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> I suggest that dataset bias is real but exaggerated by the tank story, giving a misleading indication of risks from deep learning and that it would be better

by lgas 9y ago

> I suggest that dataset bias is real but exaggerated by the tank story, giving a misleading indication of risks from deep learning and that it would be better to not repeat it but focus on established risks like AI systems optimizing for wrong utility functions. He's not arguing against having cautionary tales, he's arguing that we should base them on actual problems instead of imaginary ones.

Rotten194 9y ago
But ensuring correct datasets is a problem that people new to machine learning have to be made aware of. It's easy for people to see that NNs are good with 'noisy' data and incorrectly assume they can throw any data at it and get good results. And I think the quote is a false dilemma -- its not like we can't have multiple different stories for different problems. Make up / find a "truthy" story to spread for misoptimized utility functions if one doesn't exist, but there's no need to kill this useful story in the process.
- goialoq 9y ago
  If a failure mode has never been reported in the wild, why is it so important to tell a juicy story about it, at the expense of attention to empirically observed failure modes?
  - digi_owl 9y ago
    Google's image tech confusing black people and gorillas?
  - aqsalose 9y ago
    Sure, it wouldn't do any harm to move to relevant and well-sourced examples, or construct such demonstrations specifically for the class. This HN discussion alone has gathered several instances of classifiers overfitting or fitting to features that do not generalize outside the training set as well as it first may appear, e.g. the curious case of leopard sofa [1] linked by andreasvc [2] demonstrates more or less same problem as the tank parable (leopard detector performs quite well but in reality is not detecting the exact thing we'd expect). However, also consider that the tank parable has circulated in textbooks and undergraduate introductory lectures for several decades. The main lesson I learned from the story that after training a model, one should validate it to see if it does generalize to detecting tanks both during night and daytime. Is it really a surprise that it might be difficult find egregiously naive mistakes? [1] http://rocknrollnerd.github.io/ml/2015/05/27/leopard-sofa.html http://rocknrollnerd.github.io/ml/2015/05/27/leopard-sofa.ht... [2] https://news.ycombinator.com/item?id=15486441 https://news.ycombinator.com/item?id=15486441

by lgas 9y ago

Rotten194 9y ago

But ensuring correct datasets is a problem that people new to machine learning have to be made aware of. It's easy for people to see that NNs are good with 'noisy' data and incorrectly assume they can throw any data at it and get good results. And I think the quote is a false dilemma -- its not like we can't have multiple different stories for different problems. Make up / find a "truthy" story to spread for misoptimized utility functions if one doesn't exist, but there's no need to kill this useful story in the process.

goialoq 9y ago
If a failure mode has never been reported in the wild, why is it so important to tell a juicy story about it, at the expense of attention to empirically observed failure modes?
- digi_owl 9y ago
  Google's image tech confusing black people and gorillas?
- aqsalose 9y ago
  Sure, it wouldn't do any harm to move to relevant and well-sourced examples, or construct such demonstrations specifically for the class. This HN discussion alone has gathered several instances of classifiers overfitting or fitting to features that do not generalize outside the training set as well as it first may appear, e.g. the curious case of leopard sofa [1] linked by andreasvc [2] demonstrates more or less same problem as the tank parable (leopard detector performs quite well but in reality is not detecting the exact thing we'd expect). However, also consider that the tank parable has circulated in textbooks and undergraduate introductory lectures for several decades. The main lesson I learned from the story that after training a model, one should validate it to see if it does generalize to detecting tanks both during night and daytime. Is it really a surprise that it might be difficult find egregiously naive mistakes? [1] http://rocknrollnerd.github.io/ml/2015/05/27/leopard-sofa.html http://rocknrollnerd.github.io/ml/2015/05/27/leopard-sofa.ht... [2] https://news.ycombinator.com/item?id=15486441 https://news.ycombinator.com/item?id=15486441