Artificial intelligence @ the National Library of Norway - Svein Arne Brygfjeld, National Library of Norway Svein Arne highlighted the work of the National Library of Norway and how one of their latest projects is successfully converting analogue media to digital form, while integrating different media collections together so that users can discover and access a range of resources on specific topics online.

1. Codename Nancy
AI: lessons from the National
Library of Norway
AI @ the National Library of Norway
Svein Arne Brygfjeld
NB AILAB

2. From «cataloging rules» to «approximate, but
good enough»

3. Bsckground: The National what…?
• The National Library of Norway
• Major Norwegian memory institution under the Ministry of Culture
• All information carriers, also historical AV
• Act on legal deposit
• ~500 employees
• Two sites 1000 km apart, Oslo and Mo i Rana

4. Today
• Motivation, Nancy & the National Library of Norway
• Short introduction to Machine Learning
• What can AI do for libraries?
• What can libraries do for AI?
• Community and Conclusions

5. Why AI now?
• The future doesn’t come, we create it
• And it is possible
• Machines, software, content and metadata, human resources
• And we are libraries…
DATA
INFORMATION
KNOWLEDGE
WISDOM

6. Low-hanging fruit
• Open/available training and test sets
• Imagenet and others
• Digital library collections
• Open source democratized software
• TensorFlow and others
• Commercial services
• API’s for speech-to-text, still image and moving image analyzes, natural language
processing and more

7. Nancy
• Umbrella for AI innovations and applications at NLN
• Defined activity, NB AILAB, reporting directly to the
national librarian
• Input to conversation about AI through small scale
projects and experiments, typically weeks to months
in volume
• Currently 3-4 people on full time + some sourcing

8. Our digital shift – 2005/2006
• Status 2005
• What has happened?
• Just one thing. It is called «Internet» - all information is expected
to be found there
• 2006
• Digitize the collection
• Make the digital collection available always and anywhere

9. Our digital shift – today
• Massive digital collection with good metadata, like
• Text: Books, journals, newspapers
• Audio: Radio broadcast, music, documentary
• Images/moving images, TV
• Heavy use by external users

10. Main machine learning profiles
• Unsupervised learning
• Supervised learning
• Reinforced learning

11. ML: From exact to approximate
• Software development
is
• Precise, predictable
• IF-THEN-ELSE
• «Right» or «wrong»
• Changing to
• Approximate performance
• Experience based learning
• Good enough

12. Supervised: Training, test, use
ML Platform
i.e. TensorFlow
Algorithm & Model
«Black box»
Content
Metadata
Training
Content
Result
i.e. metadata
classification etc
Use
MetadataContent
Test
NOT
good enough
Good enough
(measures!)

13. Supervised: Training, test, use
ML Platform
i.e. TensorFlow
Algorithm & Model
«Black box»
Content
Metadata
Training
Content
New catalog data
Use
MetadataContent
Test
Books
Catalog data
Unread
books
Correct
catalog data
New
books

14. Examples from our AI lab

15. AI for libraries? (1 of 7)
Simple classification

16. Simple classification
Grouping, description & more
• Litterature groups as an example
• Four groups for commercial use
• Each book belongs to one group only
• «Nancy, in which litterature group does this book belong?»
• She is right in approx 95% of the test cases

17. AI for libraries? (2 of 7)
Dewey Decimal Classification

18. Complex classification
• Dewey Decimal Classification

19. Dewey Decimal Classification
• Nancy, could you please classify this article by 3, 4, 5 and
6 digits Dewey?
• Norart (scientific database) as metadata
• Born digital content, artificial articles to improve training set
• Result: 70-92% performance (in some rare cases, 100%)

20. AI for libraries? (3 of 7)
Language corpus

21. Making a language corpus
Some sample text
+
ML based quality
control
=
Large high quality corpus for
spoken Norwegian
(platform for machine learning)

22. AI for libraries? (4 of 7)
Supporting/changing workflows

23. Supporting existing workflow
• Our sami bibliography misses works for the years 1988-1992
• Challenge: find digitized candidates

24. AI for libraries? (5 of 7)
Commercial services rather than in-house training

25. Commercial API’s
Machine Learning based services
- Alternative to in-house training
- Typically image classification, speech-to-text, video
analyzes, natural language processing
- General in terms of use, vocabulary etc
- Several vendors like Amazon, Clarify and Google

34. AI for libraries? (6 of 7)
A complete multimedia digital library

35. Nancy’s complex challenge
• Can we make a digital library based on machine learning only?
• The case
• Complete content for January 2011
• 250 newspapers (3.000 issues, 4.400.000 articles, 209.000 images)
• Two national radio network channels (742 hrs audio)
• One national TV channel (100 hrs video)

36. Metadata production based on
Machine Learning
• Persons (names)
• Places
• Organizations
• Time
• Relations
• Subject classification
News-
papers
Radio
TV

37. Design principle
Entity and
relation
extraction,
Subject
analyzes
Geo
location
Search
platform
Text
with
time
coding
Speech to
text
Video
analyzes
Text,
OCR,
objects
& more
Articles
and
images
Text and
image
extraction
News-
papers
Radio
TV

38. Geo (map) search

39. Person search example

40. AI for libraries? (7 of 7)
Finding similar objects

41. ML without learning
ML Plattform
TensorFlow
Algorithm & modell
«Black box» Content
Result
Grouping/clustering
Use
Content
Test

42. 1. Similar photographs

43. 2. Similar books

44. What can libraries do for AI?
• Open training and test sets
• Digital content with high quality metadata
• Access to development labs as alternative
• Domain expertise
• Measuring the performance of AI systems
• Pre-trained models
• Plug-in models for various types of content

45. Conclusions?
• We understand that we don’t understand, but that we need to
• Internal workflows may change to the better and more effective
• Our view on metadata may change radically
• Our collections may be more accessible
• We may contribute better to knowledge and understanding
• All-in-all: we may be better libraries

46. • International collaboration needed
• ai4lib, ai4lam, GoogleGroups

47. Announcement
NLN AND STANFORD LIBRARIES JOINT
FANTASTIC FUTURES 2. AI CONFERENCE
DEC 4-5, STANFORD UNIVERSITY PALO ALTO

48. Thank you
Svein Arne Brygfjeld
svein.arne.brygfjeld@nb.no