Artificial intelligence is a broad term that gets applied to everything from spam filters to self-driving cars. Actually, artificial intelligence is the ability of computer systems to perform tasks that would typically require human intelligence – learning, problem-solving, decision-making. That breadth makes it easy to talk about vaguely and hard to understand precisely. This article covers the core concepts: what they mean, how they relate to each other, and why the distinctions matter.
How AI learns: Machine Learning and Deep Learning
AI systems don't follow rigid, hand-written rules. Instead, they learn from data. The field that makes this possible is called machine learning – the ability of a system to improve its performance based on experience, without being explicitly programmed for every scenario.
Machine learning comes in two main forms depending on how the model interacts with data:
- Supervised learning – the model learns from labeled data, meaning examples that already have correct answers attached. If you show a model 10,000 photos labeled "cat" or "dog," it learns to distinguish between them in photos it hasn't seen before.
- Unsupervised learning – the model learns from unlabeled data, finding patterns and structures on its own without being told what to look for. Rather than classifying, it groups – for example, clustering customers by purchasing behavior without being told what the groups should be.
Most modern AI systems use a more advanced technique built on top of machine learning: deep learning. Deep learning uses artificial neural networks with many layers, allowing the system to automatically extract features from raw data rather than relying on hand-crafted rules. This is what allows AI to handle less precisely defined tasks – recognizing what's in a photograph, understanding spoken language, generating text.
The main areas of AI application
Deep learning is the foundation for two major branches of applied AI:
- Natural Language Processing (NLP) – the field that enables computers to understand, interpret, and generate human language. NLP is what powers search engines, translation tools, and the large language models that have become widely used over the past few years.
- Computer Vision (CV) – the field that enables computers to process and understand visual information, from identifying objects in images to analyzing medical scans. Computer vision is the foundation for large vision models.
Generative AI and large models
The most visible development in AI over the past few years has been the rise of generative AI – systems that can create new content: text, images, code, audio. Rather than simply classifying or predicting, generative AI produces output that didn't exist before, by learning patterns from large amounts of existing data and applying them to generate something new.
This capability is realized through large models:
- Large Language Models (LLMs) – generative AI systems trained on vast amounts of text, designed to understand and generate human language. LLMs act as an interface: instead of writing code, you send a request in plain language and receive a response the same way. ChatGPT, Claude, and Gemini are all examples.
- Large Vision Models (LVMs) – generative AI systems trained on large image datasets, designed to understand and generate visual content. Combined with language models, they can analyze an image and discuss it in plain language.
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Why these distinctions matter
These aren't just academic categories. Understanding how an AI system learns and what it was built for helps clarify its capabilities and limitations — why some tasks come easily to it and others don't. It's the foundation for using AI tools thoughtfully rather than just instinctively.
For a full reference of AI terms used in this article and beyond, see the AI Dictionary →