DOCSLIB.ORG

Is Leibniz a Precursor of Artificial Intelligence?

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text

Load more

Sybille Kramer Sybille Kdimer is professor for phi­ Freie Universitat Berlin, FE Philosophic losophy (epistemology, themy of mind) at the Institute for Philoso­ phy, Free University of Berlin. Her publications arc on rationalism, his­ Mind, Symbolism, Formalism: tory of formalization, philosophical aspects of Artificial Intelligence, Is Leibniz a Precursor of consciousness, metaphors, theory of melancholy, computer as a medium Artificial Intelligence? and media-theory. Kramer, S.: Mind, symbolism, formalism: Is Leibniz a computer is transferable to the human mind. What hap­ precursor of Artificial Intelligence'! pens within our mind is the unconscious working of a Knowl.Org. 23(1996)No.2, p.84-87, 31 refs. computer-like mechanism. Thus the computer is an ex­ The assumption that Gottfried Wilhelm Lcibniz is a precursor planative model of the human mind: the mind is a kind of of the idea of Artificial Intelligence is misleading. The argu­ computer. ment is to distinguish between epistcmc and mind, recognition But the idea of a functional analogy between the and cognition. Leibniz interpreted forma! symbolic operations as a mere epistemological instrument, but not as a description machine and the human mind, is not a rationalistic one. of what actually happens within the mind: Leibniz denied that More over: This assumption is inconsistent with the a machine can be used as an explanative model of cognition. rationalistic concept of the mind: Leibniz (II) - as before (Author) him Descartes (12) - explicitly excluded that a machine is of use as a model of the mind. If the association ofLeibniz with Artificial Intelligence is based on the assumption 1. The Relation between Artificial Intelligence and that a machine gives us an explanative model of the Leibniz: a Common Misunderstanding human mind, then this assumption is wrong. There is a broadly shared opinion within contemporary But we have a more modest version of what Artificial theories of mind: Rationalism in 17th century and espe­ Intelligence is about: Artificial Intelligence creates real cially Gottiiied Wilhelm Leibniz' version, is a precursor machines which are capable of executing virtual symbolic of the fundamental ideas of Artificial Intelligence and machines. Computational Thcory of Mind (1,2,3). Rene Descartes' The history of the human mind comprises the evolu­ "mathesis universalis" interpreted as the project of a tion of the exterior instruments of human reasoning, universal artificial language for producing and represent­ particularly artificiallycreated symbolic systems. If such ing quantifiable knowledge (4); Gottfried Wilhelm a symbolic system is organized in form of an interpreted Leibniz' "characteristica universalis" interpreted as an calculus, it can be characterized as a "symbolic machine" instrument to derive and to demonstrate all true sentences (13). Symbolic machines are culturally created epistemie automatically (5): Are these ideas not the early versions technologies. They rationalize the process of problem­ of a research program which the pioneers of Artificial solving by means of external algorithmic processes of Intelligence, Allen Newell and Herbert Simon claimed symbol manipUlation. Interpreted in the context of this with their dictum (6): That a mechanized physical symbol non-connectionistic symbolic technologies, Artificial system is the necessary and sufficient condition for intel­ Intelligence creates automatized symbolic machines. ligent behavior? And which the Computational Theory of Leibniz was an upholder of the epistemological useful­ Mind (7, 8) condensed to the thesis that cognition is ness of symbolic machines. He developed the idea of a nothing but the computational manipUlation of mental formal system as a general instrument of knowledge representations (9, p.II)? procedures and he discovered the possibility of transform­ But to claim ArtificialIntelligence as a successor ofthe ing a virtual formal system into a real machine. Thus rationalistic philosophy in the 17th century is much to there is a relationship between the Leibnitian theory of sweeping a statement (10). It is - in a certain _sense - knowledge and the Artificial Intelligence program exte­ misleading and wrong. riorizing and mechanizing human intellectual activity. To get this sense, wc have to introduce a distinction. But to interprete Leibniz as the pioneer of Artificial­ What ArtificialIntelligence is about can be interpreted in Intelligence-as-a-model-of-the-mind constmcts a conti­ a double way: we can sketch a quite excessive or a more nuity where we actually finda significantgap. It is the gap prosaic picture. between a certain external epistemical technique and its Here is the excessive version: Artificial Intelligence is internalization