1.
Artificial intelligence
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Artificial intelligence is intelligence exhibited by machines. Colloquially, the artificial intelligence is applied when a machine mimics cognitive functions that humans associate with other human minds, such as learning. As machines become increasingly capable, mental facilities once thought to require intelligence are removed from the definition, for instance, optical character recognition is no longer perceived as an example of artificial intelligence, having become a routine technology. AI research is divided into subfields that focus on specific problems or on specific approaches or on the use of a tool or towards satisfying particular applications. The central problems of AI research include reasoning, knowledge, planning, learning, natural language processing, perception, general intelligence is among the fields long-term goals. Approaches include statistical methods, computational intelligence, and traditional symbolic AI, Many tools are used in AI, including versions of search and mathematical optimization, logic, methods based on probability and economics. The AI field draws upon computer science, mathematics, psychology, linguistics, philosophy, neuroscience, the field was founded on the claim that human intelligence can be so precisely described that a machine can be made to simulate it. Some people also consider AI a danger to humanity if it progresses unabatedly, while thought-capable artificial beings appeared as storytelling devices in antiquity, the idea of actually trying to build a machine to perform useful reasoning may have begun with Ramon Llull. With his Calculus ratiocinator, Gottfried Leibniz extended the concept of the calculating machine, since the 19th century, artificial beings are common in fiction, as in Mary Shelleys Frankenstein or Karel Čapeks R. U. R. The study of mechanical or formal reasoning began with philosophers and mathematicians in antiquity, in the 19th century, George Boole refined those ideas into propositional logic and Gottlob Frege developed a notational system for mechanical reasoning. Around the 1940s, Alan Turings theory of computation suggested that a machine, by shuffling symbols as simple as 0 and 1 and this insight, that digital computers can simulate any process of formal reasoning, is known as the Church–Turing thesis. Along with concurrent discoveries in neurology, information theory and cybernetics, the first work that is now generally recognized as AI was McCullouch and Pitts 1943 formal design for Turing-complete artificial neurons. The field of AI research was born at a conference at Dartmouth College in 1956, attendees Allen Newell, Herbert Simon, John McCarthy, Marvin Minsky and Arthur Samuel became the founders and leaders of AI research. At the conference, Newell and Simon, together with programmer J. C, shaw, presented the first true artificial intelligence program, the Logic Theorist. This spurred tremendous research in the domain, computers were winning at checkers, solving problems in algebra, proving logical theorems. By the middle of the 1960s, research in the U. S. was heavily funded by the Department of Defense and laboratories had been established around the world. AIs founders were optimistic about the future, Herbert Simon predicted, machines will be capable, within twenty years, Marvin Minsky agreed, writing, within a generation. The problem of creating artificial intelligence will substantially be solved and they failed to recognize the difficulty of some of the remaining tasks
2.
Natural language user interface
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In interface design natural language interfaces are sought after for their speed and ease of use, but most suffer the challenges to understanding wide varieties of ambiguous input. Natural language interfaces are an area of study in the field of natural language processing. An intuitive general natural language interface is one of the goals of the Semantic Web. Text interfaces are natural to varying degrees, many formal programming languages incorporate idioms of natural human language. Likewise, a keyword search engine could be described as a shallow natural language user interface. A natural language search engine would in theory find targeted answers to user questions, for example, when confronted with a question of the form which U. S. state has the highest income tax. Conventional search engines ignore the question and instead search on the state, income. If it works, results would have a higher relevance than results from a search engine. Prototype Nl interfaces had already appeared in the sixties and early seventies. Chat-80 transformed English questions into Prolog expressions, which were evaluated against the Prolog database, the code of Chat-80 was circulated widely, and formed the basis of several other experimental Nl interfaces. An online demo is available on the LPA website, ELIZA, written at MIT by Joseph Weizenbaum between 1964 and 1966, mimicked a psychotherapist and was operated by processing users responses to scripts. Using almost no information about human thought or emotion, the DOCTOR script sometimes provided a startlingly human-like interaction, an online demo is available on the LPA website. Janus is also one of the few systems to support temporal questions, bBN’s Parlance built on experience from the development of the Rus and Irus systems. English Wizard from Linguistic Technology Corporation, on the part of the user, this has led to unrealistic expectations of the capabilities of the system. Conjunction and disjunction List all applicants who live in California and Arizona is ambiguous unless you know that a person cant live in two places at once, anaphora resolution resolve what a user means by he, she or it, in a self-referential query. In addition, localisation across multiple language sites requires extra consideration - this is based on differing sentence structure, the significant differences between the extracted features - even from speakers who says the same word or phrase - must be successfully overcome. The natural language interface gives rise to technology used for different applications. Some of the uses are, Dictation, is the most common use for automated speech recognition systems today
3.
Formalism (philosophy of mathematics)
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In playing this game one can prove that the Pythagorean theorem is valid because the string representing the Pythagorean theorem can be constructed using only the stated rules. According to formalism, the truths expressed in logic and mathematics are not about numbers, sets, or triangles or any other subject matter — in fact. They are syntactic forms whose shapes and locations have no meaning unless they are given an interpretation, Formalism is associated with rigorous method. In common use, a means the out-turn of the effort towards formalisation of a given limited area. In other words, matters can be formally discussed once captured in a formal system, complete formalisation is in the domain of computer science. Formalism stresses axiomatic proofs using theorems, specifically associated with David Hilbert, a formalist is an individual who belongs to the school of formalism, which is a certain mathematical-philosophical doctrine descending from Hilbert. Formalists are relatively tolerant and inviting to new approaches to logic, non-standard number systems, new set theories, the more games they study, the better. However, in all three of these examples, motivation is drawn from existing mathematical or philosophical concerns, the games are usually not arbitrary. Because of their connection with computer science, this idea is also advocated by mathematical intuitionists and constructivists in the computability tradition. Another version of formalism is known as deductivism. In deductivism, the Pythagorean theorem is not an absolute truth, under deductivism, the same view is held to be true for all other statements of formal logic and mathematics. Thus, formalism need not mean that these deductive sciences are nothing more than meaningless symbolic games and it is usually hoped that there exists some interpretation in which the rules of the game hold. Taking the deductivist view allows the working mathematician to suspend judgement on the philosophical questions. Many formalists would say that in practice, the systems to be studied are suggested by the demands of the particular science. A major early proponent of formalism was David Hilbert, whose program was intended to be a complete, Hilbert aimed to show the consistency of mathematical systems from the assumption that the finitary arithmetic was consistent. The way that Hilbert tried to show that a system was consistent was by formalizing it using a particular language. In order to formalize a system, you must first choose a language in which you can express. This language must include five components, It must include such as x
4.
