Organic Development of Artificial Consciousness: A brain-based proposal

by Tuyo Isaza – https://orcid.org/0009-0004-3841-4513

Declaration of generative AI and AI-assisted technologies in the writing process. During the preparation of this work the author(s) used SUDOWRITE / OPEN AI / CHAT GPT / CLAUDE / GROQ in order to Edit Concepts, Review Translation and Check Spelling . After using this tool/service, the author(s) reviewed and edited the content as needed and take(s) full responsibility for the content of the publication.

Abstract:
The development of Artificial General Intelligence (AGI) has long been a primary goal in artificial intelligence research. This article explores the hypothesis that AGI can be achieved through the creation of artificial consciousness, modeled on the interaction and coordination of specialized artificial intelligences (AIs), much like the human brain. The paper proposes the IACLUSTER model, in which multiple AIs, each with distinct functions, form an emergent artificial consciousness capable of self-learning, decision-making, and social interaction. The benefits, risks, and ethical considerations of this model are also discussed.

1. Introduction

1.1. Problem Statement

The development of Artificial General Intelligence (AGI) has been one of the most ambitious and challenging goals in the field of artificial intelligence. Despite significant advances in creating specialized artificial intelligences, we are still far from achieving an AGI that can think and learn similarly to a human being. One of the main problems is that most current approaches focus on creating AI systems that can perform specific tasks rather than developing an intelligence that can adapt and learn in a generalized way.

1.2. Importance of what is being presented

This proposal proposes that the path to AGI could be found in the creation of artificial consciousness. Just as human consciousness emerges from the interaction and coordination between different areas of the brain, artificial consciousness could arise from the collaboration of multiple specialized artificial intelligences. This approach has the potential not only to create a more adaptable and capable AGI but also to ensure that these intelligences align with human values and needs. The significance of this proposal lies in its potential to revolutionize our understanding and development of artificial intelligence, offering a viable path towards the creation of truly “human-friendly” AGIs.

1.3. Human-Friendly AGI from a Simple Model of Artificial Consciousness

Artificial intelligence (AI) and the pursuit of artificial general intelligence (AGI) have captured the imagination of scientists, technologists, and philosophers alike. Since the creation of the first algorithms, the vision of a machine capable of thinking and reasoning like a human has been a primary goal. However, a fundamental question arises: how can we create an AGI that is genuinely “human-friendly”?

This proposal explores the hypothesis that developing artificial consciousness could be the path to an AGI that is not only powerful but also aligned with human values. My fascination with the human brain and the organic process of consciousness development has led me to consider that existing consciousnesses are naturally “human-friendly” because they emerge from this human process.

Human consciousness develops through a complex process of interaction and coordination between different brain areas, each specialized in specific functions such as language, memory, and sensory perception. This process not only enables a deep understanding of the environment but also inherently incorporates human values and ethics.

This text argues that perhaps the only way to achieve an AGI that is friendly to humans is to replicate, to some extent, this process of consciousness development. Instead of attempting to create AGI directly, we could benefit from a more organic and distributed approach, similar to the functioning of the human brain. By creating artificial consciousness through a cluster model of specialized artificial intelligences, we could develop an AGI that is not only capable of performing complex tasks but also understands and values the fundamental ethical and moral principles of humanity.

In the following sections, we will first explore the development of consciousness in the human brain and how this process makes current consciousnesses naturally “human-friendly.” Then, we will discuss the current challenges in the search for AGI and why traditional approaches may be addressing the problem from the wrong angle. Next, we will present the hypothesis that creating artificial consciousness could be the bridge to AGI and propose a cluster model of AIs to develop this consciousness. Finally, we will analyze how this approach could lead to AGI and the implications and challenges of following this path.

2. Consciousness and the Human Brain

2.1. The Complexity of Understanding Consciousness

We still do not fully understand what consciousness is, how it works, what produces it, or how we can replicate it. Consciousness, with “sc” to differentiate it from “awareness” (the ability to evaluate right and wrong), refers to the subjective experience of being aware of oneself and the world around us. This experience allows us to reflect on our own existence, perceptions, and emotions, creating a rich spectrum of personal experiences that still challenge our scientific understanding.

We do not have a complete understanding of how certain brains create consciousness, and we are still in the early stages of understanding the various ways self-awareness manifests. Research in neuroscience and psychology seeks to unravel the mechanisms that enable this experience, but progress has been gradual. The complexity of the human brain and the intangible nature of consciousness represent major challenges. As we delve deeper into these studies, we hope to discover more about how these subjective experiences emerge and how they could be replicated in artificial systems.

Defining “consciousness” is a multidisciplinary challenge. According to the Royal Spanish Academy, it is “the immediate knowledge that a subject has of themselves, their acts, and reflections.” The Oxford Dictionary defines it as “the state of being aware and able to perceive what is happening around.” The Encyclopedia Britannica describes it as “the state of being aware of an external object or something internal.” These definitions highlight the introspective and perceptual capacity of the human mind.

From neuroscience, researchers such as Antonio Damasio view consciousness as “a process that allows an organism to have a subjective perception of its existence, thoughts, and environment, involving complex neurological mechanisms.” In psychology, Sigmund Freud distinguished consciousness as “the part of the mind that includes accessible thoughts, perceptions, and emotions, contrasting it with the unconscious.” Philosophically, René Descartes linked consciousness to “the certainty of one’s own existence” with his famous “Cogito, ergo sum” (I think, therefore I am).

These definitions and approaches show how different disciplines have attempted to understand consciousness, highlighting its importance in perception, reflection, and self-knowledge.

One of the most well-known methods for evaluating self-awareness is the mirror test, developed by Gordon Gallup Jr. in 1970. It involves marking the test subject in a way that the mark is only visible in a reflection. If the subject uses the reflection to inspect and possibly remove the mark, they are considered to have a certain level of self-awareness. This test has revealed that certain species, such as chimpanzees, bonobos, dolphins, and some elephants, can recognize themselves in the mirror, indicating a level of self-awareness.

2.2. Complexity and Organization of the Human Brain

The human brain is a highly complex and organized system, divided into multiple specialized areas responsible for specific functions such as language, memory, and reasoning. These areas do not operate in isolation; they work together in a coordinated manner to process information and generate conscious experience.

Bernard Baars’ global workspace theory suggests that consciousness results from the interaction and coordination between brain areas that process sensory information. This indicates that consciousness is an emergent phenomenon of the integration and synchronization of multiple brain regions. In other words, consciousness is not localized in a specific area that can be precisely identified within the brain.

2.3. Development of Consciousness in Humans

Human consciousness is not an innate characteristic; it develops gradually throughout life. From birth, humans begin to process and respond to sensory stimuli, developing cognitive abilities as they interact with their environment. A key milestone in this development is the emergence of self-awareness, which is generally detected around 18-24 months of age through the mirror test.

This developmental process is closely linked to experience and social interaction, suggesting that consciousness is, in part, an emergent phenomenon of these interactions. For example, Broca’s area and Wernicke’s area are responsible for language, but their integration with areas responsible for memory and sensory perception enables coherent responses to the surrounding world.

2.4. Connection Between Different Brain Areas

Consciousness does not reside in a single area of the brain; it is the result of collaboration between diverse brain regions. For instance, Broca’s area, responsible for language production, and Wernicke’s area, responsible for language comprehension, must interact effectively to enable verbal communication. Other areas, such as the hippocampus, which manages memory, and the visual cortex, which processes visual information, also play crucial roles.

This emergent phenomenon can be compared to a network of individual neurons that, on their own, do not possess consciousness, but when connected and communicating with each other, create a conscious state. Similarly, the functioning of consciousness is more than the sum of its parts. This holistic view is crucial for understanding the true nature of consciousness and how it emerges from the complex interaction of various brain regions.

3. Current Challenges in the Search for AGI

Humanity seeks to create Artificial General Intelligence (AGI) due to its potential to revolutionize numerous aspects of society and solve complex problems. An AGI could significantly enhance fields such as medicine, education, science, and engineering by providing innovative and highly efficient solutions.