into internal mental processes (14). In this a kind of operative research on the human mind. Insofar view, mechanized symbolic operations function for Leibniz as human cognition can be described as an algorithmic as a methodological prescription and not as an explana­ formal operation, and insofar the computer is a machine tive model: A formal system establishes a norm how we to execute formal procedures, the functioning of the should think if we want to get hue knowledge, but it is by Knowl. Org. 23(l996)No.2 83 Sybille Kramer: Mind, Symbolism, Formalism no means a description of what we actually do if we are was only possible in the context of a media-invention: The thinking. invention of a non-linguistic art of writing. To be aware of this difference, may be a way to make Normally we interpret alphabetic writing as the spatial us more subtle and sensitive for the question what is a image of the temporal sequence of spoken discourse. But promising aim and what is a dead end in Artificial with the rise of written reckoning in the 15th century and Intelligence research. with the invention of the symbolic algebra in the 16th centmy, a kind of writing emerges, which was not a transition from spoken into written language any longer. 2. Reducing Truth to Correctness by Symbolic Ma­ This writing - it is well known under the label "formal chines language" - functions as a pure graphical construction, a Letme first demonstrate that calculization is for Leibniz genuine writing system: We may spell out a formalistic an exterior technique of reasoning. expression, but we cannot communicate within a formal­ Besides the experiment in natural science, the inven­ istic system. tion of the calculus is the most momentous scientific The rise of operative symbolism in the premodernera innovation of the early modern era. "Calculus" is under­ was possible only in the context of the discovery offormal stood not only in the restricted sense of the infinitesimal writing systems as a medium for knowledge acquisition calculus but as a general technique of reasonIng and and demonstration. demonstrating. Leibniz is - as far as I can see it - the first to get the idea ofthe general epistemic benefits of calculized operations. A benefit which is connected with the ration­ 2.2 Leibniz' Contributions to Operative Symbolism alistic project of reducing truth to correctness. But before Refering to the shift from ontological symbolism to we reconstruct this idea, we have to sketch an epochal operative symbolism Leibniz is a - perhaps the - dominant change, the threshold of which is marked by the work of figure. And it is just his insight into the functioning of a Leibniz. This change may be described as the transition formal system, that gives him the idea that reasoning and from an "ontological symbolismH to an "operative sym­ consciousness may be separated if thinking can be bolism" (15). calculized. Descartes in his "Regulae ad direction em ingellii"still 2.1 From "Ontological" to "Operative Symbolism" supported, that to operate with intellectual symbols pre­ supposes a permanent awareness of the symbolized ob­ "Ontological symbolism" means that a symbol refers to jects (4). But Leibniz discharged this awareness with the an object which exists independent of its symbolic repre­ following arguments: sentation. If our intellect operates symbolically, it really operates with the "things" the symbols stand for. Under (a) All our reasoning is based on sign processes: this condition the idea of rules to manipulate symbolic "ratiocinatio omnis in usu characterum constitit" (16). expressions which arc independent of its interpretation The reason for the indispensible semiotic nature of the cannot arise. Within ontological symbolism formalism is human intellect is, that the finite human mind is insuffi­ excluded. cient for grasping the infinitely many attributes which things possess. Thus instead of having an unmediated This changes, however, with "operative symbolism". experience with the objects of knowledge, we build sym­ Here the interpretation of symbolic systems is detached bolic stmctures to represent these objects (17). But this from its construction; the rules of forming and transform­ can be done in multiple ways. ing the symbols are not depending on their meaning any longer. Within operative symbolism the process of sym­ (b) Our natural language is the most influential repre­ bolic activity gets a certain self-sufficiency. The charac­ sentational medium. With its vagueness, its metaphoricity teristic feature of the operative symbolism is the calculus, and its grammatical variability, every day language serves a formal system which can be interpreted in different very well for our communicative behavior, Qut it is ways. inadequate for our cognitive activities
Recommended publications
  • Learning/Teaching Philosophy in Sign Language As a Cultural Issue