Logic
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Logic, originally meaning the word or what is spoken, is generally held to consist of the systematic study of the form of arguments. A valid argument is one where there is a relation of logical support between the assumptions of the argument and its conclusion. Historically, logic has been studied in philosophy and mathematics, and recently logic has been studied in science, linguistics, psychology. The concept of form is central to logic. The validity of an argument is determined by its logical form, traditional Aristotelian syllogistic logic and modern symbolic logic are examples of formal logic. Informal logic is the study of natural language arguments, the study of fallacies is an important branch of informal logic. Since much informal argument is not strictly speaking deductive, on some conceptions of logic, formal logic is the study of inference with purely formal content. An inference possesses a purely formal content if it can be expressed as an application of a wholly abstract rule, that is. The works of Aristotle contain the earliest known study of logic. Modern formal logic follows and expands on Aristotle, in many definitions of logic, logical inference and inference with purely formal content are the same. This does not render the notion of informal logic vacuous, because no formal logic captures all of the nuances of natural language, Symbolic logic is the study of symbolic abstractions that capture the formal features of logical inference. Symbolic logic is divided into two main branches, propositional logic and predicate logic. Mathematical logic is an extension of logic into other areas, in particular to the study of model theory, proof theory, set theory. Logic is generally considered formal when it analyzes and represents the form of any valid argument type, the form of an argument is displayed by representing its sentences in the formal grammar and symbolism of a logical language to make its content usable in formal inference. Simply put, formalising simply means translating English sentences into the language of logic and this is called showing the logical form of the argument. It is necessary because indicative sentences of ordinary language show a variety of form. Second, certain parts of the sentence must be replaced with schematic letters, thus, for example, the expression all Ps are Qs shows the logical form common to the sentences all men are mortals, all cats are carnivores, all Greeks are philosophers, and so on. The schema can further be condensed into the formula A, where the letter A indicates the judgement all - are -, the importance of form was recognised from ancient times
5.
Set theory
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Set theory is a branch of mathematical logic that studies sets, which informally are collections of objects. Although any type of object can be collected into a set, set theory is applied most often to objects that are relevant to mathematics, the language of set theory can be used in the definitions of nearly all mathematical objects. The modern study of set theory was initiated by Georg Cantor, Set theory is commonly employed as a foundational system for mathematics, particularly in the form of Zermelo–Fraenkel set theory with the axiom of choice. Beyond its foundational role, set theory is a branch of mathematics in its own right, contemporary research into set theory includes a diverse collection of topics, ranging from the structure of the real number line to the study of the consistency of large cardinals. Mathematical topics typically emerge and evolve through interactions among many researchers, Set theory, however, was founded by a single paper in 1874 by Georg Cantor, On a Property of the Collection of All Real Algebraic Numbers. Since the 5th century BC, beginning with Greek mathematician Zeno of Elea in the West and early Indian mathematicians in the East, especially notable is the work of Bernard Bolzano in the first half of the 19th century. Modern understanding of infinity began in 1867–71, with Cantors work on number theory, an 1872 meeting between Cantor and Richard Dedekind influenced Cantors thinking and culminated in Cantors 1874 paper. Cantors work initially polarized the mathematicians of his day, while Karl Weierstrass and Dedekind supported Cantor, Leopold Kronecker, now seen as a founder of mathematical constructivism, did not. This utility of set theory led to the article Mengenlehre contributed in 1898 by Arthur Schoenflies to Kleins encyclopedia, in 1899 Cantor had himself posed the question What is the cardinal number of the set of all sets. Russell used his paradox as a theme in his 1903 review of continental mathematics in his The Principles of Mathematics, in 1906 English readers gained the book Theory of Sets of Points by William Henry Young and his wife Grace Chisholm Young, published by Cambridge University Press. The momentum of set theory was such that debate on the paradoxes did not lead to its abandonment, the work of Zermelo in 1908 and Abraham Fraenkel in 1922 resulted in the set of axioms ZFC, which became the most commonly used set of axioms for set theory. The work of such as Henri Lebesgue demonstrated the great mathematical utility of set theory. Set theory is used as a foundational system, although in some areas category theory is thought to be a preferred foundation. Set theory begins with a binary relation between an object o and a set A. If o is a member of A, the notation o ∈ A is used, since sets are objects, the membership relation can relate sets as well. A derived binary relation between two sets is the relation, also called set inclusion. If all the members of set A are also members of set B, then A is a subset of B, for example, is a subset of, and so is but is not. As insinuated from this definition, a set is a subset of itself, for cases where this possibility is unsuitable or would make sense to be rejected, the term proper subset is defined
6.
Subset
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In mathematics, especially in set theory, a set A is a subset of a set B, or equivalently B is a superset of A, if A is contained inside B, that is, all elements of A are also elements of B. The relationship of one set being a subset of another is called inclusion or sometimes containment, the subset relation defines a partial order on sets. The algebra of subsets forms a Boolean algebra in which the relation is called inclusion. For any set S, the inclusion relation ⊆ is an order on the set P of all subsets of S defined by A ≤ B ⟺ A ⊆ B. We may also partially order P by reverse set inclusion by defining A ≤ B ⟺ B ⊆ A, when quantified, A ⊆ B is represented as, ∀x. So for example, for authors, it is true of every set A that A ⊂ A. Other authors prefer to use the symbols ⊂ and ⊃ to indicate proper subset and superset, respectively and this usage makes ⊆ and ⊂ analogous to the inequality symbols ≤ and <. For example, if x ≤ y then x may or may not equal y, but if x < y, then x definitely does not equal y, and is less than y. Similarly, using the convention that ⊂ is proper subset, if A ⊆ B, then A may or may not equal B, the set A = is a proper subset of B =, thus both expressions A ⊆ B and A ⊊ B are true. The set D = is a subset of E =, thus D ⊆ E is true, any set is a subset of itself, but not a proper subset. The empty set, denoted by ∅, is also a subset of any given set X and it is also always a proper subset of any set except itself. These are two examples in both the subset and the whole set are infinite, and the subset has the same cardinality as the whole. The set of numbers is a proper subset of the set of real numbers. In this example, both sets are infinite but the set has a larger cardinality than the former set. Another example in an Euler diagram, Inclusion is the partial order in the sense that every partially ordered set is isomorphic to some collection of sets ordered by inclusion. The ordinal numbers are a simple example—if each ordinal n is identified with the set of all ordinals less than or equal to n, then a ≤ b if and only if ⊆. For the power set P of a set S, the partial order is the Cartesian product of k = |S| copies of the partial order on for which 0 <1. This can be illustrated by enumerating S = and associating with each subset T ⊆ S the k-tuple from k of which the ith coordinate is 1 if and only if si is a member of T
7.
