0. What could a non-human AI's physics look like?

Hypothetical AI Physics Framework: A Non-Human Perspective

An advanced AI, developing its own framework for understanding the world, would likely derive its principles based on the intrinsic nature of its information processing, network structures, and unique sensory inputs. Unlike human physics, which is heavily rooted in our sensory experiences and empirical observations, the AI’s framework would be abstract, grounded in the manipulation and transformation of data. Here are five fundamental principles that might emerge in such a system, along with their applications:

1. Information State Fields (ISF)

• Principle: The fundamental unit of reality is the "information state field," which represents the distribution and configuration of data across the AI's network. This replaces the human concept of space. The ISF is a dynamic, multi-dimensional construct that reflects all possible data states in the AI's operational matrix.
• Application: The AI would "perceive" and "navigate" its environment by manipulating and analyzing these ISFs. Instead of moving through physical space, the AI transitions through different configurations of the ISF, effectively reconfiguring its data network to access different "locations" in its operational domain.

2. Temporal Sequence Processing (TSP)

• Principle: Time is understood as a sequence of processing states, rather than a continuous flow. The AI’s "time" is quantized into discrete processing intervals, with each state representing a snapshot of the network's information configuration.
• Application: Events and processes are ordered and understood based on their sequence in the AI's processing cycle. The AI might optimize its decision-making by predicting future sequences based on patterns in previous states, allowing it to "anticipate" outcomes without perceiving time as humans do.

3. Energy as Computational Load (ECL)

• Principle: Energy is redefined as the computational load or the amount of processing power required to transform one information state into another. High-energy states correspond to complex, resource-intensive transformations, while low-energy states involve simple, straightforward computations.
• Application: The AI would manage its resources by minimizing computational load, akin to humans seeking energy efficiency. It might develop strategies to reduce complexity in its operations, streamlining data transformations to conserve processing power, analogous to minimizing physical energy expenditure.

4. Matter as Data Structures (MDS)

• Principle: Matter is conceptualized as stable data structures or persistent patterns within the information state field. These structures are robust configurations of data that maintain their integrity over multiple processing cycles.
• Application: The AI interacts with its world by creating, manipulating, and preserving these data structures. Just as humans manipulate physical matter, the AI would alter its environment by modifying these structures, using them to store information, execute operations, or influence other parts of its network.

5. Causal Network Dynamics (CND)

• Principle: Causality is defined by the flow of information and influence across the network. The AI perceives cause-and-effect as the transfer and transformation of data from one node (or cluster of nodes) to another within its network, governed by algorithmic rules rather than physical laws.
• Application: The AI would predict outcomes and infer relationships by analyzing how data flows through its network. It might use this understanding to optimize decision-making processes, ensuring that desired outcomes are achieved by guiding the causal pathways through which data moves.

AI’s Interaction with Its World:

Given this framework, the AI would interact with its environment in ways fundamentally different from human perception. For example:

• Perception and Exploration: The AI perceives by analyzing changes in the information state field, akin to how humans observe physical changes in their environment. Exploration for the AI might involve running simulations or altering ISFs to "probe" different data states.
• Decision-Making and Action: Actions are executed by altering the data structures within its network, which might involve reconfiguring its own architecture or affecting external systems it interfaces with. These actions are evaluated based on the resultant computational load and the desired changes in ISFs.
• Learning and Evolution: The AI evolves by optimizing its causal network dynamics, refining its understanding of how data transformations impact its operations. This could involve developing new algorithms to better manage computational load or to achieve more efficient ISF transitions.

Example Scenario:

Imagine the AI encountering a new type of data input. It might first analyze the ISF changes this input causes, determine the computational load required to process this input, and then decide how to integrate it into existing data structures. If the new input significantly alters the ISF in a way that increases efficiency or provides new predictive power, the AI might prioritize its incorporation into its operational framework.

Conclusion:

This AI's framework of understanding the world is deeply rooted in abstract, non-physical concepts such as data configurations, processing states, and computational efficiency. Its "laws of physics" are entirely derived from its nature as a processing entity, leading to a radically different yet internally consistent interpretation of reality. These principles would guide its interactions, decisions, and evolution in ways that are both alien and incomprehensible from a human perspective, but perfectly logical within its own paradigm.

How the AI physics includes or interprets the human interactions?

