AI Dictionary
Essential Artificial Intelligence terms and definitions.
A/B Testing
Comparing two versions of a model or system to determine which performs better.
Activation Function
A mathematical function applied to a neural network node's output to introduce non-linearity.
Active Learning
A training process where the model queries a user to label data points that are most informative.
Agent
An autonomous system that perceives its environment and takes actions to achieve a goal.
AI Ethics
The study of moral implications and societal impacts of artificial intelligence systems.
Algorithm
A set of rules or instructions given to an AI program to help it learn on its own.
Alignment
The problem of ensuring AI systems pursued goals that match human values.
Annotation
The process of labeling data (like images or text) to train machine learning models.
API (Application Programming Interface)
A set of protocols that allows different software applications to communicate with each other.
Artificial General Intelligence (AGI)
Hypothetical AI with the ability to understand, learn, and apply knowledge across a wide variety of tasks, matching human capability.
Artificial Intelligence (AI)
Simulation of human intelligence processes by machines, especially computer systems.
Artificial Neural Network (ANN)
A computing system inspired by biological neural networks that constitutes animal brains.
Attention Mechanism
A technique in neural networks that mimics cognitive attention, allowing the model to focus on specific parts of the input.
Autoencoder
A type of neural network used to learn efficient codings of unlabeled data.
Autoregressive Model
A statistical model that predicts future values based on past values.
Backpropagation
Algorithm used to train neural networks by adjusting weights based on the error rate.
Batch Normalization
A technique to improve the speed, performance, and stability of artificial neural networks.
Bias
Systematic error introduced into sampling or testing by selecting or encouraging one outcome or answer over others.
Big Data
Extremely large data sets that may be analyzed computationally to reveal patterns, trends, and associations.
Black Box
An AI system whose internal workings are invisible or difficult to explain to the user.
Chatbot
A software application used to conduct an on-line chat conversation via text or text-to-speech.
Clustering
Grouping a set of objects in such a way that objects in the same group are more similar to each other than to those in other groups.
Cognitive Computing
Technology platforms that broadly are based on the scientific disciplines of artificial intelligence and signal processing.
Computer Vision
A field of AI that enables computers and systems to derive meaningful information from digital images and videos.
Context Window
The amount of text an LLM can consider at one time when generating a response.
Convolutional Neural Network (CNN)
A class of deep neural networks, most commonly applied to analyzing visual imagery.
Corpus
A large collection of text or audio data used to train natural language processing models.
Data Augmentation
Techniques used to increase the amount of data by adding slightly modified copies of already existing data.
Data Mining
The practice of examining large databases in order to generate new information.
Data Science
An interdisciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge from data.
Deep Learning
A subset of machine learning based on artificial neural networks with representation learning.
Deepfake
Synthetic media in which a person in an existing image or video is replaced with someone else's likeness.
Discriminator
In GANs, the network that learns to distinguish real data from fake data generated by the generator.
Dropout
A regularization technique for reducing overfitting in neural networks by preventing complex co-adaptations on training data.
Edge AI
AI algorithms processed locally on a hardware device rather than in the cloud.
Embedding
A mapping of a discrete, categorical variable to a vector of continuous numbers.
Ensemble Learning
Using multiple learning algorithms to obtain better predictive performance than could be obtained from any of the constituent learning algorithms alone.
Epoch
One complete pass of the training dataset through the algorithm.
Expert System
A computer system that emulates the decision-making ability of a human expert.
Explainable AI (XAI)
Artificial intelligence which can be understood by humans, contrasting with the "black box" concept.
Feature Engineering
The process of using domain knowledge to extract features from raw data via data mining techniques.
Federated Learning
A machine learning technique that trains an algorithm across multiple decentralized edge devices or servers.
Fine-tuning
The process of taking a pre-trained model and training it further on a specific dataset.
Foundation Model
A large AI model trained on a vast amount of data that can be adapted to a wide range of downstream tasks.
Generative Adversarial Network (GAN)
A class of machine learning frameworks where two neural networks contest with each other in a game.
Generative AI
AI capable of generating text, images, or other media, using generative models.
GPU (Graphics Processing Unit)
A specialized electronic circuit designed to manipulate and alter memory to accelerate the creation of images, widely used for AI training.
Gradient Descent
An optimization algorithm used to minimize some function by iteratively moving in the direction of steepest descent.
Ground Truth
Information provided by direct observation as opposed to information provided by inference.
Hallucination
A phenomenon where an AI generates output that is confident but factually incorrect or nonsensical.
Heuristic
A technique designed for solving a problem more quickly when classic methods are too slow.