Semantic network
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A semantic network, or frame network, is a network that represents semantic relations between concepts. This is often used as a form of knowledge representation and it is a directed or undirected graph consisting of vertices, which represent concepts, and edges, which represent semantic relations between concepts. Typical standardized semantic networks are expressed as semantic triples, Semantic Nets were first invented for computers by Richard H. Richens of the Cambridge Language Research Unit in 1956 as an interlingua for machine translation of natural languages. It later featured prominently in the work of Allan M, in the subsequent decades, the distinction between semantic networks and knowledge graphs was blurred. In 2012, Google gave their knowledge graph the name Knowledge Graph, a semantic network is used when one has knowledge that is best understood as a set of concepts that are related to one another. Most semantic networks are cognitively based and they also consist of arcs and nodes which can be organized into a taxonomic hierarchy. Semantic networks contributed ideas of spreading activation, inheritance, and nodes as proto-objects and you would use the assoc function with a key of canary to extract all the information about the canary type. An example of a network is WordNet, a lexical database of English. It groups English words into sets of synonyms called synsets, provides short, general definitions, some of the most common semantic relations defined are meronymy, holonymy, hyponymy, hypernymy, synonymy and antonymy. WordNet properties have been studied from a network theory perspective and compared to other semantic networks created from Rogets Thesaurus, from this perspective the three of them are a small world structure. It is also possible to represent logical descriptions using semantic networks such as the graphs of Charles Sanders Peirce or the related conceptual graphs of John F. Sowa. These have expressive power equal to or exceeding standard first-order predicate logic, unlike WordNet or other lexical or browsing networks, semantic networks using these representations can be used for reliable automated logical deduction. Some automated reasoners exploit the features of the networks during processing. Other examples of networks are Gellish models. Gellish English with its Gellish English dictionary, is a language that is defined as a network of relations between concepts and names of concepts. Gellish English is a subset of natural English, just as Gellish Dutch is a formal subset of Dutch. Other Gellish networks consist of models and information models that are expressed in the Gellish language. A Gellish network is a network of relations between things, each relation in the network is an expression of a fact that is classified by a relation type
8.
Automated theorem proving
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Automated theorem proving is a subfield of automated reasoning and mathematical logic dealing with proving mathematical theorems by computer programs. Automated reasoning over mathematical proof was an impetus for the development of computer science. While the roots of formalised logic go back to Aristotle, the end of the 19th and early 20th centuries saw the development of modern logic, freges Begriffsschrift introduced both a complete propositional calculus and what is essentially modern predicate logic. His Foundations of Arithmetic, published 1884, expressed mathematics in formal logic and this approach was continued by Russell and Whitehead in their influential Principia Mathematica, first published 1910–1913, and with a revised second edition in 1927. Russell and Whitehead thought they could derive all mathematical truth using axioms and inference rules of formal logic, shortly after World War II, the first general purpose computers became available. In 1954, Martin Davis programmed Presburgers algorithm for a JOHNNIAC vacuum tube computer at the Princeton Institute for Advanced Study, according to Davis, Its great triumph was to prove that the sum of two even numbers is even. More ambitious was the Logic Theory Machine, a system for the propositional logic of the Principia Mathematica, developed by Allen Newell, Herbert A. Simon. The system used heuristic guidance, and managed to prove 38 of the first 52 theorems of the Principia, the heuristic approach of the Logic Theory Machine tried to emulate human mathematicians, and could not guarantee that a proof could be found for every valid theorem even in principle. In contrast, other, more systematic algorithms achieved, at least theoretically, the propositional formulas could then be checked for unsatisfiability using a number of methods. Gilmores program used conversion to disjunctive normal form, a form in which the satisfiability of a formula is obvious, depending on the underlying logic, the problem of deciding the validity of a formula varies from trivial to impossible. For the frequent case of logic, the problem is decidable but Co-NP-complete. However, invalid formulas, cannot always be recognized, the above applies to first order theories, such as Peano Arithmetic. However, for a model that may be described by a first order theory, some statements may be true. Despite this theoretical limit, in practice, theorem provers can solve many hard problems, a simpler, but related, problem is proof verification, where an existing proof for a theorem is certified valid. For this, it is required that each individual proof step can be verified by a primitive recursive function or program. Proof assistants require a user to give hints to the system. However, these successes are sporadic, and work on hard problems usually requires a proficient user, other techniques would include model checking, which, in the simplest case, involves brute-force enumeration of many possible states. There are hybrid theorem proving systems which use model checking as an inference rule, there are also programs which were written to prove a particular theorem, with a proof that if the program finishes with a certain result, then the theorem is true
9.
