How smart applications work

Research on machine learning, artificial intelligence and intelligent applications has accompanied the development of computerization since its inception, but at the beginning the possibilities of their effective use have been limited by the computing power of computers. The development of new generation processors, computing devices and GPUs has made practical the application of machine learning assumptions on a larger scale.


Solutions based on machine learning and intelligent applications allow for much more efficient data processing and automatic response according to established assumptions.

An important element of solutions based on machine learning is the possibility of automatic modification of algorithms by the program. The system checks the quality of its own output data, comparing it to the assumed values, and then automatically modifies its algorithms to improve the quality of these results, so it does not require continuous intervention and manual change of parameters.

The machine is still no substitute for all the tasks of a real analyst, although appropriate, well-prepared solutions based on machine learning will relieve it very much at work. The machine will replace it in viewing statistics, sheets and charts, but it should still be programmed for this purpose, periodically check the effects, provide new data, and modify the algorithms of neural networks.

Creating and optimizing models

Deep machine learning is based on computational operations that take place in neural networks, consisting of a set of hierarchically ordered, non-linear functions. The structure of the neural network model is modeled to some extent on the connections between nerve cells of the living body’s brain, and the data in this model are analyzed and processed non-linearly, in multidimensional matrices and using fuzzy logic methods, i.e. not zero-one, but taking intermediate states into account , gradual, contradictory or ambiguous.

Optimization of the neural network requires the analysis of the output data, and then the appropriate selection of input data, and modification of the function, so as to maximally correspond to the assumptions. The effectiveness of machine learning depends on the quality of algorithms and input data, and finding the right patterns is fraught with certainty.

In summary, the machine sorts huge amounts of input data and selects those that it considers valuable. It uses them in the way given by a neural network programmer, and at the same time modifies its own algorithm to a certain extent in order to fulfill the given assumptions.


Tensorflow is an advanced, flexible and convenient tool for creating, processing and analyzing multidimensional and multi-layer data matrix in deep learning. Built-in diagnostic tool Tensorboard enables a convenient analysis of data flow in the form of expandable, scalable charts.

Tensorflow is a product of Google, which originally developed this system for internal needs, and in 2015 released it under an open license for free use. Currently, Tensorflow libraries and tools are available on all popular devices and operating systems.

Examples of applications

In practice, solutions based on machine and deep learning in robotics, automation, in production processes, quality control, data aggregation, for identifying people and objects, voice recognition are used, wherever there is a need to automate processes or tasks.

Modern automated cleaning robots use machine learning techniques for indoor orientation or for dirt detection algorithms. Devices responding to voice commands are configured to understand these commands as accurately as possible and respond appropriately to them, for example, including emotional tone of the voice, changes in key tones or even simple hoarseness.

Anti-spam filters that use machine learning are more effective than traditional ones and better capture unwanted content, and also modify their own spam detection algorithms, thanks to which they automatically adapt themselves to changing spam techniques.

Marketing content is personalized automatically based on data about shopping habits and customer interests.

Search algorithms use machine learning when sorting and matching content. The use of machine learning methods for the analysis of Internet traffic enabled the operation of large data sets and translated into an increase in the accuracy of results, whereas in predictive analytics, the use of machine learning increased the accuracy of predictions.

This technology is also implemented in many areas outside the strict IT industry. In medicine, e.g. in epidemiology, in the analysis of the spread and prevention of diseases or in the diagnosis of the diagnosis of diseases in patients.

In the end: progressing work on autonomous vehicles is another thing possible thanks to artificial intelligence and machine counting. Such a car has to analyze very quickly the huge amount of information obtained from cameras and car sensors, and react immediately in situations where human life is at stake.

The possibilities of using artificial intelligence and machine learning are of course much more.

So if you need an effective tool to analyze and process large amounts of information or very large data sets, where traditional methods analysis is very difficult or impossible, or to automate tasks and processes, contact Greenlogic and we will design and implement a system tailored to your expectations based on machine learning, or we will advise alternative or complementary solutions that may better suit your requirements.

połączenia neuronowe
Napis machine learning w kolorze płyty głównej