Of course, he's the same roommate who later got arrested for wire fraud and grand theft.
It seemed smart, as it would give you the same four quarters if you used a Canadian Dollar or an American one. Or maybe not, as one kid found out when he stuck a $5 bill in and got 4 quarters.
Being 12, and therefore far more clever than the designers of this machine, the kids then proceeded to cut our a few thousand little rectangular strips of paper, roughly dollar sized, and clean that poor machine out.
And being 12, and having Ms PacMan machines as the only way of disposing of that giant sack of quarters, when it became apparent that there was no way to spend them fast enough, it naturally degenerated into a game of "Throw the quarter in the Ocean".
Out popped pound coins.
He now works for GCHQ which is funny.
Edit: just to add that these change machines were really naff and obviously built by the lowest bidder.
then I googled it and realized its british slang:'https://www.google.com/search?q=naff
It's the same with many words with the -ize suffix - we seem to have subconsciously accepted -ise to the point that spellcheckers demand it, when in actual fact the OED form of a word typically uses -ize.
Most British newspapers use the -ise form. The Oxford University Press use the -ize form. So it isn't as clear cut as you think.
I recall reading that the -ise alternatives have some roots in Australian us of the language.
DIP switches are set for various options one being accept face up (two-way), other options turn on or off acceptance of certain denomination bills and maintenance features such as calibration.
Even now modern acceptors allow this, although four-way acceptance is often preferred by the customer.
So he just moved on to even more serious crimes.
I'm embarassed to admit that in middle school my friend and I did something similar as his dad ran a printing business out of his garage. It surprised us then that this worked so easily. Luckily we only used them on vending machines and we quit doing it before we got caught. It makes me wonder what more sophisticated criminals can get away with.
Tl;dr: 100$ bills produced in all likelihood by a foreign government (suspects are North Korea, Syria, Iran) or maybe criminal gangs are so well done that they are practically indistinguishable from the original.
The numbers printed seem to be pretty low though, and not nearly enough to endanger the economy in any way. Probably its hard to launder the money?
As far as counterfeit currency being a danger to economies, check out Operation Bernhard [1] where Nazi Germany forged British bank notes to flood the economy, with the goal of causing rapid inflation that would be damaging to the government.
There's a good movie as well, from 2007, about the operation: Die Fälsche (The Counterfeiters) [2]. Definitely worth a watch.
This guy made beautifully artistic bills that didn't look official currency and then made purchases with them at their face value: $100, $500, etc.
They were exceptionally good, right down to the watermark, security strip, etc. etc. Even when I had a known real one and a known fake one, I couldn't tell them apart.
Even after they told me what to look for, I couldn't tell.
http://www.esquire.com/features/benjamin-hundred-dollar-bill....
http://www.esquire.com/features/benjamin-hundred-dollar-bill...
Quality is a factor in the price so (for example) a bag of 100 £1 coins might cost £60 but you might not be able to use them in vending machines due to the weight being wrong. Notes again have their price depending on quality and cut.
A pretty picture cut to the right size would work. I think it was enough to prevent casual errors rather than strict validation.
There's been consolidation in the industry, so there's really only the US and JAP makers left, and the US maker (MEI) is so far ahead of the rest of the world, there's no point.
The simplest machines do use 2 narrow optical detectors, but as their algorithms are considered good enough that you have to basically destroy a bill to use them, what's the point.
See my other post about SVM/PCA in this thread.
LMGTFY
"Principal components are linear combinations of original variables x1, x2, etc. So when you do SVM on PCA decomposition you work with these combinations instead of original variables."
"What do you do to the data? My answer: nothing. SVMs are designed to handle high-dimensional data. I'm working on a research problem right now that involves supervised classification using SVMs. Along with finding sources on the Internet, I did my own experiments on the impact of dimensionality reduction prior to classification. Preprocessing the features using PCA/LDA did not significantly increase classification accuracy of the SVM."
I can see how that relates to currency detection.
Support vector machines are a machine learning algorithm that works by taking data points in some (usually) high-dimensional space, and classifies them based on where they lie in relation to a boundary that (mostly) divides the positive examples from the negative ones. So one way a bill detection SVM might work is by using images of the bills are being transformed into points in that high-dimensional space by treating individual pixels as different dimensions, and deciding if they're valid banknotes (and the denomination) based on where in that space a given point falls.
