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’.

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.

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Since starting work on the Lunch with Philip and Greg I’ve battled a little with the multicam. Largely because I’m using it in an atypical way, although I suspect setups like mine will become more common in the future.

My solution was Automator actions, triggered by Function keys and activating an AppleScript, so that the mode is first switched to Video Only (for angles 1 or 2) or Audio only (3, 4 and 5) before switching to the angle.  It reduces a lot of repetitive strain injury potential!

The tutorial is over at FCP.com, but here’s a little background.

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While Augmented Reality and Virtual Reality often get conflated, they are very different beasts.

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Only a few days ago I wrote about smart APIs that developers can use to enhance their apps. Today, John Hauer on TechCrunch postulates that he could not find one job that someday won’t be dehumanized.

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Google today launched a new API to help parse natural language. An API is an Application Programming Interface, that developers can use to send data to, and get a response back. Natural Language Parsing is used to understand language that is available in computer-readable form (text). Google’s API joins an increasingly long list of very smart APIs that will understand language, recognize images and much more.

A lot has changed since I last wrote about Advances in Content Recognition late last year.

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We’re all aware that technology changes the workplace. Jobs disappear; sometimes to be replaced by other jobs that didn’t exist before. During the industrial revolution we were replacing manual labor with machines. The coming revolution is for white collar “knowledge” jobs. How soon will yours be among them?

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Terence Curren and I recorded our thoughts on NAB 2016.  Topics covered include general impressions of NAB 2016, and why Terry did not attend this year; Blackmagic Design Resolve; Avid’s business; market fragmentation; HDR and expanded color gamut; Studio Daily’s Top 50 influencers (including Philip); Zcam; Lytro cam; VR; innovation; Apple watch and NDA’d Final Cut Pro X preview.

Episode 71: NAB 2016

A recent articles, and project, demonstrate an increasing trend to automate certain types of production: generally that which is highly predictable. One example uses new technology to build news videos from text articles; the other builds multiple videos based on the same XML template.

These types of technologies are but another in a series of developments on templatorization or automatic editing. Naturally, at the heart of all automated processes is metadata.

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For the Lunch with Philip and Greg project we shoot 4K and extract 1080 out of the larger image (or scale the image down). Working on the edit of the next to be published, this very moment shows why I find a big advantage in 4K for acquisition.

Click to enlarge

You can see from the multicam thumbnail why I wanted to crop this image, even though the shot on Greg is the best choice for that moment. And yes, cutting around eating is one of the early challenges of this project. We’re developing better strategies as we gain experience.