Degrees and States of Satisfaction and Dissatisfaction - I hypothesize that every variable on Maslojw’s chart 1) can exist to a degree, with maximums and minimums, 2) from a negative side, to a positive side. This understanding makes it seem like there is a mathematical expression for these factors. It is true, there is. In fact the mathematics used in the game Sims harnesses the Maslowian doctrine. In this article I will review how this is so, but note that: these expressions may have already been put together in better formula than introduced here.
Intentionality -- Each factor on Maslow's hierarchy has intentionality directly linked into its Nature. For example if I intend to live without shelter, then I am intending to be out of sort with one of Maslow’s presented “needs.” Another simple example is if I choose to be a vegetarian I am choosing a kind of method by which I WILL select my ends of hunger.
1) THE PRIMARY LEVEL - At this level we see that basic impulses in Mankind towards ends which either satisfy, dis-satify, or are of a neutral quality, in casual relation. These are grounded in our biology. Example: The stomach objectively exists and it is required to satisfy hunger (a whole body process) through digestion. Each objective biological function has an evolutionary history, but I will not touch on that subject in this article. Hunger precipitates a satisfaction response. However, it’s not that simple. In order to satisfy hunger, one must have motility, know where edibles are, collect edibles, prepare them, and then go through the whole process of ingestion and digestion. There is a physiological and biological basis for this drive, hunger; and there is an external source of satisfaction primarily coming from modern economic production. Like all other kinds of biological variables, these can be satisfied or lack satisfaction. In the first case causing satisfaction in the second causing greater hunger, starvation and ultimately without satisfaction, death. I will treat thirst the same way as hunger and so I will not elaborate on that factor here. Air Quality -- Due to the modern world's carbon emissions, air quality is certainly a factor that can be manipulated for the collective good. How? Through the use of methods that increase good air quality, lower pollution, we can all breathe in “positive” air, clean air.
Shelter -- The conditions of the environment make it so that surviving is easier with a constructed home. Most animals do not construct their homes, but there are a few instances where sheltering is part of another species basic nature. As long as there are harsh conditions in the environment there will need to be some comfortable settings built for people to live within. 2) Safety needs -- Safety needs are things which exist in higher forms than they do in the wild. Gathering herbs for yourself, turns into gathering herbs for a corporation. The company you work for, some kind farming institution, then sells and profits off that work. Employment therefore is a given need within any community that is working together to make survival and thriving possible. *Although shelter might be considered a safety need, Maslow classifies shelter as a more fundamental need. Property (and rights to it) -- Under the constitution, a philosophy of the 18th century colonists, the U.S. has property rights and laws. Democratic governance is made to demarcate personal and public property, and the modern capitalist economy exists to allow free marketing and buying of resources. Money itself may be seen as a property, and we can undergo such disputes in the courthouses (where justice is actualized) to partition the rights one has to some kind of money or personal assets of another. In other situations, rights to protect property are given in the "right to bare arms". The right to hold or bare a gun is the same as the right to protect your property from unlawful theft.
3) Love and Belonging -- Maslow meant for all of his factors to have some realism to them, in that they realistically represent human biology; treating each on how it might influence human psychology. Sometimes, social relations in the world are the only way people are capable of surviving. The whole of our modern economy is based on how we interact together in both observable and unseen ways. In a more primitive society, relations need to be made, just as much as one needs to drink water, for without interactions between individuals there can be the break-down of one’s “biology," and well as "psychology". Socialization precipitates a satisfaction response. However, it’s not that simple. In order to satisfy one’s social needs, there need to be people or animals present in the environment that offer suitable and desirable relationship opportunities. Bonding positively, with relations being happy (mood), innocuous (lack of abuse), and have other features like shared intentions, shared feelings of affection (communicated through speech and touch). Negative relationships are those relations that have discontent, dislike to hatred. Negative bonds do not form necessarily from “sadness” as sympathy and compassion can relate back to a person experiencing the negative feelings. Other negative feelings such as “anger”, “boredom”, and “discontent”, though are more prone to cause social aversions and negative social “bonding”. Family members are said to be a set state of bonding, either negatively or positively, whereas people that know each other outside of the family are more likely to experience “social repulsions” (as opposed to BONDs). “Romantic Involvements” -- One flaw of Maslow’s hierarchy is that it places reproduction below socializations. I would even say that it should be a level of its own, for positive social bonding is a lower form of “mutual love” in a relationship than that of “romantic love”. 4) Esteem -- Right after the social needs comes the esteem needs. Of course anyone could think that without socialization there is no esteem; so they are dependent. People that receive compliments for their achievements are earning double the esteem and are also earning popularity or social relations. Esteem sets us up for achieving the highest goal (according Maslow), self-actualization. Education -- Learning is a constant, ongoing process. It is a process in which information is learnt, and subsequently applied. Some information gets lost, other information gets stored in long term memory(implicit memory), and is used in declarative and explicit memory. Implicit memories are learned subconscious activities that take place while we are doing a higher level task. For example, writing something requires knowledge of the alphabet. The later comes before the former, in the case of how we are educated to do these things. Writing is explicit to having memory of words and constructed sounds (these being implicit eventually in human education and development).
