Artificial intelligence (AI) and machine learning (machine learning or ML) are terms that are often used interchangeably, but they have fundamental differences. Let's give a general description of each concept and how they are related.
artificial intelligence (AI)
AI is a broad field that focuses on creating systems capable of performing tasks that, when performed by a person, require intelligence. This can include things like logical reasoning, planning, pattern recognition, language comprehension, and visual perception.
AI can be classified into two main types:
- Weak (or narrow) AI. Systems that are designed and trained for a specific task. They have no consciousness or general understanding.
- Strong (or general) AI. Systems with generalized intelligence that can understand, learn and apply knowledge in different domains, just like a person.
Machine learning (machine learning, ML)
ML is a subfield of AI focused on developing algorithms that allow systems to learn from data and improve their performance over time. Instead of programming specific rules, ML models “learn” by detecting patterns and making generalizations based on data.
There are different approaches within ML; among others:
- Supervised learning. The model is trained on labeled data so that it learns to predict labels or results.
- Unsupervised learning. The model looks for patterns in unlabeled data, such as clustering or dimensionality reduction.
- Reinforcement learning. The model learns by making decisions and receiving feedback in the form of rewards or punishments.
Main differences
- Scope. AI is a broader term that includes any simulation of human intelligence, while ML is specific to the use of data and algorithms to learn and make predictions or decisions.
- Methods. AI can use a variety of methods, including but not limited to ML. ML always involves learning from data.
- Programming vs. learning. Traditional AI can rely on specific rule programming, while ML relies on the ability to learn and adapt from data.
In short, ML is a technique within AI that focuses on learning from data. All ML applications are forms of AI, but not all AI uses ML. The choice between using more traditional AI or ML methods depends on the specific task, the data available, and the goals of the system.