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Thinknowlogy - A fundamental approach to Artificial Intelligence / knowledge technology

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    Thinknowlogy a fundamental approach to AI Knowledge Technology Thinknowlogy a fundamental approach to AI Knowledge Technology Presentation Transcript

    • ThinknowlogyA fundamental approach toArtificial Intelligence / knowledge technologyA webinar initiated by CompegenceDecember 15, 2012© 2012 Menno Mafait http://mafait.org
    • Thinknowlogy - A fundamental approach toArtificial Intelligence / knowledge technology 1 Introduction 2 The current approach to AI 3 A fundamental approach 4 Demo 5 Questions
    • Thinknowlogy - A fundamental approach toArtificial Intelligence / knowledge technology 1 Introduction Short CV • Name: Menno Mafait • Education: Computer science (undergraduate level) specialization: telecommunications; • Experience: Software Tester with several companies, currently developing Thinknowlogy.
    • Thinknowlogy - A fundamental approach toArtificial Intelligence / knowledge technology 1 Introduction Some rules during this session • Questions can be posed during this session in the chatbox of TeamViewer. I will have a look now and then, and try to answer your question before switching topics; • Only if your question is important to your understanding, enable your microphone in the TeamViewer window and interrupt me with your question; • Please try to describe your question briefly.
    • Thinknowlogy - A fundamental approach toArtificial Intelligence / knowledge technology 1 Introduction The relation between Artificial Intelligence (AI) and knowledge technology Semantics / meaning is a subset of intelligence. • Artificial Intelligence is (or should be) based on intelligence; • Knowledge technology is (or should be) based on Semantics / meaning. So, knowledge technology is a subset of AI.
    • Thinknowlogy - A fundamental approach toArtificial Intelligence / knowledge technology 1 Introduction 2 The current approach to AI 3 A fundamental approach 4 Demo 5 Questions
    • Thinknowlogy - A fundamental approach toArtificial Intelligence / knowledge technology 2 The current approach to AI A few questions: • Why is Siri considered stupid by some users? • Why do search engines still show lists with links - rather than just answer the question of the user? • Why does Watson need 2800 CPU cores “to find a needle in the haystack of unstructured texts”? Why doesnt it just structure those texts while the knowledge is acquired? Wouldnt that use significantly less CPU power during the search?
    • Thinknowlogy - A fundamental approach toArtificial Intelligence / knowledge technology 2 The current approach to AI What is Siri? Siri is an iPhone app that allows the user to speak to their iPhone. Siri will then respond with text on the screen as well as a voice. Siri is able to read, write and send text messages (emails, tweets, etc.) on the users command, to read and organize the users agenda, to answer some questions like about the weather forecast, and to find shops and restaurants in the neighborhood.
    • Thinknowlogy - A fundamental approach toArtificial Intelligence / knowledge technology 2 The current approach to AI But why is Siri considered stupid by some users? Siri is a clever combination of speech technology, GPS, search engines and pre-programmed responses. It gives the user a feeling that the system is quite human. However, the combination of technologies is intelligent, not the app itself. For example, Siri will probably respond to the sentence “The Chinese is near the restaurant” by providing the location of the nearest Chinese restaurant rather than processing this knowledge.
    • Thinknowlogy - A fundamental approach toArtificial Intelligence / knowledge technology 2 The current approach to AI Why do search engines still show lists with links - rather than just answer the question of the user? Because current technologies filter down the sentences to mainly keywords and discard the “useless” words, by which most of the meaning is lost irrecoverably. So, despite all effort done to develop the Semantic Web, current knowledge technologies are still unable to understand the human language. The next best thing: Let the user be the intelligent factor, guided by current semantic technologies.
    • Thinknowlogy - A fundamental approach toArtificial Intelligence / knowledge technology 2 The current approach to AI What is Watson? Watson is a supercomputer system developed by IBM to support professionals by finding a keyword from a description, e.g. by providing a list of possible deceases from a description of symptoms. In fact, Watson is a reversed Wikipedia. To prove the power of Watson, IBM initiated a Jeopardy competition between human competitors and Watson.
    • Thinknowlogy - A fundamental approach toArtificial Intelligence / knowledge technology 2 The current approach to AI What is Watson? Jeopardy is a quiz show in which a cryptic description must be answered by a keyword, in the form of: “What is {noun}?” or “Who is {proper noun}?”. So, Watson isnt able to answer in sentences, only to find keywords.
