Realtime AI is Not a Myth

Transcript

1. 1
   Query
   images
   Result
   images
   Automatic generalization
   Dataset: wikiart images - https://www.wikiart.org/.
   No image pre-processing performed. No textual data used.
   Feature vectors generated automatically with deep learning.
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2. 2
   Walkthrough …
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   Query image is woman
   with long neck, small
   eyes and narrow face
   leaning to one side.
   Results (left to right) are
   in character with query
   image.
   Note: most art is unique
   and so identical
   similarity will be rare.
   

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3. 3
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   Add 2nd query image.
   Given one or more
   images, finds other
   relevant images in
   ranked order.
   Industry focus is on
   squeezing every pixel
   from image but
   metaphorically two
   eyes are better than
   one.
   

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4. 4
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   A modern art couple is
   3rd query image.
   First result is duplicate
   of modern couple. The
   2nd is in style of the
   long-necked woman.
   Remaining results are in
   style of modern couple.
   Portraits #3 and #8 are
   in character with first
   two query images.
   

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5. 5
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   The 4th query image is
   woman with long’ish
   neck and headscarf.
   Results include females
   with head-coverings,
   long necks and leaning
   heads.
   Result #8, in style of
   modern couple, has
   long neck and face.
   

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6. 6
   In a nutshell …
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   Given one or more items,
   Thingy finds other relevant
   items in ranked order
   

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7. 7
   What is a thing?
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   Items (“things”) can be images, audio,
   video, db records, documents, genes …
   in fact, all datatypes.
   Multi-modal? Yes, eventually.
   

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8. 8
   Dynamic AI
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   1. Automatic generalization – in realtime
   2. Interactive – query with one or more items;
   add and remove items from query. Gives
   consumers control
   3. Dynamic – add new data at anytime
   And, much more …
   

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9. 9
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   Thingy
   

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