Okay, so before I start I will have to warn you, basically this is almost completely math except for one slide from biology and then I was supposed to actually make a reference implementation but I never quite got to that point. So you have been warned that will be math, quite a bit of it, okay? So anyway, today I'm going to talk about neural networks and deep learning which is what they call nowadays. So the main impetus for this is that because I actually had to implement a number of neural networks mostly just for learning purposes as well as some MMOCs. So one of the problems I was actually having was that I have actually very little clue as to what's going on in terms of neural networks. So what I did was actually as kind of part of this talk was to try to derive neural, basically the back propagation algorithm which is very key to neural networks and how it relates to all of it. So the main trust of this is that I will actually kind of relate this to nonlinear regression and kind of show how it relates to how you kind of look at neural networks in terms of what I would call a bigger class of problems and a way of looking at things mostly in terms of regression, okay? So but before I do that, let me quickly go through what real neurons are and kind of give you a feel of why it has almost nothing to do with neural networks. Okay, so this is a real neuron. This is an image of the LGMD. This stands for the large globular motion detector neuron which I used to work on at one point in time.  This is one neuron. And what this neuron is capable of doing, it actually can do pattern recognition and it can actually do collision detection. So think about that this one neuron, not a network neuron, just a single neuron, is capable of doing all the computations required that most what is this called automatic cars and self-driving cars are capable of doing. So quite a big chunk of that computation which is done by neural networks in those cars is done by a single neuron. And this neuron is actually in the eye of a very, very simple animal, the locus, specifically, sister circle of Americana. It's just kind of behind the eye here, over here. So this is a real neuron, neural networks have very, very little to do with any of this. This is just a reference. Okay, so the way I'll build up neural networks is actually from the viewpoint of regression. So the way we define regression in abstract formulation is, firstly, by defining kind of the two things. The target, which is the, what will we call the training data set. And the prediction, which is what your model actually generates. X will basically be your feature set, feature vector. And what you're very interested in is actually deriving what these betas are, which are the parameters of the model. So the model is usually written down as something, as a function. Which takes in your feature set with a set of parameters which you do not know. And you want to find out. And it will make an estimation or a prediction over here. So basically it describes a mapping from the feature vector and the parameters to the prediction. And what we want is we want error to be very small in some sense. So we want h bar to be very, very close to h, essentially. So that's an abstract formulation for regression. So to make this more concrete, what we are saying is this. Given a target vector y and feature vectors xi, which equals to x. So this is basically a matrix where i is basically the index for the observation. We wish to find a beta, which is the parameters of the model. So we want to touch that the target vector y and the prediction vector is very small. In other words, we're looking at a minimization problem over here. So what we're doing here is that over all possible values of beta, we want this particular minimization function f, which takes in y and y hat, which is equivalent to the model with respect to the targets. And we want this to be as small as possible and find the betas, which are the parameters for it. With respect to all i. So this is basically the cost function of regression. So the next part of this is in order to find this beta, we need to use gradient descent, which is a method of doing optimization. So the minimizer beta, which we were talking about previously, which is our parameters, we use the gradient descent algorithm to basically, which is written over here, to compute what this set of betas are going to look like. So what this algorithm does is that it takes the current best guess of beta, and you basically subtract off a learning factor, which is the lambda over here, and you multiply this by your gradient of your cost function, which you have to find out. So, which is also dependent on the current parameters. So, and then you estimate from the current step, what your next step is going to be using the gradient operator with the learning rate, beta zero, which is the initial guess that you need to start up this algorithm, can actually be randomly initiated. So most people would do would be to initialize a random vector from zero to one. Stop condition is defined when the beta plus one, and the beta minus, the next step, and the future step, sorry, the current step, and the future step is very, very small, which means that basically the algorithm is not going anywhere, and you're very close to a proper solution, essentially. So this is just some conditions that must be met. So this is Lipschitz, which is a kind of, a weaker kind of uniform continuity, and the local convergence is dependent on lambda. So what happens is that for all problems, it is not necessarily true that you will converge and it depends on lambda. So it's possible to actually have divergence of solutions. This is kind of a picture of how it works. So you start off with an initial guess over here, and this is basically the minimizer, so this will be where your betas are going to be, and you will slowly move, the algorithm basically moves bit by bit towards the beta. So at each iteration here, which are the axis instead of betas, you see getting closer and closer to the minimal solution, which is over here. The other thing to note, just wanted to draw this quickly so that everyone gets a feel of this. So you can also represent it something like that. This is a one-dimensional version of this kind of image. So let's say this is the minimizer. One of the problems which gradient descent has is that your lambdas, your guesses are actually dependent on the lambdas. So if you do it correctly, like you start somewhere here, you do it correctly, you get here, and then eventually here, and then eventually here, so you find the true solution. But if your lambdas are too big, you sometimes can go over here, and then you'll try to go here or something, and then it just gets completely lost, and then it never converges. So this is one of the problems with a gradient descent from a theoretical point of view, and it actually also happens in practice if you don't select your lambdas correctly based on the problem that you have. And unfortunately, a lot of this is kind of iffy. So you kind of have to just guess what the best approximating solution is going to look like. Okay, so let me give you a concrete example to some extent. This is a classical linear regression written in this abstract framework. So once again, this is the model, F, which takes in your feature vector, which is a set of matrices, multiplied by a vector of betas, which is your model, sorry, your parameters. And what you want to do is you want to minimize this in the square norm, the square of the L2 norm, in other words, your square error. So the way, so what happens is that if we write this down, we get something like that, Minimum F, which you saw previously, which is equivalent to this, and then when you do the minimization, which is if it isn't the L2 square, so this is the L2 norm, you get something like this, which is the minimizer, I'm sorry, the minimizing cost function, which you see over here. Which I think you probably see in very conventional text in regression analysis. So what we do in order to find that beta, the parameters, is that we can use applied gradient descent. Once again, you see this is what we saw previously in the previous couple of slides. But now we substitute in the cost function, which is over here, which we have to take a gradient off. So the gradient, you can do some computation and you come out with a gradient something like this, where these are, once again, these are the future vectors. And then you basically multiply it by the target, which is over here. And what happens is that this is classically what you do in linear regression. If these two are equal, which is what you want, you want them to be the minimum, so these two are very small. So if these two are equal, so these two are zero, you basically get the normal equations which you use classically to solve linear regression. But in the case of nonlinear regression, you have to use gradient descent as one of the methods that you apply. There are also other methods which are high order and allow you to converge to faster solutions, but I'm not gonna talk about that in this particular case. So, now we get on to neural networks. So the way I perceive neural networks is that neural networks is just an extension of regression, except in a nonlinear case. So, instead of having that very nice x times beta function, which, let me just quickly write it over here, which is our model. It's on beta. You now have a much, somewhat more complicated set of equations over here. So, once again, one is the target vector and x is the feature vector. The input layer of our model is described by this particular equation. So, x is equal to A1, the first input layer. And then the hidden layers are described as beta multiplied by A. So, once again, you see something like linear regression coming out. This is passed through a nonlinearity G which is still part of the hidden layer. And then it's output to a final output layer by just multiplying it again by another set of parameters. So, basically, this model has beta one as a parameter and beta two as a parameter. And then you apply, once again, a very simple cost function what we saw previously, which is the least square error cost function or L2 norm squared. And the other thing to keep in mind when you do equations like this or neural networks like this is that the nonlinearity which is over here, G, must be differentiable function. So, usually what you have is things like a hyperbolic function which is a very standard case that you use in the neural networks. I will actually not use a specific function because I like the more general formulation. You can substitute whatever function that you like. So, this is basically kind of what it looks like in pictures. The input layer, so this equation over here is the first set of nodes. The hidden, sorry. The hidden layer, these two set of equations describe this and of course naturally the output layer over here is described over here. So, one of the things to keep in mind is that matrices, which you can write, which people usually think of as something like that, a one, one, a one, two, blah, blah, blah, a one, sorry, two, one, a two, two, blah, blah, blah, blah, blah, blah, blah, blah, blah, blah. They actually describe a network very easily. So, what happens is that you think of it as the first index i, so let's say this is index i and this is index j and basically the values of the, if they are actually, if the value actually exists, this is actually a real weight. If the values does not exist, so