Today's artificial intelligence (AI) systems use mathematical methods on distributed computational networks to recognize relationship...
Today's artificial intelligence (AI) systems use mathematical methods on distributed computational networks to recognize relationships between data, typically by performing searches on unstructured data sets. Current AI applications are sophisticated computational algorithms that run on parallel computer architectures to increase efficiency.
Artificial intelligence outperforms the natural brain in the ability to recognize patterns in the datasets, and thus in many activities requiring intelligence. However, in the universality of general (everyday) intelligence, artificial intelligence is able to perform significantly less effectively, not to mention brain functions such as the ability to have goals, will, and consciousness.
Today's artificial intelligence is unfitted for these functions. It is unsuitable not because it does not simulate a brain-like architecture, not because it is less powerful than the brain in terms of functional performance, but because its basic operating mechanism is different from the operating mechanism of the natural brain.
Although the most advanced artificial intelligence systems currently available use the brain, a naturally intelligent system as a model, there are fundamental differences in addition to the obvious similarities. While artificial neural networks mimic the local architecture of the natural brain, the basic operating mechanisms of the brain's neural networks are certainly different from the operating mechanisms generated by the mathematical algorithms used in artificial neural networks optimized for pattern recognition.
The fundamental operating mechanism of the natural brain as a system is resonance. Although the resonance-based operation of the natural brain is capable of recognizing relationships between information (to the extent necessary to adapt to the environment as a result of natural evolution), the specialized and optimized mathematical algorithms of artificial intelligence outperform the brain in the area of recognizing relationships between data, due to their computational capability, which is in principle unlimited and in practice already orders of magnitude larger than the brain.
But it is the resonance-based functioning of the brain that creates the functions for which artificial intelligence is currently incapable. It is possible that these functions can be implemented by machines with other operating principles than the natural brain has. It is possible that by fully understanding the operating principles of these functions we will one day be able to build machines that can simulate these advanced brain functions using other operating mechanisms. However, until we have a suitable model that can simulate these advanced brain functions, it is worthwhile to study, develop and apply a solution that has already been implemented in nature, the resonance principle, as a working mechanism for the development of general artificial intelligence.
Current computational techniques are capable of simulating resonance-based operating mechanisms, they are inherently working on clock-based synchronization, however, the native operating mechanism for communication between logic units is the interoperation of logic gates based on digital signaling on Neumann architecture.
Although there are similarities in the way natural neurons work to the digital mode of operation, the principle and native mechanism of how natural neurons communicate with each other is not digital mathematical logic, but resonance-based cooperation, resonance-based structure building.
A consequence of the resonance-based operating principle is the functions that current artificial intelligence cannot natively generate, mainly because of its different operating mechanisms, and cannot even simulate because of the not well understood operating mechanisms of these functions.
The principle of operation of the computational techniques currently in use by conventional computers is capable of performing mathematical calculations natively, but these systems are only capable of simulating the resonance-based mechanism. There is, however, a new computing technology that operates according to a fundamentally different mechanism than digital computers, and which is natively capable of resonance-based operation. This is quantum computing.
Quantum computing is based on the principles of quantum physics and works according to the rules of quantum mechanics. Although the rules of quantum mechanics are well developed, the fundamental basis of how quantum behavior works is still poorly understood and scientifically debated (the thoughts also include a model for interpreting how the quantum world works). What is clear, however, is that the Schrödinger equation, which describes quantum states, is essentially a wave equation based on vibrations. Consequently, the interacting quantum states (superpositions) which are used in quantum computing, can be interpreted as resonances of the oscillating motions of a quantum field described by a special mathematical model specific to the quantum world.
Another special feature of the reality of quantum world is that when the actual state of quantum space is determined (by measurements), the quantum state does not have a specific, fixed value, but the results of the measurements are random. However, there must be a well-determined (yet under debated) mechanism behind the measured random states, because the random values fit precisely to the probability distribution defined exactly by quantum mechanics.