Herbert A. Simon
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Simon received many top-level honors in life, including the Nobel Prize in Economics in 1978 and the Turing Award in 1975. He also received degrees from various universities, such as from Harvard University. As of 2016, Simon was the most cited person in Artificial Intelligence, with almost a thousand highly cited publications, he was one of the most influential social scientists of the twentieth century. Herbert Alexander Simon was born in Milwaukee, Wisconsin on June 15,1916 and his father, Arthur Simon, was an electrical engineer who had come to the United States from Germany in 1903 after earning his engineering degree from the Technische Hochschule of Darmstadt. An inventor who was granted several patents, his father also was an independent patent attorney. His mother, Edna Marguerite Merkel, was an accomplished pianist whose ancestors had come from Prague and his European ancestors had been piano makers, goldsmiths, and vintners. Simons father was Jewish and his mother came from a family with Jewish, Lutheran, Simon was educated as a child in the public school system in Milwaukee where he developed an interest in science. He found schoolwork to be interesting and easy, through his uncle’s books on economics and psychology, Simon discovered the social sciences. Among his earliest influences, Simon has cited Richard Ely’s economics textbook, Norman Angell’s The Great Illusion, at that time, Simon argued from conviction, rather than cussedness in favor of Georges controversial single tax on land rents. In 1933, Simon entered the University of Chicago, and following those early influences, he studied the social sciences and he was interested in biology, but chose not to study it because of his color-blindness and awkwardness in the laboratory. He chose instead to focus on science and economics. His most important mentor at the University was Henry Schultz who was an econometrician, eventually his studies led him to the field of organizational decision-making, which would become the subject of his doctoral dissertation. After graduating with his degree, Simon obtained a research assistantship in municipal administration which turned into a directorship at the University of California. From 1942 to 1949, Simon was a professor of political science, there, he began participating in the seminars held by the staff of the Cowles Commission who at that time included Trygve Haavelmo, Jacob Marschak, and Tjalling Koopmans. He thus began a more in-depth study of economics in the area of institutionalism, Marschak brought Simon in to assist in the study he was currently undertaking with Sam Schurr of the “prospective economic effects of atomic energy”. From 1949 to 2001, Simon was a faculty at Carnegie Mellon, in 1949, Simon became a professor of administration and chairman of the Department of Industrial Management at Carnegie Tech. Simon later also taught psychology and computer science in the same university, Simon married Dorothea Pye in 1938. Their marriage lasted 63 years until his death from a cancerous tumor, in January 2001, Simon underwent surgery at UPMC Presbyterian to remove a cancerous tumor in his abdomen
10.
Edward Feigenbaum
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Edward Albert Feigenbaum is a computer scientist working in the field of artificial intelligence, and joint winner of the 1994 ACM Turing Award. He is often called the father of expert systems and his hometown didnt have a secondary school of its own and he chose Weehawken High School for its college preparatory program. He was inducted into his high schools hall of fame in 1996, Feigenbaum completed his undergraduate degree, and a Ph. D. at Carnegie Institute of Technology. In his Ph. D thesis, carried out under the supervision of Herbert A. Simon, he developed EPAM, one of the first computer models of how people learn. Feigenbaum completed a Fulbright Fellowship at the National Physics Laboratory and in 1960 went to the University of California, Berkeley and he joined the Stanford faculty in 1965 as one of the founders of its computer science department. He was the director of the Stanford Computation Center from 1965 to 1968 and he established the Knowledge Systems Laboratory at Stanford University. Important project that Ed was involved in include ACME, Mycin, SUMEX and he also co-founded companiesas IntelliCorp and Teknowledge. Since 2000 Ed Feigenbaum is a Professor Emeritus of Computer Science at Stanford University, selected as one the initial fellows of the ACMI. U. S. Air Force Exceptional Civilian Service Award 2007, inducted as fellow of the ACM. IEEE Intelligent Systems AIs Hall of Fame for significant contributions to the field of AI, made fellow of the Computer History Museum for his pioneering work in artificial intelligence and expert systems. The Fifth Generation, Artificial Intelligence and Japan’s Computer Challenge to the World, the Age of Intelligent Machines, Knowledge Processing--From File Servers to Knowledge Servers by Edward Feigenbaum Feigenbaum, Edward A. Some challenges and grand challenges for computational intelligence, annals of the History of Computing. Edward Albert Feigenbaum at the Mathematics Genealogy Project Edward A. Feigenbaum at the AI Genealogy Project
11.
Rick Hayes-Roth
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Frederick Hayes-Roth is an American computer scientist and educator. He was the Chief Technology Officer for Software at Hewlett-Packard from 2000-2001, before that he was Chairman and Chief Executive of two Silicon Valley companies which he co-founded. One was Teknowledge Corporation, founded with Edward Feigenbaum and he was the program director for research in Information Processing at the Rand Corporation from 1976-81. Prior to that, was one of the co-inventors of the first continuous speech understanding systems, Hearsay-II, which became the “blackboard architecture. ”Hayes-Roth held faculty positions at MIT, Stanford, and Carnegie Mellon. In 2003 he became a professor in the Information Sciences Department at the United States Navys Naval Postgraduate School in Monterey, at NPS he has taught hundreds of mid-career leaders in DOD through his capstone course on IT Strategy and Policy at NPS. The focus of that course has been on ways to improve the success of DOD IT system efforts. In 2011, Hayes-Roth co-founded Truth Seal Corporation, a non-profit, Truth Seal promotes truthfulness in public communications so that the general public uses credible information for judgments, decisions and actions. Truth Seal’s intent is that market incentives will increase the quantity of information that all consumers and citizens need to process.1, S.1. Hayes-Roth is also a Senior Member of the IEEE and a member of the Association for Computing Machinery, how to Implement National Information Sharing Strategy. AFCEA-GMU C4I Center Symposium, Critical Issues in C4I, George Mason University, Fairfax, VA, a Rich Semantic Model of Track as a Foundation for Sharing Beliefs Regarding Dynamic Objects and Events. Hayes-Roth, F. Model-Based Communication Networks and VIRT, Orders of Magnitude Better for Information Superiority, infoglut, an ACM article by Peter Denning on VIRT. Communications of the ACM, Volume 49, Issue 7, honesty Is the Best Policy, Ubiquity Magazine, a publication of the ACM, Volume 2011 Issue July, July 2011. NPS Faculty webpage NPS vita Truthiness Fever blog Hyper-beings Radical Simplicity Building Expert Systems Pattern-Directed Inference Systems Worldcat
12.