for example, if you have something like that, a, a, zero, zero, you would have basically a matrix which goes something like that and in this case, you would have four, hold on, this is a one, a two, so this would be, give me a sec. Yeah, this basically has weights going in this direction and this in this direction and this one only has weights going in this, sorry, yeah, and this has weights going backwards and this only has weights which are going in this direction. So, this is the kind of networks which can be described by matrices. So, the matrix representation is another way of looking at neural networks which is quite useful from a computational point of view. Okay, so this comes to what we're actually very interested in, which is back propagation. Back propagation is the fundamental algorithm which is used to solve neural networks, essentially. So, what this algorithm does is only one thing. You just want to compute what the derivative of f is. So, if you can see over here, when we were looking at gradient descent in the general case, what back propagation does is creates a fast way of computing this particular number and that's it. The rest of it is basically standard regression, more or less. Okay, so this is basically a definition of back propagation. So, this particular gradient is actually this special delta which is usually called a delta rule multiplied by a, which is the activation we saw previously. These are the activations. And if j, which is the output of the neuron, sorry, output of the node is on an output node, then it just boils down to this simple equation, which is the coefficient minus the target at the top level. Alternatively, if this is at one of the internal nodes, then you basically have to use this at equations. This is the derivative of the non-linearity and this is just a z that you saw previously with the delta, which we actually took from one of the inner nodes multiplied by the beta, which is the parameters of the previous nodes. And you use gradient descent to minimize beta. So, the way to derive back propagation is basically to apply chain rule continuously. So, it's very simple. It's just basically continuous application of chain rules. So, you start with the derivative that you're looking for. You basically do a applied chain rule and you take out the two z parameters, which are kind of the internal node variables. And then you basically take a derivative of this. This is basically just a chain rule. And you see basically very quickly that this particular term is actually equivalent to just the A term over here, which you can see, how do I show you this? Oh yeah, so basically just by deriving from the original term over here, if you take the derivative of the beta over here, you see of z over here, you just get the A term. So, this basically goes to one and you just get A. So, very simple. You just get A over here. And this we defined as a delta, which I'll show why it is, why is that case. So, one thing to note about this set of equations is that the parameter beta j i depend only on the z j's, because if you look at a set of equations, they don't appear anywhere else except for this particular one. So, only for this particular layer. So, for example, this particular layer, the z three, the beta two's basically only depend on the z three. Similarly, sorry, get away with it. The z three depends only on the beta twos and the z two only depends on the beta ones. Okay? So, this basically breaks the chain rule down and makes it very, very simple to understand. Okay? And, okay. So, basically, that deals with this particular, this breaking this down into a very simple set of equations. Definition of beta and basically, we just make the equivalent that this delta is equivalent to this partial derivative of z j. So, we see that very simply that this particular term, if it's an outer node, is equivalent to z j minus y j i. See this, because what we are doing is we are just taking the derivatives of this particular term. And it's a simple calculation to in order to generate that particular value, which is this. But internal nodes are a bit more complicated. So, what happens with the internal nodes is that you have to take partial derivatives over all the input nodes. So, you can't just take derivatives over just itself. You have to actually take partial derivatives of all the other internal nodes. So, you basically take a summation over them to actually extract them to get something like that. And once again, you see this is basically just the definition of the internal nodes. So, you take the delta from the previous internal nodes. This is quite simply when you just do the right derivation, which is over. Here, it's just the derivative of this with respects. Sorry, this with respects to the previous layer. So, you get something like that. So, we're doing a bit of substitution. You basically get the original definition that I showed you previously. So, it's a kind of very, very simple concept. It's just repeated applications of chain rule in order to get that set of equations. So, once you actually have that in single elementary components, you can basically rewrite them as these delta rules in matrix or vector notations. So, these are all vectors instead and you just put in the vectors over here and matrix over here. And that's a vector again. And you can very simply compute what the derivative of the beta one and the beta two is based on what you saw in the previous based on this series of equations. So, it's quite straightforward. Okay, and what happens is that at this point, it's very easy to implement them on a computer because essentially, vectors are just one dimensional arrays and matrices are two dimensional arrays and you just apply some form of impact which is the linear algebra packages which is available in NumPy or if you want it off the bar and you can get the results quite easily. So, that's basically it and this is the reference I use which is the machine learning by Bishop. It's one of the very good references. There's also a bunch of references on the web but I found them not to be that particularly useful. So, that basically covers my talk. Thank you very much. If anyone has any questions, if not, I'll be around after this. So, that's about it. And I guess Raymond wanted to find out whether there's any interest for applied version. Oh yeah, so. So, a presentation. Applied version? Okay. So, I mean, the thing about this is that there are lots of packages around which basically does all of this that you see. Even Python basically has a bunch of them, TANOS, Tiano, Tiano is one of the packages. Then you have things like TensorFlow which are available. So, it's very easy to basically use them now. The problem is of course that you kind of need an understanding of what goes behind the scenes. That's why I don't like to implement things without at least a bit of understanding what the underlying app is. So, I don't know. Does anyone want to understand those packages at all here? Yes? One person? Okay, then I might. I think that there may be a little bit more of those people with packages because you have different kind of transition functions but you know neurons. Yeah. And different architectures of the nets that suit different applications. Oh yeah, okay. So, I'm not super familiar with those. I just, I mean, I've just used them mostly playing around with them. So yeah, I really can't say too much about those. That's all I can say. Sorry about that. Yeah. Any other questions? Actually, you can find a tutorial online on deeplearning.net. They have a variety of tutorials. So, what they give you is that the model and the definition of the layer and everything you just need to understand. I think the thing deep learning that the actual deep network is very complicated when you compare with this one. So, it's very hard for a newbie to quote it as a form of beginning. So, you need to refer to a tutorial. So, one source of a tutorial is deep learning. You can find a TNO tutorial. Very good. Okay, yeah. I have one question. Sorry, after that you, yeah, sorry. How do you make this slide with a very beautiful metamatic equation? Which software do you use to make your slides? Oh, to make my slides? This is just a latex. Latex is a standard for, like, if you want to write your equations, it's latex. Okay, okay. The other thing you can use is, I use a lot, our presenter also allows you to use quite a lot of mathematician. Just, BIML are just a simple latex. Yeah, so this is basic latex and BIML. Yeah. So, yeah, sorry, I had a question. Thanks so much for your presentation. Yeah. It's very important for me. So, I have two questions. Why is, I don't know why some of you are learning out of it. I mean, those are not totally answerable. Is that too many? That's what they say it's supposed to be. So, yeah, okay. So, because I come more from a regression background and this deep learning, sorry, this neural networks and deep learning stuff to me is actually just a, more like an application of that. And I don't know it very well. I'm just getting into it also. So, the way I perceive it is basically it allows you to stack a whole hierarchy of models which feed into each other. That's kind of the idea. So, the one we are looking at is as neurons. Neurons, but they are actually other neural, they are other neural models out there. Things which take into account, things like the synapses and channel conductances and stuff like that. But it's not really a real neural network in the biological sense. It's more like a stacking of non-linear models from my point of view. Which I think brings me to my second question. So, how do you throw around choosing these non-linear models? He has everything to choose from. So, okay, so firstly, as I said, I'm a really real beginner. So, from what I can understand, it's most of it is heuristics. The underlying theory is completely messed up. Firstly, there's no, the algorithms that in most of the time they use, for example, stochastic gradient descent. Stochastic gradient descent is the gradient descent method which I showed you just now, but instead of looking at all of the records at one point in time, they basically select subcategories of records. So, they don't use all of the data. Things like that, no guarantee of convergence at all. So, we have no idea why they even kind of converge, how to make them work in general, theoretically from a theoretical point of view. So, it turns out that a lot of things are basically tuned. You kind of play around with the parameters until you get something which works, which is, yeah, which is the case. I'm not so much a big fan of that, but that's like four neural networks, yeah. Okay. So, that will not be the ideal case, right? When the classic case converges into a global node. Yeah, no, this is no way. So, for these, even for general non-linear equations, generally the best you can hope for is convex problems. No guarantee of convex problems, but apparently they're from, I've seen a paper which I've not read because the theory is quite deep that for some reason, the more layers you stack, you get, the well is actually deeper for some reason. But it's still there, you can still get trapped in local minnows very easily. Yeah, that's all I can say. Yeah. Oh yeah, and the other thing is that, I think they basically use regularization to, in order to prevent overfits and things like that. So, that kind of helps, but I think that's a bit more of an ad hoc way of doing things, but yeah, any other questions? All right. Okay, yeah. Thank you.


