Machine Intelligence

In the past, when artificial intelligence research and applications were mentioned, only some kind of crazy laboratory studies came to mind. Today, applications that transform everything into smart are called artificial intelligence. Smart objects, smart systems, smart factories, smart state, smart cities have been included in our social, social and business life as a result of these applications that add the adjective “smart” to all concepts that have emerged in the past.

Perhaps the most important component, or even the backbone, of many countries’ digital strategies is artificial intelligence applications. In recent years, artificial intelligence applications are now seen as a base technology and strategies are created accordingly. On the other hand, artificial intelligence applications are one of the most controversial technology issues today. The debates are mainly based on the ethical boundaries of these practices and the legal ground that does not exist yet. In general, artificial intelligence aims to understand this by considering the human brain as an exemplary model and to find “new inventions” and new applications from there. It is possible to limit this general definition to some concepts. In fact, the algorithm related to this field to make it more insightful in terms of the machinery industry,


Artificial intelligence is a special application method of algorithm and sequential problem solving method with the help of computer. The algorithm itself is not “intelligent”. The working conditions of the Deep Blue program, which defeated the world chess champion Kasparov in 1996, were a system that was known for certain and consisted of unchangeable conditions. So although it was a perfect system, it wasn’t smart. When we talk about smart algorithms, we are talking about self-learning systems, and the first application of this is machine learning.


In this system, data is formed by transferring metadata created with certain information to the computer. To tell from a classic example, 100 dog pictures are entered into the computer, and besides this, information (metadata) that these pictures show dogs is added. The computer program tries to create an example and order from the pictures shown to it over the statistical probability according to the variable function state. Makes a determination as to whether the picture shown is a dog. Systems such as automatic translation and autonomous driving, which are already realized with the use of text and voice, are good examples of machine learning. Although such systems are called artificial intelligence applications, in fact, in these examples, computers evaluate the data they have and reach a conclusion or make a prediction through high probability detection.


One of the most complex and complex systems of the machine learning system is called deep learning. The main theme of the system is a kind of artificial neuronal networks system modeled on the human brain. A very comprehensive and large data entry called big data is made into this system. This big data is not designed data (machine learning has more designed data) and it develops the process by transferring the information it needs in different planes to a different plane. At this point, we are talking about a multi-purpose and versatile detail analysis capability. Thanks to this ability and as a result of spontaneous processes, surprising results emerge. Although this system is quite new, in its infancy, the ability of human intelligence, Despite its flexibility and versatility, it can be called an underdeveloped system. Currently, 100,000 neuronal networks can be built in the most advanced deep learning system, and it is estimated that this number will reach over 10 million in the near future. Applications that will consist of 10 million neuronal network systems will probably surprise us humans even more, but considering that the estimated neuronal networks in the human brain are around 85 billion, it can be said that artificial intelligence is still in its infancy.


There is an artificial intelligence research institution established in Germany in 1988. This institution is run by member companies. Apart from this, although some units of Fraunhofer Institutes and universities have also focused on this issue, the leadership in artificial intelligence applications in the world belongs primarily to the USA and China.

Germany has created a new strategy in order not to fall behind in artificial intelligence applications in the world, to maintain and improve its competitiveness. Under the theme of “Artificial Intelligence-Made in Germany”, the federal government announced a support program of 3 billion euros and expects an equal amount of investment from the private sector. According to this program, which will be valid from 2019;

  • A support-motivation program to create quota for 100 new professors in universities only for this field and to attract scientists and research people from abroad to Germany,
  • Exemption of investments of enterprises in this field from tax,
  • Establishment of artificial intelligence business knowledge centers on a state or regional basis,
  • It is envisaged to establish real laboratories where artificial intelligence applications can be tested.

Machine learning, deep learning, machine-human interaction and self-learning systems draw attention at the center of the German artificial intelligence strategy.

Turkey’s digitalization strategy should also be planned according to sectors and even sub-sector groups. This work should be started immediately, especially for export products. In this planning, the three concept contents I mentioned have to be handled and executed together. We do not need to think about how this happens, there are countless experimental applications before us. We need to analyze them well and start taking quick steps in this regard as soon as possible.

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