Ling Zhang
- Sep 17
- 2 min read

Demystifying AI: Understand Key Concepts and Connection

Making AI easy to understand, one concept at a time

Artificial Intelligence (AI) is transforming industries, revolutionizing how we approach complex problems. Whether it’s generating realistic images, translating languages, or even answering questions like a human, AI is behind the magic. However, to truly understand how AI works, we need to break down some key components that make this all possible. Let’s explore these concepts through a simplified relationship chart that outlines how everything connects.

Artificial Intelligence (AI) stands at the top as the overarching technology, enabling machines to perform tasks that require human-like intelligence. Beneath AI, we find Machine Learning (ML), a subset of AI, where computers learn from data rather than following explicit instructions. Within ML, there’s a more specialized subset called Deep Learning, which uses neural networks to learn from unstructured data like images, text, or audio.

Demystifying AI: Understanding Key Concepts and Connection

At the heart of today’s AI advancements are Foundation Models (FM), such as GPT-4 and DALL·E. These models are trained on massive amounts of data and can be fine-tuned to perform specific tasks, making them versatile tools for various applications. Generative AI, a key capability of foundation models, allows machines to generate new content—be it text, images, or even music.

A specific type of foundation model is the Large Language Model (LLM), like the one powering ChatGPT. These models process vast amounts of text and understand the relationships between words, making them useful for tasks like summarization or translation. Transformers, a core technology of LLMs, help models comprehend context by using attention mechanisms, improving their ability to handle sequential data like sentences.

Finally, we have the support systems: GPUs (graphics processing units) provide the computing power needed to train these complex models, while MLOps manages the full lifecycle of machine learning, from data management to deployment.

Here’s how it all connects:

1. AI is the overarching technology.

2. ML is a subset of AI, focusing on learning from data.

3. Deep Learning is a type of ML, specialized in unstructured data.

4. Foundation Models use deep learning and can be fine-tuned for specific tasks.

5. Generative AI capabilities emerge from foundation models, enabling content generation.

6. LLMs are a class of foundation models focused on text, powered by transformers.

7. GPUs and MLOps support the training and operational lifecycle of these models.

Why This Matters

Understanding these key AI concepts helps demystify the incredible capabilities AI offers today. As businesses and industries embrace AI, knowing how machine learning, foundation models, and their underlying technologies fit together can help you navigate this fast-moving space with confidence.

By breaking down these complex terms into simpler connections, we hope this chart helps you grasp the relationships between AI’s building blocks, empowering you to engage with AI in a more meaningful way.