    Learning/Teaching Philosophy in Sign Language As a Cultural Issue

    DOI: 10.15503/jecs20131-9-19 Journal of Education Culture and Society No. 1_2013 9 Learning/teaching philosophy in sign language as a cultural issue Maria de Fátima Sá Correia [email protected] Orquídea Coelho [email protected] António Magalhães [email protected] Andrea Benvenuto [email protected] Abstract This paper is about the process of learning/teaching philosophy in a class of deaf stu- dents. It starts with a presentation of Portuguese Sign Language that, as with other sign lan- guages, is recognized as a language on equal terms with vocal languages. However, in spite of the recognition of that identity, sign languages have specifi city related to the quadrimodal way of their production, and iconicity is an exclusive quality. Next, it will be argued that according to linguistic relativism - even in its weak version - language is a mould of thought. The idea of Philosophy is then discussed as an area of knowledge in which the author and the language of its production are always present. Finally, it is argued that learning/teaching Philosophy in Sign Language in a class of deaf students is linked to deaf culture and it is not merely a way of overcoming diffi culties with the spoken language. Key words: Bilingual education, Deaf culture, Learning-teaching Philosophy, Portuguese Sign Language. According to Portuguese law (Decreto-Lei 3/2008 de 7 de Janeiro de 2008 e Law 21 de 12 de Maio de 2008), in the “escolas de referência para a educação bilingue de alunos surdos” (EREBAS) (reference schools for bilingual education of deaf stu- dents) deaf students have to attend classes in Portuguese Sign Language.
  • Edsger Dijkstra: the Man Who Carried Computer Science on His Shoulders

    Edsger Dijkstra: the Man Who Carried Computer Science on His Shoulders

    INFERENCE / Vol. 5, No. 3 Edsger Dijkstra The Man Who Carried Computer Science on His Shoulders Krzysztof Apt s it turned out, the train I had taken from dsger dijkstra was born in Rotterdam in 1930. Nijmegen to Eindhoven arrived late. To make He described his father, at one time the president matters worse, I was then unable to find the right of the Dutch Chemical Society, as “an excellent Aoffice in the university building. When I eventually arrived Echemist,” and his mother as “a brilliant mathematician for my appointment, I was more than half an hour behind who had no job.”1 In 1948, Dijkstra achieved remarkable schedule. The professor completely ignored my profuse results when he completed secondary school at the famous apologies and proceeded to take a full hour for the meet- Erasmiaans Gymnasium in Rotterdam. His school diploma ing. It was the first time I met Edsger Wybe Dijkstra. shows that he earned the highest possible grade in no less At the time of our meeting in 1975, Dijkstra was 45 than six out of thirteen subjects. He then enrolled at the years old. The most prestigious award in computer sci- University of Leiden to study physics. ence, the ACM Turing Award, had been conferred on In September 1951, Dijkstra’s father suggested he attend him three years earlier. Almost twenty years his junior, I a three-week course on programming in Cambridge. It knew very little about the field—I had only learned what turned out to be an idea with far-reaching consequences. a flowchart was a couple of weeks earlier.
  • The Turing Approach Vs. Lovelace Approach

