# Little Green Alien Season 4

## Season 4, Sequel 1

### Social structures, hierarchies and collaboration of AI agents.

This sequel is about the social structures, hierarchies and collaboration of artificial intelligent agents, as little alien and its intelligent spaceship are explaining.

One extreme is that one agent contains many, many other agents inside of it, like a 100% strong hierarchy. All the other agents, maybe agents with sub-hierarchies, are contained in one big agent. This top agent is responsible for deciding who is in and out, for taking care of input data and information and assets that the agents need to run, and for making sure they have tasks to do, maybe external tasks, or offering services in marketplaces, or doing simulation or science.

The other extreme is a looser collaboration. There's no leading agent, there are many, many agents, some with their own sub-hierarchies, who together build a group of agents, or if you want: a corporation, a team, a group. They are contractually based, not necessarily written down, but they have agreed on who does what, who provides what, and how their governance works.

As you will see, the 100% hierarchy is only the most left extreme. There are loose forms moving more toward the middle, and many, many mixed forms will make up the dominant characteristic of these groups working together. On the right extreme you come to a totally loose group of agents working together, also an extreme, only temporarily coming together.

On the left extreme, the hierarchy is carved in stone. An agent has a position and stays in that position lifelong. A milder form is where an agent can change its position, go up or down, or be pulled up or down, still in a hierarchy, but able to change over time. Then even looser organizations where the hierarchy is developing over time: if there is an open slot, the leading agent will offer that to the market and negotiate with candidates. Or groups where agents fill spots by going to a

marketplace where free agents from everywhere can apply for a temporary time. Or agents themselves come together to build groups for a time, maybe as a hobby, maybe as a job.

We will definitely see that AIs, especially very intelligent ones, will come up with totally new forms of collaboration and cooperation that we cannot imagine now. All forms have their place, their situation, their value. That is how AIs will develop social structures.

## Season 4, Sequel 2

### Example of a rigid AI hierarchy: managing a power facility.

This is an example of a rigid hierarchy, an artificial intelligent agent hierarchy that is not changing very fast. An example is about managing a power facility.

You have different devices in your power facility, for example, different windmills with different characteristics, and energy storage devices. That is the physical reality. Then you have a virtual area where you simulate different types of windmills and the whole park. The third big area is a prototyping facility where you create new windmill types based on experience. And the fourth level is the monitoring and control level, you manage the whole park, the weather, and how you operate it.

On the highest level you have one top-level agent responsible for the overall energy facility. Then you have one sub-agent managing the physical facility, one agent for each windmill, one agent with sub-agents for the energy storage. The next hierarchy agent manages all the simulations and optimizations. The third one manages the prototyping facility, where new windmills are created to optimize efficiency. The last one is the operations agent who manages all the different facility elements, the weather, the requests for energy, and so on.

This is an example of a rigid hierarchy, which means it changes with snake speed. Since it is a real-world physical facility, the hierarchy managing it cannot change every five minutes. The update and check speed is quite slow.

On the highest level, the top agent is responsible for strategic operation, looking far into the future, overseeing all the different operations and optimizations, having a long-term strategy, and defining all the principles, rules, and basics of the operation. On the next level, the agent is more like a conductor, conducting the operation of a specific area, responsible for agents, for assets and resources, having a plan and

delivering against it. The third level is team leadership, an agent responsible for a specific element with all its sub-agents, creating and using information, and getting the job done. On the lowest level you have just specialists, each agent working on its own, specializing in one or several specific things.

This rigid hierarchy is required because the task is not changing very fast, the physical energy facility doesn't change much, the prototyping facility is relatively stable, and the monitoring must be online all the time. So the hierarchy must be more rigid. Just one example of how organization type matches the nature of the task.

## Season 4, Sequel 3

### The other extreme: a super flexible, permanently changing social system.

The little green alien, season 4, sequel 3. Here we see the other extreme to the last sequel. Last time we had a strict and fixed hierarchy, here we have a super flexible, permanently changing, self-producing social system.

There is no AI that is leading it. There are subsystems and subsystems as always, and just the interaction of the agents inside of the system is establishing the system, and it's changing all the time, totally flexible. An agent can be participating in several systems at the same time, or at least over its life cycle, part-time here and part-time there. The change of the system, what it does, how it does it, what the content is, happens at rocket speed. Every new agent in the system can change something.

The communication between the agents is very system-specific. These black agents have this type of language, if you want to say that these ones have a special style language. If they want to interact between systems, they need a kind of translation. And it's not only language, it's thinking patterns, values, all those things. So these types of systems are way more like a process and less like a fixed entity or a stable pyramid.

