Why This Guide Matters
Artificial intelligence isn’t just changing technology, it’s reshaping entire industries, careers, and how we solve problems. But AI comes with its own language, and understanding these terms is your key to participating in the AI revolution.
This isn’t another dry technical dictionary. Together with our AI Team, we’ve crafted short and practical definitions that connect AI concepts to real-world applications. What makes this different:
- Real-world examples for every concept
- Connections between related terms
- Practical applications, not just theory
- Regular updates as AI evolves
A
Agentic AI
Think of this as AI with initiative. Instead of waiting for your commands, agentic AI systems make decisions and take actions on their own—like a digital assistant that doesn’t just answer questions but actively helps you achieve your goals. These systems learn from experience and adapt to new situations, making them powerful allies in business automation.
→ See how Agentic AI is transforming business strategy in 2025.
Algorithm
The recipe that tells a computer how to solve problems. Just like following a cookbook, algorithms are step-by-step instructions that help machines learn patterns, make decisions, and perform intelligent tasks. Every AI system runs on algorithms—they’re the invisible engines powering everything from your Netflix recommendations to self-driving cars.
Artificial General Intelligence (AGI)
The holy grail of AI research. AGI would be AI that matches human intelligence across all areas—reasoning, creativity, emotional understanding, and learning. We’re not there yet, but when we achieve AGI, it will be as comfortable writing poetry as it is solving complex math problems.
Artificial Intelligence (AI)
Simply put: machines that think and learn. AI gives computers the ability to perform tasks that typically require human intelligence—recognizing faces, understanding speech, making decisions, and solving problems. It’s not science fiction anymore; it’s the technology behind your smartphone’s voice assistant and your bank’s fraud detection.
→ Discover AI’s impact on the financial industry.
Automated Machine Learning (AutoML)
AI that builds AI. AutoML tools handle the complex technical work of creating machine learning models, making AI accessible to people without deep technical expertise. It’s like having an AI assistant that can create other AI assistants for specific tasks.
B
Bias (AI Bias)
When AI systems reflect human prejudices or unfair assumptions. This happens when training data contains biases or when algorithms inadvertently discriminate against certain groups. Addressing AI bias is crucial for creating fair and equitable AI systems.
Big Data
Massive datasets that are too large and complex for traditional processing methods. Think of all the data generated by social media, sensors, and digital transactions—big data is the fuel that powers many AI applications by providing the information AI systems need to learn.
C
Chatbot
Your AI conversation partner. These programs simulate human conversation through text or voice, handling everything from customer service inquiries to complex problem-solving. Modern chatbots can understand context, maintain conversations, and even show personality.
Computer Vision
Teaching computers to see and understand images and videos. This technology powers facial recognition, medical imaging analysis, quality control in manufacturing, and autonomous vehicles. It’s what lets your phone recognize faces in photos or helps doctors detect diseases in medical scans.
Convolutional Neural Network (CNN)
The AI specialist for visual tasks. CNNs are designed to process images by detecting patterns, edges, and features—much like how human vision works. They’re the technology behind photo tagging, medical image analysis, and autonomous driving systems.
Clustering
Finding hidden groups in data without being told what to look for. Clustering algorithms discover natural groupings—like identifying customer segments based on purchasing behavior or grouping similar news articles by topic.
D
Data Mining
The art of finding gold in data mountains. Data mining uses statistical and AI techniques to discover patterns, trends, and insights buried in large datasets. It’s how companies identify new opportunities, predict customer behavior, and make data-driven decisions.
Deep Learning
AI that learns through multiple layers of understanding. Deep learning networks stack many layers of artificial neurons, each learning increasingly complex patterns. It’s the technology behind breakthrough AI achievements in image recognition, language translation, and game-playing.
Decision Tree
A logical flowchart that AI uses to make decisions. Decision trees break down complex choices into simple yes/no questions, making them easy to understand and explain—perfect for applications where you need to understand how AI reached its conclusion.
