
Ask HN: Why is there so much focus on deep learning and AI? - varghe
Deep learning has become very popular in the past few years.  To my understanding, to operate efficiently a lot of data needs to be fed to the deep learning module.  Therefore, bigger companies like Google or Facebook can efficiently use it to enhance some of their operations, e.g., search, image recognition, translation, etc.<p>What I do not understand is why people give deep learning a lot more credit beyond these applications?  Average and small sized companies do not have access to a large amount of data, so deep learning can do very little for them (?).  Not only that, for those companies, deep learning is a way out of understanding the problem they are facing; more than likely, the person that suggests using deep learning cannot explain why the network behaves in a specific way in the case of their problem.<p>I might be mislead in some of these aspects, so please feel free to correct my assumptions and views.  I feel like that my lack of understanding for the AI field in general is causing unnecessary doubts and questions for me.
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ankurdhama
Here is quick history of the AI field.

We need to creating thinking machines, but we dont know what is thinking so
lets take any human activity that requires thinking, oh chess is such a game,
lets build algo to play chess, yeh it works and beats humans, but can we say
it thinks? hmmm hmmm not really coz this algo can't play any other game ..
dammit this is not AI. We are stuck.

We need to design algos that can do a lot of different tasks and not just one
like playing chess also these algo should be able to extend their capability
rather than redesigning them. Hmmm lets use general purpose "search" algos
(LOGIC programming) and also feed lot of "knowledge" into the algo as input.
Yay we got expert system and they almost looks like thinking. Let's see what
else can we use them to do and someone shouts "lets see if they can recognize
image", dammit that's so hard to "represent" using "knowledge bases". We are
stuck.

Lot's of people kept working on how to "represent" the algo and inputs for the
problem of image classification and similar problems. Came up with various
"machine learning algos" including "deep learning" algos which seems to work
really really well but of course they have their own demands like huge amount
of data and computing power etc. But are they really "thinking" ??? They can
be easily fooled, they are like black boxes nobody can extract some useful
knowledge from trained network such that it can be used somewhere else just
like general purpose knowledge we humans have. This is the latest shiny thing
as of now. But we don't have general purpose AI yet.

We always want silver bullet and we always hope that we will get there by
creating hype but in the end the hype dies and people move on to next hype.

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analognoise
Silicon valley is faddish and has a goldfish-like attention span.

