What if we put computers to think mathematically?

When we think about programming and the computer's memories every single memory unit in the computer hardware has a certain address. The artificial intelligence connects and disconnects those memory points into the new orders. And that can make a computing process that mimics thinking. Every single memory address is like a piece of the puzzle. If every single memory unit has a certain number that makes it possible to point certain points from the computer memory. 

When we think about things like thinking we could easily connect those memory units with orders that the large language model, LLM gets by using the numeric values of the memory units and then calculate them with the ASCII marks. In the ASCII system, every single mark on the keyboard has a numeric value. 

For example, the letter A has a numeric value 61 in decimal and 41 in hex. A little a (a) has values 97 in decimal and 61 in hex. That's why it's not the same as the letter big or small in passwords. The numeric system is also important. The hexadecimal ("Base-16" system where the 10 comes after 16) and regular decimals are different. 

In that system 10 is marked in the numeric line like this. 0,1,2,3,4,5,6,7,8,9,A,B,C,D,E,F,10. In binary system 10 comes after 9 like this. 0,1,2,3,4,5,6,7,8,9,10.

Same way every single color has a numeric form in the computer memory. The system is known as RGB.  The system can use CCD cameras to make observations. 

Another thing is to use the values that fit to computer or programmer better. The color red can have a numeric value "200" and then the depth of that color can have 99 states. The system can turn every color into its own numeric value. 

The deepest red can be the 299. The data that CCD camera pixels give can be numeric. The system can see what numeric value every pixel gives and then it can make the model about things that it sees. So all data that travels into the system can turn into numeric. 

Token ring. If we think of this model as the computing cycle of the AI.  The system connects data into that data cycle. Every point in the cycle. There is the computer's image.

This can be the new way to handle large language models, LLMs are not to turn their mathematical models for words. The system can translate data that users input there into the mathematical model. Then the LLM starts to operate and process data in the mathematical form. That kind of thing can be lighter for computers than the words that we use. Mathematics is easier for computers, and when we think about the ability to turn words into mathematical form, we must remember that ASCII codes are basically numbers. Those numbers can sum, division, and multiplicate easier than words. 

That means the LLM can turn every single word that it has into numbers. Then that system can make calculations using the numbers. The ability to handle data in numeric form makes those systems more effective. The system can use the "token ring" type data handling, or computing model. The token ring model is known from data networks. However, the same model can introduce how the system surrounds data in it. Every time, when the system makes the data cycle it connects information into that data cycle. 

The system makes a certain number of calculations in every round. In those calculations, the system connects data from the sensors and memories in the data flow. The system doesn't need to show that information to the users before it drives it through the cycle as many times as ordered.

https://www.geeksforgeeks.org/ascii-table/

https://www.quantamagazine.org/to-make-language-models-work-better-researchers-sidestep-language-20250414/

https://www.rapidtables.com/web/color/RGB_Color.html

https://en.wikipedia.org/wiki/Hexadecimal

https://en.wikipedia.org/wiki/RGB_color_model