dcgan_code

here vector math is akin to reasoning. Higher level control could be achieved through vector algebras deriving actions and things directly from the robots internal vector space of motor-torques and raw-pixels Thus internal vectors are like thoughts[5] which can interpret, reason, plan, and control. Karpathy shows internal vectors can bridge modalities from words to pictures[6]. If the higher level control vectors of actions and things could translate to the modality of language as verbs and nouns, perhaps the robot could discuss its plans, and receive orders. [1] - Thelen's Infant Development http://www.ncbi.nlm.nih.gov/pubmed/17107442 [2] - Levine, Finn, Darrel, Abeel http://arxiv.org/abs/1504.00702 & [2b] - Distributed Grasp Learning http://googleresearch.blogspot.co.uk/2016/03/deep-learning-f... [3] - Mikolov, Chen, Corrado, Dean http://arxiv.org/abs/1301.3781 [4] - Radford, Metz, Chintala https://github.com/Newmu/dcgan_code#arithmetic-on-faces [5] - Hinton https://youtu.be/IcOMKXAw5VA?t=32m55s [6] - Karpathy https://youtu.be/ZkY7fAoaNcg?t=38m31s

see what the transformation is that's been done to the first two images, and then apply that transformation, disentangled from the identity of the what the transformation was applied to, to a third image. The vector of the third image, with the transformation applied to it, is then visualised by the decoder network. So the key is to not just combine some vectors otherwise you'd end up with a mess, the key is to disentagle the identity so that the relevant part of the vector associated with the change, such as rotation, can be transferred. I think the link between this kind of operation and word2vec is clearer in the paper Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks[1] (submitted for ICLR 2016 it seems[2]) which shows more explicitly the vectors being added and subtracted for images "smiling woman" - "neutral woman" + "neural man" = a series of generated images of a smiling man. Woman is the identity in the first two, by subtracting the identity is effectively removed and the "smile" is largely what remains in the vector to be transferred onto the neutral man identity as a transformation. I suppose the more general point is that we are going to see more and more complex use of various kinds of thought vectors[3] and more and more attempts to try to improve their accuracy. This area is just going to keep exploding I think because the practical applications, such as they showed in this paper for animations, will keep driving work on it in multiple domains. The current pace of research, which has led to[4], and the results in this paper and in [1] are just mind-boggling. [0] http://arxiv.org/abs/1210.5474 sections 1+2 have a good overview, and 4 has related work [1] https://github.com/Newmu/dcgan_code [2] http://arxiv.org/abs/1511.06434 (first submitted in 19th Nov 2015) [3] http://arxiv.org/abs/1506.06726 [4] http://www.arxiv-sanity.com/

gan.html [3] https://github.com/Newmu/dcgan_code [4] http://arxiv.org/abs/1511.05644

et can be trained or distilled from another net - but deep nets are overwhelmingly better at the initial learning and discovery. Neural nets have been related to Kadanof's spin glasses, suggesting learning is alike to phase transitions or sand pile collapse where small local changes can produce profound global changes. https://charlesmartin14.wordpress.com/2015/04/01/why-deep-le... Generally when training nets the learning initially learns a lot very quickly, most of the error vanishes at the start. Word2Vec demonstrates that nets learn very powerful representations, that word2vec vector algebra is semantic points to unexpectedly powerful representations. Similar semantic vector math can be performed on images and the same vectors can translate modalities, e.g. text to images. https://github.com/Newmu/dcgan_code#arithmetic-on-faces Natural Evolution produces efficient solutions. I propose the successes of deep learning so far are partially explicable because they are working within human culture and perception, relearning our very efficient solutions - akin to distilling a deepnet into a shallow one. This hypothesis will be tested if embodied deep neural nets using re-inforcement learning discover their own efficient solutions to performing tasks in the real world - robotics. IMHO Peter Abeel's deep net, learning to robustly output robot motor torques directly from camera pixels will show if embodied deep nets can do discovery rather than relearning what we know. http://www.cs.berkeley.edu/~pabbeel/research_rll.html