Category: Metadata
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Speech-to-Text: Recent Example
A 46 minute interview transcribed with only 15 of 4100 words needing correction. That’s over 99.98% accurate.
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IBM Watson is a Sports Guru?
It seems machine learning is powering more and better extraction of all kinds of metadata.
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Modern Logging and Pre-Editing Approaches: “House Hunters” Style Reality TV
This is the first time I’ve taken a deep look at a TV show and worked out what I think would be the perfect metadata workflow from shoot to edit bay. I chose to look at Pie Town’s House Hunters franchise because it is so built on a (obviously winning) formulae, and I thought that might…
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2017 – 2018 Introspection
What technologies took my interest in 2017, and what will happen to them in 2018.
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Lumberjack, IBC and FCPX World
Lumberjack System will be previewing something extremely exciting at the FCP X World Event at IBC.
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Wimbledon use IBM Watson AI to “Serve up Highlights”
The report isn’t clear on exactly how Watson’s “AI” is being used but the article says that they are “now curating the biggest sights and sounds from matches to create “Cognitive Highlights,” which will be seen on Wimbledon’s digital channels.” Apparently using Watson cognitive services to recognize a significant moment, and pull it together with…
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Apple buys (another) Facial Recognition Company – AI Based
Apple have made another purchase around facial recognition, this time a machine learning startup
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Lumberjack Story Mode in Action
With Lumberjack System we don’t focus enough on Story Mode. Of late Transcript mode and Magic Keywords have taken the main focus, and of course the primary real-time logging and pre-editing tools are well known by now. But Story Mode is ultimately move valuable if the project continues more than a one or two day shoot.…
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2016 in Review
An exciting and wonderful year.
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Introducing FindrCat
Introducing FinderCat: simple media organization in macOS Finder from you FCP X Keywords.