What Is Artificial Intelligence? A Beginner's Guide to AI, Machine Learning, Deep Learning, and LLMs

If you're curious about artificial intelligence but don't have a technical background, you're in the right place. In this beginner-friendly article, we'll break down what AI really is, clarify common misconceptions, and walk through how machine learning, deep learning, generative AI, and large language models (LLMs) fit into the broader AI landscape.

Let’s start with the big question: What is artificial intelligence?

Understanding Artificial Intelligence (AI)

Artificial Intelligence, or AI, is not a single technology — it's an entire field of study, similar to physics or biology. At its core, AI refers to machines that can simulate human intelligence — things like learning, problem-solving, and decision-making.

Within AI, there are several subfields:

• Machine learning (ML) is a subset of AI.

• Deep learning (DL) is a subset of machine learning.

• Generative AI and Large Language Models (LLMs) fall under deep learning.

Think of it like this:

• AI is the umbrella.

• Machine learning is one branch of AI.

• Deep learning is a further specialization of machine learning.

• LLMs, like ChatGPT or Google Bard, live at the intersection of deep learning and generative AI.

What Is Machine Learning?

Machine learning is a method where computers learn from data to make predictions or decisions — without being explicitly programmed. You feed the model historical data, and it learns patterns to make forecasts on new, unseen data.

Supervised vs. Unsupervised Learning

• Supervised learning uses labeled data. For example, you may have restaurant data showing total bill amounts and tip amounts. Based on this, a supervised model can predict future tips.

• Unsupervised learning works with unlabeled data, finding natural patterns or clusters. For instance, if you have data on employees' years of service and income (but no labels like job role or department), an unsupervised model might still detect high-performers vs. low-performers.

Pro Tip: Supervised learning adjusts its predictions based on feedback from the training data. Unsupervised learning does not.

What Is Deep Learning?

Deep learning is a type of machine learning that uses artificial neural networks — systems inspired by the human brain. These networks have multiple layers, and the deeper the layers, the more complex patterns the model can learn.

Deep learning models are capable of semi-supervised learning. For example, a bank might label 5% of its transactions as fraudulent or not, and the deep learning model learns from that small sample to detect fraud in the remaining 95%.

Discriminative vs. Generative Models

• Discriminative models classify data. For example, is this email spam or not?

• Generative models create new content based on patterns in training data. For example, generate an image of a dog or write a poem.

What Is Generative AI?

Generative AI creates new content — text, images, audio, or video — based on input data and learned patterns. Unlike traditional models that output numbers or categories, generative models produce entirely new content.

Examples of Generative AI:

• Text-to-Text: ChatGPT, Google Bard

• Text-to-Image: DALL·E, Midjourney, Stable Diffusion

• Text-to-Video: Imagen Video, CogVideo

• Text-to-3D: Shap-E (used in gaming and 3D modeling)

• Text-to-Task: AI that performs functions like summarizing emails or generating code

If the output is text, speech, an image, or audio, it’s generative AI.

What Are Large Language Models (LLMs)?

Large Language Models (LLMs) are deep learning models trained on massive amounts of text data. They are often pre-trained on general data and then fine-tuned for specific use cases.

Think of it like training a dog: first, it learns general commands like sit or stay. Later, it's fine-tuned for specific tasks, like being a guide dog or police dog.

LLMs can handle a wide range of language tasks:

• Text summarization

• Question answering

• Text generation

• Document classification

Organizations across industries — like healthcare, retail, and finance — can fine-tune LLMs with their own data to create powerful custom applications.

Why This Matters for Business and Research

Artificial intelligence isn't just for tech giants. SMEs, governments, NGOs, and researchers can harness AI — particularly machine learning and deep learning — to solve complex problems, predict outcomes, and automate decision-making.

That’s where we come in.

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