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As I wrote, 'function in the laymen sense of the word'. And no one actually reading this can, without malice, misunderstand this: the author uses a nice and smo

As I wrote, 'function in the laymen sense of the word'. And no one actually reading this can, without malice, misunderstand this: the author uses a nice and smooth y=f(x) as an example, and shows that it can be approximated with sigmoids. Nothing more, nothing less. And he does a good job showing this.

GTP 5y ago
You say no one can misunderstand what the author wrote without malice, but there is a saying that reads "you should not attribute to malice what can be attributed to ignorance". Especially among people outside of CS, there is currently a lot of hype regarding ML and AI in general, if one of those people looks around to check if a neural network can really do anything and founds this, what would they understand?
- nautilius 5y ago
  You're telling me you were confused by what the author could possible mean with his nice and smooth y=f(x) and that you misunderstood him that really he was implying that GAI is just around the corner? The whole discussion here is one long list of "Gotcha!", missing the point and the context of the article completely. You think these non-CS people you evoke will be too dense to understand that the author is just approximating a simple function, and then will leverage their non-CS background and immediately conclude that ANN can 'solve' Cantor-dust, non-computability and the halting problem? That's a very specific set of background your non-CS people will have to have to fall for that. [Edit:] What do you think they'll make of "Multilayer feedforward networks are universal approximators"? ("This paper rigorously establishes that standard multilayer feedforward networks with as few as one hidden layer using arbitrary squashing functions are capable of approximating any Borel measurable function from one finite dimensional space to another to any desired degree of accuracy, provided sufficiently many hidden units are available. In this sense, multilayer feedforward networks are a class of universal approximators.")
  - GTP 5y ago
    The people I'm referring to just don't know that there exist non-computabke function. So they wouln't draw specific conclusion about problems like the halting problem and silmilar because they don't know them, they would just conclude that any problem they might face can be solved by a NN. That's all.
    - deleted 5y ago
      [deleted]

GTP 5y ago

You say no one can misunderstand what the author wrote without malice, but there is a saying that reads "you should not attribute to malice what can be attributed to ignorance". Especially among people outside of CS, there is currently a lot of hype regarding ML and AI in general, if one of those people looks around to check if a neural network can really do anything and founds this, what would they understand?

nautilius 5y ago
You're telling me you were confused by what the author could possible mean with his nice and smooth y=f(x) and that you misunderstood him that really he was implying that GAI is just around the corner? The whole discussion here is one long list of "Gotcha!", missing the point and the context of the article completely. You think these non-CS people you evoke will be too dense to understand that the author is just approximating a simple function, and then will leverage their non-CS background and immediately conclude that ANN can 'solve' Cantor-dust, non-computability and the halting problem? That's a very specific set of background your non-CS people will have to have to fall for that. [Edit:] What do you think they'll make of "Multilayer feedforward networks are universal approximators"? ("This paper rigorously establishes that standard multilayer feedforward networks with as few as one hidden layer using arbitrary squashing functions are capable of approximating any Borel measurable function from one finite dimensional space to another to any desired degree of accuracy, provided sufficiently many hidden units are available. In this sense, multilayer feedforward networks are a class of universal approximators.")
- GTP 5y ago
  The people I'm referring to just don't know that there exist non-computabke function. So they wouln't draw specific conclusion about problems like the halting problem and silmilar because they don't know them, they would just conclude that any problem they might face can be solved by a NN. That's all.
  - deleted 5y ago
    [deleted]