cognition-transfer-protocol-(ctp)

.....0. introduction
.....1. protocol
.....2. practical applications

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<-- 0. INTRODUCTION -->
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Data transference is a well honed science at this juncture. Not only have computer scientists been handling the transmission of data from one geographical location to another through means of wires, lasers and air waves for a handful of centuries now, but, biology has been handling signal transmission for literally billions of years. And yet, in the world of TCP/IP, FTP, REST and HDMI, we still haven't found a solution for one of the most essential state transfers ever: Cognition.Channels exist, presently, to transfer cognition, but their formats are grossly undocumented and the conversion is gory and tremendously lossy. The field of semiotics has well explored why this transmission is primed for failure, glitch and misinterpretation. The major shortcomings of this communication can be split into 3 categories:

• Neurology
• Internal State Clarity
• Transmission Channel

In this RFC, I hope to show you how the Cognition Transfer Protocol resolves each of these challenges and the benefits we gain through a clear channel of communication between us and our peers.

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<-- 1. PROTOCOL -->
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<-- a. Neurology -->

Information transfer between two entities is simple if both entities are structurally identical. Especially if what is being transferred is information about state. Knowing that you're sending information to an entity that is precisely the same as you, has all of the required structures to interpret things the same as you have, is an easy problem to solve. So long as each entities have a transmitter and a receiver, they can communicate through some channel and both communicate perfectly, with the only limitation being the channel of communication and its level of noise.However, communication, even between two computation devices, is rarely (if ever) this straightforward. Differences in hardware architecture, available peripherals, kernels and Operating Systems can create massive discrepancies between the signals generated and expected by two entities. The solution for this discrepancy is to create an interface.INTERFACE: a plane surface regarded as the common boundary of two bodiesInterfaces are merely the meeting point between two systems or bodies, it does not need to exchange information between it. The interface itself is merely a location, whether positional or conceptual. The optical nerve serves as an elongated interface between the optical photoreceptors of your eye and neurons of your Visual Cortex (porous interface). But, equally, in some sense, the shared wall between two apartments serves as interface between those apartments (solid interface). The difference is, that wall, by design, is meant to exchange as little information through it as possible. With that clarification in mind, know that, henceforth, I will use the word Interface to refer to Porous Interfaces or Exchange Interfaces.A well designed interface facilitates the transfer of information between these two bodies, even if they would not understand one another through direct communication. In some sense, the kernel is this in your computer. It serves as an intermediary between the raw mechanics of the machine and the operating system. More transparently, TCP/IP serves as an interface between machines, allowing two disparate devices to communicate, no matter how different they are.And if there is an architecture of disparity, it is the human brain. No two human minds are precisely attuned to one another and they are always undergoing personal growth and species evolutions. Entire structures may be missing, under or over developed. Damage to certain regions can make some fauna of thought inaccessible to some people. Thus our first challenge was to formalize brain architecture enough to permit transmission. The beginning of this transmission, and our first solution within CTP, is the Interpretation Layer. During the initialization process of most contemporary Electrobe Swarm Systems (ESS), there is Primary Symbol Index (PSI) created. This index is a list of impulse patterns within your mind that signify certain linguistic symbols. Within this index, a plaintext version of the symbol is saved with an array of Impulse Fingerprints (IF). Depending on the symbol, this array can store Visual, Auditory, Olfactory, Gustatory and Somatosensory Fingerprints, each of which have their own subcategorizations. These fingerprints are Stateless identifications that are identified through an Impulse Matrix Series (IMS), meaning that they do not take into account the context or environment of the brain, they simply identify that a signal(s) at this interval(t) on these neurons (n[]) refer to this symbol(S).In the Interpretation Layer of CTP, we check the signals we're pulling from the Thought Buffer (TB) and compare its contents to the PSI. The Interpretation Layer puts out a compressed imaging of the thought, stored as an array of symbols. The raw Cognitive Matrix Image (CMI) is transferred up to the next layer along with the comparably small symbols array.

