Have you been to Isolde in New York? Love their lasagna. No, I haven't been to Isolde. I usually go to two places. One is called Norma. It's here on 33rd and 3rd Avenue. It's just Sicilian, very authentic, I love it. The other one, which is absolutely the best pizza in the world, it's called Sorbillo. It's very close to NYU. The pizza is just like in Naples, and it's like you can't find anything better on this planet. So, you know, you should go to Sorbillo. And if you mention my name, you get discount, right? So, yeah, I have a special power. Yeah. Chicago pizza is not pizza, it's pie. So if you call it Chicago pie, it's fantastic. It's not pizza. Napoli pizza is the only pizza. Okay. All right. So, what do we talk today about? Today, we talk about something called the trackbacker-upper. What are we trying to do today in this lecture? So this is the first lecture where you're going to have me basically reading with you along in a paper, okay? Such that we can figure out whether things make sense. This is how we educate ourselves in the research world. You don't have necessarily Jan on your side all the time, so you had to read papers and you had to make sense of what's written there. Usually, I transcribe them again back with Taipora just to fix the main topics in my mind, right? And to commit to time, right? Such that I can check later what's going on. So let's get started. So what is this setup? What are we trying to do here? So we are trying to design by self-learning of a nonlinear controller to control the steering of a trailer track while backing up to a loading dock from an arbitrary initial position. Only backing up is allowed. So what does this mean? What we are trying to do is going to be learning how to drive a track, which is maybe not that complicated if you go forward, but we are trying to park a track, so we just go backward and only backward. So if you try to park your car in a parallel parking, it's a little bit complicated, perhaps sometimes, and not always you can get it done with one maneuver. If you have a trailer track attached to your back, it's a mess. We are figuring out this very soon. So how does this trailer track work? We have two items. We have a cab, which is the top right part there. So we have an angle of the cab with respect to the x-axis, and then we have the coordinates x and y of the joint between the cab and the trailer. Then in the part below, we have the trailer, and therefore we have the x and y location of the back of the trailer track, and then we have the theta trailer, which is telling us what is the angle of this trailer track with respect to the x-axis. Our objective is to drive this guy backwards until the back of the trailer hits basically the dock location, which is represented there with the x-dock and y-dock coordinate. Moreover, we would like to have the theta trailer, which is the angle of the trailer to be zero, such that basically the trailer is orthogonal to the docking station, and basically the rear part of the rear, front and rear, rear part of the, is rear back, right? I'm speaking English. I guess it's correct. So the rear part of the trailer should be parallel to the docking station and as close as possible to the location of the dock. So far, makes sense, right? Yeah? Okay. All right, so let's read a little bit more further in this paper. So we have some state variables, there are six, and which is not quite, I think this is perhaps not correct, but we'll figure this out soon. So the state variables are the theta cub, which is the angle of the truck, then x-cub and y-cub, which is the Cartesian position of the yoke, which is the part behind the cub, and then you have x and y trailer, which is the position of the rear of the trailer, plus the theta trailer, which is the angle of the trailer, okay? So basically how it goes is the following. So the whole procedure is the following. The truck backs up until it hits the dock and then it stops. The goal is to back the trailer to be parallel to the loading dock, right? So the goal is to have the backside of the trailer to be parallel of the docking station. And then having the x trailer and y trailer, the location of the back of the trailer as close as possible to the x-dock and y-dock, okay? Are you with me so far? So the initial position is going to be randomly set. And then we have to back up until we, so the objective is to back up until we hit the trailer and the dock with the back of the trailer. And we are exactly like orthogonal, okay? There are a few difficulties with this. And we're going to figure out right now what are these, all right? So we go CD work, GitHub, Pdl, maybe go conda, activate Pdl, right? Now we have Jupyter notebook. So in this case, we are going to be going over this truck backer upper. So something I really like to do in Jupyter notebook is go use actually the Unicode. Since we are using Python 3, we can use Unicode, right? So to write pi there, I just do backslash pi, then I press tab, you get pi, okay? Or you can do alpha, beta, comma, okay? This is not coding. This is notebooks, okay? You can do ugly things like this. All right. So I just initialize some libraries. This is our setup. I initialize some object here. We don't care right now. And we start by visualizing this guy here, okay? And I think I had to unzoom a little bit because you can't see. There we go. All right. Cool. Okay, so now we start the interactive part. We try to draw this guy. So let me go, let's say with zero angle of the steering wheel. And I keep, you know, executing this one, right? So this guy here keeps going. And then what's happening here after the next step is that my computer now is going to be, is complaining. And there you go. The track is jackknifed. What does it mean the track is jackknife? You simply broke your track because you drove into yourself. That's very interesting, but you can drive inside yourself with these tracks, okay? So let me reset the state with something a bit decent. Maybe like, oh, okay. Just someone, volunteers. What is going to be now the angle? You'd like me to steer the wheel. You can give me anything from zero to 45 degrees, I think. Minus 10. So I go minus 10, which is going to be turning to the right 10 degrees. And I go one, two, three, four, five, six, seven, eight, nine, 10 steps, okay? Off. Yeah. If I keep going, you're going to be jackknifeing soon, okay? So what should I do right now? Someone else? Oh, maybe still you. Plus 15. Okay. Let's try plus 15. You go one, two, three, four, five, six, seven, eight, nine, 10. Okay. What next? Should I keep going? Yeah. One, two, three, four, five, six, seven, eight, nine, 10. We are going to be going off screen. Maybe alternate plus and minus, then you go straight, right? If I keep going like this, you can see that this track is going to be going outside. And we're going to be going basically game over. So you don't have a driving license. So minus 15, okay. Do you agree all? Should I go with minus 15? I go with minus 15. One, two, three, four, five, six, seven, eight, nine, 10. Okay. No, we cannot drive forward. Plus 45. We messed up. No, no. I don't think you messed up. I think it's still good. Zero. Someone said zero. Let's try with zero. Not yet. Okay. What do you want me to do? Plus 45. Okay. Plus 45. Uh-oh. I can keep going, but then we are going to be just, uh-oh, yeah, one more. Minus 45. Okay. Let's go minus 45 twice. Then twice more. Then I'm going to go, oh, eight, no, six more. So we go 10. One, two, three, four, five, six. Okay. So if I keep going like this, we are going to be going off the screen, right? So I think you got the picture, right? Is it twice? Twice more? No, it's an older message. Okay. The point here is that this stuff, it's a bit, you know, tricky. You can get a, you can get like a hand of it, and maybe it's going to be like the final solution is going to be like something that we go, we keep going like this. And then we go in the other direction to rotate something like plus 10, uh, then it's going to be killing each other here. So plus maybe 25, maybe, let's see, maybe plus 20, see? So we can turn around. And now we should basically undo these things. So if I go like, um, I think plus 40, yeah, there you go. And we can go basically straight, uh-oh, uh, steal something, 20 maybe, okay. And then we can go zero. See, I've been teaching this forever. So you know, I can actually drive this thing, okay. So there you go. Yeah. Half is a tracker. There you go. See? Oh my God, I'm actually managing to do this. We had to fix a little bit. Hong Kong. Yeah. Hong the horn. I actually had my horn there.  Sorry. Okay. Fine. So this actually considered success. Uh, okay. Now my computer now is complaining because there is some lag. I should have turned a little bit before anyhow. The point was that, uh, it's not, okay, the point is that it's not quite straightforward to, uh, you know, to figure out how to drive this guy, uh, because it's highly non-linear. Okay. So we had to figure out how to implement a high non-linear, uh, system, uh, how to invert a high non-linear, uh, kinematic model, basically, uh, such that if we had a kinematic model, you know how the vehicle behaves, right, given an action. If you invert the kinematic model, you figure out what action you would like to take in order to, you know, drive to a final destination. But since we don't know how to do this analytically, or maybe we didn't know in the 90s, we can train a neural net, right? And then we can simply, uh, figure out whether we can, uh, do some, you know, we can learn how, whether we can learn how to, you know, drive backwards. Okay. Are you excited? Are you interested to know how we can do this? Yeah. No? Yes. Okay. Cool. All right. Okay. Okay. So the overall, um, the overall, basically, um, objective here is going to be that given a initial position, uh, in this map here, given initial position, I'd like to know what sequence of actions, which is going to be basically what sequence of steering angle for the wheels I should apply in this six dimensional space, right? So it's not just 2D, because 2D, so this, this, uh, screen here is going to be the 2D screen, but then you have six values, right? You have the X, Y of the cabs, X, Y of the trailer, the two angles. So you have a six dimensional space. And then in this six dimensional space, you'd like to infer, like you'd, sorry, you'd like to, yeah, you get a network, which is telling you for this specific configuration of the track or the trailer track, you should be outputting this specific, um, value, right? So your regression network somehow is going to be outputting you a scalar, which is going from minus 45 to plus 45, I think, um, yeah, or minus, yeah, I think so. I think that's the range, um, for every position, right? So our goal is going to be training a neural net that goes from points in this six dimensional space to one scalar, which is going to be the scalar that allows me to, um, you know, get a, you know, a sequence of scalars. So but one scalar at a time, uh, such that you would like to, uh, drive these track backwards to the final destination. And did I, did I make sense? Does it make sense? Maybe I was confused. Is it okay? All right. All right. So let's figure out how to get all this stuff going, uh, back to the slides. Training. Boom. Okay. Cool. Let's go to the two stages. Um, the first one, we had to train a network, a neural net, to be an emulator of the track and trailer kinematics. Uh, this is how the paper, these people have done, right? So that's not the only option, but that's what we are going for, uh, then a second stage involves training of a neural network controller to control the emulator, which is going to be basically doing the task that we were doing right now when we were, you know, coming up with a value for the steering angle such that we can reach destination, uh, and you know, destination like success is determined by two points, two factors, uh, closeness to the docking station and the, um, orientation of the trailer. So this is the overall diagram. You're going to have a neural net, a controller, which was me right now, uh, following your suggestions through the chat, which is providing you these, uh, steering