The Next Simulation
How quantum web objects are applied through inference-based logic in making new discoveries about the world.
Whether we want to believe it or not, we live in a simulation of perception.
We appear as holograms.
These holograms represent the 3D world we perceive as “reality”.
One might consider that we are 4D, 5D, 6D or 7D beings experiencing a 3D reality. Spirits inhabiting holographic bodies, and interacting with other holographic forms, in order to experience the dualities of the perceived physical world.
Light, energy, sound and magnetism comprise the unseen waves that comprise us and connect us. What this author describes as the Internet of Waves. There is very real science behind this.
The Internet of Waves
A next paradigm thesis based on the phenomenon of quantum entanglement.
As explored in this author’s last piece, this science can be applied to web objects that represent the physical world in its true forms.
Some believe we are conscious beings seeking higher or lower realms, what is considered “ascension” or “descension”. That all of it is connected to a Source, or something greater than ourselves that represents everything that is.
The difference between now and next is in whether we want to see this reality for what it really is and what it really can be, or, if we want to see this reality for what it really isn’t or can be.
And, that we are also the sources of whatever realities we create, and choose to see or experience.
As such, a core question set arises out of intuition and inference: What is real, and what is really possible in making our realities “better” than they are right now?
And the next logical question surfaces: If we are in fact living in a simulation, can the next simulation align us closer to our true, physical realities?
The first step in developing a new simulation of physical realities is to acknowledge that elements like plants are objects we can observe in their natural forms. These can be translated into representational web objects.
For example, plants can be observed in their natural states, and their geometries can be mapped out.
Taking this a step further, these geometries form a calculus of various equations from which we can see how these natural objects are interconnected and interoperable.
And, we have plenty of quantum research to support this.
The most compelling research explains how molecules achieve superpositional states. If there are superpositional states — reflected in 1s and 0s, or in quantum terms, +1s and +0s — then we also know that our mappings show critical interdependencies between living organisms.
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So, you might consider a plant as a leading signifier of living operations in the natural or physical world.
Now let’s examine how this looks in terms of compiling information in the natural world or physical world with holographic representations.
The simplest way to understand how we can model information in its holographic forms is in three primary geometric constructs: a square, a cube and a tesseract.
Squares (2D) represent containers of information, such as what we see in flat media, or social media.
Cubes (3D/4D) represent dimensions of those containers, such as what we see in motion graphics and data visualizations.
Tesseracts (5D and above) are animated renderings of various containers, either conjoined, compared or aligned, to establish connections between points of information.
5D models use methods such as extrusion, to visualize complex systems, and the connections between them.
These types of models are then used to create multi-dimensional objects.
Let’s consider that there is no one model to see or understand information in holographic form.
In the world of data science, we call this a “model-free approach”.
The vast majority of data harvesting methods employ reductionist approaches (such as market batch analysis) and look for things like statistical anomalies, or they make statistical connections that are not fully representational of the human or the environment in which they live. 23andMe is a good example of this.
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Emergent approaches look at specific groupings of data that are at once specific to the person and the environment, and are reflective of certain characteristics to which both represent an interplay between them.
It used to be the case that we could analyze these interplays in online communities, by mapping the semantic or ontological pairings between groups.
What we were unable to do, of course, is get a good understanding of why these groups or communities were connected in the first place, or, what might cause them to disband or have tensions between them.
The correlations between the themes and topics of discussion would enable us to place them in physical locations, but much more digging would be required to get a handle on the real dynamics driving their interactions, and more importantly, how to actually help these people in terms of treatment innovations.
Remember too that this is precisely where semantics have limited us in our information discovery, and the applications thereof.
While the social technologies we have built have been very innovative, they still remain as 2D/3D containers that cannot enable us to see humans or environments in their full, natural forms.
If we can accept that the world we perceive is a large, complex system comprised of many subsystems, all of which are interconnected, then we can consider that all of our challenges and opportunities reside in the effort to see as much as we can in natural form, starting at any specific point of inference.
Each point of inference “draws in” other interconnected points, such that we can actually see causes and effects.
Unfortunately, our current information systems focus on the effects, or correlations, to then make assumptions about “what might be really going on”.
For example, externalities, or side effects, are costs or benefits that are imposed on a third party who did not agree to incur those costs or benefits. Externalities arise throughout commerce systems as side effects of production or supply chain processes that typically do not take into account all the risk they assume when they operate.
Typically, externalities are not factored into risk calculations in their full breadth, or simply not at all.
This is where new frameworks come in.
For example, this author has developed a holistic framework for factoring in externalities called natural risk amortization that uses an algorithmic quotient to determine price and demand variants based on production, distribution and consumption dynamics.
It has always been the case that all the information we need is already “out there” and “in here”, meaning within our grasp of physical reality.
We can either see it, or intuit it, or both. Information in the form of data can substantiate what we see or intuit, rather than just confirm our preexisting biases and beliefs.
With the ease, convenience and speed of modern technology, it is also the case that we tend to be less curious and less vigilant about seeking information, simply because “answers” can be fed to us in a Google search, whether they are true or not, complete or incomplete. Even worse, when we are fed answers, we tend to not ask more questions.
Going forward, information discovery cannot be relegated to predeterminations about the world.
As such, discovering the “unknown unknowns” is a noble and worthy pursuit.
Now let’s look at what information discovery means in terms of practical application.
Here we begin to see the “prisoner’s dilemma” that natural language and natural math present when information is not fully representational of nature itself.
In short, what this means is that we cannot solve complex problems by making assumptions about the natural world, no matter how sophisticated our automations are (as is the case with deep learning).
We must either test our assumptions against physical reality, and/or make the web objects we use fully representational of nature, and its natural laws.
In doing so, natural math and natural language emerge in the patterns we see when we connect objects in fully representational, natural form.
VISUALIZING MULTI-DIMENSIONAL INFORMATION
Conventional wisdom still maintains that if we connect a bunch of devices, we can see the world for exactly what it is. Yet, this is based almost solely on optics, rather than the true nature of physical forms, as described earlier.
Where optics must be driven by structured data that is fed into the system by algorithmic predeterminations, or non-deterministic math, (like deep learning AI), the common logic is that the quantum object is one part fractal of the simulated digital reality, and one part a data assumption about that simulated digital reality.
Which brings us to a sobering realization about this approach to the quantum object: That we, and our environments, are still viewed as “things” to observe, rather than living organisms that naturally evolve.
Alas, we may have found a solution for this.
As mentioned in the last piece on Quantum Web Objects, the team at ORA has designed its HALO software to fully represent objects in their multi-dimensional wave forms. This means that each object is configured according to the quantum dynamics of the natural world, and that each object is fractal of the complex systems it represents.
The team has already applied this groundbreaking approach in work with large software providers and health clinics, and we are forging a partnership that promises to redesign the web as an interconnected system of quantum objects that bring us back to nature, in the full majesty of all of its forms, its mysteries, and its offerings.
THE NEXT SIMULATION
Holistic approaches to data and their visualizations are already on avail.
Now we have the unprecedented opportunity to solve environmental, social and health problems at quantum scale. This is the next simulation we are building.
In doing so, we will transform the world according to our true natures.