Supporters have expressed
enthusiasm for the use of deep learning networks to do many individual tasks,
and even jobs, traditionally performed by people. However, results of the five
experiments in this study showed that it's easy to fool the networks, and the
networks' method of identifying objects using computer vision differs
substantially from human vision.
The goal of the experiments was
not to trick the networks, but to learn whether they identify objects in a
similar way to humans, or in a different manner. Humans see the entire object,
while the artificial intelligence networks identify fragments of the object. There
are dozens of deep learning machines, and the researchers think their findings
apply broadly to these devices.
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