Since the middle of the 20th century, some scientists already started thinking about a possibility of creating an artificial intelligence. After tremendous progress realized in several scientific fields, Artificial Intelligence commonly called AI becomes a relevant tool in different sectors of activities. It is used in social media monitoring, self-driving cars, virtual travel booking etc.
When people speak about AI, they usually come with the term AI Model. But what does that mean actually? In this post, I will be trying to explain this concept as simple as possible so that a non-technical person can have a basic idea of what an AI Model is.
Let’s start with the word model. In this context, the model we are referring to is a mathematical function that will reflect a real-world situation. You will define a set of instructions for this function to execute and you will give it some parameters to work with. Depending on the parameters you provide, you will get different output coming out of this function. The same way, the AI model will use a set of instructions (an algorithm), some input (data) in order to make prediction or decision. Each use case will require a specific algorithm. For example, an algorithm for image recognition might say to look for certain features in an image (the input data), such as the shape of an object or the color of its background. Another algorithm for natural language processing might say to analyze the grammar and sentence structure of a text.
There are different types of AI models, each suited to a different type of task. Some of the most common include:
Supervised models: These models are trained on a labeled dataset, that means the computer is told what each input data represents. For example, the computer might be given a set of images and told which ones are boats, which ones are airplanes. The strategy behind the supervised models is to learn the relationship between the inputs and outputs, so that they can accurately predict the outputs for new data that they have not seen before.
Unsupervised models: These models are trained on an unlabeled dataset, that means the computer is not told what the inputs represent. The strategy behind those models is to find patterns or structures in the data that the human might not have noticed. For example, an unsupervised model might look at a set of images and group together those that have similar features, such as texture or color.
Semi-supervised models: These models are a combination of both supervised and unsupervised models. They are given as input labeled and unlabeled data, and the goal is to make use of both types of information to make more accurate predictions.
In conclusion, an AI model is a computer program that will perform specific tasks by following a set of rules (algorithm) and using a set of data as input. There are different types of models for different types of tasks, and the choice of model depends on the nature of the task, the amount and quality of data available.