| We explain in depth (cf. especially the cites below) why the theory and design of Cyc failed to achieve the results sought. Our system is not statistical (it is entirely causal) so it clearly cannot be derived from LLMs. While the system does support NLP applications (and tends to use construction grammar for handling syntax), those NLP applications (and construction grammar) have nothing to do with the core system itself. We explicitly reject formal logic and FOPC as the basis of our inference; as the history of the discipline and our work show, not only are these formalisms not powerful enough to achieve AGI, but the epistemology baked into them is entirely incompatible with the real world. The system does enable simulation, which is at the core of intelligence. Our system does not require human input for bootstrapping - in fact, it explicitly rejects any use of human cognition and/or human-derived information, as the profound biases found therein cannot be removed. The system separates knowledge creation from verification and application. Knowledge can be created via any means, including via human effort, but this is irrelevant because the system is explicitly constructed so that no human beliefs 'leak' into the knowledge. The paper lays out a very specific protocol we use to ensure that if humans help in knowledge verification this strict rejection of human inputs will not be violated. In some cases humans may be called upon to validate knowledge, but they may only state that an atom is incorrect, not adapt it to match their thinking. If incorrect, new atoms can be created, but these must again be validated via the same process. All knowledge is proven correct independent of human judgment; if there is any ambiguity that the knowledge is correct, it must be redone until it is obviously correct. All operations of the system are entirely unguided by humans (as they must be, but they are also provably correct and safe) - thus, it does not 'parasitize' human intelligence. In the end, the paper does do what it says on the tin - prove that AGI has been achieved. Selected cites relevant to discussion of Cyc and traditional knowledge-based systems: Olsher, Daniel. (2014) Semantically-based priors and nuanced knowledge core for Big Data, Social AI, and language understanding. Neural Networks 58:131‐147. Olsher, Daniel (2013) COGVIEW & INTELNET: Nuanced Energy-Based Knowledge Representation And Integrated Cognitive-Conceptual Framework For Realistic Culture, Values, and Concept-Affected Systems Simulation. IEEE Symposium on Computational Intelligence for Human‐like Intelligence(CIHLI) 82‐91. |