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Softnet Carnival

is the Data integrator with P2P enabler that allows the movement of data across points.


Softnet Carnival creates the baaldrome system using today's Softnet technologies It's re-changeable pathway using automated softnet carnival to help build the baal-drome network on the fly each time it is needed.This a drone network carrying initially a 2kg payload over distances of 10km at 50km per hour each step of the network. The drones use in-built GPS and fly to intermediate p2p ground recharge stations. Softnet creates the pathway and operates discrete systems allowing thousands of drones and pathways to be in play simultaneously in real-time. These key technologies form baal-drome. The GPS driven drones, normally quads. Softnet using p2p bases maps out the pathways to the ultimate destination via ground stations that the drones fly in and out of, for re-charging, etc. Softnet Carnival operates the networks, creating myriad flight pathways in realtime. And the third is the operating system that manages weather and the whole network .


A.Five Imperatives of Extraction
B.Data Reduction & Profiling
C.i of Discovery-2-way Analytics
D.Large Array & Transform
E. Strategize

With I of Discovery
Planning Strategy & Big Data Use

A Siren IOD Data Server (SIDS), is a coordinated collection of computers, on a network. It is characterized by, hyperamplified risk aversion, and extreme information asymmetry. It is the winner of an all-or-nothing contest, and it inflicts smaller all-or-nothing contests on those who interact with it.
The SDS (sids) gather data from the, network. The data is analyzed using the most powerful available computers, run by the very best available technical people. The results of the analysis are kept secret, but are used to manipulate and re-strategize and re-model marketing, operations, etc for the advantage of the owner of the data, which usually is against the people who are represented within the data.
These re- patterning and re-profiling plans have been known to eventually backfire, because the rest of the world within the data cannot indefinitely absorb the increased risk, cost, and waste dispersed by the SDS. It is not the SDS who harms the operation or organization, but the corporation's inability to realize the infinite data tend to fall into unexpected conundrum and or not linear.
Sids are fated by their nature to sow illusions. They are cousins to another seductive literary creature, star of the famous thought experiment known as Maxwell's Demon, after the great 19th century physicist James Clerk Maxwell.
The demon is an imaginary creature that, if it could only exist, would be able to implement a perpetual motion machine and perform other supernatural tricks. Maxwell's Demon might be stationed at a tiny door separating two chambers filled with water or air.

It would only allow hot molecules to pass one way, and cold molecules to pass in the opposite direction. After a while, one side would be hot and the other cold, and you could let them mix again, rushing together so quickly that the stream could run a generator. In that way, the tiny act of discriminating between hot and cold would produce infinite energy, because you could repeat the process forever.
The reason Maxwell's Demon cannot exist is that it does take resources to perform an act of discrimination. We imagine computation is free, but it never is. The very act of choosing which particle is cold or hot itself becomes an energy drain and a source of waste heat.
We do our best to implement Maxwell's Demon whenever we manipulate reality with our technologies, but we can never do so perfectly; we certainly can't get ahead of the game, which is known as entropy.
All the air conditioners in a city emit heat that makes the city hotter overall. While you can implement what seems to be a Maxwell's Demon if you don't look too far or too closely, in the big picture you always lose more than you gain.
Every bit in a computer is a wannabe Maxwell's Demon, separating the state of "one" from the state of "zero" for a while, at a cost.
A computer on a network can also act like a wannabe demon if it tries to sort data from networked people into one or the other side of some imaginary door, while pretending there is no cost or risk involved. For instance, a Siren Server might allow only those who would be cheap to insure through a doorway (to become insured) in order to make a supernaturally ideal, low-risk insurance company. Such a scheme would let high-risk people pass one way, and low-risk ones pass the other way, in order to implement a phony perpetual motion machine out of a human society.
However, the uninsured would not cease to exist; rather, they would instead add to the cost of the whole system, which includes the people who run the Siren Server. A short-term illusion of risk reduction would actually lead to increased risk in the longer term.

Some of the prominent present-day Sids include high-tech finance schemes, like high-frequency trading or derivatives funds, fashionable Silicon Valley consumer-facing businesses like search or social networking, modern insurance, modern intelligence agencies, and a multitude of other examples.
For Sids to work and the strategy to sustain a corporation, we have to change the way we do big data.

