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Frank Rosenblatt, Alan M. Turing, Connectionism, and Artificial Intelligence John M

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Frank Rosenblatt, Alan M. Turing, Connectionism, and Artificial Intelligence John M Proceedings of Student-Faculty Research Day, CSIS, Pace University, May 6th, 2011 Frank Rosenblatt, Alan M. Turing, Connectionism, and Artificial Intelligence John M. Casarella Seidenberg School of CSIS, Pace University, White Plains, NY 10606, USA [email protected] Abstract: Dr. Frank Rosenblatt is commonly associated with Connectionism, an area of It was Frank Rosenblatt who in 1957 began cognitive science, which applies Artificial Neural looking at the McCulloch and Pitts [20] model of Networks in an effort to explain aspects of the neuron and started investigating neural human intelligence. Other notable networks. From his work came a new model he connectionists include Warren McCulloch, called the “perceptron”. Although perceptrons Walter Pitts, and Donald Hebb, but it is Alan differed only slightly from previous neural Matheson Turing, a man of unique insight and networks, Rosenblatt made major contributions great misunderstanding, who is noticeably to the field through his experimental absent from this list. He is commonly associated investigations of the properties of perceptrons with the development of the digital computer, (using computer simulations), and through his employing his paper tape Universal Turing detailed mathematical analyses, basing the Machine. There are many who associate him perceptron model on probability theory rather with providing the foundation for defining than on symbolic logic. He was influenced by Artificial Intelligence, specifically the Hebb‟s concepts and was the first to associate development of the Turing Test as the standard the term “connectionist” with artificial neural to be met in determining if a machine exhibits networks. It was in 1958 when Rosenblatt intelligence. His contribution to AI goes beyond provided a definition to the theoretical basis of his test, laying down the foundation of connectionism in his statement, “stored Connectionism, providing insight into and information takes the form of new connections, supporting later contributions to the key models or transmission channels in the nervous system of Perceptrons, Artificial Neural Networks and (or the creation of conditions which are the Hierarchical Temporal Memory model. functionally equivalent to new connections)” [21, 22]. Introduction Dr. Rosenblatt was not primarily interested with the invention of devices for artificial intelligence, The dawn of Connectionist Theory is commonly but rather with investigating the physical traced back to McCulloch and Pitts and their structures and neurodynamic principles related to model of the Neuron. Further strength was “natural intelligence” [22]. He believed the added upon the publication of Donald O. Hebb‟s perceptron was first and foremost a brain model, influential book The Organization of Behavior not an invention for pattern recognition. (1949) [19], the source of the Hebbian approach Although, never brought to its maturity, the to neural learning studied in connectionism perceptron plays a vital role in artificial today. The contribution to connectionism by intelligence and in connectionist theory. By his Rummelhart, McClelland and the PDP Group own admission, Rosenblatt did not believe the [17, 18] cannot be minimized, yet all of them model was complete and summed up perceptrons show no awareness of Turing‟s early in this passage from his 1962 book (page 28): contribution to the field. "Perceptrons are not intended to serve as detailed copies of any actual nervous system. They're D4.1 simplified networks, designed to permit the and the human. This is the basis of the Turing study of lawful relationships between the Test, a test integrally linked with answering the organization of a nerve net, the organization of question he poses and for determining machine its environment, and the 'psychological' intelligence. Additional details shall not be performances of which it is capable. Perceptrons presented, as it is best for one to read the original might actually correspond to parts of more article. extended networks and biological systems; in this case, the results obtained will be directly Although his famous paper was published in applicable. More likely they represent extreme 1950, Turing was harboring thoughts of machine simplifications of the central nervous system, in intelligence as early as 1941 according to Donald which some properties are exaggerated and Michie [2]. Michie remembers Turing would others suppressed. In this case, successive talk about the possibility of computing machines perturbation and refinements of the system may (1) learning from experience and (2) solving yield a closer approximation.[22]" Dr. problems by means of searching through the Rosenblatt‟s contribution to Artificial space of possible solutions [2]. He was also Intelligence, Connectionism and in providing the attempting to make a comparison between a foundation for Neural Nets and the HTM Model digital computer and the human brain. In a are significant, but he too was unaware of series of broadcast lectures, one given in May of Turing‟s early contributions. 