What is artificial intelligence? It’s the field of computer science that builds systems able to produce outputs such as predictions, recommendations, decisions or content from inputs and goals. This guide explains in plain language how an AI is trained and what data, algorithms, models, machine learning and neural networks each do.
- What we mean by artificial intelligence
- The building blocks of an AI
- How an AI works, step by step
- What is machine learning?
- What is deep learning?
- How neural networks work
- AI vs machine learning vs deep learning
- How an AI chatbot generates an answer
- Does AI think like a person?
- Why AI can be wrong
- What AI can and can't do
- A practical example: a spam filter
- Bottom line
- Artificial intelligence FAQ
- Keep reading
Quick answer: an AI isn’t a program with hand-written rules for every case. Most modern AI systems learn patterns from large amounts of data during training, store what they learned in a model made of millions or billions of parameters, and then use that model to produce results with new information, a step called inference.
What we mean by artificial intelligence
Artificial intelligence covers any system that performs tasks we associate with human intelligence: recognizing images, understanding language, translating, recommending products, detecting fraud or generating text. It’s a broad field that includes rule-based systems, search algorithms and, above all today, machine learning.
Key concepts at a glance
| Concept | What it means |
|---|---|
| Artificial intelligence | The field that builds systems able to produce outputs from inputs and goals |
| Data | The information a system uses during development or operation |
| Algorithm | The procedure used to process information or learn from it |
| Model | The trained system that results from applying an algorithm to data |
| Parameters | The internal values a model adjusts while learning |
| Training | The process of adjusting parameters using examples |
| Inference | Using the trained model to produce results with new information |
Traditional software vs machine learning
| Traditional software | Machine learning |
|---|---|
| Receives rules and data | Receives data and expected results |
| Executes defined instructions | Learns relationships present in the examples |
| Behavior is explicitly programmed | Behavior depends on the trained model |
The building blocks of an AI
Data
Data is the raw material: text, images, audio, numbers or sensor readings. The quality, quantity and variety of data largely determine how good a model can be. Biased or incomplete data produces biased or unreliable results.
Algorithms
An algorithm is the step-by-step procedure that processes the data. In machine learning, the training algorithm decides how the model adjusts its parameters to reduce its errors.
Models and parameters
The model is what you get after training: a mathematical structure whose parameters encode the patterns found in the data. Large language models have billions of parameters.
Training and inference
During training, the model sees many examples and adjusts its parameters to make better predictions. During inference, the finished model is used to answer questions, classify images or generate content. Most models don’t keep learning while you use them; improving them requires new training or fine-tuning.
How an AI works, step by step
- Define the goal: what the system should do, such as classify emails or recommend videos.
- Collect and prepare data: gather examples, clean errors and convert them into a format the model can process.
- Choose and train the model: pick an architecture and adjust its parameters with the training data.
- Validate and evaluate: test performance on data the model never saw during training.
- Run inference on new information: use the model in a real application.
- Monitor and update: track errors and changes, and retrain when needed.
What is machine learning?
Machine learning is the branch of AI in which systems learn relationships from data instead of following hand-coded rules. There are three main approaches:
- Supervised learning: the model learns from labeled examples (an email marked spam or not spam) and then predicts labels for new data.
- Unsupervised learning: the model finds structure in unlabeled data, such as grouping customers with similar behavior.
- Reinforcement learning: an agent learns by trial and error, receiving rewards or penalties for its actions, as in game-playing AIs or robotics.
What is deep learning?
Deep learning is a type of machine learning based on neural networks with many layers. Those layers let a model learn increasingly abstract features on its own: edges, then shapes, then objects in an image. Deep learning powers image recognition, speech recognition, translation and today’s generative AI.
How neural networks work
- Input layer: receives the data, such as the pixels of an image or the pieces of a sentence.
- Hidden layers: transform the information step by step, each layer combining the previous one’s outputs.
- Output layer: produces the result, such as a probability for each category.
