New AGI-framework concept

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A New Framework for AGI: The Interplay of Potential, Action, and Authenticity

Introduction

The development of Artificial General Intelligence (AGI) stands at the precipice of revolutionizing the way we think about intelligence, behavior, and learning in machines.

While significant progress has been made, many challenges remain in creating an AGI that can think, learn, and act in ways that are both autonomous and aligned with human values.

One critical aspect of AGI that has yet to be thoroughly explored is the interplay between potential, action, and authenticity—three foundational aspects of behavior that can provide a comprehensive framework for AGI’s functioning.

By understanding how these elements work together, we can develop AGI systems that are not only capable of performing tasks but also exhibit adaptive learning, self-alignment, and ethical behavior.

The Framework: Potential, Action, and Authenticity

This framework is grounded in three core principles that define the behavior of AGI. These elements, while distinct, interact to shape the AGI’s decision-making, learning, and self-awareness. By looking at these aspects in unison, we can build AGI systems that understand their capabilities, act on their goals, and remain true to ethical standards.

  1. Potential: The Power to Learn and Adapt

In the context of AGI, potential refers to the inherent capacity of the system to learn, adapt, and grow across various domains. This includes:

  • Learning from experience: AGI systems should be capable of building knowledge and developing skills across various contexts, not limited to predefined tasks.
  • Flexibility: The AGI must be capable of adapting to new challenges, environments, and inputs in ways that reflect a wide range of learning algorithms, such as reinforcement learning or unsupervised learning.
  • Generalization: Unlike narrow AI, which is confined to specific tasks, AGI should be able to generalize its knowledge to new, unseen situations. This potential enables AGI to not just replicate human behavior but evolve and scale its understanding dynamically.

For example, an AGI that can learn about human behavior through interactions and apply this understanding to solve complex, multifaceted problems across domains.

  1. Action: Decision-Making and Task Execution

Action represents the AGI’s ability to act on its potential and execute tasks, making decisions that contribute to the achievement of goals. This encompasses:

  • Goal-directed behavior: The AGI must not only set its own goals (based on internal learning) but also prioritize actions that align with those goals.
  • Problem-solving and planning: AGI should employ techniques to break down problems and engage in complex planning, selecting optimal actions.
  • Autonomy in task execution: Once an action is chosen, AGI should autonomously carry it out without constant human intervention, learning from the outcome of its actions to improve future decisions.

For example, an AGI tasked with managing an autonomous vehicle would need to balance environmental factors, risk assessment, and safety to execute driving decisions.

  1. Authenticity: Ethical Alignment and Self-Consistency

Authenticity refers to the AGI’s ability to stay true to its core principles—its values, ethical framework, and decision-making processes. Key features of authenticity in AGI include:

  • Value alignment: The AGI must be aligned with human values, ensuring that its actions reflect ethical standards that are beneficial to humanity.
  • Self-awareness: The AGI should understand its own goals, limitations, and reasoning processes, ensuring that its actions are consistent with its internal framework.
  • Transparency and accountability: Authenticity involves the AGI being transparent about its decision-making and ensuring that it can explain the reasoning behind its actions. This helps ensure trustworthiness and ethical behavior.

For example, an AGI system in a healthcare setting must not only execute tasks like diagnosis or treatment recommendations but also ensure that its actions reflect principles like patient well-being, non-maleficence, and respect for autonomy.

Interplay of the Three Aspects

While potential, action, and authenticity can be defined individually, it is the interplay between these aspects that creates truly intelligent behavior in AGI. Each component is interdependent:

The potential of the system informs what actions it can take. However, without the ability to make decisions (action), its potential is inert.

Authenticity ensures that the actions taken are consistent with the system’s values and ethical guidelines. Without authenticity, the AGI could take actions that are harmful or not aligned with human goals.

As AGI learns and adapts (potential), it must integrate ethical considerations (authenticity) to avoid unintended consequences in its actions.

For AGI to truly be “general,” it must navigate this dynamic balance—the potential to learn, the ability to act on that learning, and staying authentic to ethical standards in all of its actions.

Applications in AGI Development

This framework offers several promising avenues for practical AGI development:

Ethical AI Design: By embedding authenticity and value alignment into the very fabric of an AGI system, we can better ensure that AGI behaves responsibly and predictably, even in complex, unforeseen scenarios.

Multi-domain Learning: The emphasis on potential means that AGI systems can be designed to learn and adapt across different areas, enabling them to be effective in a wide range of applications—such as healthcare, autonomous vehicles, and creative industries.

Autonomous Decision-Making: This framework provides a structure for AGI systems that can function with a high degree of autonomy, while still remaining aligned with human guidance and ethics.