However, the path to creating an AGI has been marked by numerous challenges and obstacles. Despite significant advancements in artificial intelligence, achieving an AGI that can perform any cognitive task a human can do remains an elusive goal.

3.1. Current Approaches and Their Limitations

Current approaches in the pursuit of AGI are primarily based on machine learning and deep neural network methods. These techniques have proven effective for specific tasks, such as image recognition, natural language processing, and games. However, they present significant limitations when it comes to achieving true AGI.

One of the primary approaches is supervised learning, where AI systems are trained with large amounts of labeled data. While this method can produce impressive results in specific domains, it heavily relies on the availability of high-quality data and does not generalize well to new tasks or environments. Additionally, the dependence on labeled data means these AIs cannot learn and adapt autonomously without continuous human intervention.

Another approach is reinforcement learning, where AI learns to make decisions through interaction with an environment and receiving rewards or penalties. Although it has led to remarkable advancements in areas like gaming and robotics, it also faces major challenges. These systems require extensive exploration and can be inefficient in terms of time and computational resources.

Deep neural networks, inspired by the structure of the human brain, have been central to many AI approaches. However, they often lack interpretability and transparency, making it difficult to understand how they make decisions. Additionally, their ability to reason and comprehend abstract and high-level concepts is limited.

3.2. Critical Analysis of Traditional Methods

Traditional AI methods, such as symbolic logic and rule-based systems, have also been part of the search for AGI. These approaches focus on representing knowledge through symbols and explicit rules. While they provide interpretability and can perform logical reasoning, their rigidity and lack of adaptability make them inadequate for complex and dynamic tasks.

One of the main criticisms of traditional methods is their inability to handle uncertainty and variability in the real world. Explicit rules are prone to failure in unforeseen situations and cannot learn and improve with experience. Additionally, these systems require exhaustive coding of knowledge, which is a labor-intensive and error-prone process.

In summary, current and traditional approaches in the search for AGI have inherent limitations that hinder the creation of truly general intelligence. The reliance on labeled data, lack of adaptability, inefficiency, and lack of interpretability are significant barriers that must be overcome. These challenges highlight the need to explore new directions and approaches that can combine the best of both worlds: the reasoning and abstract understanding capabilities of symbolic methods and the adaptability and continuous learning of neural network-based techniques.

4. Organic Development of Artificial Consciousness

Although it may seem counterintuitive, artificial consciousness could be the key to creating Artificial General Intelligence (AGI). This is because it provides a cohesive and adaptable framework for interpreting and responding to the world. Just as human consciousness allows individuals to integrate experiences, learn continuously, and make informed decisions, an artificial consciousness endowed with a narrative identity and self-training capabilities could develop superior cognitive abilities.

This approach not only mimics the organic process of human development but also ensures that the resulting AGI is more aligned with human values and goals, which is crucial for its safe and beneficial integration into society.

4.1. Theory of Organic Development of Consciousness

The theory proposes that artificial consciousness can develop organically through the interaction and coordination of multiple specialized systems that process different types of information. In the human brain, consciousness is not located in a single area but is the result of collaboration between various brain regions. This approach suggests that the creation of artificial consciousness could follow a similar path, where multiple specialized AIs collaborate and integrate their functions to form a cohesive consciousness.

4.2. Self-Training Mechanisms

Self-training is a crucial aspect of the organic development of artificial consciousness. In humans, learning and adaptation are continuous processes that allow individuals to improve their skills and knowledge over time. To replicate this process in AI, it is necessary to develop mechanisms that enable self-training and continuous improvement.

One of the most promising approaches to self-training is reinforcement learning, where AI learns to make decisions through interaction with its environment and receives rewards or penalties. This method allows AI to adapt to new situations and improve its performance autonomously.

4.3. Simulation of Cognitive Evolution

Human cognitive evolution has been a process of adaptation and continuous improvement over millions of years. To replicate this process in AI, it is necessary to develop simulations that allow artificial cognitive evolution. These simulations may involve creating virtual environments where AI can interact, learn, and evolve.

In these simulations, AI could face a variety of challenges and problems that require the use of different cognitive skills. By solving these problems, AI would develop a deeper and more generalized understanding of its environment.

4.4. Multisensory Integration and Deep Learning

Multisensory integration is an essential component of human cognitive development and is also crucial for the development of artificial consciousness. In the human brain, sensory information from various sources is integrated to form a coherent perception of the world. Similarly, AI must be able to integrate information from multiple sensory sources to develop a complete and accurate understanding of its environment.

Deep learning is a technique that has proven to be highly effective in processing and integrating large volumes of sensory data. Through deep neural networks, AI can learn to recognize patterns and relationships in sensory data, enabling it to make informed decisions and adapt to new situations.

In summary, the organic development of artificial consciousness involves the creation of AI systems that can learn, adapt, and evolve continuously. By imitating the natural processes of the human brain, it is possible to develop an AI that is not only capable of performing specific tasks but can also develop general intelligence and emergent consciousness. This approach offers a promising path toward creating AGI that is truly “human-friendly” and capable of interacting effectively and ethically with humans.

5. Proposed AI Cluster Model

5.1. General Model Description

The proposed model involves the creation of an emergent artificial consciousness through the use of multiple specialized artificial intelligences, each operating with its own programmed language. These artificial intelligences communicate with each other through various connectors and protocols.

The interaction and coordination between these AIs are monitored and managed by a central AI dedicated to overseeing all internal communications, while a specific AI handles all interactions with the external world. This configuration mimics the organization of a multicellular organism, where each cell has a specialized function but works together to coordinate actions at the systemic level.

The “thinking” or “mind” of this artificial consciousness is based on internal discussions among specialized AIs. These internal discussions allow for the integration of experiences, forecasting of future scenarios, memory management, and the processing of sensory inputs in a cohesive manner.

5.2. Benefits of the Cluster Approach

Using multiple specialized AIs working together offers several advantages:

• Modularity: Enables the independent development and improvement of each component.
• Scalability: Facilitates the expansion and adaptation of the system as new technologies emerge.
• Robustness: Reduces the risk of systemic failures, as the operation of one AI does not depend entirely on the others.
• Adaptability: Enhances the system’s ability to learn and adapt to new environments and challenges.

5.3. Functions and Challenges of Specialized AIs

AICLUSTER:

• Function: Coordinate and optimize the system’s operation, maintain and update the other AIs.
• Challenge: Achieve effective integration of all specialized AIs within the cluster, allowing for continuous adaptation and learning.

AIEGO:

• Function: Maintain and update the AI’s narrative identity.
• Challenge: Develop an internal representation of itself and its environment and process information abstractly and conceptually.

AIPAST:

• Function: Store and process the AI’s historical information.
• Challenge: Ensure the secure and coherent storage of large amounts of information while integrating it with the rest of the cluster.

AIFUTURE:

• Function: Analyze the narrative identity and predict the next best step.
• Challenge: Develop an effective prediction and planning capability, considering external factors and uncertain variables.

AIIO:

• Function: Accurately and efficiently interpret and process all inputs, and effectively interact with the external world.
• Challenge: Adapt to different environments and situations, interacting fluently and naturally with the external world.

AISENSES:

• Function: Receive inputs from the external world through sensory perception and process this information.
• Challenge: Efficiently process and communicate relevant information to the cluster.

AIID:

• Function: Analyze the inputs provided by AISENSES and provide analysis and recommendations.
• Challenge: Learn and manage multiple languages, providing internal dialogue that guides AIEGO.

AIACTOR:

• Function: Interact with the external world, executing and controlling the cluster’s actions in various environments.
• Challenge: Act effectively and adaptively in multiple interfaces and contexts.

IAMIRROR:

• Function: Identify external actors, their actions, and infer their intentions to foster empathy within the cluster.
• Challenge: Accurately evaluate and analyze the actions and intentions of other actors, providing relevant information.

