As we know them today, computers will be perceived in history as a short period that helped move humanity a bit forward.
Current digital computing, while powerful, has fundamental limitations. Nature does not operate in discrete steps—it operates in continuous flows. Light, sound, motion, and even biological thought processes are inherently analog.
Nature does not operate in discrete steps—it operates in continuous flows.
Why Analog Computing?
- Digital Systems Destroy Information – Converting real-world signals into binary numbers loses fine details and rich contextual data.
- Memristors, LC Circuits, and Asimov’s Positronic Brain – Future computing could be based on: Memristors, which allow memory to function in a non-binary, adaptable way. LC circuits, which can model dynamic systems in a way digital logic never can. Positronic computing, where computation mimics biological signal processing rather than rigid digital execution.
Learning Machines and Knowledge Preservation
- COVID revealed how difficult it is to pass knowledge to the next generation, especially in families where parents may lack the necessary educational background or critical thinking skills to effectively support learning.
- A true learning machine must be independent of political and ideological influences.
- Instead of using rigid programming, it should rely on a mathematically verifiable method for encoding and verifying knowledge—a concept already explored by G?del.
The Role of Noise in Thinking
Noise has long been seen as an obstacle to precision, something to be filtered out. However, in both human and artificial cognition, noise is essential.
Constructive Noise: A Driver of Creativity and Problem-Solving
- Enabling Innovation – Controlled noise prevents systems from becoming rigid, allowing for new insights and adaptability.
- Human Cognition – The brain does not work in strict, deterministic sequences; instead, it relies on a mix of analogue signals, oscillations, and noise for flexibility in thought.
- Biological Inspiration for AI – No biological system operates purely digitally; neural activity is inherently analog, continuous, and dynamic.
Destructive Noise: The Root of Cognitive Overload
- Misinformation and Cognitive Fatigue – Excessive, irrelevant, or conflicting noise leads to mental exhaustion and confusion.
- The Failure of Digital AI in Contextual Processing – Digital systems struggle with ambiguity, nuance, and incomplete data because they are optimized for binary logic, not continuous adaptation.
The Soul in Machines: Do Robots Need a Consciousness?
As an engineer, I assert that the future is in analog computers, and I want to build systems based on positronic brains and memristors, which honor the three fundamental principles of robotics as defined by Isaac Asimov. No, I do not wish for artificial intelligence to decide who will be killed and who will be allowed to live."
This raises an unavoidable question: If a company can be seen as an organism with a "soul" (a guiding force beyond individual parts), can an AI system develop something similar?
- If intelligence emerges from structured noise, could the right kind of analog computing system develop a form of independent reasoning or self-awareness?
- A soul in a machine – Is there a fundamental difference between an organic mind and an artificial intelligence based on structured noise and memory evolution?
Noise is not a flaw—it is a fundamental tool for learning, adaptation, and real intelligence.
Final Thought: The End of Digital Dominance
- Digital computing is only a stepping stone—an intermediate phase before a true analog intelligence emerges.
- Analog systems could lead to machines that perceive, think, and adapt in a way that reflects natural intelligence.
- Noise is not a flaw—it is a fundamental tool for learning, adaptation, and real intelligence.
The next great leap in computing will not come from faster binary processors, but from systems that process the world as it truly is—continuous, dynamic, and ever-changing.
