The idea is that these models could be extended to really big complex problems with the same basic principle. Therefore, it seems that intelligence of arbitrary complexity can be achieved with these algorithms.

I don't think that claim is very bold at all, it is merely an observation. The idea that the algorithm tries to maintain entropy is very much like life as we know it. Life as we know it are mechanisms that despite the continuous dissipation of entropy in the universe around it try to achieve local points of high entropy and sustain those.

In less intelligent animals, the mechanism to resist entropy loss is defined genetically. They can not adapt to new environments because their decision making is rigidly defined in their genes. (i.e. they are like simple computer programs)

In more intelligent animals, like ourselves, instead of deriving our decisions (mainly) from our genes, we derive them from an organ (whose design is derived from genes) we call the brain (I've heard there's more decision making organs). Separating the decision making from the the dna is what makes us so highly adaptable to new environments.

There our likeness to this algorithm lies, like us this algorithm can sustain its entropy no matter what the environment is (as long as its got the tools to do so).