CAT | Interesting Technology
One of the powerful way Artificial Intelligence ‘learns’ is by using neural networks. Neural Networks are trained with a large number of examples where the result is known. The Neural Network adjusts until it gives the same result as the human ‘teacher’.
However, there’s a trap. If that source material contains biases – such as modeling Police ‘stop and frisk’ – then whatever biases are in the learning material will be contained in the subsequent AI modeling. This is the subject of an article in Nature: There is a blind spot in AI research and also the praise of Cathy O’Neil’s book Weapons of Math Destruction that not only brings up that issue, but the problem of “proxies”.
Proxies, in this context, are data sources that are used in AI programs that are not the actual data, but rather something that approximates the data: like using zip code as a proxy for income or ethnicity.
Based on O’Neil’s book, I’d say the authors of the Nature article are too late. There are already institutionalized biases in very commonly used algorithms in finance, housing, policing and criminal policy.
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.”
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: Ruslan Salakhutdinov is Apple’s first Director of AI.
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.
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.
Comments off · Posted by Philip in Interesting Technology
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.
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.
While Augmented Reality and Virtual Reality often get conflated, they are very different beasts.
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.
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.