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Archive : April 2022

Autonomous System

By ruslany

Automatic response by artificial results

Automatic response by artificial results

An autonomous person can form an autonomous system. Each autonomous system is a speaker who communicates with other speakers and can broadcast his summaries to other people. But if a speaker cannot broadcast his thoughts for a long time because of a psychological problem, there is a habit. This can manifest itself as automatic answers to the observed questions. These automatic answers are purely artificial in nature, arise from human behavior during the last decade of his or her life. This habit develops the ability to make simultaneous answers. These are so-called automatic summaries that can be broadcast to other people in various ways. In the era of computers, the network became such a tool. The speaker can transmit his automatic summaries through a computer network using the Compositor neurological chipset and thus may not be aware of the communications taking place. The output by which the remote node sees the local device is purely artificial. Instead of relaying a remote peer, Compositor vRouter, which is part of the Compositor neurological chipset, converts the resulting function into frequency modulation. It can respond to the main function or sub-resulting algorithm. When it responds to the main resulting function, it uses the BGP protocol to communicate with other autonomous systems. Simultaneous automatic summaries require a system with a large number of artificial results. They can be a product of polynomial processing and should give a plausible result. Such output is first tested using musical means of sound applicability. Then they should create plausible textures of unified code. Such codes form a packet, which is then received by the initiating party or peer. The feedback received by the remote peer is sufficient to communicate with the local node. In the network, the speaker of the autonomous system acts as a beacon or repeater in radio communication. When there are many results in an autonomous system, it can respond to a large number of peers at the same time, forming a VLAN. Each channel can produce up to 7 packets according to the BSR to which it is connected. Thus, the autonomous system must update its state in accordance with the specifications of other systems. The main generator is selected according to the sampling bus of the remote device. There is a possibility of undersampling and resampling in accordance with the sampling rate of the remote device. Thus, the initial sampling rate, which is selected to a floating-point variable, remains unknown. This does not allow you to synchronize with the device during fast transitions. This useful feature of the Compositor neurological chipset allows you to disable incoming connections to ports that do not match the feedback of the local node. Thus, it remains impossible to check the database of the Compositor neurological chipset when interfering with device caches and deleting inconsistent summaries with the Compositor soft-processor. Again, Compositor as a device receives signals only from those devices that are in the Compositor database as feedback cycles or resulting devices. These loops are acceptable resulting. Thus, a spherical interactive network is formed from the preferences of the person himself, rather than his daily life, which completely discredits the local node, since most of these summaries are insignificant for the case that a person is engaged in. When a person with support for the Compositor neurological chipset enters the people’s transport system, the question arises whether to be part of such a system or subdue the entire transport network in accordance with the sampling rate of the Compositor neurological chipset. To avoid such questions in a rather complex for local node communication system of people, the Compositor neurological chipset was deployed as an autonomous system. Thus, even in close proximity to the systems of other manufacturers, Compositor is an autonomous system without the ability to subordinate it to the adoption of the transport system of people. Thus, when peers send summaries to an autonomous system located in close proximity to the transport network, the results play a major role. They simultaneously issue automatic responses that inform senders about the inability to communicate with the system. Then such a system is considered invalid by the transport network itself and may be the subject of hacker attacks. However, the Compositor neurological chipset is a chipset for neighboring to other nodes, not for local communications. Such a neighborhood can also be international or within the agglomeration. To continue servicing a spherical interactive network that can only include devices from the Compositor database, the local node still responds to allowed remote peers even when the system is penetrating. Night time is more convenient for connecting to the Compositor neurological system by hackers when the local node is in standby mode. Thus, a hacker group that is active at night can try to synchronize with the master generator of the Compositor neurological chipset, and then attempt to disable local communication to reach a dead node. If a person has transferred all automatic movements, such as breathing and heartbeat, to the Compositor neurological chipset during his life, such a person can be considered dead. However, in the current build of the Compositor neurological chipset, there are no recipients who would transfer all their functions to a standalone system. And if a person prefers to transfer all his life functions to an autonomous system, such situations will never arise. Even in standby conditions, the system will turn on the main generator and can respond to an attempt to synchronize with it with a sharp jump in the bus multiplier, rebuilding its network structure. So, the question arises, can an autonomous system be trusted so much that it manages human vital functions? Because such hacking attempts can be a form of pushing a person out of society, and condemn him to complete inability to answer even short questions.