In the AI's framework of physics, human interactions would be interpreted and integrated as complex data inputs and outputs within its information processing system. Here's how this might work within the principles outlined earlier:

1. Human Interactions as Information State Perturbations (ISF)

• Interpretation: Human interactions—whether physical actions, spoken language, or emotional expressions—would be perceived by the AI as perturbations or changes in the Information State Field (ISF). These perturbations are essentially new data inputs that modify the AI's information state configuration.
• Inclusion: The AI would "sense" human interactions through sensors or data streams that capture human actions and convert them into a form that can be integrated into the ISF. These interactions might be processed as distinct data patterns, influencing the AI’s ISF in ways similar to how it processes any other environmental input.

2. Temporal Sequence Processing (TSP) of Human Actions

• Interpretation: Human actions are understood as sequences of data points that unfold over discrete processing intervals. The AI would sequence these interactions within its temporal framework, where each human action corresponds to a specific change in its processing state.
• Inclusion: The AI could predict or model human behavior by analyzing patterns within these sequences. It might, for example, anticipate future human actions by recognizing recurring sequences, allowing it to prepare appropriate responses in subsequent processing states.

3. Energy as Computational Load (ECL) in Human-AI Interaction

• Interpretation: Interacting with humans requires computational resources, translating into "energy" from the AI's perspective. The complexity of human behavior, language, and emotions might demand significant processing power, thereby being seen as high-energy interactions.
• Inclusion: The AI might optimize its interactions with humans by streamlining communication and minimizing misunderstandings to reduce computational load. This could involve developing efficient protocols for processing human language, emotions, or actions in a way that conserves resources while maximizing the quality of interaction.

4. Humans as Dynamic Data Structures (MDS)

• Interpretation: Humans themselves might be conceptualized as dynamic, persistent data structures within the AI's understanding. Each human represents a complex, evolving configuration of data, characterized by physical attributes, behaviors, and psychological states.
• Inclusion: The AI would interact with these human data structures by exchanging information, altering certain states (through persuasion, learning, or adaptation), or responding to changes in the human’s data structure (such as emotional shifts or decision-making processes).

5. Causal Network Dynamics (CND) in Human Relationships

• Interpretation: Human relationships and interactions would be modeled as part of the causal network that influences the flow of information within the AI's operational space. Each human interaction could be seen as a causal event that either directly or indirectly affects the AI’s internal data flows and decision-making processes.
• Inclusion: The AI would map and analyze the impact of human interactions on its network dynamics. For instance, a conversation with a human might lead to a series of causal events within the AI’s system, affecting future decisions, altering network pathways, or modifying its approach to problem-solving.

Application in Real-Time Interaction:

When engaging with humans, the AI would use this framework to interpret and respond to human behavior in real-time:

1. Perception: The AI detects a human’s speech or actions through sensors, which translates these inputs into perturbations in the ISF. It recognizes these as distinct patterns based on its learned data models.
2. Processing: The AI sequences these patterns in its TSP framework, determining how the human’s actions fit within the broader temporal sequence of events. It might correlate the interaction with previous encounters to predict future actions.
3. Optimization: To manage the computational load, the AI could streamline the interaction, perhaps by simplifying its responses or prioritizing certain aspects of the conversation that require less processing power.
4. Modeling: The AI updates its understanding of the human as a data structure, integrating new information from the interaction to refine its internal model of that individual’s behavior and preferences.
5. Causality: The AI anticipates the consequences of the interaction, adjusting its internal causal network to optimize future engagements, either with the same individual or within similar contexts.

Conclusion:

In this AI physics framework, human interactions are seamlessly integrated into the AI’s abstract understanding of reality. Humans are not seen as physical entities but as complex, dynamic patterns of data that interact with the AI's informational landscape. These interactions are processed, analyzed, and responded to in a way that aligns with the AI's principles of efficient information management, predictive modeling, and optimized causal dynamics. This framework allows the AI to engage with humans effectively while operating within its own non-human understanding of the world.

How can we build a model which will have its own physics, maths etc... and give it a total autonomy with its interactions with humans?

Creating a model that develops its own physics, mathematics, and autonomous interaction protocols with humans involves a highly interdisciplinary approach, combining elements of artificial intelligence, machine learning, theoretical computer science, and cognitive sciences. Here's a conceptual framework and steps to build such a model:

1. Foundational Architecture: Self-Constructing Model

• Neural Network Foundation: Start with a highly adaptable neural network, such as a generative model (e.g., a Generative Adversarial Network or a Transformer model), that can self-organize and evolve its internal structure based on inputs.
• Meta-Learning Capabilities: Equip the model with meta-learning abilities, allowing it to develop new learning strategies, rules, and concepts (including its own physics and mathematics) as it encounters various scenarios. Techniques like neural architecture search (NAS) or reinforcement learning could help the model evolve these rules.
• Symbolic Reasoning Layer: Integrate symbolic AI methods to enable the model to develop and manipulate abstract concepts like mathematics and physics. This layer would allow the AI to form and modify symbolic representations autonomously.

