Humans have been studying AI since the early 1900s. One of the very first AI is a computer program that can perform tasks that normally required human intelligence such as playing chess or solving mathematical problems. Since then, AI research continued to advance with the development of new algorithms and techniques for solving more complex problems. In this blog, I will look into the several models that emerged from the advancement of AI research.

An AI model is a mathematical representation of a system that can perform tasks typically requiring human intelligence like recognizing trends and patterns, and making predictions and decisions. These models are developed using algorithms and statistical methods while looking at large amounts of data. The aim is for the AI model to produce the most accurate result based on the inputs it receives. There are four examples of AI models.

The first example is AI supervised by learning models. This type of AI is trained on labelled data which means the input and output are both known and are used to make predictions on new data. The model is given a set of input data and corresponding output labels. It learns the relationship between the input and output and tries to make new data based on the pattern it learned.

The second example is an unsupervised AI model which is the opposite of the first example where both input and output are unknown. Predictions are generated by recognizing structure, patterns and relationships in the data. This type of model is most useful in exploring and understanding complex datasets.

The third example is reinforcement learning models where the AI learns through trial and error. The AI receives rewards when predictions are correct and receives penalties when its predictions are wrong. The AI is trained to aim for rewards in the long run. This type of model is used in a variety of applications such as gameplay and robotics.

The fourth example is generative models. The AI is trained and tasked to generate new data that resembles the original data it received. The goal of this type of machine learning is to learn the core distribution of data so it can create new sets of data using the same distribution.

AI models have a wide range of applications including natural language processing and predictive analytics where they perform tasks that require human intelligence with more speed, accuracy and consistency. I am excited to see how AI will help to progress humanity, especially in the field of health and space exploration.