We Cannot Create Artificial Intelligence via Computation Alone

Thanks! I defined syntax in the last round as symbol processing and semantics as mental content or meaning. However if we wish for other definitions I will present them. Syntax: A set of rules for or an analysis of this [1]. Semantic: Relating to meaning in language or logic [2]. Does the CRA affirm the resolution? Here Con argues the CRA is only one example of a computational system. If it succeeds this doesn’t entail all computational systems are not intelligent. The problem with his argument is it strawmans the Chinese Room. Con summarizes the argument in a quite fallacious form, but this isn’t the argument. The argument isn’t attempting to disprove strong AI because one setup that mimics a computer isn’t intelligent. It’s attempting to disprove strong AI because the example uses the fundamental nature of computation. Not that simply one system wouldn’t be intelligent, but because this system reflects the very nature of computation. Is the CRA computationally intelligent? Con makes a small objection at the beginning of his sentence. That the CRA was attacked as an appeal to intuition. An appeal to intuition is “an argument that because a proposition does not match one's experience of how things work in general, or how we believe they should work, then that proposition is not true.” [3] However, as I said before the CRA is based off the definition of a computation. Examples given are things like “Our intuition says that continents do not move, since we do not notice any such movement, but it turns out that they do in ways that have produced enormous movements over geologic time.”[ibid] This is something that is a posteriori, yet the CRA is deductive, relying on the definition of computation. His further objection is about the things the Chinese Room can’t do that computers can. However this misunderstands the Chinese room. The man doesn't only need to respond to text; there is no reason why the rule book can’t have him do other actions. The book can say “ if xyz comes up, then knock three times and snap his fingers once”. Since actions can be implemented, so can the action to make a new rule. If the characters “我的名字是 come up, then copy what is after those characters and reply that whenever “我叫什么名字” comes up. As for it saying two different sentences mean the same thing, there’s no reason why the Chinese room couldn’t do this. The book can tell the man to reply in a way that says they mean the same. The examples are still symbol processing. The objection misunderstands the Chinese room and thus fails. The BOP I’m sorry, but this objection is really bad. It fundamentally has nothing at all to do with the CRA or AI and is a misunderstanding of the burden of proof. It’s hardly a rebuttal. It is my job to present the argument and Con’s job to present rebuttals and for us to go back and forth with the rebuttals until the end. This is the format of the debate agreed upon. It is not my job to respond to every single reply in existence to fulfill my BOP. If Con’s objections have been answered, then the BOP has been fulfilled. Con’s view of the BOP is an impossible to meet as DDO only has a certain amount of characters. So even if he was correct, we would have to still have to do what’s described above. I doubt my opponent would find it sufficient to show I have met my BOP by showing there are unreplied replies to his objections and I doubt it would be sufficient to show that his argument in favor of AI is unsound because there is unsolved debate on the subject. This argument is a Gish Gallop [4] and misunderstands how the debate works. Connectionism Here Con says a brain stimulation may create an AI. However, simulating the formal structure of the brain is just that, a simulation. For example, Ned Block proposed a Chinese nation, where everyone in China must simulate every part of an English speaker’s brain through walkie talkies and flags [5]. Only the formal structure is simulated, not the actual mind. Furthermore, this doesn’t get around the Chinese room. Instead of one man inside a room, we could have many men inside a gym. All receiving different commands in one area of the gym and given different tokens to act as weights for adjustment. Via trial and error, they can get many things correct. They act like a connectionist system, yet there is still no understanding of Chinese. Con’s Argument Con argues AI via computation is possible because human intellect is computational. The initial problem I see with this argument is that it’s a fallacy of composition [6]. Just because one property of the mind can be simulated via computation doesn’t entail the mind itself is a computer. Even if I were to go as so far to concede parts of human intellect is computational, it still doesn’t follow that the human intellect as a whole is computational. Which could make problem solving via computation work, but not actual conscious problem solving. Furthermore, our brains don’t seem to work like a connectionist system. Connectionist machines learn via error backpropagation [7], but there is no evidence that suggests brains learn this way [8]. It’s also unclear that the connectionist machines can account for the systematic nature of the mind. Fodor and Pylyshyn pointed out someone who understands a phrase like “N7 speaks to UndeniableReality” can understand the phrase “UndeniableReality speaks to N7”, yet a connectionist machine cannot understand the latter solely because of the former [9]. What I believe the biggest problem is the fact that anything can be a computer. It is first important to understand the difference between intrinsic and observer dependant entities. Something which is intrinsic is something such as mass, force, and gravity. Basically it’s something within the nature of the entity itself. Something which is observer dependent is something like marriage, money, and government. They exist because we give value to them. The Church-Turing thesis shows any real world example computation can be also computed on a Turing machine [10][11]. Computation can be carried out on water pipes, notches, and Ned Block had an example of it being carried out with cats and mice [12]. The important question is, in virtue of what are these things computers? There is nothing about the properties of the pipes, or animals which make them computers, but because we assign a computational interpretation to them. Computation is assigned, not discovered in nature. So is the brain a computer? It’s a trivial yes, but it isn’t meaningful because no physical system is intrinsically a computer. The position isn’t coherent [13]. Con’s computational theory of the mind is filled with many problems. It’s conclusion doesn’t follow from its premises, connectionist machines don’t act like brains, and a computer isn’t something that is intrinsic in nature. We also see his first rebuttal and second rebuttal misunderstand the CRA, his third rebuttal misunderstands the parameters of the debate, and his fourth rebuttal doesn’t escape the CRA. Thanks Sources [1] http://www.oxforddictionaries.com... [2] http://www.oxforddictionaries.com... [3] http://evolutionwiki.org... [4] http://rationalwiki.org... [5] Block, N. 1978. “Troubles With Functionalism” [6] http://www.nizkor.org... [7] http://www.willamette.edu... [8] The Great Courses. Philosophy of Mind: Brains, Consciousness, and Thinking Machines Lecture 16 “Brains and Computers”. [9] Fodor, J., and Pylyshyn, Z., 1988, “Connectionism and Cognitive Architecture: a Critical Analysis,” [10] http://mathworld.wolfram.com... [11] http://www.alanturing.net... [12] Block, N. 1995 “The Mind as the Software of the Brain” [13] The Teaching Company, Philosophy of Mind by John Searle Lecture 6