Toward Grand Unified AGI – SingularityNET

In this blog post, I am going to unfold some reasonably technical ideas pertinent most directly to the fourth point in the list: How to make meta-learning work in reality, in the context of a complex multi-algorithm cognitive architecture carrying out a variety of complicated tasks. Dr. Nil Geisweiller has recently written a research blog post describing his current work on "probabilistic inference meta-learning." In his research, he discusses using OpenCog's Probabilistic Logic Networks (PLN) framework as the base-level algorithm for meta-learning, via using pattern-mining and then PLN itself to learn patterns in large sets of PLN inference examples, to learn what sorts of inferences work better in what contexts. This gets at the crux of the meta-learning problem in an OpenCog context; it is about using PLN to help PLN learn how to reason better. This blog post is complementary to Dr. Nil's, in this post I am going to describe some work currently underway to, in effect, fuse various learning/reasoning algorithms now working separately within OpenCog so that they appear as aspects of a single unified learning/reasoning algorithm. This sort of unification provides greater elegance than a situation where there are multiple markedly distinct learning/reasoning algorithms.