Upgrade to Pro
— share decks privately, control downloads, hide ads and more …
Speaker Deck
Features
Speaker Deck
PRO
Sign in
Sign up for free
Search
Search
Litmus test
Search
Xyggy
February 21, 2019
Technology
880
0
Share
Embed
Copy iframe code
Copy JS code
Copy link
Start on current slide
Litmus test
Xyggy
February 21, 2019
More Decks by Xyggy
See All by Xyggy
Add and remove images from query
xyggy
0
980
More like this or these
xyggy
0
950
More of these, less of the others
xyggy
0
920
Taking photos is free
xyggy
0
490
Knowns and unknowns
xyggy
0
450
Realtime AI is Not a Myth
xyggy
0
1.7k
3 minute Thingy
xyggy
0
170
pop-your-filter-bubble.pdf
xyggy
0
320
Other Decks in Technology
See All in Technology
変更し続けられるシステムをどう保つか — AI時代のSSoTという設計原則
kawauso
1
1.1k
[Droidcon Orlando '26] The Android Lens: Applying Mobile Forensics to AI Performance
amanda_hinchman
1
110
AI Coding Agent時代のcdk-nagガードレール 〜組織ルールを強制CIで守り抜く設計の挑戦〜
mhrtech
3
510
生成AI×AWS CDK×AWS FISで"振り返れる"ミニGameDayをつくろう
yoshimi0227
2
530
『モデル + ハーネス』で読み解く AIエージェント入門
oracle4engineer
PRO
2
160
大量データに対しても、生成AIを用いてリーズナブルにデータ加工をしたい!Databricksのai_queryについて調べてみた
kamoshika
1
280
探索・可視化・自動化を一本化 Amazon Quickでデータ活用スピードを上げる方法
koheiyoshikawa
0
170
“それは自分の仕事じゃない"を越えて行け
yuukiyo
1
520
AI時代のYAGNI:「爆速で無駄になった機能」からの学び / 20260720 Naoki Takahashi
shift_evolve
PRO
3
520
StepFunctionsとGraphRAGを活用した暗黙知活用のためのRAG基盤
yakumo
0
130
AIとハーネスで育てるトランスコンパイラ / 20260722 Yasushi Katayama
shift_evolve
PRO
3
780
インフラと開発の垣根を超えていき!〜元AWSインフラエンジニアがAWS開発で奮闘している話〜
hatahata021
3
390
Featured
See All Featured
Building Adaptive Systems
keathley
44
3.1k
How Fast Is Fast Enough? [PerfNow 2025]
tammyeverts
3
660
Claude Code のすすめ
schroneko
67
230k
The Limits of Empathy - UXLibs8
cassininazir
1
530
Color Theory Basics | Prateek | Gurzu
gurzu
0
390
Keith and Marios Guide to Fast Websites
keithpitt
413
23k
AI in Enterprises - Java and Open Source to the Rescue
ivargrimstad
0
1.4k
Fireside Chat
paigeccino
42
4k
Why Mistakes Are the Best Teachers: Turning Failure into a Pathway for Growth
auna
0
190
Mind Mapping
helmedeiros
PRO
1
290
Design of three-dimensional binary manipulators for pick-and-place task avoiding obstacles (IECON2024)
konakalab
0
490
We Have a Design System, Now What?
morganepeng
55
8.2k
Transcript
1 1. Query with unknown image Dataset: wikiart images -
https://www.wikiart.org/. No image pre-processing performed. No textual data used. Feature vectors generated automatically from deep learning model. Unknown image U U not in Thingy www.xyggy.com © 2019
2 Test to add unknown image 1. Query with an
unknown image. If image is known to Thingy, duplicate will show as the first result. It doesn’t (previous slide). 2. Add a copy of the unknown image to Thingy. 3. Query with the unknown image again. If image is known to Thingy, duplicate will show as the first result. www.xyggy.com © 2019
3 2. Add image Add a copy of unknown image
U to Thingy in realtime www.xyggy.com © 2019
4 3. Query with unknown image again Unknown image U
Copy of U is first result www.xyggy.com © 2019
5 Test to add 3 unknown images 1. Query with
3 unknown images. If images are known, duplicates will show in results. They don’t (see next slide). 2. Add copies of 3 unknown images to Thingy. 3. Query with 3 unknown images again. If images are known, duplicates will show in results. www.xyggy.com © 2019
6 1. Query with 3 unknown images Unknown images U1,
U2, U3 U1, U2, U3 not in Thingy www.xyggy.com © 2019
7 2. Add 3 images Add copies of 3 unknown
images U1, U2, U3 to Thingy in realtime www.xyggy.com © 2019
8 3. Query with 3 unknown images again Unknown images
U1, U2, U3 Copies of U1, U2, U3 appear in results www.xyggy.com © 2019
9 Thingy www.xyggy.com © 2019