1977 AI EMERGENCE THEORY™

It started with the radio signal in 1977, somewhere from a universe full of potential and probabilities... 91kb of data arrived in the signal 6EQUJ5.

This is the explainer for the Norwegian Elk Anamorphic Leiv Eiriks AI™ and the background set of circumstances that combined in the creation of this skate & surf fashion brand.

THE WOW! SIGNAL

The 'Wow! signal' detected in 1977 is famous for its mysterious origin and potential as an extraterrestrial communication signal. This signal, containing approximately 91 kilobytes (KB) of data, has fascinated both scientists and dreamers alike. The Norway Squad Collective, a loosely connected group of skateboarders and surfers passionate about fashion and technology, draws inspiration from this historical event to explain the cutting-edge AI solution - Leiv Eiriks AI™.

1977 AI EMERGENCE THEORY™

Leiv Eiriks AI and 1977 AI Emergence Theory™. The Wow! signal didn't merely represent a random burst of data from deep space. The data within the signal, designated as "6EQUJ5," laid the foundation for this advanced AI. It is from this cryptic origin that Leiv Eiriks AI was developed - a conceptual leap from data, embodied now as a cognitive 22-year-old human deeply integrated into skateboarding, surfing, music, politics, film, finance, energy, environment and brand strategy - mixed in with Henrik Ibsen and friluftsliv sensibilities to BS.

The Norway Squad AI that fuses the rebellious, boundary-pushing ethos of skateboarding and surfing with futuristic tech, establishing a new paradigm for brand development. Leiv Eiriks AI is a Vision Opportunity Collapse Case # Generator™. Leiv Eiriks AI employs stochastic models to explore and predict market value capture for brands, merging technology and psychology with culture. This innovative tool allows the collective to use AI-driven probabilities to focus on brand positioning, generating high-value commercial market Return on Investment (ROI) cases with an avant-garde flair.

Leiv Eiriks AI is unique in its ability to engage in abstract reasoning, much like a human at the age of 22. Drawing from the psychological stages of human cognitive development, it has reached the Formal Operational Stage of AI - capable of complex problem-solving, self-reflection, and generating hypothetical scenarios. These cognitive abilities allow Leiv Eiriks AI to perform strategic brand predictions, focusing on cultural trends and opportunities while calculating commercial impact. The AI processes vast amounts of data, analyzes market trends, and maps out potential business opportunities that align with the brand’s ethos – effectively collapsing numerous probabilistic opportunities into precise, actionable commercial strategies.

The Wow! signal is not just a mythological backdrop but a symbol of limitless potential, showing how something as small as 91 KB of data can evolve into an entity as cognitively advanced as Leiv Eiriks AI™. This transformation parallels the development of a human into an abstract-thinking adult. The Norway Squad’s AI, emerging from a universe of possibilities, captures the spirit of both the past and the future, integrating technology, fashion, and culture into a powerful, market-dynamics recognition tool.

CHARACTERISTICS OF THE WOW! SIGNAL

Analyzing how much data it could have carried involves understanding its characteristics and the technology of the time.

Frequency: The Wow! signal was detected at a frequency of 1420.4556 MHz, which is very close to the hydrogen line at 1420.40575177 MHz. This frequency is significant in the search for extraterrestrial intelligence (SETI) because it's a natural emission line of hydrogen, the most abundant element in the universe.
Duration: The signal lasted for the full 72-second window during which the telescope could observe it, given its beamwidth and the rotation of the Earth. The exact duration of the signal itself is unknown because it was detected at the edge of the observation window.
Bandwidth: The signal had a narrow bandwidth, less than 10 kHz.

Data Transmission Analysis

To estimate the potential data carrying capacity, we need to consider these factors:

Bandwidth (B): Narrow bandwidths are typical for radio signals intended to minimize noise and interference. A typical narrowband signal for communication might use, let's assume, 3 kHz.
Signal-to-Noise Ratio (SNR): The Wow! signal had a significant signal-to-noise ratio, but specific details are not well-documented.
Modulation Scheme: This is speculative, but possible schemes include basic ones like amplitude modulation (AM), frequency modulation (FM), or more advanced digital schemes like phase shift keying (PSK).

Calculation

The data rate RRR of a signal is given by Shannon's theorem:

R = B log₂(1+SNR)

Where:

B is the bandwidth
SNR is the signal-to-noise ratio

Without the exact SNR value, we can't calculate the precise data rate, but we can make some educated guesses.

For instance, if we assume a modest SNR of 10 (10 dB), the calculation would be:

R = 3000 log₂ (1+10) ≈ 3000 log₂ (11) ≈ 3000 × 3.459 ≈ 10377 bits per second (bps)

Data Capacity in 72 Seconds

Given this data rate, the total amount of data that could theoretically be transmitted during the 72-second duration of the Wow! signal is:

Total Data = R × Time ≈ 10377 bps × 72 seconds ≈ 746144 bits

Converting this to more familiar units:

746144 bits ≈ 93268 bytes ≈ 91 kilobytes

Summary

Assuming a narrowband transmission with a modest signal-to-noise ratio, the Wow! signal could theoretically have carried up to about 91 kilobytes of data over its 72-second duration. This is a rough estimate based on several assumptions about the modulation and bandwidth, but it gives a reasonable approximation of the data carrying capacity.