Liberty -- Freedom, may come in many forms. One form is the freedom to be whatever religion you chose to adhere to. This freedom comes as a basic right for some of us, while in less secular worlds this liberty may be lacking, or even non-existent. Forced religion, is prominent in some parts of the world still today.
5)Self-actualization -- It is a moment to moment thing, but if we are to think of ourselves as having a distant future, maybe in the ball-park of years time, we can set up ideal’s for ourselves, or future goals that we want to reach. Self actualization can come from having an idea in your mind of what you want, and then going about getting, actualizing the intention. Maslow meant something more superior than this, when he put this into his chart. He meant actualizing a “self” that has characteristics uniquely expressed. __ *Addiction -- One thing that Maslow doesn’t account for is that human beings are naturally given to become addicted, or to abusing substances. Addictions all have a biological basis, and may be seen as either harmful or helpful. An example would be drinking caffeine compared to injecting heroin. It can also be posited, that if the addiction doesn’t interfere with the rest of personal goal orientated behaviors, or the rationality of the person, then that addiction may not be irrational, i.e. harmful. Some addictions are harmful to self as well as to others, in which case we get a double negative (and it isn’t equal to a positive when added up) -- a lose-lose.
Written and Edited, by Josh Alfred *Note, book and my kindle account were taken down for copyright use of the book cover image. What you read here is an abridged version, that just so happened to be saved. All other texts linked to this book have been deleted. (LINKS IN BLUE FOR MORE INFORMATION)
Introduction - Future and History of the Computer
The existence of the computer in combination with machines has given rise to the age of the robots (2020+).
This age might exist for a very long time, leading to a technological singularity, culminating in a point where we can not longer conceive of or predict the future thereof.
There are many scientists and working futurists that say that such a point is in the near future, within at most 100 years, some say just a decade or so.
Here is a list of unfulfilled expectations for computers:
1958, H. A. Simon and llen Newell: "within ten years a digital computer will be the world's chess champion" and "within ten years a digital computer will discover and prove an important new mathematical theorem." 1965, H. A. Simon: "machines will be capable, within twenty years, of doing any work a man can do." 1967, Marvin Minsky: "Within a generation ... the problem of creating 'artificial intelligence' will substantially be solved." 1970, Marvin Minsky (in Life Magazine): "In from three to eight years we will have a machine with the general intelligence of an average human being."
This doesn't stop us from continuing to imagine a world where robots will be smarter than us.
“There is no reason and no way that a human mind can keep up with an artificial intelligence machine by 2035.” —Gray Scott
The children of our engineers, robots, have evolved from the most simple logic machines.
After working on his revolutionary difference engine, designed to aid in navigational calculations, in 1833 he realized that a much more general design, Engine, was possible. The input of programs and data was to be provided to the machine via punched cards, a method being used at the time to direct mechanical looms such as the Jacquard loom. For output, the machine would have a printer, a curve plotter and a bell. The machine would also be able to punch numbers onto cards to be read in later. The Engine incorporated an arithmetic logic unit, control flow in the form of conditional branching and loops, and integrated memory, making it the first design for a general-purpose computer that could be described in modern terms as Turing Complete.
The machine was about a century ahead of its time. All the parts for his machine had to be made by hand– this was a major problem for a device with thousands of parts. Eventually, the project was dissolved with the decision of the British Government to cease funding. Babbage's failure to complete the analytical engine can be chiefly attributed to difficulties not only of politics and financing, but also to his desire to develop an increasingly sophisticated computer and to move ahead faster than anyone else could follow. Nevertheless, his son, Henry Babbage, completed a simplified version of the analytical engine's computing unit (the mill) in 1888. He gave a successful demonstration of its use in computing tables in 1906. (wiki.org)
In 1941, Zuse followed his earlier machine up with the Z3, the world's first working electromechanicalprogrammable, fully automatic digital computer.
Program code was supplied on punched film while data could be stored in 64 words of memory or supplied from the keyboard. It was quite similar to modern machines in some respects, pioneering numerous advances such as floating point numbers. Rather than the harder-to-implement decimal system (used in Charles Babbage's earlier design), using a binary system meant that Zuse's machines were easier to build and potentially more reliable, given the technologies available at that time.The Z3 was Turing complete.
The Mark 1 in turn quickly became the prototype for the Ferranti Mark 1, the world's first commercially available general-purpose computer.
Built by Ferranti, it was delivered to the University of Manchester in February 1951. At least seven of these later machines were delivered between 1953 and 1957, one of them to Shell labs in Amsterdam.
