Artificial intelligence and artificial neural networks

Artificial intelligence (AI) is a variety of technological and scientific solutions and methods that help make programs similar to human intelligence. Artificial intelligence includes many tools, algorithms, and systems, which are also all components of Data science and artificial neural networks.

Data science is the science of analyzing data and extracting valuable information and knowledge. It intersects with areas such as machine learning and thinking science (Cognitive Science), as well as technologies for working with big data (Big Data). Data science results in analyzing data and finding the right approach for further data processing, sorting, sampling, and retrieval.

How do neural networks function, and what are they? Classification of artificial neural networks

A neural network is one of the directions of artificial intelligence, the purpose of which is to model the analytical mechanisms carried out by the human brain. The tasks that a typical neural network solves are classification, prediction, and recognition. Neural networks are able to learn and develop on their own, building their experience on mistakes made.

A neural network is a kit of neurons that are linked together by unique connections called synapses. The structure of a neural network came to the world of programming from biology. Thanks to this structure, the machine acquires analyzing and even remembers various information. Neural networks are also capable of analyzing incoming data and reproducing it from its memory.

In other words, a neural network is a machine interpretation of the human brain, which contains millions of neurons that transmit information in the form of electrical impulses.

What is a neuron?

A neuron is a computational unit that accepts data, performs simple calculations on it, and then passes it on to other neurons. They are divided into three basic types: input (blue), hidden (red), and output (green):

When a neural network consists of many neurons, the term layer is introduced. Accordingly, an input layer receives information, hidden layers (usually not more than 3), which process it, and an output layer, which outputs the result.

The biological basis of neural connectivity

There are about 86 billion neurons in our brains. A neuron is a cell connected to other such cells. The cells are connected to each other by branches. All of this together resembles a kind of network. It is the neural network. Each cell receives signals from other cells. Then it processes them and sends the sign itself to other cells.

A neuron receives a signal (information), processes it, and sends its response. The arrows represent the connections - the branches along which the information is transmitted:

It is how bypassing signals to each other; the neural network comes to some kind of decision. Thus, the decision results from the collective work of a billion neurons.

The artificial neural network

A neural network attempts to use mathematical models to simulate the human brain to create machines with artificial intelligence.

A neural network simulates the workings of the human nervous system, a feature of which is the ability to self-learn from previous experience. Thus, the system makes fewer and fewer mistakes each time.

A neural network consists of separate computing elements - neurons, located on several layers like our nervous system. Data arriving at the input of a neural network is sequentially processed on each network layer. Each neuron has specific parameters that can vary depending on the obtained results - this is the point of network training.

Neural network training

One of the primary and most important criteria is the possibility of training a neural network. Generally speaking, a neural network is a set of neurons through which a signal goes. If you feed it to the input, you will get an unknown result at the output after passing through thousands of neurons. To transform it, you need to change network parameters to get the desired results at work.

You can't change the input signal, the adder performs the summing function, and you can't change something or take it out of the system, as it would cease to be a neural network. The only thing left is to use coefficients or correlation functions and apply them to the weights of connections. In this case, we can define the definition of neural network training - the search of a set of weight coefficients which, when passed through the adder, will give the desired signal at the output. It is the concept applied by our brain as well.

But there is one nuance. If you set the coefficients of weights manually, the neural network will remember the correct output signal. In this case, information output will be instantaneous, and it may seem that the neuron was able to learn quickly. And if you change the input signal a little bit, you will get wrong, not logical answers on the output.

So, instead of specifying specific coefficients for one input signal, you can create generalizing parameters using sampling.

What does artificial intelligence entail, and how does it operate? Applications and perspectives of AI

Artificial Intelligence (AI) is a technology, or more accurately, a scientific direction, which is studying ways to teach a computer, robotic equipment, and analytical system to think intelligently as a person. Actually, the dream of intelligent robotic assistants appeared long before the first computers were invented.

Machine learning and neural networks are all examples of artificial intelligence that are used to describe sophisticated computer systems. Machine-learning-based technologies are capable of solving many problems in the real world.

Initially, functions such as reasoning and deliberate decision-making were absent from computers. Still, in recent years several important discoveries have been made in the field of AI technology and related algorithms. The increasing number of large samples of diverse data - Big Data - available for AI training plays an important role.

Many other subjects, including mathematics, statistics, probability theory, physics, signal processing, machine learning, blockchain, computer vision, psychology, linguistics, and brain research, intersect with AI technology. Those interested in philosophy are drawn to issues such as social responsibility and the ethics of developing artificial intelligence.

