Terry Curren pointed me to this example where IBM Watson (one of the Smart APIs I referred to a couple of weeks back) was tasked with determining whether or not an Artificial Intelligence could “cut” a movie trailer. This is the result, with a very interesting insight into how they did it at the end.
IBM Watson pulled the selects based on action and emotion, but an editor created the trailer from the selects. Still, being able to locate the highlights and determine emotion is a big step forward.
The extensive article by Steven Levy – The iBrain is Here – is a fascinating read on how Apple are using Machine Learning, neural networks and Artificial Intelligences across product lines. It’s well worth the time to read through, but this quote from Phil Schiller stood out:
“We use these techniques to do the things we have always wanted to do, better than we’ve been able to do,” says Schiller. “And on new things we haven’t be able to do. It’s a technique that will ultimately be a very Apple way of doing things as it evolves inside Apple and in the ways we make products.”
The ways this could all be aligned with editing? Speech-to-text; keyword extraction (just like Magic Keywords in Lumberjack System); sentiment extraction; image recognition; facial detection and recognition; speech controlled editing (if anyone really wants that), and the list goes on.
I’d like to believe the Pro Apps Team are working on this.
Buried in an article called The iBrain is Here about Apple’s use of Artificial Intelligence across a wide range of products and purposes was this gem:
Machine learning…. It even knows what good filmmaking is, enabling Apple to quickly compile your snapshots and videos into a mini-movie at a touch of a button
At one level this is certainly true, and likely. After all, Greg and I spent a summer analyzing how I made documentary-style edits. It was a fascinating experience for me, analyzing why “that” was the right place to start b-roll over an interview.
I would then have to turn that analysis into a rule of thumb that Greg could program. This was the basis of (the now gone) First Cuts app. That work will resurface at some time. It’s too valuable not to.
It’s a competition piece, so if you’d all like to go to http://indi.com/7fqks and vote for Marlon Braccia, we’d appreciate it.
Edited in FCP X I used significant amounts of speed change, chroma key, crop and blur on the background. Those in LA can see it in person, and learn how it was done in detail at the August 24 meeting of LACPUG.
Maybe I’m pushing this subject a bit hard, but I really believe we are on the cusp of a wide range of human activities being taken over by smart algorithms, also known as Machine Learning. As well as the examples I’ve already mentioned, I found an article on how an “AI” saved a woman’s life, and how it’s being used to create legal documents for homeless (or about to be homeless) in the UK.
I’ve been talking about machine learning and smart APIs recently, where I think there is great potential to make pre-editing tasks much easier. But they are not without their downside. They are built on sample data sets to ‘train’ the algorithm. If that training set is not truly representative of the whole data set, then the results will go horribly wrong.