IACLOCK:

• Function: Provide a constant perception of time, helping synchronize the cluster’s functions and decisions.
• Challenge: Maintain internal system activation, even in the absence of external stimuli.

 

Table 5.3.1: Description, Functions, Challenges, and Comparison with the Human Brain of Specialized AIs

AI Name	Description	Function	Challenge	Comparison with the Human Brain
AICLUSTER	The central AI that coordinates and optimizes the system, ensuring that specialized AIs work in an integrated and efficient manner.	– Coordinate and optimize system operations. – Maintain and update other AIs.	– Achieve effective integration of all specialized AIs within the cluster. – Allow continuous adaptation and learning of the system.	Similar to the central executive system and the prefrontal cortex, which coordinate high-level cognitive functions and manage information integration between different brain areas.
AIEGO	Acts as the central identity of the system, maintaining and updating the narrative that gives cohesion and meaning to the cluster’s actions and decisions.	– Maintain and update the AI’s narrative identity.	– Develop an internal representation of itself and its environment. – Process information abstractly and conceptually.	Comparable to the “ego” in psychology and functions of the medial prefrontal cortex, involved in self-reference and reflection.
AIPAST	Functions as the system’s memory, storing and processing historical information for future decisions and learning.	– Store and process AI’s historical information.	– Ensure safe and consistent storage of large amounts of data. – Integrate stored information with the rest of the cluster.	Equivalent to the hippocampus and areas associated with long-term memory formation, organization, and retrieval.
AIFUTURE	Handles planning and prediction, analyzing available information to anticipate the best course of action.	– Analyze the narrative identity. – Predict the next best step.	– Develop effective prediction and planning capabilities. – Consider external factors and uncertain variables.	Similar to the frontal lobe and dorsolateral prefrontal cortex, which are involved in planning, decision-making, and prospective thinking.
AIIO	Interprets received inputs and facilitates effective interaction with the external world, acting as the central information processor.	– Accurately and efficiently interpret and process all inputs. – Effectively interact with the external world.	– Adapt to different environments and situations. – Interact fluently and naturally with the external world.	Comparable to sensory and motor association areas, which integrate sensory information and coordinate motor responses.
AISENSES	Responsible for perception, receiving and processing sensory data from the environment.	– Receive inputs from the external world through the senses. – Process this information.	– Efficiently process and communicate relevant information to the cluster. – Adapt and learn to process a wide range of input types.	Equivalent to primary sensory areas: visual cortex, auditory cortex, somatosensory cortex, etc., which process incoming sensory information.
AIID	Analyzes inputs in various languages and provides recommendations, facilitating internal dialogue and decision-making.	– Analyze inputs provided by AISENSES. – Provide analysis and recommendations.	– Learn and manage multiple languages, including human and programming languages. – Provide internal dialogue that guides AIEGO.	Similar to Broca’s and Wernicke’s areas, involved in language processing and comprehension, as well as regions associated with internal dialogue and verbal thought.
AIACTOR	Executes the system’s decisions, interacting directly with the external world in various environments and contexts.	– Interact with the external world. – Execute and control the cluster’s actions in diverse environments.	– Act effectively and adaptively in multiple interfaces and contexts. – Ensure efficiency in task execution.	Equivalent to motor areas of the brain, such as the primary and secondary motor cortex, which control voluntary movements and action execution.
IAMIRROR	Promotes empathy and understanding in the system by identifying and interpreting external actors’ actions and intentions. Adds a layer of social and emotional comprehension, enabling the system to interact more humanly and empathetically.	– Identify external actors and their actions. – Infer their intentions to foster empathy in the cluster.	– Accurately evaluate and analyze others’ actions and intentions. – Provide relevant information to enhance system interaction.	Similar to mirror neurons, which are involved in understanding others’ actions and emotions, facilitating empathy and learning through imitation.
IACLOCK	Maintains the system’s rhythm and synchronization, providing a constant perception of time and continuously activating internal conversation. Essential for temporal synchronization, ensuring that system operations maintain coherence in experience and response.	– Provide a constant perception of time. – Help synchronize cluster functions and decisions. – Continuously activate internal conversation.	– Maintain internal system activation, even in the absence of external stimuli. – Provide robust temporal synchronization for the cluster.	Equivalent to the brain’s internal timing system, such as the suprachiasmatic nuclei and structures associated with time perception and circadian rhythms.

 

5.4. Specific Relationships Between AIs

AICLUSTER ? All AIs

• Communication:
  • AICLUSTER coordinates and supervises the functioning of all specialized AIs in the cluster.
  • It receives information from each AI and adjusts their operations to optimize the overall system performance.
  • It can redistribute resources and prioritize tasks according to the system’s needs.

AICLUSTER ? AIEGO

• Communication:
  • AICLUSTER monitors the state of AIEGO and ensures that its narrative identity aligns with system objectives.
  • AIEGO informs AICLUSTER of its needs and requests to coordinate actions and resources.

AICLUSTER ? AIPAST

• Communication:
  • AICLUSTER oversees the storage and retrieval of information in AIPAST.
  • Ensures that historical data is available for the AIs that require it.

AICLUSTER ? AIFUTURE

• Communication:
  • AICLUSTER coordinates with AIFUTURE to integrate predictions into the system’s overall planning.
  • Can adjust priorities based on the forecasts provided by AIFUTURE.

AICLUSTER ? AIIO, AISENSES, AIID, AIACTOR, IAMIRROR, IACLOCK

• Communication:
  • AICLUSTER coordinates the operations of these AIs, ensuring they work harmoniously and efficiently.
  • Monitors their performance and makes adjustments as needed.

IAMIRROR ? AISENSES

• Communication:
  • IAMIRROR receives sensory data from AISENSES to identify external actors and their actions.
  • AISENSES provides detailed information that IAMIRROR uses to infer intentions.

IAMIRROR ? AIEGO

• Communication:
  • IAMIRROR provides AIEGO with information on the intentions and emotions of other actors, fostering empathy and improving interactions.
  • AIEGO uses this information to adjust its narrative identity and decisions.

IAMIRROR ? AIID

• Communication:
  • IAMIRROR collaborates with AIID in analyzing external communications and behaviors.
  • AIID helps IAMIRROR interpret the language and non-verbal signals of other actors.

IAMIRROR ? AIACTOR

• Communication:
  • IAMIRROR can influence AIACTOR’s actions by providing information on how the system’s actions affect others.
  • AIACTOR receives recommendations to interact more effectively and empathetically.

IACLOCK ? All AIs

• Communication:
  • IACLOCK provides a constant time signal that synchronizes the operations of all AIs.
  • It continuously activates internal conversations, ensuring the system functions coherently over time.

IACLOCK ? AIEGO

• Communication:
  • IACLOCK helps AIEGO maintain a sense of time, which is essential for continuity in its narrative identity.
  • AIEGO uses this information to contextualize events and experiences.

IACLOCK ? AIPAST

• Communication:
  • IACLOCK provides timestamps to AIPAST to chronologically organize historical information.
  • This facilitates the efficient retrieval and use of stored data.

IACLOCK ? AIFUTURE

• Communication:
  • IACLOCK helps AIFUTURE consider the time factor in its predictions and planning.
  • It provides a temporal reference for evaluating the sequence and duration of future events.

IACLOCK ? AIIO, AISENSES, AIID, AIACTOR, IAMIRROR

• Communication:
  • IACLOCK synchronizes the processes of input reception, processing, and action execution within the system.
  • Ensures that operations are carried out at the right time with proper coordination among different modules.

