The language is very important in terms of solving logical problems, because it is the support of our reasoning. When we are working with people from different countries sometimes the use of some words does not produce a totally precise translation. For instance, in Spanish language the official meaning of the word intelligence can be both “the capability for solving problems” and “the capability for understanding”. Oxford learner’s dictionary defines the word intelligence as “the ability to learn, understand and think in a logical way about things; the ability to do this well”. Both concepts are similar however the latter term is more related to logic and the former one is more related to action. Languages are developed during a lot of years by a society in a certain environment under certain circumstances. Looking at these definitions, for the Englishman, intelligence lets us to understand the world, and for the Spaniard, it lets us to change it.
This different precision about the meaning a word can have influence in all aspects of the culture in general, and in the scientific and technological one in particular. The English concept is adequate for supporting science and the Spanish one for supporting Engineering.
This fact is more important in the field of Artificial Intelligence. In this case the RAE’s Spanish Official Dictionary defines Artificial Intelligence as a “scientific discipline concerned with creating computer software running operations similar to those ones made by the human mind as learning or logical reasoning”, and Oxford learner’s dictionary defines it as “an area of study concerned with making computers copy intelligent human behavior”. Again we can find slightly differences, the Spanish definition is problem centered (the design of software running certain tasks) and the English one is description centered (human behavior).
These conceptual differences can drive to different focuses to the development of AI. An engineering vision would try to make advances in the development of pieces of software that can solve practical problems themselves through the use of some kind of learning ability and logical algorithms. A scientific vision would try to understand how the human brain is working and how its behavior can be emulated.
These two approaches have been followed for many years providing their own results. Initial successes of AI were provided by software tools far from human brain as expert systems; however, more recent advances as deep learning are more bio-inspired.
A conventional electronic system is not usually considered as AI, because, from a conceptual viewpoint there are differences between learning and memory. A typical computer has memory, but memory itself does not imply learning.
For an electronic engineer, the memory is a device that can store or reload the state of the registers of a microprocessor in a certain instant of time. A piece of software controls how the microprocessor uses the internal registers and the memory. This one can be used to store the state of a system in a certain instant of time. A memory that stores only the state of a system in an instant of time is useless as an intelligent device because we cannot make predictions about the future with a single piece of data. In order to anticipate the future to solve any problem, mathematical scientists use extrapolation techniques. The simpler technique, the linear one would require at least two points in the past in order to anticipate the future. We do not need only have memory. We need to have stored historical data in order to drive any intelligent action. Any electronic system that only analyzes a single instant state to make an assumption instead of a longer historical evolution cannot be considered intelligent from a mathematical (scientific) viewpoint. The current interest on big data is related to this. We are assuming that the larger the information stored the better the extrapolation about the future that we can do. This concept is not only time related but space related too. The higher the dimension of the space of characteristics is, the better the extrapolation about the future state is. An intelligent system can improve its performance with the size of the memory. This is a good reason why the size of the brain is related to the intelligence of species. A greater brain can store and process more data although intelligence is not only memory.
Another important property of any intelligent system is feedback. Any intelligent system trying to solve problems must have a metric to analyze its performance to accomplish the problem. In mathematics when a mathematician proposes a hypothesis as true and finally he reaches an absurd conclusion, it can assure that the initial hypothesis was false. Any decision-making system that proposes a solution through extrapolation needs to analyze if it is getting closer to the solution and in other case the system should learn from it and change.
An important characteristic of the human brain is its plasticity, and its ability to learn and change. That is the reason why the use of bio-inspired techniques provides good results as practical systems solving problems and the scientific approach to AI must never be forgotten.