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Music for decades there has been a tug of war between connectionism and symbolism or between discrete and continuous or perhaps you might prefer the terminology between type 1 and type 2. symbolism requires symbol processing as the most suitable model of cognition which is to say the serial syntactical and universal transformation of discrete symbols in computational algorithms or in plain english the kind of code that you write in python could construct a valid model of cognition the symbols or variables if you like serve as representations of external reality in nlp the symbolists would represent lexical semantics through an ontology or semantic network typically manually created by lexicographers or domain experts on the other hand connectionism regards parallel and distributed information processing in the form of vectors and tensors as being the most salient model of cognition that is in other words the application of artificial neural networks in my opinion the duality between discrete and continuous is fascinating it seems to pervade absolutely every single aspect of reality itself now since the 1980s when connectionist methods started getting traction a whole theory arose trying to postulate an alternative and unified theory of cognition smelensky rumor heart and mcclelland they devised a new model of cognition which is called the sub-symbolic paradigm right so connectionist models may well offer an opportunity to escape the brittleness of symbolic ai systems but is it possible to marry them together right with an integrative or hybrid paradigm what's interesting with almost all of these integrative approaches is that they use a type 1 model as the first class citizen right many are looking for ways to create a type 2 interface within a type 1 model exactly as is the case for transformers and graph neural networks so all of the dominant hybrid approaches are much closer to connectionism than symbolism and personally i must say i'm quite interested in charlay's conception right which is almost turning on its head having a first-class discrete model in the inner loop and the neural network in the outer loop much like recent efforts in neural program synthesis so you basically embed meaning and grammar all together in categorical quantum mechanics what you do get then is a quantum model of language it can be called a quantum model of language i didn't like to use a term at a time now i'm totally unashamed to use the term i think at the time was suddenly deep learning start to boom boom boom boom boom and nobody nobody had any other interest in deep learning everybody everybody was obsessed with learning why not put language on a quantum computer but will there are no quantum computers yet that's not true part i'm gonna work for a company called righty and i'm gonna build them there great that is really cool let's figure out how that works now professor bob kirk met professor lambeck in the late 1990s and a few ...
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