Hello people, I'm Ginny Metherill and I am a force generation witch. As a force generation witch I'm often asked what deities I worship and in answer to that none I don't worship. However I understand and realise the potential that there is in invoking the spirit of a deity and so for today's video I wanted us to look at seeking Hecatee. So who was Hecatee? Was she an Egyptian goddess? Was she an Anatolian goddess? Was she Greek? Well in answer to that we don't actually know. Possibly she came, the name came, from Hecate the Egyptian goddess, the frog headed goddess of childbirth with which Hecatee is associated. There were many shrines and temples laid out to her including shrines within the own home. We just don't know where she comes from however there is one thing that is very definitive about Hecatee and all her guises and that is she is associated as the goddess of witchcraft and magic. The Greeks had her as a triple goddess so three faces looking in different directions and hence she was placed at shrines on crossroads. Crossroads were dangerous places from as far back as we can remember until about the 1800s certainly and so Hecatee was used as a goddess of the crossroads which she protected and helped travelers passing through. This in turn was taken to mean that she was also considered a goddess of doorways and so you often see ancient depictions of Hecatee holding two lighted flames in either hand and this is to show that she is lighting the way through the darkened doorways. The Greeks had Hecatee closely associated with dogs amongst other animals but mostly dogs and they were always female. Female dogs always having puppies in the old days and so they used to sacrifice the female dog and her puppies to Hecatee at crossroads. Should you hear the howling of howls this presages her entrance meaning should you hear dogs howling it possibly means that Hecatee is coming. Dogs were also guardians of doorways and entranceways for example Cerberus the three eddlest dog who guarded the entranceway to Hades both from keeping living people going in and the ghosts coming out. I think Hecatee became associated with ghosts and spirits through the dog association.  Her own dog was known as Hecuba. It's rather a lovely name isn't it I love the name Hecuba I don't know why it just pleases me. Part of the Hecatee's iconography i.e. things associated with her was that she was goddess of plant law both medicinally and poisoned and particular plants that were associated with her were Belladonna, Dittany, Akinite or Monkshood and Mandrake all of which are highly poisonous and also have an effect on your psychic self you know they can open your third eyes whilst killing you at the same time so in the 1700s the ladies used to put Belladonna in their eyes to make the pupils expand and become huge and dark and actually sent them completely high as a kite which I think is why they did it actually because it couldn't have been pleasant putting these things in their eyes but I think the after effects were. Hecatee has been worshiped for over 7,000 years and she has always been associated with magic and sorcery this in the changing of the plants and the poisons that she created and this sorcery and magic became known as witchcraft and it has a particularly female aspect to it. Hecatee being the head of the female witches. She was hugely popular throughout ancient Greece and they used to have a lunar meal for her and this could either be an offering at her shrine or the family would gather together and use the meal in honor of Hecatee this happened on a new moon or when you see the first crescent of a new moon after that date every month they would sit down and worship Hecatee some moon-govered foods in a meal which is a still a completely charming way to worship Hecatee. She is the goddess of the moon and is worshiped as such. She was well established in British folklore by the time of Shakespeare. He has Hecatee being the goddess which over the weird systems the three witches of Hubble, Bubble, Doyle and Trouble fame who I always rather liked. I thought they were the best bit about Macbeth myself because quite frankly I never really did Shakespeare. I'm quite keen on my literature but Shakespeare not for me young friend. Although Hecatee has dominion over the plants and their poisons she is a kind and tender hearted goddess as shown by her accompanying those spirits from the place of the living to the place of the dead where they finally rested. In Greek mythology she used to accompany the goddess Demeter from Hades into the spring the sunshine and then back again in the autumn when winter took hold. She was very kind-hearted yet implacable filled with mercy yet ruthless. So you have to be careful when invoking Hecatee in your magic about what you're invoking her for. Turnio Pagan or Wiccan roots are as an association of the crone or the third phase of the moon so she's an older witch hence why they say that sometimes younger witches like to associate with Hecatee. They depict her as a sexy woman but in fact she's not she is certainly the old crone film actor. It is the ancient Greek goddess Hecatee who was this beautiful womanly form. She really came to prominence with the Blackburn cult in Los Angeles in the 1920s. This was a cult headed up by Mary Blackburn and she used Hecatee as her goddess of worship. So the queens of the cult were buried with seven dogs and they would sacrifice many dogs to Hecatee in their rituals. A bit grim isn't it? I think the cult fell down after Mary was done for grand larceny but you know at the time it was a big deal. So how would you ask Hecatee to help you in your magic? Well she helps primarily with those things that she is associated with. So her animals are serpents, whole cats, boars, dogs and mares female horses and so if you wish to work magic with any of these animals it is a great thing to invoke Hecatee to help you with that. Raise the serpent in the spring for example which Hecatee would be most useful to do so with. Hecatee is great for the invoking for protection so warding your property if you wanted extra help with this I would use Hecatee and quite frankly extra help for warding your home is always a good thing in my opinion. However be warned if you are going to invoke the goddess Hecatee and seek her out make sure that the magic is just and true because although Hecatee is merciful she is also ruthless and you will reap what you sow when you invoke her so if you're going to send out a curse the rebound from that curse if you use Hecatee will come straight back at you so be very careful about how you invoke her and so whatever purpose you would like to have in mind I would recommend doing an altar to Hecatee you could do an altar for example to help you with childbirth she is the goddess of that after all to help you with your dogs or horses or snakes to help you when creating your potions I've said I do like a bit of potion work and finally I'd use her in spirit work if you want to commune with those souls that have passed over or under the news Hecatee to channel your spirit medium do tell me about how you invoke Hecatee and what would you invoke her for is she one of your main goddesses and do you have an altar to her let me know in the comments below otherwise don't forget to check me out on Patreon go to www.patreon.com including my coven meeting come and join us it's great fun otherwise please don't forget to like and subscribe because it really helps my channel I really need people to like and subscribe for me and I will see you in a few days


