that's what we (Kyndi) think. Good AI requires ML, NLP, and KRR. ML to acquire the knowledge on which to reason, KRR to represent and reason, and NLP as the glue that holds everything together.

Very astute. Some folks here at Kyndi used to work with Minsky. One of our favorite quotes is: "what magical trick makes us intelligence? The trick is that there is no trick. The power of intelligence stems form our vast diversity, not from any single, perfect principle." What we see now in AI/ML is not diversity, but rather attempts at the single perfect principle. We don't believe that is the path forward. Rather, you need to figure out how to tightly couple different approaches (e.g., symbolic and statistical learning approaches) to form a more powerful, flexible, and explainable model of AI.

I'm going to assume you mean the computational complexity of working with such an expressive representation. If so, then that is correct. historically, working such an expressive representation blows out computationally. But, we've resolved that issue with our graph engine. that is, its been shown that algorithms take time proportional to N-cubed where N is the number of graphs in the knowledge base. with our graph engine, we encode graph structures and ontology in a Cognitive Signature, and we can find closely matching signatures in log(N) time. the log-time algorithms scale to the size of the web.