signal, uh, at the time K, discrete time interval K. Then we have a trailer track kinematics, which are the equations that allow me to, you know, tell you what is the next configuration of the, uh, track, uh, which is again, yeah, giving you the next state, given that you, uh, provide the previous state inside and then the steering signal. And this delta here is simply a time delay, right? So it's that it tells you that this is the next state and this is the previous state. These are a little bit, um, these are like diagrams from the, uh, electronics, like, uh, electrical engineering diagrams. So you're going to see, because that's, you know, uh, there was no computer science back in the days. Right. Um, all right. So a state, uh, K is fed to the controller, which provides a steering signal K between minus one, this case and plus one, uh, for the track. So they kind of use a ton H at the output, uh, at a time index, uh, K each time cycle, the track backs up by a fixed, a small distance again. So these are the equations for the, uh, trailer track with several tracks. Uh, you have the location of the X and Y of the cup. And then you have, like, given that, you know, the distance, the, the length, both of the, the cup and also of the trailers, the one, the two, so on, you can estimate what is the, um, you know, um, the, the variation of the angle given the, uh, input phi, the phi is this negative angle, right? Because it's on the right hand side. So we have that, uh, S is the sign speed. So you can go forward and backward. And then phi is this negative steering angle, positive grade, positive angle is being on the right hand side. Right. So it's like, uh, sweep swapped. Um, and then X and Y is the location here back of the, of the cab. Uh, notice now that basically you have, uh, the X and Y position of the trailer is actually determined by the X and Y position of the cab and the distance, the one and the theta one. Okay. So before I was selling, I was telling you that, um, we had six values for the state, but as you can tell from now, we just need four values, right? You have the X and Y and you have the theta zero, which is the, um, you know, the angle of the cab and then theta one, that is the angle of the first of the trailer. Okay. So you have only four, uh, independent values right here. Uh, this X and Y of the trailer is completely determined, uh, given these other values, nevertheless, uh, question, aren't these velocities? Um, so these are the, uh, a question. Yeah. So this is how the, the position changes, right? So these are velocities, but so whenever you have like, uh, a state, the state in this case is X, Y, theta zero, theta one. And then whenever you'd like to write the equation of the motion, you're going to write that, you know, the variation in time. So the, yeah, the velocity, uh, of X and velocity over Y and the angular velocity over this guy and the angular velocity of the other guy are going to be expressed by these, uh, equations in continuous time, then we're going to be discretizing them, uh, since we are using a computer. But this is, yeah, how you express, you know, uh, a dynamic model, right? Like a model that evolves in time. Um, all right. So we have the equations. Uh, let's figure out now how we train these networks. So this is how we train the network for emulating the track, uh, trailer track kinematics on the left hand side. We provide a random input, which is going to be, is going to be like a random initial value, uh, for the, uh, for the steering, uh, angle, and then we provide these random steering angle to the track, uh, dynamics, which is going to tell me, given the previous state, what is, what is going to be the next state. And then on the bottom part, I'm going to have my first network, which is going to be trained as a regressor regressor, and it's going to be trying to minimize the difference between, you know, the actual next state and the state that is predicted. Then the difference between the prediction and the actual ground truth, it's used in order to, um, and they call these adapt, the term they are using the article is adapt the weights of these network, which is basically, uh, you know, uh, train the network. So how do you, how do you update the weights of the network, uh, the weights of the network are updated by using this error? So question now is going to be for you. Um, what kind of loss function are they using here? Since the signal that is used to update these weights, it's actually the difference between the target and the actual prediction. You should be able to tell me now. It's actually in the midterm right now. It's one of the question. MSE, why MSE? Because if you compute the derivative of the MSE, you get the difference, right? Between the target and your, um, well, you get the, the difference between your prediction and the, and the target, right? And so this, uh, this difference, uh, which is here swapped, right? Uh, is actually used here to change the values in, uh, in our course, we perform gradient descent, right? So you get the positive delta, the positive, you have prediction minus ground truth. And that you use that one to compute the partial derivatives and then you subtract that, right? To the parameter such that you go in the opposite direction here, they actually use the inverted sign, right? This prediction minus the ground truth as, you know, factor for, you know, adapting, which is, you know, changing these parameters, but basically it's the same thing, right? There's a question. Why are we training the emulator if we already have a model for the track movement? Okay. That's a very good question. Because, uh, in this case, we, we have a questions and therefore we could actually get a partial derivatives through these equations, but you might not have the equations, right? Uh, let's say you have a much more complex system. You may just observe, right? Uh, several, uh, experts driving this, you know, complex systems, and then you actually, through this sequence of, um, basically trajectory, you know, the sequence of locations, sequence of states variables, you can learn a network that is able to emulate what is the, uh, dynamics of these, which is the kinematics, sorry, of this, uh, system. Okay. That was the answer for Joseph. All right. Cool. So right now, let's see, uh, how this keeps going on. Maybe we should check them. The, let me actually switch to the, uh, to the, this guy here, right? So let, let's actually start training this stuff, right? So we, we get like, uh, torch, right? Uh, and then we're going to be running, uh, this for loop at the beginning. And so what happens here is going to be, you know, we provide some random, uh, initial steering angle. And then, you know, we use the kinematics model to learn what is the connection between previous state and next state. Whenever the truck goes outside, we just kill it. And this keeps going, right? Until we, uh, we have enough points such that we can train this network to emulate the truck, the trailer truck kinematics. Again, in this case, you wouldn't be necessary because again, we have the equations, but let's say we don't have the equations, we don't have the equations, but let's say we don't have the equations, but we just have, you know, uh, observations from the real, real world.  We can run gradients through, uh, okay. So if we keep going like that, my computer is going to crash. So right now you can see that this is taking forever. Why is it taking forever? Because there is this, uh, visualization. So in this case, I turned off the visualization such that, uh, it doesn't draw things there. And then I can run now for 10,000 times, right? Oh, oh, okay. And this happened because I interrupted the thing, right? So that's, okay, how, how lovely. Okay, boom. And this is quite quick, right? Because it doesn't have the, um, graphical, sorry, they're entering, okay? So the emulator, you're emulating what players, what people playing the game do. So the emulator, uh, let me show you, the emulator here is going to be trying to learn what is the next state given the previous state, given I provide a specific random steering signal here, right? So given a state and given a steering angle, I try to learn what is the next state. And this is coming from the ground truth which is this, uh, box here, okay? This is, it's not our current net, right? This is just a normal net. You have, you know, an input which is state plus action. And then the output is going to be just action. There is no recurrence right now, okay? The signals are always random at every step. The steering signal, it's random. The state, I collect the whole trajectory while I input a random steering angle. Okay, very long question. Does this matter that, okay, it's too long. Does it matter that the data you're collecting is a bit different from the human test and action? The data you're collecting changes directions rapidly, frequently. So there is no, it doesn't, there is no time, right? Here, my training, we haven't talked about training yet. But here I'm, so this is going to be a just normal neural net that given a input state is going to map it, map it to the output state given a specific angle for the wheels. There is no time, right? So you have one state, you have one angle, that's the output, no time right now. Okay. Okay, so we go here. These are my inputs and the outputs that I collected over these trajectories. So if you check here, I just have the initial, I have the initial state, I get the state from the track, then I have a phi, which is my angle, which is going to be something between random minus 0.5. So minus five, so from 45 to, minus 45 to plus 45. Then I have my input, so it's gonna be the state plus the angle. And then the output is gonna be the output of the tracker track, which has stepped with angle phi. All right, so we have this thing here. I can split, I can create my network. And then I can start training my network, right? And this one will keep going until forever. But then basically here we are trying to get a neural net, which is simply a couple of layers here. So linear, real or linear. We start from steering angle, steering, so steering size, which is one, plus the state size, which was six, right? Of like the x and y and angle for both, so x and y and angle for both the cab and the trailer. Then you have a number of hidden units, which are gonna be described now in the paper. Then have a real low, and then you have the final linear output, okay? MSC and SGD. All right, let's go back to the presentation. So figure three, which is this one, shows the train, how to train the emulator. So the track backs up randomly, and the emulator learns to generate the next position state factor, given the present state vector and the steering signal. And you can hear now my computer fan spinning because it's training, okay? Sorry for the bad audio. All right, so this is how we train this system, how we train the controller. So this is state transition flow diagram. C represents the controller, so it's another neural net. Whereas T represents the track and trailer emulator. So all the track kinematics or the trailer emulator, either or. So how is this thing working? So how is now time added to the system? So we start with this controller, C. This controller C, we said, provides what? The steering angle to the track or the track emulator. So the controller provides an angle given that is fed with the initial state, right? Which we can call H of the K minus one time stamp. And if we provide these previous states to the controller and the track, we basically get the next state. So you can think about these two guys as just one element of a recurring network, recurring network, which has no input. Or well, it could have an input, but the input is not connected, right? So this is like one of the multiple cells we usually see in a recurring net. But there is a different diagram inside. So how do we train this item? Well, you already know the answer, right? So you get this one down here. We don't have any input on the bottom, right? And then you stack another one, you stack another one and then you keep going until you actually start from the initial location of the track, which is the initial