    • Thinknowlogy - A fundamental approach toArtificial Intelligence / knowledge technology 2 The current approach to AI • But why did Watson need 2800 CPU cores to find “a needle in the haystack of unstructured texts” (quote from IBM) in order to beat human competitors? • Why didnt IBM just structure the knowledge base of Watson before playing Jeopardy? Wouldnt that speed-up the search itself, using significantly less CPU power during the game? Because science (and IBM) fails to develop techniques to structure or organize knowledge autonomously.
    • Thinknowlogy - A fundamental approach toArtificial Intelligence / knowledge technology 2 The current approach to AI More into depth: Search engines, chatbots (including Siri) and other natural language systems degrade the sentences to interlinked keywords and throw away the rest of the words, by which a lot of the meaning is discarded irrecoverably. Therefore, such systems are only able to find texts containing given keywords. Besides that, current AI systems are only able to act intelligently on pre-programmed situations – and therefore – chatbots fail to keep focus.
    • Thinknowlogy - A fundamental approach toArtificial Intelligence / knowledge technology 2 The current approach to AI But what about Artificial Neural Networks (ANN)? Using a metaphor to describe an ANN: a building. Neurons are not more then bricks, and an ANN isnt more than a pile of interconnected bricks, which is no building. An architect is required to design a building. However, nobody knows how to design a high-level architecture for an ANN.
    • Thinknowlogy - A fundamental approach toArtificial Intelligence / knowledge technology 2 The current approach to AI But what about Artificial Neural Networks (ANN)? And therefore, an ANN: • is limited to execute one task – and one task only; • will always be a slave, and will never become a master; • will never show intelligent behavior, because intelligence should be gained autonomously, and nobody knows how to design an ANN architecture that gains its intelligence autonomously.
    • Thinknowlogy - A fundamental approach toArtificial Intelligence / knowledge technology 2 The current approach to AI And what about the Semantic Web? The Semantic Web is assumed to add meaning to the Web, by which knowledge technology is assumed to understand the human language. The Semantic Web is a commercial success, but it still fails to understand the user, because scientists dont know what semantics is.
    • Thinknowlogy - A fundamental approach toArtificial Intelligence / knowledge technology 2 The current approach to AI What is implemented then by AI and knowledge technology experts? • Nice – but baseless – techniques. AI is only: – “inspired by nature”; – fooling the customer; – marketing; – earning big bucks.
    • Thinknowlogy - A fundamental approach toArtificial Intelligence / knowledge technology 2 The current approach to AI So, what went wrong? AI has a fundamental problem: • AI scientists started to develop techniques without defining and developing a foundation first. So, in fact, the field of AI / knowledge technology is baseless (has no foundation) and isnt scientific.
    • Thinknowlogy - A fundamental approach toArtificial Intelligence / knowledge technology 2 The current approach to AI Can you prove that AI is not fundamental? Of course. Characteristics of a mature science: • A mature science is defined unambiguously; • A mature science is based on a natural foundation; • A mature science integrates all its disciplines.
    • Thinknowlogy - A fundamental approach toArtificial Intelligence / knowledge technology 2 The current approach to AI A mature science is defined unambiguously Using the metaphor of a building again: – Before an architect starts to draw, the clients requirements must be specified clearly. • However, science started to build Artificial Intelligence without defining of intelligence first; • And science started to build knowledge technology without defining Semantics / meaning first. So, on what is AI / knowledge technology based then?
    • Thinknowlogy - A fundamental approach toArtificial Intelligence / knowledge technology 2 The current approach to AI A mature science is defined unambiguously Why does science fail to define intelligence and semantics? • According to science, intelligence should have been emerged “by itself”, like by complexity and chaos; • And natural language should have been evolved from the primal sounds of cave men. However, bright people like Leonardo da Vinci and Albert Einstein taught us the exact opposite: Intelligence is in simplicity and structure.
    • Thinknowlogy - A fundamental approach toArtificial Intelligence / knowledge technology 2 The current approach to AI A mature science is defined unambiguously Why does science fail to define intelligence and semantics? • So, what if the evolution theory is nonsense? • What if intelligence and natural language arent the chaotic result of evolution, but the organized result of an intelligent design by God?
