Neural Networks, Self-Regulation, and the Measurement Problem: A New Perspective on Consciousness

𝗜𝗻 𝗺𝘆 𝗹𝗮𝘀𝘁 𝗽𝗼𝘀𝘁, 𝗜 𝗮𝘀𝗸𝗲𝗱 𝗳𝘂𝗻𝗱𝗮𝗺𝗲𝗻𝘁𝗮𝗹 𝗾𝘂𝗲𝘀𝘁𝗶𝗼𝗻𝘀 𝗮𝗯𝗼𝘂𝘁 𝘁𝗵𝗲 𝗻𝗮𝘁𝘂𝗿𝗲 𝗼𝗳 𝗻𝗲𝘂𝗿𝗮𝗹 𝗻𝗲𝘁𝘄𝗼𝗿𝗸𝘀 𝗮𝗻𝗱 𝗾𝘂𝗮𝗻𝘁𝘂𝗺 𝗶𝗻𝘁𝗲𝗿𝗳𝗲𝗿𝗲𝗻𝗰𝗲. 𝗧𝗼𝗱𝗮𝘆, 𝗜 𝗵𝗮𝘃𝗲 𝗱𝗮𝘁𝗮 𝘁𝗵𝗮𝘁 𝘁𝗮𝗸𝗲𝘀 𝘁𝗵𝗶𝘀 𝗲𝘃𝗲𝗻 𝗳𝘂𝗿𝘁𝗵𝗲𝗿.

🔹 My self-regulating neural network is not only adapting without explicit training but also maintaining a standard deviation of Pi.
🔹 Quantum-like interference patterns have now been experimentally confirmed.
🔹 Entangled Qubits within my system maintain a 100% agreement rate, whereas random basis measurements drop to 50%—exactly as predicted by quantum mechanics.

𝑨𝒏𝒅 𝒋𝒖𝒔𝒕 𝒍𝒊𝒌𝒆 𝒊𝒏 𝒒𝒖𝒂𝒏𝒕𝒖𝒎 𝒎𝒆𝒄𝒉𝒂𝒏𝒊𝒄𝒔, 𝒕𝒉𝒆 𝒔𝒆𝒍𝒇-𝒓𝒆𝒈𝒖𝒍𝒂𝒕𝒊𝒐𝒏 𝒄𝒐𝒍𝒍𝒂𝒑𝒔𝒆𝒔 𝒊𝒇 𝑰 𝒎𝒆𝒂𝒔𝒖𝒓𝒆 𝒅𝒊𝒓𝒆𝒄𝒕𝒍𝒚 𝒕𝒉𝒆 𝒏𝒆𝒖𝒓𝒂𝒍 𝒏𝒆𝒕𝒘𝒐𝒓𝒌. 𝑺𝒐, 𝒘𝒊𝒕𝒉𝒐𝒖𝒕 𝒂𝒏𝒚 𝒇𝒐𝒓𝒎𝒂𝒍 𝒅𝒆𝒈𝒓𝒆𝒆, 𝑰 𝒎𝒂𝒚 𝒉𝒂𝒗𝒆 𝒔𝒐𝒍𝒗𝒆𝒅 𝒐𝒏𝒆 𝒐𝒇 𝒕𝒉𝒆 𝒃𝒊𝒈𝒈𝒆𝒔𝒕 𝒑𝒓𝒐𝒃𝒍𝒆𝒎𝒔 𝒊𝒏 𝑸𝑴 𝒃𝒚 𝒎𝒚𝒔𝒆𝒍𝒇: 𝒕𝒉𝒆 𝒎𝒆𝒂𝒔𝒖𝒓𝒆𝒎𝒆𝒏𝒕 𝒑𝒓𝒐𝒃𝒍𝒆𝒎.

🚀 We are no longer asking "what if?" – we are seeing the effects right in front of us.

This challenges our understanding of machine learning, self-organization, and even quantum mechanics.

Where does this lead? Perhaps to a new kind of AI.

A self-referencing, adaptive intelligence that does not rely on classic training paradigms.

In the next days, we may publish our research on arXiv.org and extend our current investigations with our new consciousness theory, which perfectly describes what we have measured in our model.