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Just because machine learning uses the word “learning” doesn’t mean it “learns” in the same way a human mind does — that analogy is doing a lot of load bearing

Just because machine learning uses the word “learning” doesn’t mean it “learns” in the same way a human mind does — that analogy is doing a lot of load bearing in your argument, and needs proving why the program’s nature of creative remixing (for lack of a better word) is the same as a human’s. Right now it seems like you’re just reusing the same word for two phenomena we don’t understand, and therefore claiming they’re equivalent. See Marvin Minsky’s comment regarding “suitcase words”.

albertzeyer 4y ago
But effectively learning here really means the same thing: Based on the input (source code), you will adapt the synaptic weights between neurons, in a similar way for humans and for the artificial neural networks. Of course, it's not exactly the same. There are some differences in the details, and the artificial neural network is really much more simplified, and thus also less efficient at learning. But why is this relevant for the copyright question?
- belorn 4y ago
  I can easily write an simplistic suggestion algorithm by doing the following: Hash the content. If the content has been viewed by the user in the past, halve the numerical hash value. Then sort the list. Doing this to a music list will create a list that is biased toward music the user has listened to before but will still seem random enough to look like intelligent suggestion that the system has learned to identify. It is just math that mimics learning.
  - albertzeyer 4y ago
    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?
- feet 4y ago
  The implementation of artificial neural networks isn't even close to how a brain works.
  - albertzeyer 4y ago
    Yes but what difference exactly plays a role for the copyright question? And why?
    - feet 4y ago
      Different networks with different dynamics for different processing and control mechanisms? Feedback that gives us self awareness? That's not even scratching the surface of differences
Andrew_nenakhov 4y ago
> and needs proving why the program’s nature of creative remixing (for lack of a better word If I ask Stable Diffusion to create a picture of Elon Musk wielding lightnings and riding a giant blue sparrow over a desert during a storm, the result would be more creative than what could be produced by most humans. I believe that counts as a proof.
- BeefWellington 4y ago
  This is necessarily not the case. The ML model has only been trained on things produced by humans, therefore its derived works. We have no idea what an ML model that hadn't been trained by looking at human art would actually produce, and whether or not it is creative. Also it's pretty clear you haven't worked with many artists from that statement.
  - Andrew_nenakhov 4y ago
    > The ML model has only been trained on things produced by humans, therefore its derived works. Humans are also trained only on things produced by humans. The only exception is nature, but ML model can be trained on photos of nature, too. Also, you are missing the point. > Also it's pretty clear you haven't worked with many artists from that statement. 1. I employed quite a few artists over the past 15 years. 2. I wasn't talking about artists. I was talking about regular humans. The vast majority of them are absolutely unable to create anything resembling Elon Musk riding on a giant sparrow.

albertzeyer 4y ago

But effectively learning here really means the same thing: Based on the input (source code), you will adapt the synaptic weights between neurons, in a similar way for humans and for the artificial neural networks. Of course, it's not exactly the same. There are some differences in the details, and the artificial neural network is really much more simplified, and thus also less efficient at learning. But why is this relevant for the copyright question?

belorn 4y ago
I can easily write an simplistic suggestion algorithm by doing the following: Hash the content. If the content has been viewed by the user in the past, halve the numerical hash value. Then sort the list. Doing this to a music list will create a list that is biased toward music the user has listened to before but will still seem random enough to look like intelligent suggestion that the system has learned to identify. It is just math that mimics learning.
- albertzeyer 4y ago
  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?
feet 4y ago
The implementation of artificial neural networks isn't even close to how a brain works.
- albertzeyer 4y ago
  Yes but what difference exactly plays a role for the copyright question? And why?
  - feet 4y ago
    Different networks with different dynamics for different processing and control mechanisms? Feedback that gives us self awareness? That's not even scratching the surface of differences

Andrew_nenakhov 4y ago

> and needs proving why the program’s nature of creative remixing (for lack of a better word If I ask Stable Diffusion to create a picture of Elon Musk wielding lightnings and riding a giant blue sparrow over a desert during a storm, the result would be more creative than what could be produced by most humans. I believe that counts as a proof.

BeefWellington 4y ago
This is necessarily not the case. The ML model has only been trained on things produced by humans, therefore its derived works. We have no idea what an ML model that hadn't been trained by looking at human art would actually produce, and whether or not it is creative. Also it's pretty clear you haven't worked with many artists from that statement.
- Andrew_nenakhov 4y ago
  > The ML model has only been trained on things produced by humans, therefore its derived works. Humans are also trained only on things produced by humans. The only exception is nature, but ML model can be trained on photos of nature, too. Also, you are missing the point. > Also it's pretty clear you haven't worked with many artists from that statement. 1. I employed quite a few artists over the past 15 years. 2. I wasn't talking about artists. I was talking about regular humans. The vast majority of them are absolutely unable to create anything resembling Elon Musk riding on a giant sparrow.