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The data is not labelled in the same way. The child has still acquired their knowledge solely from sensory experience. How do they know to apply the spoken labe

The data is not labelled in the same way. The child has still acquired their knowledge solely from sensory experience. How do they know to apply the spoken label "mom" to the recurring pattern in their visual data stream? With modern data sets and labelling, so much of the problem domain is deeply hard-coded into the system. It doesn't have to learn what letters and words mean and how to identify them in a totally arbitrary* visual or audio stream. It just gets a relatively minute amount of structured data that it has an embedded understanding of what to do with. Sure there are things like OCR and speech-to-text, but I don't think you could just run your streams through those, there's just so much subtle information loss. In order to make AI that really has a chance of reaching what humans would call intelligence, I firmly believe it has to make meaning out of some kind of raw sensory experience analogous to ours. *Ok, not totally arbitrary, a human would not learn language from a video of a forest, and that's where the "labelling" comes in, from observing other people using language, but a child can still learn any language from just that, and the labels are often vague, inaccurate, contradictory, complex, abstract, etc. There's no master training set with the right answers, you have to decide for yourself. And humans were also capable of bootstrapping language from nothing. I just don't see modern supervised learning systems ever doing things like that.

IanCal 9y ago
Actually you can learn a lot of stuff unsupervised, and then just use supervised learning to extract the information from within the net. So you can just throw huge amounts of letters at a deep net then later look at the activation at the top and say "Ok, so when neurons X, Y and Z are on it's when it's looking at a 1". I'm pretty sure you can just connect two at the top and have it say the number it sees. > In order to make AI that really has a chance of reaching what humans would call intelligence, I firmly believe it has to make meaning out of some kind of raw sensory experience analogous to ours. And why must it be able to do this from scratch? Why must it be hampered with the same limitations we have?
nl 9y ago
Unlabelled datasets are valuable in themselves. Self-Taught Learning: Transfer Learning from Unlabeled Data http://www.andrewng.org/portfolio/self-taught-learning-transfer-learning-from-unlabeled-data/ http://www.andrewng.org/portfolio/self-taught-learning-trans...

IanCal 9y ago

Actually you can learn a lot of stuff unsupervised, and then just use supervised learning to extract the information from within the net. So you can just throw huge amounts of letters at a deep net then later look at the activation at the top and say "Ok, so when neurons X, Y and Z are on it's when it's looking at a 1". I'm pretty sure you can just connect two at the top and have it say the number it sees. > In order to make AI that really has a chance of reaching what humans would call intelligence, I firmly believe it has to make meaning out of some kind of raw sensory experience analogous to ours. And why must it be able to do this from scratch? Why must it be hampered with the same limitations we have?

nl 9y ago

Unlabelled datasets are valuable in themselves. Self-Taught Learning: Transfer Learning from Unlabeled Data http://www.andrewng.org/portfolio/self-taught-learning-transfer-learning-from-unlabeled-data/ http://www.andrewng.org/portfolio/self-taught-learning-trans...