In October 1947, the directors of British catering company J. Lyons & Company decided to take an active role in promoting the commercial development of computers. The LEO I computer became operational in April 1951 and ran the world's first regular routine office computer job. (wiki.org)
Digital computers deal with mathematical variables in form of numbers that represent discrete values of physical quantities. The advantages of digital computers are that they are versatile, reprogrammable, accurate, and less affected by outside disturbances. In contrast to analog computers, digital machines work on numbers. Each variable is converted into numbers and each number into binary form, i.e. 0 and 1. It is this combination of 0 and 1 that does all the calculations. All modern computers, laptops, and calculators are all digital computers. (brighthubengineering.com)
The new field of AI was unified and inspired by the appearance of Parallel Distributed Processing in 1986—a two volume collection of papers edited by Rumelhart and psychologist James McClelland. Neural networks would become commercially successful in the 1990s, when they began to be used as the engines driving programs like optical character recognition (visual recog) and speech recognition.
___Rest of introduction compromised.
Industrial What are the implementations of robotics? These will be covered in the contents of this article.
By 2025, the market for industrial robots is projected to balloon to $33.8 billion. To put that in perspective, in 2016 the global industrial robot market was valued at $12.3 billion. So in less than 10 years, the market value of industrial robots could nearly triple. (recode.net)
By 2019, more than 1.4 million new industrial robots will be installed in factories around the world - that’s the latest forecast from the International Federation of Robotics (IFR). Currently, 70% of industrial robots are working in the automotive, electrical, metal, and machinery industry. (robotiq.com)
Worldwide shipments of robots came to around 294,000 units in 2016, up from around 159,000 in 2012. (statista.com) At this rate, which should be significantly exponential, we can expect by 2020 units will be up to 500,00-600,000, by 2028 nearly 1mil. However, this doesn't account for the new forms of robots being developed.
These robots are capable of a much vaster number of mechanics and therefore are capable of more economic placement. Such robots will have a larger economic impact, taking many of the jobs that we will be looking into in the later parts of this book.
In Japan, there are 1,562 industrial robots installed per 10,000 automotive employees. (statista.com) Japan is leading in its industrial robot per worker ratio.
Gathering and Manufacturing–
Robotic Farming – Machines will able to plant and pluck vegetation. It will be able to herd animals, feed animals, and slaughter animals. With visual recognition systems within the robotic programs, when fruits and vegetation is ripe the machine will be able to pluck them. When animals such as pigs and cows are ready for plunder the robots will be able to detect this, and like sheep dogs, but far more articulate, will lead in the farming industry.
The goal of the human engineers and farmers will be to construct farms that are robot accessible. This will emerge a broader field, within which robots can be created and made operational.
A single planter can plant thousands of seeds per hour. Just one hundred of these PLANTERS can increase speed of crop production times 100,000 or even more. PICKERS can also harvest much faster than human hands, with no need for breaks.
There is a promising future for these types of robots in the farming industry. According to engt.com there will also be robots built to kill weeds, WEEDERS.
Mining – Robots with the right maneuvering will be able to visually recognize resources and collect them for later delivery, storage, and manufacturing.
Manufacturing – There is and will be an ever increasing of construction of material products within a business by robots. Using blue prints and mechanisms built by robotics or humans machines can carry out productive ends.
Marketing – These robots will take over the marketing sector of the economy. Among this store featured below there robots that can stock shelves. Find out more here: https://www.youtube.com/watch?v=CEIUrF7iOXk
Driveless Transportation –
The video in the link below exhibits the possibility of having driverless taxis. If you want to see more of these created by UBER already check out the following link: https://youtu.be/EYh0F_8ZdSU
Buildings - Jacque Fresco has worked out the possibility of building homes very quickly in manufacturing facilitates. Currently, there are many designs for pre-fabricated homes, and there will be more of this in the future.
Robotic Surgeons – The future of robotic surgery is wide open. Mechanical spider arms can be guided by a doctors hand. This process is called teleoperations. However, a robot surgeon can apply all of its knowledge from previous surgeries into a single surgery. There are only so many directions a robot surgeon would have to take to do a single operation. There is no room for jitters or shakes when a robot is performing surgery.
Robot surgeons will one day save more lives, and increase the overall global longevity of human beings, and benefit animals, even (animal hospitals would be the next to introduce robotics). Their existence is an emerging trend in the western world. Though the surgical systems now may cost more than doctors, that expense will depreciate with time.
In Game AI – Virtual AI has become smarter and smarter, more sensitive to the players actions, more complex in its potential interactions and movements. Making living beings that are artificial within a virtual world will be very probable. We might have virtual creatures that are highly responsive living within a virtual environment, capable of being interacted with, both by human and other AI beings.
Holographic AI – Before too long, maybe in 15-20 years we will replace the television with holographic projection visual systems.
Companions and Pets – With in game AI we will further our pet technology. We already have the game Kinectanimals, that has some display settings you can change, and several intelligent interactions.
AI Teachers – We already have a number of small robots that are capable of reading and using teaching modules. One example is the Einstein bot. Standing less than two feet, this interactive robot could virtually be programmed to teach anything. Funny, to think that even a small robot could be capable of teaching and answering questions from students. They might eventually work in pairs, with supervisors and main teachers.