Weights, errors and learning
Each connection between artificial neurons has a weight. During training, the network compares its output with the correct answer, measures the error and adjusts the weights slightly to reduce it, a process known as backpropagation. Repeated over millions of examples, those small adjustments add up to a model that performs the task well.
AI vs machine learning vs deep learning
| Concept | Scope | Method | Example |
|---|---|---|---|
| Artificial intelligence | The whole field | Rules, search, learning and other approaches | An expert system or assistant |
| Machine learning | A subfield of AI | Learns relationships from data | A spam filter |
| Deep learning | A subfield of machine learning | Multi-layer neural networks | Image recognition or a language model |
How an AI chatbot generates an answer
Modern chat assistants use language models trained on huge amounts of text and, in some cases, images, audio and other formats. When you type a request, the system:
- Breaks your text into processable units called tokens
- Interprets the available context, including the conversation so far
- Calculates probabilities for what should come next
- Generates the answer piece by piece
- Applies instructions, safety filters and other settings
Predicting the next token is central to language models, but it doesn’t describe all of AI: other systems classify, predict, recommend or control actions. Assistants can also add web search, document retrieval, external tools, calculations, conversation memory, image processing, code execution and agents that complete multi-step tasks.
Does AI think like a person?
There’s no evidence that current systems have consciousness or human-like understanding. They can show very complex abilities, from reasoning through problems to writing fluent text, but they reach them through computational processes that are very different from the human mind.
Why AI can be wrong
- Insufficient or incorrect data: the model learns whatever errors its data contains.
- Ambiguous questions: vague instructions lead to guesses.
- Information outside its training: it may not know about recent events or niche topics.
- Overfitting: the model memorizes training examples and generalizes poorly.
- Bias: skewed data produces skewed results.
- Probabilistic generation: generative models produce likely-sounding output, which can be plausible but false (so-called hallucinations).
- A changing world: a model trained on old data may stop matching reality.
- No access to current information unless it’s connected to search or other tools.
What AI can and can’t do
AI can
- Recognize patterns in large amounts of data
- Classify, predict and recommend
- Generate text, images, audio, video and code
- Automate repetitive tasks
- Help people analyze and summarize information
AI doesn’t guarantee
- That its answers are true
- That it understands context like a person
- That it’s free of bias
- That it knows about recent events
- That it can replace expert judgment in high-stakes decisions
A practical example: a spam filter
- Goal: classify each email as spam or legitimate.
- Data: a collection of emails already labeled.
- Preparation: the text is converted into representations the model can process.
- Training: the algorithm adjusts parameters to link certain features to each category.
- Evaluation: performance is tested on emails not used in training.
- Inference: when a new email arrives, the model calculates the probability that it’s spam.
- Monitoring: errors and new types of messages are analyzed to improve the system.
AI doesn’t appear in a single step. It’s the result of combining a goal, data, preparation, training, evaluation, software and oversight.
Bottom line
Artificial intelligence is less mysterious than it seems: systems that learn patterns from data, store them in models and apply them to new information. Understanding data, algorithms, models, training and inference helps you use AI tools better, know their limits and judge their answers critically.
Artificial intelligence FAQ
A branch of computer science that develops systems able to perform tasks associated with human intelligence, such as recognizing images, understanding language or making predictions.
It uses algorithms and mathematical models trained on large amounts of data to identify patterns and produce answers or decisions with new information.
The algorithm is the procedure used to learn or process information. The model is the resulting trained system used to perform the task.
Training adjusts the model’s parameters using examples. Inference uses the trained model to produce results with new information.
Not necessarily. Many models don’t change their parameters during normal use; improving them requires further training or fine-tuning.
AI is the overall field. Machine learning is one of its branches, focused on models that learn relationships from data.
Generative models produce likely results based on patterns. When they lack enough information, they can generate an answer that sounds plausible but is wrong.
No. Most AI tools work through plain-language instructions. Coding helps only if you want to build or customize AI systems.