Jun. 14, 2020
An AI (artificial intelligence) model is a program that has been trained on a set of data (called the training set) to recognize certain types of patterns. AI models use various types of algorithms to reason over and learn from this data, with the overarching goal of solving business problems. There are many different fields that use AI models with different levels of complexity and purposes, including computer vision, robotics, and natural language processing.
As mentioned above, a machine learning algorithm is a procedure that learns from data to perform pattern recognition and creates a machine learning model. Below is a sampling of just a few simple machine learning algorithms:
AI and machine learning algorithms are fundamentally mathematical entities, but can also be described using pseudocode, i.e. an informal high-level language that looks somewhat like computer code. In practice, of course, AI models can be implemented with any one of a range of modern programming languages. Today, various open-source libraries (such as scikit-learn, TensorFlow, and Pytorch) make AI algorithms available through their standard application programming interface (API).
Finally, an AI model is the output of an AI algorithm run on your training data. It represents the rules, numbers, and any other algorithm-specific data structures required to make predictions about unseen test data.
The decision tree algorithm, for example, creates a model consisting of a tree of if-then statements, each one predicated on specific values. Meanwhile, deep neural network algorithms create a model consisting of a graph structure that contains many different vectors or weights with particular values.