    The Turing Approach Vs. Lovelace Approach

    Connecting the Humanities and the Sciences: Part 2. Two Schools of Thought: The Turing Approach vs. The Lovelace Approach* Walter Isaacson, The Jefferson Lecture, National Endowment for the Humanities, May 12, 2014 That brings us to another historical figure, not nearly as famous, but perhaps she should be: Ada Byron, the Countess of Lovelace, often credited with being, in the 1840s, the first computer programmer. The only legitimate child of the poet Lord Byron, Ada inherited her father’s romantic spirit, a trait that her mother tried to temper by having her tutored in math, as if it were an antidote to poetic imagination. When Ada, at age five, showed a preference for geography, Lady Byron ordered that the subject be replaced by additional arithmetic lessons, and her governess soon proudly reported, “she adds up sums of five or six rows of figures with accuracy.” Despite these efforts, Ada developed some of her father’s propensities. She had an affair as a young teenager with one of her tutors, and when they were caught and the tutor banished, Ada tried to run away from home to be with him. She was a romantic as well as a rationalist. The resulting combination produced in Ada a love for what she took to calling “poetical science,” which linked her rebellious imagination to an enchantment with numbers. For many people, including her father, the rarefied sensibilities of the Romantic Era clashed with the technological excitement of the Industrial Revolution. Lord Byron was a Luddite. Seriously. In his maiden and only speech to the House of Lords, he defended the followers of Nedd Ludd who were rampaging against mechanical weaving machines that were putting artisans out of work.
  • CODEBREAKING Suggested Reading List (Can Also Be Viewed Online at Good Reads)

    CODEBREAKING Suggested Reading List (Can Also Be Viewed Online at Good Reads)

    MARSHALL LEGACY SERIES: CODEBREAKING Suggested Reading List (Can also be viewed online at Good Reads) NON-FICTION • Aldrich, Richard. Intelligence and the War against Japan. Cambridge: Cambridge University Press, 2000. • Allen, Robert. The Cryptogram Challenge: Over 150 Codes to Crack and Ciphers to Break. Philadelphia: Running Press, 2005 • Briggs, Asa. Secret Days Code-breaking in Bletchley Park. Barnsley: Frontline Books, 2011 • Budiansky, Stephen. Battle of Wits: The Complete Story of Codebreaking in World War Two. New York: Free Press, 2000. • Churchhouse, Robert. Codes and Ciphers: Julius Caesar, the Enigma, and the Internet. Cambridge: Cambridge University Press, 2001. • Clark, Ronald W. The Man Who Broke Purple. London: Weidenfeld and Nicholson, 1977. • Drea, Edward J. MacArthur's Ultra: Codebreaking and the War Against Japan, 1942-1945. Kansas: University of Kansas Press, 1992. • Fisher-Alaniz, Karen. Breaking the Code: A Father's Secret, a Daughter's Journey, and the Question That Changed Everything. Naperville, IL: Sourcebooks, 2011. • Friedman, William and Elizebeth Friedman. The Shakespearian Ciphers Examined. Cambridge: Cambridge University Press, 1957. • Gannon, James. Stealing Secrets, Telling Lies: How Spies and Codebreakers Helped Shape the Twentieth century. Washington, D.C.: Potomac Books, 2001. • Garrett, Paul. Making, Breaking Codes: Introduction to Cryptology. London: Pearson, 2000. • Hinsley, F. H. and Alan Stripp. Codebreakers: the inside story of Bletchley Park. Oxford: Oxford University Press, 1993. • Hodges, Andrew. Alan Turing: The Enigma. New York: Walker and Company, 2000. • Kahn, David. Seizing The Enigma: The Race to Break the German U-boat Codes, 1939-1943. New York: Barnes and Noble Books, 2001. • Kahn, David. The Codebreakers: The Comprehensive History of Secret Communication from Ancient Times to the Internet.
  • Mind Body Problem and Brandom's Analytic Pragmatism