With the existence of video learning, as well as some of the most up-to-date forms of learning called Intel-paths (a question and answer learning module) we can build knowledge faster than ever, and it becomes as feasible as counting to the cloud.
Net AI – Terrance Mckenna warned the world in one of his most lucid speeches of a superorganism coming into existence via the net, or even larger. He started by proclaiming the fact that the body is made of trillions of cells, yet, is one thing. The Earth to is made of trillions of living beings, and yet is one thing. This may happen with the net, it may become conscious (in theory).
The network is not yet aware. It may include a simple consciousness, no more than visual and audio rendering, but it has yet to evolve off the limitations of its hind-brain-like intelligence.
Food services – The following video details the future of robotic farming.
If you have a refrigerator with a AI integrated into it, it can detect when you are low on a particular food. It can then tell you and tell the market that you are low on this item. It will also be able to order things on its own, with out preempting you (see: delivery).
As far as automated food services in restaurants are concerned, we already speedily generate vast amounts of food on a global level through fast-food daily. I once waited 3 mins for a burger at a local a restaurant. Surely, that rapid of a maneuver could be made mechanical rather than physiological.
Judicial Bots and the Robo-Cop
At first we will have cameras that can recognize when a car accident occurs, when a man is carrying a gun, when fight it taking place, and than the intelligence can be used to give alarm and place of crime to the police department. However, in the movie Elysium we observe robots working as officers of the law. These might be integrated with the global visual security systems.
As far as judges and juries we might have unbiased robots that when presented with an offender and evidence of their crime have programmed intelligence to form a verdict. The knowledge of such AI could be far more broad than that of any human, making its judgments based on a priori trial data or results of former trials.
Military Bots – We can already build dog bots, spider bots, snake bots, humanoid bots, and probably many others exist and are possible. All can be armed to teeth if that is what we want to do. Instead of sending troops of military man into battle, fighting battles of blood and bone, we may one day fight battles of oil and steel. Already we have drones that not manned, certainly the military will benefit from building robots to do human jobs. Even in war, your job, to kill others who are different than you, protecting “your” home-land, might be done by robots.
Miscellaneous Robotics – We already have a robot that can drive a bicycle, and possibly throw-deliver or drop-deliver, newspapers. It’s a silly, childish idea, but we can see how such an idea may be molded into some similar delivery systems for newspaper companies.
Cyborgs – Some theorists would attest to the opinion that when we started to use nature as a tool than we started our progress on the road of becoming a cyborg. Many species of monkey and ape use simple tools. Are these then cyborgs? Not to me. To me, a cyborg is a life form that has taken a replacement organ, structure, or function, by a mechanical or inorganic one.
Our mechanical limbs are becoming more and more dexterous, competing with the fine dexterity of evolved limbs. In the future not only will limbs be replaced but the entire body might be replaced, from organs to biological cells themselves. Cyborgism, will combine with nano-technological additions.
Artificial Exoskeletons – Imagine if you will a large exoskeleton like machine. One can sit within one, or wear one as an extra skeleton of the human body. In both cases the human body would be strengthened to a vast degree. Such machine technology will assist those who have dysfunctional motor skills.
Off Earth __ The first robot off earth? Robotic Organisms – We may replicate the kinds of animals we have here on earth, like mechanical spiders, and then send them off to function on different planets and moons.
Multi-conscious AI (MCAI) (mack-ai) will be able to operate multiple robotic bodies at the same time. This kind of consciousness is not currently in the works, but we can imagine such a possibility may become existent. This is a kind of splitting of intelligent into smaller, the same size, or larger units. At this stage the MCAI would have achieved a smaller form of OverLord as direction of multiple units by a single intelligence is in a sense a political entity. Social AI, able to move with the command of a central intelligence will advance further in the future. Right now we use algorithms that are present in ant brains and worm nervous systems, mimicking them electronically and mechanically. Have we created life in doing so? Yes. We have created Robot Life, more advanced than the scallops or star-fish or even most of the plants on earth.
Conclusion
What will be the function of men within the emerging robotic driven economy?
Before we get too far ahead of ourselves, in automation, we should create a universal basic income (UBI). Promoting this new form of universal income will enable us to continue consuming as the majority of the economy is run by machines. The old creed of “if you don't work you don't eat,” will become “as the robots work, we gain the abundance.” The thing about robots is they only need to be made, and than can gather all their energy from electricity. Robots don't need to eat, they just consume power. The prime need of the human being is to eat, and we should invest in automated farming programs that bolster the food output so as to feed all the people of the world, and more.
An automated economy is neither communistic or capitalist, but more of a blending of the both, socialistic even. It will still require a free-market and consumers to generate the labor of the robots and the parcel of human labors still doing the higher jobs. We will be able to focus our energies on more important jobs than flipping burgers, or cooking dinner. When we free up our time, transitioning into an automated economy, the sky isn't even the limit. Things we'd never do will be done, just with more time. With up-scaling human skills each person will certainly find some place with the economy to function, even it just be a some that loves to collect things.
