The Path To Artificial Intelligence

The Path To Artificial Intelligence

Artificial intelligence is already part of our reality, and we should live with this thought. The bottom line, however, is that she is constantly learning. So we are dealing with a technology that is not yet fully “invented”, and we operate rather in the area of ​​machine learning.

As a result, a “machine” is to be created, capable of adapting to changing conditions. It will analyze data and make syntheses in the form of conclusions on this basis, which will provide a basis for decision making. We intuitively assume that artificial intelligence, similar to the human – in the process of learning – has the ability to perceive and understand the environment. The process is verified at the level of cognitive intelligence, i.e., effective decision-making in conditions of uncertainty and unpredictability. Following this path, one can come to the conclusion that artificial intelligence defined by analogy to a human does not exist and will never arise. The intuition characteristic of a human will not emerge from the analysis of data and its correlations. Even the most perfect machine will be devoid of this feature.


The analytical possibilities of exploring big data sets and the conclusions drawn on their basis can effectively support decision-making at the tactical level of management. However, at the strategic level, decisions are not made solely on the basis of facts arising from numbers. The most important element of strategic thinking, apart from analysis, synthesis and planning, is intuition. While historical data can be used to set trends, intuition is able to reach beyond the horizon defined by the logic of numbers. Perhaps this is the secret of great discoveries matching historical and technological breakthroughs. By approaching strategic thinking in a way that prefers intuition, the basic weakness of one of the most popular methods of strategic analysis – SWOT – is exposed.


It is not difficult to imagine what will happen if, when analyzing data from the past (historical), we look for the optimal path to the future with our heads turned back. In order to make a strategic turn, at some point, we have to start looking straight ahead. Take the first step into the unknown, towards the future. In other words, artificial intelligence is able to analyze specific values ​​of indicators, their mutual relations, and changes. However, he cannot understand the importance of this data in making strategic decisions, even seemingly as simple as the answer to the question about the optimal bank account.


Intellectual depth depends on contextual independence. At the data level, both factors have the lowest value. As a result of the process of establishing relationships, information is generated, which in the next phase – matching schemas – turns into knowledge. The first stage is possible without the human mind. Computers are enough to organize and group data. Examples include a bank account and historical statements and an analysis of operations as a basis for assessing financial liquidity. The second stage – matching schemas and structuring information – takes place thanks to the involvement of the human mind and intellect.


Knowledge is characterized by a high degree of contextual independence and intellectual depth. It is at this level that decisions are usually made at the tactical level of management. Achieving this state through intellectual depth also implies the highest degree of contextual independence. Another process of data transformation is understanding the principles, culminating in wisdom. Wisdom is a property of the human mind, unattainable even by the perfect machines, the operation of which depends on computing power and algorithms. There is also the concept of genius associated with wisdom. It is hard to expect that a man-made machine can be endowed with this quality.

Synthesis – the area of ​​cognitive science

Cognitive science studies, inter alia, thinking, perception, awareness, learning, decision making, and intelligence. Thanks to internalization and neurophysiological processes, messages transform into knowledge under the influence of the perception of the environment. There is an interaction between messages and physiological processes. Knowledge, subjected to both neurophysiological and psychological processes, leads to understanding through internalization. Psychological processes and understanding interact with each other. Moreover, knowledge is also subject to psychological processes. In turn, thanks to their actions, interacting neurophysiological and psychological processes influence the environment.

Work on the development of artificial intelligence is part of the area of ​​cognitive science.

To simplify it, it can be said that artificial intelligence – as a result of machine learning – is to result in a digital copy of the “average” human built on “averaged” data.

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