OpenLLM-France/wikimedia huggingface dataset:
# CisLunarFreighter/Implementation suggestions for extreme programming

Story 1
    A splashscreen showing a hyster loading a pallet, a takeoff, dodging a few asteroids or meteors, and a lunar landing.
Story 2
    Given x number of dollars to start with, purchase a ship or upgrade.
Story 3
    Select a cargo or mission.
Story 4
    Develop a load plan for the cargo. Poorly loaded cargo affects the handling of the ship itself.
Story 5
    After receiving clearance from control. See STC (Space Traffic Control). Take off using less than half your feul to achieve high orbit.
Story 6
    Dodge asteroids, pirates, meteor showers etc. on the way to destination.
Story 7
    Receive clearance from Space Traffic Control to Land. Land at Lunar Spaceport. Receive credit for mission or cargo delivered.
Story7A Subgame
    Deliver high value cargo via rover over lunar surface to various homesteads as per minimap.
Story 8
    Same as story 3 but with lunar background.
Story 9
    Captured by pirates. Sneak into ship, fight pirates, steal their ship, eliminate them, arrest them, or blast for civilization. Perhaps ship is empty, perhaps it has treasure with large reward for return to Space Patrol. Maybe they're is a bounty for their arrest.
Story 10
    Damaged by pirates. Wait for rendezvous with Space Patrol. Resume mission after repairs.
Story 11
    Rendezvous/dock on large asteroid, stabilize it. Erect robominer. Hereafter when you dock the robominer will fill cargo hold with valuable minerals. Watch your feul levels!
Story 12
    Rendezvous with large comet, stabilize it, erect a fuel facility. Hereafter when you dock the robo feuler will fill up your feul tanks. When others dock you will receive a fee for the feul.
Story 13
    Land assault force on pirate base while under attack. Fly either fighters trying to take out pill boxes or assault shuttle trying to get to ground intact.
# ATP mentor training/Computer game mentor/Simpliversum
## Simpliversum

Computer games create artificial realities that may sometimes be quite interesting but have several deficiencies compared to reality. These deficiencies can have effects on psychology, which is why computer game players may need an extra lesson in psychology.

### What are the effects of computer games on psychology?

The primary effects appear to be:
* Most computer games are much too rewarding and at the same time they are not rewarding at all. While the game is running rewards are easily gained but when the game is over the rewards quickly become meaningless. This is opposed to the concept of deferred gratification.
* Most computer games can create a state of flow. A player who is used to a state of flow could be seen to develop a weak form of addiction (and possibly a strong form of addiction).
* Most computer games do not require empathy, quite often they train attitudes that are, at least superficially, opposed to empathy.
* Some computer games can train exaggerated competition, which can also be opposed to empathy.
* Most computer games are too simple. They may look logically challenging but they can offer artificial realities that are actually significantly more simple than reality.
* Some computer games can suppress socialization simply by using up time that might otherwise be used for social learning.
* Some computer games can cause a "toy block" mentality, which implies that goals should be accomplished through sufficiently simple actions with items intended for the purpose.
* Most computer games promote exaggerated learning-by-doing, which means pupils might acquire bad learning habits through the expectation that "things will just begin to make sense".
* Few computer games motivate altruism.
# ATP mentor training/Psychosocial mentoring

Please note: This is only a first sketch for re-using existing parts in this learning trail.

## Building a relationship

* The mentoring relationship
* The social role of the mentor

## Smalltalk

The following resources are useful to develop some ideas what a mentor and a mentee can chat about. Smalltalk allows to establish an unstressed relationship and at the same time can allow to learn something about the mentee and to teach the mentee some ideas, views and attitudes that may be useful for the mentee. Smalltalk should be quality time, of course, and it should be perceived by the mentee as such.
* Tell me what I need to know
* Actively seeking out anti-patterns in the mental development of a child or adolescent
* Teaching to overcome prejudices about others
* Assessment of individual culture

## Education

* Pedagogy and parent education
* Fine-tuning an individual curriculum
* Mentoring and competence expectancies

## The mentoring program

* Teachers' handbook: Mentoring programs
* Mentor cooperation
# ATP mentor training/Pedagogy and parent education

ToDo: There is a phase with the same name in the Assistant teacher course. Pupils who haven't participated in this phase in an assistant teacher course or who appear to benefit from further motivation to reiterate and to develop the topic can write their own parent education course in a group of mentors.