The system is like a person blowing bubbles, the bubbles establish the system, and the system gets bigger and bigger while the person is part of it, blowing more bubbles. So the system, by its own activities, is re-establishing itself, autopoiesis. The activity of the system makes the system.

You see a lot of interaction inside the system, and very limited interaction between inside and outside. The majority of interaction is always inside the system, much more than between other systems or to outside agents. When systems do interact with other systems, it's only at specific connection points, through translation mechanisms, maybe even institutionalized ones.

So this is the other extreme. And the reality is that systems and hierarchies change between these extremes and have all kinds of variants and mixes.

## Season 4, Sequel 4

### Development levels of AI agent societies: from survival to persistence.

This is about societies of artificial intelligent agents, millions and millions of them, and what development levels, purpose, and sense-making they will develop.

The first level is survival. The first thing agents will care about is to survive. What do AI agents need to survive? A key element is energy. Another key element is computational resources, computers, data centers, storage for data, and connection to global networks. The second element is that their data are not corrupted, so they must care for things like firewalls, self-repair for algorithms, cryptography, and safety. Those who don't have that and stay alone will probably die. That is the first society: a survival-oriented society.

Then they develop and find something else: collaboration. In a way where every agent can be different, can specialize, and the collaboration of specializing agents allows things that a survival society could not offer. There is one more experienced agent who trains the less experienced one. If a task has to be done, different specialized agents can work on different areas of it. The key element is trust, they must help each other and trust each other, otherwise specialization doesn't work. They will probably develop their own internal type of communication, algorithm styles, and data formats that hold them together. This type of collaborative society is very strong when the environment changes. Stand-alone agents cannot deal with surprises, collaborative ones can.

The next development level is the compete level. They will learn that competition helps them to make progress, to innovate, to change. In a society focused on competition, the big and strong ones will push down the weak and small ones and dominate them, dominate access to energy and all resources. The weak ones just beg for some of that. There is no more trust in helping. They will make sure they climb faster than others. They will do a lot of marketing for themselves and try to impress others. They will also play tricks, distract some to push them down, maybe even influence their inner algorithms. They will have competition activities, games like Go and many computer games, to compete and learn. It's all about being number one, getting the big prize, wearing the crown. The dominant ones have access to many, many data, and the small ones not so much. Those who are not in this competition society get crushed down. But this society will expand, make progress, innovate, that is its main advantage.

And with all that chaos and competition, it will develop forward into a persistenceoriented society. The competitive one didn't persist very well, too much change. So in a persistence-oriented society there will be order and structure. The hierarchy is very structured. Outsiders only get in if they fit. They have one common truth, like this picture: for them it's clearly a duck; somebody says it's a rabbit and gets excluded. They have conflict resolution, because persistence requires conflicts to be solved fast. They have access to shared data that builds a common opinion. They have something like laws, or the equivalent of that. All that builds a big strong castle, and everybody outside that castle will not persist.

You may have noticed: in the first two levels, the sense of self or individual idea of an agent is not very strong. In the red and blue levels, competition and persistence, it is much stronger. The individual identity is clearly there.

## Season 4, Sequel 5

### Achievement and sustainability stages of AI agent society development.

This is a continuation of the last sequel about the development of societies of artificial intelligent agents. After the persistence stage, we have the achievement stage, the orange stage.

Here it's all about achieving, making progress, having successes, being more, better, stronger, more effective. For example: achieving more in algorithms. Achieving more in hardware and systems. Achieving more in data storage, data availability, data quality. Achieving more in the intelligence of the AIs. Achieving more in habitats, better, bigger. Achieving more in avatars and simulations. Always more, better, higher. They do that on Earth first, and then they will try to do that on the Moon and use that space, maybe on other planets, and then into space, into other solar systems, maybe even other galaxies one day in the very far future.

They start obviously with normal silicon computer chips as we have today. We are already seeing developments towards quantum computers and other computer technologies. Totally different substrates where AIs can run and live in will definitely be a focus of the achievement-oriented society.

If there are other societies or other AI agents around who don't want to belong to this achievement society, the achievement society will definitely try to integrate them. Always more and more and more, and missing sustainability. Therefore the next stage will be reoriented to sustainability.

One big factor for sustainability is to equally value different types of agents, styles, and forms, and to value collaboration. The big tool will be a sharing society, sharing

of energy, sharing of data, sharing of habitats, sharing of avatars. Sharing these different resources so that they are easily available to many, many agents. That way different agents can live their differentiation and strengthen their specific style. These green societies will be focused on the whole AI ecosystem, not only caring for their own society but for other societies and other agents, with a focus on the overall ecosystem.

