Ask HN: What are for you the most interesting applications of deep learning? - jorgemf
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innagadadavida
Face/people detection/tracking at scale like the Chinese are doing. Despite my
moral/ethical compass revolting on this, it is one of the most impactful
applications so far. It is not just face detection that it does, there are
other more complicated things like gait/activity based classification. Route
tracking, people interaction tracking, etc. while some of this is just hype,
clearly a lot of money is flowing to solve these Chinese government problems.

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sam0x17
I think there is going to be a revolution in using synthetic imagery / 3D
renderings to train neural networks. Right now the biggest limiting factor imo
is acquiring data sets and labeling them. That problem vanishes if you
generate photorealistic images. Why this isn't a huge, huge, huge thing
already I don't know, but I'm doing it.

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csmckay
Text generation is neat. The first few iterations are nonsensical but then
real words start appearing, then phrases start making sense, then the correct
punctuation comes along and the machine is spitting out sentences, and before
you know it the machine has created readable paragraphs. I have no idea what
I'm doing, but it blows my mind.

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toomuchtodo
Biomedical/genetic engineering, as well as drug discovery.

ML could be the human augmentation we need to Cure All The Things.

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eb0la
Data Generation. All kind of dvelopments arround GANs are really exciting. I
bet on GANs on videogames.soon for generating content the same way fractals
did it 15/20 years ago (see The Sentinel, Archipelagos...)

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jacob9706
When I first started my adventure into deep learning I became convinced that
even a simple dense network could be used for storing and generating
variations of 3D content.

I never took it beyond a simple dense network for a blade of grass, but I'd
like to re-approach this using Tensorflow.JS. You can see the stupid simple
POC at: [https://jacob-ebey.github.io/gen-3d/](https://jacob-
ebey.github.io/gen-3d/) if you're interested.

Having experience working in CAD/CAM software, I have a feeling that the
struggle in an approach like this will be maintaining meaningful output
topology when adjusting parameters.

The simple approach in the grass POC was to have a single network input that
was between 0 and 1 for each grass model. If we had 4 grass models, the
training batch would have 4 entries with the first input being 0, the second
being 0.25, etc... Then to generate a "grass" model, you could feed in any
value between 0 and 1 to get a new model.

The catch here is that to maintain a meaningful output, the input models must
have the same "shape". I.e the same number of vertices and indices as the
indices from a base model are mapped to the new vertex values spat out by the
network.

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billconan
robotics, control