    • Thinknowlogy - A fundamental approach toArtificial Intelligence / knowledge technology 2 The current approach to AI A mature science is based on a natural foundation For example, the science of electricity is based on the natural phenomenon of a flow of electrically charged particles. However, • the current approach to AI doesnt define a natural source of intelligence. So, AI has no natural foundation; • and knowledge technology fails to understand the meaning of the user, because it isnt based on a natural source of Semantics.
    • Thinknowlogy - A fundamental approach toArtificial Intelligence / knowledge technology 2 The current approach to AI A mature science integrates all its disciplines For example: The basic rules of electricity apply from micro-electronics to high-voltage systems. However, the disciplines of AI / knowledge technology are separate “islands”. For example: Ontology (automated reasoning) is based on formal (=cryptic) language and Natural Language Processing (NLP) is based on natural language. So, Ontology and NLP have no common foundation, and therefore they are incompatible.
    • Thinknowlogy - A fundamental approach toArtificial Intelligence / knowledge technology 2 The current approach to AI Summary: • AI is not defined unambiguously; • AI has no natural foundation; • AI doesnt integrates all its disciplines. Conclusion: The current approach to AI and knowledge technology is not fundamental, and therefore not scientific.
    • Thinknowlogy - A fundamental approach toArtificial Intelligence / knowledge technology 2 The current approach to AI From a positive point of view: The field of AI and knowledge technology is still completely open, and provides an unprecedented opportunity for those who will succeed to develop a scientific foundation for AI and knowledge technology.
    • Thinknowlogy - A fundamental approach toArtificial Intelligence / knowledge technology 2 The current approach to AI • But what is the definition of intelligence and semantics? • How can a fundamental base then be developed for AI and knowledge technology? • And how can intelligence and semantics be implemented on a natural foundation?
    • Thinknowlogy - A fundamental approach toArtificial Intelligence / knowledge technology 2 The current approach to AI Any questions so far?
    • Thinknowlogy - A fundamental approach toArtificial Intelligence / knowledge technology 1 Introduction 2 The current approach to AI 3 A fundamental approach 4 Demo 5 Questions
    • Thinknowlogy - A fundamental approach toArtificial Intelligence / knowledge technology 3 A fundamental approach Using a metaphor: the dream to fly like a bird. The dream of flight had: • an experimental phase; • and a fundamental phase.
    • Thinknowlogy - A fundamental approach toArtificial Intelligence / knowledge technology 3 A fundamental approach The experimental approach to the dream of flight For a many centuries, people tried to fly like a bird. “Inspired by nature”, a few covered themselves with feathers or strapped a wing construction to their arms in the hope they could fly. But they failed, because feathers and flapping wings arent essential for flight. Proof: Airplanes can fly, but they no feathers, nor flapping wings. Also the current approach to AI is “inspired by nature”.
    • Thinknowlogy - A fundamental approach toArtificial Intelligence / knowledge technology 3 A fundamental approach The fundamental approach to the dream of flight The Wright brothers however, experimented for years to understand the essence of flight: • Lift (air lifting); • Thrust (propulsion); • Drag; • Weight (including center of gravity). (No feathers, nor flapping wings.)
    • Thinknowlogy - A fundamental approach toArtificial Intelligence / knowledge technology 3 A fundamental approach The fundamental approach to the dream of intelligent machines / information systems How can we achieve a fundamental approach for the dream of intelligent machines / information systems, like the Wright brothers did for the dream of flight?
    • Thinknowlogy - A fundamental approach toArtificial Intelligence / knowledge technology 3 A fundamental approach Characteristics of a mature science: • A mature science is defined unambiguously; • A mature science is based on a natural foundation; • A mature science integrates all its disciplines.
    • Thinknowlogy - A fundamental approach toArtificial Intelligence / knowledge technology 3 A fundamental approach A mature science is defined unambiguously So, lets define: • Intelligence (for Artificial Intelligence); • Semantics (for knowledge technology).
    • Thinknowlogy - A fundamental approach toArtificial Intelligence / knowledge technology 3 A fundamental approach A mature science is defined unambiguously The definition of intelligence (for Artificial Intelligence): “Intelligence is a naturally occurring phenomenon, which can be described as the capability of autonomously associating (combining), discriminating (differentiating, distinguishing), learning (from mistakes), planning and predicting, with the aim to reach a predefined goal.”
