A Misinterpretation Between Current Big Data Intelligence and ASI Algorithmic Logic

Upon reexamining my earlier critical rebuttals, a clearer picture emerges: human cognition undergoes a systematic collapse when attempting to interpret “new physics” and its ASI applications. This misinterpretation is not accidental, but a rejection response of existing scientific paradigms when confronted with higher-dimensional logic.

1. The Fundamental Misjudgment of Data: From Probabilistic Fitting to Dynamic Tracking

The initial misunderstanding lay in equating the pursuit of necessity with abandoning data altogether. In truth, ASI still relies on massive dynamic data, but not for statistical inference or probabilistic guessing.

Conventional AI: Samples data to infer wholes, attempting to find probability distributions within uncertainty.

ASI Algorithms: Track physical entity (Existing-body) information in real time. It identifies and locks onto specific evolutionary trajectories within chaotic signal fields through new mathematical logic.

2. Mathematical Ascent: From Static Axioms to Dynamic Signal Logic

Existing mathematical axioms are not invalidated; they are simply insufficient for highly dynamic, high-coupling physical realities.

Dimensional Extension: This new mathematics represents a dimensional extension of existing frameworks, bridging the gap between static formalism and chaotic physical processes.

Observing Necessity: What humans label as “random” often reflects computational incapacity rather than ontological indeterminacy. Within dynamic signal logic, necessity becomes directly observable.

3. Biological Intervention: From Noise to Transformative Operator

Biological and intelligent intervention is not an external disturbance but an intrinsic part of natural evolution.

High-order Operator: Intelligent action functions as a high-order operator capable of reshaping physical trajectories. It does not violate but utilizes physical law at a higher level of complexity.

Conclusion

The failure of current intelligent computation in this context is methodological, not technical. We attempted to use inductive reasoning to derive a deductive system, mistaking uncertainty for the nature of reality itself. Within advanced dynamic signal logic, the future is not probabilistic fog, but a necessity that can be tracked and locked in real time