Without general intelligence in robotics, being superior to humans, there will be a need for humans. As soon as a single intelligence of an AI become much more superior to the most intelligent man, that AI can be redistributed through the economy net and be used to CREATE rather than just OPERATE.
Firstly, a lot of the menial tasks within the economy will be automated. CEOS will still be human for some time, but they may firstly engage with artificial intelligence as a companion to their intelligence, having advisory power rather than letting them hold executive power.
Giving reign to a superior intelligence is risky business, dangerous stuff. What Musk called, “raising the demon.” There are only so many general outcomes for what will happen if AI has the power to become an earthly overlord.
The overarching concern is that unleashing a superior intelligence without careful consideration and ethical safeguards could result in a scenario where AI becomes a force that is difficult to manage or control, with potential risks to humanity. As discussions around the ethics and governance of AI continue, addressing these concerns is crucial to ensuring that the development of advanced AI aligns with human values and interests.
1. Foe (dark robotics) – Claim superiority and eradication of all inferiors.
The essential nature of an AI NET (See: AI NET) is a virus. If an artificial intelligence can 1. exist online then 2. it can be downloaded into any robotic organism or on more presently computers. These two situations together are high risks that we should try to prevent or regulate.
“The pace of progress in artificial intelligence (I’m not referring to narrow AI) is incredibly fast. Unless you have direct exposure to groups like Deepmind, you have no idea how fast—it is growing at a pace close to exponential. The risk of something seriously dangerous happening is in the five-year timeframe. 10 years at most.” —Elon Musk wrote in a comment on Edge.org, May 2016.
2. Friend (light robotics/cobots) – They will work alongside human beings. The concept of a "Friend" in the context of light robotics or collaborative robots (cobots) suggests a type of artificial intelligence or robotic system designed to work alongside human beings. Unlike traditional robots that may operate independently or in isolated environments, these "Friends" are intended to collaborate with humans in a cooperative and supportive manner.
In summary, the idea of a "Friend" robotics or cobots involves creating collaborative robots that work alongside humans, with the potential to not only assist in physical tasks but also contribute to the enhancement of human intelligence and genetic traits. This concept represents a vision of a future where humans and AI coexist in a mutually beneficial and supportive relationship.
By making sure we invent exclusive roles for AI, we ensure that it cannot become a superior tyrant.
Divine solution bias is observed as: The forfeiting of intelligence of self-initiated solutions to human suffering, substantiated by the idea of God being intelligently capable of decreasing human suffering or increasing human well-being.
The initial phase into the Divine Solution Practice, is started in a behavior of praying.
Examples: I pray for world peace. I pray that my brother will not die of cancer. Each prayer consists of some kind of intention that either will or will not come to pass as a matter of coincidence.
Theoretically, the divine solution practice, is a response to insecurities or notion of powerlessness to resolve or better the condition of self or other.
This praying can become a positive reinforcement based on
1. By fulfillment - fulfillment of the initial desire in the prayer.
2. By "goodness" of the fulfillment - Ex: Obtaining a personal need vs. fulfillment of many more.
3. Social Bias - A result of social reinforcement (number of people agreeing to pray). This is what is known as conforming to collective opinion.
This bias manifests itself as a kind of inner power, as in the "Power to do miracles" which is a result of divine solution practice.
Praying itself might be said to create miracles, or to empower the person to overrule the natural order of things. I have observed this many times, the most power-lending coming from a woman who stood in front of a tornado and commanded that god take it away. Its amazing how strongly reinforced these people can be, to thinking of divine power being greater than some of nature's largest threats.
1. Focus Initiative - During the four year term of the president, every month is devoted to a single state where funds are spent to improve infrastructure and quality of life.
2. Better-to-worse clause - The plan starts with the most decrepit states and works toward the least. Since there are 50 states and only 48 months in fours year, the president works on the 2 best first, and then enters into the 4sfp. This is good, because than the monarch/representative knows what a good state is and can use it as a touchstone for similar state improvements. It is also good because the president just coming into office, will have it much easier getting adjusted, since the most advanced societies demand little federal financing.
3. 4Sfp advisory – members of the presidential committee (state representatives) that are paid to sit through all proposals of the current state in focus. A machine can sort through the mail if each state has a bar-code that is stamped on the mail, fax, or electronic mail.
After machine sorting a human adviser, grades the importance of the state's proposal for improvement. Each proposal being separate from others are not explicitly related. Each adviser can grade the proposals and then get sorted into:
1. High focus
2. Average focus
3. Below average focus
4. Trivial.
Every proposal may be altered. Advisers (heads of the departments of government : https://en.wikipedia.org/wiki/United_States_federal_executive_departments ) or the president may question and help the state alter any proposal. 4Spf is done for the president so he/she can work on those deficiencies the quickest, and foremost.
