It differs from the understanding of artificial intelligence according to John McCarthy when they proceed from the assumption that artificial systems are not obliged to repeat in their structure and functioning the structure and the processes occurring in it that are inherent in biological systems. Proponents of this approach believe that the phenomena of human behavior, its ability to learn and adapt is the result of the biological structure and characteristics of its functioning.

This includes several directions. Neural networks are used to solve fuzzy and complex problems, such as recognizing geometric shapes or clustering objects. The genetic approach is based on the idea that a certain algorithm can become more efficient if it borrows better characteristics from other algorithms (“parents”). A relatively new approach, where the task is to create an autonomous program - an agent that interacts with the external environment, is called the agent approach .

(redirected from the “Quasi-biological paradigm”)

Biocomputing (or the quasi-biological paradigm [1] ) (English Biocomputing ) is a biological area in artificial intelligence that focuses on the development and use of computers that function as living organisms or contain biological components, so-called biocomputers.

The forefather of the biological direction in cybernetics is W. McCulloch, as well as the subsequent ideas of M. Conrad, which led to the direction — biomolecular electronics. Unlike understanding of artificial intelligence according to John McCarthy, when they proceed from the assumption that artificial systems are not obliged to repeat in their structure and functioning the structure and the processes occurring in it, inherent in biological systems, supporters of this approach believe that the phenomena of human behavior, The ability to learn and adapt is a consequence of the biological structure and features of its functioning.

Often the quasi-biological paradigm is opposed to the understanding of artificial intelligence according to John McCarthy, then they talk about:

• ascending (eng. Bottom-Up AI ) AI, on which the quasi-biological paradigm is based
• top -down AI (English Top-Down AI ) - the creation of expert systems, knowledge bases and inference systems that mimic high-level mental processes, and usually speak of rational AI

Content

• 1 “The von Neumann paradigm” vs. "Quasi-biological paradigm"
• 2 Directions in research
• 3 References
• 4 See also
• 5 Notes

“The Von Neumann Paradigm” vs. "Quasi-biological paradigm"

The “von Neumann paradigm” is the basis of the overwhelming majority of modern information processing tools. It is optimal when mass problems of sufficiently low computational complexity are solved.

The quasi-biological paradigm today is much richer in its content and possible applications than the initial approach of McCulloch and Pitts. It is in the process of developing and exploring the possibilities of creating effective means of information processing on its basis.

K. Zaener and M. Konrad formulated the concept of an individual machine , as opposed to the von Neumann universal computer. This concept is based on the following provisions:

1. A universal machine cannot solve any problem as efficiently as a machine specially designed to solve it;
2. A hard program implies sequential execution of operations, i.e. inefficient use of computing resources;
3. The program is easy to destroy, if from the outside to introduce random changes. Therefore, it is impossible to make small changes step by step and gradually change the structure of the program.

Therefore, the main features of an individual machine are as follows:

1. The physical structure of the machine determines the solution to a specific problem;
2. The evolution of the machine after the introduction of control stimuli leads to such a state and / or structure of the machine, which can be interpreted as solving the desired problem.

Research directions

Biocomputing allows you to solve complex computational problems by organizing calculations using living tissues, cells, viruses and biomolecules. Often use deoxyribonucleic acid molecules, on the basis of which a DNA computer is created. In addition to DNA, protein molecules and biological membranes can also be used as a bioprocessor. For example, molecular models of the perceptron are created on the basis of bacteriorhodopsin-containing films [1] .

• Molecular calculations
• Biomolecular electronics
• Artificial Neural Networks
• Evolutionary computing
• Neurocomputing ( partially )

Links

• L. B. Emelyanov-Yaroslavsky, Intellectual quasi-biological system, M., "SCIENCE", 1990
• A. S. Mikhailov, V. M. Tereshko, Pattern Recognition by Reaction-Diffusion Systems, Mat. modeling, 1991, volume 3, number 1, pages 37-47
• VG Yakhno, Models of neural-like systems. Dynamic information conversion modes, 2003
• V. Yu. Popov, DNA Nanomechanical Robots and Computing Devices, 2008
• Conrad, M. and Zauner, KP (2000) Molecular Computing with Artificial Neurons. Communications of the Korea Information Science Society, 18 (8). pp. 78-89.
• ERCIM News

see also

• Brain model
• Blue brain project
• Nanobot
• DNA computer
• Neurocomputer
• Molecular computer
• Perceptron
• Bionics