hidden state or which is basically the initial track location and configuration, right? And then how on the right hand side, so on the right hand side, you keep going, right? And then whatever until the end where you reach the final point, which final condition can be a few of the following. Run out of steps, jackknife or third part, you hit one of the edges and then I check what is the distance between your back and the docking station and then I check what is your angle, right? Your orientation of the track, which should have been horizontal, right? Okay, so those are the answer to the question, what is the terminal conditions? There are three terminal conditions. Jackknife running out of steps or hitting edge and whenever you hit an edge, you try to, you want to actually minimize now, right? What is the distance between the docking station and then you want to try to minimize whatever angle you get, right? For the trailer, cool. So figure five, training the controller with back propagation. As you saw before in the previous diagram, where we were training the emulator, we have something similar with this feedback loop. The final state, so after you complete this whole trajectory, the final state will be ending with some specific configuration. I enforce this final state to be as close as possible to my target state, which means you wanna have the X trailer as close as possible to the dock trailer, the Y trailer as close as possible to the Y dock, and then the final angle of the trailer, horizontal, so it's equal to zero. Then you make the difference and you send the difference back, basically to adapt these weights. But of course it's not just a line, the whole thing goes through, so usual as usual chain rule, right? So this actually travels through all previous TCs, right? So it goes inside modules. The visualization shows that only the C, the controller blocks are updated, also proportionally to the final error, which implies an MSC loss. So again, the thing that we came up with before. Cool. So the initial position is set at random. The track backs up until it stops. Final error is used for back propagation. And this is back propagation through time with a variable unrolling period K, right? So the weight changes in C, the controller are taken as the sum of the tentative changes. What is the meaning of this sentence? Well, now you maybe appreciate why PyTorch is accumulating the gradients every time, do you see the point? So in this case, you get this gradient coming back from the future back to the past. And then the meaningful thing is gonna be accumulating all these gradients, right? Because we have reused the same module multiple time, when you go and perform back propagation, this back propagation will sum up things as many times as we went through this module, right? That's why PyTorch accumulates gradient whenever you compute back prop. If it would be just computing them and replacing whatever it was before, then you just have the gradients of the oldest iteration, right? Or the one most back in the past, right? Instead in this case, which is written here, which says that the old tentative changes are summed together means you accumulate the gradient over time, okay, cool. Finally, repeat another initial position back up until it stops, right, done. So the network detail, this is architecture, diagrams of network architectures back in the 90s. You start with six values for the state, X and Y and theta, X and Y and theta for the two items. Now you get through these items here and that they are called potentiometers, which are basically variable resistance, which are again representing these variable weights. Variable weights as in weights that can be tuned, okay? So these are tunable weights. So weights in a neural net, okay? But again, this is the symbol for a tunable weight, a tunable resistor. Now you have 24, 25 of these guys. So from six, we go to 25. And then from 25, we go to one, which is the steering signal. So first, a fine transformation squashing. Second, a fine transformation squashing.  So you go from seven, which is six of the state plus the steering angle to 45. So you have a fine transformation squashing. And then from this final 45, you have a fine transformation to six, right? Which is the final, you know, next state. Cool. So that's it, right? So analogous to a neural net having a number of a fine transformations equal to four times the number of backing up steps. So for every time, for every trajectory, we train a net which has a length that is depending on how many time steps this specific track took in order to reach, one stopping, how do you call it? One stopping, what was the word? Condition, right? Stopping, yeah, I guess, stopping condition. All right, so the number of steps varies with the initial position of the trailer or the track and the trailer, right? So you have, you train the core item of this network which has several steps. So have several layers. The length of this network changes based on each specific episode. And then we train this stuff with deck propagation. And each module is the same module replicated multiple times. Therefore, the gradients have to be some as you go through, right? That's what PyTorch does. Makes sense? Cool. So these are a few examples. You start with this initial position and then after a few steps, the network manages to reach the destination. Another one is here. They started with the trailer pointing away from the dock and you can see here how we managed to circulate around and to get back to the correct location. And the final state, again, is horizontal, right? So that's, it looks almost horizontal. Or this one, you start orthogonal in a jackknife position, right?   And then this is also really annoying but the network doesn't complain. Be like a neural network. No, just kidding. Okay, additional resources. Full working demo is offered here on this link. I'm gonna be showing you because in the notebook, the code stops there. So here I just show you how we train the emulator and we got like an MSC loss down to very, very tiny value, like 0.0 point, which is this, whatever. Okay, you can read this number, right? And then this is my value for the testing set, right? So this is gonna be 38 micro MSC, if you want to call it. Here, if someone wants an extra grade, maybe, you can add the code for training the controller. And it's not that trivial because, I mean, we cover how to make it work in the class, but it's a bit finicky. So I just show you a final version. You can even copy from here if you want. And I really like, if you can submit would be nice.  So here we can start with a random position and then you can do drive using the controller. Let's increase the speed. Okay, and boom, another random position. Okay, it's too easy. Oh, let's make something a bit, you know, annoying. So trailer, let's go this one, let's say 180 degrees and let's have this one zero, change angle. Okay, so this is really annoying. Go. See, back and there we go, boom, okay. So here you have the code, it's training in your browser. You can go also at the bottom of this page and you have the source and GitHub. So you can, or are even, you know, encouraged to port this project here, which is written in JavaScript to PyTorch. It's gonna be a very good exercise for you to learn. We would have actually a running notebook which is, you know, left as an exercise for you and you will get an additional grade, I guess. All right, so that was it for today's class. I hope you enjoyed. Let me see if there are questions. What if we train a policy gradient? Yeah, we don't know about reinforcement learning. I don't know anything about reinforcement learning, sorry. I'm still a bit confused about the architecture. Can we look at the diagram of that again? Yeah, of course. Here, so basically the controller goes from six to 25 and then from 25 to one. And they use some kind of tonnage, right? Your activation. So six to 25, 25 to one, and this is the controller. And instead for the trailer truck emulator, we go from seven, which is six, right? The X and Y and the angle times two, six plus one, seven. So from seven to 45 and then from 45 to six. So this is my predictive model, the model that predicts the future given the past and the actual input action. And that's how I implemented this one here. So I have my state size, which is six, X and Y and theta times two and steering is one, just the angle. And we say we have 45 units, right? So we have 45 units here. So we go from steering signal, like steering size plus state size, which is seven to this 45. Then I have Rilu. Again, it looks like they use a tonnage here. So okay, you can write tonnage here. And then you have a linear that goes from this 45 to state size, which is six, which is going to be the next output here, right? And so there is a linear output. And again, this is also something that we just put in the midterm, right? I mean, I give you the answer of the midterm right now, basically. So where are you enforcing that the emulator actually emulates the way the simulation works? The way the simulation works. Yeah, here. So emulator training. Yeah, we haven't, I didn't show you this one. You're right. So here I just get the length of the training inputs and I get a random permutation. So that I pick at random. So I have my phi, my angle and the state, which are going to be the seven coordinates is going to be picked from the ith location of this training inputs. And these training inputs are coming down from here whenever I extracted, right? So you have training inputs. Inputs are going to be appending to this list, which is this list, right? Input list. You append the phi, which is the steering angle, and then the initial state, which comes from the track. So this is my input and then the output is going to be my next, the output step, like, sorry, the output state of the, of the track. So if you go back here, you get the phi, the angle and the state are going to be this first item of this training input. Okay, at the location, sorry. The ith item of training input. And then you get the next state prediction is going to be the output of this emulator, which has a linear layer as output, right? Cool. Then you have the next state, which is going to be, going to be the output at the ith location. And then you have loss, which is the criterion, which was an MSC, criterion, MSC. So you have the MSC between the next state prediction, which is this one and the next state, the true next state. Then I do basically stochastic gradient descent, right? I clean up the gradient. I see. So you're basically training the emulator before you're training the rest of the net. Oh, I train the emulator only before. So we train two models. First model, you train the emulator. Then whenever the emulator is trained, you train the controller, which is driving the emulator, but the emulator is not longer trained. The emulator is trained only once before. Then we use the network in order to train the controller. So you're first training the emulator and then you're using the network you trained as the emulator to train the controller? Correct, yeah. So whenever I'm here, I first, I already trained the trailer emulator, right? So to train the emulator, as I show you right now the code, then I can show you also the slide. So this was the training of the emulator. The kinematics, you have the next state given the previous state and the angle. And then you enforce this emulator to learn to basically do a regression and you try to copy the output.  And to train the controller, you basically have this chain of blocks, which is controller and trailer track, the track network. And then you get basically a trajectory.   