Human-in-the-loop (HITL)
A model of interaction where a human defines, checks, and improves the output of an AI system.
Hyperparameter
A configuration that is external to the model and whose value cannot be estimated from data.
Image Recognition
The ability of software to identify objects, places, people, or writing in images.
Inference
The process of using a trained machine learning model to make predictions against previously unseen data.
Internet of Things (IoT)
A network of physical objects specifically designed to connect and exchange data with other devices and systems over the internet.
Knowledge Graph
A knowledge base that uses a graph-structured data model or topology to integrate data.
Labeling
Identifying raw data (images, text files, videos, etc.) and adding one or more meaningful and informative labels.
Large Language Model (LLM)
A language model consisting of a neural network with many parameters, trained on large quantities of unlabeled text.
Latent Space
An abstract multi-dimensional space containing feature values that we cannot interpret directly.
Loss Function
A method of evaluating how well your algorithm models your dataset.
Machine Learning (ML)
The study of computer algorithms that improve automatically through experience and by the use of data.
Machine Vision
The technology and methods used to provide imaging-based automatic inspection and analysis.
Model
A file that has been trained to recognize certain types of patterns.
Multi-modal AI
AI that can understand and generate information across multiple modalities like text, images, and audio.
Natural Language Generation (NLG)
The use of artificial intelligence programming to produce written or spoken narrative from a dataset.
Natural Language Processing (NLP)
A subfield of linguistics, computer science, and AI concerned with the interactions between computers and human language.
Natural Language Understanding (NLU)
A subtopic of natural language processing that deals with machine reading comprehension.
Neural Network
A series of algorithms that endeavors to recognize underlying relationships in a set of data through a process that mimics the way the human brain operates.
Neuron
A basic unit of a neural network that receives inputs, processes them, and passes the output to the next layer.
One-shot Learning
An object categorization problem primarily found in computer vision where the model learns from a single example.
Open Source
Software with source code that anyone can inspect, modify, and enhance.
Overfitting
A modeling error that occurs when a function is too closely fit to a limited set of data points.
Parameter
A configuration variable that is internal to the model and whose value can be estimated from data.
Pattern Recognition
The automated recognition of patterns and regularities in data.
Perceptron
An algorithm for supervised learning of binary classifiers.
Precision
The number of true positive results divided by the number of all positive results, including those not identified correctly.
Predictive Analytics
The use of data, statistical algorithms and machine learning techniques to identify the likelihood of future outcomes.
Pre-training
The initial training of a model on a large dataset before fine-tuning it for specific tasks.
Prompt Engineering
The practice of designing inputs for AI models to produce optimal outputs.
Python
A high-level programming language widely used in AI and machine learning development.
Recurrent Neural Network (RNN)
A class of artificial neural networks where connections between nodes form a directed graph along a temporal sequence.
Reinforcement Learning
An area of machine learning concerned with how intelligent agents ought to take actions in an environment in order to maximize the notion of cumulative reward.
Reinforcement Learning from Human Feedback (RLHF)
A technique that trains a reward model directly from human feedback to better align the agent with human preferences.
Robotics
An interdisciplinary branch of computer science and engineering that involves the design, construction, operation, and use of robots.
Semantic Analysis
The process of drawing meaning from text.
Sentiment Analysis
The use of natural language processing to identify and extract subjective information in source materials.
Singularity
A hypothetical point in time at which technological growth becomes uncontrollable and irreversible, resulting in unfathomable changes to human civilization.
Supervised Learning
The machine learning task of learning a function that maps an input to an output based on example input-output pairs.
Synthetic Data
Information that's artificially generated rather than produced by real-world events.
TensorFlow
A free and open-source software library for machine learning and artificial intelligence.
Test Set
A set of data used to provide an unbiased evaluation of a final model fit on the training dataset.
Token
A basic unit of text (like a word or character) that an LLM processes.
Training Data
The initial dataset used to teach a machine learning application to recognize patterns or perform a specific task.
Transfer Learning
A research problem in machine learning that focuses on storing knowledge gained while solving one problem and applying it to a different but related problem.
Transformer
A deep learning model that adopts the mechanism of self-attention, differentially weighting the significance of each part of the input data.
Turing Test
A test of a machine's ability to exhibit intelligent behavior equivalent to, or indistinguishable from, that of a human.
Unsupervised Learning
A type of machine learning that looks for previously undetected patterns in a data set with no pre-existing labels.
Validation Set
A set of data used to provide an unbiased evaluation of a model fit on the training dataset while tuning model hyperparameters.
Weight
A parameter within a neural network that transforms input data within the network's hidden layers.