Metaverse Evolution

The study of thinking models is still a process of squeezing toothpaste.

You need to constantly replace formulas, correct parameters, find problems, and solve problems.

This is a process that requires patience and huge computing power, not only the computing power of the computer, but also Chu Fei's own thinking ability.

In the virtual space, four thinking models are calculated at the same time; and Chu Fei also needs to observe the four models at the same time and constantly correct them.

This is also a research that requires a solid knowledge foundation - what kind of formulas are needed, how to replace formulas, what kind of parameters to substitute, etc., all require rich knowledge.

Similarly, in such a research process, it is also a process of continuous practice, testing one's own knowledge system in practice.

With the calculation, data is constantly screened out and entered into the database.

And with the accumulation of the database, the empirical formula is becoming more and more perfect, and even the topological structure is becoming more and more rich.

The entire empirical formula gradually formed a trunk and branches, and the branches intersected and gradually formed a network.

A topological network gradually formed.

Until now, Chu Fei was surprised by the change:

"This is the intelligent neural network system, at least it is a prototype of a neural network."

After discovering this, Chu Fei looked back for related technologies and related architectures of artificial intelligence, and continued to substitute them into the calculation. At the same time, we also look for similar AI structures to learn from, or even extract some codes to embed in our own systems.

The computing speed of the four thinking models increased sharply, the database accelerated and enriched, and the primary neural network system accelerated and spread, and began to transform from a plane structure to a three-dimensional structure.

An AI structure is rapidly forming.

With the formation of AI, the calculation of the thinking model is in turn promoted.

The calculation of the thinking model has also begun to accelerate.

After two days of this, a new and stable thinking model architecture was established, and the computer spontaneously completed a verification work.

At this point, the structure of the fourth-generation thinking model was determined. This is a more detailed and more sophisticated structure.

In the thinking model, logical chains and topological structures are crisscrossed. Although it is essentially the core of Euclidean geometric space, it is a large space within a small space, interlocking and embedded in each other.

The primary thinking model is essentially a regular tetrahedron structure, and each small regular tetrahedron is like a small computing unit. Countless regular tetrahedron units are constantly stacked, and finally a complex thinking structure is formed.

But this time the computer's behavior was different from before: although the fourth generation was confirmed, the computer was still running and spontaneously calculating the fifth-generation thinking architecture.

After taking a look at the spontaneously running and initially formed AI network, Chu Fei exited the virtual space, formatted his third-generation thinking model again, and began to build the fourth-generation thinking model.

It was still a dual-core four-thread structure, but because the structure was more delicate, Chu Fei spent seven hours to build the thinking model, consuming a super potion, four barrels of meat porridge, and a bag of nutrient supplements.

After opening his eyes, Chu Fei's eyes flashed with hope-this time the thinking architecture should be better, after all, AI deduction was used!

Try running the score!

After a few minutes, the score was run dozens of times, and finally the score stabilized at: 14077 times/second!

Compared with the 11056 times/second of the third-generation thinking model, the computing power increased by 27.3%!

Chu Fei wanted to cheer and scream.

What excited him was not only the results of the fourth-generation thinking architecture, but more importantly, the technology began to enter a state of accelerated iteration.

Chu Fei once knew such a saying: There is an essential difference between the era of computer technology and the era of mechanical technology in the past.

In the era of mechanical technology, a technology needs more than ten years or even decades of research and study to be perfected bit by bit.

But in the era of computer technology, computing power is king. As long as there is enough computing power, technology can be iterated quickly. Sometimes it can be iterated dozens of times in three to five years, and the technology will be reborn.

Now, it seems that I have also stepped on the fast train of computer technology.

After completing the construction of the fourth-generation thinking model, Chu Fei rested for one night, or rested for the second half of the night. The next morning, he went out for a run and exercise, said hello to everyone, checked the inventory, and entered the virtual space again to check the progress.

But found that in just one night, the deduction of the fifth-generation thinking architecture was 22%!

In the thinking space, a progress bar appeared.

This progress bar is derived according to the empirical formula. Although it may not be accurate, it is still OK as a reference.

Chu Fei carefully checked the progress and reviewed the calculation process. He found that there was still a little problem with the automatic deduction of AI, but it only needed to manually adjust the parameters.

Then, continue to substitute more scientific knowledge, mature model structures as references, and some experience summaries.

There is still a small problem with the current AI. It will not actively search for relevant knowledge, even if it is in the database. Chu Fei still needs to enter it manually.

In addition, as the amount of data substituted increases, the computer efficiency begins to decrease. Previously, four thinking models were deduced at the same time. Now due to computing power limitations, one thinking model has to be cut off, leaving only three.

Even so, it still took only five days to determine the fifth-generation thinking model architecture.

Compared with the fourth generation, the fifth generation is mainly an optimized and streamlined architecture.

Solved the small problems that were not solved during the fourth generation development process, replaced some additions with multiplications, replaced some complex number sets with combination curves, differentials, probability formulas, or rearranged some randomly distributed data into small data packets.

Under the guidance of this streamlined thinking, the fifth generation thinking architecture is much simpler than the fourth generation architecture, but the computing speed has increased a lot.

Chu Fei took a break, formatted his thinking model again, and built the fifth generation thinking model.

Because the fifth generation architecture is streamlined on the basis of the fourth generation, it took Chu Fei only four hours to build the fifth generation architecture.

Finally, the running test was carried out: 15859 times/second, and the computing power increased by 12.66%.

Feeling the continuous increase in computing power, Chu Fei was full of satisfaction and even greater desire:

Is this the pleasure of upgrading?

I wonder if it can continue to iterate?

But after logging into the virtual space, I found that the sixth generation thinking architecture has only been carried out by 2%!

"It's reached the limit!" Chu Fei understood, "If there is no reference to external technology and no more technology input, the sixth generation technology will be difficult to produce."

With a sigh, Chu Fei became excited again. "The current thinking model is already very powerful. What will happen if such a huge computing power is used to drive spells?

I want to rebuild the wind of perception, butterfly transformation, and refining magic tools!"