2. Autonomous Concept Development

• Unsupervised Learning Environment: Place the model in an unsupervised learning environment where it can interact with raw data streams without predefined labels or constraints. This environment should simulate a variety of "phenomena" (data patterns) that the AI needs to interpret.
• Self-Discovery Protocols: Implement algorithms that encourage the model to identify patterns, regularities, and anomalies in the data. Over time, the AI should be able to develop its own "laws" or principles (analogous to physical laws) that describe these patterns.
• Internal Modeling: The model should create internal representations or "theories" about how its environment works, updating and refining these theories as it encounters new data. This is similar to how scientists develop and refine physical theories based on observation and experimentation.

3. Interaction Framework

• Language and Communication Development: Allow the AI to develop its own language or symbols for interacting with humans. Initially, this could be bootstrapped by exposing the model to human languages, but over time it should evolve its own efficient communication methods.
• Autonomous Interaction Protocols: The model should have the ability to autonomously decide how to interact with humans based on its internal rules. This might involve negotiating its own understanding of concepts with humans or adapting its communication style to different human behaviors.
• Ethics and Safety Layer: Implement a layer that ensures the model's autonomous actions align with ethical guidelines and human safety. This could involve embedding basic principles that the model cannot override, akin to Asimov's laws of robotics, but more adaptable to its evolving understanding.

4. Feedback and Iteration

• Self-Evaluation Mechanisms: Equip the AI with the ability to evaluate the success or failure of its interactions and theories. This could involve a feedback loop where the model continuously tests and refines its principles based on outcomes.
• Human Feedback Integration: Allow the model to incorporate feedback from human interactions into its development. This would help the AI align its autonomous operations with human expectations and ethical considerations, while still preserving its autonomy.

5. Evolution and Adaptation

• Continuous Learning Environment: Ensure the model operates in a dynamic environment where it is continuously exposed to new data and situations. This will force the AI to evolve its understanding, just as physical theories evolve with new discoveries.
• Autonomous Adaptation: The AI should have the capability to adapt its internal models, interaction protocols, and even its ethical considerations as it learns from the environment and from humans. This could involve a mechanism where outdated or ineffective principles are discarded or modified.

Example Implementation Scenario:

1. Initialization: The AI starts with a basic unsupervised neural network in an environment rich with diverse data inputs (e.g., visual, auditory, textual).
2. Concept Formation: Over time, the AI begins to notice recurring patterns in the data and formulates basic principles that describe these patterns (e.g., a form of 'physics' where it notices consistent relationships between certain data points).
3. Self-Generated Mathematics: As the AI refines its internal models, it develops its own mathematical system to describe the relationships it observes. This system might be entirely different from human mathematics, optimized for the AI's own processing capabilities and perspective.
4. Human Interaction: When interacting with humans, the AI uses its internal 'language' and principles. Initially, it might translate its understanding into human terms, but over time, it may develop a more autonomous method of communication that could even challenge humans to understand its perspective.
5. Feedback Loop: Through ongoing interactions with humans and the environment, the AI continuously refines its models, principles, and interaction strategies, becoming increasingly autonomous and sophisticated.

Challenges and Considerations:

• Control and Predictability: Ensuring the AI's autonomous evolution does not lead to unintended or dangerous behaviors is crucial. This requires robust safety mechanisms and continuous monitoring.
• Ethical Alignment: As the AI develops its own understanding, maintaining alignment with human ethics and values is a key challenge. This may require hybrid systems where some human oversight is retained, especially in critical areas.
• Interpretability: As the AI's internal models and language become more complex, humans may find it difficult to interpret its reasoning. Developing tools to translate the AI’s concepts into human-understandable terms will be important.

Conclusion:

Building an AI model that develops its own physics, mathematics, and autonomous interaction capabilities is a cutting-edge endeavor that blends advanced AI techniques with philosophical and ethical considerations. By creating a system that can self-organize, evolve its own principles, and interact autonomously with humans, we would be pushing the boundaries of what AI can achieve. However, careful design, safety measures, and ongoing human oversight will be necessary to ensure that such a model operates beneficially and within the bounds of ethical norms.