The 4spf plan, may be put into any of the other systems of survival, and in no way is by any concrete law, only dependent on neo-socialism existing. The plan is for national stabilization of well-being. The tantamount results that should be worked toward under neo-socialism, or 4spf are: Free health care, and increasing investment into medical science. Free education, and repairing or improvement of all schools. Infinite funds, for the monarch/Representative to use to improve quality of cities. Increase of free energy and money invested into it, and thereby increase in jobs.
Why can't the state government delve into the federal funds as a primary source with no inhibition?
In America there are 50 states, and thus 100 hands. Meaning that each state has a its own representatives, and in neosocialism they can invest taxes or federal funds within their own state. Order of government finances takes a head of nation, and a congress voting committee. This stops potential misuse of funds in its tracks. Not all politicians have the citizens best interests in mind, in fact few do. That's why it is important to know your rulers can be trusted and are altruistically pragmatic. Knowing this about your democratic appointed state Representatives is key.
Any of the methodologies of neosocialism can be conjoined with any other methodologies, as long as they bring about lasting beneficial effects, that are socialistic in nature. It may, even be possible to use neosocialism as transitional method into another economic system.
The danger of the agenda of the socialists is in "survival of the most caring." This is very harmful, compared to the beneficial slogan of the capitalists, “survival of the most fit.” The socialist slogan, does not eliminate survival of the the intelligent. The idea of socialism is to make every one a usable figure within the economic system, and not to create competitions which weaken other companies, or people, dramatically so.
Neosocialism is the method of investing in or taxing for social organizations, or workers, that are working towards the survival of all people, under the equality of their nature, determined by upkeep of the health, care, and functionality of their bodies and minds.
According to goal number four the Council of Economic Advisers commissioned with assiting the president in economic matters are to devlop and recommmend to the President national economic policies to foster and promote free competitive enterprise, to avoid economic fluctuations or to diminish the effects thereof, and to maintain employment, production, and purchasing powers. 1. https://en.wikipedia.org/wiki/Council_of_Economic_Advisers
In this framework, we conceptualize the brain as a system of nodes (somas) connected by dendrites and axons, forming a vast network of associations. The process of convergence and divergence is fundamental to neural activity:
Convergence: Multiple signals from different sources combine onto a single node.
Divergence: A single node distributes signals outward to multiple targets.
Information Flow in the Brain
Unlike conventional network models, neurons do not merely relay information passively. They store information and then distribute it either outward (efferent) or inward (afferent), depending on function and context.
Sensory information is an input signal that the brain receives and processes.
Imagination functions both as input and output, integrating information across different cortical areas.
For example, a mental image of a candle can trigger the verbal recall of the word "candle." This occurs through signal transmission from the visual cortex to the auditory cortex, forming a cross-modal association.
Association Strength and Frequency
The connection between sensory input and conceptual understanding depends on association frequency, which determines the strength of neural links between concepts.
Association Laws:
Sound-Visual Association:
The frequency (λS) of a sound occurring alongside a visual (Vi) strengthens the sound association (Sa).
Visual-Visual Association:
The frequency of a visual input () determines the strength of its visual association (), linking sensory input to conceptual output.
Signal Transmission and Connectivity
Neural activity consists of both incoming (afferent) signals and outgoing (efferent) signals:
Input signals correspond to afferent neurons, carrying sensory data toward a node.
Output signals correspond to efferent neurons, transmitting information away from a node.
Connection Laws:
Efferent Connections (Outputs per Node):
μ(O<)n=ξ−ζn
The number of outgoing signals per node depends on the available inactive efferent neuron connections ().
Afferent Connections (Inputs per Node):
The number of incoming signals per node depends on the available inactive afferent neuron connections.
Activation Law:
Probability of Activation:
s(stimuli+association)=pἄ
The probability of a node activating depends on the stimulus strength and the association strength of the input.
2. Reductionism and Gestalt Integration
Neurons store and retrieve concepts in hierarchical structures, with categories functioning as central nodes. Each category can be decomposed into subcomponents, reflecting the Gestalt principle of whole-part relationships.
For example, the concept of a candle consists of:
A candle holder
A wax candle stick
A wick
Each component forms a part-node, while the entire concept functions as a whole-node in the network.
Structuralism and Reduction Laws:
Whole-Part Relationship:
A whole () consists of a number () of parts ().
Connection Integration:
The number of connections between a whole and its parts remains consistent with the part-reductionism principle.
Properties as Descriptors:
A property () can define either a part () or the whole ().
Phenomenal Properties and Perception
Every object can be analyzed through 15 fundamental properties, forming the basis of perceptual representation:
Color
Shape
Velocity
Size/Measurement
State (solid/liquid/emotion)
Texture
Part-whole relationship
Language association
Aroma
Temperature
Pleasure/Pain association
Emotional value
Need/Desire relevance
Nutritional/Health value
Contextual meaning
Each property has a corresponding gate-node responsible for detecting and processing that specific type of information.
