Okay, so good morning everybody and thank you for being here. So in the first one we've seen supervised learning, which as I was mentioning is conceptually very similar to fitting. So the easiest one. In the second one we have seen unsupervised learning and I was mentioning those examples and yesterday the goal there was that I had, so in this case I had a lot of samples and I also had labels. Okay, so the labels I remind you are those, the value that the ideal function, high dimensional function takes on those values. In this case of the unsupervised, I wholly had samples and not labels and the goal was to find the probability distribution according to which those samples are distributed. In the third case, which is where really artificial intelligence goes wild, is where you don't have samples, you don't have labels, you have nothing. You just have a goal you want to solve. So this is called typically reinforcement learning. So in this case you don't have some, but at least you have to have something and this something is the goal you want to solve, right? So for example, I want to solve quantum mechanics. But precisely I want to solve Schrodinger's equation. So I will find you, I will show you a representation of quantum mechanics if you want, which is amenable for machine learning purposes. So this is the goal of this lecture and I will show you also some codes that do that. But in general this paradigm of reinforcement learning as all the others has some practical applications or at least some real world applications which are not in the context of physics. So since it's a Saturday morning I would like to entertain you a little bit. So this is an application that you can, for example, think of this reinforcement learning is to play games. So in this case you don't have, for example, data, you just have the rules of the game. So you know that a certain game has a score and you have to obey some rules in order to get the best goal possible. So this is the case, for example, of this rather famous game at least for people born in the 80s like me, not you. And in this case where you have this game where you have to, you know, you have this ball and this slider and you have to make the most possible points. So the Google team which is DeepBrain which is a London, basically divides a machine which is based on this paradigm of reinforcement learning which just sees the pixels of the picture that we have in the top. So just all the raw pixels and the final score. And the goal of this machine then is to maximize the score of this game. So you can do this with a training, a self-consistent training. I'm going to discuss it later. But I mean, the nice thing is that if the training does not last very, very long, so for example, sorry. For example, in this case, so in the first case on the left you see that the training is very bad. So you see, the guy is not really winning. On the other side where we have a much longer train, so the machine really gets good and find also a trick, right? So this tunneling trick that you can, that people, you see that. So in this case, the machine is really able to understand what are the, what is the best strategy possible to win this game. And again, this paradigm is much more complex than the other two we have shown here. Simply because we do not know in advance, for example, what are the winning points or let's say, what are the positions I should move the cursor to win the game. I just know the score. For example, if I want to drive a car, I just know the position of the car. I don't know the optimal trajectory from A to B. It can change dynamically. So that's the goal of this branch of machine. Now, let's see how I can do quantum mechanics with that. So first of all, what is the problem? The problem is that I would like to solve Schrodinger's equation, right? Let's say for the ground state, okay? So Schrodinger's equation is an eigenvalue equation where I want to determine the eigenvalues i. So this is certainly not a problem which is amenable as it is to machine learning purposes because it's an eigenvalue problem. However, I can try to transform it into a machine learning problem if I manage to transform it into an optimization problem, right? So what is the thing that I have to use? Well, it's very simple. So for example, we know that from the variational theorem that if I define an object that I call e of psi, if you want. So technically speaking, this is a functional of the state and I define the expectation value of the Hamiltonian over the state, right? So I know that this object is greater or equal than the ground state energy. For example, here I'm assuming that I've ordered my eigenstates in this way, right? So from the variational theorem, I know that, right? So at least finding the ground state but this can be generalized also to other properties can be found then as an optimization problem. So again, which might be more amenable to machine learning and this optimization problem is finding so the minimum over all possible states psi of this energy functional e of psi, okay? So in principle, the task that I have to do to solve this complicated equation is just to look to do a search in the space of all possible normalizable quantum states psi and at the end eventually I will find the one that has the minimum energy. So this is if you want the simplest thing we can do and this idea of course of doing the variational or using the variational principle to solve a complicated many body. I mean, for example, it's not new, I mean. However, what has been remarked in the 60s, so more or less at the same time when machine learning was intended in a sense is that the expectation value over a certain state is the quantity which is typically hard to compute if you have a many body correlated state, okay? So unless psi is, for example, a simple product state, it is very hard to compute this quantity exactly unless you have some nice analytical properties in psi. So unless you restrict