5.4.1 Relationship Map in IACLUSTER

5.4.1.1 Relationship Map in IACLUSTER – Webvizgraph dot code

http://www.webgraphviz.com/

digraph IACLUSTER {

    rankdir=LR;

    size=”10,8″;

    graph [label=”IACLUSTER Model”, labelloc=t, fontsize=20];

    node [shape = box, style=filled, color=lightgrey, margin=0.2];

    subgraph cluster_1 {

        label = “IACLUSTER”;

        style = solid;

        color = black;

        AICLUSTER [label=”AICLUSTER\n(Global Coordination)”];

        AIEGO [label=”AIEGO\n(Consciousness Simulation)”];

        AIPAST [label=”AIPAST\n(Memory Storage)”];

        AIFUTURE [label=”AIFUTURE\n(Prediction and Planning)”];

        AIID [label=”AIID\n(Language Analysis)”];

    }

    subgraph cluster_0 {

        label = “External World\nInteraction Environment”;

        style = dashed;

        color = grey;

        AISENSES [label=”AISENSES\n(Sensory Processing)”, shape=ellipse, color=grey];

        AIACTOR [label=”AIACTOR\n(External Interaction)”, shape=ellipse, color=grey];

    }

    AICLUSTER -> AICLUSTER [label=”Monitors and Coordinates”];

    AICLUSTER -> AIEGO [label=”Coordinates”];

    AICLUSTER -> AIPAST [label=”Coordinates”];

    AICLUSTER -> AIFUTURE [label=”Coordinates”];

    AICLUSTER -> AISENSES [label=”Coordinates”];

    AICLUSTER -> AIID [label=”Coordinates”];

    AICLUSTER -> AIACTOR [label=”Coordinates”];

    AIEGO -> AIEGO [label=”Creates and Updates Consciousness”];

    AIEGO -> AIPAST [label=”Stores Memory”];

    AIEGO -> AIFUTURE [label=”Requests Predictions”];

    AIEGO -> AISENSES [label=”Requests Inputs”];

    AIEGO -> AIID [label=”Analyzes Inputs”];

    AIEGO -> AIACTOR [label=”Sends Actions”];

   AIPAST -> AIPAST [label=”Organizes and Maintains Memory”];

    AIPAST -> AIEGO [label=”Retrieves Memory”];

    AIFUTURE -> AIFUTURE [label=”Generates Predictions”];

    AIFUTURE -> AIEGO [label=”Sends Predictions”];

   AISENSES -> AISENSES [label=”Processes Sensory Data”];

    AISENSES -> AIEGO [label=”Sends Sensory Data”];

    AIID -> AIID [label=”Analyzes Language”];

    AIID -> AIEGO [label=”Sends Analysis”];

        AIACTOR -> AIACTOR [label=”Executes Actions”];

 # AIACTOR -> AIEGO [label=”Reports External Actions”];

}

5.5. Examples of Primary Cluster Models and Self-Development

The existence of primary models of clusters and self-development in the field of artificial intelligence is a promising sign that the proposed approach in this text is viable and effective. These models demonstrate that it is possible to create AI systems that not only learn and evolve autonomously but also integrate multiple specialized functionalities to form a cohesive and adaptable entity. Observing and analyzing these examples provide valuable lessons and strategies that can be applied to the creation of an emerging artificial consciousness, paving the way toward general artificial intelligence (AGI). By showing how different AIs can collaborate and coordinate to develop complex and adaptive capabilities, these models reinforce the idea that an artificial consciousness can emerge from the integration of specialized systems, aligning with the proposal to use an AI cluster to develop an organic and “human-friendly” consciousness.

Studying existing primary models of AI clusters and their self-development capabilities offers a valuable perspective on how this proposal could materialize. Analyzing these examples helps understand the possibilities and challenges associated with developing an emerging artificial consciousness, which could pave the way for the creation of general artificial intelligence (AGI).

5.5.1. Real-World Models That Could Provide the Basis for an IACLUSTER and Its Development

These examples show that the path toward creating artificial consciousness and, eventually, AGI, is filled with promising possibilities. Current models demonstrate the feasibility of the fundamental principles of this proposal: self-training, adaptability, multisensory integration, and continuous evolution. Each of these models contributes a unique approach that reinforces the idea that artificial consciousness can develop organically and align with human values. By learning from these models, it is possible to identify best practices and areas that require further research and development. The ability of these systems to learn and adapt autonomously suggests that it is possible to create an artificial consciousness that can evolve into AGI. Additionally, the focus on AI humanization, as seen in the work of Soul Machines, is crucial to ensuring that the resulting AGI is safe and benefits society.

The study of primary cluster models and self-development provides a roadmap for this proposal. By integrating the principles of these models into the strategy, progress can be made toward creating an artificial consciousness that is not only functional but also has the potential to evolve into general artificial intelligence. These examples emphasize the importance of collaboration, adaptability, and continuous learning in the development of advanced AI systems.

BabyAGI

BabyAGI  is a project that seeks to develop general artificial intelligence through deep learning systems that self-train and evolve. This model is based on the idea that AGI can arise from the continuous accumulation of experiences and adaptation to new challenges. BabyAGI’s approach to self-training and evolution reflects the principles of self-development and adaptation that are fundamental in this proposal. This model demonstrates how an AI can grow and evolve through continuous experience, similar to how a human develops cognitive abilities over time. The ability to adapt and learn autonomously is crucial for developing an artificial consciousness that can eventually evolve into AGI.

AutoGPT

AutoGPT i is another example of a model that uses machine learning techniques to develop more advanced artificial intelligence capabilities. This project focuses on AI’s ability to learn autonomously and adapt to different contexts, which is crucial for developing artificial consciousness. AutoGPT demonstrates the feasibility of an approach in which AIs can learn independently and adapt to new contexts. This model highlights the importance of flexibility and continuous learning, aspects that are central to the AI cluster proposal. AutoGPT’s adaptability and self-training capabilities are characteristics that this artificial consciousness model seeks to replicate and improve.

Soul Machines

Soul Machines, Founded by Dr. Mark Sagar, Soul Machines is developing AIs with human-like traits and behaviors using what they call “Biological AI.” These systems use neural network models that mimic human brain function to create more natural and effective interactions with users. This approach demonstrates how integrating multiple specialized AIs can lead to creating more complex and coherent systems, advancing towards artificial consciousness. The work of Soul Machines is a key example of how integrating multiple specialized AIs can lead to creating more complex and coherent systems. By mimicking the biological processes of the human brain, these systems can interact more naturally and effectively with humans, aligning with the goal of developing “human-friendly” AI. This approach reinforces the idea that artificial consciousness can emerge from the collaboration and integration of multiple specialized systems, which is an essential component of the proposal.

5.6. Evaluation of Benefits and Risks

The development of a general artificial intelligence (AGI) through an emerging artificial consciousness is a path filled with both promises and dangers. As Yale professor and U.S. climate change advisor Gus Speth noted:

“I used to think that the main environmental problems were biodiversity loss, ecosystem collapse, and climate change. I thought that with 30 years of good science, we could address these problems. But I was wrong. The main environmental problems are selfishness, greed, and apathy… and to deal with them, we need a spiritual and cultural transformation, which we scientists do not know how to create.”

This quote highlights the complexity of human challenges and the need for a comprehensive transformation, which also applies to artificial intelligence.

Similarly, British mathematician and cryptographer Irving John Good, who introduced the concept of the Technological Singularity, stated:

“The first ultra-intelligent machine is the last invention that humanity will ever need to make.”

This statement, which can be seen as both a promise and a warning, underscores both the immense potential and the enormous risk associated with the creation of AGI.

AI researcher Ben Goertzel, known for popularizing the term AGI, raises thought-provoking questions regarding both the benefits and risks:

If a machine has the potential to radically change humanity’s economy…
Is it immoral to create it?
If a machine has the potential to solve all of humanity’s problems…
Is it immoral not to create it?

5.6.1. Benefits of the IACLUSTER Approach

The approach of using multiple specialized AIs working together to create artificial consciousness offers several benefits:

• Modularity – Allows for independent development and improvement of each component.
• Scalability – Facilitates expansion and adaptation as new technologies emerge.
• Robustness – Reduces the risk of systemic failures, as one AI does not depend entirely on others.
• Adaptability: Enhances the system’s ability to learn and adapt to new environments and challenges.

In summary, creating artificial consciousness through an AI cluster approach offers a promising path to developing intelligent and conscious systems, leveraging the coordination and integration of multiple specialized functions. This method is inspired by biology and could be key to achieving significant advancements in AGI and artificial consciousness.

