Deep learning is having a rough year. Here’s the Fail of the Week. Can you see a person in this image? A TV? Neither can I. From https://arxiv.org/abs/1809.05966 .pic.twitter.com/iwywaTjOpq
CEO/Founder of http://Robust.AI ; cognitive scientist, and best-selling author. New book: http://Rebooting.AI : Building Artificial Intelligence We Can Trust
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Deep learning is having a rough year. Here’s the Fail of the Week. Can you see a person in this image? A TV? Neither can I. From https://arxiv.org/abs/1809.05966 .pic.twitter.com/iwywaTjOpq
as much as I generally agree w/you, if you look closely you can see what might be a person reflection where it says person (but it’s vague) & TV isn’t a horrible fail… there’s enough DLs to critique with really bad errors- maybe go easy on the “silly, but plausible"
with "superhuman" abilities come superhuman mistakes?
It is possible that this is not a mistake. Suppose there is a sculpture to the right of the image frame that is being reflected. This could give us this image. [A bit implausible, I admit, because the sculpture would need to be upside down.]
That’s the kind of reasoning deep learning can’t do, but AI needs to do
This is a very good example showing the possible errors in a ML system based on function approximation only. The common sense logic based AI needs to be integrated with such ML systems in high risk scenarios to make the precision near perfect.
The technical term for this area is “common sense reasoning” which was an area of interest for many prominent AI pioneers around 60s 70s. There are still undergoing efforts in constructing formal mathematics for such reasonings such as the one that uses nonmonotonic logic etc.
review ernie davis and i wrote in CACM (paywalled, sorry): https://m-cacm.acm.org/magazines/2015/9/191169-commonsense-reasoning-and-commonsense-knowledge-in-artificial-intelligence/abstract …
Read the abstract. Very nice explanation about the problem.
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