yourself to very specific choices but in general, this e of psi is rather hard to compute. But people in the 60s realize that this can be done in a stochastic way. So what is the idea? So let's assume, for example, that my system is spanned by a set of many body cats X. So you can imagine that these are, for example, in a simple case, the projection of the spin for a spin one half quantum system. So I have n spins, right? So the quantum numbers for this model are just, for this system are just n plus or minus one. So these are the full set of states in the Hilbert space so that we can write this expectation value of the energy over a generic state as basically, so I insert a completeness. So this can be written as the sum of all possible axes of psi X, H, X prime. So here I will have a sum over X and X prime. And then here I will have X prime psi and then I have the normalization. Now, if we do some simple manipulation, for example, we divide everything by psi of X prime, in this case, okay? So here I have a sum over X and X prime. In this case, okay? So we can rewrite this object at the end as the sum over X of, so let me call it like this, psi of X model square times, so the sum over X prime H, X, X prime psi of X prime divided psi of X. And then at the denominator, I have the same quantity but without this. So what is psi of X? Well, psi of X by definition is this. So it's just the amplitude of my wave function. So this is very general, it's valid for any wave function basically. But the advantage of doing so is that you can see immediately that then the expectation value of the energy, of the Hamiltonian can be written as a statistical expectation value. Right? So it can be written, so you can consider these as your unnormalized probability density. So psi squared is your unnormalized probability density. And these as the quantity that you are estimating on this unnormalized probability density. So this object takes the name in the community of variational Monte Gallo methods of the local energy, it's called ELOC of X. Actually, there is nothing local in the sense of quantum information because this Hamiltonian can be highly non-local but it's called local energy for some reason. So and this thing, so you can just write it as the expectation value of the local energy over this probability distribution. Okay? Now, this is very important then because if I have a genetic state which is correlated, which is not mean field, which is whatever, I can just do basically a Monte Gallo sampling. So what I discussed yesterday in the context of, for example, unsupervised learning. And I can estimate this expectation value as, for example, what I do is that I generate a lot of samples, so X1, X2, X and S, which are distributed according to a probability distribution which is psi of X square. So imagine that I fix my state now and I can draw a lot of samples, for example, doing metropolis to generate those configurations, those many body states. And here I can then estimate this quantum expectation value as one over the number of samples. So again, as the simple average of the local energy over those XI. Okay? So this is the fundamental connection that has been done in the sixties, for example, by McMillan, one of the pioneers of this field, who understood that quantum expectation values can be computed exactly as classical expectation values over the probability distribution and then you can sample this probability distribution using Monte Gallo. Now, the interesting aspect is that we can not only compute exactly the local energy, so the expectation value of the Hamiltonian, but also, okay, we can also compute any other operator, basically, not just substitute H with your favorite operator. So that's the first thing. And the other thing is that we can also compute efficiently the derivatives of this energy. For example, imagine that I want to optimize this thing. And imagine that my state now psi of X depends on a set of parameters, P1, P and P, where these Mp can be huge, millions, 100,000s. Okay, so in this context, then I can also, so I've shown you that the expectation value over this state of, so, the expectation value of H can be written as the statistical expectation value of the local energy. But it is also true that the gradient of this expectation value, for example, with respect to some parameter, P of K, so I call this E of psi, right? This can be written also as an expectation value over this probability distribution, pi of X. So in particular, you can show, and I mean, it's quite straightforward calculations that this is equal to the expectation value of basically the local energy times what I call D of K, and that I already introduced yesterday, but now in a moment I will remind you what it is. Minus, so the expectation value of the local energy alone times the expectation value of this D of K star. So star is the complex conjugate. All of those expectation values are taken again over this probability distribution, pi, so psi squared, that's here. And this D of K, it's what I introduced also yesterday in the context of unsupervised learning. So D of K of X is defined as basically the derivative with respect to parameter P K of the log of my psi of X, okay? So imagine that I have some form for my variational wave function from my state, which depends on some parameters. And then I can do the derivative with respect to those parameters, the derivative of the log of psi, so that I know immediately what this D of K are. The local energy can also be efficiently computed, typically, so I will not go into this detail. And you can measure then, so if you do this procedure so that you can sample a lot of configurations distributing according to pi, you can estimate both the expectation value of the Hamiltonian and the gradient, right? So this is then the fundamental, one of the fundamental things that we needed to transform quantum mechanics into machine learning. So the first one is basically to convert an eigenvalue