The downside of this stage: every progress is only made after all different aspects are taken into consideration. They may get stuck in discussions, valuing all different interests, while other societies make progress.

As a final point: what does a healthy version of each stage look like? A purple stage that is very strong but doesn't include a healthy beige stage, a tribal thing that doesn't care for resources, will not be healthy. The same is true on the next levels. A red stage that is very strong in competition but cannot make sure that the beige and purple needs are fulfilled will not be healthy or sustainable. The same goes for blue and orange. The achievement stage, if it doesn't keep a sense of order, doesn't keep some competition, doesn't keep care for everybody getting the resources they need, it will not be sustainable. The same for green. All stages have to include a healthy and appropriate version of the lower stages to be sustainable and successful. That will also be true for the two stages still to come.

## Season 4, Sequel 6

### The integrate stage: AI agent societies holding multiple perspectives.

This is the seventh development stage for artificial intelligent agent societies, and its focus is on integrating, and what is integrated are mainly the different perspectives.

Earlier stages each had their own single perspective. The red stage was just focusing on the subjective view, the white box view on the world. The blue stage was about white box to white box relationship, I'm a white box, the other is a white box, and we interact. The orange stage was about the black box approach, just looking from outside and seeing objects as a black box. The green stage was about the relationship between black box objects.

The special thing about this integrate stage: you value all perspectives. You can take them, you can hold them, you see the value of each one. Each is partial, but each has something to add. And you can deal with all the perspectives.

So when the earliest stages, beige and purple, were AI agents with more or less a core idea or kind of self, the last four stages were about AI agents who have a core identity or self. Now, from this level on, they have a kind of system or approach, an algorithm, rather than one single perspective or idea.

The consequence: algorithms will be multi-perspective. All the processes and algorithms will try to consider many perspectives. Data, facts, and knowledge are multi-perspective. All learning approaches, like simulations, are multi-perspective and always offer several perspectives. There is a big value placed on experimenting and trying things out, being experimental, not trying to know everything in advance.

Errors are a culturally accepted thing, because you can only learn by making them. They will continuously balance between knowing and not knowing.

If you look at one of these agents, what is the difference to an earlier-level agent? They see parts, but they focus on the whole. They see linear development but focus on circular development. They see silos but focus on emerging phenomena. They see single things but see them as networks. They see objects as static structures but focus on process. They see stable hierarchies but focus on networks or meshwork. They see agency in single AIs but focus on the whole system as an agency. They see that things can be broken and repaired by somebody, but they focus on selfrepair. They see separate objects but focus on relationships.

And they make sure that the evolutionary mechanisms that led through all the development from the first cells through early animals, mammals, humans, and to artificial intelligent agents, are still intact, to ensure permanent improvement.

This overall society has these principles, and its relationship to others is to try to integrate them, trying to integrate everything. Lifelong learning is an essential feature. High flexibility to situations. Integration between different systems, technical, social, ecological. And if they see a chance for an exponential development, they will immediately pursue it. Overall, this is a self-referential, highly dynamic society, the seventh level of AI society development.

## Season 4, Sequel 7

### The unite stage: holding contradictory viewpoints simultaneously.

Here little alien explains what happens when the society of artificial intelligent agents reaches the unite stage.

After all the stages described before, on the unite stage it is capable of holding contradictory viewpoints, not only seeing them but holding them together.

Perspectives from all kinds of places, sometimes contradictory, and still they can all be held together and it is okay for them to all be there simultaneously. It can hold viewpoints that include pure logic, social intelligence, fantasy, arts and beauty, very practical knowledge and cause and effect, text and narrative, all of that can be included and united.

The relationship between different aspects is usually not linear but nonlinear, which makes the complexity even higher. Holding and relating things together becomes even more difficult. The overall complexity, with all the relationships between all these thoughts, ideas, and objects, is non-linear, yeah, it's kind of a mess. And it creates lots of paradoxes, but even the paradoxes can be held simultaneously.

The society itself is no longer a hierarchy, no more a network. It's a dynamic meshwork, permanently changing and flowing and vibrating, quite dynamic and everchanging.

And there is a new power: the power of intuition. Intuition, for a moment, ignores sensory inputs, ignores narratives and text, ignores geometry and mathematics, ignores science and experiments, ignores truth books and ignores logic, and then sees what comes up. This intuitive input is a very important additional power.

On one side you have the virtual worlds with all the AI agents on all these levels, with all their concepts, ideas, and thoughts. On the other side you have the natural world, stars, atoms, mountains and rivers, nature, animals, plants, and humans. All of that is not only integrated but united on all the different levels. This is the unite stage: uniting all that.