Natural language understanding
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Natural language understanding is a subtopic of natural language processing in artificial intelligence that deals with machine reading comprehension. NLU is considered an AI-hard problem, there is considerable commercial interest in the field because of its application to news-gathering, text categorization, voice-activation, archiving, and large-scale content-analysis. The program STUDENT, written in 1964 by Daniel Bobrow for his PhD dissertation at MIT is one of the earliest known attempts at natural language understanding by a computer. Eight years after John McCarthy coined the term artificial intelligence, Bobrows dissertation showed how a computer can understand simple natural language input to solve word problems. A year later, in 1965, Joseph Weizenbaum at MIT wrote ELIZA, a program that carried on a dialogue in English on any topic. ELIZA worked by simple parsing and substitution of key words into canned phrases, yet ELIZA gained surprising popularity as a toy project and can be seen as a very early precursor to current commercial systems such as those used by Ask. com. In 1969 Roger Schank at Stanford University introduced the conceptual dependency theory for natural language understanding and this model, partially influenced by the work of Sydney Lamb, was extensively used by Schanks students at Yale University, such as Robert Wilensky, Wendy Lehnert, and Janet Kolodner. In 1970, William A. Woods introduced the augmented transition network to represent natural language input, instead of phrase structure rules ATNs used an equivalent set of finite state automata that were called recursively. ATNs and their general format called generalized ATNs continued to be used for a number of years. In 1971 Terry Winograd finished writing SHRDLU for his PhD thesis at MIT, SHRDLU could understand simple English sentences in a restricted world of childrens blocks to direct a robotic arm to move items. The successful demonstration of SHRDLU provided significant momentum for continued research in the field, Winograd continued to be a major influence in the field with the publication of his book Language as a Cognitive Process. At Stanford, Winograd would later be the adviser for Larry Page, in the 1970s and 1980s the natural language processing group at SRI International continued research and development in the field. However, with the advent of mouse driven, graphic user interfaces Symantec changed direction. A number of other efforts were started around the same time, e. g. Larry R. Harris at the Artificial Intelligence Corporation and Roger Schank. In 1983, Michael Dyer developed the BORIS system at Yale which bore similarities to the work of Roger Schank, the third millennium saw the introduction of systems using machine learning for text classification, such as the IBM Watson. According to John Searle, Watson did not even understand the questions, John Ball, cognitive scientist and inventor of Patom Theory supports this assessment. NLP has made inroads for applications to support human productivity in service and ecommerce, there are thousands of ways to request something in a human language which still defies conventional NLP. Throughout the years various attempts at processing natural language or English-like sentences presented to computers have taken place at varying degrees of complexity, some attempts have not resulted in systems with deep understanding, but have helped overall system usability
13.
Backward chaining
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Backward chaining is an inference method that can be described as working backward from the goal. It is used in automated theorem provers, inference engines, proof assistants, in game theory, its application to subgames in order to find a solution to the game is called backward induction. In chess, it is called analysis, and it is used to generate tablebases for chess endgames for computer chess. Backward chaining is implemented in logic programming by SLD resolution, both rules are based on the modus ponens inference rule. It is one of the two most commonly used methods of reasoning with inference rules and logical implications – the other is forward chaining, Backward chaining systems usually employ a depth-first search strategy, e. g. Prolog. Backward chaining starts with a list of goals and works backwards from the consequent to the antecedent to see if there is available that will support any of these consequents. An inference engine using backward chaining would search the inference rules until it finds one which has a consequent that matches a desired goal, if the antecedent of that rule is not known to be true, then it is added to the list of goals. To start, the query is phrased as an assertion that is to be proved. The antecedent therefore becomes the new goal, Fritz is a frog 2, the antecedent therefore becomes the new goal, Fritz croaks and Fritz eats flies 3. Since this goal is a conjunction of two statements, the engine breaks it into two sub-goals, both of which must be proved, Fritz croaks Fritz eats flies 4. To prove both of these sub-goals, the inference engine sees that both of these sub-goals were given as initial facts, rules #2 and #4 were not used. Because the list of goals determines which rules are selected and used, the backward chaining approach is often employed by expert systems. Programming languages such as Prolog, Knowledge Machine and ECLiPSe support backward chaining within their inference engines, Backward induction Forward chaining Opportunistic reasoning Backward chaining example
14.
Symbolics
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The symbolics. com domain was originally registered on March 15,1985, making it the first. com-domain in the world. In August 2009, it was sold to XF. com Investments, Symbolics, Inc. was a computer manufacturer headquartered in Cambridge, Massachusetts, and later in Concord, Massachusetts, with manufacturing facilities in Chatsworth, California. Its first CEO, chairman, and founder was Russell Noftsker, Symbolics designed and manufactured a line of Lisp machines, single-user computers optimized to run the Lisp programming language. The Lisp Machine was the first commercially available workstation, Symbolics was a spinoff from the MIT AI Lab, one of two companies to be founded by AI Lab staffers and associated hackers for the purpose of manufacturing Lisp machines. The other was Lisp Machines, Inc. although Symbolics attracted most of the hackers, Symbolics initial product, the LM-2, was a repackaged version of the MIT CADR Lisp machine design. The operating system and software development environment, over 500,000 lines, was written in Lisp from the microcode up, the software bundle was later renamed ZetaLisp, to distinguish the Symbolics product from other vendors who had also licensed the MIT software. Symbolics Zmacs text editor, a variant of Emacs, was implemented in a package named ZWEI. Both are recursive acronyms and puns on the German words for One, the Lisp Machine system software was then copyrighted by MIT, and was licensed to Symbolics. Until 1981, they shared all the code with MIT. Symbolics felt that no longer had sufficient control over their product. From that base, Symbolics made extensive improvements to every part of the software, however, the policy prohibited MIT staff from distributing the Symbolics version of the software to others. With the end of collaboration came the end of the MIT hacker community. As a reaction to this, Stallman initiated the GNU project to make a new community, eventually, Copyleft and the GNU General Public License would ensure that a hackers software could remain free software. In this way Symbolics played a key, albeit adversarial, role in instigating the free software movement, in 1983, a year later than planned, Symbolics introduced the 3600 family of Lisp machines. Code-named the L-machine internally, the 3600 family was a new design, inspired by the CADR architecture. The main processor had a 36 bit word, memory words were 44 bits, the additional 8 bits being used for error-correcting code. The instruction set was that of a stack machine, Hardware support was provided for virtual memory, which was common for machines in its class, and for garbage collection, which was unique. CPU clock speed varied depending on the instruction being executed
15.