So it's backprop through time. In order to adapt, they use the word adapt in this paper, the weights of the controller such that it managed to map the original initial random position to this final target position and orientation configuration. Let's call it configuration. So we start with a random initial configuration which is corresponding to position and angles. And then you enforce that the final output of this sequence of modules is going to be giving you the final, the docking station and zero on the backside of the track. So this is two parts, training. Training first, the emulator, finish that, you train the controller in order to actually reach a specific goal. And the number of red boxes here is variable and it depends on every episode because you don't know how many steps is gonna be required for you to reach destination in every given initial condition. So many words I said, oh my God. Does it make sense? Yes. Okay, very good. Next question. So someone did ask this. We were about why we needed to train the emulator instead of just using the model physics. Yeah, the answer was that not every time you actually have the equations of the kinematics, right? So let's say, and this is gonna be coming up next lesson I think whenever I'm gonna be talking about my own research, I'd like to figure out what is the behavior of other cars when you drive in a highway. But I can't control other cars. I have no idea what is the behavior of other cars, right? So the only way I can learn how other cars react to my actions is actually through observations. And therefore we run, like, you know, we have some cameras mounted on a 30 story building watching, you know, looking at the highway station, the highway section. And then you basically figure out what is the interaction between the vehicles. And therefore we had to learn this forward model. It's called predictive model, which is figuring out what is going to be the reaction of other vehicles given to your own actions. So in this case, yeah, you don't need the emulator, but they train these emulators such that it's more generic. You don't need to have differentiable equations, okay? I see, okay. One more question is, so when you said the length of this recurrent network will be variable, there was something about having it be like four times the length of the number of steps or something like that. Could you go into that a bit more? So here we assume that each length of each episode is capital K, okay? So the lower K is K is the actual index where you go like zero, one, two, three, four, five, whatever. So each episode has, you know, whatever capital K number of steps, right? So capital K is gonna be different for every episode, right? Okay, so each of these two items here, this guy here has two affine transformations and this guy here has two affine transformations. So overall the network that you are seeing here has four affine transformations times capital K. Oh, I see, okay. So that's basically one neural net, right? So it's a neural net which is, it's not even a recurrent neural net. It's just a feed forward neural net with four times capital K number of affine transformations and then you train with back prop in order to enforce always the same target, right? So that's actually funny to see this way. So you have a neural network which label or target is gonna be always the same, but then the network can change length and the input can be different, right? So you have a network, you have a network, you input different things, you can change the length, the height of the depth of the network, but you always have the same target here, right? So usually when you do regression or classification, you have a fixed network, you input different inputs like here and then you have different targets on here, right? Targets or classes if you're doing classification or yeah, labels, right? In this case, you have different inputs. The target is always fixed and you have different lengths. So you train a network, a variable depth network with variable inputs and a fixed target. How cool is that, huh? I love it. Do we enforce like a maximum length of... Yeah, yeah, yeah, of course, of course, of course. Otherwise you can, ooh. Do we need an emulator to train the neural net emulator? Is the point of having a neural net emulator to have a differentiable emulator? Yeah, that's the point, right? The whole point is to have a differentiable emulator, which is not always the case, okay?  More questions? I have a question more related to implementation. So it seems like for training the controller, we freeze the weights for T. And so, but we still need to pass the gradients through in order to update for each of the cells, is that right? So I guess would we then, when we define our optimizer, we're just essentially just feeding it the controller parameters, but when we call the backwards, it's all still connected to the same network or I guess graph. Yeah, so this is gonna be exactly by torch. Examples, what we have seen last week, when we were going through the DC gun. So you have like a optimizer, hold on, what is it? Yeah, so you're gonna have an optimizer from the one network and I have an optimizer for the other network. That's it. Whenever you have the training, you actually have two distinct training. In this case, when you train generative adversarial network, you will have both network stepping in the same loop, but you can have one training thing first, you step with the first network, then you go later below, you're gonna have the other network stepping there. So you had two optimizers, which are adapting if you use this word from this paper, the weights a different time, right? It's actually two different networks which don't even train at the same time. So you first train the predictive model, the forward model, finish. Whenever you finish that, you use that as a mean to send back gradients through the future, from the future. How do we choose capital K? Capital K is the number of iterations that are necessary in order for you to reach any edge.  Whenever you hit something, that's capital K. So actually, whenever we drive forward, we use the kinematic model, such that you have a truthful final location. But then in order to run the gradients backwards, you're gonna be using the emulator network. So you use the kinematics model to do the forward pass and then you do the backward pass through using the emulator network. I think this can be done better with policy gradient and deep RL model, okay? We don't know about RL, RL for us doesn't make sense. I'm just repeating what the boss says. You can try, right? You can definitely try out and see whether it works. Again, this stuff was done in the 90s, right? So that's, yeah, it's old stuff, but it's always, you know, I would call it pertinent. There you go. More questions? Hi, Alfredo. Sorry, I'm still confused about the training for controller and emulator. So the emulator is trained at each time step because we are comparing the predicted state versus the truth. This one, right? Yeah, at each time step. So we are updating the parameters of a new word for every time step? Oh, well, you can also do a batch if you want, but you can do stochastic gradient, it's okay. So you can provide like new sample and then you update new sample, update and so on, right? Or you can actually, you know, step after several time steps. Okay, okay. And when we hit the big K, we just do that. This is not about the emulator, right? There is no capital K here. Oh, it's like, I'm confused because it's like we do the training for enumerator first and then when we hit the terminate state, we then do back prop for controller. So first you train this network, finish, done. Second step, you train the controller, next next vector, right? So you train first network, this one, with random numbers here, with random steering signal. There is no controller here, right? Yeah. Okay, cool. So you first train this emulator such that it can replicate these dynamics. Okay. Then second part is gonna be training the controller and the controller is training this manner.   And now you're doing gradient descent with regards to the controller. Only see, yes, yes, yes. Okay, okay, yeah. I got it, thank you. Okay, that's awesome. That's it, I think. And so you made it until the end of the lesson. Congratulations. All right, so how can you better get an understanding of what we covered today? So there are a few things you can do to keep going, right? Comprehension, you have some questions. Ask them just in the comment section below. We'll answer everyone. News, if you follow me on Twitter, under AlfCNZ, you can get the latest information as I post them online. Update, if you subscribe to the channel and turn on the notification bell, you will not lose any of the videos I'm posting here. Appreciation, again, if you like this video and the content and everything I do, I would really appreciate if you put a like on the video. Searching, every video has an English transcription which can be searchable and you can find it in the video description below. Language. You speak Italian, you speak Spanish. You speak Korean, Turkish, we have now. All these translations are now available for you. I'm planning to add more languages as we have volunteers. PyTorch, if you'd like to have a concrete understanding and digest better this topic, I highly recommend to you to try to train the controller yourself. And if it works, you can submit a poor request and that's actually the next point. And so such that we're gonna have now an notebook which will have a running controller. So this, again, is left to you as an exercise such that you can actually master this topic, okay? All right, thank you again for listening. I'll see you next time. Bye-bye.


TLO what's poppin? We are on Twitch. We are not live, but you can leave a like comment subscribe turn on your post notification bells Man, let's continue to grow the family for Chicago to the UK My do not glorify sensationalize and condone any of the facts and stories told I'm here simply to educate myself others in the history and the current state of issues around the world Following presentation is intended may be intended for mature audiences It contains graphic descriptions crying scenes in adult dialogue and strong language viewer discretion is advised Just want to put that out there before we get into it You know when we are on socially criminal, you know what time it is man. W channel one of the best channels Only being socially criminal if you ever see me, please I should put UK gang documentaries on here, too Or make another channel for them. I don't know how you're able to post these but so Don't forget we are partner with the respect my blueprint podcast man And blueprint breakdown This is the channel These are the videos and this is the group one right here There should be another one coming out soon Link link to this one is down in the description now if you're looking for any of my old content It is over here Remember you see this in the corner under me. No, you don't let me move. Let me move. Let me move Let me show you this problem over here 850 other people reacted to your real Come on now Hey, I salute I saluted You know, it's crazy man about social media.  