Since SVMs are designed to work well in high-dimensional data, you're correct that principal component analysis doesn't normally help them do better. Oftentimes it makes them perform worse. More likely, the reason they're using doing dimensionality reduction is to cut down on the size of the SVM's model. That could help in two ways: If you're using a really massive number of training examples, then dimensionality reduction can help cut down on the time it takes to train the SVM, or the space you need to store your training set. And if you're trying to fit the SVM into an embedded system, then dimensionality reduction would allow you to produce an SVM that runs well on lower-cost hardware.
Just as a simple example (stolen from the Caltech course which I highly recommend) if you look at points on a plane that form a circle and try to separate them with a line you're going to fail. I.e. your points are (x,y) and those in the circle are your fake dollar bill and those outside aren't. But you can take all these points and apply a non-linear transform, e.g. (x, y, xy, xxy, yyx, x+y, xx+y2), you get the idea... It turns out that now you can* separate the data into what's inside and outside the circle. The problem is you just increased your so called VC dimension of the model and you might overfit the data and not learn anything. SVMs let you get infinite numbers of combinations, without overfitting and with cheap calculations... Pretty neat.
PCA: Basically you represent your measurements as a covariance matrix about the data set you care about. You then find the eigenvalues and the eigenvectors of that matrix. These basically tell you the hyperplanes which most accurately represent your data sets. Unfortunately, I can't get into more details about how this is used for bill detection -- go read the patents and papers yourself.
SVM: Basically, you have a bunch of datasets, and you an unknown data point, and you want to figure out which dataset your new data point belongs to. Well, you're not a clever person, and neither am I, so you just come up with the "cloud that surrounds" your N-dimensional shapes. This is your Support Vector.
A Support Vector Machine is just "hey, I've got a bunch of characteristic datasets, find the minimum structure for each dataset that surrounds the cloud, and then let me compare them." In practice, it gets really thorny to find the minimum vector, so people use something called the Kernel Trick to simplify that into something more manageable. (Basically, it's a higher dimensional transform that maps your dataset into even higher dimensions which likely will simplify the data as there's probably an underlying structure to your data you don't know. You try a bunch of kernels, and take the one that works best for you.)
Again, I can't tell you how it relates to bill detection. I'm embargoed. Go look at the patents and papers yourself.
1) Features are everything 2) So is experience
When people buy machine learning experts they buy both of these things. Anyone can learn the math, it takes time to get good with it.
The majority of people will just take another bill out of their wallet and try again, so it is really a non-issue.
Generally, however, the vending business is quite cheap. They buy the low end machine because there's not really a reason to bother buying the more expensive ones. However, go to a casino, or a foreign country that uses large bills, and you'll find the story is totally different. The machines are incredibly good.
* Source: I worked in this industry for years.
What sort of features?
I think the real security is still in features that require expensive equipment to duplicate. Is it really that hard to use cheapo photodetectors to verify differential transmission/reflection (watermark), angle-dependent coloration (hologram), or to do some primitive spectroscopy (UV even) with a plastic lens and $10 CCD?
SVM might be a easy way to aggregate the features, but in that case it's just a calibration method and doesn't give any indication of the underlying security.
Again, the security given in these detectors is SO good that even if I were to give you complete knowledge of the system, you can't beat them. I can't admit to having made counterfeits, but I can say that I've seen _all_ of them, and they do not work.
As much as I wish I could make a bet with you and test this, I wish for trouble from the secret service even less, so I guess that's off the table.
This phrasing is gold. "Our product is so good, the government buys it because it's better than the best thing out there." :)
I.e. they are the best systems out there?
it just fatten someones pockets who then pass a percentage down to the policy makers.
The laundry/vending machines are an interesting case because there's a decent chance that the the vendors would just be taking bags of coins to the bank where, presumably, the Pesos would be kicked out correctly. Even then, if there were only a small number of pesos, they probably just passed them on at someone else's vending machine, or in other cash person-to-person transactions.
It's hilarious to me that, while you can argue about what currencies are backed by, at the end of the day all that matters is that something spends. If something spends like a quarter, it's a quarter.