E
Explainable AI (XAI)
AI that shows its work. Instead of being a “black box,” explainable AI systems provide clear reasons for their decisions and predictions. This transparency is crucial for building trust and ensuring AI decisions can be understood and validated.
Expert System
AI that captures human expertise in specific domains. These systems encode the knowledge and decision-making processes of human experts, helping organizations preserve and scale specialized knowledge across fields like medicine, finance, and engineering.
Edge Computing
Bringing AI processing closer to where data is created. Instead of sending all data to distant cloud servers, edge computing processes information locally—enabling real-time AI responses for applications like autonomous vehicles and smart manufacturing.
F
Feature Engineering
The art of preparing data for AI success. Feature engineering involves selecting and transforming the input variables that help machine learning models make better predictions—like choosing the right ingredients for a recipe to ensure the best outcome.
Federated Learning
Collaborative AI that protects privacy. This approach trains AI models across multiple devices or organizations without sharing raw data, enabling the benefits of shared learning while keeping sensitive information secure.
Fine-tuning
Customizing pre-trained AI for specific tasks. Instead of training from scratch, fine-tuning adapts existing AI models to new domains—like teaching a general language model to understand legal documents or medical terminology.
G
Generative AI
AI that creates new content from scratch. These systems can generate text, images, music, code, and more based on patterns learned from training data. Generative AI is revolutionizing creative industries and content production.
GPU (Graphics Processing Unit)
The powerhouse behind modern AI. Originally designed for graphics, GPUs excel at the parallel processing required for AI computations, making them essential for training and running complex AI models.
H
Human-in-the-Loop (HITL)
AI that works with human oversight. HITL systems incorporate human judgment and expertise into the AI process, combining artificial and human intelligence for better outcomes and maintaining human control over important decisions.
Hallucination
When AI confidently generates false information. Hallucinations occur when AI systems, particularly language models, create plausible-sounding but incorrect content—highlighting the importance of verification and human oversight.
→ Learn how to prompt to avoid hallucination in our guide.
I
Internet of Things (IoT)
The network of smart, connected devices that generate data for AI analysis. IoT devices collect real-world information that AI systems can use to make intelligent decisions about everything from energy management to predictive maintenance.
Inference
Putting trained AI to work. Inference is when AI models make predictions or decisions on new data – it’s the deployment phase where AI provides real-world value by applying learned patterns to solve actual problems.
Intelligent Automation
The marriage of AI and automation. This combination creates systems that can handle complex, variable tasks with minimal human intervention – going beyond simple rule-based automation to include learning and adaptation.
L
Large Language Model (LLM)
AI systems trained on vast amounts of text to understand and generate human-like language. LLMs power chatbots, content generation, translation, and coding assistance. They’re the foundation of conversational AI and many modern AI applications.
→ Learn how LLMs enable agentic AI systems.
M
Machine Learning (ML)
AI that improves through experience. ML enables computers to learn patterns from data and make predictions without being explicitly programmed for every scenario—it’s the foundation of most modern AI applications.
MLOps (Machine Learning Operations)
The practice of deploying and maintaining AI systems in production. MLOps combines machine learning, software engineering, and operations to ensure AI models work reliably in real-world environments.
→ Discover how to transform AI experiments into production-ready systems with MLOps.
Model Training
The process of teaching AI systems. Training involves feeding data to algorithms and adjusting their parameters based on performance, similar to how students learn from practice problems and feedback.
Multi-modal AI
AI that processes multiple types of information simultaneously. These systems can understand text, images, audio, and video together, enabling more comprehensive understanding and richer interactions.
N
Named Entity Recognition (NER)
Named Entity Recognition (NER)
Teaching AI to identify specific items in text. NER systems can spot and classify entities like people, companies, locations, and dates within documents—enabling everything from automated data extraction to content analysis and knowledge graph construction.
Narrow AI
AI designed for specific tasks only. Unlike science fiction’s all-knowing AI, narrow AI excels at particular jobs like recognizing faces, playing chess, or recommending movies—representing virtually all AI systems in use today.
