Artificial neural networks teach computers to think through a learning process. Instead of programming solutions by hand, the software learns by imitating the biological processes in the human brain. In fact, artificial neural networks are created on the principle of biological networks, which means that we can train them to perform those processes that humans do not perform quite consciously.
This technology is currently registering major advances in research and application. But what areas of application are the technology suitable for, how it is connected to machine learning, and what are the limits of a learning machine?
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Neural networks use a network of nodes and analogs of human synapses to process data. Input data flows through the system and generates outputs.
Then the neuronal network compares the output data with the original data. For example, you would like to train a computer to recognize an image of a certain device. To recognize it the computer must run through millions of images of different devices through the network to see which images are appropriate. Then the human must confirm which images must be accepted. The system gives preference to the pathway in the neural network that led to the correct answer. Over time this network will improve the accuracy of its results.
Machine learning is a term that is used to describe all the technologies that are used to teach a machine to improve itself. Specifically, it refers to any system in which the computer’s performance in performing a task gets better just by having more experience with that task. However, artificial neural networks belong to machine learning, but there are many other ways to teach a computer.
Artificial Intelligence includes subfields such as knowledge-based (expert) systems, pattern recognition, robotics, natural language processing, and machine translation, in addition to Machine Learning. However, machine learning is currently considered one of the central and most successful artificial intelligence disciplines.
Machine learning helps people work more efficiently and creatively. For example, they can use machine learning to organize and edit their images faster. With machine learning, they can also leave boring or laborious work to the computer. Paper documents such as invoices can be scanned, saved, and filed independently by learning software.
Potential areas of application of artificial neural networks are those where human intelligence is ineffective and traditional computations consume a lot of time. Nowadays neural networks are primarily used in pattern recognition, automated forecasting, recognition processes, automation of decision-making; control, coding, and decoding of information, etc.
Neural networks are also used in the field of telecommunications – design and optimization of communication networks (finding the optimal path of traffic between nodes), and price and production management. Neural networks are an extremely powerful modeling method, allowing the reproduction of extremely complex dependencies. Anyway, since modern neural networks have very great abilities and different uses, their popularity is growing, and this industry is also developing. Artificial neuronal networks are being taught to play computer games, recognize voices, etc. Who knows, maybe there will be neural networks, who will not just play in online casinos, but also bet on betting sites professionally? Just think about such a situation: a neuronal network registers itself on a betting platform, logs in – 22Bet login, and starts betting and winning as a person experienced in placing online bets. There are also some projects about how neural networks can be applied in the online gaming industry, and cybersports. So, there are many new opportunities for the entertainment industry.