It is a kind of rolling shutter effect, but you're misunderstanding the finish camera's design - they capture a single column of pixels and the horizontal axis is actually time. This is why the background of photofinishes is a bunch of streaks.
I agree that 1880-1915 was certainly a period of radical change, but aviation in 1915 was way less mature than you are suggesting, and would not become so for several decades.
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.
True, they are not cheap tools, but SawStop makes high quality equipment. Their prices are a bit cheaper than Powermatic [1], and the saws compare favorably in performance-oriented reviews.
SawStop isn't really a homeowner tool, but it is certainly within an accessible price range for enthusiasts, schools, and professionals.
With 35 years experience, I expect that you have a pretty wide professional network - consider whether you're using those connections to get warm introductions and pointed to good-fit jobs to the greatest degree possible.
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.
Interesting, I did not realize square drive was uncommon in the UK. It's a really nice screw head to work with, and as someone mentioned elsewhere in the thread, becoming more common in certain woodworking applications in the US.
I think from your comment you may be from Europe, so in that case for added context, you may be underestimating how uncommon Pozidriv screws and screwdrivers are in the United States. I have never seen a Pozidriv screw on any manufactured product other than Ikea furniture in the US. You cannot buy Pozidriv screws or drivers at major hardware stores (try searching "Pozidriv" on Lowes, Home Depot, or Ace Hardware), with the exception of one or two drivers that are in stock online only.
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.
You're not wrong, but it is more complicated than that. Lidar gives you an array of 3D points and intensity, corresponding to where the lasers bounced back, and how strong the reflection was. Roughly speaking, from there you have to decide 1) which sets of points belong to the same object, 2) what those objects are, and 3) what those objects intend to do in the future.
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.
In the consumer/household space, sure. 3D printing is huge in prototyping and hardware R&D though. High-end printers are even good enough for mass production runs now.
I was a member at TechShop RDU for a couple years. It started as an independent makerspace, got acquired by TechShop in 2011, then closed in 2013. It was a really cool place and I miss it. A lot of interesting characters there.
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.
The point is with a rocket, if you can't land at your original destination, you probably don't have the capability to safely land anywhere else. Maybe I'm overlooking a scenario you have in mind, but IMO you're looking at two classes of problems:
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.
Make a point of frequently going to art museums and sculpture gardens. Read the placards and learn to recognize certain artists, spend time looking at the pieces that grab your attention and think about them. Don't feel like you have to enjoy every piece. This will build your appreciation/understanding for two reasons:
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.
Same boat here, I greatly prefer Square Cash (money goes directly between checking accounts so there is no second step to empty your balance), but the network effects of Venmo are too strong at this point. The all-transactions-private setting is easy to turn on.
Not that anyone believes that this car is really going to production anytime soon, but as an additional datapoint: the car in the first image is sporting $40,000 worth of lidar on the roof (5 x Velodyne VLP-16).
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.
Strange, my experience with Inbox is nowhere near that bad. It is noticeably slower than Gmail classic, but load time is ~2s and actions like search take a fraction of a second. This is all on a 2012 MBP.
Is Illinois really a good choice for contrast with Columbia? Yes it is a state school, so it has less elite-sounding brand than Columbia, but UI actually outranks Columbia in most STEM fields.
See Animats' answer [1] to a similar NN suggestion in this thread.
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.
Can you give an example? I'm not aware of anything in 2016 that would consistently pass this test.