

Microsoft Challenges Google’s Artificial Brain With ‘Project Adam’ - digital55
http://www.wired.com/2014/07/microsoft-adam/

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chriskanan
As an academic researcher in object recognition, I'm starting to wonder if I
can still make significant contributions in this area, given that I'm
competing with larger teams with far greater resources at Google, Microsoft,
etc. For people like me, it is probably time to pivot to new problems in
machine perception; the challenge is getting funding for them.

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iandanforth
There's a lot of room left! Can you get accuracy at 120 fps? Can you do that
in an embedded system? Can you use temporal and contextual queues to build a
self-supervised system? Can you handle stereo and depth? Can the system handle
a wide variety of image sizes? Can it handle rotation? Can it handle
obstruction?

There is so so much more to vision than ImageNet! Go visit your robotics lab
and they'll give you a giant laundry list of things they need but don't have.

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chriskanan
Sure, I agree with you that there is more to vision than ImageNet (and many-
category object recognition), and that's why I indicated I'm looking for other
computer vision problems to work on that I think I can solve.

Incidentally, these deep convolutional neural networks do handle rotation very
well (see Table 1, Krizhevsky et al):
[http://arxiv.org/pdf/1301.3530.pdf](http://arxiv.org/pdf/1301.3530.pdf)

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AppliedTheor
This is a nice result. I'm sure a lot of people had this particular idea but
never got to actually do it. Not surprising because this is actually why to
human brain scales so well. This was also hinted by AutoEncoders where pieces
of a net are built separately bottom-up. I'm guessing a final settling phase
would also be useful here.

One unfortunate thing is that different runs will produce different nets. But
if training is radically faster and accuracy differences are small than this
is one new technique to add to the toolbox.