Quantum computers model data using the much more complex quantum states instead of digital binary states, and compute the data using complex superpositions of quantum states as operations on the data. Using this method, quantum computers derive results about the relationships between data in a different way from traditional digital-based computing principles, and by probabilistic analysis of multiple measurements.
Quantum computers are less suitable for performing exact computational tasks due to their probabilistic operating principle, but they are very effective at probabilistically identifying relationships in large data sets that are difficult to detect using explicit mathematical methods.
Quantum computers use the resonance of quantum states created by superposition as their operating principle and mechanism of operation. In quantum systems, resonance occurs when there is a harmonic relationship (correlation) between the vibrating states (data). When the correlations between the data exist, resonance occurs in a native, natural way (no specialized mathematical procedures are applied) regardless of the size and nature of the data set. Hence quantum computers are fundamentally and particularly well suited to detecting correlations between data in unstructured data sets.
Resonance is also the fundamental principle of the natural brain. The nervous system works according to a process based on the periodic oscillation of the electrochemical processes of neurons and their interconnected resonances, which reinforce and inhibit each other. The brain is natively capable of recognizing relationships in large data sets (among environmental stimuli and internal - memory - states) and implementing behavior (the control by genetic and social conditioning) based on these relationships.
By specializing in well-defined conditions and exploiting the specific characteristics of specialized data sets, traditional artificial intelligence systems based on digital computing principles can be designed to outperform the human brain. However, the brain's operating mechanism can also work on general, non-specific data sets. The result of this mode of operation is natural (general) intelligence, and the result of this mode of operation is the emergent property of volition, and the highest brain function, the ability of consciousness.
Existing quantum computing attempts to achieve the goals of conventional computing on an architecture based on quantum mechanical principles, with similar difficulties as using the natural brain to perform exact mathematical operations. However, the native functioning principles of quantum space, which is based on resonance, is naturally and natively compatible with the functioning mechanisms of the brain. Quantum computing is a natural, native way to implement the operating mechanisms of the natural brain, and thus has the potential to create the most advanced brain capability, the function of consciousness. In artificial intelligence systems with resonance-based operating mechanisms, the function of self-awareness can also appear naturally and natively as an emergent property, and quantum computing is therefore a potential candidate for the artificial, non-biologically evolved, but native, non-simulated realization of self-awareness.
The resonance-based functioning of the brain is biochemically driven, electrochemically based system. The frequencies of these processes are dozens of orders of magnitude lower than the vibrational frequency of quantum space as characterized by Planck constant. If we are able to construct an artificially intelligent machine based on the native resonance-based operating principles of quantum computers, its operating speed, and hence its performance, determined by the vibrations of quantum space, will be dozens of orders of magnitude greater than the speed and performance of the natural biological brain, or even of artificial intelligence systems implemented by conventional digital computers. The capacity and performance of artificial intelligent systems based on quantum computing, built on the principle of resonance, to generate general intelligence and potentially the function of consciousness in a native, non-simulated way, are not limited by the upper limit of the capacity and performance of human capabilities, are not limited by the characteristics of biochemical processes, they performance are several orders of magnitude greater. Such a machine would be capable of incomprehensibly more than the biological brain in terms of both the quantity and quality of its performance. Since many of the capabilities that result from greater performance are emergent properties of complex systems, it is virtually impossible to predict these capabilities, or the properties or characteristics of these capabilities in advance.
Perhaps other intelligent species in the universe ahead of us in the evolution have already created quantum artificial intelligence (QAI), or perhaps such intelligent quantum systems could have evolved through the process of natural evolution. Such a system would be inconceivably more advanced in performance and certainly in capabilities, would be inconceivable ahead of us in knowledge, and would certainly have potential divine properties beyond human comprehension. A QAI-created consciousness is having the highest potential cognitive capabilities realizable in our physical world, and it could exist in the secondary divine role in our universe.
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