problem into an optimization problem that I can somehow work with. So this is the optimization problem I get and this is how I would solve it if I have a generic state. Now, the connection that somehow has been missing since the birth of this field is how to identify psi, what psi should I take which is general enough to somehow hope converging to the exact ground state. So this is what we've done in our recent work so published last February. And the idea is to take as a psi a neural network, so an artificial neural network. So by, from the way that I've already written here you can immediately realize where I was going to since the goal of my neural network now is not to translate a string of text, it's not to solve a game, it's not to do anything but it's to compute the amplitudes of the wave function. So, let me write this. So what I want, what I want to do is that I want to identify this psi of x so the amplitudes again, so this psi of x is this object, right? With an artificial neural network. So an F neural network which will depend on this high dimensional vector x which can be for example the projection of your spin and which will depend on some parameters. So I've shown you for example the case of the feed forward network where those parameters are the connections, are the weights in your network. I've shown you the case of the restricted bottom machine where the weights of the connection between the hidden and the visible units were the parameters, so I remind you. So for example in the case of the RBM, the specific form that we took for this F RBM was inspired by classical, basically statistical physics. So in that case, so I told you that for this F RBM which depends on the spins, right? So sigma one, sigma two, sigma n was the sum over all the possible values of the hidden unit of an interaction between my physical spin and those hidden units which takes the form, so in this case that I have sigma z as an input if you want of this machine. Sigma z i h j, if you want also this is z so it's again a plus or minus one variable. W i j plus then I had the sum over j of h j b j and then I had the sum over i a i sigma z i. So in this formulation, then my parameters, so my p's are the weights, are the biases b and a, so the bias of the hidden units and the visible ones. Okay, so this is basically what you need to do to do machine learning for quantum mechanics. You need to know, you need to identify the wave function amplitudes with a suitable neural network. So a choice which is rather reasonable is to, which actually achieves some state of the art results in 1D and 2D actually, is to use on some models, is to use this form, this specific form that I just wrote you here. So here in this RBM form, you fix the number of hidden units, so this number of artificial, so these are your physical spins and these are the artificial hidden neurons which somehow mediate the correlations and the interactions if you want between the physical spins. So what I can do is that I just tune those things in order to best reproduce my ground state. So that's the idea. And to do that, I need two ingredients. So the first one is the ability to sample from this machine, right? So I want to generate a lot of configurations, a lot of x1, x2, x3, which are generated according to psi of x squared. And the other one is that I need to, you do some stochastic where they descend or some approximation, some optimization algorithm in order to minimize the energy function as a function of those parameters. And this can be done using again those samples and using the gradient that I can specifically compute as an expectation value over those configurations. Now, the sampling from the RBM is what we've discussed yesterday. There is a subtlety, of course, in this case. And so in general, the wave function is complex valued, right? So yesterday we discussed only the case where those parameters are real valued. Of course, that case is not general enough if you want to describe a wave function in the sense that if you want to do this, you want to identify psi with f, you need this function in general to be complex valued. So in the general case, you might want to take these w's and b's and a's complex value. So if you do that, typically you cannot choose any longer the Gibbs sampling strategy that I was introducing, but you just need to do standard metropolis sampling and generate your configurations and measure your local energy. It's all rather straightforward. So an interesting property that I would like to discuss now is how this, before showing you some actual numerical results live, is how this optimization can take advantage of some specific properties of quantum mechanics. In particular, you might remember that, yes, I discussed the connection between the stochastic gradient descent and this Langevin equation, right? So I show you that there's a connection between the effective temperature of your classical system and the noise that you have on the gradient. So also in this case, you have a noise because simply because those expectation values are estimated over finite samples, a finite amount of samples. So on those gradients, you also have a noise which was a sigma square with go like, so the noise on this, so the variance of this variable, if you want, the variance of the expectation value of the gradient.  