Artificial intelligence (AI) en machine learning (ML) zijn gerelateerd, maar ze zijn niet hetzelfde. 2.260 / 5.000 Artificial intelligence (AI) and machine learning (ML) are related, but they are not the same. Here is a quick overview of the difference:
- Artificial Intelligence (AI): AI is a broader concept that focuses on creating machines that mimic human intelligence. The goal of AI is to develop systems that can reason, solve problems, learn, understand, and make decisions like humans do. AI encompasses several subfields, such as natural language processing, computer vision, and robotics.
- Machine Learning (ML): ML is a specific branch of AI that focuses on developing algorithms and models that allow machines to learn from data automatically, without being explicitly programmed to do each task. It is primarily about improving performance based on experience (data). ML uses mathematical models to recognize patterns and make predictions.
In summary:
- AI is the overarching concept of intelligent machines.
- ML is a way to realize AI by having machines learn from data.
Machine learning is thus a subcategory of AI, but AI encompasses many more techniques than just machine learning.
Core models (AI algorithms)
Machine Learning (ML): ML models learn from data by recognizing patterns and making predictions. ML can be further divided into:
- Supervised learning: A model is trained with labeled data (input-output pairs).
- Unsupervised learning: The model searches for hidden patterns or structure in data without labeled output.
- Reinforcement learning: The model learns through rewards and punishments based on actions and outcomes.
Deep Learning: A subfield of ML that uses multi-layered neural networks (so-called deep neural networks). These are particularly suitable for tasks such as image and speech recognition.
- Convolutional Neural Networks (CNNs): Used for image processing, with filters that help detect patterns.
- Recurrent Neural Networks (RNNs): Used for sequential data such as time series or text.
- Transformer Architectures: Modern neural networks that form the basis for advanced language models such as GPT and BERT, by efficiently handling long sequences of text data.