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Additional details are present in the unpublished rule (document 2019-27649 [1][2]). -- cut -- Geospatial imagery “software” “specially designed” for training a Deep Convolutional Neural Network to automate the analysis of geospatial imagery and point clouds, and having all of the following: 1. Provides a graphical user interface that enables the user to identify objects (e.g., vehicles, houses, etc.) from within geo…
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The "graphical user interface" bit is weird. I suppose you are fine if the software is driven entirely by keyboard shortcuts.
IMO that's an indication that this was made for a specific piece of software and trying to limit the collateral damage.
Additional details are present in the unpublished rule (document 2019-27649 [1][2]). -- cut -- Geospatial imagery “software” “specially designed” for training a Deep Convolutional Neural Network to automate the analysis of geospatial imagery and point clouds, and having all of the following: 1. Provides a graphical user interface that enables the user to identify objects (e.g., vehicles, houses, etc.) from within geo…
Gosh! There are like thousand github repos that do these stuff. This could become a tool to prosecute a lot of unwanted people like DeCSS.
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The main restriction is "“specially designed” for training a Deep Convolutional Neural Network to automate the analysis of geospatial imagery and point clouds".
And having a GUI. It appears you can simply export a binary framework (with or without source) and let the buyer build an interface on top of it. Building an interface is not terribly advanced work and can be done by fresh bootcamp grads even...
Although saying "well technically" as you get dragged off for waterboarding may not make you feel better.
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It is meaningful if the US is ahead of any cooperating bloc of powers in any covered area of image recognition. This is much broader than being ahead of specifically China on the whole. For it to not be true would essentially imply that no new research is happening in the US.
I doubt the US is ahead in this area. China gains heaps upon heaps or practical experience in CV by sheer virtue of the breadth of its surveillance networks. Not to say we aren't doing the same here in the US, but efforts seem to be much more scattered
Or is it that training data provides experience, which improves the algorithms? Or the application of the algorithms?
Earlier quoted context omitted.
The "graphical user interface" bit is weird. I suppose you are fine if the software is driven entirely by keyboard shortcuts.
IMO that's an indication that this was made for a specific piece of software and trying to limit the collateral damage.
This assumes the US is ahead of China in image recognition. Is there any justification for that?
We're in an early and explosive growth stage of AI where well-established statistical knowledge is having an unreasonable effect when combined with computing power. I've yet to see any AI platform that is mindbending vs doing basic math with a multivariate normal distribution. The eyewatering stuff is the number of Hz of computing power being thrown into simulating Go games, etc, etc. Assuming that among 1.4 billion…
None of the convnets are Gaussian. It was one of the big reasons convnets came to be at all, to model highly kurtotic distributions like natural images. Chinese convnet research is absolutely state of the art already, indeed.