Determinants of AI Development
17.3 Determinants of AI Development
Let us notice that most AI models presented in the second part of the book have been defined on the basis of ideas which are outside computer science. Cognitive simulation, semantic networks, frames, scripts, and cognitive architectures have been developed on the basis of psychological theories. The models of standard reasoning, and non-monotonic reasoning are logical theories. Genetic algorithms, evolution strategies, evolutionary programming, genetic programming, swarm intelligence, and artificial immune systems are inspired by biological models. Mathematics has contributed to Bayes networks, fuzzy sets, rough sets, and standard pattern recog- nition. Theories of linguistics have influenced the development of syntactic pattern recognition. Artificial neural networks simulate models of neuroscience. Physics delivers methods based on statistical mechanics, which make algorithms of problem solving and learning algorithms more efficient. It seems that only rule-based systems have been defined in computer science.
Thus, the development of Artificial Intelligence treated as a research area has been influenced strongly by the theories of the scientific disciplines mentioned above. It seems that AI will be developed in a similar way in the future.
Now, let us try to identify the most important AI prospects of the disciplines mentioned above. The main scheme of AI determinants is shown in Fig. 17.1 . As we have concluded in the previous section, the crucial barriers in the areas of general problem solving, automatic learning, Natural Language Processing, plan- ning, and creativity result from our lack of psychological models of two generic cognitive processes, namely concept comprehension and pronouncing a judgment. Any research result relating to these processes would be very useful as a starting point for studies into a computer simulation of these processes.
Communication between humans and AI systems and between AI systems (multi- agent systems) requires much more effective NLP methods. Advanced models of
244 17 Prospects of Artificial Intelligence
Computer science
engineering and algorithmization
Models of
Models of cognitive
of methods
organisms and processes
their behavior
Models of Linguistics
Brain Neuroscience semantics
Models of theory of Models based on mind and
mechanical statistics and epistemology
quantum mechanics
Logical calculi
Mathematical
Philosophy
for world
formalization of
Fig. 17.1 Determinants of AI development
syntax analysis developed in linguistics are successfully used in AI. Let us hope that adequate models of semantic analysis will be defined in linguistics in the very near future.
If advanced neuroimaging and electrophysiology techniques in neuroscience allow us to unravel the mysteries of the human brain, then this will help us to construct more effective connectionist models.
Models of organisms and their physiological processes and evolutionary mecha- nisms will be an inexhaustible source of inspiration for developing general methods of problem solving.
Further development of new logical calculi of a descriptive power that allows us to represent many aspects of the physical world would allow a broader application of reasoning methods in expert systems. Mathematics should help us to formalize models of biology, psychology, linguistics, etc. that could be used in AI.
As we have discussed in the second part of the monograph, AI methods are very often computationally inefficient. In order to develop efficient AI methods we should use computational models that are based on mechanical statistics or quantum mechanics delivered by modern physics.
New effective techniques of software and system engineering should be developed in computer science. This would allow us to construct hybrid AI systems and multia- gent systems. The algorithmization of methods which are based on models developed in various scientific disciplines is the second goal of AI research in computer science.
17.3 Determinants of AI Development 245 Hopefully, philosophy will deliver modern models of theory of mind and episte-
mology. As we have seen in Sect. 17.1 they play an important and inspiring role in progress in Artificial Intelligence. Finally, let us notice that AI researchers should cooperate more strongly, because of the interdisciplinary nature of this research area. What is more, any AI researcher should broaden his/her interests beyond his/her primary discipline. The development of a new discipline, cognitive science, should help us to integrate various scientific disciplines that contribute to progress in Artificial Intelligence.
Bibliographical Note Fundamental issues of theory of mind are presented in [45, 120, 134, 158, 186, 224,