Master AI Terminology

Core AI Concepts

​

AI (Artificial Intelligence) – The simulation of human intelligence processes by machines, including learning, reasoning, and problem-solving.

​

ML (Machine Learning) – A subset of AI that enables systems to learn and improve from data without being explicitly programmed.

 

DL (Deep Learning) – A specialized subset of machine learning that uses artificial neural networks with multiple layers to analyze and process data.

 

NLP (Natural Language Processing) – A field of AI that enables machines to understand, interpret, and respond to human language.

 

Neural Network – A computing system inspired by the human brain, consisting of layers of nodes that process information.

 

Algorithm – A set of rules or instructions used by AI systems to perform specific tasks or solve problems.

 

Supervised Learning – A type of machine learning where models are trained on labeled data, allowing them to make predictions.

 

Unsupervised Learning – A type of machine learning where models analyze and group data without labeled outcomes.

 

Reinforcement Learning (RL) – A learning method where an agent learns to make decisions by receiving rewards or penalties.

 

Generative AI – AI models that create new content, such as text, images, audio, or code, based on input data.

 

Transformer Model – A deep learning architecture commonly used in NLP tasks, including models like GPT and BERT.

 

GPT (Generative Pre-trained Transformer) – A model developed by OpenAI that generates human-like text based on input.

 

LLM (Large Language Model) – A powerful NLP model trained on vast amounts of data to understand and generate text.

 

Computer Vision – A field of AI that enables machines to interpret and analyze visual information from images and videos.

​

AI Techniques and Methods

 

Classification – The process of assigning data points to predefined categories or labels.

 

Clustering – Grouping similar data points together without predefined labels.

 

Regression – A technique used to predict continuous values based on input data.

 

Data Preprocessing – The practice of cleaning and transforming raw data to prepare it for model training.

 

Feature Engineering – Creating new input features or modifying existing ones to improve model performance.

 

Transfer Learning – Reusing a pre-trained model on a new but related task to save time and resources.

 

Fine-Tuning – Adjusting a pre-trained model’s parameters to improve its performance on a specific task.

 

Prompt Engineering – The practice of designing effective prompts to guide the behavior of generative AI models.

 

Vectorization – Converting data, such as text or images, into numerical vectors for machine learning models.

 

AI Applications and Tools

 

Chatbot – An AI-powered application designed to simulate human conversation with users.

 

Virtual Assistant – AI software that performs tasks or services based on voice commands or text input.

 

Recommendation Engine – An AI system that suggests relevant products, content, or information to users.

 

Autonomous Vehicle – A self-driving vehicle that uses AI to navigate without human intervention.

 

Sentiment Analysis – Using AI to analyze text and determine the sentiment or emotional tone.

 

Speech Recognition – The ability of AI systems to convert spoken language into text.

 

Image Recognition – AI technology that identifies and categorizes objects within images.

 

Fraud Detection – The use of AI to identify suspicious patterns and prevent fraudulent activities.

​

AI Models and Architectures

 

CNN (Convolutional Neural Network) – A deep learning architecture commonly used in image processing.

 

RNN (Recurrent Neural Network) – A neural network model that processes sequential data, often used in language models.

 

GAN (Generative Adversarial Network) – A model that pits two neural networks against each other to generate realistic data.

 

BERT (Bidirectional Encoder Representations from Transformers) – A pre-trained NLP model developed by Google.

 

T5 (Text-to-Text Transfer Transformer) – A model that converts NLP problems into a text-to-text format.

 

VAE (Variational Autoencoder) – A generative model used to compress and recreate data.

Perceptron – The simplest type of neural network, consisting of a single layer of nodes.

​

Ethics and Challenges in AI

 

Bias in AI – The tendency of AI models to reflect and perpetuate biases present in the training data.

 

Explainability – The ability to understand and interpret how an AI model arrives at a decision.

 

Fairness in AI – Ensuring that AI systems provide equitable outcomes for all user groups.

​

AI Ethics – The study and application of moral principles to the development and use of AI

technologies.

​

Adversarial Attacks – Attempts to manipulate AI models by introducing deceptive input.

​

Data Privacy – Protecting user data from unauthorized access or misuse by AI systems.

​

Model Drift – The gradual degradation of model performance as new data patterns emerge.

​

Advanced AI Concepts

​

Autonomous Learning – AI systems capable of adapting and improving without human intervention.

​

Federated Learning – A technique where AI models are trained across decentralized devices without sharing raw data.

​

Edge AI – AI models that run locally on devices instead of in the cloud.

​

AI Model Deployment – The process of making trained AI models available for real-world applications.

​

Synthetic Data – Artificially generated data used to train AI models when real data is limited.

​

AI Pipeline – The sequence of steps involved in developing, training, and deploying AI models.

​

Future of AI Technologies

 

AGI (Artificial General Intelligence) – Hypothetical AI that can perform any intellectual task a human can.

 

ASI (Artificial Superintelligence) – A theoretical form of AI that surpasses human intelligence.

 

Quantum AI – The intersection of quantum computing and AI to solve problems beyond classical computers.

 

Self-Supervised Learning – A training method where models generate their own labels from input data.

​

Neural Architecture Search (NAS) – The automated process of discovering the best neural network architecture.

 

AI-Related Acronyms Quick Reference

• AI – Artificial Intelligence

• ML – Machine Learning

• DL – Deep Learning

• NLP – Natural Language Processing

• CNN – Convolutional Neural Network

• RNN – Recurrent Neural Network

• GAN – Generative Adversarial Network

• LLM – Large Language Model

• AGI – Artificial General Intelligence

• ASI – Artificial Superintelligence

• VAE – Variational Autoencoder