To be honest. Don't know.
My contribution is writing software that optimizes software.
Current CPU forecasts for AGI demands deep pockets, HUGE teams spreadout, closed door company secrets, ETC.
That's bull donkey. Solve the software problems first. Coordinating AI's development is only yielding loss in translations, waisted debates, profressionalls accidently reinventing the wheel.
Softwares problem is it's writers scalability. Does any one person no what's happening at all angles/stages anymore?
Our unnecessary complexities outgrown us. Many software fields like Applications development creates frameworks rather a narrow AI automating the field. Web, Mobile, & Desktop Applications development are architecturally split up... why? Why ? Why?
Everything something in common, yet those fields treat each screen like a project. Terrible.
I have a project hidden away. It's called autotemplater. It's a semi ML solution for automating content display. All one must do to the wield that tool is fill out the data such as a paragraph, image, title. No need to tell it positions, colors, sizing, etc. It runs through the permutations.
In short on that note. There are a few fundamentals which desperately need automation to save everyones mind.
Logic,
Security ... Memory Inspection,
Interfacing,
Performance Optimization.
Interfacing is like a UI, or an API, or how anything reacts with another.
Voice is an interface. Vibrations are an interface.
My contribution is writing software that optimizes software.
damn
Heres an example of 1 input & output someone gave me.
Quickly figured out there algorithm.
@James4Deutschland
Input: ["group","groupChar"]
Output:
[[True, False, False, False, False], [False, True, False, False, False], [False, False, True, False, False], [False, False, False, True, False], [False, False, False, False, True], [False, False, False, False, False], [False, False, False, False, False], [False, False, False, False, False], [False, True, False, False, False]]
specDev is considered this definition
https://en.wikipedia.org/wiki/Fifth-generation_programming_language
However, as larger programs were built, the flaws of the approach became more apparent. It turns out that, given a set of constraints defining a particular problem, deriving an efficient algorithm to solve it is a very difficult problem in itself. This crucial step cannot yet be automated and still requires the insight of a human programmer.
This module might help us with finding constraints faster
this project is put on hold. All my software inventions require one thing to be created. A numbercruncher. The number cruncher is an ML based math learning machine. We should not program the computer mathematics we already know, rather it needs to identify these concepts itself through pattern recognition.
An example is identifying what are prime numbers. Instead of writing an algorithm for isPrime, it should be able to come to a conclusion from calculations saying, "realized these numbers are not nicely dividable"
Everything in math relies on number theory. Understanding numbers will be the guide to all rationalities. A computer cannot produce an equation on it's own until it becomes aware of one numbers relationship to other numbers.
We could start off with something simple.
See how long it takes for the machine to realize the relationships of operators outputs of counting by one and counting by 2.
To us we know that counting by 1 will produce every number between m->n.
And count by 2 is half of m->n.
Also the predictable calculations you could perform.
if you count by 1 and divide all numbers from m->n by 5 or count by 1 and multiply by 2. will the machine realize that the digits are equivalent 2,4,6,8,0 placeholders as equal to just counting by 2