Using Technology to Solve Problems
The best apps come from a need: either personal or an obvious industry need.
The Advantage of Web APIs
APIs for Web Services improve over time, and everyone who uses them gets the improvement for free!
Maybe 10 Years is Enough for Final Cut Pro X
Our industry is evolving ever faster. Why shouldn’t our tools? Maybe apps should have a best before date.
Looking back on 2017 on the Digital Production BuZZ
I was honored to be invited – as one of many – to provide my thoughts on 2017: what technologies were important, what major changes happened.
Here is a link to the full show -Â Â
Here is a link to the Transcript -Â Â
Or if you want to go direct to my segment:
MP3:Â
2017 – 2018 Introspection
What technologies took my interest in 2017, and what will happen to them in 2018.
Lumberjack, IBC and FCPX World
Lumberjack System will be previewing something extremely exciting at the FCP X World Event at IBC.
NAB 1998 in Retrospect
NAB 1998 was my first. This week I rediscovered my ‘review’. It’s interesting to see what’s changed, and what has not.
Transcription Services: State of Play
Transcription is transitioning from expensive luxury to commodity service. Where are we at right now?
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.
The Danger of AI Modeling
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.