Foundations of Modern AI: A Beginner’s Guide to the Key Concepts

Artificial intelligence can feel confusing from the outside, but most of the buzzwords boil down to a few core ideas. This post walks through the basic concepts so anyone can start understanding and using AI in their work.

AI, ML and Deep Learning

AI (Artificial Intelligence) is the umbrella term for “machines that exhibit intelligence.” It includes everything from classic rule-based systems to modern techniques like machine learning, deep learning, generative AI, and robotics.

Machine Learning (ML) is a subset of AI where systems learn patterns from data and improve over time without being explicitly programmed for every rule. In practice, ML often refers to more traditional algorithms that came before today’s deep learning wave.

Deep Learning is a modern approach inside ML that uses neural networks with many layers to learn complex patterns such as images, speech or natural language. The ideas date back to the 1950s but really took off in the 2010s thanks to better hardware and huge datasets.

Generative AI, LLMs and Transformers

Generative AI uses deep neural networks to create new content - text, audio, images, or video - based on what it has learned. Made famous by ChatGPT.

A Large Language Model (LLM) is a deep learning model trained on massive amounts of text so it can understand and generate language. An LLM doesn’t have to be generative in the flashy sense (images, video, etc.), but generative AI systems that work with text often rely on LLMs under the hood.​

The Transformer is the neural network architecture that made today’s LLMs possible. Introduced in 2017, it uses a mechanism called “self-attention” to handle long pieces of text efficiently, which is why modern models are so good at reasoning over multi-paragraph prompts and documents.​

Parameters, Training and Inference

A parameter is an internal setting of a model that controls how it turns input into output. Modern frontier models have billions or even trillions of parameters, which is part of what gives them their flexibility and power.​

There are two main stages when working with models:

• Training: feeding data into the model so it can adjust its parameters and learn patterns.

• Inference: using the trained model to make predictions or generate content on new, unseen inputs.​

  
  

As a user or builder, you mostly work in the inference stage: calling APIs or tools that run already-trained models.

Agents and the AI Stack

In practice, many tools now talk about agents. The term is flexible, but usually means systems built on top of LLMs that can break a goal into steps, call tools (APIs, databases, CRMs) and make decisions with some autonomy rather than just answering a single prompt.​

All of this sits in a broader AI stack:

• Frontier labs build the core models (OpenAI, Anthropic, Google etc.)

• Cloud providers and managed AI (e.g. large cloud platforms like AWS, GCP, MS Azure) host them and make them accessible.

• Frameworks and platforms (for orchestration, vector databases, agent tooling, etc.) help teams build real products and automations.

• Commercial products on top (copilots, chat assistants, vertical AI tools) package everything into user-friendly solutions for work.​

  
  

How to Start Taking Advantage of AI

For someone new to the space:

• Learn the basics above so vendor claims make sense.

• Start with ready-made tools (chat assistants, copilots) to speed up writing, analysis, research and ideation.

• Gradually move to workflow automation or agents tailored to your role or business once you understand where repetitive work and decision-making live in your day.

  
  

Understanding these definitions turns AI from “magic” into a practical toolbox you can actually use, and that is the first real step into the AI space.