[radarsat1]

As much as I dislike calling on the neural net / biological net metaphor, I do think that computer science has made some headway in how "useful codes", in the sense of semantically-meaningful interpolation, can be derived from natural scene stimuli, and therefore the onus that "we do something different" is to some extent now on the neuroscientists to think about and try to prove that "reasoning" in the human sense is anything other than an algebra of latent codes, i.e., linear or non-linear combinations of codified summaries of sensory input.

[sjg007]

What do you mean by an "algebra of latent codes"?

[radarsat1]

I mean being able to combine latent codes through some form of algebra (e.g. linear combinations) and have it retain coherent semantics:

https://github.com/Newmu/dcgan_code/raw/master/images/faces_...

[deepnet]

Geoff Hinton refers to thought vectors performing reasoning by analogy using algebra [1] in his Royal Society Lecture.

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

Surprisingly this works for other modalities, Chintala, Radford & Metz found a latent semantic space in images, that adds vectors for glasses or smiles to peoples faces. [4] With a generative model new images can be created as outlined in this blog post by Soumith [5]

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