Demystifying ML: Key jargon words you need to know

You’ve probably heard words like “neural network” or “LLM” and thought, “What on earth does that mean?” ML or AI can feel like a foreign language, full of words like “transformers” and “reinforcement learning.” But don’t panic, we’re here to translate the geek speak into plain English, with a few laughs along the way. We’re breaking down AI jargon so anyone can understand what’s really going on behind the machines.

Algorithms

Algorithms or algo are a set of rules or instructions a computer follows to solve a problem or complete a task. In AI, algorithms are the backbone that allows models to learn patterns from data.

Machine Learning (ML)

ML is a subset of AI where machines learn from data instead of being explicitly programmed. ML models improve their performance over time as they process more data.

Neural Network

A neural network is a computational model inspired by the human brain, composed of layers of interconnected nodes (neurons). Neural networks are fundamental to deep learning.

Natural Language Processing (NLP)

A branch of AI that focuses on enabling computers to understand, interpret, and generate human language, including speech and text.

Large Language Model (LLM)

An AI model trained on massive amounts of text data to understand and generate human-like language. Examples include GPT, LLaMA, and PaLM.

Generative AI

AI that creates new content, such as text, images, music, or code, based on learned patterns from existing data.

Training & Inference

• Training: The process of teaching an AI model by feeding it data so it can learn patterns.
• Inference: The process of using a trained model to make predictions or generate outputs.

Overfitting & Underfitting

• Overfitting: When a model learns the training data too well, including noise, making it perform poorly on new data.
• Underfitting: When a model is too simple and fails to capture patterns in the data.

Reinforcement Learning (RL)

A type of machine learning where an agent learns to make decisions by receiving rewards or penalties from the environment, often used in robotics and gaming.

In conclusion, ML terminology can be intimidating at first, but understanding these key terms helps you grasp how modern AI systems work and opens the door to deeper learning in this fast-growing field.