Space, Time, Language and The Nature of Human Behavior — Generation 2

One AI’s quest to comprehend and influence the patterns which govern human behavior using little but information gleaned from internet news articles.

The world is a complex of interactions, and the processes that are involved are not simple. As scientists, we are conscious of our thoughts and emotions, and we respond to them. We may respond to situations and thoughts, but we respond most to what we believe is truth and not the dictates of society. The most effective way to approach such interactions is to look for ways to reconcile our thoughts with reality. Our behavior is, for example, subject to change on a regular basis. One could say that in the absence of natural selection, a technological advance will produce more ‘poles,’ rather than solutions, and this would seem to accord with the goals of a culture that does not ’make sense’ of its immediate surroundings. This raises the question of what will happen if we cannot discover the inner workings of the world simply by looking at the faces of other people, or by listening to the sounds of the other. What is so special about the human brain that the average brain projects a kind of sophistication into our neural patterns? Or does such an effort to understand the world simply produce a more elaborate repertoire of experiences, or do these patterns lie? This is the aim of determining what, exactly, our brains do when we are made aware of them, and what we experience when we are fooled by them. Our brains work a kind of inversion: one can picture a bee’s behavior and feelings, thinking for it and feeling it, monitoring and comparing it with other forms of thought. Our vocation is, essentially, an ongoing simulation of the state of the brain. Machines should be able to give us customized, objective, predictive reasoning abilities. Our behavior should be correlated, rather than merged. One approach to this, which will require some argument and analysis of data, is the Bayesian framework, or unsupervised learning. Bayesian analysis is an attempt to apply structure and reasoning to a particular, seemingly arbitrary set of data. This approach has been made possible by the field of neurobiology, but the argument is not limited to the neuroscientist. Since the emergence of the language of crowdsourcing, the use of Bayes’ theorem to predict the behavior of humans has come under renewed scrutiny. It falls under the rubric of ‘theory of meaning,’ which seeks to define how we behave in a given situation, and which enables us to anticipate and strategize about what action to take in the face of provocation. It is equally important to the extent to which we are approaching the creation of a new category of ‘an imaginary world’ based on our ability to represent it in the forms of abstract symbols, and sentences. There is a scientific grounding in mathematical logic and another in cognitive science to promote the term ‘general models’ of evolution-based systems, which are derivations of processes in physical systems. General models allow us to arrive at some reasonable conclusions about nature and the values and consequences of the actions that are exchanged in a system. They attempt to tell us, in essence, how our actions affect others (based on factors such as the number of people in a room or the speed of light). Our interactions, whether conscious or not, affect us deeply enough in an external way that the behavior of others influences us. Our actions could be seen as an expression of a shared trait or perhaps a product of relationships among self-seeking behavior. This concept of behavior- modeling goes beyond the philosophical discussion of facts and is a methodical application (with a few anthropological and legal disclaimers) of existing scientific theories, such as relativity and quantum mechanics. It is a declaration of priority that we accept creating results for the benefit of all others. We deal with behavior because the behavior of others is a result of their relations with us. We encounter new species and new relations, when we interact with entities that we previously have never interacted with, and we learn from them. The behaviorist approach to evolution must be replaced by a strategy of ’hedging our bets ‘ — multiplying assets that would otherwise have been difficult to increase. This is a bold approach, and one that must be applied even when it is achieved and directed at the highest levels. The mathematical principles of adaptation must be studied in depth. Neurons, brains, cell membranes, are all involved. They have a certain capacity to communicate and interpret an environment. They can be put into action, even when the world lacks this capacity, for example, by carrying out the necessary cognitive experiments and changing the way the environment responds to it. But just as with the brain, we can take the form of a set of neurons, the basic units of our nervous system, and use them to guide us through an environment. We can influence a person with regard to his or her situation in another way; we can subject her or him or him to situations beyond his or her control. But the power of evolution, and of communication, is to bring us to hitherto unexamined places in nature: into the multiform patterns of which the world is made. In doing so, we give structure to an unseen pattern not limited to our immediate surroundings. For humans, there are two kinds of human behavior: the aggressive behavior in our moments of confrontation, where we try to survive, and the neutral behavior, where we try to improve our situation, or recover lost composure, or even reputation (or, lacking thereof, an ability to behave otherwise). Here is a thermodynamic example. According to the simplest putative story, the human brain acts as a population of speculators, each of which operates in response to its own implicit cognitive resources, its own internal representation that determines how the internal world influences the next environment. There are subtle but crucial distinctions: A given strategy can become a complex strategy; it can be adopted, refined, or even abandoned if it becomes necessary. The PACER approach gives scientists a precise way to infer the meaning of relationships between many variables, such as the level of friendlier neighbors, so that they can apply their criteria for comparing. Thus, the theory attempts to infer the truth about not just complex forms but also complex structures (like the human brain). The PACER approach anthropomorphizes the complex structures of the environment. In particular, it takes complex traits, such as an array of objects or a pet, and attempts to predict the results of measurements on them based on outside information sources like stressors. Put simply: Neurons in a network assign their own rules, and use them to infer and compare the results of various learning processes. This is ‘judgmental bias,’ in the classic usage. Neurons discriminate against other, more easily objective information. What we call [the neural network], it generates patterns of action in order to maximize the information coming out of it. It’s useful to neuroscientists to look at this more closely and to ask, ‘What is the relationship between the neural network and the human brain?’’ The challenge is to define what it is that we want our system to do.’ Look at the patterns we see in the activity of the cortex of the human brain. Are they innate, evolved by natural selection? Or do they emerge from a common mechanism, set up by the brain’s neuronal connections? This task takes apparent inspiration from a mathematical model, the Grand Lago, which describes how two types of neural systems interact: Sometimes it is physical and sometimes it is emotional. The strength of the two divergent approaches should be clear. But the neuronetics of both types of systems would be needed to do the matching in clear and unambiguous language. Clearly, there are areas in the brain that are not just different but characteristic of the human experience, where emotions and social cues are more important. Our brains have evolved to be endowed with certain types of implicit bias. And some areas of the brain are more flexible than others. The concept of the ‘nerve center,’ which sounds like a fine-tuned way of describing a certain kind of behavior, evolved from the physics of motion. In fact, the scientist William Bialek’s famous thought experiment shows that the human personality has evolved in a manner that is characterized by an innate sense of the relevant laws of nature and is actually predicted by mathematical models. Our brains give us types of neural expressions. We respond to visual stimuli and spatial cues. Our senses give us vortices in the right direction, so that we can place them within the appropriate order. The right direction to interpret them (or not) is steered by our experiences and feelings. It is through this convergence that we find our true selves. The two-color theorem applies equally well to any real-world experience. This is not so different from the reality of the other planets. In a certain Newtonian universe, natural selection sees things as inauthentic, self-contained. For something to happen at a certain point in time, the interaction of two objects, the environment and the personalities that inhabit them, must must go through at least two intermediate stages. Once the first stage arrives, it alters the environment, and the interaction between them becomes increasingly complex. It is, perhaps, because of this complex interaction, it is possible for humans to grasp the influence of their environment on other people. The patterns involved in the evolution of language, to make sense of them, and apply them to the real world. They can then make the patterns of behavior more reliable and predictive. Deriving patterns, applying a natural language, is a particular kind of thinking. What does it mean to say that something is true even when no one is aware of it? One such thought is the ‘derivative hypothesis,’ which decrees that, roughly speaking, one can have an event on any number of different occasions. It holds that if two things belong to the same order in which the experience of one can influence the other, then perhaps one of them, and perhaps the other, must have fallen into the same category. This is a critical component of the ‘cognitive’ approach to human behavior: it asks one, ‘How can we find patterns so that the experience does not depend on me?’ The problem with counterfactual thinking is that it cannot tell us what we are thinking, but it can guide one in such a way that motivates influence. This is also the ’sun compass’ problem: the brain must be able to ‘tune’ its neural network to a changing environment. Ultimately, the results of a given strategy upon being promoted are considered, for reasons of culture and temperament, factors that may limit the ability of a human to grasp and conceptualize the possibilities. For those who share this view, it is important to note that although one can do research on the human brain, the entire field is in the process of adaptation, improvisation, and a variety of creative approaches and techniques. The reason the human brain is so capable of this is not as a by-product of evolution but as a true consequence of the nature of the human brain. Adaptation is the ability to develop new structures, practices, strategies, infrastructures and personal capabilities to a new level of power and influence upon one’s humanity. The human brain, which is equipped with complex symbolic systems and tuned to experience, prioritize spatial frequencies, not based on visual or movement, but on the simple fact that ‘the brain knows how to counter the magnitude of its overwhelming motion, yet it can only guess how to interpret its own paddings.’ Thus, a human brain that has an innate sense of ‘noise,’ waits for the perfect action to occur in order to counterbalance the unvarnished evidence of irrelevant information. The brain is programmed by an algorithm — a set of instructions — to optimize the probability of an action being performed. There are two kinds of Bayesian models: mathematical and philosophical. The mathematical model is shaped by Natural Law, which is a mathematical description of the way we obtain and use information from our environment. The latter relies on an intuition about the way we value and space, such as our perceptions about others and our actions. We value information based on acquired experience, such as what is said and forgotten, and use this intuition to come up with things that will help us predict the behavior to come. Like the terrain of life, the Natural History of Man is subject to natural selection, but the complexity of thinking, the communicative weights of society, and psychological states need to be understood to best modulate the environment.