    • Thinknowlogy - A fundamental approach toArtificial Intelligence / knowledge technology 3 A fundamental approach A mature science is defined unambiguously The definition of semantics (for knowledge technology): “Semantics is a naturally occurring phenomenon, which can be described as the capability of autonomously associating (combining) and discriminating (differentiating, distinguishing), with the aim to reach a predefined goal.” So, Semantics is actually a subset of Intelligence, and knowledge technology is a subset of AI.
    • Thinknowlogy - A fundamental approach toArtificial Intelligence / knowledge technology 3 A fundamental approach A mature science is based on a natural foundation “Intelligence is a naturally occurring phenomenon …” So, an intelligent system should derive its intelligence from a natural source – rather than artificially created sources, like semantic vocabularies and statistics.
    • Thinknowlogy - A fundamental approach toArtificial Intelligence / knowledge technology 3 A fundamental approach A mature science is based on a natural foundation “Intelligence […] can be described as the capability of autonomously […]” But what is autonomy?
    • Thinknowlogy - A fundamental approach toArtificial Intelligence / knowledge technology 3 A fundamental approach A mature science is based on a natural foundation To illustrate autonomy by a known Chinese saying: “Give a man a fish and you feed him for a day. Teach a man to fish and you feed him for a lifetime.” So, an intelligent system should be able “to fish” – e.g. building its own semantics autonomously from a natural source – rather than being fed constantly from an artificial source, like semantic vocabularies and statistics.
    • Thinknowlogy - A fundamental approach toArtificial Intelligence / knowledge technology 3 A fundamental approach A mature science is based on a natural foundation Thinknowlogy is able “to fish”: by deriving its intelligence autonomously from a natural source (grammar): There are rules of intelligence contained within grammar. (will be explained later in this presentation), by which it is able to preserve the meaning at natural language level throughout the system.
    • Thinknowlogy - A fundamental approach toArtificial Intelligence / knowledge technology 3 A fundamental approach A mature science integrates all its disciplines Proof of the pudding! Thinknowlogy integrates several disciplines with natural language: • Programming in natural language; • Reasoning in natural language; • Intelligent answering of “is” questions in full sentences; • Detecting some cases of semantic ambiguity.
    • Thinknowlogy - A fundamental approach toArtificial Intelligence / knowledge technology 3 A fundamental approach What is Thinknowlogy? Thinknowlogy is grammar-based software, designed to utilize the intelligence contained within grammar, in order to create intelligence through natural language in software, which is demonstrated by:
    • Thinknowlogy - A fundamental approach toArtificial Intelligence / knowledge technology 3 A fundamental approach • Programming in natural language; • Reasoning in natural language (=reasoning expressed in natural language): • drawing conclusions, • making assumptions (with self-adjusting level of uncertainty), • asking questions (about gaps in the knowledge), • detecting conflicts in the knowledge; • Building semantics autonomously (no vocabularies): • detecting of some cases of semantic ambiguity; • Intelligent answering of “is” questions (by providing alternative answers as well).
    • Thinknowlogy - A fundamental approach toArtificial Intelligence / knowledge technology 3 A fundamental approach Demo – Programming in natural language: • Playing the game Connect-Four only by reading the playing rules, written as readable language; • Greeting program written as readable text; • Solving the Tower of Hanoi problem with the rules written as readable text. Remark: With some additional development, the system will be able to execute business rules, written as readable text.
    • Thinknowlogy - A fundamental approach toArtificial Intelligence / knowledge technology 3 A fundamental approach Demo – Reasoning in natural language: Most reasoners work at the level of linked keywords. And therefore, they are unable to produce in sentences in natural language. The reasoning capability at natural language level of Thinknowlogy is worldwide unique: • Drawing conclusions; • Making assumptions (self-adjusting level of uncertainty); • Asking questions (about gaps in the knowledge); • Detecting conflicts in the knowledge.
    • Thinknowlogy - A fundamental approach toArtificial Intelligence / knowledge technology 3 A fundamental approach Demo – Intelligent answering of “is” questions • Most scientific Question Answering (QA) systems are only able to provide a keyword as answer; • Other QA systems – like chatbots – are able to answer in full sentences. However all its sentences are written by humans. Only Thinknowlogy is able to answer with knowledge (either from humans or from the reasoner) in a full sentences. And in case no answer could be found, it tries to find an alternative, that could answer the question of the user.