On tiktok, I do a completely different thing than I do on YouTube So people on tiktok don't really know I have a YouTube Like it's crazy like people like nobody know each each one of these platforms. I'll be doing something different That's that's the point. I'm trying to say anyway battered stab and star UK crime documentary Let's get into this It's a little suspicious. I already liked I'm already said Don't forget if you watching this video in the In the premiere man, you're a first responder and I appreciate y'all for being here in this premiere because you can't skip you can't fast forward You can't do none of that so hit the like button man shots in love man my goal for this video a hundred fifty likes man That's the light for y'all That's the light That's I'm talking overall first responders y'all getting how y'all live I know y'all go show the love and support that y'all supposed to because y'all just being here In the premiere is lit This time 35 years on and still Britain's biggest unsolved missing person inquiry There's still no sign of the London estate agent Susanna lamp lose Susanna left her office in Fulham in west London at 12 40 on Monday Susie lamp blue her car was found about an a 12 Notice the series. I've not but I've seen this in phone jaggers 1240 on Monday Susie lamp blue Her car was found abandoned and unlocked in a street near the River Thames The prime suspect who's not what he seems Financially and career-wise. I've achieved what I've wanted to achieve This is someone who's literally pretending to be another person so this charm and intellect seems superficial He's faking it I'm just now looking for what the next thing to achieve, but he's kind of overshooting it a bit here And I see that a lot with psychopathic personalities and later The other side of domestic abuse She's trying to project a gentle meek mild vulnerable individual When the truth is she's an aggressive. I could tell that's already gonna make me mad this one Lot of that goes violent Like I sat there and watched the whole My name is Michael Barley in July of 1986 I was a detective sergeant at Fulham police station And got assigned to the Susie lamp blue case 28th of July 1986 Susie lamp blue 25 an estate agent leaves her office at midday her diary recording an appointment with a mr. Kipper She's never seen again Susie's fiesta was parked here, but just over the entrance to this this garage It was almost obstructed the entrance The boot of the car was about here And it's part facing that way But the driver's door was unlocked and say there was a hat on the back parcel shelf The handbrake was off and you could see in the driver's door pocket There was something which turned out to be her purse those sorts of things just thought this isn't right So she got snatched out the car With a hundred offices assigned to the case and an in oh man, don't forget. We do have patreon Investigation that spread from London to the continent the hunt for Susie dominated the news for months The vivacious young estate agent Well bad, this is the patreon for anybody that's new link is down in the description This is everything that's on there and I got two notes two new shows beginning real soon. This is England and one other one I'm wearing a person is believed to have been lured into a trap Police are worried this might be a murder hunt But with few firm leads and immediate suspects being ruled out All the police had to go on was the artist's impression of the mysterious mr. Kipper Just over a year later October 1987 a smart young man arrives for an appointment a minute ago 87 at a dating agency a hundred miles away in Bristol I guess this was the 80s so there was no tender or plenty of fish or BLK or Christian mingle He's well presented. He's well groomed And even the Stuff we were working at the dating agency said he's attractive. He's got charisma Financially and career-wise I've achieved what I've wanted to achieve. I'm just now looking for what the next thing to achieve It's all a charade because he's even using an alias This is someone who's literally pretending to be another person What do nasty Dangerous men look like well. He was the internet before the internet was even out. Well, they look like this His real name is John Kanan a man with a very different backstory Born and raised in Sutton Coldfield near Birmingham John Kanan came from a comfortable background With a doting mother and an ex-RAF father who was a successful car salesman privately educated locally Aged 14 John Kanan committed his first sexual attack more followed culminating in an assault at a local precinct in 1981 John Kanan goes into a knitwear shop the woman is on The phone and she has her two-year-old son With her and he pulls out a knife and puts it to a throat Bundles her into the back office and puts it to the child and said do as you're told or I'll cut him the woman's mother arrives He binds her Hand and foot He rapes the woman as her mother stands there saying Please God leave her alone. She's pregnant Absolutely horrendous attack Police put out a description of the man There's a distinctive Part of his appearance. He's eyebrows.  He moved to prison And then rolled at a dating agency under a false name John Kanan crops up as John Peterson So not only is he got a brand new name For the purposes of attracting a whole new raft of women But he's got a whole new history and a whole new personality to go with it Now if you join up to a dating agency with a name that's not your own You're not planning on having a long-term relationship. Are you financially in? Career-wise I've achieved what I wanted to achieve. So he's a Bristol businessman and he's made his fortune So he's ready to settle down Spend his time with the right woman Yes, I've admired a few people like Gandhi I Knew he was gonna say something like this Gandhi God Malcolm X philosophers like Bertrand Russell Ah present-day people like Prince Charles Who's socially aware, but he's kind of overshooting it a bit here and I see that a lot with Psychopathic personalities He doesn't seem to realize when he takes it too far but also Classically he gets words wrong. He's He's not actually that good with English and he loves big words and Quite often misuses them. I'm just looking now. I'm in a sedimentary period. Yeah, we're Financially and career-wise. I've achieved what I've wanted to achieve. I'm going through a bit of a sedimentary period Sedimentary is a geological process by which rocks were formed, right? 3.9 billion years ago or so he might have meant sedentary But all that means is that you that you sit down a lot He probably wasn't trying to convey that impression either. He just gets it completely wrong. I'm just looking now I'm in a sedimentary period now. We call this a malapropism because he's using the wrong choice If you're in college, we work at 9 to 5 and it's not what you want. It's not making you happy. I seen that There's a movie on Netflix where the guy did the same thing and it was like the onion dome They took them to the office and they had to solve the murder of himself and somebody was actually trying to take him out He was saying the wrong words playing the role of somebody smart Propism because he's using the wrong choice and What this signals to us is that this is someone who's literally pretending to be someone else He's taking on a persona, which is much bigger than he is in terms of how socially aware he is or Is educational level if you like and his language choices are effectively giving him away at this point We all do this on occasions. I certainly Mess up my words all the time But I think that it's very interesting that some research has shown that people who are very high on psychopathic traits Tend to use more malopropisms and that's because their brain is taxed when they're talking about subjects that they can't emotionally connect to When we saw the video from the dating agency my initial reaction was This is a man. This is a convicted Criminal rapist, but he's trying to portray himself as a businessman who has been amazingly successful Trying to be alluring. He's trying to tell him what they want to hear basically he capping Bad cap because of who he really is it would be good cop if he wasn't really like, you know if he didn't have that history, but But right now it's terrible But he's financially stable that he's achieved everything he wants to achieve these were clearly lies But the interview was no more It's just a beautiful country It's clean. It's precise Well, yes, that's right and what we see is a micro expression which is a combination of contempt and immediately after Fear Contempt is a unilateral expression. It happens on one side of the face And one corner of the lip will move up to the center of the cheek Activated by what we call a buccanator muscle in the cheek And that cheek dimpler action is a sign of contemptuous Judgment, so why would he feel contempt? Maybe because he's being quizzed on a place. He doesn't know too much about it's just a beautiful country It's clean. It's precise. I Never heard somebody describe a place as precise as Clearly he don't know what he so if you look at his eyes, they've widened. We see the whites around the eye If you look at the eyelids, they're stretched Vertically with the top lid high the bottom lip low now when the mouth moves sideways at the same time like we're seeing here Activated by the rhizaurus muscle connected to the lip corners on the cheekbone We know Beyond reasonable doubt. He is experienced in fear. So this charm and intellect seems superficial He's faking it two days after this interview Kanan reverted to type with a vengeance He tried to kidnap a woman near the Bristol waterfront, but her screams forced him to run away But the next day was a different story Shirley Banks has been married for six weeks She and her husband Richard They have arranged a date night And she goes into the broad meat center in in Bristol and selects a blue and white print dress She makes her way back to her car Amen Attacks her and gets into the car with her she goes missing Three weeks later more than a hundred miles away in Lemington spa Police arrest a man after he uses a knife to threaten a shop assistant His name John Kanan He was brazen too. He's like broad daylight Like Maybe sometimes not broad daylight people were definitely around and this is possibly visible and they rapidly find his car and there are handcuffs a replica gun and Absolutely crucially a tax disc from Shirley Banks's orange mini But when police traced that orange mini it looked very different What John Kanan had taken that into his garage Resprated and changed the number plate and he changed the number plate from HWL 507N to SLP 386S He chose those letters and he chose them in that order the obvious significance is SLP used to for Susie Lamploo He collected trophies and put in slick little names on them that will he test people like SLP Stand for Susie Lamploo the only person that can possibly tell us is of course John Kanan 14 months and two weeks earlier July the 25th 1986 Wormwood Scrubs west London John Kanan is completing a five-year jail sentence for rape As part of his rehabilitation He's in the prison's day release hostel and has a job locally But doesn't spend the evenings locked up There are insecure windows people get out and particularly Kanan was known to get out at night and He was drawn as if to a honeypot to fool him three miles up the road Wine bar drawn locked up Hostel and has a job of his rehabilitation He's in the prison's day release hostel and has a job locally But doesn't spend the evenings locked up There are insecure windows This is two separate events now And y'all give him this type of leeway.  He commits his next guy The photo fit of mr. Kipper the man seen with a woman who looked like susie lamploo on the day she went missing Bore a striking resemblance to john kanan After he was jailed for life for the murder of sherry banks in bristol and other violent sexual attacks Offices investigating the susie lamploo case questioned john kanan in prison They believed the changed car number plate meant an even stronger link to susie lamploo He said about the car that the car belonged to a bristol businessman And that he was the man who was responsible for the murder of susie lamploo and i said to him Is that man you john? and he said yes Then he went no, no, I meant it's not me. I'm backtracked And we carried on questioning him And he didn't come back to that point. I couldn't get it from him anymore. We had a break But I actually believe for that very brief moment in time That john kanan was telling the truth I don't know whether this is a slip of the tongue or rather a slip of the ego And you know you tend not to make mistakes when you're talking about something as serious as murder The fact that john kanan said yes It was enough for me That that he was a man responsible You can't prosecute somebody for murder or one word But sometimes killers just can't help taking You can't prosecute somebody for murder on one word You can if the question is did you do it and the word is yes that Ownership of what they've done even if it's in a very momentary way because They are secretly very proud of what they've done. So they want other people to know it Even if it's just for a split second. So it feels to me like it's a very significant moment It feels to me that it's an admission of guilt Susie lamplu's mother Diana was convinced john kanan had killed her daughter Part of my body has died We merge it But it would take a review of susie's case 14 years after her disappearance before police once again questioned john kanan Tapes of the interview in a custody suite were leaked to a national newspaper Our