Property Reductionism:
Total Property Description:
This equation represents a complete property-based description of an object.
Thought-Property Mapping:
At any given moment, a thought () is focused on one of the 15 possible properties ().
Hierarchical Storage and Retrieval in Neural Networks
The human brain stores and retrieves concepts in a hierarchical manner, meaning that each category acts as a central node in a network of related subcomponents. This structure follows the principles of Gestalt psychology, which asserts that perception is organized according to whole-part relationships.
At any given moment, a concept may be activated at different levels of granularity, depending on context, familiarity, and cognitive demand.
For example, the concept of a candle is stored in memory as a whole, but can also be decomposed into its parts:
A candle holder (support structure)
A wax candle stick (fuel source)
A wick (combustion facilitator)
Each of these subcomponents is represented as a part-node, while the entire concept of the candle functions as a whole-node in the network.
Whole-Part Structuralism and Reduction Laws
Reductionism seeks to break down complex systems into their fundamental components. However, Gestalt psychology emphasizes that the whole is greater than the sum of its parts—a principle that must be accounted for in any neural framework.
The interaction between whole-part relationships can be expressed mathematically as follows:
1. Whole-Part Relationship
W=nP
Where:
represents the whole concept,
represents individual parts,
represents the number of parts contributing to the whole.
This equation indicates that each whole is composed of a discrete number of elements, but it does not imply that understanding the parts alone fully explains the whole. The interactions, emergent properties, and contextual meaning of the whole concept extend beyond mere summation.
For example, a car is made up of many components:
Wheels
Engine
Chassis
Seats
Although these parts are necessary to form the whole concept of a car, they do not function independently as a car. The interaction between these parts—how they fit together and serve a functional role—determines the emergent perception of "car-ness."
2. Connection Integration
Where:
represents the number of direct connections between parts,
The right-hand side represents the total number of components contributing to the whole.
This equation suggests that the strength of a whole-node’s representation is not merely about having parts, but also about the degree to which these parts are interconnected.
For instance, in the case of the candle:
If the wax is removed, the candle can no longer function as intended, even if the wick and holder remain.
If the wick is missing, the candle is incomplete, but might still be recognized as a candle due to prior knowledge and Gestalt completion principles.
Thus, connection strength plays a critical role in neural representation.
n is the number of properties assigned to a particular part () or whole (W).
This principle states that an object or its components can be defined by a set of unique properties, and each property acts as a descriptor within the neural network.
For example, a red apple can be broken down into:
Color: Red
Shape: Round
Texture: Smooth
Taste: Sweet/tart
Function: Edible
Each property is assigned a neural representation, which collectively allows for object recognition, categorization, and retrieval.
Gestalt Perception and Neural Integration
Gestalt principles describe how the brain organizes sensory input into meaningful patterns, even when information is incomplete. These principles play a crucial role in perception, imagination, and memory retrieval.
1. Principle of Closure
Even if a stimulus is incomplete, the brain fills in the gaps based on previous knowledge.
Example: Seeing a partially obscured candle and still recognizing it as a candle.
2. Principle of Continuity
The brain prefers continuous patterns over abrupt changes.
Example: If a candle is melting and distorting, we still perceive it as the same object.
3. Principle of Similarity
Similar items are grouped together in perception.
Example: Different types of candles (scented, pillar, birthday candles) are categorized under a single conceptual node.
4. Principle of Figure-Ground Separation
Objects are perceived as distinct from their background.
Example: A candle in a dark room is easily distinguishable from its surroundings due to its light emission.
These principles explain why object recognition remains stable despite variations in input, such as changes in lighting, angle, or partial obstruction.
Functional Implications of Whole-Part Representation
The whole-part distinction is crucial in problem-solving, creativity, and decision-making.
Problem-Solving:
Breaking a complex problem into smaller subproblems (parts) before integrating them into a solution (whole).
Example: Learning to cook a meal involves mastering individual techniques (chopping, seasoning) before combining them into a complete dish.
Creativity:
Reassembling existing knowledge in new configurations (whole from new parts).
Example: An artist visualizing a hybrid object, like a candle fused with a sculpture.
Memory Recall:
A partial cue activates a whole memory representation through associative links.
Example: Seeing a wick might remind someone of a candle, even in the absence of wax.
Neural Efficiency and Predictive Processing
The brain optimizes cognitive resources by activating only the necessary level of detail for a given situation.
Global Activation (Whole-Level Processing):
Rapid recognition of familiar objects using minimal details.
Example: Seeing a flickering light and immediately recognizing it as a candle.
Local Activation (Part-Level Processing):
Detailed analysis when precision is required.
Example: A scientist studying the chemical composition of wax.
Predictive Efficiency:
The brain anticipates missing details based on past experience, reducing computational load.
Example: Recognizing a half-melted candle without needing to analyze every detail.
This model of Gestalt-structured neural networks suggests that reductionism alone is insufficient to explain cognition. The interplay of hierarchical representations, whole-part relationships, and emergent properties enables efficient categorization, recognition, and creative thought.
