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> Juergen Schmidhuber has made noises to the effect that deep nets have shown intuition and things like that. The only thing wrong in this sentence is your sar

> Juergen Schmidhuber has made noises to the effect that deep nets have shown intuition and things like that. The only thing wrong in this sentence is your sarcastic tone. Neural nets, deep or not, rely on intuition to produce output, so it is completely true that deep nets show intuition. What is intuition? It's a feeling that something is of a certain way that is not obvious or immediately obvious/self evident. And translated to ML terms, it is some amount of certainty about something. Intuition is an intrinsic feature of any ML algorithm.

YeGoblynQueenne 10y ago
>> What is intuition? It's a feeling Do you see the problem with this line of thought? You're now arguing that machine learning algorithms have feelings. Well, I got feelings too and your assumption I'm being sarcastic makes me really, really sad T_T
- cLeEOGPw 10y ago
  You are mixing feelings with emotions. Sadness is a feeling, but more specifically an emotion. A generic feeling is just amount of fitness to the pattern. The better the network is fit to recognize specific pattern (machine or human alike), it can be said the better "feeling" of the pattern it has. By the way, emotions won't be that difficult to implement once the feeling part will work.
  - YeGoblynQueenne 10y ago
    >> A generic feeling is just amount of fitness to the pattern. This is nonsense and the rest of your comment is nonsense on stilts.
    - cLeEOGPw 10y ago
      Looks like someone's amount of fitness to the pattern got hurt.
YeGoblynQueenne 10y ago
On a more serious note (and therefore a different comment) you're not even, like, factually correct. What you call "certainty", more commonly known as confidence, which you seem to think is a component of all machine learning algorithms [1], is actually a characteristic of probabilistic classifiers and makes sense primarily in a Bayesian belief network context in any case. If you train an SVM for instance, you would normally not learn much about its degree of confidence to its results (unless you explicitly output probabilities). Also like I say it's called confidence not "certainty" and it absolutely refers to the degree of confidence of an observer of a given phenomenon, and not some mental state of a system that computes the probabilities by which that observer sets his or her degree of confidence. Not to mention that in any other context, such terms as "belief", "confidence", and so on are only used metaphorically and - assuming a modicum of reason - only sparingly. [1] You say that in the context of machine learning intuition is "some amount of certainty" and so "an intrinsic feature of any ML algorithm". Correct me if I misunderstood that.
- cLeEOGPw 10y ago
  Well, I'm not native English speaker, but certainty and confidence means pretty similar things, although probably I should have used confidence instead. And for your criticism about all ML algorithms having degree of confidence - can you give me an example where an algorithm would not have it? And therefore would not have intuition? ML are made for partial knowledge problems - means that they don't SOLVE a problem, they take prior experience and GUESS the answer. That is intuition by definition.

YeGoblynQueenne 10y ago

>> What is intuition? It's a feeling Do you see the problem with this line of thought? You're now arguing that machine learning algorithms have feelings. Well, I got feelings too and your assumption I'm being sarcastic makes me really, really sad T_T

cLeEOGPw 10y ago
You are mixing feelings with emotions. Sadness is a feeling, but more specifically an emotion. A generic feeling is just amount of fitness to the pattern. The better the network is fit to recognize specific pattern (machine or human alike), it can be said the better "feeling" of the pattern it has. By the way, emotions won't be that difficult to implement once the feeling part will work.
- YeGoblynQueenne 10y ago
  >> A generic feeling is just amount of fitness to the pattern. This is nonsense and the rest of your comment is nonsense on stilts.
  - cLeEOGPw 10y ago
    Looks like someone's amount of fitness to the pattern got hurt.

YeGoblynQueenne 10y ago

On a more serious note (and therefore a different comment) you're not even, like, factually correct. What you call "certainty", more commonly known as confidence, which you seem to think is a component of all machine learning algorithms [1], is actually a characteristic of probabilistic classifiers and makes sense primarily in a Bayesian belief network context in any case. If you train an SVM for instance, you would normally not learn much about its degree of confidence to its results (unless you explicitly output probabilities). Also like I say it's called confidence not "certainty" and it absolutely refers to the degree of confidence of an observer of a given phenomenon, and not some mental state of a system that computes the probabilities by which that observer sets his or her degree of confidence. Not to mention that in any other context, such terms as "belief", "confidence", and so on are only used metaphorically and - assuming a modicum of reason - only sparingly. [1] You say that in the context of machine learning intuition is "some amount of certainty" and so "an intrinsic feature of any ML algorithm". Correct me if I misunderstood that.

cLeEOGPw 10y ago
Well, I'm not native English speaker, but certainty and confidence means pretty similar things, although probably I should have used confidence instead. And for your criticism about all ML algorithms having degree of confidence - can you give me an example where an algorithm would not have it? And therefore would not have intuition? ML are made for partial knowledge problems - means that they don't SOLVE a problem, they take prior experience and GUESS the answer. That is intuition by definition.