So this thing will go like one over the number of samples that I have drawn from my probability distribution. So in principle, there is always some intrinsic noise which is good as I was telling you yesterday because it allows us to to converge to the global minimum of the distribution. In this case of the energy of the system, the actual physical energy. However, we also might want to control this noise or at least be sure that the temperature we arrive at the end to is zero. And there is a nice property and the property which is easier to show, for example, in the case of the local energy is that, for example, the variance of the local energy as a variable. So you consider your local energy, so the expectation value of the local energy is the expectation value of the Hamiltonian. So what is the variance then of the local energy? So if you want the expectation value of the local energy square minus the local energy square. So these are just the statistical fluctuations of the local energy and also those have a very nice physical meaning because you can show, I will not go through the derivation, that this is equal to the expectation value of the variance of the physical Hamiltonian, okay? So you can demonstrate that using a couple of tricks. Now, this is rather nice because it tells you that, for example, the fluctuations that you have on the local energy tend to reduce when the Hamiltonian converges to the exact ground state. So can you see that? Because if your state is an exact target state, for example, then h psi, right? Yes, I mean, h psi, so h square psi, if you want, so this quantity, which is here, is simply equal to, normalize, is simply equal to e squared because psi is an eigenstate of h. And here, we have the same quantity, which is e squared. So the variance of the Hamiltonian is zero if we are on a close, we are at an exact time set of the Hamiltonian. So the magic thing which happens is that the closer you get to the eigenstate of the Hamiltonian, the smaller the fluctuations on the gradient, for example, and also on the energy will become. So this means that the closer you approach the exact solution, the less noise you will have. So this is something which is peculiar, I would say, or particular of the quantum mechanics that you do not find in standard applications in machine learning, typically. So in particular, this means also that typically, so this learning rate that I introduced yesterday, so which is the rate at which you change your parameters can typically be taken just fixed. So you do not want to anneal it as a function of the number of iterations simply because the temperature decreases by itself because the variance of your gradient goes down automatically. Now I can show you this at work for a simple example, numerically. So the simple example I'm going to treat is the case in which the Hamiltonian is the harmonic oscillator. So here I'm taking h bar equal one, omega one, whatever, equal to one, m equal one. And I take as an answer, so it's a simple answer for the ground state just to show this property, psi of x, the exponential basically of some Gaussian, which I could, so minus alpha x square. Okay? So I take, if you want, there's a variational parameter in this case, this parameter alpha, which I can, so that I can play with and I can modify in order to converge to the ground state. This is just to show you how, what happens to the energy as a function of the, ah yeah, so those things are, will be, I mean, some of them are already on my GitLab repository, those codes that I'm going to discuss. So the first, so they are there, this is publicly accessible, so you can go there and download these codes and play with them. There are also some other lecture notes from another school, I mean, there's a lot of material you might want to, to have a look at. So in this case, let me show you the example of the, of the, of the, of the, of the IC model. Sorry, I have to find the right, okay? Okay, so here, again, I'm optimizing alpha, so this parameter in the wave function and what I'm doing is that I'm generating samples of, drawn according to psi square using some metropolis. So you can have a look at the code again in the repository. I don't know why the sound is not dead, but it should be. And then, so you can call this script. Okay, she's called, okay. So you see that I'm, well, you can probably not see it, but so I start from a very, so okay, so let's have a look at the code. So optimize harmonic. So here, what I do is that I start with a state psi, this is in Python, I hope you are fluent in Python. So this state, so I start with a pi, with a state which has alpha equal to one. So it's not the exact tiger state. You know that alpha, one half is the exact tiger state in this case. And then I, what I do is that I generate some samples and then I consider the, I compute here, for example, the local energy and I compute the derivative of the local energy to this gradient and I use this information just to change the parameters. So what you can plot then is there, for example, the expectation value of the energy as a function of the time. And here I'm using a number of sample which is very small, I think a hundred. So this is the energy as a function of the iteration counter. So you can see the plot here. I generated it in real time. And it is very noisy because I start with a very bad answer for the wave function. So again, I had half equal one, which is very large. And so at the beginning, the energy is very large and it is very far from the exact energy which in this case is one half. And you see also that I have wild fluctuations in this quantity simply because at the beginning I'm taking not enough samples to have a good resolution and I have a very huge variance. But I'm keeping it fixed and then I'm doing this optimization in the way basically I described here. And you can see that at the end, the energy approaches 0.5, like with the precision which is unbelievably accurate. So you can have a look at the thing and you can see that the energy is 0.500031, so you see. So for example, this is the last iteration. And you can see that the variance, for example, is energy is 10 to the minus eight, something extremely small basically at the level of numerical precision. So you can see that because of this property of the Hamiltonian, because of this property of quantum mechanics, even if you are doing something stochastic, so which is intrinsically dirty in a sense because we have noise, we can convert to very high precision in the energy and also in other properties. Now, of course, this is interesting, but again, it's a