In a complaint filed with the inspector general's office, Mr. Grusch alleges that the information in question has been illegally withheld from Congress for decades and he has been subjected to retaliation from some in the intelligence community for his attempt to disclose these truths.
It's probably because they're moving the crafts somewhere else and, now that they've been let out of their lead cage, the technology is automatically trying to gain access to our communication systems.
The article states that it could be a way to use advances in
computer vision to succeed where RNNs have difficulties. (Kinda sceptical about the success of this technique but that's the claim.)
This is a relatively common trick in ML - mapping temporal data to image form so one can use the relatively efficient convolutional operations on it. An example would be mapping audio data via a short-time fast fourier transform (https://en.wikipedia.org/wiki/Short-time_Fourier_transform) and using 2d convolutions on that for processing.
Architectural improvements in 1D convolutions (atrous convolutions) have possibly surpassed these techniques in some areas though, I'm not sure.
Having been privy to the coarse reality of about 20 students who survived 5 years of medical school, followed by FY1, FY2, and junior positions in the NHS, the intellectualized "best effort" mulling-over in the article couldn't be further away from reality.
As a student doctor not quite yet exposed to the true harshness of reality I feel that this article does give some perspective into the issues that I have grappled with so far in my training. The frustrations, the conundrums, the uncertainty, the humanity.
