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> It is also the reason why they cannot be trusted in the most serious of applications which such decision making requires lots of transparency rather than a mo

> It is also the reason why they cannot be trusted in the most serious of applications which such decision making requires lots of transparency rather than a model regurgitating nonsense confidently. Doesn't this criticism also apply to people to some extent? We don't know what the purpose of individual brain neurons is.

TaylorAlexander 4y ago
People are better understood intuitively. We understand how people fail and why. We can build trust with people with some degree of success. But machine models are new and can fail in unpredictable ways. They also get deployed to billions of users in a way that humans do not, and deployed in applications that humans do not. So its certainly useful to try to explain neural networks in as great of detail as we can.
- istjohn 4y ago
  Or we can build trust using black box methods like we do with humans, e.g., extrapolating from past behavior, administering tests, and the like.
  - TaylorAlexander 4y ago
    We can, but the nice thing about neural networks is the ability to do all kinds of computational and mathematical manipulations to them to basically pick them apart and really find out what’s going on. This is important not just for safe deployment but also for research on new methods that could be used to make them better. Plus we need this ability to help avoid neural networks with intentionally hidden features that appear to behave linearly in certain regimes but are designed with a strong nonlinear response when special inputs are applied. You could have all the tests you want for a self driving car based on real world conditions but some bad actor with access to the training system could create a special input that results in dangerous behavior.
    - int_19h 4y ago
      The more fundamental problem is the sheer size of them, and this is only going to get worse as models grow larger to become more capable. Being able to look at the state of individual neurons during inference is very convenient, but that does not by itself make it possible to really find out what's going on.
theodorejb 4y ago
As a person I can at least tell you what I do and don't understand about something, ask questions to improve/correct my understanding, and truthfully explain my perspective and reasoning. The machine model is not only a black box, but one incapable of understanding anything about its input, "thought process", or output. It will blindly spit out a response based on its training data and weights, without knowing the difference whether it true or false, meaningful or complete gibberish.
- int_19h 4y ago
  As a person, you can tell what you think you do and don't understand, and you can explain what you think your reasoning is. In practice, people get both wrong all the time. People aren't always truthful about it, either, and there's no reliable way to tell if they are.

TaylorAlexander 4y ago

People are better understood intuitively. We understand how people fail and why. We can build trust with people with some degree of success. But machine models are new and can fail in unpredictable ways. They also get deployed to billions of users in a way that humans do not, and deployed in applications that humans do not. So its certainly useful to try to explain neural networks in as great of detail as we can.

istjohn 4y ago
Or we can build trust using black box methods like we do with humans, e.g., extrapolating from past behavior, administering tests, and the like.
- TaylorAlexander 4y ago
  We can, but the nice thing about neural networks is the ability to do all kinds of computational and mathematical manipulations to them to basically pick them apart and really find out what’s going on. This is important not just for safe deployment but also for research on new methods that could be used to make them better. Plus we need this ability to help avoid neural networks with intentionally hidden features that appear to behave linearly in certain regimes but are designed with a strong nonlinear response when special inputs are applied. You could have all the tests you want for a self driving car based on real world conditions but some bad actor with access to the training system could create a special input that results in dangerous behavior.
  - int_19h 4y ago
    The more fundamental problem is the sheer size of them, and this is only going to get worse as models grow larger to become more capable. Being able to look at the state of individual neurons during inference is very convenient, but that does not by itself make it possible to really find out what's going on.

theodorejb 4y ago

As a person I can at least tell you what I do and don't understand about something, ask questions to improve/correct my understanding, and truthfully explain my perspective and reasoning. The machine model is not only a black box, but one incapable of understanding anything about its input, "thought process", or output. It will blindly spit out a response based on its training data and weights, without knowing the difference whether it true or false, meaningful or complete gibberish.

int_19h 4y ago
As a person, you can tell what you think you do and don't understand, and you can explain what you think your reasoning is. In practice, people get both wrong all the time. People aren't always truthful about it, either, and there's no reliable way to tell if they are.