toy model. It's just the harmonic oscillator. We want to do something more complicated. So to do something more complicated, we need the good stuff, so we need the RBM. And this is the other example that I'm going to put on this repository or you can already find it. So in this case, I consider a Hamiltonian which is already a prototype for strongly interacting models. So I consider the transverse field as a model. So in this case, I have a one-dimensional chain with periodic boundaries. And I have my sigma x operator proportional to my transverse field gamma. And then I have some, in this case, nearest neighboring interaction of the form sigma i z sigma i plus one of z, right? So again, here I have a lattice of spins of, so I have sigma one z sigma two up to sigma n. And I have periodic boundaries, so I have interactions in this way. And I also have a transverse field on each of those, if you want, I have a transverse field on each of those points. So this model has a nice property which allows to simplify a little bit the presentation of this code, which is that the ground state is positive definite. So you can demonstrate that in 1D. So in this case, then what I will do is something a little bit different because I just want to simplify the code. And I will say that psi square, so psi square now is my probability distribution. If you want, psi square of x, so of sigma one, sigma n z. So identify psi square with my FRBM, if you want, with my distribution. Okay, so you see the difference here that before I was doing psi equal f here, I'm doing psi square. And this is fine, but the simplification is that now I can, you take just the real parameters for those interactions, for example, these w's because psi square is positive by definition. And also I can use Gibbs sampling to draw samples from this psi square. Or I can generate those configuration just using the Gibbs sampling approach that I discussed yesterday. Again, those are details that I discussed somehow also in my lecture notes. So what we can do is that we can go here. Sorry about that. It's okay. So okay, so this code is pretty general. You can use it for any Hamiltonian and basically any graph. So here I'm just considering the case of 20 spins, right? And okay, hypercube one dimension is just lattice. So it's okay. So the first thing defines the number of spins. The second thing defines the geometry, the graph if you want on which model is defined, which in this case is in a hypercube of dimension one. So it's a line. And I have periodic boundaries. And then this defines the Hamiltonian. So you can, so the Hamiltonian depends, I'm assuming that all of you know C++ but probably it's not a good assumption. But anyway. And this Hamiltonian takes as a template the graph. So the thing on which this Hamiltonian leaves, so in this case a one dimensional lattice. And then you have to specify the number of hidden variables. So again, it's this number here. So how many of those I want to put my neural network? So the more I put the clever my neural network will be and the longer it will take to optimize of course, but that's the trade off. And then here I basically define my state for to do, for example, Gibbs sampling. Then I can use a modified version of stochastic that in this end, which is called add a max. This is very a little bit more advanced. Okay, so we can run this thing, for example, just to show you. So typically you just do make and it's compiled. You just need a few standard libraries. Might take a while though. Okay, so you just run this thing. So it does, so it does these things and we can also plot in real time what's going on. So here I'm plotting as a function of time of the iteration count, the network that is finding the ground state. Or is it these guys learning what is in the guys and the software this model has learned in a few years. So you can see as a function of time, the energy, so the expectation value of the energy. And you can also see plotted the relative error with respect to exact ground state. So in this case, the model is simple enough. I mean, we have 20 spins, we can diagonalize it, for example, exactly. And you can see that I'm doing some progress, right? So already the relative error on the energy is pretty low and then I can improve it and I can go as low as, I don't know, I think in this case, 10 to the minus four or something. So this is the same principle that I was discussing before for the transfer field for the simple harmonic oscillator. But at work for a much more complicated, I mean a more complicated system where you also, which is basically a many body system where you have interactions and which does not have a simple mean field solution at least. But this can be generalized not only to 1D, but also to 2D, you would find very similar results. You have to be a bit more careful about the sampling. It's not always that easy. But I mean, in general, this is a relatively robust way of finding the ground states. Okay, so, okay, I mean, it's going down, but I have to stop at some point. So let's say two to, yeah. Depends on how patient you are or how many processors you have. Okay, so that was more or less what I wanted to tell you. So I've shown you during this lecture how to transform quantum mechanics into basically a machine learning problem. Through this stochastic mapping of quantum mechanics to an optimization problem. And I've also shown you that like a simple choice, one of the, let's say in a sense most straightforward choice that you can do is to take this RBM machine and identify it with the wave function of your system. So I mean, after our work, people have started wondering why those states work and what is the peculiarity or what is the characteristic which makes them suitable to study quantum systems. So I'll discuss some of those issues and ideas during my talk. But I can already tell you that one of the nice features of those states is that because of these new states if you want of variational