You don't necessarily have to train it to see elephants, but Neural Networks fail in pretty interesting ways. This that are very obvious to you and I have the ability to confuse a CNN[1].
These look like a digital Rorschach test. With the minor difference that a human "knows" the image is not really an object and is being asked to interpret in some way; where the machine has no "knowledge" nor "understanding" but has an imperative to match this input to something in its repertoire of knowledge.
As a human, if you asked me to match these synthetic images to a most-likely real-world match, then my responses will also look "confused."
Those aren't too bad (out of context of the Tesla article), I can see how the ai might misinterpret those images. The bagel one, for example, is pretty on point.
Those examples show that ML algorithms "see" things much differently from us. It also shows they're very vulnerable to nonsense data. How can I be reasonably sure that some random play of shadows and lights, that would never fool a human, will cause a failure mode in a neural network in a vision-only self-driving car?
Would these be the same graffiti artists that are currently maliciously painting over road warning and direction signs to fool human drivers? Say, whiting out a 'low bridge ahead' sign, so that trucks and double-decker buses crash into it...? Because, nobody is doing that now, and if they tried it, they would be prosecuted, probably for murder or attempted murder.
I don't understand where some people get the belief that automated vehicles will suddenly become targets of murderous or sociopathic pranksters?
> I don't understand where some people get the belief that automated vehicles will suddenly become targets
There is precedent when it comes to the automation of people's jobs (truckers, cab drivers, courier services) and fanatical condemnation of technological advancement; see everything from the Luddite's infamous wooden shoes to the spiking of trees to stop logging.
In the forests near my hometown, some environmental fanatics will string up wire across motorcycle and ATV tracks, or create blockages and attack riders with crowbars and pipes. I wish I were kidding.
As for current graffiti artists - they're taking down stop signs and road markers, defacing handicapped parking signs with stickers, tagging any and all road signs, and so forth. They're minor nuisances for human drivers, who can usually easily identify compensate for such changes. Computers, even those running neural networks, are not able to compensate so easily.
I guess the CNN was trained only with real images. They must add some [random noise], [crap], [glitch] and [collage] images to the training set. This may reduce the efficiency of the NN in the real world, but it will make more difficult to find funny examples of misclassification.