1951, Turing provided some additional insight into what he was thinking. In reviewing In the Beginning… Turing‟s typescript, it was found he believed digital computers could be used in such a manner The mid to late 1950s is often looked upon as the they could appropriately be described as brains. beginning of artificial intelligence. This, He continues by saying, “although digital unfortunately, is incorrect. History, by way of computers might be programmed to behave like discovery and re-discovery of the writings of Dr. brains, we do not at present (1951) know how Alan M. Turing, places artificial intelligence‟s this should be done. As to whether we will or true origins to approximately 1950, and possibly will not eventually succeed in finding such a as early as 1941. To most people, it was program, I (Turing), personally am inclined to Turing‟s article entitled Computing Machinery believe that such a program will be found. Our and Intelligence [1] which affords him his fame, main problem is how to program a machine to but this is only part of Turing‟s contributions to imitate the brain, or as we might say more AI. The significance of this paper could not briefly, if less accurately, to think [3-5]”. have been anticipated at time of its publication, Turing‟s level of understanding of intelligence yet its impact on artificial intelligence cannot be and artificial intelligence was far more advanced disputed. Within this paper, Turing poses the than previously understood, specifically in how question, “Can Machines Think?” we learn. It was Turing‟s [4] understanding, in trying to imitate an adult human mind, we should To determine the answer to this question, he consider three issues: the initial state of the provides the reader with a “game” which first mind, the education it has been subject to, and takes place between an interrogator, a man and a the other experiences it has been subject to (that woman. These three individuals are separated cannot be described as education). His final from each other. The interrogator can ask thoughts show we should try to create a questions of either of these individuals via computational model a child‟s mind and then “teletype” interface. The man will attempt to “educate” it to obtain the model of the adult convince the interrogator he is the woman and brain. It would difficult not to see the correlation the woman will be truthful. The objective is for to perceptrons, neural networks and especially to the interrogator to correctly conclude who is the the hierarchical temporal memory model. man and who is the woman. Turing now alters this game and replaces the man or the woman In the ensuing years, there remained many with a machine. It is now the objective of the unanswered questions concerning his vision of interrogator to differentiate between the machine artificial intelligence, his views on intelligent D4.2 machinery and the continuous debate as to the into networks in a largely random manner, meaning of the “Turing Test” in defining referred to by Turing as “unorganized intelligence. There are arguments attempting to machines”. His invented neural network was show the fallacy of Turing‟s concept of machine called a “B-type unorganized machine”, which intelligence, such that a machine would need to consisted of artificial neurons and devices be conciseness (be aware of itself), but is this a capable of modify the connections between valid argument? Maybe Turing was looking them. His model was very different in that every beyond a simple definition of machine neuron in the network executes the same logical intelligence, but was unable to complete his operation of “not and” (NAND): the output is 1 work due to his untimely departure from this if either of the inputs is 0. If the both inputs are Earth. 1, then the output is 0. Turing selected NAND because every other logical or Boolean operation Some of his critics have found fault in the can be accomplished by groups of NAND behavioral approach of the Turing Test [6]; neurons [10, 11]. French [7] discusses whether passing the Turing Test is a sufficient or a necessary condition for It was Turing‟s contention machines [4, 10, 12] machine intelligence and he asks whether the test could be constructed which would simulate the can be passed at all. Perhaps it is Hayes and behavior of the human mind very closely. He Ford [8] who provided a more provoking goes further by stating these machines “will concern in a moral objection concerned with the make mistakes at times and at times they may artificial constraints the setting imposes on the make new and very interesting statements, and participants of the game and to express their on the whole the output of them will be worth inability to find a practical use for the Turing attention to the same sort of extent as the output Test. They ask why we put forth so much effort of a human mind.[4]” He looked at the creation to build a machine to imitate a human.
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