On desktop you should see a little arrow in the upper left or right that lets you do the same, and an x to knock it out altogether.
Purchase price maybe: $16,200 (2005)
Invested at 7% for 10 years: 31867 (2015)
Sale price maybe: $10,000
Net loss: $21,867
Insurance: approx $600/year * 10 = $6,000
Gas: 9,000 miles / 25 mpg: About $1260
Registration: $30 * 10 = $300
Inspection/etc: $60 * 10 = $600
Total: about $32,027
Price per mile: 32027/9000 = $3.55
THREE FIFTY FIVE PER MILE. WHAT WAS I THINKING?
(And I'm still being conservative and not counting inflation/investment gains on the upkeep and other things.)
For comparison, AAA's compositive average cost per mile for a car driven 10,000 miles per year (their lowest category) is $0.783. A freaking SUV is only $1/mile. (Note: This is not to suggest I should have bought an SUV; my WRX was far cheaper than one would have been. Just that I'm an unusually poor choice of person to own a car.)
I'd like to apply for a job as the poster-child for Uber and Zipcar, please. Uber especially. Thank goodness they've come to Pittsburgh.
I also think planning helps to be productive. But sometimes I forget to plan. I have to plan to plan.
For the last few weeks, Googlers have been obsessed with an internal visualization tool that Alexander Mordvintsev in our Zurich office created to help us visually understand some of the things happening inside our deep neural networks for computer vision. The tool essentially starts with an image, runs the model forwards and backwards, and then makes adjustments to the starting image in weird and magnificent ways.
In the same way that when you are staring at clouds, and you can convince yourself that some part of the cloud looks like a head, maybe with some ears, and then your mind starts to reinforce that opinion, by seeing even more parts that fit that story ("wow, now I even see arms and a leg!"), the optimization process works in a similar manner, reinforcing what it thinks it is seeing. Since the model is very deep, we can tap into it at various levels and get all kinds of remarkable effects.
Alexander, , and Mike Tyka wrote up a very nice blog post describing how this works:
There's also a bigger album of more of these pictures linked from the blog post:
I just picked a few of my favorites here.
- Google IncSoftware Engineer, 2011 - present
- Oasys Design Systems Inc.Principal Engineer, 2010 - 2011
- Synopsys Inc.Sr R&D Engineer, 2007 - 2010
- AccentureAnalyst, 1999 - 2000
- Univ. of Illinios at Chicago
- Tsinghua University
- Boom Beach
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