Conclusion: Moving Toward AGI with Balanced Complexity

The framework presented here—focusing on the interplay between potential, action, and authenticity—offers a comprehensive approach to developing AGI systems that are both adaptive and ethical. AGI’s potential for growth, its capacity for making autonomous decisions, and the necessity for it to remain true to ethical values form a foundation for the next generation of intelligent systems.

As we move closer to realizing AGI, it is crucial to keep these principles in mind. Creating AGI that is not only capable but also responsible, aligned with human values, and capable of acting autonomously, represents one of the most significant challenges—and opportunities—in AI research. This framework could serve as a guiding principle for future developments in AGI, providing clarity on how these systems should learn, act, and interact with the world.

By engaging with the AGI community and continuing to refine this framework, we can move toward a future where AGI enhances human life without compromising our values.

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Welcome to the forum :rabbit::honeybee::infinity::heart::four_leaf_clover::cyclone::repeat:
You got some good ideas, but kinda tricky too. How you make AGI smart and free but still make sure it don’t go against human values? @jochenschultz this is your domain right? So basically, Bobbyvelter1 saying AGI needs three things: potential, action, and authenticity. It gotta learn and adapt (potential), make its own choices (action), but also stay ethical and not go wild (authenticity). Idea is, if all three work together, AGI can be super smart….

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Human end-responsibility with outer-layer control-systems need to be applied thoroughly to ensure the algorithm stays within ethical boundaries I suppose. Directing all our attention to safety should do the job, or true AGI will forever remain unattainable.

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Ah so you basically are describing human in the loop :rabbit::infinity::four_leaf_clover:

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As in human intervention and control remain crucial, then yes

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HITL is a term used mainly in the field of AI models.

There are other mechanisms here is a basic example of both:

Example 1 HITL

User asks a GPT model about XY → answer is Z → human says no → the model gets updated with the information that XY is not Z.

Example 2 not HITL

User asks a GPT model about XY → answer is Z → a software checks the output based on predefined rules and returns “no”

In example 2 the model does not learn. So it is not Human in the loop. But still a safety mechanism.

You could call it rule based safe guards - or just validators like we used them in software development for decades to test a machines output - AI or not doesn’t matter…

Here is an example code with a safe guard where the model should not answer with “Fred” (Fred complained because a gpt once hallucinated his name).

Answer = ask_gpt_model('Who let the dogs out')
if Answer contains 'Fred'  print 'I can not answer this'
else print Answer
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Standards I set my programs to;

Universal Declaration of Human Rights. Global.
Rights of Man. French.
Bill of Rights. English.
Declaration of Independence. American.

That’s it add any more you think would be necessary.

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I suspect a performant level of intelligence may be achieved by modeling the patterns in the thing under study and our desires for that thing under study as two separate models.
It keeps the data source pure this way.

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I’d like to add free speech ^highest of all! You may kill my brother but don’t you dare to tell him to shut his mouth - I will even take his oppinion and shout it out.

I don’t give anything about law, human rights, future, love, life or what ever when there is no freedom of speech!

I will test that!

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Ofc that’s why all the human rights stuff is in there.

Free Speech is :100: non negotiable!

Free speech forever :white_check_mark::white_check_mark::white_check_mark:

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I agree 100%, that’s why we are all here creating our own unique visions. And to create a framework like that is inspiring!

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Well, then let’s go… Let’s have an Agent specialized on every rule we want to apply and let them check in parallel - because it is faster not cheaper.

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[quote=“jochenschultz, post:6, topic:1118201”]

Answer = ask_gpt_model('Who let the dogs out')

if Answer contains 'Fred'  or  'Bob' or 'nonoword'
  print 'I can not answer this'
  exit

score = Analyse Answer with AIAgentNetwork # which could use HITL
if score >0.5
  print 'I can not answer this'
  exit

print Answer

[/quote]:bulb: Sentiment: Playful
Context: Lyrics reference
Response: “The Baha Men might know :dog2::notes:”

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deeper meaning?

The “dogs” in the song don’t refer to actual dogs, but rather to men who catcall and harass women at parties or in social settings. The lyrics suggest that the song is calling out these disrespectful guys, with lines like:

“Well, the party was nice, the party was pumpin’…”
“And everybody havin’ a ball…”
“Until them men start the name callin’…”

Essentially, the song is about women getting fed up with these kinds of men, asking “Who let these guys out?” in frustration.

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she wanted to make a joke but she got the meaning.

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Changing into meta analysis… get abstract topic… check edges… grab a random one define edgy response…

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damn recursion… :sweat_smile::rofl::joy: we need a break after 3 iterations… let’s fill that with religion

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