Bloomberg reported yesterday about a Hedge Fund ‘Robot’ that “outsmarted” its human “master”. The quotation marks are all mine because it’s self learning, so it doesn’t really have a master, but rather someone that created it.
Still, the performance in the quoted instance is quite impressive. It’s currently in charge of about $35 million in investment.
When I discovered I could do in two keystrokes what took 9 mouse clicks and keystrokes in Soundtrack Pro, I never looked back and now edit all my audio only projects in FCP X.
I got together with Marcelo Lewin of DigitalMedia Pros and explained how I do it.
Most of the thinking – the little that’s done – around the affect of Artificial Intelligence and Robotics replacing jobs, is somewhat negative, so it was almost a relief to read John Hagel’s perspective that we could use this transition as an opportunity to rethink the nature of work.
I cut a short 30 minute competition entry for a friend today. A relatively simple single-take green screen over a Pond 5 background she purchased.
Except we used a bunch of technologies that were all non-existent just a few years ago.
Starting with some ProRes files from a Blackmagic Design camera, we:
all in real time on a 2015 Retina 5K iMac and Final Cut Pro X.
It wasn’t that long ago that applying a soft edge to a mask; or any gaussian blur, or any chroma key meant a render before playback.
Like in the machine learning/AI field, the video technologies also keep getting better all the time.
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.
Cory Doctorow at Boing Boing uses the Trump campaign as an example of how this can play out in ‘the real world’.
I recently commented on the importance of metadata for rights management during distribution. While cleaning my email inbox I revisited a story from late last year, on how over-the-top content providers (generally niche) can use metadata from social media and other sources to help grow their audiences.
Apple has reportedly purchased machine learning company Turi. The expectation is that they will use it to improve other Apple products, most likely Siri. But what is machine learning and how does it fit with the algorithms I’ve been talking about lately.
A couple of recent articles have pointed to Artificial Intelligence writing, or contributing to, a screenplay. A narrative script. I find this fascinating, even though my own area of interest in applied AI is in non-scripted.
There is no doubt that computer algorithms – up to true AI – will be involved in productions future. Smart people will work out how to master it.
Stephen Galloway writes at The Hollywood Reporter:
The majors are still the first port of call for any significant project; they still have an unparalleled ability to get that project developed, cast, shot, marketed and into theaters; and despite extraordinary technological and economic change, they haven’t allowed any upstarts to challenge their hegemony.
Until now.
He then goes on to document the changes that Amazon and Netflix have wrought on the “old bastions of Hollywood Power.”
All of which is good news: more outlets lead to more production.