Glencore sank a shaft 8,530 feet under Ontario from the bottom of a mine it closed in 2009, and put five lithium-ion locomotives and 23 battery machines down there so it would not have to pump cooled air a mile and a half to keep the crews working //

So why do batteries win underground while they’re still fighting for every point of market share up here? Run a diesel engine in a closed parking garage and you’ll have your answer inside a minute. A mine is that garage stretched over miles of tunnel, with warm rock on every side. Every diesel machine down there’s a heater with an exhaust pipe, and the mine has to push enough fresh air past it to keep people alive and cool enough to work. //

Push air down a shaft and it compresses, and compressed air heats up, at roughly 29 degrees Fahrenheit for every mile of depth (10 degrees Celsius per kilometer) in engineering studies of deep mines in Northern Ontario. At Onaping’s depth, the intake air shows up dozens of degrees warmer than it left the surface, before the rock or a single machine adds anything.

Obviously, I modified the order entry and wholesaler systems to work with each other.

Luckily my 50 line receiver only had to deal with 1200 baud, but it did have to transparently deal with 7-bit even parity, 7-bit odd parity and 8-bit no parity. //

baud rate was simple: 115200, 9600 or occasionally 1200

115200? Hark at Mr Fancypants and his expensive toys.

occasionally 1200

Occasionally? You mean you let your expensive modem just sit there unused? Wasting all that time getting the occasional table close enough to the 'phone inlet so the two could connect!

Next you'll be telling me everything ran at 8N1! //

Back when I was a lad, we fought in the gutter to get 110 baud connections. //

Luxury! We didn't have baud rate, we had pigeon rate and we was happy to have it. //

You had pigeons?

We just had stones to throw. Made for pretty short range transmissions.

A human deputy sitting beside the road in 1987 might never have noticed. He probably wouldn’t have cared unless there was some reason to stop the vehicle in the first place.

The computer doesn’t get bored.

It doesn’t look away.

It doesn’t forget.

And increasingly, that is the privacy question Americans need to be discussing.

This isn’t really about whether Dad should have gone to the Secretary of State and spent the money to register the replacement trailer correctly. He should have.

The interesting question is whether every minor violation that was once practically invisible should become permanently detectable simply because technology has made detection cheap.

There is a profound difference between a police officer seeing something suspicious and investigating it and building a technological environment in which everyone’s ordinary movements can be recorded, searched and reconstructed later.

For most of American history, privacy wasn’t created entirely by law.

Some of it came from friction.

Following someone required manpower. Checking plates required effort. Remembering where somebody’s pickup was Tuesday afternoon required an actual human being who saw it Tuesday afternoon.

Government theoretically possessed considerable investigative authority, but exercising that authority cost something.

Technology is removing that friction.

A camera doesn’t need overtime.

A database doesn’t need coffee.

An algorithm doesn’t say, “I’m not spending twenty minutes investigating whether Earl’s 1993 Harbor Freight trailer has the correct plate.”

It simply remembers.

And that creates a strange new world where activities that were always technically discoverable can potentially become automatically discoverable.

Those aren’t necessarily the same thing.

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Claims follow scrutiny over monitors installing adware without user consent

A cybersecurity expert has demonstrated how computer-generated patterns can successfully prevent surveillance cameras from detecting vehicles - such as the controversial AI-powered Flock licence plate readers that are becoming increasingly common on American streets.

Bill Swearingen, founder of SIXCYBER, has spent the past year running an impressive 31 million tests developing what he calls noRecognition. noRecognition is a reinforcement learning model that generates patterns capable of defeating the algorithms built into surveillance cameras to detect objects.

The patterns do not prevent cameras from actually recording footage, so a human watching the video would still see a car. But what fails is the software use to identify objects, logs vehicle licence plates, and triggers alerts.