[olsher]

The reason that AGI has proven so elusive is that traditional techniques can't achieve it - as our work shows, this includes all statistical approaches. Statistical approaches can only be benchmarked statistically, and are mathematical techniques, so they require math, data, etc. It is critical to note that this proof method, while popular within Computer Science for the reasons just noted, is not a 'strong' method of proof - as but one example it can never reach the levels of proof that can prove propositions 'for all n' within mathematics.

The only way to prove AGI is by showing that the right properties hold of a system at its top level. Generality means a guarantee that the system can handle any future problem seen or unseen, but we can't generalize from examples; showing it working for 10 cases doesn't tell you if it will work for the 11th.

Given that we prove our system 'for all n', the proof that we provide in this paper is in fact far stronger than even what you've asked for here. We employ the strongest possible proof mechanism available.

As for formal descriptions and applications, these are extensively provided in the papers noted in an earlier comment (cited in the proof paper). See especially "Semantically-based priors and nuanced knowledge core for Big Data, Social AI, and language understanding" for a mathematical treatment of waste entropy and how and why atoms work at a fundamental level.