An interesting article, but (understandably, if frustratingly) very light on the details. It reminds me of some of the work being done in the Artificial General Intelligence (AGI) community. In AGI, you are looking to come up with more universal approaches to mimicking intelligence, rather than bake-an-architecture-solve-a-specific-problem.<p>In particular, this reminds me of some of the operational logic work done in OpenCog (<a href="http:&#x2F;&#x2F;opencog.org&#x2F;" rel="nofollow">http:&#x2F;&#x2F;opencog.org&#x2F;</a>) and, especially, Pei Wang&#x27;s Non-Axiomatic Reasoning (<a href="http:&#x2F;&#x2F;cis-linux1.temple.edu&#x2F;~pwang&#x2F;papers.html" rel="nofollow">http:&#x2F;&#x2F;cis-linux1.temple.edu&#x2F;~pwang&#x2F;papers.html</a>). I&#x27;ve liked Non-Axiomatic Reasoning for a long time, and this sounds in the same broad area.<p>Pei Wang, incidentally, came up with the best operational definition of intelligence I&#x27;ve seen (and one I apply to other systems and approaches): Intelligence is the ability to act appropriately with limited knowledge and limited resources (including time and space).<p>For what it&#x27;s worth, my (published, although very open to change) position is that a layered architecture (akin to old-school subsumption architectures) is probably going to be most effective here. Combining the &#x27;symbol creation&#x27; abilities of a deep neural network with the evidential reasoning, generalisation, and planning capabilities of a cognitive layer will allow us to get the best of both worlds.