We Cannot Create Artificial Intelligence via Computation Alone

As you have stated in your outline of the debate, I am required to rebut your argument in this round and I have the option of making an argument if I wish. I will do both of these. I would also like to take this opportunity to request that you please define ‘syntactic’ and ‘semantic’, as they appear to be central to your argument (or rather we should say Searle’s argument, since you have used it verbatim from Searle’s 1990 publication [1] and mistakenly attributed it to his 1984 publication). Rebuttal 1: A counter-example does not confirm the notion As Pro has stated at the top of his argument, Pro is attempting to show that computation is not sufficient to produce a mind by illustrating a hypothetical example where computation mimics a particular expression of intelligence (language comprehension), but does not result in true understanding, or intelligence. I would not agree that Pro has succeeded in showing this. However, if we assume for the moment that Pro has, and if we are precise in describing what pro has shown, we can summarize it as follows: (P1) There are examples of computational systems which mimic some aspect of human intelligence without producing true intelligence(P2) The existence of such examples is sufficient to show that computational systems cannot produce intelligence(C1) Therefore computational systems cannot produce intelligence. P1 is true, but the conclusion does not follow from the premise because P2 is false. Though Pro has succeeded in providing a counter-example to the notion, a counter-example does not confirm that artificial intelligence cannot be created from computation alone. Therefore, while I will rebut Searle’s CRA, I do want to point out that it is not actually necessary, as far as I can tell (I accept that a reformulation of the argument may reinsert relevance of the CRA to the notion). Searle’s CRA, even if in the future is definitively shown to be true, does not disprove that intelligence, or even artificial intelligence, cannot be produced from computation alone. It proves only that the CRA is not a successful implementation of an artificial intelligence. Rebuttal 2: The room is not a computational intelligence (strawman of the CRA) The point of the CRA is that it presents a system which exhibits what would seem like intelligence based on computation alone, but when it is revealed how the system actually operates, it should become intuitively obvious that the system itself is not intelligent (this has been attacked as an appeal to intuition fallacy in the CRA [7,8]). However, the system does not exhibit properties of intelligence in the first place, as it is an entirely syntactic system. The room, as you have presented it, is only able to respond in one given way depending on the input, which it must have a preprogrammed response for. It cannot respond to novel inputs, change its responses over time, or interpret two different sentences to have the same meaning. Since we have programs which can actually model semantic information [3,4,5] in language and generate novel responses to novel questions, as well as change its responses over time, (A1) in Pro’s post is a questionable premise, and the CRA as Pro has presented it is no longer relevant, even if we do not realize it is a strawman to begin with. By Pro’s own definition of a program (which the CRA is meant to be an example of), there is no semantic information processing. Therefore, there is a contradiction in the CRA, since if (A1) in Pro’s argument is true, and programs are only syntactic by necessity, then the room in the CRA cannot give meaningful responses to most inputs in the first place, and all it shows is that a syntactic program is a syntactic program. Rebuttal 2 can be summarized as follows (P1) The CRA attempts to show that simulating intelligent behavior via computation cannot produce intelligence(P2) The CRA, as presented by Pro, does not simulate intelligence(C1) The CRA does not show that simulating intelligent behavior via computation cannot produce intelligence Rebuttal 3: Unrefuted Replies to the CRA Here I will simply point out that there are several currently unresolved debates regarding the CRA [6]. By this I mean that the replies have not been sufficiently refuted to show that they do not disprove Searle’s CRA, and therefore the CRA has not yet been proven valid. Each of the replies has several versions which the CRA has not yet been able to satisfactorily dismiss. There are several, but I will discuss mainly the brain simulation / connectionist reply. Rebuttal 3 is summarized as follows: (P1) Pro has based his argument on the CRA(P2) To meet his BoP, Pro must show that there is no valid falsification of the CRA(P3) There is currently a list of replies to the CRA which have yet to be refuted or dismissed(C1) The CRA is insufficient to meet Pro’s BoP Rebuttal 4: Connectionist Reply The connectionist reply is essentially that a sufficiently accurate simulation of a brain (X), or a sufficiently complex computational network with similar properties and functions as the brain (Y), could be called intelligent. (P1) If either X or Y is possible, then artificial intelligence via computation is possible(P2) Neither X nor Y has been proven or disproven(C1) We cannot conclude that artificial intelligence via computation is not possible. Furthermore, the progress towards AI in connectionist models has been staggering from the late 80’s onwards [2]. Many functions of intelligent agents have been simulated with algorithms using computations which are not formally defined (i.e., the functions of the algorithms are learned, not defined [9,10]), including object recognition [9,10], natural language processing (semantically) [3,4,5], and much more. Argument: Human Intelligence is Computational The current paradigm in neuroscience, supported by decades of neuroscience research and computational simulation [9,10,11,12], is that the brain is a computational network of computational units called neurons. Neurons can be accurately simulated as binary output units which compute non-linear functions of their summed inputs weighted by the strength of their synaptic connections to input neurons, and learning can be simulated through algorithms which change these synaptic connections by simulating the dynamics information propagation in the brain. Even complex functions of human intelligence have been simulated using such computational networks, such planning, imagination, and creativity [13], and scientifically, we have yet to find evidence for anything other than computation that is involved in the production of intelligence in the brain. Sources: [1] Searle, John (1990) Is the Brain’s Mind a Computer Program?. Scientific American 262 (1): 26-31.[2] Cole, David (2004) The Chinese Room Argument, in Zalta, Edward N., The Stanford Encyclopedia of Philosophy.[3] Elci, A, Kone, M, Orgun, M. (2011) Semantic Agent Systems. Springer Publishing.[4] Shuklin, D E, (2004) The Further Development of Semantic Neural Network Models. Artificial Intelligence. 3: 598-606[5] Cambria, E, White, B. (2014) Jumping NLP Curves: A Review of Natural Language Processing Research. IEEE Computational Intelligence Magazine. 9(2): 48-57.[6] http://en.wikipedia.org...[7] Crevier, D (1993) AI: The Tumultuous Search for Artificial Intelligence. New York, NY: BasicBooks.[8] Dennett, D (1991) Conscoiusness Explained. The Penguin Press.[9] Haykin, S (2009) Neural Networks and Learning Machines. Pearson Education.[10] Tang, Y, Abdel-rahman, M (2012) Multiresolution Deep Belief Networks. International Conf on AI and Statistics[11] Churchland, P, Sejnowski, T J (1992) The Computational Brain. Computational Neuroscience. The MIT Press.[12] Baron, R (2013) The Cerebral Computer: An Introduction to the Computational Structure of the Human Brain. Psychology Press[13] Byrne P, Becker S, Burgess N (2007) Remebering the past and imagining the future: a neural model of spatial memory and imagery. Psychol Rev