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I don't understand how this could have been unknown - surely every paper that presents an image recognition system has a small section looking at the cases the

I don't understand how this could have been unknown - surely every paper that presents an image recognition system has a small section looking at the cases the system got wrong? Edit: I said "surely", but, no: GoogleNet's paper has no such section: https://static.googleusercontent.com/media/research.google.com/en//pubs/archive/43022.pdf https://static.googleusercontent.com/media/research.google.c...

mjburgess 6y ago
It's hard to tell whether experimenters are delusional or dishonest; I think it's a mixture in practice. Researchers have no incentive to refute their own success; and neither do their peers who are producing similar "research" themselves. The whole game is to state enough metaphorical propositions until they seem concrete. "New AI interprets conversations and writes stories!" they say... Their evidence? Run it enough times until a subsample appears to match our expectations, and show that subsample. What about all the other counter-examples? That's compared with "errors humans make too!!!!" When, of course, if you look at those counter-examples they completely destroy the interpretation of the system as understanding anything. It is ruled out by the data analysis procedure: measurement data is fed to algorithms which assume it is sampled randomly. The whole purpose of "intelligence" (, understanding, etc.) is to uncover the non-random underlying model which explains the data distribution. Thus statistical AI simply cannot do what it is claimed of it. All we are left to do, for marketing and financial reasons, is play a game of metaphors and superstition. Personally I find it infuriating. It's profoundly religious/superstitious and a debasement to scientific practice: of course, computer scientists aren't scientists; and here is where the problem arises. They have no empirical sense of what a model of the world actually is; nor of what a "mere statistics of measurement can do".
- hntrader 6y ago
  Delusional - absolutely. Such a large proportion fall into the trap of iteratively overfitting to the test set without much of a care for the basics such as data quality, what question to ask, etc. It's a problem.
- tasogare 6y ago
  Marketable names (buzzwords) are very powerful. If we’d replace AI by more realistic terms such as "curve fitting", "hyperparameters tuning" or "decision tree building" the limitations would be very clear from the state, and the hype could be kept in check. A supervisor of mine tried once to make me switch to more AI-based thing by telling me my research field was niche, and asking if "AI in education" wasn’t something bigger and more interesting (I replied no). In retrospect I should have asked him the question back by rephrasing it like proposed above.
  - BlueTemplar 6y ago
    I believe the term is "computer". Especially these days, when human computers have basically disappeared as a job.

mjburgess 6y ago

It's hard to tell whether experimenters are delusional or dishonest; I think it's a mixture in practice. Researchers have no incentive to refute their own success; and neither do their peers who are producing similar "research" themselves. The whole game is to state enough metaphorical propositions until they seem concrete. "New AI interprets conversations and writes stories!" they say... Their evidence? Run it enough times until a subsample appears to match our expectations, and show that subsample. What about all the other counter-examples? That's compared with "errors humans make too!!!!" When, of course, if you look at those counter-examples they completely destroy the interpretation of the system as understanding anything. It is ruled out by the data analysis procedure: measurement data is fed to algorithms which assume it is sampled randomly. The whole purpose of "intelligence" (, understanding, etc.) is to uncover the non-random underlying model which explains the data distribution. Thus statistical AI simply cannot do what it is claimed of it. All we are left to do, for marketing and financial reasons, is play a game of metaphors and superstition. Personally I find it infuriating. It's profoundly religious/superstitious and a debasement to scientific practice: of course, computer scientists aren't scientists; and here is where the problem arises. They have no empirical sense of what a model of the world actually is; nor of what a "mere statistics of measurement can do".

hntrader 6y ago
Delusional - absolutely. Such a large proportion fall into the trap of iteratively overfitting to the test set without much of a care for the basics such as data quality, what question to ask, etc. It's a problem.
tasogare 6y ago
Marketable names (buzzwords) are very powerful. If we’d replace AI by more realistic terms such as "curve fitting", "hyperparameters tuning" or "decision tree building" the limitations would be very clear from the state, and the hype could be kept in check. A supervisor of mine tried once to make me switch to more AI-based thing by telling me my research field was niche, and asking if "AI in education" wasn’t something bigger and more interesting (I replied no). In retrospect I should have asked him the question back by rephrasing it like proposed above.
- BlueTemplar 6y ago
  I believe the term is "computer". Especially these days, when human computers have basically disappeared as a job.