Intuitive programmers look into the random data and find rules or invariants, then, by training multiple instances they find how invariants are linked, inventing a mathematical representation of a similar class of events happening in nature. Geometric Musical Language (GML) maps periodic events as clocks arranged in a 3D space, groups the clocks with higher-level clocks until it reaches a global integrator clock. So, GML extraction is intuitively creating a model replacing human programmers. Then we perfect it instantly using Phase Prime Metric (PPM), which is used to build any sensor in invariant computing, PPM’s role is to add all possible symmetries which should be there in an ideal GML structure, but we miss them because of weak sensors. Thus, the GML-PPM combination would let a sensor retrieve an accurate model by communicating with the environment and asking questions whether there exist the missing symmetries or not. Therefore, GML-PPM combination is a human bias-free protocol to replace human scientists and thinkers, for computers, of course, programmers. So, it reduces a huge cost and time for any inquisitive entity who wants to understand nature and predict the future for safe living.
The invariant bank is a concept that is at the core foundation of invariant computing. In this bank, we store elementary 47 sound patterns, each pattern is connected to a specific perception or a phenomenon. We call it resonance currency. Similarly, we store 108 invariants, which could reduce spatial, temporal, topological and symmetry variations occurring in events around us. This is invariant currency. The invariant bank stores 15 actions related to 15 primes, which further reduces complexity. This is action currency. The invariant bank even links basic geometric fractals as argumentative transformations and build 15 prime logical operations, to retrieve arguments from random events and construct future. Geometric shape links human perceptions and conscience with emotions. So, this is conscience currency. These four currencies together builds an architecture of confusion, where in an unknown data we do not see the known but hunt for most confusing feature of an event. We enter inside, and continue to make journey finding more and more confusions until we reach facts. The temple like architecture of confusion, AOC formed is the financial protocol that consolidates invariant banking. The bank is more efficient than existing artificial intelligence protocols because AOC does not require information encoding, decoding, data interpretation, finding intuitive logic, build theory and fitting with many instances to validate and perfect it. AOC protocol running invariant bank could deliver readymade solution to any unknown situation with zero shot learning, which is hallmark of the contemporary AI adventure.
One of the biggest problem of deep learning or existing AI is that the engine that produces remarkable intelligent output is a black box, we don’t know the physical significance of each function or combination of functions using which we deliver the result. It is not the case for GML, because every clock is a variable, center of clocks connected together to build a higher clock that is an invariant. A combination of variables and invariants is the equation and formulae used by modern science as foundation of scientific practice. We provide an alternative way to build it, because, we also generate the 3D spatial distribution of clocks that could be used to create scientific models and theories from scratch. Thus, we dramatically transform user experience. The user of our products could feel the data with an audio-visual effect that tells what does every clock or sound stands for. Feelings, perceptions were never incorporated as part of existing scientific process to understand nature, that we had to do by creating analogies and cartoons and thus, making popular science or pop-science. Now, its a emotional perceptional encoding of knowledge in the human mind and an instant learning that was never there.