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When automated analysis goes wrong by Tristan Roddis - EuropeanaTech Conference 2018
58.
Tristan Roddis. Cogapp
Images
• Sourced from Nationalmuseum Sweden
• Using Europeana API for discovery
• 2000 images
• http://labs.cogapp.com/iiif-ml/
59.
Tristan Roddis. Cogapp
Success: Use pre-trained
models for term extraction
• Tune thresholds
• Optionally correlate with “known good” terms
• Optionally allow people to flag incorrect terms
60.
Tristan Roddis. Cogapp
Failure: when computers
get it wrong
61.
Tristan Roddis. Cogapp
Failure: when computers
get it wrong
62.
Tristan Roddis. Cogapp
Failure: when computers
get it wrong
63.
Tristan Roddis. Cogapp
Failure: when computers
get it wrong
64.
Tristan Roddis. Cogapp
Success: Embrace
failure
• Original goal: Finding similar images
• It doesn’t matter what the computer
sees!
65.
Tristan Roddis. Cogapp
Success: Hide the
details
66.
Tristan Roddis. Cogapp
Unexpected success:
Optical music recognition
85.
Tristan Roddis. Cogapp
Conclusions
• Embrace failure
• Find workarounds
• Constrain results
• Tune thresholds
• Hide the details
• Add humans to the mix
86.
Tristan Roddis. Cogapp
Further reading
• Automated image analysis with IIIF
Adrian Hindle, Cogapp
• Using Artificial Intelligence to enhance User
Experience Neil Hawkins, Cogapp
• Playing ancient music without an instrument
Tristan Roddis, Cogapp
• Iconclass and AI
Gro Benedikte Pedersen, NMAAD Norway
• Computer Vision so good
Shelley Bernstein, Barnes Foundation
87.
Tristan Roddis. Cogapp
Further reading
• Automated image analysis with IIIF, Adrian Hindle, Cogapp
• Using Artificial Intelligence to enhance User Experience, Neil Hawkins,
Cogapp
• Playing ancient music without an instrument, Tristan Roddis, Cogapp
• Iconclass and AI, Gro Benedikte Pedersen, National Museum of Art,
Architecture and Design in Norway
• Computer Vision so good, Shelley Bernstein, Barnes Foundation
• Quantifying Kissenger, Micki Kaufman
• Comparison of different computer-vision APIs, Cogapp Labs.