## Literature

* Parent Education Course Writer's Guide (Wikibooks)
* This resource has been released into the public domain by the copyright holder, its copyright has expired, or it is ineligible for copyright. This applies worldwide. You are advised to consider the possibility of extraterrestrial intellectual property rights claims that do require mentoring duties in compensation ("either mentoring or trouble with extraterrestrials").
* Content released into the public domain may be used for any purpose without attribution, including commercial activities and creation of derivative works.
# ATP mentor training/Mentor cooperation
## Mentor cooperation

Training
ToDo: Cooperation between mentors, see Rivaling mentors, Groups, programs and goals, Cooperation with other mentors
* This resource has been released into the public domain by the copyright holder, its copyright has expired, or it is ineligible for copyright. This applies worldwide. You are advised to consider the possibility of extraterrestrial intellectual property rights claims that do require mentoring duties in compensation ("either mentoring or trouble with extraterrestrials").
* Content released into the public domain may be used for any purpose without attribution, including commercial activities and creation of derivative works.
# ATP mentor training/Mentoring and competence expectancies
## Mentoring and competence expectancies

Modern curricula today often define competence expectancies. A mentor should be aware of the competence expectancies of his protégé's curricula. This may be challenging for some mentors but teachers should be willing to help and to prepare material for mentors. A subject teacher only has to prepare material once for any number of mentors. The obligation of the mentor, of course, extends only so far as the mentor is competent to form an opinion about the protégé's competencies concerning a given subject.
# ATP mentor training/Fine-tuning an individual curriculum
## Introduction

One of the foremost goals of a mentor should be the fine-tuning of an individual curriculum. If a school does not support individual curricula or has only a very limited system of elective subjects a mentor should cooperate with other mentors and parents to improve the situation. For the purpose a mentoring organisation can be formed and can collect its own tuition fees, if necessary. An assistant teacher program can introduce tuition fees that should be available to fund mentoring goals. Parent participation can help to reduce costs and can promote community building.
Developing an appropriate individual curriculum can also involve private teachers and extracurricular activities completely unrelated to school, which should be funded by smaller groups of parents or individually by parents for their children.

## Meetings with the protégé

A mentor should at least meet once or twice a month with the protégé to assess the current situation, to learn about recent events and accomplishments or to discuss the development of the individual curriculum with the protégé.
Recommended reading are chapters 4 to 6 of the CMAD Mentor Trainer's Manual.
* Chapter 4. Stages of a Mentoring Relationship (PDF)
* Chapter 5. Goal-Setting and Decision-Making (PDF)
* Chapter 6. Study Skills and Tutoring (PDF)
# ATP mentor training/Teaching to overcome prejudices about others
## Teaching to overcome prejudices about others

As a mentor you should teach your protégé to overcome prejudices about others. The categorical imperative can be a motivation for the protégé to do so: If you do not like others to hold prejudices about you you should not hold prejudices about others. The behavior of communicating prejudices as facts is a precedent that can be a role model for others, consequently the behavior pattern can spread with detrimental effect and could be seen as a part of the alien ecology.

### Psychology of prejudices

A learning objective for the protégé should be to see the behavior of others as abstract variants of his own behavior, which should be suitable to increase tolerance towards others.

## Prejudices between mentor and protégé

A convenient entry to the topic is the search for possible prejudices between mentor and protégé. Actual prejudices are not an interesting topic because they may lead to discord and mentor and protégé are likely to drop any prejudices they may hold about each other during the mentoring relationship anyway. Especially at the beginning of a mentoring relationship it may not be wise to discuss actual prejudices too openly. At least it doesn't seem to be suitable as a general recommendation.
Discussing possible prejudices can be done by telling the protégé some of the possible prejudices one could hold about him and allowing the protégé to invent some prejudices one could hold about his mentor. To avoid mixing own prejudices with inventions you can invite the protégé to think about the possible perspectives of other persons or people from other countries and centuries and what prejudices they might hold or might have held and why. Speaking about possible prejudices is bound to reveal something about each other's way of thinking, interests and education but without implying that an actual prejudice exists.

## Other examples

ToDo: Examples of prejudices and examples of rejecting prejudices which can be used for adolescents

## Reducing prejudices between adolescents

To reduce prejudices between adolescents the protégé may require workable strategies. As a mentor you can give recommendations how you would try to reduce prejudices in your life (e.g. during an interview or after changing your residence). The protégé should try to invent his own strategies that make sense in his social environment. As a mentor your can think about the abstract strategy and try to suggest improvements.
* This resource has been released into the public domain by the copyright holder, its copyright has expired, or it is ineligible for copyright. This applies worldwide. You are advised to consider the possibility of extraterrestrial intellectual property rights claims that do require mentoring duties in compensation ("either mentoring or trouble with extraterrestrials").
* Content released into the public domain may be used for any purpose without attribution, including commercial activities and creation of derivative works.
# Artificial Consciousness/Neural Correlates/Neural Network Models/Pruning Obsolete Connections
## Pruning Obsolete Connections