Now the plan is built on three pillars and today I'd like to tell you about some of the key initiatives That will be implementing as part of it The first pillar is job creation Job creation with a common-sense government that opens doors instead of closing them One that makes things simpler for business and Getting out of the way when we can Now the second pillar of our jobs plan is about Continuing to build smart infrastructure, and I don't just mean bricks and mortar. I mean people too We need to support the human relationships that help us succeed The unity we must foster and the education we have to strengthen Now the third pillar of our jobs plan is about opening up markets for BC goods We'll focus especially on China and India where economies are growing and the middle class is coming to the fore The middle class in India alone is expected to be ten times the size of Canada's entire Population in just 20 years. I know government doesn't have all the answers and We need your help We don't create jobs You do that. I'm optimistic I'm optimistic as we look toward tomorrow. I Know that our province can lead this country But only if we're at our best if we're united if we're optimistic if we're confident. I Intend to be the chief salesperson for our province as we take our historic place in leading this great country It's our time to say to Canada Canada starts right here. Thank you very much


What's good Josh your boy Ross back out again with another video and today I will be checking out WWE top-step moments of all time Shout out to the homie D Rose 1528 he Instagram Will he send me a DM on Instagram saying I should check this video out. So that's what I'm gonna do for you guys I'm gonna probably make this alive. This will be my first live Video on on my personal YouTube channel. So those that are in the chat the few y'all are in the chat What's good man? Welcome. Welcome to the to the Ross zone. What? You know I'm saying that was pretty lame. It's cool But I appreciate y'all joining me whoever's in the chat appreciate y'all joining me best believe We know the best moves of August has came out me and Dub We're trying to work things out scheduling wise so we can link up and record that if we don't record it today It will be recorded tomorrow, and it'll probably be up tomorrow for sure We got a lot of vids. We know that joining Lucas ADHD. He dropped the track We're gonna be checking out all that stuff. Hopefully tonight or tomorrow for sure And we'll be dropping that all the different videos and content y'all want us to check out throughout the week We know the chisel adonis. He dropped some stuff. We got you guys It's just scheduling has been kind of different since we you know since he's uh, he doesn't stay in the same place He used to so best believe we got you we read the comments for sure. So let's check this out man Let's get right into it. Once again those in the chat appreciate y'all joining with me joining in with me and Let's see Some of these clips. This is a old video came out round last year Last year in April. So we'll check it out. Oh Yeah, I remember that one. I remember that he busted him open hard way That's you don't see blood like that.  Look at this again. Oh my god. Oh Yeah, he's done. That was funny. Oh My goodness There's some stiff moments, bro. Oh, oh my gosh Whoo, yeah, damn. Oh, this is this is WWF. This is all Who Jesus? No, I don't think he was supposed to land like that. Oh, oh Yeah, he wasn't like that. That was nasty.    Oh Wait, did he give there's no way he got busted open from that Did he? Was that a blade job?  I think yes.  Come on. I gotta do better Old-school Kofi. Hi damn I Remember that he he actually messed up John Cena's nose.       I Love it. Oh jeez sus, bro Time in on that has to be perfect. Oh Yo Yo, this is old school That's crazy.   Oh Wow Cesaro can really go. They just never really Gave him like the chance to really run as a main event guy. Oh, yeah, that I remember that Yep, I remember that Brock less than them elbows man. I remember that I remember that oh Wow, oh Oh Geez I'm not sure but I think that's the move that Force heartbreak. I say heartbreak break heart to retire. I think that was the move I'm not sure y'all comment down below. Let me know. I'm not a hundred percent great with my wrestling history But I do believe that was the move that caused bread heart to retire. So let me know. Let me know if I'm right down below Man not the infamous point. Oh I don't know why they be laying like that cheeks up.  So he got a concussion from that What Yo black with the knees and the kicks. I Remember that one. That was pretty dope. That was pretty cool timing on that one. Oh Oh On the back is neck Jesus and Dolph knows how to sell more. Yo, yo Yo, those next spots be yo, that's Look at this. Oh my god. Oh my god. That's right joey styles with the Emphatic. Oh my god. Oh Here we go. I Love that little combo. He does What noise was that? God damn Yo, that's whoo, look at his nose man NXT got a lot of stiff moments. I love it. I Love it.   So for those who say wrestling is fake storylines in the outcome of the matches are predetermined, but It takes a lot of Choreography and training to be able to do what they do in the ring, man So when stuff like that happens where people accidentally get dropped on a neck or get concussions like that Takes years off their profession and sometimes years off their life, man So much respect to all those that pursue wrestling and those that are in the wrestling biz It doesn't even have to be WWE it can be anywhere It's just the fact that you're putting your body on the line to entertain people like me and all around the world It's much respect to you guys, man And you know, hey, that's better than me because I'm not there's nothing You couldn't pay me nothing to get destroyed by Brock Lesnar to get busted open hard way by Brock Lesnar or Get suplexed into oblivion now. I'm good. I like my neck It'd be hurting from time to time but not not pain like that So but I hope y'all enjoyed the video. I hope y'all enjoyed it in the chat, man You'll be some more lives on this channel. I'll be premiering more videos on here If you guys want to let me know comment down below Like I said, we got some more videos lined up for this weekend. So best believe stay tuned. We got you guys We just trying to get things situated schedule on eyes But I appreciate y'all kicking it with me and I see y'all on the next video. Peace


I call upon the adventurers amongst us. Go forth, but careful where you tread. This journey will not be smooth, for untold glory lies ahead. A moment of shining quality. And they've done it. It was about determination and desire. Maybe I'll let victory take you high, lure you to my pinnacle. Just remember, one minute you're off, the next you're down. My spirit's unpredictable. FA Cup story. Be not afraid to dream of glory and play with fire. My will is to make impossible the truth and prove certainty a lie. Fabulous finish. Some say it's the look of the draw. I say it's the draw of the look.  In my world, every game's an adventure, for I'm the Emirates FA Cup.


