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HOW DOES G-101 ALGORITHM WORK?




Computer algorithms work based on input and output. They take the input and apply each step of the algorithm to that information to generate an output. For example, a search engine is an algorithm that takes a search query as an input and searches its database for items relevant to the words in the query. Common examples include: the support of analytics to determine investment values, the means we use to solve a long division problem, the functionality of a search engine, and even the process of doing laundry are all examples of an algorithm. Indeed, the evolution of life is a full display of algorithms directing the actions of cells that make up our bodies. This means that below the level of consciousness there are trillions of algorithmic processes constantly occurring within our bodies. There are six steps to creating an algorithm: (1) Determine the goal of a set of defined rules (2) Access historic and current data. (3) Choose the right models. (4) Fine-tuning. (5) Visualize your results. (6) Running your algorithm continuously. In this framework, there are four types: (i) Brute Force algorithm. (ii) Greedy algorithm. (ii) Recursive algorithm and (iv) Subjective Probability algorithm. The Subjective Probability algorithm (“SPA”) is what G-101 is based on. The term and definition behind SPA were conceived by an affiliate of Northridge Corporation more than 25 years ago and a leading pioneer in the field. Our version of SPA G-101 under the SPA format is the closest approximation to consciousness. However, no computer or artificial intelligence will ever become conscious. But a form of consciousness under a “best guess” scenario can be created by the execution of software with an exclusive data pool or rhythms of quantifiable statistics to answer a specifically defined question. How G-101 works: Through accommodation, G-101 may generate information that is initially conceived as unreliable, false, unorthodox, or misplaced (identified as “novel data”) only to be unified by cognitive dissonance to mitigate the conflicting data by either updating G-101's interpretation of the model to disqualify the variable or assimilation as factual. Thus, the new model within the G-101 matrix perceives the added information as useable or as a discarded sequence for further interpretation. When the model is updated, the novel information is no longer an anomaly but a new chain of data. By expanding the data fields, G-101 literally establishes a new category that is able to redirect the program, in essence, to change its mind, keeping what is known as facts but updating the interpretation. Thus, settled viewpoints and new values create classified estimations that rate the new data with a confidence factor whether it is trustworthy or falsity. Within the subjective probabilities, matrices are sets of “attitudes” which are combined to generate a neoteric equivalent of thinking. Therefore, the underlying factor of the accumulative data generates a perception, which when applied with the support statistics creates a conclusion, which was not available in the initial analysis. It’s like saying, “the G-101 algorithm can change its mind.” Indeed, calculated facts and persuasion become entangled with the corrupted data. Therefore, typical Recursive Subjective Probability algorithms used for evaluating investment values are only reliable 47% of the time. You would be better served by flipping a coin than applying an expensive algorithm to answer investment-based inquiries. This fact further illustrates that Wall Street is a high-class betting parlor since perception is more important than fact. There is no truth only perception. The reason for this chaos is persuasion. When data is revised in stages it allows for conventional thinking to be modified, which usually produces novel results. There are computer systems that actively guide and systems that actively misguide. G-101 treats persuasion as a presentment proficiency tool (“PPT”) to establish a more coercive standard. The conclusion behind this element is that data is only useful when its success rates can be measured. Allowing an algorithm to change its mind sounds impossible, but its intention to understand the higher truth increases the chance for success. G-101 is a mind-bender and within this context, its level of reliability has no equal. LEARN MORE ABOUT G-101 ALGORITHM. Ask for Report G-101 – Success is Dependent on Effort.

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