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0 pointsalbertzeyer3y ago0 comments

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?

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belorn3y 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.

albertzeyerOP3y 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.

belorn3y 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.

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feet3y ago

The implementation of artificial neural networks isn't even close to how a brain works.

albertzeyerOP3y ago

Yes but what difference exactly plays a role for the copyright question? And why?

feet3y 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

j / k navigate · click thread line to collapse

0 pointsalbertzeyer3y ago0 comments

0 comments

belorn3y 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.

albertzeyerOP3y 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.

belorn3y 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.

1 more reply

feet3y ago

The implementation of artificial neural networks isn't even close to how a brain works.

albertzeyerOP3y ago

Yes but what difference exactly plays a role for the copyright question? And why?

feet3y 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

j / k navigate · click thread line to collapse