Hi everyone, Joe for jaspyscasebrakes.com coming at you with jaspys six box Stanley Cup Finals quick hit mixer Number one one spot gets you two teams in this break. We comboed up the ducks and coyotes. No cracking There are the boxes right here. There they are for visual earners Should be nice big thanks to this group for making it happen Thanks to the people bought spots straight up and congrats again to the people who won spots in series one s1 break one So it's W up like sir mix a lot. Oh double up. Oh, oh ducks coyotes comboed It's not a good combo coyotes will eat ducks, right? Let's roll it and randomize it three and a four seven times her names and teams one two three four five six and Seventh and final time three and a four seven times. There's seven right there. We got Brittany down to Stephen Three and a four seven times for the teams one two three four five six Good luck seventh and final time after seven. We got the Oilers down to the Panthers after seven Game four on the background end of the first period 10th Bay up Won nothing That was a fast goal 36 seconds fastest goal to start a Stanley Cup final game since 2006 Brittany You got the Oilers Steve Birch with the flyer Stephen with the flames Steve with the abs Stephen with the penguins and preds Steve with the devils Stephen blues lightning wild Andrew with the shark Stephen with the Canadians Brittany with the Golden Knights Stephen you have the Kings of blue jackets and the ducks coyotes combo Steve Capitals and maple leaves Stephen with the red wings and conducts Steve with the Islanders Stephen with the black Hawks and Hurricanes Andrew Bruins and Senators Stephen with the Rangers little last Bob Mojo star next to your name Florida Panthers also last Bob Mojo. They say 70% of the time last Bob Mojo hits 100% of the time Andrew with the Dallas Stars and Stephen with the Jets and Sabres Let's alphabetize by team and we're gonna pause the video when we come back We're gonna see if there's any trades then we'll have the break stick around BRB All right. Welcome back ladies and gentlemen No trades no deals are done. So the list you saw on the screen is now officially printed out Thanks everyone for getting in on it. Appreciate it Let's get the let's get this out of the way first It's a little more cards here, and then I think it's just one card per box after these O'peachy Platinum's Good luck everyone. Hopefully we'll find some nice some nice stuff here Stephen you pointing out that McCar would be the big poll in 2019-2020 stuff So other Steve he thinks should be really happy. Yes, he verged Randomized the the Colorado Avalanche. We're attempting to go up 3-1 on this series Well, that was the first period is over. Let's close the poll given everybody enough time to vote and the results of the poll Who wins game for tonight? It's pretty close. Just 51% are saying that it'll be Tampa Bay to tie up the series So here's the first box is Jack Hughes Rookie for New Jersey that'll be for Steve Birch Noah Dobson that sunset parallel and we've got Timo Meyer Blue rainbow to 149 for San Jose. That's for Andrew. Do all cards ship in this? I'm assuming so It doesn't say all cards will ship Austin Matthews right here Here Luke Dubois Sunset There's Marquis Rookie Capococco Nick Suzuki Rookie Rainbow That's for Montreal Steven you and for the Rangers. That's also Steve Steven you and we got Aaron Etblad to 199 red at Prism and that's for Florida That's gonna be also Steven you Hampus Lindholm Matt Pink Also for Steven not numbered and the autograph Eric Brandstrom For Ottawa, that's gonna be for Andrew and the Senators It's the rookie auto 24 out of 50 on that one Another box. How are we liking the hockey fans? How are we liking the ESPN coverage? Think they did they this is their first year in a long time, right? Broadcasting hockey again, I feel like they've done a pretty solid job We get a little little more hockey highlights on Sports Center I think just the interest and knowledge in hockey. I think is ticking up a little bit Which is good for the hobby John de Veris sunset.  That's for Steven you Yeah, yeah, yeah, see you man. Yeah, see you man. Yeah, there's a kale McCarr marquee rookies for Steven Steve B this time With the abs Down a goal at the moment Thomas Kavett to 299 Quinn Hughes marquee rookies No parallel just the base For Vancouver, that'll be for Steven you Ottawa. That is for Andrew Jack Hughes Marquis rookies for New Jersey Steve B We've got a Gustav Nyquist Violet pixels to 399 Marquis rookie rainbow Oliver Wallström for the Islanders Steve B with the Islanders Columbus that'll be for Steven you I like the parallels no peachy plant. They're always always pretty nice. All right Now let's get into this some clear cut one premium card inside a hard sign autograph sealed in a one-touch Thanks upper deck Pop that into a one-touch Are they covered or no, they're not covered Just make it out in the open. All right. Here comes the good stuff lays on good luck We have here a rookie and it is Buffalo Victor Olafson Steven you with the Sabres We got oh, there's a card on top of a car What we got here? Oh a redemption. It's Igor shurster again It's a clear cut metal universe Palladium rookie auto For the sure-circuit is a ranger, right? Goal tender for yes, the New York Rangers Get a little bit of that adhesive off and that'll be for Steven you with the Rangers Last spot mojo strikes again. All right, and now Still some sticky stuff on top of that But the hit is gonna be He's gonna be rookie debut upper-deck canvas Cody glass Nice, I guess this this was your auto and then we've got a bonus card out of here that'll go to Vegas That's gonna be for Brittany the Golden Knights. Oh good luck. Gilo. Enjoy it and the final clear-cut card Is gonna be an Acetate card here. It's pretty sharp Palladium auto Chris cellos Like the blue ink on there That looks really cool. That'll go to Chicago Steven you that's what we got in here hip-raid hockey emerald edition series to box 87 out of 100 I see draft day marks. Oh Nice 2018 draft pick number two Andre Shevnikov Autograph right on the K right there There it is 16 out of 35 on the numbering Carolina Steven you with a spot that he won Randomize the Carolina Hurricanes Guess this draft day marks There you go gang real fun break nice quick little Stanley Cup finals quick hit mixer from Jaspy's case breaks calm Thanks for watching. Thanks for breaking with us. I'll see you next time for the next hockey break. Bye. Bye


Hello. Oh, can you? Wow. All right. Thank you all so much for being here. I know we're all feeling really well rested after last night's party, but don't worry either way. This is not a technical talk, though I'm sure you're going to learn something anyway. Can everyone hear me okay? Awesome. Okay. So before I start, I just want to say two things. First I want to offer just a huge thanks to the NorthSek organizing team and all the volunteers here. They really make it look easy, but there's a tremendous amount of work that goes into pulling off something like this, so thank you. Second, just at the outset, I wanted to flag that while I have many fancy sounding titles and I've worked with some of the really best lawyers in Canada, I myself am not yet a lawyer and I am not here to give you legal advice. So while I'm excited to hear your thoughts and questions after the talk, if something I say makes you think that you might have a specific legal problem or a specific legal question, come see me afterwards and I'll refer you to a member of the bar. Cool. So with that out of the way, this talk is called Law Metaphor and the Encrypted Machine. At the core of what I want to share with you today is the argument that the language we use to describe complex technical problems, what words and stories we use to understand what technology is and what it does profoundly shapes the law. And in the case of encryption, this debate over metaphor is arguably a really important part of a much larger battle, one that's going to profoundly shape our lives. So I hope today that by making how the law thinks about this technology more transparent, we'll get closer to answering questions like could the government force your company to build a backdoor into its software or could a cop or a judge force you to decrypt your hard drive or hand over your password? And also should they be able to do either of those things? So there are really four parts of this talk.  For most of you or some of you, these issues are going to be obvious. They're going to be in some way part of the work you're already doing. For others, maybe you've really never thought about your work as having a moral or a political dimension, but I'm going to make the case that you probably should. Next, I want to talk a little bit about how the law works, how laws change, how we interpret them. I'll argue that metaphor, reasoning by analogy is critical to how the law thinks. It's impossible to separate law from language and from the built-in biases of human reasoning and all these cognitive shortcuts we use to understand what's going on. That tendency is even more clear when we're talking about technology. The fact that we're only starting to understand the implications of metaphor in the encryption debate is really a problem. So third, I'm going to show you a few different models for how lawyers and judges think about encryption. And we're going to work through what they might mean for us and for our rights. Finally, I want to just talk a little bit about why this matters, what role that we as technologists or designers or lawyers or citizens or troublemakers might want to play in this debate. So to keep things really simple today in this talk, I just want to focus on the data at rest about a computer or a phone using full disk encryption. As you all probably know, encryption is the process of using cryptographic algorithms to make the substance of a message, plain text, incomprehensible by turning it into ciphertext using a key. Decryption, which is the process that uses the secret key, is the process of returning the ciphertext to the original plain text. Again, as I'm sure most of you here already know, there's a huge inherent advantage mathematically in encrypting versus trying to break encryption. Bruce Schneier writes that it would take the most powerful supercomputer in the world a 46-digit number multiplied by the age of the universe to break a single key using AES 256. I'm a law student, and so I haven't checked the math on that, but I'm going to trust Bruce. So while the government can and the government does attempt to exploit other vulnerabilities in networks and computers and people, there's just no realistic incentive to try and break encryption through brute force. To quote Edward Snowden, encryption