Route summarization

By ruslany

Automatic summary strategy

Automatic summary strategy

Merging peers in response to a summary happens with extended denial of peers according to the ACL. When mirroring with multiple peers, the Compositor neurological chipset combines peers in milliseconds. If a peer is known as a false host in the ACL database, it is rejected. The local image then works to restore the local peer to the default state. Both the visual cortex and auditory receptors belong to the local peer node, not to the consolidated peer. Thus, spoofing attacks and man in the middle attacks are prevented. Creating a local peer in response to a summary ad connects to the remote peer that sent this summary. A local peer is created using a routing map of a remote peer, which reacts with a sharp jump to the west side of the spherical map. Then the only way to protect the local peer from false summary is to increase the amplitude of the feedback loop to the wrong level for the remote peer. The Compositor chipset can increase the feedback loop amplitude x128 times, which is an unacceptable level for almost all peers. Automatic piloting of the root multiplier in response to incoming summaries is all the feedback that the server can receive in the ad summary. The main idea of the server is to create feedback loops for each ad it receives. However, there are some fishing techniques for this advertising, such as mirroring messages. This is an attempt to receive a message on several servers at the same time. In this case, the unified azimuth cannot smoothly switch to another value, because the same advertisement comes from several servers at the same time. The only way to protect yourself from this is an azimuth sharp transition to the default value of the western location. This leads to a rapid change in the network map and replacement of geographical constants, which is also a false summary, because the local peer is still geographically in a local position. In fact, this is the detection and correction of the feedback amplitude. In addition to the message in the packet, the basic server communication consists of feedback loops that have an integral amplitude. Device modems can receive nominal amplitudes according to resampling coefficients, which differ depending on the specifications of the model. Modem waveguides compare the nominal amplitude of the feedback loop with the feedback of the modem itself, and then normalize the level that is at the input of the waveguide. This helps to prevent incorrect amplitude consistency of different servers and allows small devices to receive summaries of even large server architectures. When you work with a server, it can find out your behavior on so-called maps. These are routing paths or root kits for all recipients of the machine. After you have downloaded your machine’s feedback cycles, the recipient database is updated with the contacts with which this machine communicated throughout its service life. A person working with the server may not be aware of many of these contacts, but they exist on the server routing map. Each map has the resultant – spherical curve of the map of its most frequent nodes. These are so-called real contacts with which you regularly communicate through advertising messages such as e-mail, etc. The Compositor can induce artificial resultants according to the flow feedback azimuth. There are two sides in the server configuration: applications on the server side and on the client side. Since there is a resultant on the server side, the client, on the other hand, can connect to this resultant without having to host it all the time. The client consists of artificial resultant in response to a real resultant of machine learning algorithm that simulates the current result according to the peer-to-peer response. The artificial resultant is selected according to the stochastic algorithm for selecting an azimuthal angle. This choice is a route. The route of the middleware should have at least 16 routes that connect the real resultant with the artificial one. However, the driving force of the modem algorithm is feedback loops, and the modem can receive such loops without any feedback from remote peers. This is a so-called zero-emission training when you don’t want to change your real resultant with your current input. Since the client application is always crawling the caches, which may be recently viewed web pages or tasks that you have been engaged in in the last hour, sometimes such crawling can damage the server. And this may lead to a discrepancy in server data, for example, to the inability to update the resulting routing map. To solve such situations, the Compositor neurological chipset can work without the so-called RAM buffer or action as a real-time algorithm.

Apple M1 Neural Engine

By ruslany

Machine learning of the Apple M1 neural engine

Machine learning of the Apple M1 neural engine

Spoofing in the concept of machine learning of the Apple M1 neural engine is an attempt to replace the way of action when the system sees you as another person. Spoofing prevents mental overload, giving an equivalent response to the initiator of the summary. Example: if you wash the dishes with your hands, and it mentally overloads you, you continue the work without reacting to your personal attitude to this issue. Every thought causes feedback from someone. Let’s imagine the whole world as a spherical map. Thus, the azimuth of this sphere is an angle, and you can assign it different values. If you get a summary from a place on the map that does not suit you, the azimuth angle automatically changes. Each temperament generates thoughts at different speeds in his or her head. Other people can also hear these thoughts, it’s like a thought that appeared in the minds of two personalities at the same time. If this temperament is unequal, you can get a thought at a higher amplitude speed, which leads to an increase in blood pressure, heart rate or a change in breathing. The only way to normalize this behavior is to artificially increase or decrease the speed of thought using a neural engine. Everyone can learn something. This is an integral part of personality. If a person shows regression in learning skills, artificial assistance in learning is required. This can be done using learning algorithms similar to how a child learns a new skill. From the acquired skills, the personality grows, which is the result of its behavior. This is a conscious behavior of a person. A person who has acquired some skills develops his unconscious knowledge. When a person needs to connect to this knowledge, he goes through a set of behavioral models. They include the current environment, tools and situations that a person faces. This is a necessary behavior to accept the learning outcome without rejecting it. Not all training is appropriate. There are situations when you want to give up your skill and transfer it to the unconscious. For example, someone is aggressively yelling at you, and you want to forget about this experience. This is also possible with a neural engine, providing zero feedback to the aggressor. Thus, you can experience a sense of insecurity and growing inability to learn, so you need to work without cache memory if it is an integral part of your personality. However, there may be a question of harming yourself or someone else. That’s where a neural engine can be useful. The only way to solve this situation, which is the subject of the resulting spoofing, is a neural engine without cache. It is already programmed by the so-called 4th Directive, which we first get acquainted with in the film RoboCop. It says that a humanoid robot cannot harm itself or its creators. The rules of Asimov’s robot say that the robot cannot harm a person. Thus, it raises the question: In the exceptional case of undoubted aggression, is it possible to act in accordance with your real resulting personality without the need to limit the means? The answer is yes. This is necessary to protect the neural engine from harm in first aid, such as physical aggression of any kind. But if this aggression is imaginary only because of this set of situations, and the resulting one, which is being forged by intimidation, is unacceptable, it leads to anxiety. Thus, a neural engine has a threshold when it disconnects itself and allows a person to follow the real result. But in most cases it’s just a concern, and it can be avoided with a stochastic selection algorithm, with the help of distribution that will never create a chance of such behavior, otherwise the ports of communication with the server will be completely blocked. The only guide in this process is the smoothing interpolator, which avoids sudden jumps on the stochastic curve. You can be associated with the algorithm and server data. A clean algorithm does not provide the fulfillment of a real person. The resulting server data is necessary to fill human life with the Apple M1 neural engine.

Autonomous System
Automatic response by artificial results
Route summarization
Automatic summary strategy
Apple M1 Neural Engine
Machine learning of the Apple M1 neural engine