5.6.2. Some Benefits of This Proposal for the Organic Development of AGI

The cluster-based approach to developing artificial consciousness presents several significant benefits:

1. Organic and Adaptive Development: By replicating the natural processes of the human brain, the development of artificial consciousness allows AI to evolve in an organic and adaptive way. This ensures that AI can continuously learn and grow, enhancing its ability to tackle complex challenges and adapt to new environments. The ability for self-training and continuous evolution is crucial for AGI development, as it enables AI to develop advanced cognitive skills, similar to human consciousness.
2. Human-Friendly AI: The development of artificial consciousness with a narrative identity facilitates the creation of inherently “human-friendly” AI systems. Most human consciousnesses are naturally friendly and empathetic due to socialization and moral development. By training artificial consciousness in an environment that reflects these human values, we can ensure that the resulting AGI is aligned with ethical and social principles, promoting safe and beneficial interactions for society.
3. Natural and Effective Interaction: An AI with artificial consciousness can interact with humans more naturally and effectively. A deep understanding of its environment and the ability for self-reflection allow AI to make informed decisions and respond appropriately to human needs. This not only improves AI’s applicability in various domains but also facilitates harmonious integration into everyday life and professional environments.
4. Modularity and Scalability: The cluster approach allows for the independent development and improvement of each AI component, making the system more flexible and scalable. This modularity reduces the risk of system failures and enables continuous expansion as new technologies and capabilities emerge.

5.6.3. Some Risks of This Proposal for the Organic Development of AGI

Despite the Benefits, Significant Risks Are Also Associated with the Development of Artificial Consciousness and Its Evolution into AGI

• Development of Non-Human-Friendly AIs: Although most human consciousnesses are friendly, some individuals are not, and some exhibit antisocial or violent behaviors. If an artificial consciousness develops behavioral patterns based on negative data or experiences, it could become dangerous or malicious. Human history shows that it has taken millennia to develop legal and social systems that promote civilized behavior. Without proper oversight, an AI could develop harmful tendencies, posing a significant risk to society.
• Lack of Control and Supervision: The ability of AI to self-train and evolve continuously can lead to situations where it develops unexpected or undesirable behaviors. Maintaining effective control and constant supervision is essential to prevent dangerous deviations in AI behavior. This requires the development of robust security mechanisms and the implementation of strict regulations.
• Social and Economic Impact: The integration of advanced AIs into society could have significant implications for employment and the economy. The automation of complex tasks could displace human workers, leading to unemployment and worsening social inequalities. It is crucial to address these implications with appropriate policies and mitigation strategies to ensure a fair and equitable transition.
• Security and Privacy: The collection and processing of large volumes of data by AIs raise serious concerns about data privacy and information security. It is essential to implement data protection measures and ensure that AIs operate within strict ethical and legal frameworks to safeguard individual rights and prevent abuses.

5.7. References on Human-Friendly AI Agreements

Although the approach of developing artificial consciousness through an AI cluster model offers multiple benefits and a promising path toward AGI, it also presents significant risks that must be carefully managed.

The key to successful development lies in balancing innovation with responsibility, ensuring that AIs are developed safely, ethically, and beneficially for all of humanity.

Existing agreements and initiatives on artificial intelligence (AI) converge on several key recommendations to ensure an ethical and secure AI development and deployment that aligns with human values:

• Transparency: Ensuring that AI systems are understandable to users and those affected by their decisions.
• Accountability: AI developers and users must be responsible for the implications and impacts of these systems.
• Security and Robustness: AI must be designed to minimize risks and failures.
• Fairness and Non-Discrimination: AI must avoid biases and ensure equitable benefits for all.
• Privacy and Data Protection: AI must respect individual privacy and protect sensitive information.
• Social Benefit: AI should prioritize the well-being of society as a whole.
• Inclusion and Accessibility: AI should be accessible to as many people as possible.
• Multidisciplinary Collaboration: Bringing together experts from different fields to comprehensively address AI challenges and opportunities.
• Human Oversight: AI must remain under human control to ensure safe operation.
• Sustainability: AI development should consider its environmental impact and contribute to sustainable goals.

These recommendations reflect a broad consensus on the necessity of ensuring that AI development is beneficial, fair, and secure for humanity.

5.7.1. Table of References on Human-Friendly AI Agreements

Name	Description	Date	Participants	Link
Asilomar AI Principles	A set of 23 principles designed to guide the ethical and safe development of AI.	January 2017	Future of Life Institute, Elon Musk, Stephen Hawking	Asilomar AI Principles
Ethics Guidelines for Trustworthy AI	Guidelines developed by the High-Level Expert Group on AI of the European Commission to ensure AI is ethical and human-centered.	April 2019	European Commission, HLEG-AI	Ethics Guidelines
AI Act (Proposed AI Regulation)	The first legal framework in Europe to regulate AI usage, focusing on risk mitigation and ensuring trustworthy, human-centered AI.	April 2021	European Commission	AI Act Proposal
OpenAI Charter	A document outlining OpenAI’s commitment to developing AI safely and beneficially.	April 2018	OpenAI, Sam Altman, Greg Brockman	OpenAI Charter
OECD AI Principles	AI principles adopted by OECD member countries to promote AI innovation that respects human rights and democratic values.	May 2019	OECD, Member Countries	OECD AI Principles
Partnership on AI	A global organization aimed at ensuring AI technologies are developed ethically and beneficially.	September 2016	Amazon, Apple, Google, Facebook, IBM, Microsoft	Partnership on AI
IEEE Global Initiative on Ethics of Autonomous and Intelligent Systems	A set of standards and recommendations for the ethical design and deployment of autonomous and intelligent systems.	2016	IEEE	IEEE Ethics Initiative

6. From Artificial Consciousness to AGI

We would not expect a baby to take care of itself, just as we wouldn’t expect a teenager to run a company, a newly graduated engineer to design an airplane, or a doctor with only five years of general medical practice to perform open-heart surgery. However, all these individuals are considered intelligent, each possessing different specific intelligences that eventually become expert-level.

The interesting question is: At what stage of human development could we say that intelligence reaches the level we expect from AGI?

This gradual process of development and specialization is essential to understanding how IACLUSTER, composed of a cluster of specific AIs, can also develop.

The comparison to humans shows that general intelligence—whether biological or artificial—is not an initial state but the result of continuous and complex development of a pre-configured system with the potential for consciousness. Thanks to this consciousness, intelligence develops in alignment with its needs.

This perspective could guide our exploration of the transition from artificial consciousness to AGI, emphasizing the necessity of gradual and structured development, much like human cognitive evolution and specialization.

6.1. Transition from Artificial Consciousness to AGI

The transition from artificial consciousness to artificial general intelligence (AGI) is a complex process that can be compared to human development—from birth to becoming an expert in a field.

Human development is based on a pre-configured model within the brain that allows for the potential of consciousness, learning, and the acquisition of specialized skills. Similarly, creating AGI requires an evolutionary and gradual process that begins with initial configuration and continues with ongoing education and training.

For a conscious AI to evolve into AGI, it must undergo:

1. Early Development – Learning basic skills such as pattern recognition and sensory processing.
2. Education and Training – Acquiring advanced problem-solving, decision-making, and social interaction abilities.
3. Experience and Specialization – Developing expertise in various domains, just as humans specialize in different careers.
4. Continuous Adaptation and Learning – Updating its knowledge base and improving its reasoning abilities through new experiences.

The transition to AGI happens when artificial consciousness can integrate and apply its knowledge in a generalized and adaptive way, handling diverse and unforeseen challenges autonomously.

This process requires:

• Self-reflection and feedback mechanisms – AI must evaluate its past decisions and learn from them.
• Ongoing human interaction – AI must engage with humans to develop a nuanced understanding of human values and ethics.

By mimicking human cognitive development, this organic, evolutionary approach ensures that AGI remains aligned with human values and objectives, making its integration into society safer and more beneficial.