The development of AI technology is motivated by the fact that jobs with numerous variables necessitate extremely complicated solutions that are difficult to comprehend and algorithmize manually. Modern machine learning and AI technologies, when combined with appropriately selected and prepared "training" data for the systems, can enable computers to "think" by themselves - to program, compose music, evaluate data, and make independent judgments based on it.

How does artificial intelligence (AI) work?

Artificial intelligence is the capacity of a computer or a robot to do tasks normally associated with intelligent beings. The terminology is widely used to describe a project aimed at developing systems that have human-specific cognitive processes, such as the ability to reason, generalize, and learn from past experiences.

In its most basic form, AI is a rudimentary simulation of brain neurons. Signals are transferred from neuron to neuron, and after the process, a result is obtained.

Artificial Neuron Description

An artificial neuron, often known as a neural network, is a mathematical function intended as a model of biological neurons. In artificial neural networks, artificial neurons are the basic building blocks. As a mathematical model of the human brain, artificial neural networks were developed.

Individual neurons are complex-structured living cells in them with branched offshoots capable of exchanging signals with other neurons through regular connections and one more significant node responsible for impulse transmission from the neuron. Some of the links are responsible for the neuron's excitation, and some are responsible for inhibition. The impulses that the neuron transmits to other neurons will depend on which signals and the connections the neuron "enters."

The artificial neuron does not need a physical carrier. Basically, it is a mathematical function. Its task is to receive information (e.g., signals from many other artificial neurons), process it in a certain way, and then output the result. In an artificial network, neurons are usually divided into three types:

  • input - each of these neurons receives an "input" element of initial information,
  • Intermediate - process the information,
  • Output - output - output the result.

The neural network itself is created in layers, like a pie. One of the outer layers contains input neurons, another one contains output neurons, and there can be one or more intermediate ones between them. Each neuron of the medium network is connected to a set of neurons from two surrounding layers. Communication between neurons is provided through weights - numerical values that each neuron calculates based on the data received from the previous layer of the network.

When creating artificial neural networks, scientists were guided by the structure of the human brain.

The difference between artificial intelligence and human intelligence

Intelligence can be defined as the general mental capacity for reasoning, problem-solving, and learning. Because of its public nature, intelligence integrates cognitive functions such as perception, attention, memory, language, or planning. A conscious attitude toward the world distinguishes natural intelligence.

The question of how AI differs from natural intelligence is actually a philosophical one rather than a strictly scientific one. The fact is that no artificial intelligence that exists today has reached a high enough level of development to compete with humans on an equal footing.

There is a point of view expressed by philosopher John Searle back in the 1980s. He introduced the terms "strong AI" and "weak AI." A robust artificial intelligence, according to scientists, can realize itself and think like a human. Weak AI is incapable of this.

Today's AIs are unequivocally weak because none of them have developed self-awareness yet. They do only what they have been trained to do, and in a sense, they can be considered programmed to do so. They have no accurate understanding of what is behind these things.

However, many researchers believed that computer systems could never equal humans since they rely on learned knowledge and practical experience to make sense of their activities. Computers do not have it by definition - hence they cannot develop the intelligence of their own.

But these statements were made when neural networks were just taking their first steps. Today, looking at their success in learning, it is easy to believe in the reality of AI, which will be able to become equal to humans or even surpass them.

The Basic Principles of Intelligence

We can compare human thinking with artificial intelligence based on several standard parameters of the organization of the brain and the machine. The activity of a computer, like a brain, involves four steps: encoding, storing, analyzing data, and delivering results.

The human brain and AI can self-learn based on data from the environment. Also, human brains and machine intelligence solve problems (or tasks) using specific algorithms.

Artificial intelligence technological principles

Machine learning (ML) is a self-learning algorithm-based AI development principle. Human involvement in this approach is limited to loading an array of information into the machine's "memory" and setting goals.

Deep learning is a mixed-method, the main difference being the processing of large data sets and the use of neural networks.

A neural network is a mathematical concept that mimics the structure and function of nerve cells in a biological organism. Accordingly, ideally, it is a self-training system. If we transfer the principle to the technological basis, a neural network is a set of processors that perform a single task in a large-scale project. In other words, a supercomputer is a network of many ordinary computers.

Deep learning refers to a separate principle of AI, as this method is applied to detect patterns in vast amounts of information. For such a human-impossible job, the computer uses advanced techniques.

