What is Artificial Intelligence and Machine Learning?

Artificial intelligence (AI) and machine learning (ML) are often used as if they mean the same thing. They don’t. AI is the broad goal of building machines that perform tasks we associate with human intelligence. ML is one way of getting there: systems that learn patterns from data instead of following hand-written rules. This guide explains each term, how they relate, and where you encounter them.

What Is Artificial Intelligence?

AI is a field of computer science concerned with systems that reason, perceive, understand language, and make decisions. The term comes from the proposal John McCarthy wrote for the 1956 Dartmouth Summer Research Project, the workshop generally treated as the founding event of the field.

AI is not one technique. Early AI relied heavily on hand-coded rules and logic (expert systems, search, planning). Today the field includes natural language processing, computer vision, robotics, and machine learning, with machine learning now behind most practical applications.

Narrow AI vs. General AI

• Narrow AI performs specific tasks: a chatbot, a recommendation engine, an image classifier, a voice assistant.
• Artificial general intelligence (AGI) would match or exceed human ability across any intellectual task. It does not exist yet.
• Superintelligence is a speculative idea of AI that surpasses humans in every domain.

According to IBM, nearly all current AI is narrow AI and AGI remains hypothetical. Even large language models such as ChatGPT are considered narrow because their skills are bounded by their training data.

What Is Machine Learning?

Machine learning is the subset of AI in which algorithms learn patterns from training data and use them to make predictions on new data, without being explicitly programmed for each case. The term is usually traced to Arthur Samuel’s 1959 paper Some Studies in Machine Learning Using the Game of Checkers, which showed a program learning to play checkers better than the person who wrote it.

A spam filter is a simple example: rather than listing every spam phrase by hand, developers train a model on emails already labelled “spam” or “not spam,” and the model learns which features predict each class.

Main Types of Machine Learning

IBM’s overview of machine learning paradigms describes the main approaches:

• Supervised learning: trains on labelled examples to predict a known output, such as a price (regression) or a category (classification).
• Unsupervised learning: finds structure in unlabelled data, such as customer segments or unusual transactions.
• Semi-supervised learning: combines a small labelled set with a larger unlabelled one, reducing labelling cost.
• Reinforcement learning: an agent learns by trial and error to maximise a reward. DeepMind’s AlphaGo was trained partly by playing against versions of itself using this method.

Deep learning is a branch of ML that uses multi-layer neural networks. It underpins modern image recognition, speech recognition, and large language models.

AI vs. Machine Learning: The Key Difference

All machine learning is AI, but not all AI is machine learning. A rule-based system that follows fixed if/then logic is AI but does not learn from data. An ML model learns its behaviour from examples.

Where You See AI and ML Together

In most current products, ML is the component that makes the AI system work:

• Voice assistants use ML models for speech recognition and language understanding.
• Self-driving systems combine ML-based perception (identifying cars, pedestrians, lanes) with planning and control logic.
• Banking uses ML to score credit risk and flag suspicious transactions.
• Streaming and retail platforms use recommendation models trained on viewing and purchase history.
• Healthcare uses ML to analyse medical images and support diagnosis.
• Manufacturing uses ML to predict equipment failures from sensor data.

Skills Needed to Work in AI and ML

• Programming, mainly Python
• Frameworks such as PyTorch, TensorFlow, and scikit-learn
• Mathematics: linear algebra, probability, statistics, and calculus
• Data preparation, feature engineering, and model evaluation
• Deploying and monitoring models, often on cloud platforms

ML engineers typically focus on training and evaluating models. AI engineers more often build the surrounding system: combining models with rules, data pipelines, and user interfaces.

Summary

AI is the broad field; machine learning is the data-driven approach that now powers most of it. When a vendor describes a product as “AI,” it is worth asking which part is learned from data, what data it was trained on, and how its accuracy is measured. If you are looking for tools or development partners, the AI & ML Marketplace lists AI software and ML development companies by category.