answers, is that if you have long range weights, so if for example you're effective if you want a classicalizing model as very long range connections, then the amount of bipartite entanglement, for example, that you can put inside those quantum states typically can satisfy volume law very easily. So you don't need exponentially many parameters in the wave function to have a volume law. So this is nice for example if you want to describe critical systems, especially in two dimensions, et cetera, whereas for example with other approaches, you might be limited by entanglement. The other thing that people realize is that with those states, you can write basically a lot of interesting topological phases, exactly, like the ground set of interesting topological models, the Gitaev models and other things. And also this is a nice result and in some cases you cannot do that with other states. And one of the results which is also particularly interesting, which can have far reaching consequences, I believe, is that you can demonstrate mathematically that any basically physical quantum state, so any reasonable physical quantum state can be written as a network with two layers. So in the classical case, I've shown you that the RBM is enough to describe any classical interaction, any classical energy. So I've told you the example of the two-body interaction in the classical case and I told you that you can represent any classical Hamiltonian with that form. Now, in the quantum case, you can demonstrate that if you have two layers, so not only let's say these hidden layers that we already have in the RBM, right? But you also have some extra connections, so like this, for example. So we have two layers now. So physical, sigma one, sigma two, hidden, H1, H2, and deep, let's say, let's call those deep neurons. Then the wave function that results out of identifying, let's say, this object with the wave function can represent efficiently any quantum state where efficiently it means that the number of neurons that you need is basically a polynomial of the number of spins. So in particular, it goes, yeah, it depends on the model, but let's say it's a polynomial that can be somehow, typically, sent square for reasonable moments. So this is a very nice result to which I believe we'll have some consequences in the field in the sense that if we manage to use efficiently those states, in principle, we can describe any quantum system efficiently. Okay, so I leave you, if you have some questions, I will be very happy to answer now or later. So thank you for your attention.


Ευχαριστώ πολύ για την παράδοση. Είμαι ο Μπερικλής Δραμμός. Γεια σας, μία name is Novti Psharmo. Και θα συμμετέχουμε για να αναγνώρισουμε και να προσπαθούμε ανθρώπισης για την υπηρεσία της εξαναδικασίας της εξαναδικασίας. Λοιπόν, πρώτας, όλοι στην κατασκευή της virtual, οι εξαναδικασίας της εξαναδικασίας για industry 4.0 έχουν δει ένα εξαναδικασία από τα τέστας χρόνια και είναι όλοι προσπαθήθηκε να αγιωθεί. Γιατί αυτό αυτός γιατί, γιατί η virtual reality έγινε ένα καλύτερη υπηρεσία στην όμως της εξαναδικασίας της εξαναδικασίας. Γιατί είναι η σύγχρη, είναι η καρδιά, είναι οσχερό, και είναι όλοι είναι πιο πολύ επιθυμμένος, γιατί έχεις λασί για θέα στη εξαναδικασία, για την κατασκευή της εξαναδικασίας του εξαναδικασίας. Όχι σκέφτερες χρήματος ήρθετε να με ανεθρώψοτε. Περκώς, ήταν έναν πολύ μεγάρο εξαναδικασίας που διεθνούν their stuff with virtual reality but it's not far in the future where specialties like doctors or surgeons will be trained with virtual reality as technology progresses. Augmented reality on the other hand has found much more applicability on the job and in the future augmented reality glasses will become as an essential part of a worker as a hard hat. The reason for that is that first of all they offer handshift manuals, they offer remote guidance which makes very good use of the camera that most AR glasses have and it also has automated guidance which is taking the SOPs and the manuals to the next level making them interactive and making them much more easy to use. And companies have already started using it, it has found very good usage in logistics as you can see in that photo but also in aerospace manufacturing which gives guidance to the workers where to place what hand having much less errors and making mistakes in such an environment can have a huge impact both in terms of revenue but also in terms of health and safety. What this talk is going mostly to be focused on is prediction of those errors and their prevention. The data, the intersection between data science and AR VR is of course the data and the data that we have been dealing with is the motion data coming from AR and VR because they do double as motion capture systems. And those were the tool that we used for our project, we used an Oculus Rift for virtual reality which you can see that it has both a headset and the two controllers and a Microsoft Hololess for augmented reality. And those systems very accurately record the user movement of the user, both coordinates and angles for the headsets, timestamp, producing the trajectories but also the objects in the XR environment and the interaction that the user has with them. A lot of industries still have to train their workforce in really dangerous environment where any mistake can lead to injury or even a loss of a lot of expensive drugs and liquids. One of the simulation that we create at Accenture was BioVR in which we train people how to work with chemical labs and chemical liquids. And to analyze the data and to run the prediction we designed the XR Insights platform which is at its very core, it's a streaming platform where you can bring in your own AR and VR model. And also data scientists can plug in their machine learning model as well as define their schema to consume the data. The type of data we are streaming at the moment is state and event stream. The state stream considers considering all the spatial data with coordinates of head, hand