AI Terminology
Understanding the language of artificial intelligence
The field of artificial intelligence is full of specialized terminology that can be confusing for newcomers. This glossary provides clear explanations of key AI terms and concepts to help you better understand the discussions around AI assistants and related technologies.
A
Artificial Intelligence (AI)
The simulation of human intelligence in machines that are programmed to think and learn like humans. AI encompasses a wide range of technologies and approaches that enable computers to perform tasks that typically require human intelligence.
Algorithm
A step-by-step procedure or formula for solving a problem. In AI, algorithms are the rules and procedures that guide how a machine learning model processes data and makes predictions or decisions.
Artificial Neural Network (ANN)
A computing system inspired by the structure and function of the human brain. ANNs consist of interconnected nodes (artificial neurons) organized in layers that process information and learn patterns from data.
Augmented Intelligence
An approach that focuses on AI's role in enhancing human capabilities rather than replacing them. Augmented intelligence emphasizes human-AI collaboration where AI systems assist humans in making better decisions.
Autonomous Systems
Systems that can operate, make decisions, and take actions without direct human intervention. Examples include self-driving cars, autonomous drones, and certain types of robots.
B
Bias (in AI)
Systematic errors in AI systems that can lead to unfair or discriminatory outcomes. AI bias often stems from biased training data or flawed algorithm design and can perpetuate or amplify existing social inequalities.
Big Data
Extremely large datasets that may be analyzed computationally to reveal patterns, trends, and associations. Big data is characterized by volume, velocity, variety, and veracity and often serves as the foundation for training advanced AI models.
Backpropagation
A training algorithm used in neural networks to calculate gradients and adjust weights. During training, backpropagation works backward from the output layer to update connection weights based on the error between predicted and actual outputs.
C
Computer Vision
A field of AI that enables computers to derive meaningful information from digital images, videos, and other visual inputs. Computer vision applications include image recognition, object detection, and facial recognition.
Clustering
An unsupervised learning technique that groups similar data points together based on their characteristics or features. Common clustering algorithms include K-means, hierarchical clustering, and DBSCAN.
Chatbot
A software application designed to conduct conversations with human users, typically via text or voice. Chatbots range from simple rule-based systems to sophisticated AI-powered assistants capable of understanding and generating natural language.
Convolutional Neural Network (CNN)
A specialized type of neural network designed for processing grid-like data such as images. CNNs use convolutional layers to automatically detect features and patterns in visual data and are fundamental to many computer vision applications.
D
Deep Learning
A subset of machine learning that uses multi-layered neural networks to learn from vast amounts of data. Deep learning enables AI systems to automatically discover representations from raw data through multiple levels of abstraction.
Dataset
A collection of data used to train, validate, and test machine learning models. The quality, size, and diversity of datasets significantly impact the performance and fairness of AI systems.
Decision Tree
A tree-like model of decisions and their possible consequences. Decision trees are used in machine learning for classification and regression tasks and provide transparent, interpretable decision-making processes.
E
Edge AI
AI systems that process data locally on a device (at the "edge" of the network) rather than in the cloud. Edge AI reduces latency, enhances privacy, and enables AI applications to function with limited or no internet connectivity.
Embeddings
Numerical representations of objects (like words, images, or users) in a continuous vector space. Embeddings capture semantic relationships and allow AI systems to process complex information efficiently.
Explainable AI (XAI)
AI systems designed to be transparent and understandable to humans. XAI focuses on making AI decision-making processes interpretable, allowing users to understand why and how AI systems reach specific conclusions.
F
Feature
An individual measurable property or characteristic of a phenomenon being observed. In machine learning, features are the input variables used by models to make predictions or classifications.
Federated Learning
A machine learning approach where models are trained across multiple devices or servers while keeping the training data decentralized. Federated learning enhances privacy by enabling model training without sharing raw data.
Fine-tuning
The process of taking a pre-trained model and further training it on a smaller, task-specific dataset. Fine-tuning allows leveraging knowledge from large general models while adapting them to specialized applications.
G
Generative AI
AI systems capable of creating new content such as text, images, music, or videos. Generative models learn patterns from existing data and generate novel outputs that exhibit similar characteristics.
Gradient Descent
An optimization algorithm used to minimize the error in predictive models by iteratively adjusting parameters. Gradient descent calculates the direction of steepest descent in the error surface and updates model parameters accordingly.
GPU (Graphics Processing Unit)
A specialized electronic circuit designed to rapidly manipulate and alter memory to accelerate the creation of images. GPUs are widely used in AI for their parallel processing capabilities, which significantly speed up deep learning training.
H
Hyperparameter
Parameters that define the structure and training process of machine learning models and are set before training begins. Examples include learning rate, batch size, and number of hidden layers in a neural network.
Hallucination (in AI)
When an AI system generates content that appears plausible but is factually incorrect or made up. Hallucinations are a common issue in large language models and can lead to the presentation of false information as if it were true.
Human-in-the-loop
AI systems that incorporate human feedback and oversight into their operation. This approach combines the efficiency of automation with human judgment, helping to improve system performance and address ethical concerns.
