On this page
- 1 AI Terminology Explained for Beginners (With Examples)
- 1.1 Artificial Intelligence (AI)
- 1.2 Machine Learning (ML)
- 1.3 Deep Learning
- 1.4 Neural Networks
- 1.5 Training Data
- 1.6 Model
- 1.7 Inference
- 1.8 Supervised Learning
- 1.9 Unsupervised Learning
- 1.10 Reinforcement Learning
- 1.11 Epoch
- 1.12 Overfitting & Underfitting
- 1.13 NLP (Natural Language Processing)
- 1.14 Tokens
- 1.15 Embedding
- 1.16 Vector
- 1.17 Prompt
- 1.18 Generative AI
- 1.19 LLM (Large Language Model)
- 1.20 GPT (Generative Pre-trained Transformer)
- 1.21 Fine-tuning
- 1.22 Zero-shot / Few-shot Learning
- 1.23 API (Application Programming Interface)
- 1.24 Final Thoughts
- 1.25 elated Posts:
- 1.26 Leave A Comment Cancel reply
AI Terminology Explained for Beginners (With Examples)
Artificial Intelligence (AI) is reshaping how we work, build software, and interact with technology. But if you’re new to AI, the jargon can feel overwhelming. In this post, I’ll break down the most commonly used AI terms in a simple, clear way — so you can learn confidently, even if you’re just starting.
On this page
- 1 Artificial Intelligence (AI)
- 2 Machine Learning (ML)
- 3 Deep Learning
- 4 Neural Networks
- 5 Training Data
- 6 Model
- 7 Inference
- 8 Supervised Learning
- 9 Unsupervised Learning
- 10 Reinforcement Learning
- 11 Epoch
- 12 Overfitting & Underfitting
- 13 NLP (Natural Language Processing)
- 14 Tokens
- 15 Embedding
- 16 Vector
- 17 Prompt
- 18 Generative AI
- 19 LLM (Large Language Model)
- 20 GPT (Generative Pre-trained Transformer)
- 21 Fine-tuning
- 22 Zero-shot / Few-shot Learning
- 23 API (Application Programming Interface)
- 24 Final Thoughts
- 25 elated Posts:
Artificial Intelligence (AI)
AI is the broader concept of machines being able to carry out tasks in a way that we consider “smart” — like recognizing images, translating languages, or answering questions.
Machine Learning (ML)
ML is a subfield of AI. It means teaching computers to learn from data. You don’t program the answer — you provide examples, and the system figures out the patterns.
Deep Learning
A type of ML that uses neural networks (like a simplified version of the human brain) to learn complex patterns — used in voice recognition, self-driving cars, and ChatGPT.
Neural Networks
These are layers of “neurons” that pass signals, allowing the model to learn relationships in data.
Training Data
The input data you give to the model to learn. Example: Spam vs. non-spam emails.
Model
A model is what you get after training. It takes input and gives output. ChatGPT is a language model.
Inference
The process of using a trained model to make predictions or give answers.
Supervised Learning
Learning with labeled data — like images tagged as “cat” or “dog”.
Unsupervised Learning
The model gets data without labels and finds patterns — used in recommendations or clustering.
Reinforcement Learning
AI learns by trial and error using rewards/penalties. Example: learning to play chess.
Epoch
One full pass of your training dataset through the model during learning.
Overfitting & Underfitting
- Overfitting: Memorizes training data, poor on new data.
- Underfitting: Too simple, doesn’t learn well.
NLP (Natural Language Processing)
Helps machines understand human language — like summarization, translations, chatbots.
Tokens
Small pieces of text a model understands. “ChatGPT” might be 1–2 tokens.
Embedding
Converts words into numbers (vectors) that machines can process.
Vector
Numerical representation of data. Example: “king – man + woman = queen” in vector math.
Prompt
Your question or instruction to an AI model.
Example: “Write a blog post on AI terms.” → That’s a prompt!
Generative AI
AI that creates things — text, code, images. ChatGPT, DALL·E, and Copilot are examples.
LLM (Large Language Model)
A powerful AI trained on massive text data. GPT-4 is one example.
GPT (Generative Pre-trained Transformer)
The architecture behind ChatGPT. Pre-trained on large data, then used for generation.
Fine-tuning
Training an existing model like GPT on your own data to make it specialized.
Zero-shot / Few-shot Learning
- Zero-shot: The AI does a task without seeing any example.
- Few-shot: It sees 1-2 examples first, then does the task.
API (Application Programming Interface)
A way to connect your app to a model like ChatGPT programmatically.
Final Thoughts
Understanding these terms is your first step toward building your own AI tools, training models, or even making money by offering AI services!
💬 Want to build your own ChatGPT in PHP or Laravel?
Stay tuned at statelyworld.com for hands-on tutorials!