Is There a Smarter Path to Artificial Intelligence? Some Experts Hope So
Maybe good AI requires a combination of symbolic approaches for the knowledge representation, deep parsing for NLP and semantics&#x2F;discourse models, and machine learning for pattern recognition tasks of any kind.<p>The currently trendy &quot;AI&quot; looks more like massive data mining with powerful ML to me, that&#x27;s very good for certain tasks but brings us nowhere near real AI. The knowledge representation problem has not yet been solved. In order to get even just a convincing simulation of AI, let alone real AI, we need a large common sense knowledge base &#x2F; computational ontology.<p>One perhaps promising approach are geometric meaning theories&#x2F;concept representations that allow for logical combinations. For example, Diederik Aerts works in this area. However, to be honest, I don&#x27;t have the math skills to evaluate his approach. Generally speaking, logical modelling is too limited for good concept representations - especially classical 90s AI like in default reasoning and other nonmonotonic logics -, whereas traditionally geometric representations suffer from problems with representing logical inference and quantification. IMHO, that&#x27;s a problem worth looking at. (Admittedly, I&#x27;m a bit biased towards symbolic AI like the people in this article.)