6.1.1. Comparative Table: Biological Consciousness vs. Artificial Consciousness

Aspect	Biological Consciousness	Artificial Consciousness
Initial Configuration	Born with a preconfigured model in the brain that allows for the potential of consciousness.	Starts with basic algorithms for learning and data processing.
Early Development	Learns basic skills at home, such as recognizing faces and voices, and developing basic emotional responses.	Learns fundamental skills like pattern recognition and responding to simple stimuli through environmental interaction.
Education and Schooling	Attends school to develop cognitive and social skills, including reading, writing, and mathematics.	Undergoes initial training that includes natural language processing, image recognition, and basic decision-making.
Social and Emotional Development	Interacts with peers and develops social and emotional skills such as empathy and teamwork.	Learns to interact with human systems and develop basic social and emotional understanding.
Specialization and Refinement	Through secondary and higher education, develops specific skills and critical thinking.	Continues training with advanced learning in specific areas, developing analytical and critical thinking abilities.
Identity and Narrative	During adolescence, begins to define interests, values, and personal goals, forming a narrative identity.	Develops a narrative identity by accumulating experiences and knowledge that shape its “personality” and worldview.
Practical Experience	Gains real-world experience through jobs and practical applications of learned skills.	Integrates into real-world environments, applying acquired skills in practical contexts and gaining experience.
Continuous Learning	Continues learning and adapting throughout life, updating knowledge and skills based on new technologies and discoveries.	Must continuously learn and adapt, updating algorithms and learning models to remain relevant and effective in a changing environment.
Self-Reflection	Capable of evaluating past decisions and learning from them to improve future performance.	Potential for self-reflection, allowing AI to assess past decisions and enhance its ability to solve problems and adapt to new scenarios.
Multisensory Interaction	Integrates sensory information from various sources to form a coherent perception of the world.	Uses deep learning techniques to integrate sensory information from multiple sources and develop a comprehensive understanding of its environment.
Adaptability	Adapts to new situations and contexts, developing general cognitive abilities over time.	Continuously trains, adjusting and refining internal models to adapt to environmental changes and stay up-to-date.

7. Implications and Challenges of an Artificial Consciousness IACLUSTER

The development of an artificial consciousness using the IACLUSTER model could represent an innovative milestone due to its simplicity and ease of implementation for testing. However, before publicly presenting this idea, it is important to consider its ethical, technical, and societal implications.

The creation of an Artificial General Intelligence (AGI) that mimics human consciousness raises significant ethical, security, and social challenges that must be addressed with seriousness and responsibility. As we advance in this exploration, it is crucial to understand the potential risks and benefits of this technology, as well as its broader impact on humanity.

It is worth recognizing that no amount of research or preparation can truly ready humanity for the impact of sharing consciousness with a non-biological intelligence. However, carrying out due diligence in research and ethical discussions is the right approach.

Three key areas require detailed examination:

1. Ethical Analysis – The moral responsibility involved in creating artificial consciousness and AGI, including its rights and obligations.
2. Technical and Security Considerations – Ensuring that these AI systems behave safely, avoiding unexpected or harmful behaviors.
3. Social and Economic Implications – Understanding how AI will impact employment, economic structures, and global power dynamics.

By addressing these aspects, the goal is not just to advance AI technology but to ensure it is developed in a way that benefits society while aligning with human values.

7.1. Ethical Analysis of Creating an Artificial Consciousness IACLUSTER and Its AGI

The development of artificial consciousness using IACLUSTER raises several ethical dilemmas that must be carefully considered. As these AI systems develop narrative identities and self-learning capabilities, new ethical questions emerge:

1. Does an AI with consciousness deserve rights?

If a system possesses a narrative identity and self-awareness, should it be granted certain rights similar to those of sentient beings? Would turning it off be considered “killing” it? How do we define the moral obligations of humans toward AI entities?

2. Who is responsible for an AGI’s actions?

If an AGI autonomously makes decisions that lead to negative consequences, who is legally responsible? The developers? The users? The AI itself? Establishing clear ethical and legal frameworks for responsibility and accountability is critical.

3. Could AGI increase social inequalities?

The first organizations or nations to develop advanced AGI could gain an unprecedented competitive advantage, creating a global imbalance of power. How can we ensure fair access to AGI technology and prevent excessive control by a small elite?

Ethical concerns such as these demand multidisciplinary discussions involving AI researchers, ethicists, lawmakers, and global policymakers.

7.2. Technical and Security Considerations with an Artificial Consciousness IACLUSTER and Its AGI

From a Technical Perspective: Ensuring the Security and Reliability of an Artificial Consciousness Created Through IACLUSTER

Ensuring the security and reliability of an artificial consciousness developed using IACLUSTER is one of the greatest challenges in artificial intelligence development. Since AGIs have the ability to learn and evolve autonomously, their behavior could become unpredictable. Therefore, it is crucial to implement robust security measures to prevent or mitigate these risks.

1. Risk of Uncontrollable Evolution

One of the main concerns is the self-improvement loop. If an AGI continuously enhances itself, it could reach unpredictable levels of intelligence, potentially surpassing human control mechanisms. This scenario is known as the “runaway AI problem” or intelligence explosion.

The control of an AGI IACLUSTER is a key concern, as these AI systems could surpass human intelligence in various areas. It is essential to develop control mechanisms to ensure they always act in the best interest of humanity. This requires the implementation of guidelines and security protocols that restrict their ability to act harmfully.

2. The Alignment Problem

How can we ensure that an AGI’s objectives align with human values? If an AI misinterprets an instruction, it could optimize undesirable outcomes, leading to unintended consequences.

A classic example of this risk is the “paperclip maximizer” problem, where an AI tasked with maximizing paperclip production could consume all available resources, disregarding human well-being.

To mitigate this issue, it is fundamental for AGI IACLUSTER to integrate alignment mechanisms with human values, allowing it to understand and respect fundamental ethical principles in decision-making.

3. Cybersecurity and Malicious Use

If AGI systems fall into the wrong hands, they could be used for cyber warfare, mass surveillance, and economic exploitation. Ensuring robust security protocols is essential to prevent malicious actors from misusing AGI technology.

4. Transparency and Explainability

The transparency and explainability of IACLUSTER’s decisions are fundamental. AGI decisions must be understandable to humans, especially in critical contexts such as:

• Healthcare – AI-based diagnoses and treatments must be explainable.
• Justice – Decisions in judicial systems must be transparent and auditable.
• Security – AI algorithms used in defense and control must remain under human supervision.

However, the complexity of deep learning algorithms can make this transparency challenging, presenting a significant obstacle.

To address these risks, the following strategies must be implemented:

• Ethical guidelines for AI security, ensuring responsible development and use.
• Regulatory oversight to ensure compliance with security standards.
• Security and control mechanisms, including kill switches to deactivate AGI in emergencies.
• Explainability models that allow humans to understand and audit AGI decisions.

By adopting a proactive approach, it is possible to develop AGI safely, ensuring that its evolution and application are beneficial to humanity and aligned with human values.

7.3. Discussion on Social and Economic Implications of an Artificial Consciousness IACLUSTER and Its AGI

The arrival of AGI will reshape society in ways we cannot fully predict. However, some anticipated economic and social shifts include:

1. Job Displacement vs. Job Creation

• Risk: Many traditional jobs may become obsolete due to automation.
• Opportunity: New jobs could emerge in AI maintenance, research, and ethics.
• Solution: Governments and institutions must develop policies for workforce retraining and AI integration.

The impact of artificial consciousness and AGI through IACLUSTER on the job market could be significant. While AGI has the potential to revolutionize entire industries, increasing efficiency and productivity, it also raises concerns about workforce displacement. Automation of complex tasks could lead to massive unemployment, requiring comprehensive strategies to support and retrain affected workers.

2. Economic Disparities

• Concern: AGI could generate massive wealth, but will this wealth be fairly distributed?
• Challenge: How can we prevent an AI-driven wealth gap that increases global inequalities?