experts watched the extracts posted online First kanan answers questions about susie lamplu I I don't really see as things currently What else there is to be said? I don't really see As things currently stand What else there is to be said? and the the anger Mimic because I believe this is faked Is on the first part and the second part of the sentence I don't really see as things currently stand What else there is to be said? I'm going to pretend to be angry But he lets himself down with this instant drop Instant increase which is not characteristic of anger. He's not feeling angry And unfortunately for him what he's adding is a qualifier what we call an exclusion qualifier as things currently stand In other words what you've got me on so far is just circumstantial And therefore I'm not going to give you anything else because I'm feeling fairly secure at the moment Mrs. Lamplu thinks I've done it She's accused me of doing it. He uses done it and doing it So we've got examples of being deliberately ambiguous and distancing It is a form of even distancing himself. The little things that these people catch on to like I completely missed when he said done it and doing it like The little things that these professionals catch up like I wonder how it would be being in a relationship with this woman Just out of curiosity like you could not You got to be faithful as you should in a relationship But if you think you funny cheat on her or lie or do anything behind her back. Oh, no You had another thing coming Let's see. She got a ring on her finger man. Let's check it out So from even naming what happened to Susie Lamplu and we know that Susie Lamplu was murdered So that's why we've got both ambiguous language and distancing happening at that point I've answered only question you've put to me I've invited mrs. Lamplu herself up to the prison Franklin prison to have a quiet word to me To to question me or if her husband wants to question me And I've said I'll go on the lie detector test the truth drug or anything you want to put me on And so far We've not had a denial One of the 27 indicators of deception is evasiveness And evasiveness is characterized by not answering a question If you didn't do it, you say I didn't do it. He's not taking the opportunity here So why is he holding back? John Canaan has always maintained that he had nothing to do with Susie Lamplu's disappearance I'm trying as I've always tried for 12 years to try and help you to try and help persuade you that it wasn't me Our experts gave their view of this denial All he needed to say was it made me for whatever reason he qualifies that it doesn't do that And he goes to this helping frame to try and help you to try and help persuade you that it wasn't me Why does that it wasn't me come after So much additional packaging. What he's saying Is I'm trying and I've been trying for 12 years to convince you that it wasn't me That is not really a denial Canaan then said more More than anyone had expected My life, um, I think was that believe me. I'm genuinely sorry for Um, one or two things I haven't been caught for now.  You can almost see the whites of the eye We call that the sclera around the iris and the pupil As we move into a micro expression of why died fear He's experiencing a fear state and that could be because he's just semi-confessed By saying I've committed crimes I haven't been caught for Um, I think was that believe me. I'm genuinely sorry for One or two things I haven't been caught for If we unpack this we have a credibility qualifier believe me and then we have That's how these rappers be in the booth When they Admitting they crimes over over rap, maybe Yeah, yeah She can stop them Have interesting language choices. So genuinely sorry for relates to many crimes But not all crimes So that suggests to me that there are some crimes that is He's actually admitting to us on camera. That is not genuinely sorry for So if he was genuinely sorry for all of his crimes, why isn't he in that police station right now? telling us About those are the crimes Part of the reason is he's a criminal.  So why would there be any fear attached? He's just gonna make it hard for you Withalding that information back. So this isn't about him easing his conscience at all. This is about him Maintaining things that the police haven't yet found out One or two things I've been caught for it's a not thing to say, isn't it? But I think he's trying to bargain with the police what he's trying to say is Look, I have done terrible things that I've been not caught for But this isn't one of them So he's trying to deflect attention away from Susie Lamplu's murder and look like he's actually giving A bit of useful information to the police look like he's trying to be honest But actually all he's trying to do is serve his own needs Canaan then criticized the police Claiming they had planted stories in the newspapers Blaming him for Susie Lamplu's murder What you've done the press gonna be depressed man the press make their own story Is a dam site more serious than you will ever know Because you are affecting Um, my own position my own legal position and you're affecting quite frankly my case I'll be honest with you. I think you're not that you're not involved in a prosecution You're involved in a persecution which for me is a great line. Um This is somebody that um Thinks about some of the choices Um, that was a great line That we are looking at it objectively that was a great line They were non-objective Make in terms of language But was volume is down now. This is someone from most of the interview who has had the volume up Um, and now we get the volumes down That volume down tends to undermine the words that he's saying at that point So there's a sense in which that he knows this isn't persecution And and this is about the police trying to get to the truth Though the police wanted Canaan charged the prosecutor ruled against wanting more conclusive evidence Heartbreaking news for Susie's family We are greatly distressed And indeed considerably angered that after all these years It is still not possible To prosecute the person who both we and the police believe murdered Susie The detective who questioned that person John Canaan more than 30 years ago Still believes he is guilty and will never forget the prime suspect's momentary admission I think the three main reasons why I think John Canaan Is responsible and he is still the main suspect one He couldn't account for his movements On the 28th of July Satisfactorily to us The car the mini that he changed that number plate to SLP. I think that's very significant And thirdly He said yes to a simple question Oh, no, bro. Is it me or is It be seeming like they got more than enough evidence to get a conviction They found her car in his garage like they found her car Spray with a new plate in his like possession or somewhere in his possession Uh Will link to him having possession of it He said yes, I would as as soon as he would have said yes, I would all right that concludes it Get out of there like 35 years on the Susie lamp blue case remains unsolved and the search continues 25 years of policing this one sticks with me It's not a success of policing This one from the policing perspective It's not a success It's not a success from the policing perspective We haven't found the body. We haven't found Susie We can't give satisfaction to the family So there's a bit of a sense of failure. I feel on on our part on my part because we weren't able to resolve it But the answer The answer to this lies in one man's head And that's John Canaan Good morning Susie lamp blue trust Diana and Paul lamp blue worked tirelessly to Pull something positive out of the wreckage of this situation And they created the Susie lamp blue trust And the trust does some remarkable work Trying to keep people safe keep women safe in particular and advocating for victims silver lining If there can be one this is a really heart wrenching case And it feels just as relevant now 35 years on That it did at the time So I feel as a woman Susie could have been literally any one of us And is every one of us whenever we feel afraid and unsafe If John Canaan knows anything anything at all about her disappearance He's got a moral obligation to tell the police To give her family some sort of As a as a perfectly sane person Who's on that side of the line? Yes, you would think like that. But as John is that guy You don't have no moral obligation to do nothing. He's a he's a M-u-r-d-e-r-e-r What morals closure some sort of justice Maybe even bring her body back home I think that he understands this I think that he knows this But if he is responsible, I just don't think that he cares 10th of june 2017 A 21 year old man is rushed to the a and e unit at bedford hospital His name alex skill He was found to have second and third degree burns over a number of areas of his body He had stab wounds scarring scalding burn marks alex tells police the person who's attacked him is his 22 year old girlfriend, jordan Jordan worth presents herself as a Meek and mild almost innocent person here Where she's trying to project a gentle meek mild vulnerable individual When the truth is She's an aggressive violent abuser of a partner At the surface level You would think well, how could she possibly be carrying out this type of abuse against alex? She went to university She's got a couple of children Seen from a well-mannered background Or how possibly Could a woman like this be carrying out domestic abuse Against a man. It just doesn't seem possible or plausible And that that's that's that's what that's what women like is behind behind The thought of oh man, nobody's gonna think this is possible Look at look at how my life has set up Nah, bro It could happen to anybody This could be anybody Jordan worth and alex skill had met as teenagers Jordan worth was a Student who had achieved a degree in finance. She had done a large amount of charity work The couple seemed happy But the smiles masked the truth The situation was very different behind closed doors One of the key features of domestic violence is the Jekyll and Hyde thing Facing police questions, Jordan worth tried to give a very different version of events She was to say to officers that she herself had been the subject Of violence at the hands of alex She did admit In due course that she struck him, but she Effectively was seeking to downplay the matter to police I thought it's Just wanna sort everything out Start again to him.  