A related problem is "one shot learning", [1].
I searched for "face detection" and got "face recognition" videos. I felt like a linear model would have been more useful.
"One of the strongest reasons for drawing the line of separation clearly and broadly between science and art is the following:—That the principle of classification in science most conveniently follows the classification of causes, while arts must necessarily be classified according to the classification of the effects, the production of which is their appropriate end."
Essays on some unsettled Questions of Political Economy
http://www.gutenberg.org/ebooks/12004?msg=welcome_stranger#E...
Deep Learning is finding associated effects. It does not find the underlying causes. It is a mode of technical rather than scientific advance.
https://arxiv.org/abs/1601.01705v4 (Learning to Compose Neural Networks for Question Answering) comes close to breaking this barrier.
https://github.com/Newmu/dcgan_code/raw/master/images/faces_...
The other widely reported vector algebras in a semantic space were discovered by Mikolov et al when producing ~300 dimensional vectors for a billion word Wikipedia corpus.
If one performs vector algebra and ~= is near by cosine distance then using Mikolov's Vectors[3].
King - Man + Woman ~= Queen
France - Paris + Gernmany ~= Berlin
Karpathy shows trained nets can be assembled like lego across modalities, slice off the classifier to reveal the rich semantic 'thought vector' layer of an Imagenet trained Alexnet, plug in a RNN sentence generator using word2vec and ( some over simplification ... ) you get a convincing image captioner [6].
The thought vectors are akin to high level representations of the world and can cross modalities . Text to Images using thought Vectors ( from hnnews discussion [7] )
So the vectors of though are in some way a an AI mentalese or encoding of a symbolic representation of the world derived from the data and can ( again drastic over simplification ) transfer modalities and even between previously unlinked languages [8]
Also see Anything2Vec https://gab41.lab41.org/anything2vec-e99ec0dc186
[1] https://youtu.be/izrG86jycck?t=25m58s
[2] The paper Geoff Hinton is reffering to : Sequence to Sequence Learning with Neural Networks by Ilya Sutskever, Oriol Vinyals, Quoc V. Le https://arxiv.org/abs/1409.3215
[3] Efficient Estimation of Word Representations in Vector Space by Tomas Mikolov, Kai Chen, Greg Corrado, Jeffrey Dean https://arxiv.org/abs/1301.3781
[4] Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks Alec Radford, Luke Metz, Soumith Chintala https://arxiv.org/abs/1511.06434
[5] https://code.facebook.com/posts/1587249151575490/a-path-to-u...
[6] SF Machine Learning: Automated Image Captioning with ConvNets and Recurrent Nets by Karpathy https://youtu.be/ZkY7fAoaNcg?t=38m31s
[7] https://news.ycombinator.com/item?id=12366684
[8] https://github.com/Babylonpartners/fastText_multilingual
> I haven’t found a way to properly articulate this yet but somehow everything we do in deep learning is memorization (interpolation, pattern recognition, etc) instead of thinking (extrapolation, induction, etc). I haven’t seen a single compelling example of a neural network that I would say “thinks”, in a very abstract and hard-to-define feeling of what properties that would have and what that would look like.
> All the while I'm thinking: this thinking process this person goes through as he analyzes this data: THAT is what Machine Learning SHOULD do
-- Andrej Karpathy
Deep learning for image recognition works because our visual world is made up of structured hierarchical features: Dark/Light, Texture, Edge, Part of Object, Object, Scene. Deep learning layers create increasingly higher-level features in a computationally feasible way.