Thoughts on Real-Time Pattern Detection and AI Principles

Detecting Patterns: The Basics

1. Simple Pattern Detection:

   • Consider the sequence: ABCABC.
   • The distance between A and B is one character.
   • The distance between B and A is two characters.
   • Key Point: AB is a stronger pattern because it occurs with less distance between the values than BA.

2. Pursuer and Evader Points:

   • Each character has a pursuer point and an evader point.
   • Pursuer points move towards the evader points of characters that precede them.
   • Evader points move away from the pursuer points of characters that precede them.

• In the sequence ABCABC:

  • B's pursuer point moves strongly towards A's evader point because they are one character apart.
  • A's evader point moves more weakly away from B's pursuer point because they are two characters apart.

• Key Insight: B's pursuer point is chasing A's evader point more strongly than A's evader point is escaping, leading to their co-location. This co-location indicates a strong pattern based on frequency and proximity.

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Principles for Advanced AI

1. Real-Time Data Processing:

   • Machine learning should operate on data in real time, enabling immediate responses to new information.

2. Parallelization:

   • Every character, byte, or primitive value fed through the AI should, in principle, be able to be processed fully in parallel to every other piece of information.

3. Running Calculations:

   • Values critical to decision-making should be computed continuously, on the fly, to support real-time processing.

4. Threshold-Based Decisions:

   • Use thresholds to make decisions quickly and efficiently. If a choice hinges on whether something exceeds or doesn't exceed a threshold, the decision can be made rapidly, which is crucial for real-time operation.

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Conclusion:

• By applying these principles alongside the basics of pattern detection, we can develop AI that operates effectively and powerfully in real-time environments.