works. Properly implemented strong crypto systems are one of the few things that you can rely on. The problem, of course, is that everyone gets to rely on it. Encryption provides protection for government officials and banks and software companies and human rights activists, just as it does for child pornographers and terrorists and criminals. This poses huge problems for the law, whether in criminal investigations or commercial disputes, intelligence gathering, national security, and at the outer edge, what encryption does is create spaces that the government can't reach. Potentially spaces the government can never reach. So in the words of Philip Rogaway, cryptography rearranges power. It configures what we can do from what. This makes cryptography an inherently political tool. So what happens when encryption is standing in the way of a police or anti-terrorism investigation? Where there might be evidence on an encrypted phone, like in the Apple and FBI case, or on an encrypted computer, like in the case of Laurie Love, the UK activist who recently had a series of altercations with the law. It would seem like the obvious solution to these kinds of problems is basically to put a system in place that would let police officers or courts force individuals to either hand over the unencrypted contents of an encrypted device or to give up the secret that would let government authorities do it for themselves. This is compelled decryption. And that's essentially what's been done in the United Kingdom under a law called the Regulation of Investigatory Powers Act. RIPPA gives all kinds of government officials like judges and border agents, military, certain kinds of police, the power to compel decryption. In cases where someone refuses in order to decrypt, they can be put in prison, in some cases for up to five years.  There are a lot of cases where it's preferable for someone to refuse a court order and face some jail time rather than hand over evidence of what could potentially be a much more serious crime. By contrast, in the US, one of the leading cases on the issue is called the Boucher. We're all familiar with people like pleading the fifth on television, right? Yeah, okay. So that's that's your right against self-incrimination. It's the idea that the government can't force you to be a witness against yourself. In that case, in Boucher, the court ended up basically using the Fifth Amendment and accepting the argument that by forcing someone to hand over their key or to decrypt a machine, you're forcing them to self-incriminate. The court decides that on the basis of like the process of surrendering your key is like testimony. So we'll come back to that idea. And because of that, citizens are protected in most cases from being forced to decrypt their devices under the Fifth Amendment. In Canada, our Supreme Court hasn't actually addressed this issue yet. But it's unlikely that a cop or a court in Canada would have the power to make you decrypt a personal device here either. This is because we have similar protections against self-incrimination in Canada through Section 11 of the Charter of Rights and Freedoms. Because compelled decryption requires the actual participation of the person who's accused of a crime, it engages your constitutional rights. This right exists at least in some degree to some degree, because we believe you ought to have the choice about whether or not you cooperate with authorities. So like I mentioned, while our Supreme Court hasn't yet dealt with this question, there's been a pretty good case here in Quebec called the Crown versus Boudreau Fontaine. In that case, our Court of Appeal decided that a police officer who ordered a man to enter his laptop password as part of an investigation, not only violated his right to silence, but also his right to be presumed innocent, his right not to be conscripted against himself, and his protections against self-incrimination. So the short story is that from a constitutional perspective, at least, forcing someone to hand over their encryption keys poses some serious problems. And even where that option is available, like in the UK, it's not really a viable end game solution. So these protections, both the technical ones and the constitutional ones, are a source of endless frustration for law enforcement and intelligence. James Comey, who's the director of the FBI and one of the most vocal opponents of strong encryption, recently said at a Senate committee, he was there to speak about terrorism. He said, there's no doubt that the use of encryption is part of terrorist tradecraft now, because they understand the problems we have getting court orders. Of course, terrorists use all kinds of things that the US government doesn't seem to have a huge problem with, like guns and cars. Guns killed over 13,000 people in the US last year. Even more people died in motor vehicle accidents, but James Comey isn't worried about cars and guns. It seems to me conspicuous to single out just one technology that in a lot of cases, in most cases, is keeping us all a lot safer when we're willing for more freedom or convenience or efficiency to accept so much more risk in other dimensions of our lives. But this is all part of a larger narrative that the FBI and others call going dark. That is, they say, as more and more data becomes encrypted, law enforcement and intelligence are losing access to the material they need to do their jobs at a critical cost to national security. Comey and other backers of this narrative have argued that courts should have the ability to compel decryption. And failing that, they've argued for the creation of backdoors and encryption technology. They want to encourage or, in some cases, force people like you to design your products in ways that would give government access to encrypted data without its owner's consent. That was a big part of the issue at stake in the Apple and FBI case. But there are at least two very convincing counterarguments against the proposal for encryption backdoors. The first is that many of the world's leading computer scientists have said over and over again that building backdoors fundamentally breaks the technology. They say that it's a problem of putting keys under door mats, that there's no way that we know of to design a system that only the good guys can access and that can only be used lawfully and not by our adversaries. Backdoors can be exploited potentially by anyone and therefore we have the right to use the technology and security for everyone. Of course, even if the government could force companies domestically to build systems they can access, the bad guys would just use gray market software that hasn't been backdoor. There's a second argument, too. One that strikes against the demand for encryption backdoors and against the idea that the government should be able to force you to decrypt your own devices. Rogue away, the cryptographer I mentioned earlier calls the going dark narrative a brilliant discourse of fear of crime, fear of losing our parents protection, fear of the dark. Because this story as spooky as it may be isn't really an honest one. Things just noted we know that nearly every part of our lives in some way can be monitored or captured by government surveillance. Without any meaningful civilian oversight, without any transparency about how our information is collected, without any idea about how long it's kept, who it's shared with, what it can be used for. In reality, even with the widespread use of encryption, nothing is truly going dark. As Peter Swire has shown, even where law enforcement loses access to specific information in specific contexts, those losses are more than offset in massive gains in the form of new material that was never before available to law enforcement. We're actually living in what he calls a golden age of surveillance. And look, I'm speaking to you, even if you're one of the people, and I know you're in the audience thinking, I've got a top secret security clearance and I've seen things so threatening and so horrible that we need this kind of surveillance power. We need the ability to see inside the encrypted machine. Because even if you're okay with giving government that power, and I'm not, but maybe you are, I think people really underestimate how easily that power can be abused. Tools that we might be okay with using to hunt down terrorists or child predators quickly end up being used to go after copyright violations or shoe parking tickets. This is what we call mission creep, or function creep. It's hard to take away these toys once government has them, and it's hard to say no to perfect enforcement if you have the technical capacity to do it. Here's one of my favorite examples of that, where an MC catcher was used to hunt down for a guy who stole some chicken wings. But more than that, governments change. What's legal today might be illegal tomorrow. Every one of us has been a criminal, at least in some small way, at some point in our lives. And people working in security should be particularly worried, because judges and politicians are really bad at understanding what you do, which means that your work is especially easy to criminalize. As Biela Coleman mentioned yesterday in her keynote, people using perfectly legal technology like Tor, which technology beyond its other practical uses is literally keeping human rights activists alive, are routinely branded as suspects and as criminals by government. Here in Canada, a 19-year-old straight-A student just pled guilty to a criminal charge because he embarrassed the government when he showed that the Canadian Revenue Agency was vulnerable to heart bleed. This kid might have been reckless, but he was no criminal, and he had no intention to cause harm. He was doing security research, like all of you do, and now he's under house arrest and he's going to have a criminal record for the rest of his life. I did a study last year where we looked at 30 different attempts from all around the world over the last 25 years to write a bill of rights for the internet. We studied all of these projects that we're trying to figure out how we ought to protect and entrench our freedoms in this new digital space, and people listed the kinds of rights you would expect, like freedom of expression and access to the network and privacy. But something that surprised me was that in almost a third of the documents that we looked at, they actually spelled out the right to use encryption specifically as a right, as fundamental as freedom of association. But maybe it's not that surprising after all. The government's inability to compel disclosure of an encryption key or to force a tech company to steal it for them is the only thing standing in the way of the government's ability to secure total