Natural Language Processing (NLP)
Teaching computers to understand human language. NLP enables machines to read, interpret, and generate human language—powering everything from voice assistants to language translation and sentiment analysis.
Neural Network
AI’s attempt to mimic brain structure. These networks consist of interconnected artificial neurons that process information and learn from data, forming the foundation of deep learning and modern AI breakthroughs.
O
Overfitting
When AI memorizes instead of learning. Overfitting occurs when models learn training data too specifically, including noise and outliers, leading to poor performance on new data—like a student who memorizes answers without understanding concepts.
Optimization
Finding the best solution among many possibilities. In AI, optimization involves adjusting model parameters to minimize errors and maximize performance—the process of making AI systems as effective as possible.
Optical Character Recognition (OCR)
Technology that converts images of text into editable digital text. OCR enables computers to read printed or handwritten text from documents, photos, and scanned images.
P
Predictive Analytics
Using AI to forecast future events. Predictive analytics analyzes historical data to identify patterns and make predictions about future outcomes—helping businesses anticipate customer behavior, market trends, and operational needs.
Prompt Engineering
The art of communicating with AI effectively. Prompt engineering involves crafting input instructions to get optimal outputs from AI models—like learning the right way to ask questions to get the best answers.
→ Learn how to prompt like a master with our guide.
Python
The programming language of AI. Python’s simplicity and extensive AI libraries make it the preferred choice for machine learning and AI development—it’s the common language that unites the AI community.
→ We’re looking for engineers with strong Python expertise to join our AI Team. Apply here.
R
Retrieval‑Augmented Generation (RAG)
AI that combines knowledge retrieval with content generation. RAG systems first search for relevant information from knowledge bases, then use that information to generate accurate, contextually relevant responses—overcoming the limitations of static training data.
Reinforcement Learning (RL)
AI that learns through trial and error. RL systems interact with environments, receiving rewards or penalties for their actions, gradually learning optimal strategies—like how video game AI learns to play by trying different moves and seeing what works.
Robotic Process Automation (RPA)
Software robots that automate repetitive tasks. RPA handles rule-based processes like data entry and invoice processing, but unlike agentic AI, it follows predefined rules without learning or adapting. Learn about the evolution from RPA to agentic AI systems.
S
Supervised Learning
AI learning with a teacher. Supervised learning uses labeled examples to train models, showing them correct inputs and outputs so they can learn to make similar predictions on new data.
Sentiment Analysis
AI that understands emotions in text. Sentiment analysis determines whether text expresses positive, negative, or neutral feelings—crucial for social media monitoring, customer feedback analysis, and market research.
T
Transformer
The neural network architecture that revolutionized AI. Transformers use attention mechanisms to process information more effectively, forming the foundation of modern language models like GPT and BERT.
U
Underfitting
When AI models are too simple to capture important patterns. Underfitting occurs when models lack the complexity needed to understand the underlying data relationships—like using a straight line to describe a curved relationship.
User Experience (UX) in AI
Designing AI interactions that feel natural and helpful. AI UX focuses on making AI-powered interfaces intuitive, accessible, and valuable to users – ensuring AI enhances rather than complicates human experiences.
V
Vector Database
Specialized databases for AI similarity search. Vector databases store high-dimensional numerical representations of data, enabling AI systems to quickly find similar items – crucial for recommendation systems and information retrieval.
Virtual Assistant
AI-powered helpers that respond to voice or text commands. Virtual assistants like Siri, Alexa, and Google Assistant use multiple AI technologies to understand requests and provide helpful responses.
W
Workflow Automation
Using AI to streamline business processes. Workflow automation reduces manual work and improves efficiency by automatically handling routine tasks and decision-making processes.
Keep Learning
Artificial intelligence moves fast, and new terms emerge constantly. We regularly update this guide to include the latest AI concepts and terminology. Bookmark this page and return often as you deepen your AI knowledge.
This guide is regularly updated to reflect the latest developments in AI terminology and concepts. Last updated on July 2025.
Ready to apply these concepts?
Explore our practical AI resources and discover how these technologies can transform your business.
