AI Glossary

Active Learning: A machine learning approach that involves the algorithm selecting the most informative data points to label, rather than labeling all data points, to improve model performance.

Adversarial Attacks: Techniques used to fool or manipulate machine learning models by introducing specially crafted inputs.

Algorithm: A set of instructions or rules that a computer follows to perform a specific task or solve a problem.

Artificial General Intelligence (AGI): A theoretical future state of AI in which machines possess human-like intelligence and are capable of performing any intellectual task that a human can.

Artificial Intelligence (AI): A field of computer science that aims to create intelligent machines that can mimic human behavior and perform tasks that normally require human intelligence.

Attention Mechanism: A technique used in deep learning that allows neural networks to selectively focus on important features in input data.

Augmented Intelligence: The use of AI technologies to enhance human intelligence and decision-making rather than replacing it.

Autoencoder: A neural network that learns to represent input data in a compressed form, and then uses that representation to generate similar data.

Bias: Systematic errors or distortions in data or algorithms that result in inaccurate or unfair predictions.

Big Data: A term used to describe the large and complex sets of data that are generated by modern digital systems.

Chatbot: An AI-powered program designed to simulate conversation with human users, typically through text or voice interfaces.

Cognitive Computing: A field of AI that involves simulating human cognitive processes such as perception, reasoning, and learning, with the aim of creating more human-like AI systems.

Computer Speech Recognition (CSR): A field of AI that focuses on recognizing and interpreting human speech.

Computer Speech Synthesis (CSS): A field of AI that focuses on generating human-like speech from text.

Computer Vision (CV): A field of AI that enables machines to recognize and interpret visual information from the world around them.

Computer Vision: A field of AI that focuses on enabling machines to interpret and analyze visual information from the world, similar to how humans perceive and understand images.

Convolutional Neural Network (CNN): A type of neural network used primarily in computer vision that is designed to recognize patterns in visual data.

Data Mining: The process of extracting insights and patterns from large data sets.

Data Wrangling: The process of cleaning, transforming, and preparing raw data for analysis and machine learning.

Deep Learning (DL): A type of machine learning that uses neural networks with multiple layers to learn and make predictions based on large amounts of data.

Deep Reinforcement Learning: A type of reinforcement learning that uses deep neural networks to learn from trial and error interactions with an environment.

Domain-Specific Language (DSL): A programming language designed for a specific domain, such as machine learning or robotics, that is optimized for that particular domain's requirements and constraints.

Edge Computing: A computing paradigm that involves processing data at the edge of a network, closer to the data source, rather than in centralized data centers.

Ensemble Learning: A technique that involves combining the predictions of multiple machine learning models to improve overall performance.

Expert System: An AI system designed to replicate the decision-making capabilities of a human expert in a specific field.

Explainability: The degree to which a machine learning model's decisions can be understood or explained by humans.

Explainable AI (XAI): A subfield of AI that focuses on developing models and techniques that allow for transparency and interpretability of machine learning decisions.

Fairness: The degree to which a machine learning model's predictions are unbiased and do not discriminate against certain groups of people.

Federated Learning: A distributed machine learning approach that trains models on decentralized data sources while keeping the data locally stored and private.

GAN (Generative Adversarial Network): A type of neural network that generates new data by pitting two neural networks against each other in a game-like framework.

Human-in-the-Loop (HITL): An approach to machine learning that involves human experts guiding the learning process by providing feedback or labeling data.

Hyperautomation: The combination of AI, machine learning, and automation technologies to automate complex business processes and decision-making.

Hyperparameter: A parameter in a machine learning algorithm that is set by the user and affects the model's performance.

Knowledge Graph: A structured data representation of knowledge that enables machines to understand relationships between concepts and entities, often used in natural language understanding and recommendation systems.

LSTM (Long Short-Term Memory): A type of RNN that is designed to handle long-term dependencies in sequential data.

Machine Learning (ML): A subset of AI that involves training computer algorithms to learn from data and make predictions or decisions based on that learning.

Multi-Agent Systems: A field of AI that focuses on developing systems that involve multiple agents (i.e., individual entities with their own goals and behaviors) interacting with each other.

Multi-Task Learning: A machine learning approach that involves learning multiple tasks simultaneously, with the goal of improving overall performance on all tasks.

Natural Language Generation (NLG): A subfield of NLP that involves generating human-like text based on input data.

Natural Language Processing (NLP): A field of AI that focuses on enabling computers to understand, interpret, and generate human language.

Natural Language Processing (NLP): A subfield of AI that focuses on enabling machines to understand and generate human language, including tasks such as speech recognition, machine translation, and text analysis.

Natural Language Understanding (NLU): A subfield of NLP that involves understanding the meaning and intent behind human language, rather than just the surface-level text.

Neural Network (NN): A type of computing system inspired by the structure and function of the human brain, which is used in deep learning to learn and make predictions based on data.

One-Shot Learning: A type of machine learning that involves learning from only one or a few examples, rather than requiring large amounts of labeled data.

Optical Character Recognition (OCR): A field of AI that involves recognizing and interpreting text in images or scanned documents.

Optimization Algorithm: A method used to train machine learning models by minimizing the error between predicted and actual values.

Overfitting: When a machine learning model is too complex and starts to fit the training data too closely, resulting in poor performance on new data.

Predictive Analytics: The use of statistical algorithms and machine learning techniques to predict future outcomes based on historical data.

Quantum Computing: A computing paradigm that uses quantum-mechanical phenomena to perform calculations, with the potential to revolutionize AI and other fields.

Recommendation Systems: A type of AI system that provides personalized recommendations to users based on their previous interactions and preferences.

Recurrent Neural Network (RNN): A type of neural network used primarily in natural language processing that is designed to process sequences of data, such as sentences or speech.

Reinforcement Learning (RL): A type of machine learning that involves training computer algorithms to make decisions based on feedback from their environment.

Robotics: A field of engineering and AI that involves the design, construction, and operation of robots.

Semi-Supervised Learning: A type of machine learning that combines supervised and unsupervised learning to learn from partially labeled data.

Sentiment Analysis: A technique used in NLP to classify the sentiment of a piece of text as positive, negative, or neutral.

Speech-to-Text: The process of converting spoken language into text, often used in applications such as virtual assistants and transcription software.

Supervised Learning: A type of machine learning where the algorithm is trained on labeled data, meaning data that has already been categorized or classified by humans.

Synthetic Data: Artificially generated data that mimics real-world data, used for training machine learning models without compromising privacy or confidentiality.

Tensor: A mathematical object used to represent multidimensional arrays in deep learning.

Text-to-Speech: The process of generating human-like speech from text, often used in applications such as virtual assistants and accessibility tools.

Transfer Learning: A technique that allows a model trained on one task to be adapted and used for another task without having to train the model from scratch.

Underfitting: When a machine learning model is too simple and fails to capture the patterns in the data, resulting in poor performance on both the training and new data.

Unsupervised Learning: A type of machine learning where the algorithm learns to identify patterns in unlabeled data, meaning data that has not been categorized or classified by humans.

Variance: The degree to which a machine learning model's predictions vary when trained on different subsets of the data.