I
Inference
The process of using a trained machine learning model to make predictions or decisions on new, unseen data. During inference, the model applies what it has learned during training to generate outputs for new inputs.
Image Recognition
The ability of AI systems to identify objects, people, places, and actions in images. Image recognition is a fundamental computer vision task with applications ranging from photo organization to medical diagnostics.
Internet of Things (IoT)
A network of physical objects embedded with sensors, software, and other technologies that connect and exchange data with other devices and systems over the internet. IoT generates vast amounts of data that can be leveraged by AI systems.
L
Large Language Model (LLM)
A type of AI model trained on vast amounts of text data to understand and generate human language. LLMs like GPT, LLaMA, and Claude can perform a wide range of language tasks, from translation to creative writing to coding.
Learning Rate
A hyperparameter that determines how much a model's parameters are adjusted in response to the estimated error each time the model weights are updated. The learning rate influences how quickly or slowly a model learns from data.
M
Machine Learning
A subset of AI that enables systems to learn from data and improve from experience without being explicitly programmed. Machine learning algorithms build mathematical models based on sample data to make predictions or decisions.
Multimodal AI
AI systems that can process and understand multiple types of information, such as text, images, audio, and video. Multimodal AI enables more natural and comprehensive human-computer interaction.
Model Drift
The degradation of a machine learning model's performance over time as real-world data changes. Model drift occurs when the statistical properties of the target variable change, making the model's predictions less accurate.
N
Natural Language Processing (NLP)
A field of AI focused on enabling computers to understand, interpret, and generate human language. NLP technologies power applications like translation services, sentiment analysis, and conversational AI assistants.
Neural Network
A computational model inspired by the structure and function of biological neural networks. Neural networks consist of interconnected nodes (neurons) that process information and learn from data through adjustments to connection strengths.
O
Overfitting
A modeling error that occurs when a machine learning model learns the training data too well, capturing noise and random fluctuations rather than the underlying pattern. Overfitting results in poor performance on new, unseen data.
P
Prompt Engineering
The practice of designing and refining input prompts to elicit desired outputs from large language models. Effective prompt engineering can significantly improve the quality, relevance, and accuracy of AI-generated content.
Privacy-Preserving AI
AI technologies and methodologies designed to protect user privacy while still delivering valuable functionality. Techniques include federated learning, differential privacy, and secure multi-party computation.
R
Reinforcement Learning
A type of machine learning where an agent learns to make decisions by taking actions in an environment to maximize some notion of cumulative reward. The agent learns from the consequences of its actions rather than from explicit examples.
Recurrent Neural Network (RNN)
A type of neural network designed to recognize patterns in sequences of data, such as text, time series, or speech. RNNs maintain an internal memory that allows them to process sequences of varying lengths.
S
Supervised Learning
A machine learning approach where models learn from labeled training data. The algorithm learns to map inputs to outputs based on example input-output pairs, with the goal of accurately predicting outputs for new, unseen inputs.
Sentiment Analysis
The use of natural language processing to identify, extract, and quantify subjective information from text data. Sentiment analysis determines whether text expresses positive, negative, or neutral attitudes toward a subject.
T
Transfer Learning
A machine learning technique where a model developed for one task is reused as the starting point for a model on a second task. Transfer learning leverages knowledge gained from solving one problem to improve performance on a related problem.
Transformer
A neural network architecture designed for sequence processing that relies entirely on attention mechanisms. Transformers have revolutionized natural language processing and form the foundation of modern language models like GPT and BERT.
Training Data
The dataset used to teach a machine learning model. Training data contains examples with known outcomes that the model uses to learn patterns and relationships, which it can then apply to new, unseen data.
U
Unsupervised Learning
A machine learning approach where models learn patterns from unlabeled data. Without explicit guidance on what to predict, unsupervised learning algorithms discover hidden structures and relationships within the data.
V
Validation Data
A subset of data used to provide an unbiased evaluation of a model during training. Validation data helps tune hyperparameters and prevent overfitting by providing feedback on model performance on unseen data.
Vision Transformer (ViT)
A type of transformer model adapted for computer vision tasks. ViTs process images by dividing them into patches and treating these patches as sequences, similar to how transformers process words in text.
Further Learning Resources
Expand your understanding of AI terminology and concepts with these recommended resources:
Books
- "Artificial Intelligence: A Modern Approach" by Stuart Russell and Peter Norvig
- "The Hundred-Page Machine Learning Book" by Andriy Burkov
- "Deep Learning" by Ian Goodfellow, Yoshua Bengio, and Aaron Courville
Online Courses
- Elements of AI - Free online course by University of Helsinki
- Andrew Ng's Machine Learning Specialization on Coursera
- Fast.ai's Practical Deep Learning for Coders
Podcasts
- The TWIML AI Podcast
- Practical AI
- The AI Alignment Podcast
Newsletters & Blogs
- Import AI by Jack Clark
- The Batch by DeepLearning.AI
- Distill.pub for accessible explanations of machine learning concepts