This document discusses content AI technologies and their applications. It provides an overview of key content AI areas like images, speech, video, tagging, information extraction, classification, process automation, machine reading, question answering, and machine translation. The document also discusses challenges around trust in AI, including algorithmic bias, privacy concerns, and the need for explainability of AI systems and their results. It provides examples of how AI systems can exhibit unintended bias if not developed carefully, as well as perspectives on responsible development and application of AI technologies.

1. Content AI: From
Potential to Practice
Seth Grimes
@SethGrimes

2. Content AI 2

3. Content AI 3

4. Content AI 4

5. Content AI 5

6. Content AI 6

7. Content AI 7

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.

10. 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…

11. Content AI 11

12. Content AI 12

13. Content AI 13
Clustering

14. 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

15. https://xbpeng.github.io/projects/SFV/index.html

17. Content AI 17
Knowledge graph and query
http://blog.bruggen.com/2013/12/fascinating-food-networks-in-neo4j.html

18. 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.

19. Content AI 19

21. “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

22. “Neural Machine Translation,” https://nlp.stanford.edu/projects/nmt/Luong-Cho-Manning-NMT-ACL2016-v4.pdf

23. 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

24. 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.”

25. Content AI 25
https://medium.com/s/story/im-an-amazon-employee-my-company-shouldn-t-sell-facial-recognition-tech-to-police-
36b5fde934ac

26. 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.

27. Content AI: From
Potential to Practice
Seth Grimes
@SethGrimes