The Synthetic Psyche methodology studies the human mental processes behavior, with the aim of replicating them in intelligent software acting independently from the context and nature of the data.
The Synthetic Psyche is a group of AI algorithms whose main objective is not limited to understanding the human brain functions, but rather include the study of how the human cognitive processes work, with the objective to emulate them.
One of the most important principles of this approach is that, with the Syntetic Psyche, we simulate each cognitive process involved in all cases that require both intelligence and experience.
By simulating our cognitive processes, it isn’t necessary to develop a Neural Network for each function.
If we simulate intuition, the same process can be used in all verticalizations.
For example: if we develop a classical Deep Learning algorithm that can play chess, the same algorithm can’t play Go. With the Synthetic Psyche, the same process used to win at Chess can be used to play Go, Poker and other games, furthermore it can be used to solve any type of other problems like, for example, completing a sale successfully.
The Synthetic Psyche approach, is much more complex than the classical approach, as it implies a deep knowledge of the functional processes of the human mind.
Below there is a practical example of using the Synthetic Psyche methodology in the preliminary stages of an AI algorithm design: Simulation and synthesis of Linguistic Intelligence
By “the functional processes of the human mind”, I mean all types of human intelligence like linguistic intelligence, emotional intelligence, insights, and so on.
When we talk about “language” in the field of AI, we often use terms such as Natural Language Processing, Natural Language Understanding, Semantic Analysis, and more recently “ChatBot”.
I would now like to dwell quickly on the reason why human beings communicate with each other.
At the origins of human communication, human beings interacted with each other through a sophisticated “code” made of expressions and signs, emotions or needs, that allowed them to communicate but also to promote social exchange.
Language was back then, and still is, a “mean” to convey one’s thoughts to other beings also in order to obtain advantages.
Language is therefore a tool through which the human beings express the result of their
mental processes as well as their level of intelligence.
It follows that pretending to analyze the language, without the logic necessary to understand a) reference contexts, b) intentions, c) emotions and d) communication strategies without having an internal need or a precise target does not make any sense.
In light of all the above, we are convinced that a correct understanding of the language (any kind of language, not just natural language) could bring AI to the level everyone expects.
In the model of Synthetic Psyche, the Linguistic Intelligence is composed by different levels of analysis (Syntax, Semantics, Pragmatics, Semiotics) and aims to:
- understand the context of reference: the context is formed by N attributes and the decision on the weight that each attribute holds within the context is a prerogative of the Deductive Logic process;
- identify the intention behind any external stimulus (Semiotics-Pragmatics): if, for example, I ask “Do you know what time it is?” at the semantic level, the answer “yes, I know it” could be correct, but adding the intention analysis, the system will understand that the I intend to know the exact time;
- study and understand the language structure: a 6-year-old child, in most cases, speaks correctly but would not be able to make a logical analysis of a sentence. The reason lies in the fact that human beings learn the language by “emulation”, that is, they learn to understand the role of each word, within the various contexts and with a precise intention, only by experience;
- formulate answers or questions according to the output of the other synthetic mental processes: as already said, language is only a mean to express solutions, needs or emotions. The decision-making processes are entrusted to other software components.