Artificial Intelligence and Models
In the present Spring of Artificial Intelligence (AI), an area of knowledge has been forgotten. It is a relevant area, without which AI cannot progress in areas such as social sciences, urbanism, health (beyond prognosis), or software development. We are talking about complex knowledge representation.
In practice, specific AI (Narrow AI) is the type of AI we all work with today. It is the debate around this Narrow AI that we were committed to improving, mainly because it can support the digital transformation of our productive structures and our society. To this end, we are also committed to the 2030 sustainable development goals and are closely following the UN “AI for Good” conferences.
Knowledge is represented by models. The model is a simplified, independent, and useful conception of reality. Through rules, with its own assumptions and semantics, it translates what is known about this reality. It can be continuously extended or refined, namely by incorporating new standards. It can represent different aspects of reality differently, to study them more easily. It can be evaluated. It can be tested. It allows simulations, anticipating behaviors, or predicting developments.
Whether due to inherent difficulty or to purism, anything that requires more than a simple decision, or a sequence of simple decisions, is not being currently considered AI. In fact, in the several conferences we attended, we have seen that software programming is not considered part of today’s Artificial Intelligence applications. Quidgest intends to change this mindset.
To put a computer to write very complex information systems, just as Quidgest does, even if with quality and (naturally) much higher speed than a team of several programmers, has to fight to be considered AI. After all, it is not among the official disciplines of current AI. It is not ML; it is not data classification; it is not natural language processing. It will be, at best, robotics.