In just two years, the Artificial Intelligence technologies available through IBM Watson has exploded. Artificial Intelligence is a rapidly growing field.
The New York Times has an interesting article that provides a background to the business behind Watson.
Also new for today is an article at the Mac Observer about Apple’s AI intentions.
In this episode we discuss the future of Avid and how AI will affect post production. Only one subject has a positive looking future!
Indirectly – by forming the Partnership on AI – Facebook, Amazon, Google, IBM and Microsoft to promote “best practices” in Artificial Intelligence. I just hope that includes not wanting to wipe out humans!
The full name of the new partnership is: The Partnership on Artificial Intelligence to Benefit People and Society. That’s a worthy goal. Here’s hoping they go a measure toward achieving it.
It seems the smartest way to make money right now is to have a startup in speech recognition, machine learning, neural networks or other Artificial Intelligence related startup.
TechCrunch reported late last week the Apple had acquired another machine learning company:
After buying Perceptio at the end of 2015 and Turi just a few months ago, Apple has now acquired an India/US-based machine learning team, Tuplejump.
Presumably to beef up its efforts in AI and machine learning across the company.
Not to be left behind, Google:
…said that it’s acquired API.ai, a startup with tools for speech recognition and natural language understanding….
In addition to its developers tools, Api.ai offers a conversational assistant app with more than 20 million users.
I would expect the purchase is to beef up their speech recognition in its AI assistant Google Now.
Google have open sourced it’s Show and Tell model for automatically captioning images. This is an excellent example of how neural networks work: train the model with examples – in this case human captioned images – and then let it loose on new images. From the Venture Beat article:
Google trains Show and Tell by letting it take a look at images and captions that people wrote for those images. Sometimes, if the model thinks it sees something going on in a new image that’s exactly like a previous image it has seen, it falls back on the caption for the caption for that previous image. But at other times, Show and Tell is able to come up with original captions. “Moreover,†Shallue wrote, “it learns how to express that knowledge in natural-sounding English phrases despite receiving no additional language training other than reading the human captions.â€
As the article points out, there are many more players looking to do the same thing. Imagine how much easier life would be in editorial if all the B-roll came in organized like this.
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.â€
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.
UPDATE:Â Ruslan Salakhutdinov is Apple’s first Director of AI.
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)Â First Cuts app. That work will resurface at some time. It’s too valuable not to.
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.
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.