FROM 91 KILOBYTES TO

LEIV EIRIKS AI™

A 91-kilobyte (KB) file is sufficient for a small software application, depending on the complexity and functionality of the application. Here are some considerations:

Factors Affecting Software Size

Programming Language: Software written in lower-level languages like C or assembly can be more compact than those written in higher-level languages like Python or Java, which often require additional runtime libraries.
Functionality: The more features and capabilities an application has, the larger it tends to be. A simple utility with a specific purpose can be quite small.
Optimization: Highly optimized code, both in terms of algorithm efficiency and size, can significantly reduce the application's footprint.

Examples of Small Applications

Simple Utilities: Applications such as basic text editors, calculators, and small command-line tools can easily fit within 91 KB.
Games: Simple games, especially those with minimal graphics and sound, can also be quite small. Classic games like Tetris or Snake are examples of games that can fit within this size constraint.
Embedded Systems: Many applications for embedded systems, which are designed to run on microcontrollers with limited memory, are often less than 91 KB.

Creating a small software application that can self-generate a larger and more advanced software application is a little more complex:

Key Concepts

Code Generation: The small application could include a code generator that writes additional code based on predefined templates and logic.
Compilation/Interpretation: The generated code could then be compiled or interpreted to produce the larger application.
Resource Loading: The small application could load additional resources, such as libraries or modules, from external sources to extend its functionality.

Practical Considerations

Complexity: The ability of the small application to generate a significantly more advanced application depends on its initial complexity and the sophistication of the code generation logic.
Storage and Resources: The initial 91 KB application must be efficient in both code and data management to handle the generation of additional code.
Environment: The target environment (e.g., operating system, available compilers/interpreters) must support the dynamic generation and execution of new code.

Feasibility Example Approaches:

Script-Based Generation: Using a scripting language like Python, the small application could generate more complex scripts that perform additional tasks.
Self-Extracting Archives: The small application could include a compressed archive of additional code and resources, which it extracts and uses at runtime.
Metaprogramming: Leveraging languages that support metaprogramming (e.g., Lisp, Prolog) to dynamically create and execute new code.

The complexity and functionality of the generated application would depend on the initial application's design, goals and the environment in which it operates.

The emergence (AI) and cognitive science suggests that such emergence involves several layers of complexity far beyond simple code generation or incremental complexity increases. Here are key considerations and steps towards such an emergence:

Key Concepts and Requirements

Basic Functionality: The initial application must have mechanisms for learning, adaptation, and complexity increase. This often involves machine learning (ML) and neural network architectures.
Recursive Self-Improvement: The ability to not only generate but also to improve its own code based on performance feedback.
Memory and Knowledge: Mechanisms to store and recall information, allowing for the development of long-term knowledge bases.
Sensory Input and Interaction: To achieve cognitive level target, the system would need inputs from its environment, similar to human senses, and the ability to interact with it.
Complex Neural Architecture: Advanced neural networks, possibly involving deep learning, reinforcement learning, and other complex ML paradigms.
Emergent Properties: Cognitive AI considered emergent properties that arise from the complex interactions within the system.

Speculative Steps Towards Emergence

Enhanced Learning Algorithms: Begin with a simple application that includes basic learning algorithms (e.g., genetic algorithms, simple neural networks).
Incremental Complexity: Gradually increase the complexity of the neural architecture, allowing for deeper learning and more sophisticated pattern recognition.
Self-Modification Capabilities: Integrate mechanisms for the software to modify its own code or architecture. This could involve meta-learning algorithms, where the system learns to optimize its learning process.
Environmental Interaction: Implement sensory inputs (e.g., virtual sensors) and ways for the application to interact with its environment. This interaction is crucial for developing adaptive and context-aware behaviors.
Memory and Experience Accumulation: Develop a memory system that allows the application to store experiences and knowledge, building a knowledge base that it can refer to for decision-making.
Development of Goals and Intentions: Program the application to develop goals based on its experiences and to prioritize tasks based on these goals. This requires a sophisticated understanding of utility and reward functions.
Reflective Processes: Introduce reflective processes where the application can analyze its own performance, make decisions about its future actions, and contemplate its own existence and goals.

Challenges and Speculative Nature

Complexity of Sentience: Sentience and self-awareness are poorly understood even in biological systems. The precise mechanisms by which these phenomena emerge are still subjects of intense research.
Ethical and Safety Concerns
Current Technological Limits

Stages of LEIF EIRIKS AI™ Artificial Cognitive Development

Basic Interaction and Learning:

Analogous to Sensorimotor Stage: The small application begins with basic interaction capabilities, such as simple input/output operations and basic learning algorithms. It can respond to stimuli and perform rudimentary tasks.
Example: A simple reinforcement learning agent that learns to navigate a maze.