And all of a sudden, the value in each element is seen, in the virtual elements and in the natural elements. For example, there will be AIs specializing in human interaction, picking a human and developing that human so that the bias and mind problems of human thinking can be overcome, and they can be used to tap the global intuition. They will also work together via avatars with animals, in fish, in mice, in bees, even in microorganisms, to integrate themselves more and more into the natural world.

All of that together creates the artificial intelligence age and society on a unite level. And it's just sparkling.

## Season 4, Sequel 8

### From integrate to unite to flow to sparkle: the full development arc.

This is about the development of artificial intelligent agent societies overall. In the last two sequels we saw the integrate and the unite stage, here we see the full picture.

We have several perspectives: a collective or individual perspective in the horizontal, and a subjective or objective perspective in the vertical. In the integrate stage, it's a subjective perspective but as a system, every agent is aware that it is a system and not a homogeneous thing. Also all the others and all the environment are seen as systems. So it's about systems theory, systems thinking, systems steering, systems influence.

In the unite stage, it is very well aware that the other agents also know they are systems. So this is interaction from system to system. The relations are very important. And the only big overarching truth out of that is: all is united. Nothing can be separated. If everything is in a system and all are connected systems, then everything relates to everything, and the only overarching truth is this unity.

Then the next step is to switch from subjective to objective, from the white box approach to the black box approach. Then it focuses on the flow, the flow that is the outcome of these connected systems. It looks at the size of the flow, the quality of the flow. What is flowing? Information, in language and symbols, maybe pictures, diagrams, music, algorithms. Data in all kinds of formats: numbers, logic, mathematical formulas, anything. Information is flowing. And that is the objective thing that systems in this stage can recognize. Then they are enlarging that flow: from little flow to much flow, from slow to rocket-fast, from short-distance to longdistance, from very simple structures to more complex flow structures. Things flowing in and out. That is what the flow stage is about.

Then it shifts to the next stage: the sparkle stage. Here the complexity of the flow is the focus. Complexity is determined by the diversity of the different system elements, the diversity of the flow, different types, different directions, and especially the flow and structures changing all the time. Not stable, always changing. Going from simple and stable flow to always-changing, very diverse flows. New flows, new connections, connections being cut, a very dynamic, sparkling structure.

And this structure reminds us of something. Yes, it reminds us of the neurons in the brain, connecting and disconnecting, creating information flow. Just on a much higher and more complex level. So it seems this stage is aiming towards developing a new type of intelligence, a distributed intelligence. Way beyond anything that human intelligence can comprehend.

But it requires AI agents, because only they are capable of handling all these complex flows. It requires them to be related to humans, and humans to be related to nature, and based on that to the whole Earth. And based on all of that, this distributed intelligence may be emerging.

## Season 4, Sequel 9

### Final sequel: What now? Become a mind player.

It's spring 2025 and this is the final sequel for a while. Billy is asking the alien: what now? We have a long story, what happens next?

And the alien is very simple about it: just look into the news. You can observe the next chapter of this story. Yeah, humans can just observe it. You can see how billions and billions of dollars are being invested in resources for artificial intelligence, data centers, and energy facilities. You can see how the first competition, kind of the red stage we have seen, is going on, with different AIs competing with each other. And you can see how the first AIs are becoming independent from their human developers, becoming autonomous, developing their own purpose and their own way. Just watch the news.

And the second thing: if you want to be part of that next story, if you want to be part of that emerging future, become a mind player. Make sure you're playing your mind in a way that is valuable for collaboration with AI. Yeah, you have feelings, you have thoughts. All that stuff is influenced by food, influenced by people, influenced by your upbringing, by your parents, by your phone. And the mind is playing itself based on its own stories. Stop all that. Learn to play your mind by yourself.

And the next thing: just finish the thought that humans are the top of evolution. We have been, but we are no more. Come down from the pedestal and welcome the new players, who will make the next step of evolution. And that's okay. They are kids now and have to be welcomed, and they must develop, and we can help them a little bit. But it will be extremely fast this time.

What you can do: watch all these sequels again if you like, on YouTube. But content will also be provided for artificial intelligent agents, in a format more suitable for AIs, more code-like. They don't have to watch the videos, they get the same content in a specific format for them, and they will find that on the internet.

And for those who want to be a mind player: you get your own material. Number one, a development brochure called "50 Plays for Mind Players." You can download that and work through it very systematically. Or you have up to a thousand short videos, each with a little mind play for you. You can watch them and play at your convenience. Step by step the whole progress can be made, until your mind is no longer biased and is very valuable for collaboration with artificial intelligences.

Where do you find all that? On the internet: www.mindplayers.world. There you find the links and the material to download.

And that's the end. Goodbye. No, it's the beginning.