We Cannot Create Artificial Intelligence via Computation Alone

Thanks for an excellent set of rebuttals! I’m glad that this will be challenging. Definitions Pro’s definition of syntax is misquoted from the source and thus incoherent. The full definition is Syntax: A set of rules for the arrangement or analysis of words and phrases. Pro’s definition of semantic is too vague, as it does not even omit syntax. I expect Pro will agree with Semantic: the relationships among potentially syntactic objects and what they denote [2] I wish to add Computation: the execution of an algorithm [8] 1) The CRA does not affirm the resolution The BoP is on Pro, which means that it is up to Pro to prove the resolution that AI cannot be achieved through computation alone. The resolution is not “the CRA cannot be disproved”. The most direct way to meet the BoP is to provide evidence indicative of any aspect of intelligence that is not computational. But Pro’s entire argument relies on the CRA, so Pro must show that the CRA is both correct and sufficient for proving that AI is not possible via computation. 2) The CRA is not correct or sound a. False equivalence Pro states that the man in the room is functionally equivalent to someone who speaks Chinese. This is a false equivalence to the entire system, because the man is just a part of the system and it is the whole system that is meant to appear to speak Chinese. Pro would have to prove that the system doesn’t speak Chinese, which contradicts the argument.b. The CRA is self-defeating If A1 is true (programs are only syntactic) and A3 is also true (semantics can’t be derived from syntax), then the system cannot simulate semantic understanding in the first place (it wouldn’t pass the Turing Test) [1]. To pass the Turing test and simulate understanding Chinese, the system would have to consistently output semantically correct responses. Therefore it must be able to distinguish between semantically correct phrases (A) and syntactically correct phrases that are semantically incorrect (B), and also incorporate new syntax in a semantically correct way. Distinguishing between A and B would require semantic understanding. Otherwise the system wouldn’t be able to see the absurdity in “The pizza was so hot it screamed”. To integrate new words, the system would need to be able to learn new words how to use them in semantically correct ways. Therefore it would have to understand how a new word relates to other syntactic and semantic objects, requiring semantic understanding. If the system overcomes either of these, it has semantic information processing somewhere. If it distinguishes between C and I by having a list of each (this can’t be done in a finite time), then the author of the paper that the man receives infuses the semantic understanding. If the system can correctly incorporate new syntax, then the author of the paper must incorporate the semantic relationships between new words and previous words. Therefore, we are left with only three options:i. If the CRA presents a system that passes the Turing test, then the system is able to process semantic information, thus violating (A1)ii. If pro shows that (A1) holds and the system passes the Turing test (produces consistently correct semantic output) then (A3) is violated, since the system achieved semantic coherence through syntax aloneiii. The system does not pass the Turing test, making the entire basis of the CRA moot with respect to proving the resolution that AI cannot be achieved via computationc. Premise (A1) is false Programs are capable of achieving semantic coherence through computation. Computational semantics incorporates semantics into computation so that natural language programs and computational reasoning can make use of semantic representations and perform correct semantic inference [3]. One example extracts meaning from text, builds semantic representations, and performs semantic inference [4]. Thus it is impossible for the CRA to prove the resolution, as it itself is not proven nor sound. 3) The CRA is insufficient Pro’s CRA fails to take into account progress in AI and computational neuroscience since the mid 80’s. Intelligent systems exist which both learn and extend beyond syntax so Pro’s CRA does not represent all computational systems or reflect the nature of computing.a. Computational systems can learn The CRA fails to represent learning, and therefore does not represent all computational machines. As Pro agrees, computational systems are capable of learning, though Pro may not be aware that this extends to semantics [5,6,7]. The CRA has no mechanism by which to learn, and therefore the CRA does not represent all programs or the nature of computation. b. Connectionist models and brain simulations Pro rejects the notion that an accurate brain simulation can result in AI because it only simulates the formal structure of a brain. This is incorrect. Brain simulations and connectionist models also simulate the dynamics, plasticity, and abilities of the brain [5,6]. Thus the refutation is not grounded in a true understanding of connectionist models and brain simulations. Modern connectionist models are not even programmed to perform certain computations or solve certain problems. They learn instead. Deep Belief Networks learn semantic representations and perform semantic inference [6]. They are also able to imagine new semantic concepts based on what they are already familiar with (music composition [7]). Together with 2c, this means the CRA is merely an example of a computational system and Rebuttal 1 from Round 2 holds. Argument: Human Intelligence is Computational The argument isn’t that aspects of human intellect can be simulated via computation and therefore it is computational. The argument is that biological neural networks are computational systems which compute algorithsm and the brain is simply a large neural network and neural networks are computational systems [9,10]. Therefor this is not a fallacy of composition. The rebuttal against connectionist machines is based on a misunderstanding. Backpropagation is only how certain connectionist models learn, and these models are not meant to simulate brains. Backpropagation is used to learn classification [5]. Other connectionist models are more concerned with being biologically plausible [11]. Pro’s final rebuttal is that computation isn’t inherent to natural systems and is just in our interpretation. Since humans can compute, there must be at least some computation in happening in our brains. There are also several fields of science devoted to studying computation in nature [12]. However, what makes our brain computational is that all of the aspects of intelligence we identified is explainable by neural computation running algorithms. [1] http://www-formal.stanford.edu... [2] https://www.cl.cam.ac.uk... [3] Blackburn & Bos (2005) Representation and Inference for Natural Language: A First Course in Computational Semantics [4] McNamara (2010) Computational Methods to Extract Meaning from Text and Advance Theories of Human Cognition. Topics and Cog. Sci. 3(1): 3-17 [5] Haykin (2009) Neural Networks and Learning Machines [6] Lee, Grosse, Ranganath, Ng. (2009) Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations. Conf on Mach Learning [7] Learning to Create Jazz Melodies Using Deep Belief Nets (2010) Conf on Comp Creativity. http://www.cs.hmc.edu... [8] http://www.alanturing.net... [9] Churchland, Sejnowski (1992) The Computational Brain. Computational Neuroscience. The MIT Press. [10] Baron (2013) The Cerebral Computer: An Introduction to the Computational Structure of the Human Brain. Psychology Press [11] Ciresan, D. (2012). Multi-column deep neural networks for image classification. Computer Vision and Pattern Recogntion [12] http://en.wikipedia.org...