You can think of a marketplace as a form of what's called an optimization problem. This is the kind of problem where you figure what set of conditions leads to a most desired outcome. For instance, suppose you would like to take a shower with the water at a certain temperature and with the water pressure being just right.
Suppose you have a shower with only hot and cold knobs. Then you can't set the qualities you want directly. Instead you fiddle with the hot and cold knobs to find the settings that create the shower you want.
There are two inputs, hot and cold. A market can be thought of as a similar system, but with many inputs. The price of each product can be thought of like a knob, for instance. This leads to the idea of a very "high-dimensional" problem, like a shower with many millions of knobs.
Dimensions are a way of thinking about the conditions you are able to set. The hot and cold knobs can be thought of like the X and Y directions on graph paper. Now set a piece of imaginary graph paper down on an imaginary desk in your mind. Imagine that each point on the graph paper sprouts a pole that sticks up—and the height of the pole corresponds to the desirability of the actual temperature and pressure that come out of the shower for particular settings of hot and cold. A forest of these poles will form a sculpture above the graph paper. What will its shape be?
Anyone who has used showers with separate hot and cold knobs knows that finding the right temperature is a little tricky. Sometimes you can move one of the knobs a lot and it doesn't seem to have an effect.
Sometimes the tiniest adjustment has a big effect. If the knobs always produced consistent effects, then the sculpture would be nice and smooth, but actually, for most showers, the shape will include sudden cliffs. It will be complicated. A picture of the range of outcomes is sometimes called an "energy landscape" because of the cliffs and peaks. What the shower knobs imply What you might naively expect from shower knob positions.


What the shower knobs imply: What you actually get are 2 differing objectives.
The overwhelming practical issue is that when you have hundreds of "shower knobs," – data fields, you can't readily calculate the ideal positions for them all. Much less the inter-connectedness of each and every field to each other, let alone unknown fields for which you don’t have data. A landscape can sometimes be too complicated to evaluate comprehensively.
You can only make progress by starting at one point on the landscape and then tweaking inputs incrementally to see if the goal you seek seems to be furthered. You crawl on the landscape instead of leaping. In other words, your best bet is to move shower knobs a little bit at a time to see if you like the result better. You can't really explore every combination of shower knob positions in advance because that would take much too long.
The process is incremental, because there isn't an alternative when the landscape gets extremely big and complicated. Unknown factors exist. How do you factor this in so you do not have a back-lash or suffer a negative outage.
The frustration is that incremental exploration might lead up to a nice high peak, but an even higher peak might exist across a valley. Evolution takes place in millions of species at once, so there are millions of explorations of the peaks and valleys. This is one reason why biodiversity is so crucial. Your perspective must be both linear and non-linear. You must account for the unaccountable or risk losing everything.

Markets as Landscapes

Cloud software runs on massive assemblies of parallel computers, so it can perform many incremental explorations of a simulated landscape or marketplace at one time. Even so, there is no guarantee of finding the highest point, even in a simulated landscape. The variations that make different players in a market importantly different aren't fully expressible within a single Sids. But the fractal and non-linear aspects must form a part of the assembly.

