The Essence and Evolution of Intelligence Processing: Mutual Interplay of Pattern Recognition and Simulation
Introduction:
Pattern recognition and simulation are more than mere concepts; they are the core elements that underpin intelligence processing.
This article opens with an exploration of how both neural networks and Turing machines can actualize these foundational aspects, elucidating why both artificial and biological brains possess intelligence.
It further delves into the profound reasons behind the utilization of simulation as a biological survival strategy. Although the simulation model exists in the virtual world of our minds, it has tangible applications in the real world. This transcends mere future prediction, serving as the bedrock for developing academic concepts and theories within the simulation of the virtual world.
The final section of this article sketches the probable architecture of future artificial intelligence at the physical layer, emphasizing the multifaceted interplay between pattern recognition and simulation.
This comprehensive examination promises to offer insights into the very fabric of intelligence, its growth, and its potential to shape our understanding of both the virtual and real worlds.
Pattern Recognition and Simulation
I assume that pattern recognition and simulation are the essence of intelligence processing.
Pattern recognition can be realized with neural networks. The essence of neural networks is arithmetic operations and transmission functions by neurons, and the superposition of states by their network structure.
Arithmetic operations, transmission functions, and the superposition of states can handle both continuous and discrete states. Continuous states can also be referred to as real numbers, analog, tacit knowledge, etc. Discrete states correspond to integers, digital, explicit knowledge.
Simulation can be realized with Turing machines. A Turing machine is composed of a memory space to hold the state, a sequence of instruction codes to change the state, and a processor that sequentially reads the instruction codes and changes the state in the memory space.
General computers realize Turing machines with digital states, digital instructions, and digital processors. It is possible to simulate analog states by making the number of digital states extremely large. Computers use this principle to process not only integers but also real numbers in a simulated manner. Instructions and processors are generally digital, but it is possible to simulate analog instructions and processors by adding randomness or noise.
Mutual Simulation of Neural Networks and Turing Machines
A neural network can simulate a Turing machine, and vice versa, a Turing machine can simulate a neural network.
The human brain, which has nerve cells, has native pattern recognition capabilities as it is essentially a neural network. Also, it can somehow perform simulation by simulating a Turing machine on that neural network.
General computers are Turing machines, so they have native simulation capabilities. Also, they can somehow perform pattern recognition by simulating neural networks on a Turing machine. Current AI (Artificial Intelligence) is performing simulation by simulating Turing machines on these simulated neural networks.
Nature of Pattern Recognition and Simulation
Pattern recognition is the process of memorizing and recognizing patterns. Simulation is the process of memorizing and simulating mechanisms.
Nature and Definition of Patterns and Mechanisms
Patterns are rules and order, mechanisms are processes and chaos.
Thus, patterns can be described as static and synchronic existence, and mechanisms as dynamic and diachronic existence. In other words, existence is the static existence of patterns and the dynamic existence of mechanisms.
Real World and Virtual World
The real world has its unique patterns and mechanisms.
Pattern recognition and simulation take place within intelligence. Therefore, within the virtual world of intelligence, patterns and mechanisms exist.
By observing and learning the virtual world, intelligence can create corresponding real-world patterns and mechanisms within the virtual world.
This allows intelligence to recognize reality and predict the future, utilizing pattern recognition and simulation.
Academics as a Way to Utilize the Virtual World
Furthermore, patterns and mechanisms can be created within the virtual world independently of observing the real world. Many of these virtual existences are pure imagination, fiction, or fantasy. However, some can be useful for recognizing and predicting the real world. This exists in the realm of academia.
Mathematics and physics theories contain patterns and mechanisms that cannot be learned solely through direct observation of the real world.
These exist within the virtual world of intelligence, and their usefulness is confirmed, establishing them as theories. Among these, mathematics defines areas that are useful apart from real-world verification once axioms are established, and physics requires areas to always be proven useful for real-world recognition and prediction.
Similarly, chemistry is the study of observing real chemical substances and reactions, and making assumptions and proving them within the virtual world of intelligence. Biology is the study of observing and proving against living organisms, sociology is the study of observing and proving against society, and humanities are the study of observing and proving against culture and humans.
Reasons for the Evolution of Intelligence
Basic intelligence learns to recognize and predict reality through the study of patterns and mechanisms in the real world. Then advanced intelligence looks for things that are useful for recognizing and predicting reality within the patterns and mechanisms devised, constructed, and inferred within a virtual world within the intelligence itself.
If this intelligence needs to maintain its own survival, this ability to recognize and predict will affect its ability to survive. For this reason, it is believed that living organisms have evolved from having basic intelligence to having advanced intelligence in the competition for survival.
Future of Intelligence Evolution
Pattern recognition can be parallel processed. Therefore, current AI typically uses semiconductor chips called GPUs, which are good at parallel processing.
Simulation uses the state determined in the previous process for the next process, so it can only be processed in series. Therefore, it is better to use a semiconductor chip called CPU, which is good at serial processing. However, if there are parts in the model being simulated that can be parallel processed or that require pattern recognition processing, you can use GPUs, which are good at parallel processing, for those parts.
Current AI is acquiring the ability to write programs, so in the near future, the simulation part will likely shift to a method that utilizes the native simulation capability of computers.
In addition to current computers (so-called von Neumann computers), the practical application of quantum computers is also coming into view. Since quantum computers can natively process the superposition state, which is one of the essences of neural networks, they may be able to achieve higher pattern recognition capabilities than the brain or AI. If this happens, quantum computers could handle pattern recognition, and von Neumann computers could handle the simulation part, possibly creating intelligence with unimaginable high performance and low energy costs.
In Conclusion
In this article, I have organized the concepts of pattern recognition and simulation as the essence of intelligence processing.
With this as the axis, I have organized the principles of computer science, such as neural networks and Turing machines, and the hardware of processing, such as the brain, von Neumann computers, and quantum computers, and shed light on the history and future of intelligence.
I also explored the relationship between intelligence and the real world. The discovery that patterns and mechanisms are the basis of the existence of the real world was a significant revelation as I organized this article. This led to an understanding that recognizing and predicting the existence of the real world is the purpose of pattern recognition and simulation, which are the essence of intelligence processing.
I also began to see the relationship between the virtual world within intelligence and the real world. The virtual world includes mere fantasies and fictions. On the other hand, I was able to gain an understanding that it is sublimated into scholarship by demonstrating its usefulness to the real world.
This article focused on the processing aspect of intelligence. Another major aspect of intelligence is learning. Based on learning, pattern recognition and simulation become practical. Therefore, I would like to examine learning further in the future.
Although I focused on intelligence, it is not the only mirror that reflects the patterns and mechanisms of the real world. My hypothesis is that the phenomenon of life is also a mirror reflecting the real world. Based on the organization of this article, I would like to delve into the processing in the phenomenon of life in the future.
