AGI (Artificial General Intelligence): A theoretical form of AI that can perform any intellectual task a human can do, demonstrating generalized learning and adaptability.
Algorithm: A step-by-step set of rules or instructions for solving a problem, often used in AI and machine learning models.
ANI (Artificial Narrow Intelligence): AI systems designed to perform specific tasks or solve particular problems (e.g., chatbots, recommendation systems). Often referred to as “Weak AI.”
API (Application Programming Interface): A set of protocols and tools that allow software applications to communicate with each other, often used to integrate AI services into other systems.
Artificial Intelligence (AI): The simulation of human intelligence by machines, enabling them to perform tasks like reasoning, learning, and problem-solving.
ASI (Artificial Superintelligence): A hypothetical future AI that surpasses human intelligence across all fields, including creativity, problem-solving, and decision-making.
Attention Mechanism: A technique in neural networks, especially transformers, that allows models to focus on relevant parts of input data.
Autoencoder: A type of neural network used for unsupervised learning, typically for dimensionality reduction or feature extraction.
B
Backpropagation: A training method for neural networks that adjusts weights based on error gradients to minimize loss.
Bayesian Networks: Probabilistic models that represent variables and their conditional dependencies using a directed acyclic graph.
Benchmarking: The process of evaluating AI models using standard datasets and performance metrics.
Bias (AI Bias): Systematic errors in AI outputs caused by biased training data, which can result in unfair or inaccurate predictions.
C
Chatbot: An AI-powered program that simulates conversation with users, often used for customer service or virtual assistance.
Computer Vision (CV): A field of AI that enables machines to interpret and analyze visual information from images and videos.
Concept Drift: When the statistical properties of a dataset change over time, potentially degrading an AI model’s performance
D
Dataset: A structured collection of data used to train, validate, or test AI models.
Deep Learning: A subset of machine learning that uses neural networks with many layers to analyze large amounts of data and identify patterns.
Diffusion Models: A class of generative models that progressively refine random noise into coherent outputs, used in AI-generated images.
Dimensionality Reduction: Techniques like PCA (Principal Component Analysis) used to reduce the number of input variables in a dataset.
E
Edge AI: AI computations performed on local devices (e.g., smartphones, IoT devices) instead of cloud servers, reducing latency.
Embedding: A numerical representation of data (e.g., words, images) used by AI models to capture semantic meaning.
Ethics in AI: The study and implementation of guidelines to ensure AI is used responsibly, addressing concerns like privacy, bias, and accountability.
Evolutionary Algorithms: Optimization techniques inspired by natural selection, used to improve AI models over iterations.
F
Federated Learning: A decentralized approach to machine learning where models are trained across multiple devices without sharing raw data.
Feature Engineering: The process of selecting and transforming data variables to improve AI model performance.
Fine-Tuning: The process of adapting a pre-trained AI model to perform specific tasks by training it on a smaller, task-specific dataset.
G
GAN (Generative Adversarial Network): A type of neural network consisting of a generator and a discriminator that work together to create realistic data, such as images.
GPT (Generative Pre-trained Transformer): A type of large language model designed to generate human-like text by predicting the next word in a sequence.
Gradient Descent: An optimization algorithm used to minimize loss in machine learning models by adjusting weights iteratively.
Graph Neural Networks (GNNs): AI models designed to process graph-structured data, useful for social networks and recommendation systems.
H
Hallucination (AI): When an AI model generates outputs that are incorrect, misleading, or entirely fabricated.
Heuristic: A problem-solving approach that finds approximate solutions efficiently but does not guarantee the best result.
Hyperparameter: Settings in a machine learning model (e.g., learning rate, batch size) that are tuned to optimize its performance.
I
Imbalanced Data: A dataset where some classes are underrepresented, leading to biased AI model predictions.
Inference: The process of using a trained AI model to make predictions or generate outputs based on new data.
IoT (Internet of Things): Devices connected to the internet that can collect, share, and analyze data, often integrated with AI for automation and decision-making.
J
Joint Probability: A statistical measure used in machine learning to calculate the likelihood of two events occurring together.
Jupyter Notebook: An open-source interactive computing environment used for writing and running AI and machine learning code.
K
K-Means Clustering: An unsupervised learning algorithm that groups data points into clusters based on similarity.
Kernel Trick: A method in support vector machines (SVMs) that allows them to operate in higher-dimensional spaces for better classification.
Knowledge Graph: A structured representation of information, showing entities and their relationships to enhance AI’s understanding.
