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How is this relevant to what I wrote? You are not learning with a neural network in your example. You are not adapting synaptic weights.

belorn 4y ago
{a_{j}(t+1)=f(a_{j}(t),p_{j}(t),\theta _{j}),} The mathematics of artificial neural networks is math. It is only math. One can make it very complex or very simple but in the end it is just math and pointers.
- albertzeyer 4y ago
  I'm not sure I understand your argument. With math, you can just describe everything, including the human brain. My original argument was specifically about neural networks, that I don't really see the principal difference in how a human learns from reading code, and how Copilot has learned from reading code.
  - belorn 4y ago
    The science to understand and map the human brain has come quite far, but we have yet to make a functional mathematical model where one can input a bunch of number and then compute what a human person is thinking. People have theorized and written novel about the idea of uploading the human mind into the computer for several decades now, but we are unlikely to even reached 1% of the road to get there. Among technology philosophers it is still highly debated if we will ever get there. Right now it is impossible to accurate describe the human brain in the form of math. What we can do is write simplified models that either describe or mimics behavior for which, if we apply abstractions, we can call predictive models. Their effectiveness are quite poor but that has never stopped people from trying to use poor predictions models to predict the future.
    - albertzeyer 4y ago
      Yes but how is that relevant to my arguments? My statement on human brain was: In principle, you can describe it with math. This doesn't mean that we know how to do that yet. My statement on Copilot was: Comparing learning of the human brain to learning of the artificial neural network, both are still very similar, much more similar to other (machine or other) learning methods. Sure, there are differences. But my point is: Those differences, why are they relevant for the copyright question?
      - throwaway290 4y ago
        Since you equate human mind with a neural network (questionable to me but OK), let's swap this around and call them both minds and see how it works out: The mind that can acknowledge and appreciate your work in this scenario (Copilot) does literally nothing of its own free will except 1) take your code and 2) give it to me, possibly combined with someone else's code. This is the sole purpose of its entire existence and full range of its capabilities. Is this enough of a difference compared to a human mind when copyright is concerned? It spares me from knowing that you exists, that you wrote a library that does this thing I need, that I can contribute to it, etc. In such a scenario, what is the motivation for you to make your library publicly available in the first place (other than generate revenue for Microsoft or whoever I pay for access to the network)? Does copyright have relevance to OSS now?

belorn 4y ago

{a_{j}(t+1)=f(a_{j}(t),p_{j}(t),\theta _{j}),} The mathematics of artificial neural networks is math. It is only math. One can make it very complex or very simple but in the end it is just math and pointers.

albertzeyer 4y ago
I'm not sure I understand your argument. With math, you can just describe everything, including the human brain. My original argument was specifically about neural networks, that I don't really see the principal difference in how a human learns from reading code, and how Copilot has learned from reading code.
- belorn 4y ago
  The science to understand and map the human brain has come quite far, but we have yet to make a functional mathematical model where one can input a bunch of number and then compute what a human person is thinking. People have theorized and written novel about the idea of uploading the human mind into the computer for several decades now, but we are unlikely to even reached 1% of the road to get there. Among technology philosophers it is still highly debated if we will ever get there. Right now it is impossible to accurate describe the human brain in the form of math. What we can do is write simplified models that either describe or mimics behavior for which, if we apply abstractions, we can call predictive models. Their effectiveness are quite poor but that has never stopped people from trying to use poor predictions models to predict the future.
  - albertzeyer 4y ago
    Yes but how is that relevant to my arguments? My statement on human brain was: In principle, you can describe it with math. This doesn't mean that we know how to do that yet. My statement on Copilot was: Comparing learning of the human brain to learning of the artificial neural network, both are still very similar, much more similar to other (machine or other) learning methods. Sure, there are differences. But my point is: Those differences, why are they relevant for the copyright question?
    - throwaway290 4y ago
      Since you equate human mind with a neural network (questionable to me but OK), let's swap this around and call them both minds and see how it works out: The mind that can acknowledge and appreciate your work in this scenario (Copilot) does literally nothing of its own free will except 1) take your code and 2) give it to me, possibly combined with someone else's code. This is the sole purpose of its entire existence and full range of its capabilities. Is this enough of a difference compared to a human mind when copyright is concerned? It spares me from knowing that you exists, that you wrote a library that does this thing I need, that I can contribute to it, etc. In such a scenario, what is the motivation for you to make your library publicly available in the first place (other than generate revenue for Microsoft or whoever I pay for access to the network)? Does copyright have relevance to OSS now?