## Foms 2018 @tracey_pooh
TV, A/V, VR/AR/3D, Docker/Kubernetes 2018 Oct 15-16
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https://github.com/traceypooh/slides https://traceypooh.github.io/slides/foms2018 --- # The Internet Archive ## is doing things - AI / Machine Learning - Third Eye - Face-o-matic - ARKit - youtube-dl recording - tvOS app - Kubernetes _(* not really A/V but muse)_ - do _not_ talk to me about this ;-) --- # Internet Archive ## archive.org - WayBack Machine - past copies of 300B+ pages - 20M books, lendable - 5M videos, 5M audio & live concerts - 3M images - 300K software items & emulation (in JS) --- --- --- https://archive.org/tv --- - OCR 'lower third' - chyrons - overlaid text on broadcasts - not captions or descriptive text - editorial / summarizing in nature - 4 TV channels, 24x7, ~1 min from realtime - CNN - MSNBC - Fox News - BBC News --- # Chyron filtering - tesseract OCR - free; errors - simhash - groups 'nearly the same' - character flips - word off in time - look for vowels - pick 'most seen' group every minute - and tweet ---
  AFTER WH MEETING, SCHUMER DISHES
  WHEN HE THOUGHT NIC WAS OFF
  
--- # bots - twitter bots - https://twitter.com/tvThirdEye - https://twitter.com/tvThirdEyeB - https://twitter.com/tvThirdEyeF - https://twitter.com/tvThirdEyeM - https://twitter.com/tvThirdEye/lists/all --- https://tweetdeck.com --- # API - https://archive.org/details/third-eye - TSV - cropped imagery - raw + filtered OCR --- # Research - How cable news networks covered Kavanaugh-Ford hearing - Aaron Williams, Danielle Rindler, Chris Alcantara, - Kevin Schaul & Kevin Uhrmacher - https://www.washingtonpost.com/graphics/2018/politics/kavanaugh-ford-hearing-chyrons/ --- # Research - audio fingerprints - presented keynote paper on
CSPAN floor speeches and vocal pitch
Bryce Dietrich, UIowa - discovered 375K political Ads - find sound bites of speeches --- # CNNs - Convolutional Neural Network - filtered neural network - each layer uses output from prior layer as input - multi-node connections (but not "fully connected") --- # CNN Example - feed in image - node looking for eyelash - node looking for iris - could feed to node looking for eye - meanwhile... nose node - all feed to face recognizer node - could feed to "is this Barack Obama?" --- # Face-o-matic https://blog.archive.org/2017/07/19/introducing-face-o-matic/ --- # ARKit / OpenFace - implementation of FaceNet - https://cmusatyalab.github.io/openface/demo-3-classifier - similar to tensorflow (Torch..) --- # Image Matching - pixel diff algorithms (MAE, RMSE, MSE) - perceptual hashing pHash.org - image => _8x8 grayscale_ - convolve to 8x8 image with DCT - reduce to _64bit_ number - hamming distance Int64 pairs --- ### pHash - to gray 8x8


--- # OpenFace Training - 3+ images per person/face - avoid 'overfit' - align eyes + nose (nostrils?)

--- ### miniARchive --- ## bonus round --- ## GitLab + Auto DevOps + Kubernetes + Docker https://archive.org/details/auto-devops --- ## appleTV TV app https://github.com/traceypooh/TVArchive tvOS + TVJS + TVML is cool! --- # Webamp ## Don't click the llama! https://archive.org/details/otr_cbsradiomysterytheater --- # The End