The word “neural networks” can 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. Most probably you’ve seen hundreds of neural network generated pictures and wondered about their possibilities.
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: are they needed for simple tasks, like posting a Tombstone RIP slot review, or some more difficult activities? And most importantly, what can they be useful for and what can they do, apart from making bright pictures?
What Is a Neural Network
Neural networks are a type of machine learning in which a computer program operates on the principle of the human brain, using various neural connections. To greatly simplify, it’s a miniature human brain, only 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 on the basis of past experience – i.e. on its own, using previously acquired data. It’s literally like you yourself in your childhood: first your parents helped you, taught you and guided you, and then you started to figure out how things work, make your own conclusions based on that and find ways to solve problems.
That sounds kind of creepy, doesn’t it? It seems like artificial intelligence is about to get out of control and take over the world – just like in the famous movies. But the existing neural networks are still far away from full-fledged artificial intelligence, at least because they aren’t yet able to program and create themselves, and there are a lot of different programs that are not connected to each other.
Why We Need Neural Networks
The basic principles of neural networks were formed in 1943 by Americans Warren McCulloch and Walter Pitts, neurolinguists 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 information perception by the human brain.
For almost 50 years, the mathematical models became more complex and sophisticated, but it wasn’t until 2007 that big data opened up the possibility of using neural networks for machine learning.
So why do we need neural networks? Today they are most commonly used for big data analysis, prediction, matching, classification, and pattern recognition in a wide range of scientific and socioeconomic research fields, from enterprise management and image recognition to predicting international conflicts and searching for traces of life on other planets.
Modern neural networks work according to several basic principles. If to describe them as simply as possible, they are as follows:
- A neural network is loaded with a certain amount of specific data necessary for an experiment or research.
- Information is transmitted using artificial synapses from artificial neuron to artificial neuron, from layer to layer, 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. An array of data is loaded into a neural network, that is, a complex mathematical model created in advance, like an empty container. This can be scientific works, literary works, image collections and so on.
If you load into a neural network collections of works by world literary classics, the output will be able to write its own text in the style of Shakespeare – to simplify and exaggerate as much as possible. The generation of images is the same way: 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 to find different patterns and coincidences when analyzing huge databases, for example to find criminals or to make predictions for several years ahead based on previously obtained studies.
Types of Neural Networks
All neural networks can be divided into several types: single-layer, multilayer, forward propagation, recurrent.
Single-layer networks immediately produce a result after some data set is loaded into them. Multilayer networks run the input information through several intermediate layers, and their principle of operation is more similar to a biological neural network. The output is obtained after all layers have been processed and analyzed.
Dissemination networks are most often used for pattern recognition, classification and clustering of data – they are directed in one direction and cannot redirect information back. You input the data, you get the answer.
Recurrent networks redirect information back and forth until they get the end result. They use the effect of short-term memory, from which information is augmented and reconstructed. Such networks are more often used for forecasting.
Each neural network can be categorized into several other types. Homogeneous and hybrid networks – depending on neuron types, trainable and self-training – depending on training method, and analog, binary or image networks – depending on input signals type.
Tasks and Applications of Neural Networks
Besides already described above tasks of pattern matching, prediction, information clustering or text and image generation in the style of various writers and artists, neural networks also solve other tasks, which you probably have not guessed about.
Virtually every modern flagship smartphone now has a neurochip that helps analyze and classify a lot of incoming data. Phone cameras have learned to apply automatic settings and filters while taking pictures of a wide variety of subjects, understanding if you’re shooting food, nature or architecture. Searching by picture, 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 the image gallery 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 scope of neural networks is expanding, simplifying our interaction with the digital world.
Pros and Cons of Neural Networks
Obviously, the very invention of neural networks was aimed at bringing as much benefit to humanity as possible. Their main advantage over other complex mathematical models is the recognition of more complex and deeper regularities, allowing them to solve any task assigned to them.
If properly tuned, neural networks are capable of producing frighteningly accurate results, but neural networks can also be inaccurate, and their results can be too approximate or only remotely resemble something you would like to see. Consequently, you cannot fully rely on neural network results, but you 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 its infancy, they are still far away from full-fledged AI. This is due to the fact that 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 contain no more than 10 billion. No matter how sophisticated mathematical models neural networks are based on, they still fall short of the human brain.