AI Terminology

AGI - Artificial General Intelligence: AI systems with the ability to understand, learn, and apply knowledge across different domains, akin to human-level intelligence.

 

AI - Artificial Intelligence: The simulation of human intelligence processes by machines, especially computer systems.

 

AIaaS - Artificial Intelligence as a Service: Cloud-based AI services provided by third-party vendors, allowing businesses to access AI capabilities without investing in infrastructure.

 

AIoT - Artificial Intelligence of Things: The integration of artificial intelligence technologies into the Internet of Things ecosystem, enabling IoT devices to process and analyze data locally.

 

ANN – Artificial Neural Network: is a method in artificial intelligence that teaches computers to process data in a way that is inspired by the human brain. It is a type of machine learning process, called deep learning, that uses interconnected nodes or neurons in a layered structure that resembles the human brain. It creates an adaptive system that computers use to learn from their mistakes and improve continuously.

 

API - Application Programming Interface: A set of rules and protocols that allows different software applications to communicate with each other.

 

AR - Augmented Reality: A technology that superimposes computer-generated images, sounds, or other data onto a user's view of the real world, often through a smartphone or wearable device.

 

BERT - Bidirectional Encoder Representations from Transformers: A pre-trained natural language processing model developed by Google, widely used for various NLP tasks such as text classification and question answering.

 

Big Data - Extremely large datasets that require advanced processing techniques to uncover insights and trends, often characterized by the 3Vs: volume, velocity, and variety.

 

CNN - Convolutional Neural Network: A type of deep neural network designed for processing structured grids of data, commonly used in image and video recognition.

 

CVAE - Conditional Variational Autoencoder: A type of neural network architecture used for generating new data samples, particularly useful in generative modeling tasks with structured latent spaces.

 

Digital Twins: A digital twin is a digital representation of a physical object, person, or process, contextualized in a digital version of its environment. Digital twins can help an organization simulate real situations and their outcomes, ultimately allowing it to make better decisions.

 

DL - Deep Learning: A subset of machine learning where artificial neural networks, inspired by the human brain, learn from large amounts of data.

 

DQN - Deep Q-Network: A type of deep reinforcement learning algorithm used to approximate the optimal action-value function for a given environment.

 

Edge Computing: A distributed computing paradigm that brings computation and data storage closer to the location where it is needed to improve response times and save bandwidth.

 

ETL - Extract, Transform, Load: The process of extracting data from various sources, transforming it into a consistent format, and loading it into a destination, often used in data warehousing and analytics.

 

GAN - Generative Adversarial Network: A class of machine learning systems comprising two neural networks, pitting one against the other (generator vs. discriminator) to generate realistic synthetic data.

 

IoMT - Internet of Medical Things: A network of medical devices and applications connected to healthcare IT systems through online computer networks, aiming to improve patient care and outcomes.

 

IoT - Internet of Things: The network of physical devices embedded with sensors, software, and other technologies to connect and exchange data over the internet.

 

LLM – Large Language Model: is a language model notable for its ability to achieve general-purpose language generation and other natural language processing tasks such as classification.

 

LSTM - Long Short-Term Memory: A type of recurrent neural network architecture capable of learning long-term dependencies in sequential data, often used in language modeling and time series prediction.

 

ML - Machine Learning: A subset of AI that enables systems to automatically learn and improve from experience without being explicitly programmed.

 

MLOps – Machine Learning Operations: a set of practices that automate and simplify machine learning (ML) workflows and deployments. An organization can use MLOps to automate and standardize processes across the ML lifecycle. These processes include model development, testing, integration, release, and infrastructure management.

 

MLaaS - Machine Learning as a Service: Cloud-based platforms and APIs that provide machine learning tools and infrastructure, enabling developers to build and deploy machine learning models without managing underlying infrastructure.

 

MLP - Multi-Layer Perceptron: A class of feedforward artificial neural network consisting of multiple layers of nodes, where each layer is fully connected to the next layer.

 

NLP - Natural Language Processing: A field of AI focused on the interaction between computers and humans through natural language, enabling computers to understand, interpret, and generate human language.

 

OCR - Optical Character Recognition: The technology that enables computers to interpret handwritten or printed text characters from scanned documents, images, or other sources.

 

PCA - Principal Component Analysis: A statistical technique used to simplify datasets, reducing the number of variables while preserving most of the original information.

 

Pipeline (AI): AI pipelines are a way to automate machine learning workflows. AI Pipelines general consist of four main stages – Preprocessing, Learning, Evaluation and Prediction.

 

RAG - Retrieval-Augmented Generation: a technique for enhancing the accuracy and reliability of generative AI models with facts fetched from external sources they can cite, like footnotes in a research paper, so users can check any claims.

 

RL - Reinforcement Learning: A type of machine learning where an agent learns to make decisions by interacting with an environment to achieve a goal through trial and error.

 

RNN - Recurrent Neural Network: A type of neural network where connections between nodes form directed cycles, allowing it to exhibit dynamic temporal behavior, often used in sequence tasks like natural language processing and time series prediction.

 

SVM - Support Vector Machine: A supervised learning algorithm used for classification and regression analysis, particularly effective in high-dimensional spaces.

 

VR - Virtual Reality: A simulated experience that can be similar to or completely different from the real world, created using computer technology, often experienced through specialized headsets.