    Mind Body Problem and Brandom's Analytic Pragmatism

    The Mind-Body Problem and Brandom’s Analytic Pragmatism François-Igor Pris [email protected] Erfurt University (Nordhäuserstraße 63, 99089 Erfurt, Germany) Abstract. I propose to solve the hard problem in the philosophy of mind by means of Brandom‟s notion of the pragmatically mediated semantic relation. The explanatory gap between a phenomenal concept and the corresponding theoretical concept is a gap in the pragmatically mediated semantic relation between them. It is closed if we do not neglect the pragmatics. 1 Introduction In the second section, I will formulate the hard problem. In the third section, I will describe a pragmatic approach to the problem and propose to replace the classical non-normative physicalism/naturalism with a normative physicalism/naturalism of Wittgensteinian language games. In subsection 3.1, I will give a definition of a normative naturalism. In subsection 3.2, I will make some suggestions concerning an analytic interpretation of the second philosophy of Wittgenstein. In the fourth section, I will propose a solution to the hard problem within Brandom‟s analytic pragmatism by using the notion of the pragmatically mediated semantic relation. In the fifth section, I will make some suggestions about possible combinatorics related to pragmatically mediated semantic relations. In the sixth section, I will consider pragmatic and discursive versions of the mind-body identity M=B. In the last section, I will conclude that the explanatory gap is a gap in a pragmatically mediated semantic relation between B and M. It is closed if we do not neglect pragmatics. 2 The Hard Problem The hard problem in the philosophy of mind can be formulated as follows.
  • KNOWLEDGE ACCORDING to IDEALISM Idealism As a Philosophy

    KNOWLEDGE ACCORDING to IDEALISM Idealism As a Philosophy

    KNOWLEDGE ACCORDING TO IDEALISM Idealism as a philosophy had its greatest impact during the nineteenth century. It is a philosophical approach that has as its central tenet that ideas are the only true reality, the only thing worth knowing. In a search for truth, beauty, and justice that is enduring and everlasting; the focus is on conscious reasoning in the mind. The main tenant of idealism is that ideas and knowledge are the truest reality. Many things in the world change, but ideas and knowledge are enduring. Idealism was often referred to as “idea-ism”. Idealists believe that ideas can change lives. The most important part of a person is the mind. It is to be nourished and developed. Etymologically Its origin is: from Greek idea “form, shape” from weid- also the origin of the “his” in his-tor “wise, learned” underlying English “history.” In Latin this root became videre “to see” and related words. It is the same root in Sanskrit veda “knowledge as in the Rig-Veda. The stem entered Germanic as witan “know,” seen in Modern German wissen “to know” and in English “wisdom” and “twit,” a shortened form of Middle English atwite derived from æt “at” +witen “reproach.” In short Idealism is a philosophical position which adheres to the view that nothing exists except as it is an idea in the mind of man or the mind of God. The idealist believes that the universe has intelligence and a will; that all material things are explainable in terms of a mind standing behind them. PHILOSOPHICAL RATIONALE OF IDEALISM a) The Universe (Ontology or Metaphysics) To the idealist, the nature of the universe is mind; it is an idea.
  • Thinking About False Belief: It’S Not Just What Children Say, but How Long It Takes Them to Say It

    Thinking About False Belief: It’S Not Just What Children Say, but How Long It Takes Them to Say It

    Cognition 116 (2010) 297–301 Contents lists available at ScienceDirect Cognition journal homepage: www.elsevier.com/locate/COGNIT Brief article Thinking about false belief: It’s not just what children say, but how long it takes them to say it Cristina M. Atance a,*, Daniel M. Bernstein b,c, Andrew N. Meltzoff c a School of Psychology, University of Ottawa, 200 Lees Avenue, Room E228, Ottawa, Ontario, Canada K1N 6N5 b Kwantlen Polytechnic University, 12666, 72nd Avenue, Surrey, BC, Canada V3W 2MB c Institute for Learning and Brain Sciences, University of Washington, CHDD Building, Room 373, Box 357920, Seattle, WA 98195, USA article info abstract Article history: We examined 240 children’s (3.5-, 4.5-, and 5.5-year-olds) latency to respond to questions Received 29 January 2009 on a battery of false-belief tasks. Response latencies exhibited a significant cross-over Revised 22 March 2010 interaction as a function of age and response type (correct vs. incorrect). 3.5-year-olds’ Accepted 4 May 2010 incorrect latencies were faster than their correct latencies, whereas the opposite pattern emerged for 4.5- and 5.5-year-olds. Although these results are most consistent with con- ceptual change theories of false-belief reasoning, no extant theory fully accounts for our Keywords: data pattern. We argue that response latency data provide new information about under- Theory of mind lying cognitive processes in theory of mind reasoning, and can shed light on concept acqui- False-belief reasoning Conceptual development sition more broadly. Response latencies Ó 2010 Elsevier B.V. All rights reserved.
  • An Early Program Proof by Alan Turing F