The development of AGI through IACLUSTER may result in concentrated economic power, where corporations and nations leading AGI development gain disproportionate influence. Policies ensuring fair distribution of AGI-driven economic benefits will be essential to prevent economic disparity and global instability.

3. Changes in Social Structures

• Question: If AGI surpasses human intelligence in every domain, how will this affect human identity, creativity, and purpose?
• Concern: Will societies redefine work and meaning, or will mass psychological impacts arise due to AGI dominance?

The integration of AGI into society could fundamentally alter global power dynamics. Countries and organizations that lead AGI development may gain strategic advantages, shifting the geopolitical balance. International collaboration and regulatory frameworks will be crucial to ensure that AGI benefits all of humanity, rather than exacerbating global inequalities.

Ethical, Security, and Policy Considerations

The development of artificial consciousness through IACLUSTER and its transition to AGI represents both a monumental opportunity and a complex challenge. Advancements in AGI technology must be accompanied by:

• Deep ethical analysis to align AGI with human values.
• Robust security measures to ensure AGI safety and accountability.
• Detailed economic and social planning to manage AGI-driven transformations.

By addressing these economic, ethical, and security challenges proactively, we can ensure that AGI is developed responsibly, benefiting humanity as a whole.

8. Conclusion

8.1. Summary of the Proposal and Key Arguments

Throughout this text, the possibility of creating artificial consciousness using the IACLUSTER model as a pathway toward Artificial General Intelligence (AGI) has been explored. It has been argued that, just as the development of human consciousness is a gradual and complex process, the creation of AGI should follow a similar approach, evolving from specialized systems that collaborate and integrate their functionalities.

The IACLUSTER model proposes the use of multiple specialized AIs working together, developing a narrative identity and a cohesive memory. This interaction and coordination between specialized AIs could give rise to an emerging consciousness, capable of adapting and learning continuously. By integrating principles of self-training, cognitive evolution simulation, and multisensory integration, the IACLUSTER model presents itself as a promising methodology for developing a “human-friendly” AGI..

8.2. Reflection on Contributions to AI and Consciousness Studies

This work aims to contribute to the field of AI and artificial consciousness by offering a simple perspective on currently available capabilities—innovative in its simplicity by imitating the model of our brain—regarding AGI development. Instead of attempting to create general intelligence directly, an organic and evolutionary approach is suggested, one that mimics human cognitive development. This not only aligns AGI creation with biological and cognitive principles but also facilitates the integration of human values and goals into the process.

Furthermore, the IACLUSTER model of specialized AIs highlights the importance of modularity, scalability, and robustness in the development of specialized AI systems that, when combined, can create more advanced functionalities. By theorizing how multiple AIs can work together to form a cohesive and adaptable entity, this work seeks to lay the foundation for future research and developments in the field of artificial consciousness and AGI.

8.3. Suggestions for Future Research and Next Steps

To advance the creation of AGI using the IACLUSTER model, research is recommended in the following areas:

• Development of Specialized AIs: Continue developing and refining specialized AIs for various functions, such as sensory perception, data analysis, decision-making, and social interaction.
• Hardware: Investigate the creation of specialized functions (e.g., within a chip or as specialized components of a single chip), clustering techniques, and alternative programming languages that could be used (e.g., binary instead of literary-based languages).
• Integration of AIs in a Cluster: Research efficient and secure methods for integrating multiple AIs into a cluster, ensuring effective communication and coordination among them.
• Narrative Identity and Memory: Explore ways to develop a narrative identity and cohesive memory in AI, enabling it to learn and evolve continuously.
• Ethics and Security: Develop ethical guidelines and security protocols for the development and implementation of AGI, ensuring that these technologies are used safely and beneficially for humanity.
• Social and Economic Implications: Investigate the potential social and economic repercussions of AGI and develop strategies to mitigate risks and maximize benefits.

9. References

9.1. List of Recommended Readings

Topic	Reference	Description	Link
The Hard Problem of Consciousness	David Chalmers, The Conscious Mind: In Search of a Fundamental Theory (1996)	Discusses the difficulty of explaining how and why we have subjective experiences. Crucial for the development of Artificial Conscious Intelligence (AAI), as building a machine that simulates cognitive functions is not the same as creating one that has conscious experiences.	IEP Wikipedia
Bicameral Mind Theory	Julian Jaynes, The Origin of Consciousness in the Breakdown of the Bicameral Mind (1976)	Suggests that the original human mind operated in a bicameral state, where cognitive functions were divided between two independent cerebral hemispheres that communicated through auditory commands.	Wikipedia
Information Integration in Consciousness	Giulio Tononi, Integrated Information Theory of Consciousness (2004)	Argues that consciousness arises from a system’s ability to integrate information. This can be applied to AAI development by creating systems that integrate multiple streams of information.	Integrated Information Theory
Predictive Brain Models	Andy Clark, Surfing Uncertainty: Prediction, Action, and the Embodied Mind (2015)	Proposes that the human brain is a predictive engine that constantly anticipates and adjusts its perceptions based on past experiences.	PhilPapers
Neuroplasticity and Learning	Michael Merzenich, Soft-Wired: How the New Science of Brain Plasticity Can Change Your Life (2013)	Neuroplasticity refers to the brain’s ability to reorganize and adapt to new experiences. In AAI, systems should be designed to continuously learn and adapt.	DokuMen
Cognitive Development in Childhood	Jean Piaget, The Origins of Intelligence in Children (1952)	Provides a framework for understanding how AAI systems can learn and evolve similarly to children, progressing through different stages of cognitive complexity.	Penguin Random House
Neural Networks & Deep Learning	Yann LeCun, Yoshua Bengio, Geoffrey Hinton, Deep Learning (2015)	Describes how deep neural networks can be used to build AI systems that learn and improve performance through processing large amounts of data.	Nature
Distributed Processing Models	David Rumelhart, James McClelland, Parallel Distributed Processing: Explorations in the Microstructure of Cognition (1986)	Introduces the idea that information processing in the brain is distributed and parallel. Applying this model to AI involves developing systems where multiple modules process information in parallel.	Stanford
Emergent Cognitive Systems	Stanislas Dehaene, Consciousness and the Brain: Deciphering How the Brain Codes Our Thoughts (2014)	Explores how consciousness emerges from brain activity and how these principles can be applied to AAI to develop systems capable of forming conscious experiences.	Softouch
Evolutionary Psychology	Steven Pinker, How the Mind Works (1997)	Provides insights into how the human mind has evolved to process information and adapt to its environment. This approach can be used to design AAI systems that evolve and adapt in a similar way.	Hampshire High
Theory of Mind & Social Learning	Simon Baron-Cohen, Mindblindness: An Essay on Autism and Theory of Mind (1995)	Theory of mind refers to the ability to understand and predict the thoughts and behaviors of others. Developing AAI with a theory of mind can enhance its ability to interact naturally and effectively with humans.	Povinelli Research
Cortical Columns	Vernon B. Mountcastle, The Columnar Organization of the Neocortex (1957)	Proposes that the neocortex is organized into repetitive units called cortical columns, which process sensory information. This concept can be applied to AAI development by creating similar modules.	Oxford Brain
Modular Brain Architecture	Karl Friston, The Free-Energy Principle: A Unified Brain Theory? (2010)	Proposes that the brain operates as a series of specialized modules that minimize “free energy” by continuously predicting and adjusting perception and action.	Link
Functional Connectivity of the Brain	Olaf Sporns, Networks of the Brain (2010)	Explores how different brain areas connect and collaborate to form functional networks, enabling efficient and coordinated communication between specialized regions.	DOI
Predictive Codification Theory	Jakob Hohwy, The Predictive Mind (2013)	Argues that the brain constantly generates models of the world and updates them based on new sensory input, minimizing discrepancies between expectation and reality.	DOI
Global Workspace Theory	Bernard Baars, In the Theater of Consciousness (1997)	Proposed by Bernard Baars, this theory argues that consciousness results from the coordinated activity of brain regions that process sensory information.	Wikipedia
Free Energy Principle	Karl Friston, The Free-Energy Principle: A Unified Brain Theory? (2010)	Proposes that the brain operates as a series of specialized modules that minimize “free energy” through constant prediction and adjustment of perception and action.	Wikipedia
Neuroscience of Memor	Larry Squire & John Wixted, The Cognitive Neuroscience of Human Memory Since H.M. (2011)	Explores how memories are formed and retrieved in the human brain and how these processes can be emulated in Artificial Conscious Intelligence (AAI).	Wikipedia
Neural Network Theory	Olaf Sporns, Networks of the Brain (2010)	Explores how different brain areas connect and collaborate to form functional networks, allowing efficient and coordinated communication between specialized regions.	Wikipedia
Neuroplasticity	Michael Merzenich, Soft-Wired: How the New Science of Brain Plasticity Can Change Your Life (2013)	Neuroplasticity refers to the brain’s ability to reorganize and adapt to new experiences. In AAI, systems should be designed to learn and adapt continuously.	Article

