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Content AI: From Potential to Practice

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Presentation to the AIIM Leadership Council, London, October 25, 2018.

Published in: Data & Analytics
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Content AI: From Potential to Practice

  1. 1. Content AI: From Potential to Practice Seth Grimes @SethGrimes
  2. 2. Content AI 2
  3. 3. Content AI 3
  4. 4. Content AI 4
  5. 5. Content AI 5
  6. 6. Content AI 6
  7. 7. Content AI 7
  8. 8. Content AI 8 Agenda Content AI technologies:  images, speech, and video  tagging and information extraction  classification and process automation • machine reading and question answering • machine translation • trust in AI: bias, privacy, and explainabilty Q&A Note: I use a variety of products for illustrations, without endorsement. I use certain other authorities’ material, but only if openly available.
  9. 9. Content AI 10 Method Analytics is the systematic, repeatable application of algorithmic methods that derive and deliver information, typically expressed quantitatively, whether in the form of indicators, tables, visualizations, or models. • Systematic means formal & repeatable. • Algorithmic contrasts with heuristic. • Information Knowledge AI creates and applies models for process automation. Machine learning is about data-discovered models. On to Content AI…
  10. 10. Content AI 11
  11. 11. Content AI 12
  12. 12. Content AI 13 Clustering
  13. 13. Content AI 14 Agenda Content AI technologies:  images, speech, and video  tagging and information extraction  classification and process automation  filtering, routing & recommendation  summarization, cropping, enhancement, creation  machine reading and question answering  machine translation • trust in AI: bias, privacy, and explainabilty Q&A
  14. 14. https://xbpeng.github.io/projects/SFV/index.html
  15. 15. Content AI 17 Knowledge graph and query http://blog.bruggen.com/2013/12/fascinating-food-networks-in-neo4j.html
  16. 16. Content AI 18 Question Answering Question Answering is technology that interprets and executes queries, expressed in natural language, against a knowledgebase or corpus, and returns a response in an appropriate form.
  17. 17. Content AI 19
  18. 18. “Neural Machine Translation,” https://nlp.stanford.edu/projects/nmt/Luo ng-Cho-Manning-NMT-ACL2016-v4.pdf “Massive Exploration of Neural Machine Translation Architectures,” https://arxiv.org/pdf/1703.03906.pdf
  19. 19. “Neural Machine Translation,” https://nlp.stanford.edu/projects/nmt/Luong-Cho-Manning-NMT-ACL2016-v4.pdf
  20. 20. Content AI 23 Agenda Content AI technologies:  images, speech, and video  tagging and information extraction  classification and process automation  machine reading and question answering  machine translation  trust in AI: bias, privacy, ethics, and explainabilty
  21. 21. Content AI 24 https://reut.rs/2Od9fPr Reuters: “Amazon scraps secret AI recruiting tool that showed bias against women” Amazon’s computer models were trained to vet applicants by observing patterns in resumes submitted to the company over a 10- year period. Most came from men, a reflection of male dominance across the tech industry. In effect, Amazon’s system taught itself that male candidates were preferable. It penalized resumes that included the word “women’s,” as in “women’s chess club captain.”
  22. 22. Content AI 25 https://medium.com/s/story/im-an-amazon-employee-my-company-shouldn-t-sell-facial-recognition-tech-to-police- 36b5fde934ac
  23. 23. Content AI 26 Trust in AI Many questions: • Data & content… non-operational usage rights • Algorithm… validation – robustness • System... verification • Training... bias • Results... explainability – repeatability – reproducibility – PII disclosure – discrimination • Application… Do No Evil (e.g., surveillance) Certain issues are special to ML and data-driven AI, while others also apply with engineered methods.
  24. 24. Content AI: From Potential to Practice Seth Grimes @SethGrimes

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