    An Early Program Proof by Alan Turing F

    An Early Program Proof by Alan Turing F. L. MORRIS AND C. B. JONES The paper reproduces, with typographical corrections and comments, a 7 949 paper by Alan Turing that foreshadows much subsequent work in program proving. Categories and Subject Descriptors: 0.2.4 [Software Engineeringj- correctness proofs; F.3.1 [Logics and Meanings of Programs]-assertions; K.2 [History of Computing]-software General Terms: Verification Additional Key Words and Phrases: A. M. Turing Introduction The standard references for work on program proofs b) have been omitted in the commentary, and ten attribute the early statement of direction to John other identifiers are written incorrectly. It would ap- McCarthy (e.g., McCarthy 1963); the first workable pear to be worth correcting these errors and com- methods to Peter Naur (1966) and Robert Floyd menting on the proof from the viewpoint of subse- (1967); and the provision of more formal systems to quent work on program proofs. C. A. R. Hoare (1969) and Edsger Dijkstra (1976). The Turing delivered this paper in June 1949, at the early papers of some of the computing pioneers, how- inaugural conference of the EDSAC, the computer at ever, show an awareness of the need for proofs of Cambridge University built under the direction of program correctness and even present workable meth- Maurice V. Wilkes. Turing had been writing programs ods (e.g., Goldstine and von Neumann 1947; Turing for an electronic computer since the end of 1945-at 1949). first for the proposed ACE, the computer project at the The 1949 paper by Alan M.
  • Is AI Intelligent, Really? Bruce D

    Is AI Intelligent, Really? Bruce D

    Seattle aP cific nivU ersity Digital Commons @ SPU SPU Works Summer August 23rd, 2019 Is AI intelligent, really? Bruce D. Baker Seattle Pacific nU iversity Follow this and additional works at: https://digitalcommons.spu.edu/works Part of the Artificial Intelligence and Robotics Commons, Comparative Methodologies and Theories Commons, Epistemology Commons, Philosophy of Science Commons, and the Practical Theology Commons Recommended Citation Baker, Bruce D., "Is AI intelligent, really?" (2019). SPU Works. 140. https://digitalcommons.spu.edu/works/140 This Article is brought to you for free and open access by Digital Commons @ SPU. It has been accepted for inclusion in SPU Works by an authorized administrator of Digital Commons @ SPU. Bruce Baker August 23, 2019 Is AI intelligent, really? Good question. On the surface, it seems simple enough. Assign any standard you like as a demonstration of intelligence, and then ask whether you could (theoretically) set up an AI to perform it. Sure, it seems common sense that given sufficiently advanced technology you could set up a computer or a robot to do just about anything that you could define as being doable. But what does this prove? Have you proven the AI is really intelligent? Or have you merely shown that there exists a solution to your pre- determined puzzle? Hmmm. This is why AI futurist Max Tegmark emphasizes the difference between narrow (machine-like) and broad (human-like) intelligence.1 And so the question remains: Can the AI be intelligent, really, in the same broad way its creator is? Why is this question so intractable? Because intelligence is not a monolithic property.
  • Uluslararası Ders Kitapları Ve Eğitim Materyalleri Dergisi