<-- b. Internal State Clarity -->

Communication seems challenging because it is hard to be understood by other people, but, through our research we discovered a greater challenge. It's hard to be understood by ourselves. We live in an internal world of near misses and gray signals. Boundaries are blurred and the idea of one thing, in another state of mind, could have come to be interpreted as something else.There is a very common thought experiment in which the participant is asked to think of a hill of sand. Now remove one grain. Is that hill now a pile? No? Okay, remove another grain of sand. Is the hill now a pile? At this point, the trajectory becomes obvious, however, the question still remains troubling. What is really being asked is If a hill is a large pile of the same kind, what is the boundary between a hill and a pile?. What is the greatest number of grains of sand that a pile may have? And if you had the largest pile and the smallest hill side-by-side, could you tell the two apart? And if you can't tell them apart, are they functionally different?This entire exercise may seem frivolous, however, in our line of work, it's become nearly the entire problem. These flexibilities give the human mind an incredible interpretive power. Most inspiration seems to blossom out of misunderstanding, a sort of glitch in the mind. And so, for a single mind, within itself, this can be a tremendous skill. However, for understanding, this feature throws up a smokescreen. Understanding others and certainly understanding ourselves.And to only further shroud this already tremendous opacity, we are struck with the Statefulness of the human mind. It can be easy to think of the human mind the way we think of computers. Memories are just files stored in directories, sensation is just input interpreted and displayed on a sort of UI. But, just as steam technology failed to be an appropriate proxy for the human mind, computers still fall short of describing the human mind. Memories are more like sums of brain configuration, as though the memory was accessed through a resonance in the mind. It's also a pale metaphor, but, it's akin to binaural beats. The beats create a resonance because of how related they are to one another, creating a third tone between the two. Memories are not precisely this, but they are a sort of cognitive sum. And memories are far from the only thing in the mind that behaves this way. Context is incredibly important, otherwise known as State. This state has to be parsed as well when transmitting Cognition from one agent to another. What this state provides is a greater depth of meaning that the sum of the symbols used. Though the image of a dog may be what is being transferred, the meaning comes from the remaining context of the mind. A dog can evoke fear or comfort, depending on the state of the mind fixated on the image. And when transmitting from one mind to another, we do not want to miss the meaning content of the message. If a receiver in a transaction has a fear of dogs, but the transmitter has a positive association, it is more important that we exchange the meaning of the symbol to the mind of the listener, than that we transfer the image itself.This problem is solved by the Expression Layer. Leveraging the State Pattern Library (SPL) in ESS and a TPU-accelerated Ecchaus Machine Model (TEMM), popularized by the Template Brain Transfer Paradigm (TBTP), we were able to get a reliable formalism of the current state of the person's mind. Unlike old Bolister Tables (BT), TEMM provides a relevant intelligence to State Interpretation within the mind, as opposed to a format to fit your data into. This provides a less lossy transfer packet at each frame, summing out to a compression that has 0.00076% loss, well within the acceptable criteria (0.000912%) of the Otts Specifications.Our TEMM also has advantages over TBTP. The TEMM in TBTP is based off of an antiquated Standard of Routine. While stable at the time, the ESC's Standard of Routine model of the mind showed to be problematic as brain imaging got better and our understanding of the mind was sharper. The averages denoted in the papers have continued to drift, perhaps due to better measurement or, perhaps, due to subtle human evolution. While the MIT team that currently maintains the TBTP's Slate Brain updates their data set annually to keep as close to Otts Specifications, they continue to drift closer to the threshold not only for lossiness, but also for Thought Deviations and, they only update and audit once a year.Our TEMM is peer-to-peer, creating a library of users and building the Comparator Kernel from realtime data that is compiled every day. This accounts for variations in population using the technology, updates in imaging technology and inevitable mutations in the human brain. In the three years that we have been developing this protocol, we have never returned from the other side of our 0.00076% loss and +-0.002% deviation. Our results have been consistent, even as more adopters have taken to the protocol.The output of the Expression Layer is a body with symbol and meaning, that is ready to be transmitted. That body is split up into frames (which we discuss more in the Frame Anatomy Section) and is ready to be transmitted.

<-- c. Transmission -->

It may seem that the greatest challenges are behind us at this point, but, that is not the case. Unlike TBTP, our protocol does hit closer to the OS threshold for size. Brilliantly, Evan Walters, an engineer working in Kuwait right now that has corresponded with the team for the better part of this year, realized a way to simplify our processing. TBTP and VTP, as well as a myriad of other thought protocols, use the ESS Intermediary Node to handle communication between two exotic Electrobe Clusters. ESS IN provides a delightful API for handling batch packets from another ESS IN, but, due to the incredibly small Frame of Incidence of this system, multithreading is not a possibility. Was not a possibility, I should say.This set a hard limit of the amount of information you could reasonably send and not cause cognition disruptions. Each frame needs to process in 5ns, which is less challenging now, with contemporary hardware, but, a CTP frame is 10 times larger than a TBTP frame, and TBTP has been pushing at the upper thresholds of OS Size compliance.Walters realized, through his research on Electrobe Clusters, that there were several processes which required Electrobes to communicate directly, instead of through the Intermediary. The rest of us had always assumed every communication had to happen through the Intermediary, otherwise, why would it be there? However, there is an entire Reflex Protocol built entirely on the Electrobes' ability to communicate with one another, and, fittingly, this channel is called the Cluster Reflex Channel (CRC).The CRC is incredibly lightweight and very fast (5 - 8 times faster than Intermediary Channels, depending on the distance of nodes from one another), however, it is raw. Each electrobe has very little processing power internally, when compared to the IN, and so, there is no room for extra compilers or protocols within an individual Electrobe. The signals are incredibly raw and have much more to do with a series of amplitudes. Walters wrote an incredible paper documenting his discoveries on what he called Amplitude Symbols. The only two that we care about are WR and IP. WR writes a bit of data to the 4 bits of storage within the onboard RAM of the electrobe (if you can even call it that). IP sends an impulse to the brain, it's the actual moment of communication between the electrobe and the neuron it acts with.The Transmission Layer Frame of CTP consists of 5 Slides:

• Priming Slide (CRC)
There are areas of the brain that are not component in the experience of a symbol, but, that need to be activated in order for the brain to interpret the symbol. These areas are simple to identify and target. If they are present in the brain, you turn them on. Period. The Priming slide sends an IP signal to these neurons and then a WR signal to the contingent neurons, for use later by the Symbol Slide.
• Symbol Slide (IC)
Running much slower than the Priming Slide, the Symbol Slide stores the Symbol Name which is interpreted through the IC, converted into a brain specific signal that is then transmitted to the brain, providing a symbol without emotional/meaning content. When we transmitted just the Symbol Slide, receivers saw an image in their mind's eye, but only experienced their own associations with that symbol. The symbol slide uses the WR write the Priming Slide created to determine whether or not to fire an IP signal.
• Provocation Slide (CRC)
Provocation is much like priming, but prepping the brain for the Expression content of the transmission, lighting up related areas.
• Meaning Slide (IC)
Here is where the associative and meaningful information is transmitted. The process of handling this information is a little more challenging, as no two humans experience the same emotions precisely. The SPL eases much of this process, but, CTP comes with several protocols (discussed later) that further aide in the interpretation of these signals.
• Control Slide (IC)
Sends a CLOSE signal to the IC and prepares the system for the next frame.

Up to 250 frames can be sent a second wirelessly, with the most contemporary of hardware. Tethered, we've transmitted a maximum of ~3000 frames in a second, however, there is a biological bandwidth limit at the moment that makes the efficiencies provided by this speed fairly moot. As long as the framerate does not drop below 180 frames a second, we have seen no disruptions to receiver cognition.

<-- [appended] d. Foreign Symbol Interpolation -->

In the midst of our research, we uncovered one challenge we had not anticipated. In the early stages of research, we had a fairly homogenous group of researchers utilizing the technology. This homogeneity created a problem, however, once there was a broader release.During this early phase, we would encounter an unpleasant fuzz in the head on occasion with more complicated cognitive transfer. We addressed this issue as a bandwidth concern, but, it was, in fact, a knowledge one. Complicated ideas require a greater basis of knowledge than, for instance, shape primitives or natural numbers and if the receiving agent lacks the necessary knowledge structures to understand what is incoming, signals are sent to their brain in an erratic pattern that creates confusion.Edward Tyler set to work on what he referred to as Foreign Symbol Transfer. In the last two years, he has made massive efforts at allowing thoughts to be exchanged, regardless of Knowledge State discrepancies between the agents. At the effort's onset, it seemed that it was going to be a pleasant fix to a very real problem, but, as more code was written, it became apparent that FST was so much more.FST's primary feature is an interpretive algorithm that uses IMS to create a sophisticated model of the relations between symbols in the agents brain. When knowledge is transmitted that the agent does not understand, a request is sent to the sender. An exchange process then begins, writing each packet into the receiver's memory, allowing the transmissions to not interrupt their normal functioning. Meanwhile, the exchange process compares the FST Generated IMS Knowledge Tree (FKT) between the two agents, figuring out what knowledge is required for the receiver to understand the sender. Once a clear map of the necessary architecture is created, packets are sent to the receiver and the half completed memories are overwritten, the receiver now knowing everything that was transmitted to them.This process is very demanding and does still have its limitations. If required knowledge is too disparate, the process will abort. This doesn't seem to create too much of a problem, basically just leaving the receiver with some 'fuzzy memories'. But, when it works, an agent can rapidly learn a topic from this communication.

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<-- 2. PRACTICAL APPLICATIONS -->
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The major use for this protocol seems to be immediate, pristine communication between two agents. This would expand efficiencies in interpersonal interactions, both in personal and enterprise settings. But, our research has yielded a few more promising outcomes.

Learning

Because of FST, we found outcomes of accelerated learning. Coursework could be consumed over a span of an hour, instead of dozens of hours. There is currently a team looking into expanding efficiencies and applications behind this outcome.

Expanded Empathy

Subjecting people to CTP tends to expand their appreciation for types of other experiences, thus decreasing xenophobia, sexism and general selfishness. A number of social workers and therapists have taken interest in CTP as a means of interpersonal mediation and understanding, when the usual strategies do not yield desired results.

Therapy

CTP can serve as an incredible opportunity not only to communicate with others, but to also provide clarity on one's own internal state. Several applications have been created to help patients with severe depression, anxiety and PTSD, both through Augmented CBT and a new process called Cognition Modeling, where a therapist models productive cognitions, explicitly teaching the patient some new thought strategies. Research in this field has yet to yield enough data to be conclusive, but, it is a promising application for CTP.IN SUMMATIONCommunication is challenging and lossy, especially when the channels for communication are as primitive as they are, converting experiences into phonemes and graphemes that inevitably compress and confuse the experiences. With the Cognitive Transfer Protocol, we seek to have a more direct transfer of experience, not needing to convert it so far from its source material. We fully appreciate that our contemporary understanding of physics seems to indicate that perfect information is impossible, but, with CTP, we hope to make communication as efficient, effective and clear as possible, with appreciation for physical limitations.