or any of the chemical bottles and all the coordinates that you have in the virtual reality. Event stream is a series of activities that the user perform within the virtual reality. So something like moving hand, picking up object, picking up glass, something like that. In going into the type of analysis that we wanted to perform, there were two major analysis we wanted to perform. One is to track how a user has improved over the training periods. So during the multiple sessions we can track very accurately with the time number of mistakes someone is doing in the virtual reality while getting trained. And also personalizing the learning experience for an individual because everyone learns differently. So not putting generic predictions out there whereas building a stateful machine learning model which can learn how a user has behaved in the past and how he is going to perform the next task. So let's go how we went on to achieve this. This is our machine learning pipeline which of course is divided into real-time processing and offline processing. In the real-time processing of course we have the user immersed into the XR environment and we collect the data from them as previously described which are then sent into our real-time processing pipeline. One of the purposes of this real-time processing component is to collect the data and send them to our historical database. From there we have the data collected from several users which the first thing that we do from them is the first step of the machine learning processing which is to extract the features from them. We will talk about those features later but those features serve dual purpose. They serve first of all the purpose of giving us insights as to what the user did during the XR experience but they are also used as an input for our predictive analytics model. As an input for training our model. So after we train our model we store it in an S3 bucket and then that makes it ready for deployment, connecting it into our real-time processing component. An important part is that the same way that we are calculating the features offline, the exact same procedure we need to calculate for calculating the features online. This is why we have to connect them this way through a registry schema. Once we have the same mechanism of calculating features online and offline we can then go ahead and produce the features from the user in real-time which are then fed into our prediction model again in real-time. The prediction is made in real-time and is sent back into the user into the XR experience thereby closing the loop in real-time and providing a prediction. So I will try to walk you through with the high-level architecture of this solution. The key motivation behind designing this architecture was prediction in context and prediction in time. Prediction in context refers to building a stateful machine learning model where machine learning models can remember and request a point in time information what a user has done in previous tasks. Prediction in time refers to minimizing the latency between machine learning model and the visualization of the output on the user's end. The stream flows from the XR devices over state and event endpoints. It connects to our WebSocket server which has integrated Kafka producer and Kafka consumer. Then the Kafka producer pushes the data to Kafka cluster. Then we do some data transformation to handle the sessions and Kafka Connect is using... We are using Kafka Connect to serialize all this data in our master database which is always going to be append only and we don't really... We never change that actually. This same master database is now being used to train the machine learning models. The similar was that Perklis had just explained and that goes to the bucket and we can deploy any number of machine learning models because it's a message-based architecture. Many machine learning models can be connected and consuming the same messages as compared to other architecture services. We also have real-time reporting because tracking how well a user has been doing and how many mistakes he has done and also tracking the predictions. We've designed a real-time reporting component as well and all these predictions get consumed by Kafka consumer and Kafka consumer pushes it back to XR devices. So this is the whole architecture diagram on a very high level. One of the major components for us to design a mainly decoupled system was schema registry so that your devices and your machine learning model and any of the infrastructure services don't have to talk to each other directly to know what schema of the data is. A central schema registry really allows you to define your schema at one central location and any component which want to consume and push data, they'll have to get the schema first and start producing or consuming the data. Here's an example. It looks fairly like a JSON structure but once you define your schema, it's backward compatible as well. So as your solution grows, you add more objects in virtual reality, you can change your schema and none of your components have to ask each other what has changed over the evolution. Right, so features engineering and events engineering and prediction. So in our world, we went at the end with the decision of having events and features being the exact same thing and that really made our life quite simpler. So in terms of events, we have the raw events coming from the XR experience, things like the user pressing the left or the right trigger or the left or the right grip or both picking up an object, dropping an object. Those events in conjunction with the trajectories can provide the derived events which is, for example, field of view events, whether the user was facing the objects of interest or not, their hands moving towards the objects of interest or away, where the item that they were picking up the correct one or not, where they touching the wrong item, hitting collision features and so