
Large Language Model (LLM): A very large transformer model trained on a massive dataset of text. LLMs can generate text, translate languages, write different kinds of creative content, and answer questions in an informative way.
Attention Mechanism: A key component of transformer models that allows the model to focus on the most relevant parts of the input data when making predictions.
Foundation Model: Similar to LLMs, but these models can be trained on multiple data types (text, images, etc.) and can be adapted to a wide range of tasks.
Prompt Engineering: The art of crafting effective input prompts for generative AI models to get the desired outputs.
Few-Shot Learning: A type of machine learning where a model can learn a new task from only a few examples. LLMs are particularly good at this.
Zero-Shot Learning: A type of machine learning where a model can perform a task it has never seen before, without any explicit training examples.
Hallucination: When a generative AI model confidently outputs information that is factually incorrect or nonsensical.