Executive Summary

• Event-based systems react to events, not timers or sequential code execution, thus saving energy, time, and memory.
• But time is also defined by events—without events, time itself has no meaning.
• Optimal systems integrate event-based and time-based approaches—balancing efficiency with long-term resiliency to errors and inherent uncertainty.
• Saturation mechanisms prevent failure by enforcing periodic system changes, even when event-based triggers fail.

Event-Based Approach: Core Principles

• Event-based logic is not just a technical choice—it is a foundational ideology that shapes both hardware and software architecture.
• In intelligent systems, event-driven decision-making applies to both physical actions (in-body) and cognitive processes (in-mind).

Time as an Event-Driven Concept

• Time is not an absolute entity but a sequence of events.

  • If nothing happens, the system should only store the start and end timestamps of the inactivity.
  • This aligns with natural perception—we measure time between events, not in continuous increments.

• Events define time itself:

  • A second is an arbitrarily defined unit—9,192,631,770 oscillations of a cesium atom.
  • We assume these oscillations are stable, but we have no deeper reference to verify time’s absolute nature.
  • Intelligence extrapolates time from event patterns, not from an independent inner mind “clock.”

• Time perception is event-driven:

  • A cat learns that its owner arriving home signals mealtime, using it as a time reference.
  • If the owner returns earlier, the cat misinterprets the event as the end of a full day, relying on past patterns rather than absolute time.
  • Humans make similar cognitive errors when assuming standardized time measurement is inherently correct without deeper verification.

Memory and Event-Based Data Processing

• Redundant static data should not be stored or processed.

  • If a robot stares at a white wall, storing 100 frames per second is meaningless.
  • Memory should act like keyframe-based video compression, recording only meaningful changes.

• Repeated experiences should condense into high-level knowledge.

  • If a robot navigates the same warehouse path 100 times with no new obstacles,
    • The full event history is unnecessary.
    • Only new deviations matter—otherwise, the system simply increments a success counter.

• Event-driven memory is hierarchical:

  • On a small scale, minor variations exist.
  • On a larger scale, events smooth out into patterns—when nothing significant happens, the derivative of change is zero.
  • This ties into layered system design, where information is processed at different abstraction levels.

Event-Based vs. Time-Based Processing

• All systems in our Universe are fundamentally event-based.

  • Even time itself – seconds in SI – is an arbitrarily defined sequence of events.
  • However, purely event-driven systems can fail when sensors miss events or fail to trigger.

• Optimal systems balance event-driven logic with periodic saturation resets.

  • Event-based processing ensures efficiency—acting only when necessary.
  • Time-based fallbacks prevent event-detection failures from leading to catastrophic errors.

Example: The Pipe Clogging Sensor

• A clog detector reports that the pipeline is clear.

  • If it fails silently, an event-based system may never detect the clog.
  • A pure event-driven system is vulnerable to such sensor failures.

• A hybrid approach prevents this issue:

  • The event-based system waits for a clog-detection event.
  • A time-based backup rule enforces scheduled maintenance every 50 years.
  • This prevents catastrophic failure, even if no event occurs.

• The principle of saturation applies here:

  • Over time, the probability of pipeline failure increases.
  • The expected damage from failure eventually outweighs the benefits of delaying maintenance.
  • Even if the system “could” wait longer, the cost of waiting rises, making replacement the rational choice.

Conclusion

• Event-based processing is fundamental to intelligent system design.
• Time is a byproduct of events—intelligence perceives time only through changes.
• Memory should store only meaningful changes, not redundant data.
• Layered system design allows hierarchical event abstraction, optimizing decision-making.
• Event-based and time-based approaches should be integrated to balance efficiency with long-term system stability and resilience to errors and enthropy in general.