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

CAT | Artificial Intelligence

Adobe is heading into AI/Machine Learning full steam ahead. A private room NAB demo showed picture search and dialog search powered by IBM Watson, and now this collaboration with Stanford organizes takes, matches them to lines of dialog, recognizes voices, faces and emotions, camera framing, and more and then builds dialog-focused stories in any style desired.




AI and Creativity

What is creativity? Nick Stockton examined the question in an article at Wired about the use of an Artificial Intelligence (really Machine Learning) based collaborator in creativity.  Beyond discussing the nature of creativity from a couple of perspectives it looks at the role of machines as collaborators.


Design is much more than the way things look. It encompasses every aspect of every interaction with the device, object or software package you’re using. Josh Clark published a talk he gave on behalf of his design studio big medium, that is focused on design in the era of the algorithm, or Artificial Intelligence.

It’s a very long and deep article, well worth the read if you design anything at all. I found it very relevant as we are currently designing our most complex and powerful app. Read on for a list of the topics.


A new Market Watch article spells out the results of a study that pulled data from an employment report from University of Oxford, processed into a visual chart by Visual Capitalist.

Not surprising, but retail service jobs are likely to do poorly, while high-touch occupations like nursing and teaching are likely to remain. Overall – and quite unfortunately for society – most of the job losses will be among relatively unskilled workers.

While there have been huge disruptions to employment in the past, there have been new jobs created, which is hard to foresee from this chart.

Consider the chart a career planning guide! I’m somewhat relieved that “Software Developers and Programmers” are relatively safe, although surprised as code is increasingly written by code! There is a surprising amount of creativity that goes into designing the software, that isn’t directly related to the code, so I guess that’s the safe part?

There isn’t a direct category that I could slot production and post production into, but since creativity in general will be valued, I think that creativity will continue to be valued. On the other hand, relatively routine tasks like organization and preparing for editing is likely to be vulnerable.

For an indication of when the jobs will disappear, consider this article about a paper: “When Will AI Exceed Human Performance? Evidence from AI Experts.” In this study, experts where asked their expectation as to when jobs would be eliminated. Bottom line, they could all be gone in 125 years.

We were watching the WWDC keynote address last night and the term “Machine Learning” came up so ofter, that if you were taking a shot each time, it would have been very detrimental to your health. There were at least 12-15 references during the 2.25 hour keynote.

Apple have seriously embraced Machine Learning/Deep Learning across many apps and have introduced a Machine Learning framework for running developer designed learning algorithms, even providing conversion tools for migrating from other AI platforms.

Or course, this comes as no surprise, as I wrote about the many ways Apple were integrating machine learning back in October 2016.

UPDATE: Wired also noticed just how key Machine Learning has become to Apple.

I’ve written here before, and Terry Curren and I have discussed repeated on The Terence and Philip Show, that many jobs are likely to be replaced by the combination of Artificial Intelligence and Robotics/Automation. It’s good to see people thinking and writing about these things, as does Caitlin Fitzsimmons of the Sydney Morning Herald (Australia), in an article – How to prepare for the jobs of the future when you don’t know what they are – that features an interview with Pulitzer prize-winning author and New York Times columnist, Thomas Friedman, who writes about the age of acceleration in his new book, Thank You For Being Late.

The whole article (and likely the book, which I’m about to buy) are worth the read, but I loved this paragraph from Ms Fitzsimmons:

That’s because the only way to equip children for the future of work is to develop their imagination, creativity and emotional intelligence. If the world is changing, the best thing you can do is equip them for change. They need to be emotionally resilient with a habit of self-directed lifelong learning.

A few years ago, we considered supporting transcripts in Lumberjack System. At the time our goal was to quickly prepare for an edit, and transcriptions took days and cost serious money.

Two years ago we supported the alignment of time-stamped transcripts to Final Cut Pro X Clips and a year ago, introduced “magic” keywords, derived by a cognitive service. Since Lumberjack doesn’t (yet, I might emphasize) support a speech to text service internally, what are the options and what do they tell us about the state of play for transcription in April 2017?




Machine Learning in practical terms

While researching the anecdotal history of some local property, I did what I’ve done previously: ask Siri. In this case, asking about actors dates of birth and death. In the past, these type of questions would have pulled up the relevant IMDB or Wikipedia page with Siri saying “I’ve found some links for you on the web” or similar.

It took several rounds before I realized that, while the pages were still being pulled up as before, Siri was parsing out the answer to the question I’d asked, and gave that to me directly. I never had to glance down or open my phone.

Similarly, in Mail, there is now a predictive mailbox making suggestions (usually accurate) into which email box I might want to move the selected email.

In Calendar, I find addresses being suggested for my events, based on whether I’ve been there or not, address book entries, or other information.

It’s clear to me that these are all improvements related directly the Apple’s increased use of Machine Learning across it’s software products.



A few applications of Machine Learning

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.




In Just 10 Years

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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