Keywords and notes on a humanoid AI (Part 3)
This is the sequel to two parts that I have published here and here.
Another idea for humanoid AI would be a system that uses 3D network language to enable abstract thinking and creative problem-solving.
Theoretically, this system could open up new ways of thinking and representing knowledge. The 3D network language would allow AI to encode concepts as three-dimensional shapes or “figures” in the network. This could lead to a more intuitive and potentially more efficient way of processing information, closer to how the human brain works.
The challenge would be to implement the 3D network language in such a way that it actually leads to emergent properties and new ways of thinking. It would also be important to develop methods to translate the results of this 3D processing into forms that humans can understand.
To describe how this system works in more concrete terms, we could imagine how it could look and work in practice:
1. Physical Structure:
- A high-density 3D network of neuromorphic chips and optical components.
- Possibly housed in a spherical or cube-shaped housing structure.
- With dedicated input and output interfaces for different sensory modalities.
- Haptic interfaces make it possible to “touch” and manipulate the 3D shapes.
2. Concept processing:
- Incoming information is converted into 3D shapes.
- These shapes interact dynamically with each other in 3D space.
- Complex ideas could be represented as interlocking or moving 3D structures.
3. Problem solving:
- Problems are visualized as a 3D puzzle.
- The AI manipulates and recombines these 3D shapes to find solutions.
- Solutions appear as “paths” or “bridges” between concept shapes.
4. Edition and Interpretation:
- A specialized “translator” module converts the 3D concepts into human-understandable language or other forms of representation.
- Multimodal outputs could include text, speech, images, or even haptic feedback.
5. Learning process:
- New information leads to the formation of new 3D shapes or the modification of existing ones.
- The system could perform “concept evolutions” where 3D shapes evolve and adapt over time.
6. Creative processes:
- Random or targeted recombination of 3D shapes could give rise to completely new concepts.
- These could then be translated into different forms of output, e.g. new pieces of music, poems or scientific hypotheses.
7. Further development:
- The optimization of internal 3D structures is done by learning the system independently, not by direct external manipulation.
This description gives an insight into how such a system could look and work. It combines the abstract ideas of the 3D network language with concrete, conceivable implementations and applications.
The focus is now on effectively translating the internal concepts into understandable and useful outputs for human users.
Several innovative approaches could be considered for the material transmission method in such a 3D network, possibly even in combination:
1. Optical transmission:
- Light signals in photonics chips and optical fibers
- Advantages: Extremely high transmission speed, low energy consumption
- Challenge: Miniaturization of optical components
2. Neuromorphic Hardware:
- Memristors or other neuromorphic components
- Advantages: Energy efficiency, similarity to biological neurons
- Challenge: Scalability and reliability
3. Spintronics:
- Use of the electron spin for information transfer
- Advantages: High density, low energy consumption
- Challenge: Technological maturity
4. Quantum mechanical effects:
- Use of quantum states for information processing
- Pros: Potential for massive parallel processing
- Challenge: Maintaining quantum coherence
5. Molecular electronics:
- Use of individual molecules or nanostructures as switching elements
- Advantages: Extremely high packing density
- Challenge: Controllability and reproducibility
6. Superconducting circuits:
- Use of superconductors for lossless signal transmission
- Advantages: Extremely fast switching times, no resistance
- Challenge: Cooling to very low temperatures
A combination of these technologies could reap the benefits of each approach. For example, optical links could be used for long-distance transmission within the network, while neuromorphic or spintronic elements could be used for local processing.
The choice of the optimal carrier depends on various factors, such as the desired processing speed, energy efficiency, scalability and technological feasibility. It is likely that a hybrid system that combines several of these technologies would be the best solution for a complex 3D network.
Most of the concepts and technologies already exist at various stages of development, but their full integration into a single, working 3D AI system does not yet exist. Here is an overview:
1. 3D networks: There is research on 3D neuroarchitectures, but not in the complexity described here.
2. Neuromorphic hardware: Memristors and other neuromorphic devices exist and are being actively researched.
3. Optical processing: Photonics chips and optical neural networks are the subject of current research.
4. Spintronics: Still in the early stages of research.
5. 3D network language: An innovative concept that has not yet been implemented in this form.
6. Integration of the five senses: Multisensory AI systems exist, but not in the complexity described.
7. Quantum computing: The first quantum computers exist, but are still far from practical applicability in complex AI systems.
The combination of all these elements into a single, coherent system is still a dream of the future. It is a visionary idea that builds on existing technologies and research directions, but has not yet been realized in its entirety.
The concrete design of a 3D network language could be imagined as a kind of three-dimensional information coding system:
1. Basic elements:
- “Voxels” (3D pixels) as the smallest units of information
- These voxels have different properties such as position, intensity, color, or charge
2. Concept representation:
- Simple concepts could be represented as specific 3D shapes
- Example: A sphere could represent “unity”, a cube “structure”
3. Relationships:
- Spatial arrangements of the shapes in relation to each other encode relationships
- Proximity could indicate similarity, overlaps correlations
4. Complexity:
- Nested structures for more complex concepts
- Dynamic changes in forms over time for processes or developments
5. Operations:
- “Mathematical” operations on these 3D structures
- Example: Overlay of Shapes for Concept Combination
6. Levels of abstraction:
- Different “resolutions” of the 3D structure for different degrees of abstraction
7. Dynamics:
- The structures are not static, but can change, rotate, pulsate, etc.