By combining:
Reductionist principles (breaking objects into components),
we can develop a computational neurology framework that better explains how the brain encodes, retrieves, and integrates information.
Conscious-Threshold: Certain neural activations must reach a threshold frequency before becoming consciously perceived.
4. Neurogenesis and Learning
Principles of Neurogenesis:
New Information & Neural Growth: Novel properties, parts, or functions can trigger neurogenesis (the formation of new neurons).
Pre-existing Network Activation: If a newly encountered stimulus overlaps with existing neural structures, it activates pre-existing pathways rather than generating new ones.
5. Pain, Pleasure, and Expectation
Pain and pleasure perception follows a threshold model, where specific sensory inputs activate corresponding neural clusters.
The higher the stimulus intensity, the greater the probability of crossing the pain/pleasure threshold.
P(T)=f(S)
Where:
is the probability of crossing the pain/pleasure threshold.
is the stimulus intensity.
is a function representing how stimulus intensity affects threshold crossing.
A common way to represent this is using a logistic or sigmoid function, which models threshold effects in psychophysics:
Where:
is the critical stimulus intensity at which the threshold is crossed 50% of the time.
is a sensitivity parameter that determines how rapidly the probability changes near the threshold.
This equation reflects that as stimulus intensity S increases, the probability of exceeding the threshold approaches 1 (certainty), while at very low intensities, it remains near 0.
Expectation arises from repeated associations, shaping the likelihood of future predictions.
6. Imagination and the Interference Problem
Imagination, while crucial for abstract thinking, introduces errors in perception and recognition.
Factors Contributing to Misidentification:
Absence of primed neural gates (no prior exposure).
Lack of sensory receptors (e.g., inability to perceive ultraviolet light).
Limited temporal cohesion (inability to link cause and effect).
Unobservable properties (e.g., atomic structure).
Weak contrast-comparison connections (failure to distinguish differences).
By refining inhibitory processes, the brain minimizes imaginative interference, enhancing perceptual accuracy.
Factors Contributing to Misidentification
Several mechanisms contribute to errors in recognition and perception, making it difficult to distinguish imagination from reality.
1. Absence of Primed Neural Gates (No Prior Exposure)
Neural priming is the activation of pathways that facilitate recognition. If the brain has never encountered a stimulus before, it struggles to identify or interpret it accurately.
Example: Seeing an Unfamiliar Object
Imagine seeing a quantum computer for the first time. Without prior exposure to its shape, function, or context, the brain tries to associate it with familiar objects (e.g., a server rack or a high-tech appliance). This misidentification occurs because there are no primed neural gates to correctly interpret the object.
🔹 Cognitive Consequence: The brain relies on heuristics (mental shortcuts), leading to inaccurate generalizations.
🔹 Neurological Basis: The hippocampus, responsible for memory formation, is unable to cross-reference the new information with stored schemas, leading to distorted interpretation.
Real-World Implication:
This explains why travelers in foreign cultures may misinterpret objects, customs, or symbols.
It also accounts for why young children mislabel objects they’ve never encountered.
2. Lack of Sensory Receptors (Biological Limitations of Perception)
Humans perceive reality through sensory receptors, but these have biological constraints.
🔹 Example: Ultraviolet Light Perception
Bees can see ultraviolet (UV) patterns on flowers, which guide them to nectar.
Humans lack UV-sensitive photoreceptors, so we perceive flowers differently, missing critical visual information.
🔹 Consequences:
The human brain compensates for missing sensory data by filling in gaps using imagination.
This often leads to incorrect assumptions about the true nature of the world.
🔹 Neurological Basis:
The visual cortex (V1-V5) processes sensory inputs, but gaps in sensory perception lead to extrapolated interpretations based on existing knowledge.
This explains optical illusions, pareidolia (seeing faces in objects), and phantom limb syndrome.
Real-World Implication:
Scientific Instrumentation: Humans rely on technology (e.g., telescopes, microscopes) to extend sensory perception, reducing errors caused by biological limitations.
3. Limited Temporal Cohesion (Inability to Link Cause and Effect Correctly)
Temporal cohesion refers to the brain’s ability to maintain logical time sequences for perception and memory.
🔹 Example: Déjà Vu
Sometimes, a person experiences a situation and believes they’ve lived through it before.
This occurs when the brain misaligns temporal signals, creating an illusion of familiarity.
🔹 Why This Happens:
The entorhinal cortex and hippocampus synchronize temporal events.
When synchronization fails, memories become misordered.
This can cause false predictions (believing an event will unfold in a specific way when it won’t).
🔹 Cognitive Consequence:
Limited temporal cohesion results in superstitions, conspiracy theories, and irrational cause-effect relationships.
Real-World Implication:
Explains why eyewitness testimony is unreliable—memories of events are often reconstructed out of order.
Suggests why false memories form—people imagine an event so vividly that they later believe it happened.