A AI model is a simplified computer representation reality, trained on data to learn to perform tasks that normally require human intelligence.
These tasks can include speech recognition, natural language understanding, decision making, image recognition, and many others. AI models are generally developed usingmachine learning anddeep learning.
In short, an AI model :
- Learns from data Instead of being explicitly programmed, it is trained on data sets to identify patterns, trends or relationships.
- Makes decisions or generates predictions Once trained, the model can analyse new data and provide results, whether in the form of classification, prediction or other forms of interpretation.
- Adaptable and evolving using learning algorithms (such as supervised, unsupervised or reinforcement learning), it adjusts its parameters to improve its performance over time.
Applications
- Prediction: predict sales, risks, etc.
- Classification: filing images, texts and e-mails.
- Generation : generate text, images and music.
- Task automation : driving an autonomous car, playing games, etc.
👉 Types of AI models
1. Regression models
Used to predict a continuous value from one or more input variables.
Example of use Estimating the price of a house based on its surface area, location and other characteristics.
2. Classification models
Predict a category or label from input data.
Examples of use :
- Decision trees detect whether an email is spam or not.
- Random forests Classify images (for example, distinguish between photos of cats and dogs).
- Support vector machines (SVM) identifying fraudulent transactions in the banking sector.
3. Clustering models
Segment data into homogeneous groups, without prior labelling.
Example of use Using the K-means algorithm to segment customers according to their purchasing behaviour.
4. Neural network models
Inspired by the human brain, these models learn from complex data and can be used for a variety of tasks (classification, regression, clustering).
Example of use recognition of handwritten digits in automatic form reading systems.
5. Convolutional neural networks (CNN)
Specialising in image and visual data processing, they detect and classify visual patterns.
Example of use object detection in images for autonomous driving or video surveillance.
6. Recurrent neural networks (RNN)
Designed to process sequential data (text, time series), these models take into account the order of the information.
Examples of use :
- Automatic text translation.
- Text or music generation.
- Prediction of time series, such as energy demand.
7. Long memory neural networks (LSTM)
A variant of RNN capable of storing information over long sequences.
Example of use transcription of speech into text, retaining the context of several sentences.
8. Transformer models
Use attention mechanisms to process sequences of data in parallel and efficiently, often outperforming RNNs in natural language processing.
Examples of use :
- Machine translation.
- Language comprehension for virtual assistants.
- Text generation, as in GPT models.
9. Reinforcement models
Learn by interacting with an environment, maximising rewards through a process of trial and error.
Examples of use :
- Training agents in video games (such as chess or Go).
- Control and navigation in robotics.
- Optimising financial trading strategies.
10. Generative models
Creates new data similar to the training data.
Examples of use :
- Generative Adversarial Networks (GAN) generate realistic images, such as human portraits.
Variable autoencoders (VAE) Produce designs or works of art inspired by existing examples.
11. Language models
Specialising in the processing, analysis and generation of natural language.
Examples of use :
- Machine translation.
- Sentiment analysis on social networks.
- Chatbots and virtual assistants capable of answering questions.
12. Computer vision models
Designed to interpret and analyse images and videos, they extract relevant information from visual data.
Examples of use :
- Facial recognition in security systems.
- Image segmentation for medical assistance (e.g. tumour detection on scans).
- Image analysis for agriculture (detection of plant diseases).
