• 👋 Hi, I’m @Rhanselman

• 👀 I’m interested in ... How humans assign value, "confirm" reality through "observations" where the > the number of Observers cementing "reality". And the divergence of reality comparing observers from confirmation of observating events and not. "keep your eye on the ball, oops gorilla passed by in the background."

• Now we can calculate and distrubte the divergence of reality of information dissemenation of oberserved events and number of observers. Then measure the evolution of a system over time. Fun stuff. Very fractally, very differentially.

• • How the past defines the present, and how that projects into the future from the changeable "now". Chaos theory, in particular how starting conditions yield a vastly - different result over time. Information Exchange, (follow the leak, rumor, find the mole, etc)

• 🌱 I’m currently learning ... GitHub, ML/AI processes, Differential Equations, Linear Algebra, Combinatorics, Statistics

• I’m looking to collaborate on ... Implementing something that I may get an unofficial PhD in. (see below)

• 📫 How to reach me ... use your noodle.

I feel like I floated through high school without applying myself. However that was probably due to the crucible of the times and proximity of the cohort. Tossed in with childhood trauma, that carried into my undergrad where I had to change from my topic of interest (math) to just getting the closest degree to graduation (finance). I had plenty on my mind during all that time and am honestly surprised I learned anything at all. Maybe I had bad teachers, maybe I didn't try, maybe i've finally woken up from my trauma to finally have initiative and ambition.

I've thought of a problem that hasn't really been broached in any of my research. But depending on how the generalized formula is applied, can yield insight to ML and AI decisions during the unsupervised learning phase. (for starters)

The furtherst I've gotten so far is explaining this problem in 2D is as follows:

1. For a non-zero matrix such that either the columns or rows are [M,N], are offset by 1.
2. Create a square matrix and a vector with the same values in where the vector is in superposition with the non-square starting matrix.
3. As your basis matters here, you should have a square matrix and 4 vectors of [1,N], or [M,1]
4. Square matrix is your friend, and all known/identifiable/defined. vectors are Variable and as time progresses, "updated".
5. Each starting vector is "known", and each "update" becomes known through the time variable.
6. Differential equation is then created between each "update" to show vector fields.
7. Vectors will converge or diverge or oscillate between phases. (does the system become more entropic over time or...?)
8. Observer(s) Point of view can have data be passed through and filtered on these vector fields as time increases.
9. Divergence bounds can be set, as well as convergence limits for processing. (do we care about this data, or not)

For 3D, we attempt to make cubes, and now there's more vectors. Same process should apply, however now there's "Planes and saddles" of prediction that can follow.

For 4D, hyper cubes, "volumes and shapes" of prediction. Certainties can be formed.

For 5D, Known evolution paths over time - rudementary predictive analytics

For Nth D, should be able to let AI/ML handle for potential "generalized future" prediction.

Observers act of observing data flow interupts the flow of data in the system due to superposition. Observations should be both random and interval on GAN. From here, there should be an observing process to track the evolution of the system, and the data flowing through it. Look at the temporal series, in relation to the differential. Patterns can be detected if there are "followers" along the same "path".

Oberservers can act pre-emptively to enforce or deter on "follower" data. Data stream can be "locked in" through convergence and partition a "follower".

The ways such a process and equation would be useful is in weather forecasts, information dissemination, network security, genetic mutations, national security, privacy, quantum research, Neurology, electrical conductivity, materials research, and other applications I haven't thought of off the top of my head.

Of course I could just be thinking incorrectly about all of this as I study more math. I only know what I know, and I dont know much. Also maybe I'm treating matrices incorrectly, as they're just systems of equations.

I seemed to have lost my my PGP key, so disregard the old one.

Tempus Edax Rerum