The neural Network: what it is and how it works
We live in a world where everything revolves around technologies. Probably the most important gadget that combines all the necessary features is the smartphone. Its capabilities are expanding every day. As the example, recently supercapacitor was printed on cotton fabric ink from graphene that provides charging portable electronic devices in seconds and makes mobile devices even more convenient in operation terms.
Such a development can create a real revolution in the field of “smart things” that will lead to a significant increase in capacity. In this case, there will be a need for effective management of SMART-things. And for this an efficiently functioning neural network is absolutely needed.
How an inanimate becomes alive
Today one of the most promising technologies in the world is the neural network and AI. However very few people know what it really is.
The neural network is one of many directions used in creating an artificial intelligence system. Unlike machine learning and other algorithms it is based on modeling the work of the human nervous system in the context of self-learning as well as correcting errors.
Thanks to this principle of construction the neural network is capable of independent training grant it to take into account the previous experience and make fewer mistakes each time.
It must be understood that an artificial neural network not only imitates the activities of the human brain but also has a similar structure to it. It consists of a huge number of “neurons” — computational elements. Often each such element refers to a particular layer of the network.
The input data in this structure is consistently processed on all layers of the created network. The parameters of each individual neuron can undergo certain changes depending on the result obtained from the previous sets of input data. As a result the order of operation of the entire network as a whole can change.
The main advantage of the neural network is the ability to effectively build nonlinear dependencies that allow more accurate description of information sets.
An artificial neural network is capable of solving the same problems as some other algorithms of machine learning. The difference lies only in the approach to learning and effectiveness.
Neural network: application features
Being a self-learning system presented in the form of a program, the neural network is actively used today to solve a wide variety of tasks:
Search for hidden patterns;
Pattern recognition, etc.
However, for effective performance of the functional assigned to it, the neural network must have large amounts of data that will be constantly updated. This requirement is the main condition for the development of a neural network.
Major market players
In recent years, the number of developments implemented on neural networks has increased severalfold. Many of the submitted projects practically do not differ from each other, since they are developed approximately on the same technologies.
Due to the complexity of development, as well as the need for powerful computing resources, only large companies or associations can independently create neural networks.
One of the main players in the AI market is Google, namely, its DeepMind division, which developed the Google Brain and AlphaGo network. Own development is released by Microsoft in its laboratory Microsoft Research.
They also create artificial neural networks Facebook with their subdivision Facebook AI Research, IBM, Baidu with the Baidu Institute of Deep Learning, Yandex and many others. In addition, similar developments are conducted around the world by various technical universities.
Do not write off startups (NeuroSeed, SOMN, etc.), which usually demonstrate a non-standard approach to solving existing problems.
Despite the fact that the concept of “neural network” exists for about 60–70 years, AI created on the basis of a neural network is far from perfect.
Solving the existing problems can increase the computing power. The neural network is the most “heavy” area of machine learning algorithms. Therefore, its effective work requires huge computing resources. To date, this problem is being solved by large international organizations.
The second difficulty is to limit the self-development of the algorithms being created. On small data, the neural network works poorly, which leads to an inefficient solution to the accumulating errors.
For optimal performance, a neuron must “run” on tens of millions of sets of input data. To do this, the necessary colossal amounts of information, which today only large international corporations have (for example, Google). But these databases are closed for free access.
The wayout from the situations becomes a combination of neural networks and blockade. In this case it will be possible to effectively solve the problem of data availability, and also to introduce artificial networks at any level (from household to global). And all this on an absolutely transparent and decentralized system.