Artificial Intelligence or simply AI, could be defined as a technology that enables machines to simulate human learning, comprehension, problem-solving, decision-making, creativity and autonomy.

Beyond such introduction, there is no single, simple definition of Artificial Intelligence because AI tools are capable to performs tasks under varying and unpredictable circumstances without significant human oversight and can learn from experience and improve performance when exposed to data sets.

LogicalDOC contains a general purpose AI engine with which you can solve problems even not strictly related to document management, but with the advantage of being able to benefit from all the potential of a Document Management System to manage large volumes of data necessary for training.

Models

AI models are programs that implement an algorithm designed to solve a problem in the same way it would do a human brain, you can also look at them as artificial brains enabling systems to learn from data and perform tasks like analysis, prediction, and content generation.

At the time of writing, LogicalDOC supports this set of models:

• Neural Network: useful to predict the category or nature of an object on the basis of input data
• Classifier: uses Natural Language Processing(NLP) to catalog a naturally written text
• Tokens Detector: uses Natural Language Processing(NLP) to extract tokens from a naturally written text

Samplers

Models cannot do anything without having been trained: like children, they must learn from experience in order to 'understand' how to solve a given problem.

In AI, this experience is built through a process called training that basically presents to the model a huge dataset of examples. The size and quality of the dataset impacts the model's ability to identify patterns in the data and therefore to understand the problem.

Samplers are those objects responsible for retrieving data used in training the models.