Re: [Idnet] A few ideas/suggestions to get us going
David Meyer <dmm@1-4-5.net> Thu, 23 March 2017 14:16 UTC
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From: David Meyer <dmm@1-4-5.net>
Date: Thu, 23 Mar 2017 07:16:39 -0700
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Subject: Re: [Idnet] A few ideas/suggestions to get us going
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Interestingly, Andrew also points out the need for data sets and the problem with talent pools (among many other things): https://hbr.org/2016/11/what-artificial-intelligence-can-and-cant-do-right-now --dmm On Wed, Mar 22, 2017 at 10:29 AM, David Meyer <dmm@1-4-5.net> wrote: > Folks, > > I thought I'd try to get some discussion going by outlining some of my > views as to why networking is lagging other areas in the development and > application of Machine Learning (ML). In particular, networking is way > behind what we might call the "perceptual tasks" (vision, NLP, robotics, > etc) as well as other areas (medicine, finance, ...). The attached slide > from one of my decks tries to summarize the situation, but I'll give a bit > of an outline below. > > So why is networking lagging many other fields when it comes to the > application of machine learning? There are several reasons which I'll try > to outline here (I was fortunate enough to discuss this with the > packetpushers crew a few weeks ago, see [0]). These are in no particular > order. > > First, we don't have a "useful" theory of networking (UTON). One way to > think about what such a theory would look like is by analogy to what we see > with the success of convolutional neural networks (CNNs) not only for > vision but now for many other tasks. In that case there is a theory of how > vision works, built up from concepts like receptive fields, shared weights, > simple and complex cells, etc. For example, the input layer of a CNN isn't > fully connected; rather connections reflect the receptive field of the > input layer, which is in a way that is "inspired" by biological vision > (being very careful with "biological inspiration"). Same with the > alternation of convolutional and pooling layers; these loosely model the > alternation of simple and complex cells in the primary visual cortex (V1), > the secondary visual cortex(V2) and the Brodmann area (V3). BTW, such a > theory seems to be required for transfer learning [1], which we'll need if > we don't want every network to be analyzed in an ad-hoc, one-off style > (like we see today). > > The second thing that we need to think about is publicly available > standardized data sets. Examples here include MNIST, ImageNet, and many > others. The result of having these data sets has been the steady ratcheting > down of error rates on tasks such as object and scene recognition, NLP, and > others to super-human levels. Suffice it to say we have nothing like these > data sets for networking. Networking data sets today are largely > proprietary, and because there is no UTON, there is no real way to compare > results between them. > > Third, there is a large skill set gap. Network engineers (us!) typically > don't have the mathematical background required to build effective machine > learning at scale. See [2] for an outline of some of the mathematical > skills that are essential for effective ML. There is a lot more to this, > involving how progress is made in ML (open data, open source, open models, > in general open science and associated communities, see e.g., OpenAi [3], > Distill [4], and many others). In any event we need build community and > gain new skills if we want to be able to develop and apply state of the art > machine learning algorithms to network data, at scale. The bottom line is > that it will be difficult if not impossible to be effective in the ML space > if we ourselves don't understand how it works and further, if we can build > explainable systems (noting that explaining what the individual neurons in > a deep neural network are doing is notoriously difficult; that said much > progress is being made). So we want to build explainable, end-to-end > trained systems, and to accomplish this we ourselves need to understand how > these algorithms work, but in training and in inference. > > This email is already TL;DR but I'll add one more here: We need to learn > control, not just prediction. Since we live in an inherently adversarial > environment we need to take advantage of Reinforcement Learning as well as > the various attacks being formulated against ML; [5] gives one interesting > example of attacks against policy networks using adversarial examples. See > also slides 31 and 32 of [6] for some more on this topic. > > I hope some of this gets us thinking about the problems we need to solve > in order to be successful in the ML space. There's plenty more of this on > http://www.1-4-5.net/~dmm/ml and http://www.1-4-5.net/~dmm/vita.html. > I'm looking forward to the discussion. > > Thanks, > > --dmm > > > > > [0] http://packetpushers.net/podcast/podcasts/pq-show-107- > applicability-machine-learning-networking/ > > [1] http://sebastianruder.com/transfer-learning/index.html > [2] http://datascience.ibm.com/blog/the-mathematics-of-machine-learning/ > [3] https://openai.com/blog/ > [4] http://distill.pub/ > [5] http://rll.berkeley.edu/adversarial/arXiv2017_AdversarialAttacks.pdf > [6] http://www.1-4-5.net/~dmm/ml/talks/2016/cor_ml4networking.pptx > >
- [Idnet] A few ideas/suggestions to get us going David Meyer
- Re: [Idnet] A few ideas/suggestions to get us goi… Rana Pratap Sircar
- Re: [Idnet] A few ideas/suggestions to get us goi… Henk Birkholz
- Re: [Idnet] A few ideas/suggestions to get us goi… David Meyer
- Re: [Idnet] A few ideas/suggestions to get us goi… David Meyer
- Re: [Idnet] A few ideas/suggestions to get us goi… David Meyer
- Re: [Idnet] A few ideas/suggestions to get us goi… Adeel Rehman
- Re: [Idnet] A few ideas/suggestions to get us goi… Pedro Martinez-Julia
- Re: [Idnet] A few ideas/suggestions to get us goi… David Meyer
- Re: [Idnet] A few ideas/suggestions to get us goi… João Paulo S. Medeiros
- Re: [Idnet] A few ideas/suggestions to get us goi… Pedro Martinez-Julia
- Re: [Idnet] A few ideas/suggestions to get us goi… João Paulo S. Medeiros
- Re: [Idnet] A few ideas/suggestions to get us goi… David Meyer
- [Idnet] FPS game traffic datasets... Re: A few id… grenville armitage