Re: [Idnet] A few ideas/suggestions to get us going
David Meyer <dmm@1-4-5.net> Thu, 23 March 2017 13:51 UTC
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From: David Meyer <dmm@1-4-5.net>
Date: Thu, 23 Mar 2017 06:51:36 -0700
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To: Henk Birkholz <henk.birkholz@sit.fraunhofer.de>
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Subject: Re: [Idnet] A few ideas/suggestions to get us going
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Hey Henk, On Thu, Mar 23, 2017 at 2:55 AM, Henk Birkholz < henk.birkholz@sit.fraunhofer.de> wrote: > Hello, > > maybe an excerpt from my personal point of view can be a contribution to > the discussion starting on this list. > > > In my experience, the gap between... work-flows (in lack of a better term) > how problem statements are created in the domain of > network/management/security and - in contrast - the domain of machine > learning is (aka "appears to me, subjectively") astonishingly vast. > Definitely. See the few attached slides for a some ideas I have on the topic from upcoming talks. > > One way to illustrate that gap (and there are multiple ways, I think), > using a bit of hyperbole: > > "network: least viable solution" -> "I only produce the information I > require" > > meets > > "machine learning: combination of heterogeneous most viable solutions" -> > "Please provide me with everything you got, including the best qualified > semantic annotation of characteristics and context so I can identify and > select the features that are relevant to provide a contribution". > > > Also - as trivial and repetitive as that might sound - terminology again > is key. Please note the following quote I actually encountered in my very > early days collaborating with machine learning architects: "The maximum > number of ports really is 2^16? Wow, how big can these routers be?". > While this is of course "one of these entertaining anecdotes" everybody > already heard at least once already, it also highlights quite prominently > the existing gap form a different angle. > > > In consequence, guidance that enables an individual with a specialization > in machine learning skills to just better understand the network domain > itself - maybe by illustrating very simple, well-known and already solved > problem statements - might already be a contribution of high value, just > because it is specifically provided for that group of individuals and using > terminology that is common and well-understood in that domain. > I think what you are saying is that domain knowledge is very important (key) when building ML solutions. That is for sure true. Regarding terminology: it is a mess in ML: everyone uses their own notation. Consider the notation used in [0] vs. for example, the Deep Learning Book [1]. Compare the notation in [0] to say, Chapter 6 of [1]. So we don't yet agree even on mathematical notation (though the notation of [0] isn't widely used) much less the description of networks. It goes on. I'll just point out here that this again goes to not having a "usable" theory of network (UTON, I guess :-)). Thanks, Dave [0] https://arxiv.org/pdf/1404.7828.pdf [1] http://www.deeplearningbook.org/ > > Viele Grü0e, > > Henk > > > On 03/22/2017 06:29 PM, David Meyer 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-applic >> ability-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 mailing list >> IDNET@ietf.org >> https://www.ietf.org/mailman/listinfo/idnet >> >> > > _______________________________________________ > IDNET mailing list > IDNET@ietf.org > https://www.ietf.org/mailman/listinfo/idnet > >
- [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