Basic principles of artificial intelligence construction

Knowledge of the world and oneself in it is an integral part of human existence. Reflections on the nature of consciousness have long been raised by philosophers. Neurophysiologists and psychologists have developed theories about the principles and mechanisms of the human brain operation. As in several other sciences, processes observed in nature laid the foundation for the creation of intelligent machines.

The main structural unit in the human brain is the neuron. The exact number of neurons in the human nervous system is not definitively known, while estimates suggest approximately 100 billion. Neurons, each consisting of a cell body, dendrites and axon, connect with each other forming a complex network. The points at which the connect are called synapses.

The described processes and structures served as the basis for the creation of artificial neural networks. In 1943, Warren McCulloch and Walter Pitts published the article A logical calculus of the ideas immanent in nervous activity, in which they proposed and described two theories of neural networks: with loops and without them. These theories represented a significant step in understanding the interaction of neurons and later formed the basis for the principles of constructing neuron interactions in artificial neural networks. Donald Hebb's book The organization of behavior: A neuropsychological theoryreleased in 1949 laid the foundation for neural learning.

The works mentioned above explored the processes in the human brain and were further developed in the works of Frank Rosenblatt. His mathematical model of the perceptron developed in 1957 formed the basis of the world's first neurocomputer "Mark-1", which he created in 1960. It should be noted that various versions of the perceptron are successfully used today to solve various tasks.

But let's proceed systematically. In this chapter, we will examine the mathematical models of the neuron and the perceptron:

• Neuron and principles of neural network construction. This section elaborates on the structure of the neuron and the fundamental concepts underlying artificial neural networks, as well as their importance in understanding intelligent systems.
• Activation functions are an integral part of neural networks, determining how a neuron should respond to incoming signals. This section focuses on the different types of activation functions and their role in the neural network learning process.
• Weight initialization methods in neural networks. Weight initialization is a critical step in preparing the network for training, influencing its ability to learn and converge.
• Neural network training is considered through the key components: loss functions, gradient backpropagation, and optimization methods which together form the basis for efficient network training.
• Techniques for improving the convergence of neural networks, such as Dropout and normalization, detail strategies for improving neural network performance and stability during training.
• Artificial intelligence in trading covers the practical application of the technologies discussed, exploring how artificial intelligence and machine learning can be used to analyze financial markets and make trading decisions.

Thus, the chapter provides a comprehensive overview of artificial intelligence and neural networks, covering their structure, mechanisms, and real-world applications, particularly in algorithmic trading.