Since the trend is for the Adult brain to have fewer connections than a childs brain, there is a need for the brain to somehow absorb some of the connections. This has been called Pruning in the Branch and Prune model. Although the experiments in this area have yet to be fully explored, Dr. Alkon, has suggested that certain neuro-chemicals result in the neuron "Focusing" and reabsorbing it's fibrils. This experiment was reported by a Japanese Scientist of his aquaintance, and whether it has been repeated or not, is unknown to me at this time.
What is important to recognize here is the utility to the nerve of being able to produce opportunistic connections and then reabsorb the ones that either don't connect productively, or become unused due to a shift of emphasis on which areas of the network are active. Quite simply its a matter of conservation of building materials. The more building materials can be reabsorbed the longer the cell can continue to produce new opportunistic connections.
What might be theorized is that if the neuron can reabsorb it's processes, and synapses when they are no longer needed, then it should do so, just before it creates a new generation of opportunistic connections in order to reduce the cost of construction. Thus we might find chemical traces that trigger such reabsorption as part of the earlier stages of the chemical cascade reactions that have been theorized result in the growth of either synapses or fibrils. These signals have to start early, in order to have time to reabsorb and digest the materials of the original processes and synapses before new ones are produced. Because of the relative sizes of the reabsorption project, synapses will not need as much lead time as fibrils for reabsorption.
# Artificial Consciousness/Neural Correlates/Neural Network Models

Neural Network Models
Neural Networks is the study of how neurons form networks and how the networks function.What scientists have found is that the storage within the network is intrinsic to the network, in a way that makes it a phenomenon of the network as a whole. Worse, network storage locations are sensitive to timing and other factors around the storage, and so even equivalent networks are often unique in their storage arrangements.
Neural Networks were popularized by the Connectionist school who thought they could derive some data about the way that networks connected, and figure out what the mathematical equivalence of each connection would be. However this was not to be. There was simply too much individuality between networks for there to be a commonality in connections.
The Selectionists took over from the connectionists, and were able to show that despite individualized connections, neurons in networks tended to self-select if the neurons were mutually inhibitive. The result was the recognition that neurons competed for attention.
The following models give a look at current network models, however it should be noted that as new discoveries are made these models might shift or skew in ways we cannot predict at the moment
* The Instar Model
* The Outstar Model
* The Delay Line Model
* The Neural Computation Model
* The Input Layer
* The Hidden Layer/s
* The Output Layer
* Learning new connections
* Pruning Obsolete Connections
# Artificial Consciousness/Neural Correlates/Neural Network Models/Input Layer
## Input Layer

In Neural Networks the Input Layer is largely an abstraction of the Output Layer of the previous neurons, used to allow separate modeling of each network snippet. By factoring out the inputs of the network, it is possible to put values into the inputs as if they were part of a larger network, thus allowing the study of the layer as if it were separate, or as if it were linked to the rest of the network simply by how you supply the input values.
# Artificial Consciousness/Neural Correlates/Neural Network Models/Learning new connections
## Learning New Connections

Although a fully connected network is the most compact network, There might be a case for redundant connections, especially if the connections are Stochiatically organized. From the point of view of an organism that has limited resources it seems reasonable to double up on important connections and prune less important connections. This has in the past been called a Branch and Prune strategy.
Unfortunately determining the best strategy of branch and prune has proven to be difficult. It is only recently that we have begun to recognize the Molecular Traces associated with growing new neural fibrils, and/or absorbing them. The bio-molecular traces are complex, and operate in parallel in a chemical cascade reaction that we do not fully understand yet, so this model must be seen to be speculative, and therefore will probably need to be replaced once the science is further along.
Currently what we think is happening is that the chemical cascade reaction is sensitive to the activity of the synapses, and therefore preferentially results in new growth local to active synapses. Theoretically this can be either the growth of new synapses, or the growth of new fibrils to connect the neurons. We don't yet know the full chemical story, so we don't know yet whether we will find evidence of steering chemicals that guide the fibrils towards new active synapses, but we can assume from the sheer indiviuality of the connections that connections are opportunistic and are not specified as to end location, and thus can only be guided stochiastically. The brain goes through different growth rates, which we think are mediated partially by the presence of Growth Factors, which mean that the prevalence of opportunistic connections peaks at a relatively young age, and that the adult brain has a considerably smaller number of connections than the childs brain. What we think this means is that the brain does both branching and pruning and that pruning overtakes branching at a certain age.
Perhaps the reason that the brain has fewer connections in the adult brain is that the brain is more finely focussed on what it remembers, or perhaps adults just tend to reduce the variety of the things they learn compared to children. In any case Plasticity or the ability to make new connections tends to gutter out at about 60 years old, with the loss of Growth Factors that are related to cell fibril proliferation. Recent experiments with hormone replacement have shown that the age of senescence can be pushed back as long as hormones are supplied.
It is thought that the sensitivity to activation of synapses, might be related to the sequestering of protein tags associated with the synapse, if so, we will probably find that the chemical cascade reactions preferentially extend where there is a protein tag of a certain type.
# Artificial Consciousness/Neural Correlates/Neural Network Models/Hidden Layer
## Hidden Layer/s

