AI vs machine learning: What’s the difference and why does it matter?
| 5 Min Read
Artificial Intelligence (AI) systems perform tasks that normally require human intelligence, and machine learning is a part of AI that allows computers to learn from data rather than following fixed instructions.
This article breaks down the difference between AI and machine learning and explains how deep learning and generative AI both build on machine learning to create new content.
What is artificial intelligence?
Artificial intelligence refers to the science of building computer systems that can perform tasks associated with human cognitive abilities. How complex an AI system needs to be depends on its use case. Some AI systems are designed for focused tasks, such as recognising speech, while others combine capabilities to plan actions, solve problems or make decisions.
What is machine learning?
While traditional AI systems are rule-based, following written instructions, machine learning takes a different approach. Instead of programming every decision explicitly, developers train models using examples so they can make predictions or classifications when presented with new information.
How are machine learning models trained?
Machine learning models are trained using datasets that contain examples of the problem they need to solve. During training, the model identifies relationships and patterns within the data, using these learned patterns to make predictions on new, unseen data.
For example, instead of writing thousands of rules to identify spam emails, developers train a machine learning model on large collections of spam and legitimate emails. The model then gradually learns the characteristics that accurately differentiate one from the other as it processes more data.
AI vs machine learning: Snapshot
| Aspect | Artificial intelligence | Machine learning |
|---|---|---|
| Defintion | Broad field focused on creating intelligent systems | A subset of AI focused on learning from dataA subset of AI focused on learning from data |
| Approach | Can use rule-based programming or learning algorithms | Primarily relies on statistical learning methods |
| Data requirement | May not require large datasets | Usually requires quality training data |
| Focus areas | Includes reasoning, planning, robotics, and expert systems | Includes prediction, classification, and pattern recognition |
| Examples | Rule-based assistants and planning systems | Recommendation engines, fraud detection, and image classification |
The difference between AI and machine learning also shapes the skills and knowledge required to pursue a career in these fields. Broader AI work can involve reasoning systems, planning, governance, and software development, while machine learning roles usually require stronger skills in data, statistics, and model development.
How deep learning and generative AI relate to machine learning
As AI has advanced, new techniques have emerged that build on machine learning. Deep learning is one of them, and generative AI systems use deep learning models to create content. The relationship between these technologies can be understood as a hierarchy:
- Artificial Intelligence is the broadest field.
- Machine Learning is a subset of AI that learns from data.
- Deep Learning is a subset of machine learning that uses multi-layered neural networks to process complex data such as images and speech.
- Generative AI uses advanced deep learning models to create new content, including text and video.
How Goldsmiths’ online MSc teaches AI and machine learning together
Since AI and machine learning are closely connected, professionals increasingly benefit from understanding both technologies. Goldsmiths’ online MSc in AI and Machine Learning covers core AI and machine learning before progressing into areas like deep learning, NLP, computer vision and MLOps.
Building AI Skills at Goldsmiths
Goldsmiths’ online Artificial Intelligence and Machine Learning MSc is designed to help you progress from core concepts to advanced applications. The course will develop your knowledge in areas such as:
- Machine Learning algorithms
- Deep Learning
- Natural Language Processing (NLP)
- Computer vision
- Machine Learning Operations (MLOps)
The programme also emphasises responsible AI, helping students understand ethical considerations, governance, and risk management alongside technical development. This balanced approach will prepare you to design and manage AI solutions across a wide range of industries.
Choosing the right path in AI and machine learning
AI is the broader field of creating intelligent systems that can perform tasks that typically require human intelligence. Machine learning is a key part of AI that allows systems to learn from data, while deep learning and generative AI build on these techniques to handle more complex tasks.
Recognising these differences helps organisations choose the right technologies and enables aspiring professionals to develop relevant skills. For those seeking a machine learning degree or an MSc in Artificial Intelligence, studying both disciplines together provides a comprehensive foundation for careers in one of today’s fastest-growing fields.
Frequently asked questions
What is artificial intelligence?
Artificial Intelligence refers to the development of intelligent systems capable of analysing data, recognising patterns, and solving complex real-world problems.
At Goldsmiths, you’ll explore AI across three distinct MSc pathways. New for 2026, the MSc in AI and Data Analytics combines technical grounding in AI with applied data analysis skills. The MSc in AI and Machine Learning focuses on building and deploying machine learning solutions. The MSc in AI and Creative Practice teaches the application of AI as a creative medium across film, music, design, and culture.
Is AI truly creative?
At Goldsmiths, AI is treated as a creative collaborator that expands human imagination rather than replacing it. The Artificial Intelligence and Creative Practice MSc Online is built around this premise, challenging students to critically examine and shape how AI intersects with art and creative expression.
Do I need to know how to code?
No, you don’t need prior programming experience for the Artificial Intelligence and Creative Practice MSc Online. Students come from backgrounds including arts, design, media, and the humanities, so coding doesn’t have to be your starting point. The AI and Machine Learning MSc (online) and the AI and Data Analytics MSc (online) have different requirements: it expects a quantitative foundation, although a computer science background isn’t required, and relevant professional experience is considered.
Is experimentation encouraged?
Experimentation is central to how Goldsmiths teaches AI, with the Creative Practice MSc structured around studio-style project sprints where students rapidly prototype and refine ideas in a collaborative cohort environment. Students also present original AI-driven work, such as films, interactive installations, or multimedia artworks, at a public symposium attended by industry partners.