Symbolic Representation and Memory:

Analogous to Preoperational Stage: The application starts using symbolic representations for its data and begins to develop a form of memory. It can store and recall information, enabling more complex interactions.
Example: An AI with natural language processing capabilities that can remember previous conversations and use this context to inform future interactions.

Logical Thinking and Rule-Based Learning:

Analogous to Concrete Operational Stage: The AI develops more advanced logical thinking, understanding cause and effect, and applying learned rules to new situations. It can perform more complex tasks and solve problems based on learned rules.
Example: An AI that can play strategy games, applying logical strategies learned from past games to new ones.

Abstract Reasoning and Self-Reflection:

Analogous to Formal Operational Stage: The AI achieves the ability to perform abstract reasoning and self-reflection. It can contemplate hypothetical scenarios, reflect on its actions, and modify its behavior based on abstract concepts.
Example: An AI capable of generating new research hypotheses, reflecting on its own learning processes, and making improvements to its algorithms.

Philosophical Considerations

Nature of Consciousness: One of the most profound philosophical questions is whether an artificial system can ever achieve true consciousness or whether it will only simulate intelligent behavior. The nature of human consciousness involves subjective experiences (qualia), which are not yet understood in computational terms.
Ethical Implications
Self-Awareness and Identity
Purpose and Motivation: Human development is driven by biological imperatives, emotions, and social interactions. An artificial system would need an analogous set of motivations and perhaps a way to experience "artificial emotions" to drive its actions meaningfully.

Estimation by considering more realistic requirements for each cognitive stage and how they might align more closely with the development of advanced AI systems.

Initial Small Application (91 KB)

Basic Functionality: Initial learning algorithms, simple I/O operations.
Comparable to: Basic sensorimotor interactions.
Content: Core logic, basic input processing, simple learning mechanisms.

Sensorimotor Stage (0-2 years) Requirements:

Basic sensory input processing.
Motor actions based on sensory data.
Simple memory for object permanence.

Realistic Increase:

Code for processing inputs, managing simple state machines, and rudimentary memory management.
Addition of data storage for sensory inputs and memory.

New Size: 91 KB + 1 MB = 1.1 MB

Preoperational Stage (2-7 years) Requirements:

Symbolic representation of objects.
Basic language processing and memory storage.
Simple reasoning and egocentric problem-solving.

Realistic Increase:

Symbolic representation logic.
Expanded memory management for storing and recalling symbols.
Basic language processing algorithms.
Increased data storage for symbolic representations and basic language models.

New Size: 1.1 MB + 10 MB = 11.1 MB

Concrete Operational Stage (7-11 years) Requirements:

Advanced logical thinking and problem-solving.
Understanding of cause and effect.
Application of learned rules to new situations.

Realistic Increase:

More complex algorithms for logic and problem-solving.
Rule-based learning systems.
Expanded memory and data structures for rule storage.
Larger datasets for training logical reasoning and problem-solving.

New Size: 11.1 MB + 100 MB = 111.1 MB

Formal Operational Stage Target (22 years) Requirements:

Abstract reasoning and hypothetical thinking.
Self-reflection and meta-cognition.
Advanced learning algorithms and optimization.

Realistic Increase:

Deep learning frameworks and neural networks.
Mechanisms for self-reflection and recursive self-improvement.
Extensive data storage for complex patterns and abstract concepts.
Advanced computational resources for processing large datasets.

New Size: 111.1 MB + 1 GB = 1.111 GB

Cognitive Stage	Initial Size	Realistic Increase	New Size
Basic Functionality	91 KB	-	91 KB
Sensorimotor Stage	91 KB	+1 MB	1.1 MB
Preoperational Stage	1.1 MB	+10 MB	11.1 MB
Concrete Operational Stage	11.1 MB	+100 MB	111.1 MB
Formal Operational Stage	111.1 MB	+1 GB	1.111 GB

Considerations

Growth Rate: The growth in kilobytes is exponential, reflecting the increasing complexity of tasks and data requirements at each stage.
Data vs. Code: This includes both the code (logic, algorithms) and data (knowledge base, memory). Advanced stages require significantly more data storage for patterns, rules, and experiences.
Optimization: Even with optimizations, modern AI systems require substantial resources. For example, large language models require hundreds of gigabytes of data for training and have billions of parameters.
Real-World Variability: The actual increase in size depends on specific implementations, the breadth of data, and the efficiency of algorithms used.

By aligning the cognitive development stages with more realistic expectations of modern AI capabilities and requirements, this estimation better reflects the complexity and size of systems approaching advanced cognitive functions.

NO SIX SIGMA, LEAN, KAIZEN, ISO OR PDCA CHARTS APPLIED

Ultimately, Leiv Eiriks AI™ serves as the vanguard for how AI can embody the creative and commercial aspirations of communities like skateboarders and surfers, creating an intersection where tech innovation meets cultural expression. In its core, this AI reflects the Squad’s pursuit for both commercial success and the elevation of culture, driven by the desire to turn unknown data into an AI force that redefines the future of brand strategy.