[HN Gopher] The Neural Net Tank Urban Legend
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The Neural Net Tank Urban Legend
Author : belter
Score : 20 points
Date : 2023-06-21 10:51 UTC (12 hours ago)
(HTM) web link (gwern.net)
(TXT) w3m dump (gwern.net)
| morkalork wrote:
| I vividly remember hearing that story from a friend when I was in
| highschool around the mid 2000s.
| [deleted]
| riskneutral wrote:
| Oh wow, I read this story as evidence that neural nets are not
| the future back when I was an undergrad studying machine
| learning. Maybe I should have followed my instincts back then
| because even then neural nets seemed intuitively very interesting
| even if the statistics and math professors hated them because
| they weren't derived from any first principals.
| teraflop wrote:
| A fictional variant of this story also shows up in Peter Watts'
| novel _Starfish_.
|
| In that version, the neural net is trained to manage the
| ventilation system of an underground train station, based on data
| about passenger movements. Unfortunately, it ends up simply
| paying attention to the display of a clock that happens to be
| visible through one of its cameras. And when the clock breaks, a
| bunch of people asphyxiate. A bit implausible, but memorable.
| jumploops wrote:
| My friend once asked me to help him with an autonomous RC car
| race, where we'd go to a warehouse and train the car around the
| track.
|
| After a few weekends of iterating and improving the
| model/training set, we were convinced we'd win the next race...
| only to lose almost immediately by crossing the lines on the
| track.
|
| We did some inverse model explanation work which quickly showed
| that our car was paying attention to the overhead skylights more
| than the actual tracks. Unlike the other weekends, it was a foggy
| day!
|
| A quick hack to cut out the top 50% of each training image
| brought our car back to its prior reliability.
| api wrote:
| While this may be an urban legend, I have definitely played with
| ML and seen things overfit in comical ways.
| morkalork wrote:
| It reminds me of a similar story about using deep learning on
| medical imagery for diagnosis. In one study, it supposedly
| learned the to tell which device was used to take the images.
| Apparently that was somewhat correlated with the results as
| more serious / heavily suspected cases were sent to a specific
| hospital in the region.
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