We Cannot Create Artificial Intelligence via Computation Alone

There are several problems with Pro’s rebuttal:Pro fails to refute most of my points and attacks strawmen instead.Pro fails to see that I have not dropped any of my arguments.Pro accepts the definition of semantics I provided and then uses an unrelated definition, ‘mental content’, which itself needs defining. Pro switches between definitions to make his argument work.Finally, Pro misstates my definition of semantics to say it is only about language. It says that semantics is about what language can refer to (what a dog is, not how the word dog is used). When I say 'semantic computing', I am talking about computers understanding concepts that are not in their programming; not language comprehension.The CRA does not affirm the resolutionMy original argument stands. If the CRA is proven, it does not affirm the resolution because it is only one example of computation. Pro asserts it represents all computation but provides no justification, whereas I have shown why this assertion is incorrect. The CRA is just an example because it does not take into account learning or semantics, both of which are included in modern AI (see the examples I have provided).The CRA is only syntactic and therefore does not represent all programs. Once learning and semantics are added to the CRA, it becomes self-contradictory.The resolution is not “the CRA cannot be disproved”. Since this is what Pro seems to be arguing, he must show that proving “the CRA cannot be disproved” also proves “AI cannot be achieved through computation alone”, which is what we are actually debating.The CRA is not correctPro’s response to the systems reply is the appeal to intuition fallacy I mentioned in the last two rounds. Pro has assumed that if the man does not understand Chinese then neither does the system (fallacy of composition) because it is absurd to say the system is intelligent (appeal to intuition). This is like saying that since neurons don’t understand words, brains can’t either, and that this is obvious because brains are inanimate objects. This is a fallacious rebuttal.Pro does not explain how the CRA learns. He asserts that the book can make new rules without explaining how these new rules come to be or how they can be semantically correct. No matter how, if the system can add new concepts in semantically correct ways, it must understand the semantic content of those concepts. Furthermore, in order to distinguish between semantically correct phrases and phrases which are semantically incorrect but syntactically correct, the system needs semantic understanding (not just language). Otherwise the system doesn’t speak Chinese: a contradiction.BoPPro has neither met his BoP nor understood the BoP in this debate. To meet his BoP, Pro must show that the CRA is both correct and sufficient for proving the resolution.Pro’s argument is based entirely on the CRA not being disproven. No notion can be proven simply because its premises cannot be disproven.The CRA, even if proven correct, is not sufficient to meet the BoP. An example of computation is not sufficient to prove anything about all computational systems.The CRA has already been disproven by the many existing refutations, as well as the many existing programs which do what the CRA claims is impossible. I have provided references to both of these in Round 3.ConnectionismPro incorrectly asserts that neural dynamics and plasticity must be simulated formallyto be computation. Pro ignores the literature I have provided that show direct counter-examples.Computation is the execution of an algorithm (see definition) and this is what biological brains do [6,7]. Pro strawmans the argument by misrepresenting the Church-Turing thesis, which by Pro’s own source, says that any computation can be performed by a Turing machine, not any computer [5].This invalidates Pro’s later strawman argument, claiming that I said intelligence can be achieved by simulating the brain’s structure. As I stated, one needs to simulate the structure, dynamics, AND plasticity of the brain.Pro gives examples of connectionist models as if they represent all connectionist models and as if this shows they cannot simulate brains. Here is the first line from one of his own sources: “… is a comprehensive, full-featured deep neural network simulator that enables the creation and analysis of complex, sophisticated models of the brain in the world” [1]. This contradicts Pro’s own point.Pro fails to refute my examples or arguments about connectionism. Pro also fails to refute the notion that connectionist models do not share the same limitations as the CRA. The CRA cannot represent connectionist model.Human Intelligence is ComputationalThe point of this argument is this: the current scientific theory explaining human intelligence is that it is computational [6,7]. To say that AI cannot be achieved through computation would require the current scientific explanation to be disproved. Pro is either arguing that current scientific theory is wrong, or that human intelligence is impossible.Pro is incorrect in claiming that this argument can only succeed if the CRA is refuted. The CRA can only succeed if the current theories of neuroscience are refuted.Pro strawmans the argument again. I repeat: The argument is not that aspects of human intellect can be simulated via computation and therefore [all human intellect] is computational... this is not a fallacy of composition. I also did not argue that since brains can compute they are computational.Pro misunderstands backpropagation and neural computation. Backpropagation is a technique in machine learning for engineering applications, not a simulation of the brain. The quote you have of me does not describe backpropagation. Ironically, it describes how biological neurons function by computing.To promote the idea that connectionism cannot account for the ‘systematic nature of mind’ (undefined), Pro cites a philosophy paper from 1988. Modern connectionism came about in the 90’s and revolutionized computational intelligence. The specific example Pro says is unsolvable was solved long ago and is today considered trivial [2,3].Pro’s assertion that computation is not intrinsic is without evidence. Neurons perform computation (the output is generated from the input given the current state algorithmically). If there were no observer they would still be performing computation. Computation is intrinsic to brains [4,6,7].ConclusionPro fails to meet his BoP on two accounts: the CRA was not proven correct, and the CRA was not proven sufficient for the resolution.I have shown that the CRA is not correct because it is self-defeating and based on false premises, and I have shown that the CRA is not sufficient to meet the BoP.Pro fails to understand the meaning of computation. He mistakenly considers computation to be a set of hard-coded instructions. I have provided several counter-examples.Pro fails to demonstrate any aspect of intelligence that is not computational. I have given examples of connectionist models that exhibits properties that Pro claims would not be possible.Pro’s idea of connectionism is a poor understanding based on outdated scienceInstead of arguing my points, Pro attacks strawmen, falsely claims that I have dropped my arguments, and declares victory. Yet many of my actual arguments have gone unrefuted.[1] https://grey.colorado.edu...[2] Kuhn & DeMori (1995) The application of semantic classification trees to natural language understanding. Pattern Analysis and Machine Intelligence, 17[3] Blackburn & Bos (2005) Representation and inference for natural language: a first course in computational semantics[4] Giunti (2006) Is being computational an intrinsic property of a dynamical system? Systemics of Emergence, 683-694[5] http://mathworld.wolfram.com...[6] Haier (2011) Biological Basis of Intelligence. Cambridge Uni Press[7] Dayan & Abbott (2001) Theoretical Neuroscience. MIT Press