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Overview[ edit ] The need for 10 axioms a successful project massive symbolic artificial intelligence project of this kind was born in the early s out of a large number of experiences early AI researchers had, in the previous 25 years, wherein their AI programs would generate encouraging early results but then fail to "scale up"—fail to cope with novel situations and problems outside the narrow area they were conceived and engineered to cope with.
Douglas Lenat and Alan Kay publicized this need,    and organized a meeting at Stanford in to consider the problem; the back-of-the-envelope calculations by them and colleagues including Marvin MinskyAllen NewellEdward Feigenbaumand John McCarthy indicated that that effort would require between and person-years of effort, hence not fit into the standard academic project model.
Fortuitously, events within a year of that meeting enabled that Manhattan-Project-sized effort to get underway. The project was started in July, as the flagship project of the person Microelectronics and Computer Technology Corporationa research consortium started by two dozen large United States based corporations "to counter a then ominous Japanese effort in AI, the so-called " fifth-generation " project.
The objective of the Cyc project was to codify, in machine-usable form, the millions of pieces of knowledge that compose human common sense. In slightly more detail: The CycL representation language started as an extension of RLL   the so-called Representation Language Language, developed in by Professor Douglas Lenat at Stanford University and his graduate student Russell Greinerbut within a few years of the launch of the Cyc project it became clear that even representing a typical news story or novel or advertisement would require more than the expressive power of full first order logicnamely second order predicate calculus "What is the relationship between rain and water?
Triplestore representations which are akin to the Frame -and-slot representation languages of the s from which RLL sprang are widespread today in AI.
It may be useful to cite a few examples that stress or break that type of representation, typical of the examples that 10 axioms a successful project the Cyc project to move from a triplestore representation to a much more expressive one during the period An arbitrarily large number of additional terms are also implicitly present in the Cyc ontology, in the sense that there are term-denoting functions such as CalendarYearFn when given the argumentit denotes the calendar yearGovernmentFn when given the argument France it denotes the government of FranceMeter when given the argumentit denotes a distance of 2.
The Cyc knowledge base of general common-sense rules and assertions involving those ontological terms was largely created by hand axiom-writing; it grew to about 1 million inand as of is about It is important to understand that the Cyc ontological engineers strive to keep those numbers as small as possible, not inflate them, so long as the deductive closure of the knowledge base isn't reduced.
Suppose Cyc is told about one billion individual people, animals, etc. Then it could be told facts of the form "Mickey Mouse is not the same individual as Bullwinkle the Moose". But instead of that, one could tell Cyc 10, Linnaean Taxonomy rules followed by just rules of the form "No mouse is a moose".
And even more compactly, Cyc could instead just be given those 10, Linnaean Taxonomy rules followed by just one rule of the form "For any two Linnaean taxons, if neither is explicitly known to be a supertaxon of the other, then they are disjoint".
Those 10, assertions have the same deductive closure as the earlier-mentioned facts.
The Cyc inference engine design separates the epistemological problem what content should be in the Cyc KB from the heuristic problem how Cyc could efficiently infer arguments hundreds of steps deep, in a sea of tens of millions of axioms.
To do the former, the CycL language and well-understood logical inference might suffice,but for the latter Cyc used a community-of-agents architecture, where specialized reasoning modules, each with its own data structure and algorithm, "raised their hand" if they could efficiently make progress on any of the currently open sub-problems.
At the end of the first decade,there were 20 such heuristic level HL modules;  as of there are over 1, HL modules.
Some are domain-specific, such as a chemical equation-balancer. Access to Cyc is through paid licenses, but bona fide AI research groups are given research-only no-cost licenses cf.
ResearchCyc ; there are currently over such groups worldwide with such licenses. Typical pieces of knowledge represented in the Cyc knowledge base are "Every tree is a plant" and "Plants die eventually". When asked whether trees die, the inference engine can draw the obvious conclusion and answer the question correctly.
Most of Cyc's knowledge, outside math and games, is only true by default. For example, Cyc knows that as a default parents love their children, when you're made happy you smile, taking your first step is a big accomplishment, when someone you love has a big accomplishment that makes you happy, and only adults have children.
When asked whether a picture captioned "Someone watching his daughter take her first step" contains a smiling adult person, Cyc can logically infer that the answer is Yes, and "show its work" by presenting the step by step logical argument using those five pieces of knowledge from its KB.
These are formulated in the language CycLwhich is based on predicate calculus and has a syntax similar to that of the Lisp programming language. InCyc resources were mapped to many Wikipedia articles,  potentially easing connecting with other open datasets like DBpedia and Freebase.
Much of the current work Cyc continues to be knowledge engineeringrepresenting facts about the world by hand, and implementing efficient inference mechanisms on that knowledge. Increasingly, however, work at Cycorp involves giving the Cyc system the ability to communicate with end users in natural languageand to assist with the ongoing knowledge formation process via machine learning and natural language understanding.
Another large effort at Cycorp is building a suite of Cyc-powered ontological engineering tools to lower the bar to entry for individuals to contribute to, edit, browse, and query Cyc. Like many companies, Cycorp has ambitions to use Cyc's natural language processing  to parse the entire internet to extract structured data;  unlike all others, it is able to call on the Cyc system itself to act as an inductive bias and as an adjudicator of ambiguitymetaphorand ellipsis.
Knowledge base[ edit ] The concept names in Cyc are CycL constants.Pharmaceutics, Volume 10, Issue 2 (June ). Issues are regarded as officially published after their release is announced to the table of contents alert mailing list.; You may sign up for e-mail alerts to receive table of contents of newly released issues.; PDF is the official format for papers published in both, html and pdf forms.
10 Ingredients for Successful Projects by Guest Author Editor's Note: We're re-publishing this popular post in which guest blogger, IT/Project Management consultant, and author Brad Egeland shares the ten components of winning projects.
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Posted on Oncourse Project Management Basic Principles Ten Axioms for Success from SPEA V at Indiana University, Bloomington. Barbee Davis, PMP, PHR, writes a semi-monthly feature for the Project Management Institute (PMI) international publication, Community Post, in which she guides project managers to more successful .
10 best practices for successful project management The right mix of planning, monitoring, and controlling can make the difference in completing a project on time, on budget, and with high quality.