Direct comparison
Machine Learning vs Artificial Intelligence
Machine learning is a subset of artificial intelligence: AI is the goal, ML is one way to reach it. Compare scope, methods, data, governance and careers.
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How do Machine learning (ML), Artificial intelligence (AI) compare side by side?
The table below compares Machine learning (ML), Artificial intelligence (AI) across 12 procurement-relevant dimensions, from definition through choose this term when.
Side-by-side comparison
| Dimension | Machine learning (ML) | Artificial intelligence (AI) |
|---|---|---|
| Definition | Algorithms that improve their performance on a task by learning from data rather than from explicitly programmed rules. Tom Mitchell's widely quoted formulation (1997): a program learns from experience E with respect to tasks T and performance measure P if its performance on T, as measured by P, improves with E. | The branch of computer science concerned with building systems that perform tasks which, when done by people, are understood to require intelligence: perceiving, reasoning, learning from data, making decisions under uncertainty and using language. |
| Relationship | A subfield of AI, and the technical core of most of today's applied AI. It draws heavily on statistics, optimisation and linear algebra. Deep learning is in turn a subset of machine learning. | The umbrella field. It includes machine learning but also knowledge representation and reasoning, planning, search, robotics and multi-agent systems, several of which can work without learning from data. |
| Core question | How can a model be fit to data efficiently, and how well will it perform on data it has not seen (generalisation)? How much data does a task need, and how can the result be explained and made robust? | How can a system represent knowledge, reason over it, learn, act under uncertainty and communicate, and how should such systems be evaluated and used responsibly? |
| Main methods | Supervised learning (classification and regression from labelled examples), unsupervised learning (clustering, dimensionality reduction), semi- and self-supervised learning, reinforcement learning, and deep neural networks trained by gradient-based optimisation. | Everything in the ML column plus symbolic and logic-based reasoning, knowledge graphs, rule-based expert systems, search and automated planning, and the integration of these into perception, language and robotic systems. |
| Where capability comes from | Extracted from data by an optimisation procedure. Model parameters are set by training, so behaviour is only as good as the training data, the loss function and the evaluation design. | Either learned from data (the ML route) or encoded by people as rules, models of the world or search procedures (the symbolic route). Many deployed systems combine both. |
| Data needs | Central. Training, validation and test data are all required, and performance claims are only meaningful against data kept separate from training. Data quality, labelling, leakage and distribution shift are the dominant methodological risks. | Varies by approach. A rule-based or search-based system may need little or no training data, though it still needs domain knowledge and test cases. A learning-based system needs the data requirements described for ML. |
| Typical systems | Spam and fraud classifiers, recommender systems, clinical risk-prediction models, image classifiers, speech recognisers, protein-structure and materials-property predictors, and large language models. | All of the ML examples, plus chess-playing and route-planning programs, rule-based expert and decision-support systems, robot controllers and conversational assistants that combine several components. |
| Typical research questions | Does this model generalise beyond the benchmark? Is the result reproducible given the same data, code and random seeds? Is the evaluation free of leakage? Does performance hold across subgroups and under distribution shift? | Can a system plan and act reliably in an open environment? How should knowledge and learned models be combined? How are risks, safety and societal impacts of deployed systems assessed and controlled? |
| Governance and regulation touchpoints | Mostly appears through data and model practice: training-data governance, documentation of data and models, validation and performance monitoring, explainability, and reproducibility expectations in journals and funder policies. | Regulation is written at the AI-system level, regardless of technique. The EU AI Act defines an "AI system" broadly and sorts uses into risk tiers, and voluntary frameworks such as the NIST AI Risk Management Framework and the OECD AI Principles address AI risk management and trustworthiness as a whole. |
| Training and entry routes | Typically statistics, mathematics, computer science or data science, with coursework in probability, linear algebra, optimisation and applied modelling. Domain scientists increasingly learn ML as a research method within their own field. | Typically computer science with a specialisation, or a related discipline. Broader AI training also covers knowledge representation, reasoning, robotics, human-computer interaction and increasingly AI ethics, safety and policy. |
| Funders and funding signals | Funded as a methods contribution (new algorithms, theory, evaluation) and as an enabling method inside domain science grants, for example in biomedical, physical and social science programs. Check each solicitation, because funders increasingly add data-management, reproducibility and AI-use requirements. | Funded through dedicated AI programs and institutes as well as cross-cutting programs. In the US, NSF runs a national network of AI Institutes; agencies such as NIH and DOE publish their own AI policies. Calls tend to use "AI" as the programmatic label even when the work itself is ML. |
| Choose this term when | You are describing a specific trained model, its data, its training procedure or its evaluation, for example in a methods section, a data management plan or a reproducibility statement. | You are naming the field, a funding program, a policy or regulation, or a whole system whose behaviour (not only its learned component) is being assessed or governed. |
Common questions
Common questions about Machine learning (ML) vs Artificial intelligence (AI)
Is machine learning the same as artificial intelligence?
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No. Artificial intelligence is the broad field of building systems that perform tasks requiring intelligence. Machine learning is one approach within it, in which a system learns from data rather than following hand-written rules. All machine learning is AI, but not all AI is machine learning.
Is machine learning a subset of AI?
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Yes. Machine learning is a subfield of artificial intelligence, and deep learning is in turn a subset of machine learning. The nesting is AI, then machine learning, then deep learning.
Can there be AI without machine learning?
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Yes. Rule-based expert systems, logic-based reasoning, search and automated planning can produce intelligent-seeming behaviour without learning from data. These symbolic approaches predate the current statistical-learning wave and are still used, sometimes alongside learned components.
Why do people use the two terms interchangeably?
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Because most of the AI that has become visible recently, including image and speech recognition and large language models, is machine learning, and specifically deep learning. "AI" has become the common public label for those systems. In technical writing the more precise term is usually better.
Which term should I use in a grant application or paper?
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Match the term to what you are describing. Use "machine learning" for a specific model, its training data and its evaluation. Use "artificial intelligence" for the field, a funding program, or a system being governed as a whole. Follow the wording of the solicitation, and describe the actual method either way, since a reviewer will judge the method, not the label.
How do the two differ in regulation?
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Regulation is generally written around AI systems and their uses rather than around a particular technique. The EU AI Act, for example, regulates AI systems by risk tier. A system can therefore be in scope whether its behaviour was learned from data or hand-coded, and the learned-model details matter mainly for data governance, documentation and validation obligations.
Is data science the same as machine learning?
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No. Data science is the wider practice of extracting knowledge from data, covering collection, cleaning, statistics, visualisation and communication as well as modelling. Machine learning is one of its core modelling toolkits, and it overlaps with AI at the point where models are used to build intelligent systems.