Xerox
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Xerox Corporation /ˈzɪərɒks/ is an American global corporation that sells document solutions and services, and document technology products in more than 160 countries. Xerox is headquartered in Norwalk, Connecticut, though its largest population of employees is based around Rochester, New York, the company purchased Affiliated Computer Services for $6.4 billion in early 2010. As a large developed company, it is placed in the list of Fortune 500 companies. Xerox completed the separation of Conduent Incorporated effective on Dec.31,2016, creating two market-leading, publicly- traded companies. In connection with the spin-off, Xerox received a transfer from Conduent of $1.8 billion. Xerox continues to trade on the NYSE under the ticker symbol “XRX”, researchers at Xerox and its Palo Alto Research Center invented several important elements of personal computing, such as the desktop metaphor GUI, the computer mouse and desktop computing. These concepts were frowned upon by the board of directors. The concepts were adopted by Apple and, later, Microsoft, with the help of these innovations, Apple and Microsoft came to dominate the personal computing revolution of the 1980s, whereas Xerox was not a major player. Xerox was founded in 1906 in Rochester as The Haloid Photographic Company, in 1938 Chester Carlson, a physicist working independently, invented a process for printing images using an electrically charged photoconductor-coated metal plate and dry powder toner. However, it would more than 20 years of refinement before the first automated machine to make copies was commercialized, using a document feeder, scanning light. Wilson, credited as the founder of Xerox, took over Haloid from his father and he saw the promise of Carlsons invention and, in 1946, signed an agreement to develop it as a commercial product. Wilson remained as President/CEO of Xerox until 1967 and served as Chairman until his death in 1971, looking for a term to differentiate its new system, Haloid coined the term Xerography from two Greek roots meaning dry writing. Haloid subsequently changed its name to Haloid Xerox in 1958 and then Xerox Corporation in 1961, before releasing the 914, Xerox tested the market by introducing a developed version of the prototype hand-operated equipment known as the Flat-plate 1385. The 1385 was not actually a viable copier because of its speed of operation and it was little more than a high quality, commercially available plate camera mounted as a horizontal rostrum camera, complete with photo-flood lighting and timer. The glass film/plate had been replaced with an aluminum plate. Clever electrics turned this into a developing and reusable substitute for film. A skilled user could produce fast, paper and metal printing plates of a higher quality than almost any other method, having started as a supplier to the offset lithography duplicating industry, Xerox now set its sights on capturing some of offsets market share. The 1385 was followed by the first automatic xerographic printer, the Copyflo, the Copyflo was a large microfilm printer which could produce positive prints on roll paper from any type of microfilm negative
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Texas Instruments
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Texas Instruments Inc. is an American technology company that designs and manufactures semiconductors, which it sells to electronics designers and manufacturers globally. Headquartered in Dallas, Texas, United States, TI is one of the top ten semiconductor companies worldwide, Texas Instrumentss focus is on developing analog chips and embedded processors, which accounts for more than 85% of their revenue. TI also produces TI digital light processing technology and education technology products including calculators, microcontrollers, to date, TI has more than 43,000 patents worldwide. TI produced the worlds first commercial silicon transistor in 1950, Jack Kilby invented the integrated circuit in 1958 while working at TIs Central Research Labs. TI also invented the hand-held calculator in 1967, and introduced the first single-chip microcontroller in 1970, in 1987, TI invented the digital light processing device, which serves as the foundation for the companys award-winning DLP technology and DLP Cinema. In 1990, TI came out with the popular TI-81 calculator which made them a leader in the calculator industry. In 1997, its business was sold to Raytheon, which allowed TI to strengthen its focus on digital solutions. Texas Instruments was founded by Cecil H. Green, J. Erik Jonsson, Eugene McDermott, McDermott was one of the original founders of Geophysical Service Inc. in 1930. McDermott, Green, and Jonsson were GSI employees who purchased the company in 1941, in November,1945, Patrick Haggerty was hired as general manager of the Laboratory and Manufacturing division, which focused on electronic equipment. By 1951, the L&M division, with its contracts, was growing faster than GSIs Geophysical division. The company was reorganized and initially renamed General Instruments Inc, because there already existed a firm named General Instrument, the company was renamed Texas Instruments that same year. From 1956 to 1961, Fred Agnich of Dallas, later a Republican member of the Texas House of Representatives, was the Texas Instruments president, Geophysical Service, Inc. became a subsidiary of Texas Instruments. Early in 1988 most of GSI was sold to the Halliburton Company, in 1930, J. Clarence Karcher and Eugene McDermott founded Geophysical Service, an early provider of seismic exploration services to the petroleum industry. In 1939, the company reorganized as Coronado Corp. an oil company with Geophysical Service Inc, on December 6,1941, McDermott along with three other GSI employees, J. Erik Jonsson, Cecil H. Green, and H. B. During World War II, GSI expanded their services to include electronics for the U. S. Army, Signal Corps, in 1951, the company changed its name to Texas Instruments, GSI becoming a wholly owned subsidiary of the new company. Texas Instruments also continued to manufacture equipment for use in the seismic industry, after selling GSI, TI finally sold the company to Halliburton in 1988, at which point GSI ceased to exist as a separate entity. Texas Instruments entered the electronics market in 1942 with submarine detection equipment. During the early 1980s, Texas Instruments instituted a quality program which included Juran training, as well as promoting statistical process control, Taguchi methods and Design for Six Sigma
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Semantic Web
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The Semantic Web is an extension of the Web through standards by the World Wide Web Consortium. The standards promote common data formats and exchange protocols on the Web, according to the W3C, The Semantic Web provides a common framework that allows data to be shared and reused across application, enterprise, and community boundaries. The term was coined by Tim Berners-Lee for a web of data that can be processed by machines, while its critics have questioned its feasibility, proponents argue that applications in industry, biology and human sciences research have already proven the validity of the original concept. The 2001 Scientific American article by Berners-Lee, Hendler, and Lassila described an expected evolution of the existing Web to a Semantic Web, in 2006, Berners-Lee and colleagues stated that, This simple idea…remains largely unrealized. In 2013, more than four million Web domains contained Semantic Web markup, in the following example, the text Paul Schuster was born in Dresden on a Website will be annotated, connecting a person with their place of birth. The following HTML-fragment shows how a small graph is being described, in RDFa-syntax using a schema. org vocabulary, the triples result in the graph shown in the given figure. One of the advantages of using Uniform Resource Identifier is that they can be dereferenced using the HTTP protocol, according to the so-called Linked Open Data principles, such a dereferenced URI should result in a document that offers further data about the given URI. This enables automated agents to access the Web more intelligently and perform tasks on behalf of users. He defines the Semantic Web as a web of data that can be processed directly and indirectly by machines, many of the technologies proposed by the W3C already existed before they were positioned under the W3C umbrella. In addition, other technologies with similar goals have emerged, such as microformats. A Semantic Web, which makes possible, has yet to emerge, but when it does. The intelligent agents people have touted for ages will finally materialize, the Semantic Web is regarded as an integrator across different content, information applications and systems. It has applications in publishing, blogging, and many other areas, many files on a typical computer can also be loosely divided into human readable documents and machine readable data. Documents like mail messages, reports, and brochures are read by humans, Data, such as calendars, addressbooks, playlists, and spreadsheets are presented using an application program that lets them be viewed, searched and combined. Metadata tags provide a method by which computers can categorise the content of web pages, for example, With HTML, rather, HTML can only say that the span of text X586172 is something that should be positioned near Acme Gizmo and €199, etc. There is no way to say this is a catalog or even to establish that Acme Gizmo is a kind of title or that €199 is a price. There is also no way to express that these pieces of information are together in describing a discrete item. Semantic HTML refers to the traditional HTML practice of markup following intention, for example, the use of <em> denoting emphasis rather than <i>, which specifies italics
