systems that know and understand and think and learn
Deep learning can produce some impressive chatbots, but they are hardly intelligent. In fact, they are precisely ignorant in that they do not think or know anything.
More intelligent dialog with an artificially intelligent agent involves both knowledge and thinking. In this article, we educate an intelligent agent that reasons to answer questions.
In a recent post I mentioned comments by Sir Tim Berners-Lee concerning the overlap between enterprise information models and semantic web ontology supporting the concept of linked data. Sir Berners-Lee argued that overlap is already sufficient to have a transformative effect on mainstream IT. I think he is right, but also that we are not there yet. There are many obstacles to adoption, not the least of which is the inertia of enterprise IT. Disruptive approaches to software development typically require ten years or so to cross the chasm from visionary and early adopters to the mainstream. We are only a few years into this and the technology is not ready.
First, let’s establish that there is plenty of semantics available for reuse now. There are existing models, some of which are well-designed, mature, and widely used. Unfortunately, most of what exists has little apparent relevance to enterprises. There is little on this diagram that would draw the attention of an enterprise architect, for example.