    • Thinknowlogy - A fundamental approach toArtificial Intelligence / knowledge technology 3 A fundamental approach Demo – Detecting cases of Semantic Ambiguity Disambiguation (solving ambiguity) is the biggest problem in Natural Language Processing. There are two types of ambiguity: • Grammatical ambiguity, like: “The man hit the dog with a stick”. (Did the man hit with a stick, or had the dog a stick?); • Semantic ambiguity, like “former president Bush”. George H. W. Bush, or his son George W. Bush?
    • Thinknowlogy - A fundamental approach toArtificial Intelligence / knowledge technology 3 A fundamental approach Demo – Detecting cases of Semantic Ambiguity Several techniques are developed to solve semantic ambiguity at keyword level. However, only Thinknowlogy is able to detect – not yet to solve – some cases of semantic ambiguity at natural language level. From 2013 I will start to implement solving some cases of semantic ambiguity to prove my approach in fundamental.
    • Thinknowlogy - A fundamental approach toArtificial Intelligence / knowledge technology 3 A fundamental approach From 2013 – Solving cases of Semantic Ambiguity When a sentence is entered and its context (semantics) is not clear, the system can either: • use deduction to determine which context is meant by the user; • make an assumption, when the meant context cannot be determined, but when it is quite obvious; • or ask a question, when the system has no clue about the context.
    • Thinknowlogy - A fundamental approach toArtificial Intelligence / knowledge technology 3 A fundamental approach How to build semantics autonomously? • By autonomously associating (combining) the new information with existing information, • and by autonomously discriminating (differentiating, distinguishing) the new information from existing information, using the rules of intelligence contained within grammar.
    • Thinknowlogy - A fundamental approach toArtificial Intelligence / knowledge technology 3 A fundamental approach Some rules of intelligence contained with grammar to associate and discriminate knowledge: • Indefinite article (“a”) represents a definition, a static structure; • Definite article (“the”) represents a dynamic structure, similar to a variable in a programming language; • A series of words are all from the same word type: noun, proper noun, adjective, etc; • Conjunction “or” marks a choice; • Conjunctions like “and” and “or” mark a set (an association), and distinguishes from other sets.
    • Thinknowlogy - A fundamental approach toArtificial Intelligence / knowledge technology 3 A fundamental approach Some rules of intelligence contained with grammar to associate and discriminate knowledge: • Indefinite article (“a”) represents a definition, a static structure: “A son is a male.” (no assignment); • Definite article (“the”) represents a dynamic structure, similar to the assignment of a variable in programming languages: “John is the father of Paul, Joe and Laura.”. A static structure can be derived from a dynamic structure: “John is a father.”.
    • Thinknowlogy - A fundamental approach toArtificial Intelligence / knowledge technology 3 A fundamental approach Some rules of intelligence contained with grammar to associate and discriminate knowledge: • A series of words are all from the same word type: noun, proper noun, adjective, etc., like in: “0, 1, 2, 3, 4, 5, 6, 7, 8 and 9 are numbers.”. People will get confused by a list – a series of words – having different grammatical word types, like in: “0, 1, 2, 3, 4, 5, 6, 7, 8 and blue are numbers.”.
    • Thinknowlogy - A fundamental approach toArtificial Intelligence / knowledge technology 3 A fundamental approach Some rules of intelligence contained with grammar to associate and discriminate knowledge: • Conjunction “or” marks a choice, like in: “A person is a man or a woman.”. Using such a choice is an act of intelligence: When given the specification: “John is a person.”, a substitution of both sentences will result in the question: “Is John a man or a woman?”.
    • Thinknowlogy - A fundamental approach toArtificial Intelligence / knowledge technology 3 A fundamental approach Some rules of intelligence contained with grammar to associate and discriminate knowledge: • Conjunctions (like “and” and “or”) associate a set: “Humans have two arms and two legs.” and “Humans have constructed roads and bridges.”. However, the set of “arms” and “legs” doesnt belong to the set of “roads” and “bridges”. So, by associating one set, at the same time we make a distinction with other sets, which is an act of intelligence.
    • Thinknowlogy - A fundamental approach toArtificial Intelligence / knowledge technology 3 A fundamental approach When will Thinknowlogy deliver revenue? The current approach to knowledge technology is far from fundamental. So, we need to redo basically most AI research done in the last 60 years: I estimate an additional 100 man years (10 developers during 10 years) is required to develop Thinknowlogy as a foundation for knowledge technology. And it will be difficult to find developers able to think outside the box.