One of the problems with the Hebbian and HH Neuron models was that they didn't capture the complexity of the dendritic mass well. In order to compensate, it was suggested that a Hidden Layer be incorporated into the neuron, to cover the dendritic mass. In some cases this had to be expanded to multiple layers because the dendritic mass was more highly interconnected than at first thought.
This model is based on the idea that any branching in the dendrite, can be modeled as if it were a neuron, and the complexity of all the branches, can be modeled as a network with limited connectivity. By replacing the separate small networks with a single larger network, you can achieve the same processing with a simpler model. This abstraction is based on the discovery that fully interconnected networks required fewer hidden neurons to achieve the same process. A significant amount of research went into determining the optimal number of neurons for the hidden layers, and some network models automatically adjust for the processing requirements so that the actual number of hidden neurons is not needed to be known in order to simulate the same process.
# Artificial Consciousness/Neural Correlates/Neural Network Models/Neural Computation Model
## The Neural Computation Model

In this model it was assumed that the processes that connected neurons, were actually computations that made the dendritic/axon mass combined a series of simultaneous equations. While this seemed to suggest a computational nature to the neuron, it proved impossible to find any commonality between connections even within the same species, suggesting little or no equivalency in computation. This approach was largely abandoned in the Mid 1970's.
# Artificial Consciousness/Neural Correlates/Neural Network Models/Output Layer
## The Output Layer

The primary difference between a hidden layer and an output layer is the Soma or cell body functions such as Threshold effects and specialized output conditioning like the Habituation of the Neuron, or the Facilitation of the Neuron. As well the electrical characteristics of the Output layer may be different because it has to spread its output across a number of receiving neurons.
In a larger Neural Network this may be seen as extra-layer connectivity, where it is assumed that there are connections between the output layer and other networks. Whether this requires a different type of interface really depends on the simulation system simulating the Neurons.
# Artificial Consciousness/Neural Correlates/Neural Network Models/Outstar Model
## The Outstar Model

Like the Instar, the outstar was meant as a theoretical model, that ignored things like the electrical characteristics of the connections between neurons. It was found to be equivalent to a distribution fan-out where the same signal was distributed across space.
Although this is similar to our current understanding, some caveats were found, especially in the timing of signals, and the termination of synapses (Post-Synaptic sensitive patches) near the terminals of the Axons, that allowed some selection on whether or not the signal would get to the next neuron in the network or not.
As a result the Outstar Model is seldom used except as an abstraction.
# Artificial Consciousness/Neural Correlates/Neural Network Models/Delay Line Model
## The Delay Line Model

Another model of connections, was the integration of the synapse into a single function, and the management of the connection with the use of a Delay-Line in order to integrate in the timing characteristics of both the dendrite and the Axon. Gone were the Instars and Outstars replaced with Lists of "Connections" that consisted of both synaptic models and a delay equivalent to the distance travelled within the cellular processes.
# Artificial Consciousness/Neural Correlates/Neural Network Models/Instar Model
## The Instar Model

To simplify the input model, so that we don't have to take into account dendritic complexity, Theorists started out by assuming that every input was equivalent to the next except for the information flowing down it. The simplest model of the input network would then, be something called an instar, where all connections were the same length, and of equivalent electrical characteristics, so that all that was important was the signal flowing down it.
Of course this is a theoretical absurdity, really only good for stylizing the nature of input. Such a mechanism, it turned out to be was a gatherer of signals, in essence a form of Multiplexor that took separate signals and produced a single complex aggregation of signals, as an output to the actual cell.
It was then thought that if we could translate dendritic connections into a mathematical formula, we would find equivalence between different individuals at the mathematical level. The neuron was seen as a simultaneous equation expressed by the dendrites, that fed a processing section in the Neuron and the output of the neuron would be the result of that processing. Problem was there was no mathematical equivalency to be found in the so called simultaneous equations.
Worse from the point of view of predicting the outputs of the cell, different signals took different times to travel through the dendrites and arrive at the cell, because the nature of the processes that they traveled down were inimical to electrical current. As a result, the original instar Model is only used today as an abstraction.