She's trying to create a vulnerability in the eyes of the police Start again to him. Yeah And she closes off this statement. Do you think she's actively trying to do that? Or just something that's coming natural to her like the body's instinct is to do that, bro With some Jekyll and Hyde behavior What we see here is with the left hand She grooms the side of her face and uh, takes the hair behind her ear in almost like a vulnerable pose But look at her right hand You've got the fist firmly clenched on the table How old are you? 22 and I was 21 Of what you were doing I was holding a knife And if you notice the rotation of her wrist here, she's clearly mimicking pointing the knife forward But maybe realizing how aggressive this might look she very quickly softens it off and turns it back and places her hand on the desk here So she's reliving the moment, but I'm gonna minute. This is looking too bad I'll soften off and bring back to the table Your son is into coming So she's trying to describe a Vicious attack with a knife in a light-hearted way and we are I guarantee you she's an only child too Have a gentle social smile on her face Almost trying to take the emphasis away from what was a vicious attack I was holding the knife Your son is into coming house There's laughter when she says the house and the volume drops If we put those two things together, I was holding the knife And the relationship like this right now I just wanted him to come in the house means that her motivation According to worth for holding the knife was to get alex to come in So one of the first questions we'd want to ask is why does she need a knife In order to get alex to come in the house Tell me about the knife It was a big one Kitchen I think it was a bread knife It's at this point we see an increase in hesitation and pauses which is a sign Of stress potentially it's also indicative of deception in some cases Tell me about the knife It was a big one Kitchen I think it was a bread knife and It was a big one So we can assume she chose a big one from the kitchen and she thinks it's a bread knife So in spite I'm gonna keep it a buck with y'all right now. I'm gonna keep it real. I don't even know the difference between kitchen knives All I know is butter knife And knife That's it of her demeanor in spite of how she says this That's quite a menacing thing to do She's asked So now we know it's menacing Um, it's a it's a loaded question I don't even think it went that far but it did those little blood There was a lot of blood. She's admitting she's caused alex harm, but she isn't telling us she's attacked him She's telling us she was holding the knife that then ended up in him and then it didn't go in that far Notice she doesn't say she didn't put it in that far She then contradicts herself, of course because she tells us there was a lot of blood So it would suggest it went in far enough to cause quite a serious injury Jordan worth seems quite compliant in her police interviews. She's trying to show that She's sitting forward she's paying attention and she's answering the questions Look, if you're ambitious, you're young, you want to make money Using but right now she's using the one that is You know indicative of the situation Big word But what I find quite disturbing is the fact that even though she wants to minimize what she's done to alex She's actually quite matter of fact. She seems to have lost all sense of perspective that this is absolutely Inappropriate unacceptable behavior. It shows what has become the norm for them in their household Pretty soon after their relationship started. She began to control And suggest what he wore She wanted him to wear clothes that were made by jack wilts because it was the same initials as her name As it progressed she would Effectively end up taking over his facebook profile and shutting down his contacts Also Jordan worth insisted on the color of alex's clothing and forbid him from wearing anything in gray Domestic abuse is very insidious It's a drip drip drip of behaviors And those first behaviors Jordan worth telling alex that he doesn't really suit some colors. So he should wear another color instead They they seem to be almost caring a lot of them are packaged as romance Well, I only get possessive because I really really a backwards compliment. We love you It's a whole pattern of behaviors a whole raft of different behaviors designed to Manipulate a victim and make that victim dependent on somebody And they think well, yes, because this person loves me so much Don't they that this must be right and then they try and alter their behavior To try and avoid there being any unpleasant consequences in the future Now, of course, they they find that they can't avoid unpleasant consequences And so they feel helpless they feel trapped and they feel stuck in this situation that they just can't get out of Eventually alex's skill did decide he'd had enough of jordan's control and he ended their relationship after two years together But when jordan gave birth to alex's child they reunited and eventually moved to a new address in stewartby bedfordshire He became cut off from his friends and his family He became captured completely captured and controlled by jordan So that he had no independence no means of escape Despite the arrival of a second child the abuse continued On june the third 2017 bedfordshire police are alerted Neighbours had called them to say that they felt that his life was actually at risk Police arrived at one o'clock in the morning They could see that alex had a number of very serious injuries And at that stage alex was indicating to police that he had self harmed and they were self inflicted One officer recorded the scene on his body camera The body cam footage that we see is really quite Harrowing viewing it's painful viewing because you see just how broken alex And even though he's got a policeman standing in front of him. He's too afraid to say yes, she's doing this to me. Please help me So he goes along with the pretense that in actual fact, he's a self-harmer and he's doing this to himself That shows you just how powerful a figure Jordan worth is to him With no complaint from alex skill the police made no arrests He was left at home with jordan worth But things would change dramatically Sunday june the 10th 2017 One week after police visit the home of If there's anybody going through any domestic situations on either side of the fence, man Do not let it's listen.  Get help alex skill and his partner jordan worth A second emergency call is made The thing for me is that alex's neighbors saved his life But police spoke to alex this time privately and that then started the police investigation into the matter But at first alex insists there's no problem Eventually the officer persuades alex to make a Statement just as jordan enters the room To get alex away from jordan the police tell her they're taking him to hospital alex was taken to hospital Finally confirmed the officer's suspicions Jordan worth was taken to the police station, but tried to downplay evidence of alex's injuries I think the ghost fucked my husband Tell me about this hairbrush of alex's injuries Tell me about this hairbrush that you came from the car Well Whenever we've had an argument in the car I'll Wrapped my hair, so it's on the side and I'll just hit him either wherever I can What she's trying to do is minimize her actions by saying just my hairbrush Wrapped my hairbrush and sort of hit him wherever I can So she's using vague language When she says, um, I'll just sort of hit him wherever I can It shows intent We've had an argument happens more than once My go-to is to pick up the hairbrush that is on the side and I'll just sort of hit him It suggests it's an activity that she's engaged in definitely more than once and probably regularly and they got a newborn child You think these kids don't ingest What they see at such a young age, but they do believe me they do I got a child They take all of that again, man Next there were questions about a much more serious attack So we have an interest in confession here Because the discussion now is around Worth pouring boiling water over alex Often while he's sleeping in bed boiling water And we've seen the results of that in terms of the scarring of the tissue On his arms and and around his back And it was so bad his screams It never relates, never So she Uses head shakes and says never in leads, but she falters on never It never relates, never And she repeats never again, which suggests it's undermining what she's just said There's a lack of confidence in what she's saying Trying to reassure herself with the double never It never relates, never So she didn't say I didn't pour water on him She's saying here, yes, I do pour boiling water on him, but you've got it wrong not in leads Very very weird He says that he's told to make a story up So your parents that the shower in the hotel was faulty Yeah, right, you know how much money out stewed that hotel for it was burning me like that. Come on now. Yeah, I'll be rich So And there were scars from another attack from another serious weapon When Jordan worth mentions the hammer in the police interview we have more evidence of fake language She tells us there was a hammer and then she pauses she hesitates Pours us again and then tells us but I never I never hurt him. It's what she's telling He knows is that she didn't hurt him too much Not that she didn't hurt him with the hammer I think she did more And we obviously did more because he had marks all over the place And you've accepted that you have thrown water over him. That was boiling Okay, and you have accepted that you swapped him with the knife and cut him. All right And the other time you're just Maybe like but you can't argue about those injuries because they're quite obvious because the burn marks and the cuts Finally there was the claim that you look at this man This man looks over. Let me move out the way because y'all not getting the full grasp of this picture, right? Look at this man. This man looks so peaceful in the hospital bed Wherever he is that he looks peaceful right now while somebody probably in the kitchen plotting Boiling water Jordan had stopped Alex from seeing his parents Alex seems relieved That he's now going home Back to his family. I'm gonna have ptsd for ever He feels he's being isolated from Isolating a victim from their friends and family is Very very common amongst domestic abusers Because they don't want to be found out They don't want their partner to have the support network to be able to go to and potentially leave them He states that majority of the content of his family has been stopped because you You hear that I'm a sudden this to you You hear that Stopped him having contact And also they're jealous of the relationships that their partners have with other people They want to create a world where it's just them and their partners because that way they've got far more control Would you say at any point you have made it difficult for him to see his friends and family? He made it okay. He didn't want to see any of his family. He said he hated his family Illusionability.  They might be dominant individuals. They might be very bullying individuals But at the heart of it, they're actually really very inadequate It stems from their lack of self-worth and self-esteem And the inability to believe that actually somebody could just love them for them and want to stay with them without them having to Make sure that happens by being controlling threatening intimidating Jordan Worth pleaded guilty to causing grievous bodily harm with intent And was the first woman in the uk to be guilty of the new offense of coercive control She was jailed for seven and a half years Not all as she should be domestic abuse Involves physical violence and so coercive control has now been made into an offense to recognize that It might involve threats. It might involve intimidation In this case, it involved isolating alex from his family Taking his wallet away from him not allowing him to go to work Not allowing him to sleep in the bed Not even allowing him to eat when he needed to eat So all of this has an cumulative This is not the same dude that we saw when the police came Effect of making him feel helpless Hopeless and utterly utterly dependent I'm glad you up out of there alex your kid probably with you and she is in jail currently probably Because that was in 2007 2008 2017 2018 so Tlo leave a like comment subscribe turn on your post notification if you are in any if you're in a situation like this Or know anybody in a situation like this Help them