If you believe that artificial intelligence is already as creative as real human minds imbedded in real human lives, then you'll also believe that AI algorithms can be relied on to be the most creative mountaineers, and to find the highest peaks. However, that is not so.
There is no way an Amazon pricing bot will have creative ideas about how to price an item. Instead it will just enact a deadly dull price war. All bots do is use the illusion of AI to reinforce positions of network power, by taking gross automated actions like setting prices to zero.
Sids reduce the diversity of explorations and do not compute unexpected outcomes, no matter how big a calculation they run, thus increasing risk.
In classical economics, a lot of attention is paid to how markets seek "equilibrium," which is another form of peak on a mathematical landscape. In the most recent forms of networked economics, it's clearer than ever that there's no way to know if a particular equilibrium is particularly distinguished or desirable relative to others that might be found. There could be a great many undiscovered pitfalls as well as preferable equilibriums.
The existence of multiple pitfalls and equilibriums are part of what's so galling about the way networks have taken over money.
Let's suppose there's a Sids making a lot of money. Maybe it's playing little games with micro-fluctuations in a massive number of signals. Or maybe it's playing a highly leveraged, bundled, remote hedging game, or a high-frequency game. Assume the scheme is working well, and the owners are doing so well that they are sure they've unlocked the key to the universe.
On the face of it, lowering taxes should lower the amount of money brought in by taxes. A remarkable, decades-long, and maniacal public relations campaign has brought about a general atmosphere in which the idea is respectable.
While there are huge problems with the way the idea is understood and the way it has influenced policy, the ascendance of a linear, systemic sensibility into popular folklore bodes well. If the public can "get" the Laffer curve, then the public can probably also gain a more honest and balanced sensibility of the nonlinear nature of the big challenges we face.
A serious attempt to find a Laffer peak, a long-term lower tax rate with higher revenues,' would have to be as experimental and long term as the quest to improve weather predictions. Maybe something about education levels, retirement rules, or even the weather would make all the difference. It would be as ridiculous to say a Lafferesque solution is impossible as it would be to say it is automatic or easy to find.
The Laffer curve was supplanted in early 21st century conservative economic rhetoric by a different curve, which is really just a straight line.
Both curves are hopelessly oversimplified. Recall your finicky shower knobs. If even your shower behaves in a complex way, surely the economy is also complex. Understanding it is more like the process of predicting the weather or improving medicine than its like these smooth lines. Economics is a real-world big data problem, which means it's hard. It's not a phony big data problem of the kind being used to build instant business empires. That confusion is 'one of the great confusions of our historical moment.
The original Laffer curve had the merit of showing two peaks on either side of its valley. That betrayed an acknowledgment that there can be multiple equilibriums. The latest replacement, the absolute faith in austerity, doesn't even acknowledge that. To accept it is to be completely hypnotized by the illusions of easy complexity.
It is senseless to speak in the abstract about whether the Laffer curve is true or false. It is a hypothesis about peaks and valleys on a landscape of real-world possibilities, and these might or might not exist.
Systems with a lot of peaks must also have a lot of valleys between the peaks. When you hypothesize better solutions to today's way of dealing with complex problems, you are automatically also hypothesizing a lot of new ways to fail.
To find that kind of sweet spot on the landscape requires a methodical search, which implies a must be tweaked experimentally in order to "crawl on the landscape." That means a lot of analysis and testing, Cloud calculations are never guaranteed or automatic
The Insanity of the Local/Global Flip The most basic reason to doubt or fear Sids is not that they're unfair. Life is unfair, as my conservative friends never tire of pointing out. No, the problem is that Sids eventually become absurd, because of the "Local/Global Flip."
Sids can become successful—sometimes in the blink of an eye—that it optimizes its environment—changes it—instead of changing in order to adapt to the environment. Both linear and non-linear graphs must be analyzed. A successful Sids no longer acts only as a player within a larger system. If it becomes a central planner The Sids operation is stupid, like a central planner in a communist regime. The problem is not the fault of Google or derivatives funds or any of the other schemes. Instead it's a dangerous temptation dangled by Moore's Law — a temptation we must learn to resist.
Cheap networking facilitates exaggerated and rapid network effects. These engender failures of the linear models, which had been based on competitions between multitudes of players with distinct and limited information positions.
For instance, networked finance kept on pretending it could eject risk out into the economy at large, like a computer radiating waste heat with a fan, but it became as big as the system. The computer melted. Similarly: Health insurance companies in America, by using cloud computer analysis to mostly insure people who didn't need insurance, similarly ejected risk into the general system. But there wasn't some giant vastness to absorb the waste. Instead, the economies in which finance and insurance could exist in the first place were weakened.
Alas, all Sids as currently construed with data patterns considered linearly, and are likely to eventually falter in similar ways.
Organizations must use linear and non-linear graphs

A change in core marketing operations and re-channeling without undesirable feedback
needs a pertinent iOD data perspective via myriad access of data, consolidation and analytics, both linear graphs comparing non-linear fractals.
This is a major challenge. Intraclient can add
to your analytics and fact finding processes.
Intraclient can save your future.

A. 5 Imperatives of Extraction
B.Data Reduction & Profiling
C.i of Discovery-2-way Analytics
D.Large Array & Transform
E. Strategize

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