# AI Terms, Simply Explained: Notes from My Learning Journey > Source: > Published: 2026-05-20 05:38:51+00:00 While preparing for the AWS Certified AI Practitioner exam , I thought it would be helpful to ✍️ down my understanding of some common AI and GenAI terms. These notes reflect my understanding, shaped by different learning resources, including AWS publicly available content and from experiences. This is not a textbook or a glossary. πŸ“š It’s a simple explanation of key terms, written in a way that I would have liked to read when I first started β€” with real-world analogies and no jargon. Let’s get started. πŸš€ As we all know, terms like Machine Learning , AI , Generative AI , and Agentic AI are becoming common. These are the ones we hear the most, but there are many more working quietly behind the scenes. Personally, I believe staying relevant and up to date is the key. When you understand the fundamentals right, it becomes easier to connect the dots when you work on real AI projects β€” and that confidence makes a real difference. 1️⃣ Artificial Intelligence (AI) is the idea of making computers do things that would normally require human intelligence. πŸ€– Think of it as teaching machines to solve problems, understand language, or even make decisions β€” tasks that earlier needed a person. Real-life examples we already use: πŸ“š Why it matters: AI is now behind many tools and services we use daily. Knowing the basics helps you understand how these systems are built and what’s happening behind the scenes. πŸ” Quick Note: Why Data Matters All AI systems β€” whether it's Machine Learning , Generative AI , or Chatbots β€” rely heavily on data. Data is what helps AI learn, find patterns, and make decisions. Where does the data come from? It can be collected from public datasets, user interactions, company records, or even purchased from authorized data providers. In short: No data, no AI. The better the data, the smarter the AI becomes. 2️⃣ Machine Learning (ML) 🧠 is a branch of AI focused on teaching computers to learn from data, without being explicitly programmed for every task. While AI is the broader idea of making machines intelligent, ML is one way we achieve it β€” by helping machines find patterns in data and improve over time. Real-life examples: πŸ“š Why it matters: Machine Learning powers many of the AI applications we interact with daily. Understanding how ML works helps demystify how intelligent systems make decisions based on data. 3️⃣ Artificial Neural Networks (ANN) are computer systems inspired by how the human brain works. They are made up of layers of simple units called neurons, connected to each other, and are designed to recognize patterns in data β€” much like how our brain processes information. How it works: Real-life examples: πŸ“š Why it matters: Neural networks are at the heart of many AI applications that requirepattern recognition . They help machinesprocess complex data and makedecisions more like how humans do. 4️⃣ Deep Learning is a type of Machine Learning that uses large neural networks with many layers β€” which is why it's called deep. You can think of it as a more powerful way for machines to learn complex tasks by breaking them down into smaller steps β€” similar to how we build a house brick by brick 🧱🏠, or how we first set up infrastructure before deploying an app in tech projects. πŸ–₯οΈπŸš€ Real-life examples: πŸ“š Why it matters: Deep Learning has made it possible for machines to perform tasks that once needed human-level skills β€” like seeing, recognizing, and even understanding β€” at a much higher scale. 5️⃣ Generative AI (GenAI) is a type of AI that creates new content β€” like text, images, or even music β€” based on what it has learned.🧩 You can think of it like a chef who has studied thousands of recipes and can now create a new dish using that knowledge.🍳 Real-life examples we already see: πŸ“š Why it matters: Generative AI is speeding up how we create, design, and problem-solve β€” helping us move from ideas to results much faster. 6️⃣ Foundation Models (FM) are large AI models trained on a huge variety of data β€” text, images, or both β€” so they can handle many different tasks without being specialized for just one thing. You can think of a Foundation Model like a strong base in construction β€” once built, it can support different types of buildings on top.πŸ—οΈπŸ’ Real-life examples you might know:πŸ“š Why it matters: Instead of building a new AI model for every task, Foundation Models give us a powerful starting point that can befine-tuned for specific needs β€” making AI development faster and more flexible. 7️⃣ Large Language Models (LLMs) are AI systems trained on huge amounts of text data to understand and generate human language.πŸ§ πŸ“ You can think of an LLM like a smart virtual assistant β€” or like a doctor who has seen thousands of cases and can diagnose based on experience, without having to look things up every time. πŸ©ΊπŸ“š Where you see LLMs in action: πŸ“š Why it matters: LLMs are powering a new generation of tools that can understand human language and respond naturally, helping make information and communication faster and easier.Quick Note: All LLMs areFoundation Models (FMs) , but not all FMs are LLMs β€” FMs can handle other types of data too, like images or video. Real-world example: AWS offers a service called Amazon Bedrock , where you can access different LLMs like Anthropic's Claude and Meta's Llama 2 and AWS's own Amazon Titan models to build language-based applications. 8️⃣ Natural Language Processing (NLP) is the part of AI that helps computers understand and work with human language β€” both what we write and what we say. πŸ—£οΈπŸ’» You can think of NLP like teaching a computer how to read, listen, and respond in ways that feel natural to us. Behind the scenes: πŸ” NLP uses algorithms that learn from lots of examples β€” books, conversations, articles β€” so that computers can figure out what we mean and reply in a way that feels human. It’s not hard-coded with rules β€” it learns patterns and improves over time, just like we do when we practice a new language. Two important sides of NLP: πŸ“š text-to-speech ) or turning spoken voice into written words (speech-to-text ).βœοΈπŸ”ŠWhy it matters: NLP is what makes it possible for computers to have more natural conversations with us β€” whether it’s chatting with a support bot or using voice commands on a device. 9️⃣ Transformer Models are a type of AI model designed to understand and process language more effectively.πŸ§ πŸ’¬ Unlike older models that read sentences one word at a time, Transformers look at the entire sentence all at once. What makes them special is a trick called attention β€” they figure out which words in a sentence are more important to focus on. For example, in a customer review: β€œThe food was amazing, but the service was slow.” The model pays more attention to words like β€œfood,” β€œamazing,” β€œservice,” and β€œslow” because they carry the real meaning, instead of small filler words. Why it matters: Transformers have become the foundation for many advanced AI systems, helping them understand language faster and more accurately than before.