Most people don’t understand what AI is and, just in case, they fear it, like they do everything unknown. But artificial intelligence is just the ability of artificial systems to perform some intellectual functions. Ancient animal traps were already the simplest AI devices. Today we are surrounded by a lot of smart devices – from the thermostat that regulates the temperature in the house to airplane autopilots, programs like Word or Excel, etc. However, it’s impossible to create a full-fledged AI with software alone. Those who program these systems cannot always foresee all the situations that may arise in real life. And classical neural networks are unsuitable for this task, since they cannot learn in real time.

We can expect the emergence of many PANN based ‘smart servants’ that will replace ‘hard programs’ or will control them in the interests of their ‘master’. These will be systems to which one could simply ‘explain’ what needs to be done, and they would determine how best to do it, how to do it within the law, how to ensure the safety of their owner and other people, etc.

In classical neural networks, training is based on various versions of the ‘gradient descent’ method, widely used in mathematics, which requires a huge number of iterative computation cycles, while the training time grows exponentially with the amount of data used for training. In addition, one of the most unpleasant properties of such networks is that it’s almost impossible to ‘update’ their training or ‘retrain’ them – any change, no matter how small, in a system that has already been trained for a long time requires an equally long new training.

Because of all this, building and running such neural networks requires the use of very powerful computers for training, a lot of highly paid programmers, a lot of expensive computer time, and electricity. Consequently, this leads to very expensive and slow developments in the advent of improving such networks, due to the long testing times of experimental iterations. That’s exactly why such developments are currently almost completely monopolized by industry giants.

The Omega neuron made it possible to apply a fundamentally new training method that doesn’t require many iterations, providing an almost linear dependence of PANN’s training time on the amount of data. The more difficult the task and the larger the amount of data, the greater the gap in the learning rate compared to any other modern (convolutional, etc.) neural network. This will allow for the very rapid development of such networks and products based on them, both replacing existing ones and creating completely new ones.

The first artificial neural network was the universal perceptron. It didn’t work very well for specific tasks, however. So for 60 years, development efforts went mainly into the creation of specialized neural networks ‘honed’ for solving specific intellectual problems.

There are many more or less narrowly specialized neural networks – cognitrons, neocognitrons, Hopfield, Kohonen, Kosko, Jordan Elman, Boltzmann networks, recurrent networks, resonance networks, deep learning networks, convolutional networks, etc. Bayesian networks of trust and networks of cellular automata are also close to them.

PANN allows us to reproduce the functions of any of these networks, all while doing it much more efficiently. PANN also allows to create new products, for example, highly efficient personal training systems, a ‘personal doctor’, or even an ‘Alter Ego’ (second self, the ideal secretary and agent on computer networks), etc.