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Caspian machine learning garage
- 1. Public domain intelligence 1 How to use publicly available data to enrich your solution
- 3. 3 Where to find knowledge? • Experts • Documentation • Publicly available data
- 4. 4 Nature of Business What business are you in?
- 5. 5 Nature of Business Oil&Gas Producers Distribution Financial Insurance Services Caspian Business Taxonomy Caspian AI Model Training data Public Knowledge
- 6. 6 Nature of Business: Business Taxonomy • National and International Standards • Domain Experts • Client requirements
- 7. 7 Nature of Business: Extract Public Knowledge Oil&Gas Producers Distribution Financial Insurance Services Word Expansion Search Public Resources Search Engines FT Government papers
- 8. Nature of Business: AI Models Training data Topic Modelling Word Embedding Deep Learning Model
- 9. 9 Nature of Business: Hierarchal Multi-Label problem Oil & Gas Producers Distribution Financial Insurance Services Model IModel II Model III BayesianChainRule
- 10. 10 Nature of Business: Example We offer Car, Home, Travel and Health cover, as well as Life cover, Savings, Pension and Investment services to over 31 million customers worldwide. Input Apply Model Output Oil & Gas Producers Distribution Financial Insurance Services
- 11. • 95% accuracy • Basis for customized models for different clients’ requirements • A framework for other public domain knowledge based solutions 11 Nature of Business: Results
Hinweis der Redaktion
- In Caspian we do data science differently. We focus on building systems that extract domain knowledge from experts and use that to build intelligent systems.
- We as humans, tend to agree with ourselves only around 75% of the time. Expert knowledge is very important but is usually inconsistent across experts and is not objective. Documentation is very helpful but might not be accurate and/or very difficult to extract information from.
- As an example of using public knowledge is our nature of business module. It might sound a straightforward question to answer. What business are you in? however the answer of that question is never that simple, especially with multi-national companies.
- The output of this process is a large set of labelled documents
- For each business type we extract LDA and word embedding features and build a classifier