Let’s understand the meaning of the term “neural network,” determine its degree of dissemination and usefulness for ordinary people, and determine what we need such technology for.Â
What are neural networks and how can they be useful to us?
The word “neural networks” can rightfully become one of the most popular words of this year. You must have seen it on some news portal or heard it on your YouTube channel. You’ve probably seen hundreds of neural network-generated pictures and wondered about their possibilities. And recently, you might have noticed how many of your friends have updated their avatars on social networks, giving in to the trend and uploading all their selfies to the new trendy neural network.
But what is a neural network in simple terms? When and by whom was it invented? How does it work, and what can a neural network do? What are neural networks for? Is it possible to find them on seemingly simple resources like teen patti online websites? And the main thing – what can they be helpful for, and what can they do besides making bright pictures? We have figured it out, and now we will tell you.
What is a neural network in simple terms?
Neural networks are a type of machine learning in which the computer program works on the principle of the human brain, using different neural connections. To greatly simplify, it is a miniature human brain, but its neurons are artificial and are computational elements created in the image and likeness of biological neurons.
A neural network is also a teachable system and can even be self-training. It can learn both with the help of human-defined recognition algorithms or commands and based on experience – i.e., on its own, using previously acquired data. It is like you in your childhood: first, your parents helped you, taught you, and guided you, and then you started to understand how things work, make your conclusions and find ways to solve problems on your own.
Why do we need neural networks?
The basic principles of neural networks were formed in 1943 by Americans Warren McCulloch and Walter Pitts, neurolinguistics and neurophysiologists, who stood at the foundations of cybernetics and founded the revolutionary idea that the human brain is a computer.
In 1958, American neurophysiologist Frank Rosenblatt developed the first neural network, though it is too high-profile a name for the first mathematical model of how the human brain perceives information.
For almost 50 years, the mathematical models became more complex and sophisticated, but it was in 2007 that big data opened up the possibility of using neural networks for machine learning.
How neural networks work
Modern neural networks work according to several fundamental principles. If to describe them in the simplest way possible, they are as follows:
- A neural network is loaded with a certain amount of specific data necessary for an experiment or study.
- Information is transmitted using artificial synapses from artificial neuron to artificial neuron, from layer to layer, and each neuron can have several incoming synapses with data.
- The data received by each neuron is the sum of all data multiplied by the weight factor of each artificial synapse.
- The resulting values form the output signals, which are transmitted until the information reaches the final output.
Still, sounds complicated? Then let’s try to simplify it even more. First, an array of data is loaded into the neural network, that is, into a complex mathematical model created in advance, like an empty container. It can be scientific works, literary works, image collections, and so on.
Suppose you load into neural network collections of works by world literary classics. In that case, the output will be able to write its text in the style of Shakespeare – to simplify and exaggerate as much as possible. The generation of images is the same: you load into a neural network a database of pictures in various artistic styles of different artists, and at the output, you get a completely new image created based on the uploaded data.
Similarly, neural networks allow for finding different patterns and coincidences while analyzing massive databases, for example, to find criminals or to make several years’ forecasts based on previously obtained research.
Tasks and applications of neural networks
In addition to the tasks already described above for image matching, prediction, information clustering, or text and image generation in the style of various writers and artists (for entertainment purposes only), neural networks also solve other tasks you may not have guessed.
Virtually every modern flagship smartphone has a neurochip that helps analyze and classify incoming data. Phone cameras have learned to apply automatic settings and filters while taking pictures of various subjects, understanding if you’re shooting food, nature, or architecture. Searching by sight, by word, or by the name of an object can also use a simple neural network. For example, in iOS, you can find all the pictures of cats from an image gallery just by typing the word “cat” in the search. Or recognize and copy text from a photo in Google Pixel smartphones.
Progress has progressed to the point where neural network chatbots capable of mimicking communication with a once-living or recently deceased person have emerged. They are created based on previously uploaded correspondence, notes, or diaries to a neural network.
In addition, neural networks are actively used in the financial sector, making decisions about lending to potential bank customers. Voice assistants use neural networks to recognize voice commands and process requests. Every day the sphere of application of neural networks expands, simplifying our interaction with the digital world.
Advantages and disadvantages of neural networks
The very invention of neural networks was aimed at bringing as much value to humanity as possible. Their main advantage over other complex mathematical models is the recognition of more complex and profound regularities, allowing them to solve any task.
If properly tuned, neural networks are capable of producing frighteningly accurate results. Still, neural networks can also be inaccurate, and their effects can be too approximate or only remotely resemble something you would like to see. Consequently, you cannot entirely rely on neural network results but can use them as an additional tool for solving particular problems.
Although neural networks can be called a kind of artificial intelligence, even if in their infancy, they are still far away from full-fledged AI; this is because the computational capabilities of the human brain cannot yet be repeated since the human body contains 86 billion biological neurons, while the most advanced neural networks have no more than 10 billion. No matter how sophisticated neural networks are based on mathematical models, they still fall short of the human brain.
(India CSR)
