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Nonlinear Neural Nets Smooth Wi-Fi Packets
mindless4210 writes "Smart Packets, Inc has developed the Smart WiFi Algorithm, a packet sizing technology which can predict the near future of network conditions based on the recent past. The development was originally started to enable smooth real-time data delivery for applications such as streaming video, but when tested on 802.11b networks it was shown to increase data throughput by 100%. The technology can be applied at the application level, the operating system level, or at the firmware level."
Is anyone else slightly alarmed by this news? "Neural-net" technology that shows some degree of intelligence (if you consider making fuzzy predictions intelligence). I think that checks or governing circuits should be put in place for this kind of technology so that it doesn't get out of hand by, oh I don't know, burning out transmitter circuits or something. Remember the documentary "The Terminator"? Yeah. I do. I don't want something like that to happen.
Words I undestood in the headline:
1.Smooth
Fuck...
the technology can be executed at any of those level to be effective, not all 3 at once. So that means linux could get support for it at the kernel level... someone could write an application for windows, and palms could use an updated firmware and all 3 would effectively take advantage of the algo.
Not necessarily. This improves throughput, but as a general rule wireless always adds 20ms to your ping. so 50% of that would still be a 10ms penalty.
I'm not a network engineer, but latency is more important than bandwidth for ping times and such.
For an example pay a half-life game, open the console and type net_graph 3. That'll show you your fps, ping, and in/out bandwith used.
I'm curious as to why they are using Neural Networks for this application? In the last 10 years or so, most machine learning applications have moved away from Neural Networks to more mathematically based models such as Support Vector Machines, a generative model (e.g. Naive Bayes), or some kind of Ensemble Method (e.g. Boosting). I suspect they used NN because the Matlab toolkit made it easy or someone in research hasn't kept up. I'd look for a paper to come out soon that improves the accuracy by using SVM.
If you're a fan of women, add me to your friends list.
Umm... it isn't so simple. You are missing the basic idea of a layered architecture. This is actually really cool that it can be implemented at any layer. Sometimes there are things that can't be done at the application layer because of the constraints created by the layers below it. For instance, it is pretty worthless to do routing at the application layer if you are using IP, because it is already taken care of at the network layer.
So to say that it is all just "software" misses the fact that there is a significant difference between how these peices of software work. It is really cool that this can be done at the application layer, because it will allow applications to be developed to take advantage of it with out even changing the drivers for your wi-fi card.
This is a big deal to me, because I live in an appartment complex that offers Internet access over Wi-Fi. Because there are so many people using it the connection is pretty flaky (due to collisions). I get high download rates, but poor response times. If this provides better collision avoidance I will get a significantly better connection (lower ping times and such).
Very often the term "neural network" is used
...
just as a selling point because it sounds
like something extremely advanced and "related
to artificial intelligence".
usually the neural network is just a
very simple, possibly linear, adaptive filter
which means that really contains no more
than a few matrix multiplications
yes it has some success in approximating
things locally, but terms like "learning"
are really misused
After RTFA (the second) it actually
seems that they did try two or three
things before, but really i wouldn't
"welcome our new intelligent packet sizers overlords"
just yet.
We learn from history that we learn nothing from history - Tom Veneziano
Heres a graph that I ripped out of some lecture notes. It shows how much of a problem congestion is on 802.11b networks.
.035 Mbps per host.
http://web.ics.purdue.edu/~dphillip/802.11b.gif
For a little explaination, where it says "Node 50" or "Node 100" that means that there are 50 or 100 computers on the wireless network. And the throughput numbers are for the whole network, not per host. So when 100 nodes are getting 3.5 Mbps that's
Thanks to professor Park
Why isn't there something like this for normal internet? Even the "old days" of Zmodem's big packets if it was going well, and small packets if it wasn't, is better than the fixed MTU/MRU we're stuck with now.
Gee, let's see how many buzzwords we can cram into a technology:
.NET, Java 2 USS Enterprise Edition, and GNU/Emacs - soon to include POP, IMAP, P2P and B2B functionality for enhanced productivity.
"Introducing iFluff/XP: An XML-based Object-oriented neural networking system that will synergize the modular components of your SO/HO WAN protocols, while minimizing TCO and giving five 9's reliability by branch-predicting streaming traffic through your SAN, NAS, or ASS.
iFluff/XP allows you to commoditize and monetize the super-size networkcide as rogue packets from black hats and white hats and clue bats compete for cyber-mindshare of your Red Hat hosts.
Secure your Homeland LAN and manage your digital rights with dignitude and affordability with the help of iFluff/XP's bytecode-based embedded operating system protocols interfacing through broadband Wi-Fi connectivity and virtual presense frameworks.
A user-friendly GUI is provided through an XSLT module interfacing to leading industry applications such as Mozilla,
When you're thinking of buzzword-compliiant, ISO9001 conformant, remotely-managed turnkey security solutions, remember iFluff.... TO THE XXXTREME!"
Oh god, my brain hurts now.
When I see the headline: "Nonlinear Neural Nets Smooth Wi-Fi Packets" and I only understand the words nets, smooth and packets...and none of them in relation to each other
;-)
Simple 'nuff, really...
Neural net - An arrangement of "dumb" processing nodes in a style mimicing that which the greybacks of AI (such as Minsky and Turing et al) once believed real biological neurons used. Basically, each node has a set of inputs and outputs. It sums all its inputs (each with a custom weight, the part of the algorithm you actually train), performs some very simple operation (such as hyperbolic tangent) called the "transfer function" on that sum, then sets all of its outputs to that value (which other neurons in turn use as their inputs).
Nonlinear - This refers to the shape of the transfer function. A linear neural net can, at best, perform linear regression. You don't need a neural net to do that well (in fact, you can do it a LOT faster with just a single matrix inversion). So calling it "nonlinear" practically counts as redundant in any modern context.
Smooth - A common signal processing task involves taking a noisy signal, and cleaning it up.
Wi-Fi - An example of a fairly noisy signal that would benefit greatly from better prediction of the signal dynamics, and from better ability to clean the signal (those actually go together, believe it or not - In order to "clean" the signal without degrading it, you need to know roughly what it "should" look like).
Packets - The unit in which "crisps" come. Without these, you can't use a Pringles can to boost the gain on your antenna to near-illegal values.
There, all make sense now?
Saying "Non-linear neural network" is like saying "Non-purple hamster". I mean, how often do you see a linear NN? Like, never.