informational awareness. So what I'm trying to get at here is that terrorists and child predators aren't the only threat in the world worth protecting against. And if we're concerned, like I am, about government information, it's a kind of final frontier, a possibility for space beyond the government's reach. And keeping it that way, I think that's a right worth fighting for. Because access to perfect information invariably leads to the possibility for perfect enforcement. And in the words of Yochai Benkler, imperfection is a core dimension of freedom. So how does the law think about encryption? Well, to explain that, I'm going to talk about what the law thinks generally. So, law is like code in lots of ways, but unlike code, it is not binary. Every word has multiple possible meanings. It's context dependent and certain and shifting and interpretive. Nevertheless, it has its own internal logic. So the first thing that you need to know is that the law relies heavily on categories. Change in the law is often about shifting categories, shifting boundaries of what's included and what's excluded. I'll give you an example. One of the most important categories in the law is the category of personhood. Because persons have rights that non-persons don't have. Personhood, though, is in some sort of fixed or static or objective concept. After all, for a good chunk of the 20th century, the legal model of personhood is what people shrug, like what let people sort of shrug their shoulders and say, look, we'd love to give women the right to vote, but the law says only persons can vote and the case law says that women aren't persons. So sorry. Slavery, segregation, we're both justified on the basis of this really sick assertion that black people were not persons or maybe only three-fifths of a person. The category of personhood is why anti-abortion activists are so fixated on the question of when a fetus becomes a person and the fact that over time the law has decided that corporations are persons has had its own profound legal and political implications. The second thing you need to know about the law is that the law tends to think in metaphor. If you're a jurist or a lawyer or maybe you just dated one once, you're probably going to recognize these two names on the screen, Fuller and Cardozo. Lon Fuller, who's this iconic legal philosopher, said that metaphor is the traditional device of persuasion. Eliminate metaphor from the law and you've reduced its power to convince and convert. Justice Cardozo, one of the most famous American judges in history, warned that metaphors in the law are to be narrowly watched. For starting as devices to liberate thought, they often end by enslaving it. And the history of technology law is very much a history of metaphor. For example, the whole framework of telecommunications law was actually built on a legal model that was meant for railroads. This is all just a big abstraction about interstate commerce in the Constitution. So basically everything new is folded into the law by making reference to what came before it. At the same time, the law is notoriously bad at understanding new technology and legal change is always going to lag behind technical change. The law is slow. Some people argue that this is actually part of the design of our legal system. We need to know what we're dealing with, where interests lie, what the risks are. We also know that the rate of technological change in a variety of sectors tends to track Moore's law very closely, at least for now. That means that we predict a doubling of technical capacity every 18 months or two years. By contrast, it routinely takes that long for a single case to get to trial in Canada. Years more to reach the Supreme Court and become binding to the law. And that's what we're dealing with. We want Moore to reach the Supreme Court and become binding precedent for Parliament to review an issue or amend a law. We also want to avoid legislating in the hypothetical for all kinds of good practical reasons with actual problems on the table, poverty and climate change and war. It's pretty difficult to justify spending time and resources on like legislating penalties for negligent time travelers or drafting traffic regulations for jetpacks. And speaking of time travelers and jetpacks, we're really notoriously bad at predicting the future anyway. So when the law strives for proactivity, it sometimes falls short. So all of these factors mean that the law has a degree of intrinsic reactivity to it. And that claim has been born out again and again throughout history. Copyright laws emerge from Gutenberg's machine, the printing press. And it took hundreds of years before there was any comprehensive law that addressed it. Nearly as soon as those laws were in place, we get cinema and photocopiers and tape recorders and VCR and fax machines. And eventually the internet and arguably copyright law has not yet caught up to that. So analogy and metaphor are essential because they help the law play catch up. It lets it fold in these new technologies as they arise. But metaphors aren't value neutral and they shape precedent. And metaphors are everywhere in technology. Take, for example, probably what is the most infamous failure of metaphor in technology, which is this quote from Ted Stevens in the net neutrality debate that the internet is a series of tubes. But that comment isn't actually as totally out of left field as we might imagine. Because that debate was actually rife with all kinds of metaphors, particularly about public infrastructure. Net neutrality was all about the information superhighway and fast lanes and slow lanes. And even traffic itself has a metaphorical dimension. And if the internet is like a highway, what exactly is traveling on the highway? What is data? In the copyright debate of the mid-2000s, we saw a lot of metaphors like this one. And I'm sure the person who came up with this thought they were being really clever. But really what they're doing is they're arguing that intellectual property is exactly like physical property. And that's a metaphor that record companies and the music industry really love, but it feels absurd to pretty much everyone else. We hear all kinds of other metaphors about data, particularly big data, whatever that means. Data as a natural resource is one that's been very popular in some circles. So you see language like data as an asset or reserve, data as the new oil, raw data, data mining. Sigalowski, thinking in the context of privacy and surveillance, proposed a different metaphor that I really like, which is data as nuclear waste. He invites us to consider it quote, not as a pristine resource, but as a waste product, a bunch of radioactive, toxic sludge that we don't know how to handle. Again, as Beela explained in her keynote, there's an element of metaphor and category around how we treat digital activism too.  And of course, there's the cloud. This ephemeral, distant, borderless thing that moves data away from physicality from tubes and highways into like the ether, right? And so the closer we look at the language we use to describe our digital lives, the more we realize that almost every word we use has its own metaphorical baggage. I particularly like nautical ones, like surfing and torrents and leaks and pirate. But like highways and tubes, we have all kinds of structural imagery. Portals, paywalls, firewalls, chat rooms, even the idea of cyberspace has a dimension of physicality of jurisdiction. Some metaphors, like cloud, also have a natural dimension. There are ones like web and bug and mouse and virus and shells and worm that fit into that category too. There's actually a woman named Sue Thomas who wrote a whole book on this. As someone mentioned to me the other day, a new one is cyberpathogen. And so look, some of these are potentially coincidence or background noise. And not all of them are going to have profound legal implications. But some of them do. And where the technology is complex and novel, and where the metaphor obfuscates or misleads, we can end up with really serious consequences for the law. So there are these three authors, Sonny, Suri and Lee, who coined the term metaphor vacuum, because it itself is a metaphor. They give broadcasting as an example of a metaphor vacuum. Broadcasting had originally been conceived as a point-to-point technology. So originally they were sort of using it like a messy wireless telegraph. But as people started to use it for broadcasting and mass communication, the law really struggled, particularly in copyright, to understand what it was. As a side note, the original meaning of broadcasting is itself a metaphor. It means the act or process of scattering seeds across a field. So anyway, from my perspective, what we have is a kind of metaphor vacuum in encryption technology. It seems that much of the way courts and politicians respond to the encryption problem actually sort of hinges on what they understand encryption to be. So that is that the legal outcomes that we arrive at tend to depend on the model we use to imagine the encrypted machine in a narrative sense. So I want to briefly canvas some of those metaphors because they have implications for whether or not a court can compel the encryption as well as for the broader going dark debate. So the one thing that you need to understand first is that the heart of a lot of these legal arguments is the idea that acts of encryption or decryption, because they normally use a passphrase that you store are testimonial, at least in the US and Canada. That is, to hand over your key is sort of like being forced to testify against yourself, to be a witness against yourself. And where there's testimony, it seems that the courts have accepted that you can be protected from compelled decryption on the basis that your rights against self-incrimination are engaged under the Fifth Amendment in the US or Section 11 in Canada. The US case Boucher I mentioned earlier is interesting for that reason. So in that case it's not about the contents of an encrypted drive being protected but the very act of turning over a document or providing a password to an encrypted hard drive that implicitly communicates incriminating facts, including the fact that you can open the machine at all. So for that reason it's protected. The danger with this model is that you can end up with incoherent results when you focus too much on what form the key takes rather than what the actual rights are at stake. For example, there have been at least two cases I can think of in the US where individuals have been forced to unlock an iPhone with a fingerprint because it didn't have a passphrase and so the logic is that fingerprints are biometric data and therefore non-testimonial and therefore you can be