Applied Artificial Intelligence, An Engineering Approach, Humm B.G.

Applied Artificial Intelligence, An Engineering Approach, Humm B.G.

   Why yet another book on Artificial Intelligence?
It is true that hundreds of publications on Artificial Intelligence (AI) have been published within the last decades - scientific papers and text books. Most of them focus on the theory behind AI solutions: logic, reasoning, statistical foundations, etc. However, little can be found on engineering AI applications.




Prominent AI Projects.
Some AI projects have become milestones in AI history due to their public visibility. I shortly mention just three examples.

In 1997, IBM Deep Blue beat the chess world champion Garry Kasparov. This was a milestone since chess is considered one of the most complex board games.

However on examining the computational algorithms used in the Deep Blue Application, people quickly realized the difference between the IBM approach and human intelligence. For people who believed in machines being potentially at least as intelligent as humans, this was a disillusionment (And since this belief still exists, this story is repeated for every successful AI project). For those practitioners whose goal is to build useful applications this was no disillusionment at all but an important milestone in the development of applications which exhibit behavior of human intelligence.

Contents.
Preface.
Copyright Notice.
1. Introduction.
1.1 Overview of this Book.
1.2 What is AI?.
1.3 A Brief History of AI.
1.4 Prominent AI Projects.
1.5 Further Reading.
1.6 Quick Check.
2. Knowledge Representation.
2.1 Ontology.
2.2 Knowledge Representation Approaches.
2.3 Semantic Web Standards.
2.4 Services Maps and Product Maps.
2.5 Tips and Tricks.
2.6 Quick Check.
3. AI Application Architecture.
3.1 AI Reference Architecture.
3.2 Application Example: Virtual Museum Guide.
3.3 Data Integration / Semantic Enrichment.
3.4 Application Logic / Agents.
3.5 User Interface.
3.6 Programming Languages.
3.7 Quick Check.
4. Information Retrieval.
4.1 Information Retrieval Services Map.
4.2 Information Retrieval Product Map.
4.3 Tips and Tricks.
4.4 Application Example: Semantic AutoSuggest Feature.
4.5 Quick Check.
5. Natural Language Processing.
5.1 The Big Picture.
5.2 From Letters to Sentences.
5.3 NLP Services Map.
5.4 NLP Product Map.
5.5 Quick Check.
6. Machine Learning.
6.1 Machine Learning Areas and Tasks.
6.2 Machine Learning Approaches.
6.3 Machine Learning Services and Products.
6.4 Engineering a Machine Learning Application.
6.5 Quick Check.
7. Computer Vision.
7.1 Computer Vision Applications.
7.2 Computer Vision Tasks and Approaches.
7.3 Services and Product Maps.
7.4 Example: Named Entity Recognition with the Autokeyword.me Web Service.
7.5 Quick Check.
8. Conclusions.
Literature.
Appendix.
Product Tables.
Arts Ontology.
About the Author.



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