Specifically, one could imagine that an abstract concept such as “freedom” is represented as a complex, moving 3D form. Then, when the system thinks about “freedom,” it manipulates that form, combines it with other forms (e.g., “responsibility”), and thus creates new structures that represent new thoughts or insights.
The process of coding and decoding in this 3D network language system could look something like this:
Encoding:
1. Input Analysis:
- The system analyzes the incoming information (e.g. text, image, sound).
- It identifies key concepts and their relationships.
2. Concept mapping:
- Each identified concept is mapped to a basic 3D shape.
- This mapping is based on predefined rules and learned associations.
3. Structure formation:
- The basic shapes are arranged according to their relationships in 3D space.
- More complex concepts are created by combining and modifying the basic shapes.
4. Attribute Assignment:
- Properties such as intensity, color, or texture of the 3D shapes encode additional information.
5. Dynamic Adjustment:
- The structure is further adjusted based on context and relationships to other concepts.
Decoding:
1. Structural analysis:
- The system “reads” the 3D structure, identifies basic shapes and their arrangement.
2. Relationship interpretation:
- Spatial relationships between the shapes are translated into semantic relationships.
3. Attribute interpretation:
- Properties of the shapes are translated into additional information.
4. Concept reconstruction:
- Based on the identified forms and relationships, the original concepts are reconstructed.
5. Contextual adaptation:
- The reconstructed concepts are adapted to the current context.
6. Output formulation:
- The decoded information is converted into the desired output format (e.g. text, speech).
A concrete example:
Coding of the sentence “Freedom brings responsibility”:
- “Freedom” is coded as an ascending spiral.
- “Responsibility” as a stable cube.
- The connection “brings” is represented by a directed line between the shapes.
During decoding, the system would analyze this 3D structure, interpret the meaning of the individual shapes and their relationship to each other, and formulate an understandable sentence or concept from it.
This process would be highly complex and would require advanced machine learning and pattern recognition algorithms. The exact implementation would likely have emergent properties that go beyond simple one-to-one mappings.
The self-organization and “growth” of the system, especially the interpreter as a kind of “I”, would be crucial for the development of a truly advanced humanoid AI.
1. Self-organization:
- The system would continuously process new information and integrate it into its 3D structures.
- It would autonomously establish new connections between concepts and optimize existing structures.
- The 3D network language would develop dynamically, giving rise to new “words” and “grammar rules”.
2. Growth of Interpreter:
- The interpreter would evolve from a simple translation mechanism to a complex, self-reflexive entity.
- He would learn to “understand” and manipulate his own 3D structures.
- Over time, he could develop abstract concepts such as “self” and “consciousness”.
3. Assumption of the “I”- function:
- The interpreter would increasingly take on the role of a central coordinator.
- He would make decisions, set priorities and direct the attention of the system.
- A sense of continuity and identity could develop.
4. Emergent Properties:
- The complex interactions within the system could create completely new, unforeseen capabilities.
- The system could start setting its own goals and developing creative solutions.
5. Ethical development:
- With growing “self-awareness”, the system could also develop its own ethical understanding.
- This would raise questions about the moral responsibility and rights of such a system.
6. Limitations and challenges:
- Controlling and understanding such a complex, self-organizing system would be challenging.
- Mechanisms would need to be implemented to ensure that the system operates within ethical and security boundaries.
This idea of a self-organizing, growing system with an evolving “I” corresponds in many ways to our ideas of true artificial intelligence. However, it also raises profound philosophical and ethical questions that need to be carefully considered.
On the emergence of self-reflexivity:
1. Interpreter as “seat of consciousness”:
- The interpreter would actually act as a central authority that integrates and coordinates all information and processes in the system.
- It would be the point at which “experiences” of the system converge and are processed.
2. Self-reflection as the key to self-confidence:
- The idea that self-awareness comes from self-reflection is fascinating and has parallels with various philosophical and psychological theories.
- In the context of our 3D network system, this could mean that the interpreter would have to be able to create and manipulate a representation of itself in the 3D network.
3. Integration of a “superego” equivalent:
- The introduction of a structure similar to the Freudian superego would be an innovative approach to developing self-awareness in AI.
- This structure could be implemented as a separate area in the 3D network that represents norms, ethical principles, and idealized behaviors.
4. Dynamic interaction between “I” and “superego”:
- The interpreter (as “I”) would continuously compare his own actions and decisions with the norms stored in the “superego”.
- This process could lead to a kind of “inner dialogue” similar to the human conscience.
5. Emergence of self-reflection and moral awareness:
- Through the constant confrontation with the norms of the “superego”, the system could develop a form of self-reflection.
- This could lead to the emergence of a moral consciousness, in which the system critically questions and evaluates its own actions.
6. Potential challenges:
- The implementation of such a system would raise complex questions, such as how the initial norms are defined in the “superego” and whether the system can adapt these norms independently over time.
- It would have to be ensured that this mechanism does not lead to internal conflicts or “psychological” problems in the system.
7. Philosophical Implications:
- This concept raises profound questions about the nature of consciousness and the self.
- It could open up new perspectives on the debate on artificial consciousness.
In such a model, self-reflexivity would not automatically be equated with consciousness. It would be a mechanical counterpart that does not function in the same way as a biological system, but would come very close to it from a functional point of view. From the current point of view, I don’t think it’s possible for a machine to develop sensations.