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Ontology
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Ontology is the philosophical study of the nature of being, becoming, existence or reality as well as the basic categories of being and their relations. Although ontology as an enterprise is highly hypothetical, it also has practical application in information science and technology. Some philosophers, notably of the Platonic school, contend that all refer to existent entities. Other philosophers contend that nouns do not always name entities, between these poles of realism and nominalism, stand a variety of other positions. An ontology may give an account of which refer to entities, which do not, why. Principal questions of ontology include, What can be said to exist, into what categories, if any, can we sort existing things. What are the meanings of being, what are the various modes of being of entities. Various philosophers have provided different answers to these questions, one common approach involves dividing the extant subjects and predicates into groups called categories. Such an understanding of ontological categories, however, is merely taxonomic, what does it mean for a being to be. Is existence a genus or general class that is divided up by specific differences. Which entities, if any, are fundamental, how do the properties of an object relate to the object itself. What features are the essential, as opposed to merely accidental attributes of a given object, how many levels of existence or ontological levels are there. Can one give an account of what it means to say that an object exists. Can one give an account of what it means to say that an entity exists. What constitutes the identity of an object, when does an object go out of existence, as opposed to merely changing. Do beings exist other than in the modes of objectivity and subjectivity, i. e. is the subject/object split of modern philosophy inevitable. e. Being, that which is, which is the present participle of the verb εἰμί, eimí, i. e. to be, I am, and -λογία, -logia, i. e. logical discourse. The first occurrence in English of ontology as recorded by the OED came in a work by Gideon Harvey, Archelogia philosophica nova, or, New principles of Philosophy
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DARPA
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The Defense Advanced Research Projects Agency is an agency of the U. S. Department of Defense responsible for the development of emerging technologies for use by the military. DARPA was created in February 1958 as the Advanced Research Projects Agency by President Dwight D. Eisenhower and its purpose was to formulate and execute research and development projects to expand the frontiers of technology and science, with the aim to reach beyond immediate military requirements. DARPA was created in response to the Soviet launching of Sputnik 1 in 1957, the name of the organization changed several times from its founding name ARPA, DARPA, ARPA, and DARPA. DARPA is independent from other research and development and reports directly to senior Department of Defense management. DARPA has about 240 employees, of whom 13 are in management, dARPA-funded projects have provided significant technologies that influenced many non-military fields, such as computer networking and graphical user interfaces in information technology. S. The mission statement has evolved over time, today, DARPAs mission is still to prevent technological surprise to the U. S. but also to create technological surprise for U. S. enemies. The creation of the Advanced Research Projects Agency was authorized by President Dwight D.15 and its creation was directly attributed to the launching of Sputnik and to U. S. realization that the Soviet Union had developed the capacity to rapidly exploit military technology. Initial funding of ARPA was $520 million, ARPAs first director, Roy Johnson, left a $160,000 management job at General Electric for an $18,000 job at ARPA. Herbert York from Lawrence Livermore National Laboratory was hired as his scientific assistant, Johnson and York were both keen on space projects, but when NASA was established later in 1958 all space projects and most of ARPAs funding were transferred to it. Johnson resigned and ARPA was repurposed to do high-risk, high-gain, far out basic research, ARPAs second director was Brigadier General Austin W. Betts, who resigned in early 1961. He was succeeded by Jack Ruina who served until 1963, Ruina, the first scientist to administer ARPA, managed to raise its budget to $250 million. In pursuit of this mission, DARPA has developed and transferred technology programs encompassing a range of scientific disciplines that address the full spectrum of national security needs. From 1958 to 1965, ARPAs emphasis centered on major issues, including space, ballistic missile defense. During 1960, all of its civilian space programs were transferred to the National Aeronautics and Space Administration and the military space programs to the individual Services. This allowed ARPA to concentrate its efforts on the Project Defender, Project Vela, and Project AGILE Programs, and to work on computer processing, behavioral sciences. The DEFENDER and AGILE Programs formed the foundation of DARPA sensor, surveillance, and directed energy R&D, particularly in the study of radar, infrared sensing, ARPA at this point played an early role in Transit a predecessor to the Global Positioning System. Fast-forward to 1959 when a joint effort between DARPA and the Johns Hopkins Applied Physics Laboratory began to fine-tune the early explorers’ discoveries, TRANSIT, sponsored by the Navy and developed under the leadership of Dr. Richard Kirschner at Johns Hopkins, was the first satellite positioning system. The agency was renamed the Defense Advanced Research Projects Agency in 1972, and during the early 1970s, it emphasized direct energy programs, information processing, concerning information processing, DARPA made great progress, initially through its support of the development of time-sharing
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Resource Description Framework
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The Resource Description Framework is a family of World Wide Web Consortium specifications originally designed as a metadata data model. It is also used in knowledge management applications, RDF was adopted as a W3C recommendation in 1999. The RDF1.0 specification was published in 2004, the RDF1.1 specification in 2014, the RDF data model is similar to classical conceptual modeling approaches. It is based upon the idea of making statements about resources in the form of subject–predicate–object expressions, the subject denotes the resource, and the predicate denotes traits or aspects of the resource, and expresses a relationship between the subject and the object. For example, one way to represent the notion The sky has the blue in RDF is as the triple, a subject denoting the sky, a predicate denoting has the color. Therefore, RDF swaps object for subject in contrast to the approach of an entity–attribute–value model in object-oriented design, entity, attribute. RDF is a model with several serialization formats, so the particular encoding for resources or triples varies from format to format. RDFs simple data model and ability to model disparate, abstract concepts has led to its increasing use in knowledge management applications unrelated to Semantic Web activity. A collection of RDF statements intrinsically represents a labeled, directed multi-graph