The present and future of post production business and technology | Philip Hodgetts

CAT | Artificial Intelligence

As I’m trying to figure out how and where we might use Machine Learning (ML) in our software businesses, I thought I’d review all the uses I can find beyond the more general cognitive services (like speech to text, image recognition, keyword extraction, etc) that I’ve already talked about and that – by themselves – are incredibly valuable and offer a near-immediate payoff.

I was a little shocked at the diversity of ways ML is being used. According to TechCrunch there has already been over $10 billion in Venture Capital to 1500 AI/ML startups in 70 countries, which is predicted to rise to more than four times that in 2017!

Since I was compiling this list, I thought I’d share it with you, but it’s just a sampling. Even so there are more than 40 applications described here, in addition to the Cognitive Services as stand alone ML tools.

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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.

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It’s relatively easy to get an overview of the current state of Artificial Intelligence (AI). It’s probably easier to understand the benefits of machine learning, particularlyMachine Learning (ML) that’s already applied to common tasks that we can benefit from now because we’re fitting those new technologies within existing frameworks.

What is much harder to determine, is how machine learning will be directly applied to post production processes, and what role AI will take in our collective production future.

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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.

In the Overview I pointed out that most of what is being written up as Artificial Intelligence (AI) is really the work of Learning Machines/Machine Learning. We learnt that Learning Machines can improve your tax deduction, do the work of a paralegal, predict court results, analyze medical scans, and much more. It seems that every day I read of yet another application.

There are readily available Learning Machines available for all comers, but there are ways to benefit from them without even using one.

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Over the last couple of years I’ve become more and more interested in the ways that the research being done into Artificial Intelligence (AI) might be applied to production and post production. In this article I’ll be giving an overview of what AI is at this stage of development, and what technologies are being used. Later articles will cover immediate and future applications and implications.

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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.

Philip and Greg 2017. Thanks to Kay Stammers and Tristan Parry.

Philip and Greg 2017. Thanks to Tristan Parry.

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One of the smart algorithms that developers can call on is Sentiment Analysis (by that or another name). Sentiment Analysis simply reads the sentiment – positive, neutral or negative – from a body of messages. It can also provide the same information on single ‘documents’, which could be transcripts.

MonkeyLearn – one of the providers of these smart algorithms – has an example of sentiment analysis from the current electoral cycle.

My question is, does this sort of metadata about the content of media, provide any benefit for post production processes; in sorting or organizing footage; or is this something you’d ever want to search for?

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.

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Microsoft claim a milestone with their Cordana speech to text transcription service, hitting an accuracy rate of 93.1% or a failure rate of 5.9%, which is reportedly the same accuracy as you’re paying $1 or $2 a minute for right now.

No human transcriber is completely accurate. There are generally some words that are unclear, or technical terms not known to the human transcriber that need correcting in a transcript.

I’ve also been one of the beta testers on SpeedScriber, which is built around an automatic engine, and have been very impressed with the accuracy, particularly with American accents. Accuracy dropped a bit when it had to deal with my still-mostly-Australian accent.

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