    Uluslararası Ders Kitapları Ve Eğitim Materyalleri Dergisi

    Uluslararası Ders Kitapları ve Eğitim Materyalleri Dergisi The Effect of Artifiticial Intelligence on Society and Artificial Intelligence the View of Artificial Intelligence in the Context of Film (I.A.) İpek Sucu İstanbul Gelişim Üniversitesi, Reklam Tasarımı ve İletişim Bölümü ABSTRACT ARTICLE INFO Consumption of produced, quick adoption of discovery, parallel to popularity, our interest in new and different is at the top; We live in the age of technology. A sense of wonder and satisfaction that mankind has existed in all ages throughout human history; it was the key to discoveries and discoveries. “Just as the discovery of fire was the most important invention in the early ages, artificial intelligence is also the most important project of our time.” (Aydın and Değirmenci, 2018: 25). It is the nature of man and the nearby brain. It is Z Artificial Intelligence ”technology. The concept of artificial intelligence has been frequently mentioned recently. In fact, I believe in artificial intelligence, the emergence of artificial intelligence goes back to ancient times. Various artificial intelligence programs have been created and robots have started to be built depending on the technological developments. The concepts such as deep learning and natural language processing are also on the agenda and films about artificial intelligence. These features were introduced to robots and the current concept of “artificial intelligence was reached. In this study, the definition, development and applications of artificial intelligence, the current state of artificial intelligence, the relationship between artificial intelligence and new media, the AI Artificial Intelligence (2001) film will be analyzed and evaluated within the scope of the subject and whether the robots will have certain emotions like people.
  • Vol. 62, No. 3; September 1984 PUTNAM's PARADOX David Lewis Introduction. Hilary Putnam Has Devised a Bomb That Threatens To

    Vol. 62, No. 3; September 1984 PUTNAM's PARADOX David Lewis Introduction. Hilary Putnam Has Devised a Bomb That Threatens To

    Australasian Journal of Philosophy Vol. 62, No. 3; September 1984 PUTNAM'S PARADOX David Lewis Introduction. Hilary Putnam has devised a bomb that threatens to devastate the realist philosophy we know and love. 1 He explains how he has learned to stop worrying and love the bomb. He welcomes the new order that it would bring. (RT&H, Preface) But we who still live in the target area do not agree. The bomb must be banned. Putnam's thesis (the bomb) is that, in virtue of considerations from the theory of reference, it makes no sense to suppose that an empirically ideal theory, as verified as can be, might nevertheless be false because the world is not the way the theory says it is. The reason given is, roughly, that there is no semantic glue to stick our words onto their referents, and so reference is very much up for grabs; but there is one force constraining reference, and that is our intention to refer in such a way that we come out right; and there is no countervailing force; and the world, no matter what it is like (almost), will afford some scheme of reference that makes us come out right; so how can we fail to come out right? 2 Putnam's thesis is incredible. We are in the presence of paradox, as surely as when we meet the man who offers us a proof that there are no people, and in particular that he himself does not exist. 3 It is out of the question to follow the argument where it leads.
  • Machine Learning

    Machine Learning

    Graham Capital Management Research Note, September 2017 Machine Learning Erik Forseth1, Ed Tricker2 Abstract Machine learning is more fashionable than ever for problems in data science, predictive modeling, and quantitative asset management. Developments in the field have revolutionized many aspects of modern life. Nevertheless, it is sometimes difficult to understand where real value ends and speculative hype begins. Here we attempt to demystify the topic. We provide a historical perspective on artificial intelligence and give a light, semi-technical overview of prevailing tools and techniques. We conclude with a brief discussion of the implications for investment management. Keywords Machine learning, AI 1Quantitative Researcher 2Head of Research and Development 1. Introduction by the data they are fed—which attempt to find a solution to some mathematical optimization problem. There remains a Machine Learning is the study of computer algorithms that vast gulf between the space of tasks which can be formulated improve automatically through experience. in this way, and the space of tasks that require, for example, – Tom Mitchell (1997) reasoning or abstract planning. There is a fundamental divide Machine Learning (ML) has emerged as one of the most between the capability of a computer model to map inputs to exciting areas of research across nearly every dimension of outputs, versus our own human intelligence [Chollet (2017)]. contemporary digital life. From medical diagnostics to recom- In grouping these methods under the moniker “artificial in- mendation systems—such as those employed by Amazon or telligence,” we risk imbuing them with faculties they do not Netflix—adaptive computer algorithms have radically altered have.