    • Thinknowlogy - A fundamental approach toArtificial Intelligence / knowledge technology 3 A fundamental approach When will Thinknowlogy deliver revenue? However, after those 10 years of development, Thinknowlogy will deliver a monopoly position in knowledge technology, with a situation comparable to men covered with feathers and having complex wing constructions strapped to their arms, against a fundamentally designed airplane of the Wright brothers.
    • Thinknowlogy - A fundamental approach toArtificial Intelligence / knowledge technology 1 Introduction 2 The current approach to AI 3 A fundamental approach 4 Demo 5 Questions
    • Thinknowlogy - A fundamental approach toArtificial Intelligence / knowledge technology 4 Demo Thinknowlogy integrates several disciplines with natural language: • Programming in natural language; • Reasoning in natural language; • Intelligent answering of “is” questions in full sentences; • Detecting some cases of semantic ambiguity.
    • Thinknowlogy - A fundamental approach toArtificial Intelligence / knowledge technology Thinknowlogy at startup
    • Thinknowlogy - A fundamental approach toArtificial Intelligence / knowledge technology Programming in natural language: Reading the playing rules of Connect-Four as text
    • Thinknowlogy - A fundamental approach toArtificial Intelligence / knowledge technology Programming in natural language: The playing rules of Connect-Four are executed
    • Thinknowlogy - A fundamental approach to Artificial Intelligence / knowledge technology Programming in natural language:The system has gains knowledge and keeps track of history
    • Thinknowlogy - A fundamental approach toArtificial Intelligence / knowledge technology Programming in natural language: Greeting program written as readable text (1)
    • Thinknowlogy - A fundamental approach toArtificial Intelligence / knowledge technology Programming in natural language: Greeting program written as readable text (2)
    • Thinknowlogy - A fundamental approach toArtificial Intelligence / knowledge technology Programming in natural language: Reading the rules to solve the Tower of Hanoi problem
    • Thinknowlogy - A fundamental approach toArtificial Intelligence / knowledge technology Programming in natural language: The rules of the Tower of Hanoi problem are executed
    • Thinknowlogy - A fundamental approach toArtificial Intelligence / knowledge technology Reasoning in natural language: Reading definitions about a family as readable text
    • Thinknowlogy - A fundamental approach toArtificial Intelligence / knowledge technology Reasoning in natural language: Autonomous conclusions, assumptions and questions
    • Thinknowlogy - A fundamental approach toArtificial Intelligence / knowledge technology Reasoning in natural language: When questions are answered by the user
    • Thinknowlogy - A fundamental approach toArtificial Intelligence / knowledge technology Reasoning in natural language: Another reasoning example (1)
    • Thinknowlogy - A fundamental approach toArtificial Intelligence / knowledge technology Reasoning in natural language: Another reasoning example (2)
    • Thinknowlogy - A fundamental approach toArtificial Intelligence / knowledge technology Reasoning in natural language:The assumptions have a self-adjusting level of uncertainty
    • Thinknowlogy - A fundamental approach toArtificial Intelligence / knowledge technology Reasoning in natural language: Correcting an invalidated assumption
    • Thinknowlogy - A fundamental approach toArtificial Intelligence / knowledge technology Reasoning in natural language: Justification report for the self-generated knowledge
    • Thinknowlogy - A fundamental approach toArtificial Intelligence / knowledge technology Reasoning in natural language: When conflicting knowledge is entered
    • Thinknowlogy - A fundamental approach to Artificial Intelligence / knowledge technology Question Answering:Intelligent answering of “is” questions by providing alternatives
    • Thinknowlogy - A fundamental approach toArtificial Intelligence / knowledge technology Detecting some cases of semantic ambiguity: City Boston – Multiple instances
    • Thinknowlogy - A fundamental approach toArtificial Intelligence / knowledge technology Detecting some cases of semantic ambiguity:President Bush – Re-occurrence or multiple instances? (1)
    • Thinknowlogy - A fundamental approach toArtificial Intelligence / knowledge technology Detecting some cases of semantic ambiguity:President Bush – Re-occurrence or multiple instances? (2)
    • Thinknowlogy - A fundamental approach toArtificial Intelligence / knowledge technology 1 Introduction 2 The current approach to AI 3 A fundamental approach 4 Demo 5 Questions
    • Thank youA webinar initiated by CompegenceDecember 15, 2012© 2012 Menno Mafait http://mafait.org