In brief
- AI is the umbrella term, not a synonym for one particular model or method.
- Machine learning adjusts model parameters from data and a defined learning objective.
- Deep learning uses multi-layer neural networks; generative AI produces outputs such as text, images or code.
- Reinforcement learning is a learning approach, reinforcement learning from human feedback (RLHF) is a specific method using human preferences, and AGI is a hypothetical future concept.
The terms do not describe the same level
The terms are often used as though they were interchangeable technologies. They describe different things: AI is a broad field and can refer to complete systems; machine learning and deep learning are methods; generative AI describes content-generating capability; and AGI describes a possible future level of capability.
A perfectly nested pyramid would still be misleading. Most modern generative language and image models use deep learning, but deep learning also supports non-generative tasks such as recognition, classification and forecasting.
- Artificial intelligence (AI)
- The umbrella term for machine-based systems that infer outputs such as predictions, content, recommendations or decisions from inputs.
- Machine learning (ML)
- Methods that use training data to create a model of statistical patterns rather than coding every rule individually.
- Deep learning (DL)
- A field within machine learning that uses neural networks with multiple processing layers to learn complex representations.
- Generative AI (GenAI)
- AI models that use learned structures to generate new outputs such as text, images, audio, video or code.
- Artificial general intelligence (AGI)
- A hypothetical concept of broadly capable AI. Its definition, measurement and achieved status are not settled by a common standard.
In what ways do models learn?
The word ‘learning’ does not describe the same process everywhere. During training, model parameters are adjusted to improve a defined learning objective on examples. People and organisations still determine the purpose, data, feedback, quality criteria and deployment boundaries.
Reinforcement learning therefore belongs in an introduction to machine learning. The separate article on alignment and reinforcement learning from human feedback (RLHF) then explains how human preferences can become a specialised training signal for language models.
| Learning approach | Plain-language explanation | Typical example |
|---|---|---|
| Supervised learning | The model receives examples with a desired answer or class. | Support requests are assigned to categories using reviewed cases. |
| Unsupervised learning | The method finds structures or groups without a correct answer for every case. | Similar transactions or documents are grouped together. |
| Self-supervised learning | The learning signal is derived from the data itself, such as predicting hidden or next text fragments. | A language model learns statistical language patterns from large text collections. |
| Reinforcement learning | A system learns a policy from rewards associated with actions or outcomes. | An agent optimises decisions in a simulated environment. |
| Reinforcement learning from human feedback (RLHF) | Human preferences are translated into a reward or preference signal for further model training. | People compare response variants so a language model follows intended instructions more effectively. |
What kind of task does each term describe?
For a business decision, a provider's label matters less than the required output, how that output can be tested and what the consequences of an error would be.
| Term | What it describes | Business example |
|---|---|---|
| AI | The broader system or field | A system prioritises service cases and recommends next steps. |
| Machine learning | Learning statistical patterns from example data | A model forecasts demand or detects unusual transactions. |
| Deep learning | Machine learning based on multi-layer neural networks | Image inspection detects material defects; speech recognition transcribes conversations. |
| Generative AI | Generating new content from a model, input and context | An assistant summarises reports or drafts a response. |
| LLM or SLM | A language-focused model family in different size classes | A language model processes instructions; an application and platform add data, permissions and processes. |
| AGI | A hypothetical general capability level rather than a clearly bounded current product | Not a reliable procurement category: each use case still requires its own evaluation. |
What does learning really mean in business use?
Saying that a machine ‘learns by itself’ is an oversimplification. During a defined training process, model parameters are adjusted. People select or influence the data, model family, objective function and evaluation. Poor data or an unsuitable learning objective can therefore produce systematically unhelpful results.
A trained model also does not automatically continue learning in production. Many deployed models remain unchanged until they are deliberately retrained or replaced. Continuous learning must be explicitly designed, monitored and evaluated.
- Training data provides examples and observed relationships.
- An objective function determines which type of error should be reduced.
- Validation and test data assess cases the model has not seen.
- Applications, permissions and process rules do not emerge from model training.
- New data changes a deployed model only through an intended update process.
AGI is a future concept, not a procurement category
AGI is often described as AI with very broad, human-level or superhuman capabilities. There is no universally accepted definition or binding test. Individual organisations use their own wording, but that does not create an industry-wide standard.
For a current business decision, the label is therefore of little practical value. Even a highly capable general model must be evaluated for the actual process, company data, languages, error consequences and permissions.
- Step 1
Define the required result
Specify which prediction, classification, summary or action is actually needed.
- Step 2
Choose the simplest suitable approach
Compare rules, conventional machine learning and generative models against the task.
- Step 3
Assess the complete system
Evaluate the model, data, application, platform, permissions and human approvals separately.
- Step 4
Measure with your own cases
Test quality, cost, latency and risk instead of relying on general capability claims.
Example: three AI tasks in a manufacturing company
A machine-learning model forecasts spare-part demand. A deep-learning model detects damage in product images. A generative assistant summarises approved service reports and drafts a response. All three solutions are AI, but they solve different tasks and require different tests. None needs to be called AGI to create concrete business value.
What to remember
Classify the task, learning approach and required output first. Then select and test the simplest system that meets the quality and risk thresholds, rather than basing the decision on labels such as deep learning or AGI.
Sources and further reading
These primary sources provide further detail on definitions, technical foundations or responsible use.
Content reviewed