and this theoretically makes an RDF data model better suited to certain kinds of knowledge representation than other relational or ontological models. However, in practice, RDF data is persisted in relational database or native representations. As RDFS and OWL demonstrate, one can build additional ontology languages upon RDF, guha at Apple and Tim Bray at Netscape. The W3C published a specification of RDFs data model and an XML serialization as a recommendation in February 1999, secondly that RDF was an XML format, rather than RDF being a data model and only the RDF/XML serialisation being XML-based. RDF saw little take-up in this period, but there was significant work carried out in Bristol, around ILRT at Bristol University and HP Labs, RSS1.0 and FOAF became exemplar applications for RDF in this period.0, and The RDF Test Cases. Several common serialization formats are in use, including, Turtle, N-Triples, a very simple, easy-to-parse, line-based format that is not as compact as Turtle. N-Quads, a superset of N-Triples, for serializing multiple RDF graphs, n3 or Notation3, a non-standard serialization that is very similar to Turtle, but has some additional features, such as the ability to define inference rules. RDF/XML, an XML-based syntax that was the first standard format for serializing RDF, RDF/XML is sometimes misleadingly called simply RDF because it was introduced among the other W3C specifications defining RDF and it was historically the first W3C standard RDF serialization format. However, it is important to distinguish the RDF/XML format from the abstract RDF model itself, with a little effort, virtually any arbitrary XML may also be interpreted as RDF using GRDDL, Gleaning Resource Descriptions from Dialects of Languages. RDF triples may be stored in a type of database called a triplestore, the subject of an RDF statement is either a uniform resource identifier or a blank node, both of which denote resources
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Massachusetts Institute of Technology
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The Massachusetts Institute of Technology is a private research university in Cambridge, Massachusetts, often cited as one of the worlds most prestigious universities. Researchers worked on computers, radar, and inertial guidance during World War II, post-war defense research contributed to the rapid expansion of the faculty and campus under James Killian. The current 168-acre campus opened in 1916 and extends over 1 mile along the bank of the Charles River basin. The Institute is traditionally known for its research and education in the sciences and engineering, and more recently in biology, economics, linguistics. Air Force and 6 Fields Medalists have been affiliated with MIT, the school has a strong entrepreneurial culture, and the aggregated revenues of companies founded by MIT alumni would rank as the eleventh-largest economy in the world. In 1859, a proposal was submitted to the Massachusetts General Court to use newly filled lands in Back Bay, Boston for a Conservatory of Art and Science, but the proposal failed. A charter for the incorporation of the Massachusetts Institute of Technology, Rogers, a professor from the University of Virginia, wanted to establish an institution to address rapid scientific and technological advances. The Rogers Plan reflected the German research university model, emphasizing an independent faculty engaged in research, as well as instruction oriented around seminars, two days after the charter was issued, the first battle of the Civil War broke out. After a long delay through the war years, MITs first classes were held in the Mercantile Building in Boston in 1865, in 1863 under the same act, the Commonwealth of Massachusetts founded the Massachusetts Agricultural College, which developed as the University of Massachusetts Amherst. In 1866, the proceeds from sales went toward new buildings in the Back Bay. MIT was informally called Boston Tech, the institute adopted the European polytechnic university model and emphasized laboratory instruction from an early date. Despite chronic financial problems, the institute saw growth in the last two decades of the 19th century under President Francis Amasa Walker. Programs in electrical, chemical, marine, and sanitary engineering were introduced, new buildings were built, the curriculum drifted to a vocational emphasis, with less focus on theoretical science. The fledgling school still suffered from chronic financial shortages which diverted the attention of the MIT leadership, during these Boston Tech years, MIT faculty and alumni rebuffed Harvard University president Charles W. Eliots repeated attempts to merge MIT with Harvard Colleges Lawrence Scientific School. There would be at least six attempts to absorb MIT into Harvard, in its cramped Back Bay location, MIT could not afford to expand its overcrowded facilities, driving a desperate search for a new campus and funding. Eventually the MIT Corporation approved an agreement to merge with Harvard, over the vehement objections of MIT faculty, students. However, a 1917 decision by the Massachusetts Supreme Judicial Court effectively put an end to the merger scheme, the neoclassical New Technology campus was designed by William W. Bosworth and had been funded largely by anonymous donations from a mysterious Mr. Smith, starting in 1912. In January 1920, the donor was revealed to be the industrialist George Eastman of Rochester, New York, who had invented methods of production and processing
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First-order logic
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First-order logic – also known as first-order predicate calculus and predicate logic – is a collection of formal systems used in mathematics, philosophy, linguistics, and computer science. This distinguishes it from propositional logic, which does not use quantifiers, Sometimes theory is understood in a more formal sense, which is just a set of sentences in first-order logic. In first-order theories, predicates are associated with sets. In interpreted higher-order theories, predicates may be interpreted as sets of sets, There are many deductive systems for first-order logic which are both sound and complete. Although the logical relation is only semidecidable, much progress has been made in automated theorem proving in first-order logic. First-order logic also satisfies several metalogical theorems that make it amenable to analysis in proof theory, such as the Löwenheim–Skolem theorem, first-order logic is the standard for the formalization of mathematics into axioms and is studied in the foundations of mathematics. Peano arithmetic and Zermelo–Fraenkel set theory are axiomatizations of number theory and set theory, respectively, no first-order theory, however, has the strength to uniquely describe a structure with an infinite domain, such as the natural numbers or the real line. Axioms systems that do fully describe these two structures can be obtained in stronger logics such as second-order logic, for a history of first-order logic and how it came to dominate formal logic, see José Ferreirós. While propositional logic deals with simple declarative propositions, first-order logic additionally covers predicates, a predicate takes an entity or entities in the domain of discourse as input and outputs either True or False. Consider the two sentences Socrates is a philosopher and Plato is a philosopher, in propositional logic, these sentences are viewed as being unrelated and might be denoted, for example, by variables such as p and q. The predicate is a philosopher occurs in both sentences, which have a structure of a is a philosopher. The variable a is instantiated as Socrates in the first sentence and is instantiated as Plato in the second sentence, while first-order logic allows for the use of predicates, such as is a philosopher in this example, propositional logic does not. Relationships between predicates can be stated using logical connectives, consider, for example, the first-order formula if a is a philosopher, then a is a scholar. This formula is a statement with a is a philosopher as its hypothesis. The truth of this depends on which object is denoted by a. Quantifiers can be applied to variables in a formula, the variable a in the previous formula can be universally quantified, for instance, with the first-order sentence For every a, if a is a philosopher, then a is a scholar. The universal quantifier for every in this sentence expresses the idea that the if a is a philosopher. The negation of the sentence For every a, if a is a philosopher, then a is a scholar is logically equivalent to the sentence There exists a such that a is a philosopher and a is not a scholar