Cognitive computing is one of the directions of AI, which studies and implements the processes of natural human-computer interaction, similar to the interaction between people. Artificial intelligence technology aims to fully mimic higher-order human activities - speech, symbolic and analytical thinking.

Computer vision is a branch of AI used to recognize graphic and video images. Today machine intelligence can process and analyze graphical data and interpret information according to the environment.

Synthesized speech. Computers can already understand, analyze, and reproduce human speech. We can already control programs, computers, and gadgets with speech commands—for example, Siri or Google assistant, Alice in Yandex, and others.

Furthermore, sophisticated graphics processors, which are at the heart of interactive data processing, make it difficult to envisage artificial intelligence without them. APIs - application programming interfaces - are needed to integrate AI into various programs and devices. Using API, you can easily add artificial intelligence technology to any computer system: home security, smart home, CNC equipment, etc.

The main problems of AI today

At this point in development, artificial intelligence's capabilities are limited. The following main difficulties can be highlighted:

  • Machine learning is only possible based on massive data. It means that any inaccuracies in the information strongly affect the final result.
  • Intelligent systems are limited to a specific activity. That is, an intelligent system set up to detect fraud in taxation will not be able to detect fraud in banking. We are dealing with highly specialized programs that are still far from human multitasking.
  • Intelligent machines are not autonomous. It requires a whole team of specialists and a lot of resources to keep them "alive."
  • The limits of deep learning and neural networks.

Despite all their advantages, deep learning and neural networks still have significant drawbacks.

  • Dependence on data: in general, deep learning algorithms require a considerable amount of training data to perform their tasks accurately. Unfortunately, there is not enough high-quality training data to create working models for many problems.
  • Unpredictability: neural networks evolve in some strange way. Sometimes everything goes as planned. And sometimes (even if the neural network does well), even the creators struggle to understand how the algorithms work. The lack of predictability makes it extremely difficult to fix and correct errors in neural network algorithms.
  • Algorithmic bias: Deep learning algorithms are as good as the data they are trained on. The problem is that the training data often contain hidden or apparent errors or flaws, and the algorithms inherit them. For example, a face recognition algorithm trained chiefly on pictures of white people will work less accurately on people of different skin colors.
  • Lack of generalization: deep learning algorithms are suitable for targeted tasks but are bad at generalizing their knowledge. In addition, deep learning does a poor job of processing data that deviates from its training examples.

Future Prospects for Artificial Intelligence

AI technology is just finding widespread application and has enormous growth potential in all areas. Over time, humanity will create more and more powerful computers, which will become more and more advanced in the development of AI.

Artificial neural networks are being created in the image of the human brain, albeit in a very simplified form. Perhaps one day, it will be able to scan all sections of a living person's brain, map its neurons and synaptic connections and reproduce its copy in a computer. Such a copied neural network can be expected not only to behave intelligently - it will literally be a human double, able to recognize itself, make decisions and perform actions as it does. Even memories will be copied. Theoretically, it will be possible to put such a neural network in an artificial body (in a robot). Then the person - a copy of his consciousness - can live almost forever.

It would be tough to carry out such a transfer in practice: no technology would allow one to "read" the living brain and create its "map." And we are still very far from creating an artificial neural network that would be as powerful as the brain.

The purpose of AI is to help people and take on complex or routine tasks. If artificial intelligence can one day reach the level of human thinking, it will be a milestone for civilization. We will have a capable and intelligent assistant - and we can be rightly proud that it is a creation of our hands.

At the moment, science has successfully created relatively small neural networks capable of voice recognition or image processing. No AI yet has the same abilities as the human brain.

"Power" intelligence is not related to the speed of computation but the complexity of the neural network. The human mind is still superior to any artificial neural network, even though the pace of its processes is significantly slower than that of computers.

Artificial neural networks consist of individual neurons, which are grouped into layers. Two outer layers serve as an "input," which receives input information, and an "output," from which the result is read. There can be from one to several tens, or even hundreds, of intermediate layers of neurons between them. And each neuron in a layer is linked to many others in the previous and following layers.

The more complex the network, the more layers, and neurons it contains, and the more severe and extensive tasks it can perform.

Many factors shape the human mind. We receive information about the outside world through our perceptual organs - by observing, touching, and tasting. By interacting with the environment, we gain life experience, knowledge about the properties of the world, and social skills. Our brains are constantly improving and physically changing.

Suppose we manage to create a neural network complex enough for it to evolve similarly and equip it with "sense organs" - video camera, microphone, and the like - perhaps after a while, it will be able to acquire "life experience." But this is a matter for the distant future.