About #

Nowadays, when we are talking about Artificial intelligence, we mostly think of Machine Learning, that is also called Statistical Learning. However we can achieve spectacular results via Machine Learning it has disadvantages too. It is quite cumbersome to analyze the results of learnings, and very difficult to reason for human beings.

Machine Learning is only one possible approach to implement Artificial Intelligence. The early period of AI researches, in the 50s and 60s the symbolic approach was the main direction. Its advantage against the ML approach is that the results of learnings a possible to read and understand. The harvested knowledge, and the whole reasoning process can be described and managed via traditional programming tools. The so called Semantic Web technologies can be classified into this symbolic category.

A big disadvantage of these symbolic approaches that it requires extremely big computational capacity, and resources in case we are willing to build up intelligence that tries to approximate the level of human intelligence. It is close to impossible to create and efficiently operate systems that reach this level.

Nevertheless, in order to create really intelligent system, that comparable to human intelligence, we have to rely on two fundamental capabilities:

1. The capability of the system for both supervised and un-supervised learning,
2. A technology, which makes possible the conversion of the knowledge internally represented in the “brain” of the artificial system into the symbolic space, to a representational format, that humans can understand and modify.

A Semantic Web, and the Graph Databases provide a very good basis for the symbolic representation and management of knowledge. The Google Knowledge-Graph can be classified here, which inspired the development of the Cayley system.

This cook-book applies a fundamentally practical approach to this topic.

It does not want to give a detailed, precise analysis of the above mentioned theories and technologies. It focuses exclusively on those basic terms that are inevitable to know to use Cayley .

The content is divided to the following main parts:

1. The “Basic Concepts” chapter gives a brief introduction to the most important subjects, that are required to be able to use Cayley .
2. The “Cayley overview” chapter helps you find your way among the components the Cayley “ecosystem” is build-upon. It also gives some examples about how to use these system components, and where can we find documentation to them.
3. The “Working with Cayley” chapter is the most important part of the cook-book. it demonstrates with the usage of the Cayley system with examples, and source code, that you can run. Each section begins with the basics of the given topic, and follows towards the more advanced cases.