advocates of #machinelearning, I am told that you all know that (current) #ML is limited. fair enough. but which limits are you willing to *publicly* acknowledge?https://twitter.com/NotSimplicio/status/1173373706674085888 …
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Replying to @GaryMarcus
Different ML researchers have different degrees of optimism, but the position that current methods will carry us all the way to general AI seems to be rare. And in public, AI skepticism has always been a much more popular position to take.
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Replying to @Plinz @GaryMarcus
Most AI practitioners don't look for general AI, and don't even have public opinions on it. ML is all about experimental statistics, and if you say that out loud, you will probably not meet with much resistance.
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Replying to @Plinz
and i still await examples from ML practitioners about the limits they are willing to *publicly* acknowledge.
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Replying to @GaryMarcus
I don't think that folks have problems to rattle off lists of things that are notoriously difficult, unsolved and under researched. OTOH, there are no hard proofs of insufficiency of current approaches. You'll need to move past hand waving.
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Replying to @Plinz @GaryMarcus
Most serious researchers will refrain from both stating unproven limits, and from promising success using the current set of methods.
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serious researchers should acknowledge potential limits at the end of every paper; they also shouldn’t invite the inference that if they have solve one hard problem, they have solved them all. but examine closely the ML that gets cited the most: https://arxiv.org/abs/1801.05667
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