CAT | Technology
For those in the LA area who want to understand what all the Lumberjack System hype is about, this is your chance to get an in depth look at Lumberjack System, and get your questions asked. Everyone leaves with a one month free trial.
While projecting the changes that Artificial Intelligence (AI) and Machine Learning (ML) might bring about in the future, it was interesting to look back and see just what didn’t exist 10 years ago. Keep in mind that the Internet itself is only just over 30 years old.
In September 2010 Apple purchased Swedish facial recognition company Polar Rose, and today we learn they’ve purchased Israeli startup RealFace: “a cybersecurity and machine learning firm specializing in facial recognition technology”.
What is different between the two purchases is that this latest is based on machine learning.
…the startup had developed a unique facial recognition technology that integrates artificial intelligence and “brings back human perception to digital processes”. RealFace’s software is said to use proprietary IP in the field of “frictionless face recognition” that allows for rapid learning from facial features.
Another step towards our software identifying and labelling people in our media.
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. Story mode lets us send Lumberjack logged Final Cut Pro X Events or Libraries back to the Lumberyard app to create string-outs from all the footage.
This recently became very valuable for a recent project: extracting the conversations on Final Cut Pro X from nearly 20 episodes of Lunch with Philip and Greg for an upcoming documentary.
2016 was a year of consolidation and growth for Greg and I: citizenship, green card, artificial intelligence and a house and yard dominated the year. 2017 looks like being another interesting and exciting year.
I’d like to introduce you to our first new piece of software for about two years: FindrCat. FinderCat is an easy-to-use app that converts your Final Cut Pro X Keywords into Finder Tags, so you can then filter and search for your media via Finder. In a world of Media Asset Management (MAM), and Digital Asset Management (DAM) this is a ‘no M’am’ asset organization tool.
The biggest advantage is that the FCP X keywords now travel with the media files, and will return to FCP X as keywords when re-imported, on any system.
I guess it won’t be any surprise that I have a lot of metadata entered in my Aperture photo library. In fact the lack of metadata support in Photos is the reason I can’t migrate there.
The real value of metadata is to help find photos, but sometimes the right piece of metadata is beyond value. For some paperwork related to my current husband’s ‘green card’ I needed the date of birth of my former wife. I could not remember it, but looking in my photo library, I found one taken on her 23rd birthday.
Of course I have the original date set on my photos, even those that I scanned from prints or slides. Because I had added the metadata when I knew it, I now had the all-important date I needed, and was able to file the paperwork.
Never underestimate the value of metadata!
I’ve (along with many other people) have been beta testing SpeedScriber, an unreleased app that combines the power of an API for speech to text with a well thought out interface for correcting the machine transcription. Feed the SpeedScriber output to Lumberyard (part of Lumberjack System) and extract Magic Keywords and in a very short period of time (dependent largely on FCP X’s import speed for the XML) and you have an organized, keyworded Event with a fully searchable Transcript in the Notes field.
One of the powerful way Artificial Intelligence ‘learns’ is by using neural networks. Neural Networks are trained with a large number of examples where the result is known. The Neural Network adjusts until it gives the same result as the human ‘teacher’.
However, there’s a trap. If that source material contains biases – such as modeling Police ‘stop and frisk’ – then whatever biases are in the learning material will be contained in the subsequent AI modeling. This is the subject of an article in Nature: There is a blind spot in AI research and also the praise of Cathy O’Neil’s book Weapons of Math Destruction that not only brings up that issue, but the problem of “proxies”.
Proxies, in this context, are data sources that are used in AI programs that are not the actual data, but rather something that approximates the data: like using zip code as a proxy for income or ethnicity.
Based on O’Neil’s book, I’d say the authors of the Nature article are too late. There are already institutionalized biases in very commonly used algorithms in finance, housing, policing and criminal policy.
Rather than take up more screen real estate with a new button, we repurposed an existing function in Lumberyard. Previously, any logged Keyword Range less than 5 seconds long was ignored. We figured anything that short was a mistake. Now it creates a Marker.
The Marker will be named using the Keyword as the name, but it will be applied at the starting point as a single frame Marker.