L
LLM (Large Language Model): Advanced AI models like GPT trained on massive text datasets to generate human-like text or analyze language.
Latent Variable: A hidden variable inferred from observed data, often used in generative models.
LoRA (Low-Rank Adaptation): A technique for fine-tuning large AI models efficiently while reducing computational costs.
M
Machine Learning: A subset of AI where algorithms learn from data to improve their performance on a specific task without explicit programming.
Markov Decision Process (MDP): A mathematical framework for modeling decision-making in reinforcement learning.
Meta-Learning: A machine learning approach where models learn how to learn, improving adaptability to new tasks.
Multimodal AI: AI systems that can process and generate multiple types of data (e.g., text, images, audio) simultaneously.
N
Natural Language Processing (NLP): The branch of AI focused on enabling machines to understand, interpret, and generate human language.
Neural Network: A computational model inspired by the structure of the human brain, used in AI to recognize patterns and solve complex problems.
O
Ontology: A structured framework that defines the relationships between concepts within a domain, often used to improve AI’s reasoning capabilities.
Overfitting: A modeling error where an AI model learns the training data too well, resulting in poor performance on new, unseen data.
P
Perceptron: The simplest type of artificial neural network used for binary classification tasks.
Pre-training: The initial phase of training an AI model on a large dataset to learn general patterns before fine-tuning it for specific tasks.
Prompt Engineering: Crafting specific and detailed inputs (prompts) to guide AI models like GPT for generating accurate and relevant outputs.
Propagation: The process of transmitting signals through a neural network during training.
Q
Quantum Computing in AI: The use of quantum computers to solve complex AI problems faster than classical computers can.
Q-Learning: A reinforcement learning algorithm used to optimize decision-making processes.
R
Recurrent Neural Network (RNN): A type of neural network designed for sequential data processing, often used in NLP and time-series forecasting.
Reinforcement Learning: A machine learning method where agents learn by performing actions in an environment and receiving feedback in the form of rewards or penalties.
ResNet (Residual Network): A deep learning model architecture used to train very deep neural networks while avoiding degradation problems.
RLHF (Reinforcement Learning from Human Feedback): A training method where AI models improve by incorporating human-provided feedback.
S
Self-Supervised Learning: A machine learning approach where models generate their own labels from raw data instead of relying on human-labeled data.
Semantic Search: An AI-powered search technique that understands the context and intent behind queries to deliver more accurate results.
Sentiment Analysis: A natural language processing technique used to determine the emotional tone behind a text.
Supervised Learning: A type of machine learning where models are trained on labeled datasets to predict outcomes or classify data.
Swarm Intelligence: A type of AI inspired by collective behaviors in nature, such as ant colonies or bird flocks, used for optimization problems.
T
T5 (Text-to-Text Transfer Transformer): A transformer-based model designed for various NLP tasks by converting all inputs and outputs into text format.
TensorFlow: An open-source machine learning framework developed by Google, widely used for AI research and production.
Tokenization: The process of breaking text into smaller units (tokens), such as words or subwords, for NLP models.
Transfer Learning: Reusing a pre-trained model on a new task, reducing the need for extensive training data.
Transformer: An AI model architecture designed for processing sequential data, widely used in NLP tasks.
Turing Test: A test developed by Alan Turing to determine whether a machine’s behavior is indistinguishable from that of a human.
U
Underfitting: A modeling error where an AI model is too simple and fails to capture important patterns in the training data.
Unsupervised Learning: A machine learning technique where models identify patterns in unlabeled data without predefined categories.
U-Net: A convolutional neural network architecture commonly used in medical image segmentation.
V
Variational Autoencoder (VAE): A type of neural network used for generating new data points similar to a given dataset, often in generative tasks.
Vector Embedding: A method for representing words, images, or data points as mathematical vectors to capture their relationships.
Vision Transformer (ViT): A transformer-based architecture designed for image recognition tasks.
W
Weight (Neural Networks): A parameter in neural networks that determines the strength of connections between nodes, adjusted during training.
Word Embedding: A technique used in NLP to represent words in vector space, capturing semantic meaning and relationships.
X
Explainable AI (XAI): Techniques and tools that make AI decision-making processes transparent and understandable to humans.
Y
YOLO (You Only Look Once): A real-time object detection system used in computer vision to identify and localize objects in images or videos.
Z
Zero-Shot Learning: An AI capability where models generalize to perform tasks or recognize data they have not been explicitly trained on.
Z-Score Normalization: A statistical method used to standardize features in a dataset by subtracting the mean and dividing by the standard deviation.
