CHAPTER 662: COMPLEXITY AND SIZE.

So even though the large thought units with more faces has a higher raw information transformation power, it will be slower at simple task than the smaller thought unit and will still consume more energy than the small thought unit. This makes large thought units less efficient at executing simple tasks.

The area where the larger thought unit will be able to shine is in a complicated task, such as predicting the weather. There are too many factors to consider for this task, so the small thought unit will have to take many cycles to complete the same thing that the larger thought unit would be able to complete in one cycle.

At the end of the day, thinking in the thought unit and coming to the right outcome is a matter of chance. More complex thought units have more chances to make mistakes, while simpler thought units don’t have as much room for error. But the two of them have their advantages and areas where they are better suited.

After learning all of these, he came to the conclusion that upgrading an AI logic model mainly involves increasing the complexity of its thought units while maintaining or reducing their size. But there were other things to pay attention to, such as the interaction of the thought units with each other.

After all, each thought unit is only capable of one thought. The logic model still requires other thought units in order to form a train of thought and create the illusion of reasoning.

And not all thought units in a logic model are the same. Some are simple, while others can be large in favor of the option to specialize. And the way the thought units cluster and communicate with each other is important too.

But at the end of the day, the basic thought unit is still the most important part of a logic model. Once he has mastered how to create thought units, the rest will be easy.

The more he read and learned about logic models, the more he realized that they looked similar in their structure to the quantum computers of his past life.

Specifically, thought units seem similar to qubits. This is because they are capable of parallel processing and processing information into multiple outcomes.

In classic computers, the basic units called bits can only be in the state of 1 or 0. But qubits can be 1, 0, or both simultaneously.

The state that a qubit is in is a probability until it is measured. It can be 0 and 1, and it can be a range of probabilities of 1 and 0, such as 30% 1 and 70% 0. So in essence, the state of a qubit has infinite possibilities.

In the same way, the state that a thought unit is in is all of its faces until it is done transforming information. In the case of the thought unit in the shape of a triangular prism with 5 faces, the thought unit occupies the 0, 1, 2, 3, and 4 states simultaneously.

So a thought unit can be in the state of 0, 0, 0, 0, 0, or 0, 0, 0, 0, 1, or 0, 0, 0, 1, 2, or 1, 2, 0, 2, 4, and so on for a total of 120 possible states due to the many combinations of the possible states of five faces. The state of a triangular prism is probabilistic too, but the outcome is limited to only 120 possibilities.

If there is another major difference between thought units and qubits, it is the lack of quantum advantages such as quantum entanglement and superposition.

This lack of a quantum advantage makes it difficult for the state of one thought unit to affect the states of other thought units and reduces the power of thought units when combined. But this disadvantage also makes thought units more stable and more error-free than qubits.

This error-free property of thought units is very important to Arthur because one of the main challenges that quantum computers had to overcome was the ease with which qubits produced errors or invalid operations.

Other issues that qubits faced were their unstable state, which made it easy for external interference to remove them from their quantum state, and the difficulty in manufacturing them. But thought units don’t have these problems.

Thought units need special materials and special techniques to create, but anyone with a divine sense can make them, so they are easier to make than qubits. And as long as thought units remain in their logic cores, there is very little that destabilizes or destroys them.

To top things off, thought units don’t require extreme conditions of temperature at near absolute zero in order to exist and function like qubits do. So they are easier to work with than qubits.

The more Arthur thought about it, the more he realized that he had a great opportunity here to enhance the power of thought units by comparing them to qubits.

He thought to himself, "If I can make thought units process information like quantum computers, I might be able to do something that humans were not able to accomplish in my past life."

In his past life, humans built functional quantum computers. But these quantum computers were expensive machines that only a few powerful individuals and governments could own because of the cost of maintaining and operating them.

Quantum computers were not widely adopted like classical computers, and their power was only useful for certain specific tasks, such as cryptography and molecular simulation.

But as he gazed on the structure and behavior of thought units, the hope of creating a powerful computer with the best of both quantum and classical computers became bright within him.

Currently, thought units process information in sequence, like classical computers. They have the potential for parallel computing, but they are not being used for it.

The ability for parallel computing is the main advantage that quantum computing has over classical computing. It is the foundation of what is called quantum superiority.