forced to decrypt a device. It's not totally clear whether this is the law in Canada but it's bad law anywhere. Where your fingerprint is used instead of a passphrase nothing has changed about the potential content stored on the device nothing has changed about your expectation of privacy, nothing has changed about the mathematical process taking place yet your rights have been profoundly affected. So if decryption is speech at least sometimes, what is the encrypted machine? The analogy that law enforcement and national security agencies really like to make is the idea of the computer as a locked box or a locked room. James Comey, our favorite FBI director is always going on about locked closets but he also argues that Apple was he argued that Apple was selling cars with trunks that can never be opened or apartments that can never be entered. What you gain by this analogy is that it basically treats digital and physical evidence identically. It minimizes the testimonial aspect of disclosing a private key as much as possible. You see like there's no speech involved in opening your car trunk, right? It rejects the idea that there's some sort of qualitative difference in the kind or scope of information stored on digital devices compared to what otherwise might be physically discovered. So once we make that metaphor more transparent it's clear that the going dark language about back doors and front doors is actually all part of the same framework. The idea of a combination safe might be a little bit better. It's been used in a couple of different court cases because the combination is stored in your mind so there's some testimonial dimension to it. But even by trying to introduce that aspect you're still kind of treating the device like a container which falls short of the technical reality. Encrypting a hard drive doesn't create a barrier between the data and the outside world but it means instead that the information exists only as cyber text it's scrambled. There's no intelligible data hidden inside of an encrypted machine. The data is transformed it's not enclosed. So in this light all of these metaphors box and safe and car trunk and closet simply don't reflect the operation of the technology and unlike a locked room or a combination safe encryption can't be cracked or broken into or dynamite it. It's practically unbreakable. In an amicus brief from the ACLU and the EFF a few years ago they proposed a model that sort of gets around this. They argued that asking an individual to decrypt their machine was not like opening a box full of data but instead in order to explain or transform or translate it. They argued that a court can't make you transform information that's incomprehensible into information that's going to put you in jail because that would go completely against the heart of what the Fifth Amendment is supposed to be about. To illustrate that principle they use the metaphor of translation for decryption and an undecypherable book for the encrypted machine. The idea of translating an undecypherable book is a lot closer to the actual mathematical process involved in decryption. It also more clearly illustrates the testimonial aspect of disclosing a private key but at the same time in that metaphor the idea of testimony speech translation is doing a lot of work and so I worry about the kinds of problems that we start to run into if the key is something not not testimonial like a fingerprint. The ACLU has also recently proposed in a brief the model of a puzzle rather than a book or a library. I'm still thinking about it but my instinct is that it might help fix the problem around testimony. The puzzle metaphor might also help developers in Apple VFBI like cases who don't have their own self-incrimination right to invoke because they're not the ones under criminal investigation but where a court is still trying to make them break their own software to get at someone else. In terms of the law the legal question in those types of situations is often going to be more like what are the limits of what a court can make you do by order by injunction. So the idea of forcing someone to solve a puzzle reminds us that the real moral issue here is compulsion. The idea of being forced to undertake something that incriminates yourself or someone else and not literal speech. Finally I want to refocus on the content itself because in closing I want to make sure that we don't lose sight of the values that we began this conversation with in the first place. So what kind of data is it that we keep on our devices? What kind of data do we encrypt? Is that our assets? Is it our nuclear waste? You know our Supreme Court has actually said a bit about this. When they were talking about the heightened expectation of privacy we have over our personal computers. In a case called the crown versus VU. They said that we can't imagine our devices as simple containers like cabinets or cupboards. They really reject James Comey's closet in his locked boxes. But they don't reject it because it's technically inaccurate. They reject it because of what it can reveal about us, what our devices reveal about us. And these machines are as Vannevar Bush argued as early as the 1940s an enlarged intimate supplement to human memory. They are deeply personal and imagining compelled decryption as a kind of intrusion into the human mind or our most intimate physical places makes a certain kind of sense. Especially as we start to blur the distinction between what is our machine and what is our self. The courts have spent a lot of energy trying to figure out what the device is, what the process of encryption is. But I think in choosing metaphors that we need to be careful not to lose sight of what's actually at stake. The space that's based beyond the reach of government power, space for experimentation, for secrets, for radical intimacy. So Jeffrey Kiyak and others, mostly legal scholars have argued that we need to be hesitant to analogize our employee metaphors at all. That they lead to bad law, bad politics. These critics argue that courts should be engaged in fact specific inquiries informed by technological experts about the precise nature of the technology at hand in precise cases. And he's right. But I'm skeptical to the extent which the law can actually escape this kind of thinking. To the extent that we can move past the cognitive shortcuts we use to understand the world around us. In particular he's right that metaphors can obfuscate, they can distort. But they can also potentially illuminate and make the political implications of our choices more transparent. They can reshape the boundaries of power. So we need to be careful about the choices we make and the words we use. Of course these aren't the only legal problems related to encryption technology. I skimmed through a bit of the case law again this morning and it's an area of the law that's changing in a lot of different ways, really fast. We don't know yet, for example, how these problems are going to play out at the border. There's a case in Nova Scotia involving a man from Quebec called Alain Philippon. He's going to go to trial in August and I think a lot of people in the tech community are watching that really closely. He refused to give border officials his BlackBerry password, so he was charged under the customs act with hindering a customs officer. That charge carries the maximum penalty of $2,000 fine and a year in jail. We can talk a little bit about this in the questions if you want. There's also the Crown versus Fear and Issue, which is a 2014 Supreme Court case where they decided that the police are allowed to search your cell phone incidental to arrest. The court put a few safeguards in place there, but they don't really talk about encryption and that's worrying. They make a really strange comment about whether or not you have a password that doesn't really have a huge impact on your reasonable expectation of privacy. So we're going to have to see how that plays out. I've also seen one or two cases where even when the court has recognized that it can't actually force the accused to surrender their encryption key, they infer from the fact that they're using encryption that they're up to no good. Obviously, those kinds of assumptions make bad law and bad politics, so that's a myth we're going to have to work to debunk. Anyway, what does this mean for you? Right? I think as technologists, engineers, designers, project managers, people who are actually building these systems, systems that can shape the rights people have and the risks they face, you've got some real responsibility. You have to think about the values that you're embedding and the technology you make. And what's its take here, right? Like, maybe it's some big company's money. Fine. But maybe it's someone's privacy or their reputation or their job or their immigration status or their physical well-being. For all you know, your users are the next corporate whistleblower or the next Ed Snowden or the next Dr. King. Your choices can be the difference between safety and imprisonment for people. So keep in mind what we know about encryption and the law. It's unsettled. Law enforcement is bent on building in exceptions and weakening constitutional protections. Staying on top of how the law changes is going to be an important part of how you can design meaningfully secure systems. Remember that you can't exercise your users' right against self-incrimination for them. So when the government comes knocking at your office you can be forced to hand over information even if they wouldn't want you to. That's why the gold standard is kind of a system that just doesn't let you betray your users by design. Apple has actually been a really good example of this. If you literally can't decrypt your users' data without their participation, you make sure that they have the choice about whether or not they want to exercise their constitutional rights. Obviously it's probably not possible to design systems like this in all circumstances. But that doesn't mean that you shouldn't be trying to embed these values wherever you can in your work. I think it's about respecting your users. So even where the stakes aren't that high you have to hold each other to a high standard. Think about integrity in your work because it's not just like Alice and Bob using your software. It's actually real people who will face real risks and consequences. So I've been really grateful to talk to many of you since the beginning of the conference yesterday. And I know that there are actually a lot of people in this room who take these issues seriously who care about privacy and who care about freedom. So the challenge is then how are you going to make these values a bigger part of your work? We're counting on you. Thanks.