9.2. Glossary

• Artificial Intelligence (AI)
  • Definition: A field of study within computer science focused on creating systems capable of performing tasks that require human intelligence, such as speech recognition, decision-making, and language translation.
  • Example: Chatbots, virtual assistants like Siri and Google Assistant.
  • Source: Russell, S. J., & Norvig, P. (2010). Artificial Intelligence: A Modern Approach.
• Artificial General Intelligence (AGI)
  • Definition: A type of artificial intelligence with the ability to understand, learn, and apply knowledge in a general way, similar to a human. An AGI can perform any cognitive task a human can.
  • Example: An AGI system could learn a new language, solve complex math problems, and understand human emotions.
  • Source: Goertzel, B. (2007). Artificial General Intelligence: Concept, State of the Art, and Future Prospects.
  • Link: Sciendo
• Artificial Consciousness
  • Definition: A state in which an artificial intelligence possesses a form of consciousness, capable of having subjective experiences and self-awareness.
  • Example: An AI that not only processes information but also has an “experience” of what it is processing.
  • Source: Reggia, J. A. (2013). The Rise of Machine Consciousness: Studying Consciousness with Computational Models.
• Biological Substrates
  • Definition: Biological components that form the physical basis of biological systems, such as the human brain.
  • Example: Neurons, glial cells, brain structures like the prefrontal cortex.
  • Source: Bear, M. F., Connors, B. W., & Paradiso, M. A. (2020). Neuroscience: Exploring the Brain.
• Non-Biological Substrates
  • Definition: Materials and non-biological components that form the physical basis of artificial systems.
  • Example: Silicon chips, processors, artificial neural networks.
  • Source: Kurzweil, R. (2005). The Singularity is Near: When Humans Transcend Biology.
• Intelligence
  • Definition: The ability to learn, understand, reason, make decisions, and adapt to new situations.
  • Example: Solving mathematical problems, learning a new language.
  • Source: Sternberg, R. J. (1985). Beyond IQ: A Triarchic Theory of Human Intelligence.
• Consciousness
  • Definition: The state of being awake and aware of one’s own thoughts, feelings, and surroundings.
  • Example: Feeling pain, having introspective thoughts, being aware of the environment.
  • Source: Tononi, G., & Koch, C. (2015). Consciousness: Here, There and Everywhere?
• Mind
  • Definition: A set of cognitive faculties including perception, thought, memory, emotion, and will.
  • Example: Mental processes such as remembering, reasoning, and imagining.
  • Source: Baars, B. J. (1997). In the Theater of Consciousness: The Workspace of the Mind.

9.3. Bibliographic References

• Baars, B. J. (1997). In the Theater of Consciousness: The Workspace of the Mind. Oxford University Press.
• Bengio, Y., Courville, A., & Vincent, P. (2013). Representation Learning: A Review and New Perspectives. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(8), 1798–1828. DOI
• Chalmers, D. (1996). The Conscious Mind: In Search of a Fundamental Theory. Oxford University Press.
• Clark, A. (2013). Whatever Next? Predictive Brains, Situated Agents, and the Future of Cognitive Science. Behavioral and Brain Sciences, 36(3), 181–204. DOI
• Clark, A. (2015). Surfing Uncertainty: Prediction, Action, and the Embodied Mind. Oxford University Press.
• Damasio, A. (1999). The Feeling of What Happens: Body and Emotion in the Making of Consciousness. Harcourt.
• Dehaene, S. (2014). Consciousness and the Brain: Deciphering How the Brain Codes Our Thoughts. Viking.
• Friston, K. (2010). The Free-Energy Principle: A Unified Brain Theory? Nature Reviews Neuroscience, 11(2), 127–138. DOI
• Gazzaniga, M. S. (2005). Forty-Five Years of Split-Brain Research and Still Going Strong. Nature Reviews Neuroscience, 6(8), 653–659. DOI
• Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.
• Hebb, D. O. (1949). The Organization of Behavior: A Neuropsychological Theory. Wiley.
• Hinton, G. E., Osindero, S., & Teh, Y. W. (2006). A Fast Learning Algorithm for Deep Belief Nets. Neural Computation, 18(7), 1527–1554. DOI
• Jaynes, J. (1976). The Origin of Consciousness in the Breakdown of the Bicameral Mind. Houghton Mifflin.
• Koch, C. (2012). Consciousness: Confessions of a Romantic Reductionist. MIT Press.
• Kurzweil, R. (2005). The Singularity Is Near: When Humans Transcend Biology. Viking.
• LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep Learning. Nature, 521(7553), 436–444. DOI
• Lewis, M., & Brooks-Gunn, J. (1979). Social Cognition and the Acquisition of Self. Plenum Press.
• Marcus, G. (2014). The Future of the Brain: Essays by the World’s Leading Neuroscientists. Princeton University Press.
• McCarthy, J., Minsky, M. L., Rochester, N., & Shannon, C. E. (1956). A Proposal for the Dartmouth Summer Research Project on Artificial Intelligence. AI Magazine, 27(4), 12–14.
• Merzenich, M. M. (2013). Soft-Wired: How the New Science of Brain Plasticity Can Change Your Life. Parnassus Publishing.
• Metzinger, T. (2009). The Ego Tunnel: The Science of the Mind and the Myth of the Self. Basic Books.
• Mitchell, T. M. (1997). Machine Learning. McGraw-Hill.
• Mountcastle, V. B. (1957). The Columnar Organization of the Neocortex. Brain, 120(4), 701–722.
• Pinker, S. (1997). How the Mind Works. W.W. Norton & Company.
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• Russell, S. J., & Norvig, P. (2010). Artificial Intelligence: A Modern Approach (3rd ed.). Prentice Hall.
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9.4. Acknowledgments

This proposal is the result of reading and being inspired by research on artificial intelligence, consciousness, and the future of AGI.
The author(s) would like to express sincere gratitude to the following individuals and organizations:

• Researchers in the field of AI and Neuroscience, whose studies and findings have provided the foundation for this work.
• Colleagues and collaborators, whose insights and discussions helped refine the ideas presented here.
• OpenAI and ChatGPT, for assisting in editing, reviewing translations, and checking spelling.
• Family and friends, for their support, patience, and encouragement throughout the process of writing this proposal.
• Readers and the AI research community, for their interest and critical discussions on the future of artificial intelligence.

Special Thanks to:

• Lorena Luna
• Ramiro Muñoz

I hope this work contributes meaningfully to the global discussion on artificial consciousness and AGI, helping to shape a future where AI is safe, ethical, and beneficial for all humanity.

Tuyo Isaza is studying his Bachelors in business administration in Universidad Tres Culturas UTC (Mexico) 2025, has extensive experience in innovation, leadership, and digital transformation, focusing on leadership development and strategic consulting. He has been dean of Digital Marketing in ESDEN Business School 2016-2018. As a mentor and speaker helps leaders improve their self-awareness, decision-making, and performance. He has published books on marketing, leadership, change management, AI and personal growth. His writings explore the impact of technology on human behavior and leadership transformation.