Can you give an example? I'm not aware of anything in 2016 that would consistently pass this test.

https://en.wikipedia.org/wiki/Photo_finish#Strip_photography

The SPT Airboat Line [1] was the closest to an airline in the US at the time. It operated 20 mile flights across Tampa bay for 6 months in 1914 before ceasing operations.

Flying from NY to LA was not happening on any regular basis, and took much more than a couple of hours. The first transcontinental flight in 1911 [2] took 80+ flight hours over more than a month. Fast forward to 1933, transcontinental passenger flights took 20+ hours [3]. Nonstops finally became available in the late 40s and early 50s.

[1] https://en.wikipedia.org/wiki/St._Petersburg%E2%80%93Tampa_A... [2] https://pioneersofflight.si.edu/content/first-american-trans... [3] http://www.boeing.com/news/frontiers/archive/2003/may/i_hist...

SawStop isn't really a homeowner tool, but it is certainly within an accessible price range for enthusiasts, schools, and professionals.

[1] https://www.toolnut.com/power-tools/saws/table-saws.html?man...

My apologies if this is stating the obvious, but if your applications are starting with just a resume, you're already at a disadvantage at any age. And I suppose that effect only increases with years of experience and seniority of positions applied to.

So consider that this is an extremely rare fastener format in the US market, and which looks almost identical to Phillips (the most common US screwdriver). I think many people like myself who are interested in tools and hardware are aware of Pozidriv and can identify it easily, but I can completely see why the average US consumer mistakes it for Phillips.

So yes, a lidar-equipped AV completely not sensing a pedestrian would be surprising, but you can see how it might have incorrectly classified the pedestrian, or misunderstood the pedestrian's intent.

As others have said in this thread, I think TS had a tough value proposition to build a business model around. Classes were a pain and a fairly big start-up cost for new members, but inevitable to satisfy insurance requirements. The monthly membership fee was fairly cheap considering the awesome equipment they had, but expensive if you could only manage to go a few days a month. Plus whatever storage fees for larger projects and/or the hassle of carting your stuff around.

On top of that, management at both locations I used (RDU and Arlington) seemed a bit chaotic. Sad to see them go out of business either way.

1) Problem during launch. There's been one successful use of a launch escape system in rocketry history[1], and such a system has never been developed for a shuttle type craft.

2) Problem after entering orbit. Cutting your orbital flight short isn't going to change the fact that you still have to survive re-entry stresses and have a working vertical landing system.

[1](https://en.wikipedia.org/wiki/Launch_escape_system#Usage)

1) Like all things, art depends on context. A piece is often in conversation with other works by the same artist, contemporaries, predecessors, or historical events.

2) Sculpture in particular depends on scale, perspective, and physical detail. You'll never get to appreciate these qualities through a photograph - you have to be there in person and walk around the sculpture. Look at it from different distances, different angles.

I'm sure GM is getting a healthy discount off retail from Velodyne, but production-ready lidar is still eye-wateringly expensive by OEM standards. Some solid-state lidar units (Quanergy) out there in the hundreds of dollars (and one from Velodyne in the works for 2018 release), but the fact that Cruise is still using spinning Velodyne units suggests to me that solid-state lidar needs a few more generations to come up to par.

This is the kicker. Lidar allows companies to make up for a lot of the current shortcomings of state-of-the-art computer vision.

As to how easily Uber could advance state-of-the-art computer vision, CV is an academic field with decades of research efforts built up already. To be fair, CNNs are still young-ish in terms of producing results, but Google, FB, etc all have better teams than Uber in this area. The fact that every major self-driving car project except Tesla is using lidar says a lot about how insiders see the first-to-market strategy playing out. Relying on pure CV at this point is kneecapping yourself.

There are some research groups trying robotic control with black box neural networks (pixels in, control commands out), afaik with limited success so far, particularly with this many degrees of freedom.

Deep learning has yielded some really incredible results the last 5+ years, but those results have been concentrated in a few areas (computer vision, speech recognition, game decision, ...). So there is a tendency to think NNs can do anything, but really a) setting up a NN to work with a problem is non-trivial (architecture design, data collection, data labeling, feature selection) and b) very much an open research topic for many problems, ie not guaranteed to work at all.

[1]: https://news.ycombinator.com/item?id=13121158