awesome to see these beautiful faces. We made it, Alhamdulillah. Guess what today is? The most incredible day in the week, Friday. Guess what today is? The second Juma, the second Friday in this month, right? The second one. It's awesome. So for that we say Alhamdulillah. For that we should be happy. Allah gave us an opportunity to be here again on the day of Friday and this month. So with that we say, Alhamdulillah. All praise, gratitude belongs to Allah, Subhanahu ta'ala. Nahmaduhu, when we praise him, when we ask for assistance. Allah says in his book, Ya ayyuhal ladina amanu believers addressing all of you.  Haqqa toqatihi, the way he deserves it. Don't forget what we just said.  And Allah gave you this month for what? Just for this purpose. To learn how to earn, how to become muttaqi, how to understand what taqwah is, understand how consciousness and closeness to Allah is. Let me tell you a story. It might be a little bit different khutbah. I'm going to ask questions and I'm going to give you the answer.  If it was a aid khutbah, it was a completely different ball game. Juma khutbah is different. Did you know, do you know where your ultimate home is? Did you know you were in Jannah once upon a time? And the answer is yes. You should know that. Every single one of you, including me, we were in Jannah before we came here. Let me tell you this. When you take a vacation, when you are going to go vacation after Ramadan after the Eid, you go somewhere for a vacation for a week, two weeks, a month. At some point, you've got to come home. You can vacation forever. It doesn't work that way, right?  Guess where your home is? Guess where you were before this? You were in Jannah. This is where I... What do I get this? I get it from the Qur'an. And guess what else happened? Have you like, what? Yeah. Allah spoke with you. Did you know that? Allah talked to you directly. And you responded to Allah directly. That's in the Qur'an too. Allah asked you a question and you responded. Every one of you temporarily were placed in here. This is where you are temporarily. You're going to go back. How are you going to go back? That's a question. How do you go back up there? You need to go back up. Allah wants you to go back to where you belong, to Jannah. And if we understand this and contemplate about that, I want to know what's going on with this mic, then we understand the meaning of the area that you have heard a million times at least during Ramadan or preparation to the Ramadan and all that. If you understand that, then you understand that this is something new. This is nothing new. Allah gave this from the very beginning, which is Adam, a.s. Until this promise was fulfilled by our Prophet, a.s., which is what? Which is to go back, to find a journey, to find a means.   How do you do that? You get to know someone that you wanted to know someone. What is the method of get to know someone? You have to talk to the person. You have to get to know the person. You have to make appointments. You have to make time consciously. Allah gave you this month and this revelation to do exactly that. Allah is inviting you and waiting for you. Hear me out. You might be like, yes, Allah is waiting for you. So you can make this connection. Okay. He's like, what is he talking about? What is he getting that from? When you read the ayat of Ramadan in Surah Al-Burra, when you read those ayat, this is part of that ayat. This is part of that. This ayat is part of those continuation. Which is Allah's telling you. Through the Prophet, a.s., says, when I badi one of you is asking about Allah, Allah says he's near. There's an incredible amount of, this is my favorite ayat. One of my favorite ayat. What is Allah telling you? Ask away. He's waiting for you to ask. Ask what you want to ask. And here's the interesting part that comes out of the ayat which you completely miss in the translation. Which is what? Guess what we do whenever we want to ask something, whenever we want to ask for dua. We find someone who is the most knowledgeable. We find someone who is an imam and a Mufti, a sheikh or something. Please do make dua for me. That's not what this is.  No.  Then the only criteria for you is to ask. Ask Allah whatever you want to ask. In this month, we are going through these first 10 days, right? You all heard, you know what are the first 10 days? Allah's mercy is raining upon you on these first 10 days. Allah's mercy, continuous mercy is raining upon you. Ar-Rahma. Ask away. If you ask me what happens, what do you think is going to happen when you ask? Yeah, you're playing with me here. Now, you're on a video. You have to say hello. Okay, wear this one. Okay, I'm getting instructions. Wear this. Wait, how do I do this? My apologies. I'm not techy. Okay, now, when you ask, do you have a guarantee to be heard in this day and age, in this world, in the life that you and I are together? There's no guarantee. But guess what Allah is saying? Ujibudawatadai. The single ask that you're going to make from Allah, he's accepting it. He's waiting for you to ask, why waste time? Now, if the question comes and the question is, this is one of the awesome questions I heard just before coming here. It made me a minute to think about.  How's your Ramadan going? What do you think the typical answer is? Think about it. The typical answer is, good, not bad, not bad, not bad, good. Ya, the first couple of days I had this chai problem, this coffee problem, a couple of sleep problems. It's going well. I'm okay. I'm getting there. I'm making it work. Guess what would be answered? We answered on a physical aspect of it only. But the essence, absolute essence of Ramadan is guess what? You got to go up the connection you got to make with Allah. And how do you make that connection? Through this revelation, through these words that Allah gave to you and I that we read through this Quran and through this dua that Allah says, when you make this connection, when Allah is throwing you this rope, it is one of the strongest one that it can never be broken, regardless of what it is, what circumstances, what circumstance that you're going to face. If you're going to make a connection, you're going to build a relationship, you're going to attain taqwa, consciousness of Allah, that is your key. That's your method. This is how you do it. That's what Ramadan is about. That's what Ramadan is about. If you remember the Hadith of the Prophet sallallahu wa sallam, and I'm paraphrasing it to you, someone who keeps himself and herself hungry and thirsty, gained nothing out of Ramadan because Ramadan is not about keeping you hungry and thirsty, but it is the prerequisite. You have to have some prerequisites in order to get a doer. It's about change of your attitude, change of your behavior. I'm going to give you one scenario. Think about this. You know your biggest enemy is chained, it's out of the picture, right? It's the best thing you can have.  Think about it as like, hmm, am I getting what I need to get out of Ramadan? That's the key message for all of us. Ninth day of Ramadan or so? The days of Rahma is upon us, and the next, guess what's going to happen after the tenth day? The next gate opens up. The gate of Maghfira, forgiveness. Ask away, ask for Allah's mercy, and ask for Allah's forgiveness. And when I'm just going to sit here for a few seconds, take advantage of it. Take advantage of that moment. And that's what it means in the Khutbah when the Khatib says, one of the other things you can do on Friday, and this month, and make it as part of your habit, as part of your lessons from this Ramadan. Because at the end of the day, Ramadan is for you to train yourself to change habits to become better. And what is that thing you can do? Allah says it in the Quran for you and I. Allah and His angels are praying on the Prophet. O you who have believed, pray on him and send him peace. Allahumma, pray on Muhammad and on the people of Muhammad. As you prayed on Ibrahim and on the people of Ibrahim, you are Hamid al-Majid. Allahumma, congratulations on Muhammad and on the people of Muhammad. As you congratulations on Ibrahim and on the people of Ibrahim, you are Hamid al-Majid. What is that thing you supposed to do when I just recite it? Increase your salutations, peace and blessings, sending upon our beloved Prophet sallallahu alayhi wa sallam. Make that part of your habit. You know you have the tasbih, the little things, you count 33 times or 99 times or so and you're done. That's good. That's an awesome beginning. Increase more, add more. And that's how we understand the hadith that Prophet sallallahu alayhi wa sallam said, the more you do, the more is better, which means the more you praise him sallallahu alayhi wa sallam. I'm going to end a couple of things. When you do things, do it mindfully, do it purposefully, purposefully, do it intentionally when you ask, when you praise. Once upon a time, and here's the story for you. You know Omar, the second Khalifa, the second caliph. He was in a mosque and one of the dudes sitting in a corner in the mosque, he was going astaghfirullah, astaghfirullah, astaghfirullah, astaghfirullah, astaghfirullah, and he was counting just like astaghfirullah. He came and hit him in the head and said, what are you doing? Astaghfirullah. He was like, dude, you are astaghfirullah and he's astaghfirullah. That's not how you do astaghfirullah. Make it with intention. Make it in a way that it means something. Think about why are you saying astaghfirullah? For what reason? Maybe I was snappish the other day a couple of minutes ago. Maybe I didn't yield on a traffic light. I did something. Hey, Allah, I'm sorry. Astaghfirullah. Astaghfirullah, mean it. And here's your last lesson for the day. Surah An-Nahl, ayah number 90, is our story for success, our recipe for success which Allah says do three things and avoid three things. To become successful, maybe that's one of your goals for Ramadan, if you haven't thought about it. After this prayer, go ahead and open Surah An-Nahl. 16, I believe it is. Ayah number 90 and Allah says in Allah, يأمر بالعضل والحسان وإيتها إذ القربة. وَيَنْهَا عَنَ الفَحْشَائِ وَالْمُنْكَرِ وَالْمَغْيَا عَذَكُمْ لَعَلَّكُمْ تَذَكَّرُونْ And that's your last lesson.


Hi guys, this is Linda. Welcome to Brain Education TV. If this is your first time. Oh, welcome Please consider subscribing to our channel like this video and click the bell for notifications of each new episode in this video I have another announcement to make As you know, if you've been following our YouTube channel We regularly invite our founder and creator of brain education Ilchi Lee to our channel to talk about various topics that relate to brain Education we've done two YouTube lives with him so far.  We have a link right here and it's recorded on our video So you can watch it at any time and now he's back with a third one this time The topic is about Spirituality in sex the live this time is scheduled for Thursday October 28th 2021 at 4 p.m. Pacific 7 p.m. Eastern you can join by going to our main Channel page youtube.com slash brain education TV and click the video That's at the top that will be pinned on our channel Aren't you curious what questions he will answer this time this time Ilchily will answer four questions and those questions are Number one sex is a very important aspect of having a fulfilling relationship and marriage But some may find themselves feeling sexually unhappy with their partner and also not knowing what is missing What do we need to do to prepare both?  In other words, how can we stop fighting with our sexual energy and instead develop a good relationship with it? The third question he will answer is Spiritual sexual union is typically described as yin female energy coming together with yang male energy Is that possible only between a man and a woman can same-sex couples experience a spiritual sexual union as well and The last question he will answer this was actually taken directly from a question one of our audience members asked during the last live and that is What happens to the energy in my body during sex? And then not only these but he will answer some questions during the live as time permits So if you have a burning question about this topic and you want to ask Ilchily live during the event Make sure you come and join so that you can type your question in the chat and get them answered right there So once again, it's happening Thursday, October 28 2021 at 4 p.m. Pacific 7 p.m. Eastern mark your calendar save the day tell your friends tell your family tell anybody who you think would be Interested in learning more about spirituality in sex. We'll see you all there Bye


What's going on, everyone? Sam here with late sports cards, ripping one box of 22-23 Panini Origins basketball hobby for Parth M. So good luck here, Parth. See what we got in here for you, my friend. Nice little Origins box. It's been good so far, man. Been enjoying Origins. Marshawn Bochamp, 71-75 for the Bucks. What'd you forget? Ah, fair. Fred Van Vleet, Shy Gildress Alexander, Christian Coloco for the Raptors, as well as Christian Braun, 26 of 99 for the Nuggets. Redemption is E.J. Liddell on the Elevation Signatures. That is for the Pelicans, E.J. Liddell. Elevation. And nice one here, going to the Rockets. We've got Jabari Smith Jr. Congrats on that one there. Let's go, Parth. Very nice, my friend. That is awesome. Jabari Smith. That'll do it for your break there, Parth. Thanks again for your support. Of course, man. Thank you for your support. We'll get them right out to you, buddy.