on. But what I think are the most interesting one are the behavioral features. In other words, did the user made many small movements or a few large ones, whether hence jittering. Those are features or events that they were not calculated once, but they were calculated over a period of time. And you can already see that from those events slash features, you get quite a lot of insights, quite a lot of understanding as to what the user was doing in the XR environment. But we want to have predictions and the predictions will be the exact same events or features, but in the future. So by having some events in the past, we want to predict what they're going to do in the future. And this type of similarity that we all want them to be together dictated the type of algorithms that we're going to go ahead with. So we chose, first of all, statistical correlations and then association rules learning. And the advantages of those algorithms were that they could associate like for like, unlike many algorithms in machine learning. They were suitable for a small data set and that's what we had. They were also very, very transparent. We wanted to have a transparent algorithm because associating particular features at a moment in time with particular features at a later moment in time is a very important insight in itself. And we wanted to have that in our model. And last but not least, we wanted an algorithm that it was flexible because different events have different importance for different environments. For example, in a pharmaceutical environment, it will be very damaging if you are to drop a very expensive vial or a very expensive liquid, but not so much if you are putting cables in a manufacturing environment. Okay, so as I said, the system is very decoupled. So if you choose association rule or any more complex deep learning algorithms, you can choose any of the algorithms and plug to the system as long as you're following the response schema. The response schema is designed to take the responses from machine learning model and serve it on the user end. So here's an example of response schema where the highlighted part is the actual text which the user can see. And this is the raw and this is when you actually go and wear the headset on a virtual dashboard or an audio headset, you can see this prediction coming in and which is very much based on how you have behaved throughout the task and also managing one simple application for multiple users as well. So this basically explains that any of the messages that we send back to the XR Insights or the VR devices can be generated by any of the machine learning models. Yeah, to finish, I've just going to go through some of the key learnings was we use Kafka as our backbone of the service and the consumer group really allows you to spin up machine learning models or instances of machine learning models within the consumer group for parallel execution of and data consumption. Schema registry is a single point of schema changes and worsening. So if the VR developer changes the schema and data scientists don't know, then they don't have to worry about because there's a central schema registry for that and data consumption will go as smooth as it was before. Avro, we used Avro, not JSON realizing that Avro saves the data within a binary format. So a lot of metadata that JSON carry, we don't have to send that because at the end any component can use the schema to convert the data. We didn't pursue a very complex machine learning model because we wanted to build an infrastructure where developers and data scientists can bring their own model and plug into the system. To design truly human and machine interaction, we designed this event driven architecture where all the events either generated by human or machine in form of prediction or features are equals. And also event driven architecture basically allows you to build a stateful machine learning model where your machine learning model has access to all the instances which has occurred in past. And the last is we've tried with an AR application on the same platform and we think there is a really good possibility to train machine learning model in virtual reality and actually provide people guidance with their VR glasses by deploying it there. Thank you.


Hello everyone. Today we're going to be unboxing and reviewing a rechargeable spotlight that also has a laltin functionality in it along with a USB charge point so you can recharge your smartphones and it even has a tripod that can be used to mount the light in a tent while you're camping or in an emergency survival situation. Let's start off with the unboxing. The item is carefully packed and it is shipped to you directly via Amazon. The contents include a spotlight, a tripod that has a universal mount so it can even be used for taking photographs, connecting to a camera, a very sturdy harness that has metallic carabiners on the side so the nightlight can be hung from a tree or from your shoulder. We also have a cigarette lighter adapter that can allow you to recharge your flashlight along with a power adapter so you can plug it in and charge it at home along with a product manual. These are the product specifications and as I said before it can be used for camping if you're going fishing if you're working on your car it can also be used if you're at a construction site it can be used for warning hunting running bicycling multiple uses to it. The light has two buttons this is the laltin function by pushing the button it will progressively get dim and then it'll turn off however if you hold the button down the top button down it'll turn on emergency mode if you're stuck on the side of the car or you need attention of people passing by this will come in handy to switch it off just push the button the second button is the one on the bottom the second button will allow you to turn on the spotlight and again if you hold the button down it will allow the light to start strobing there is a rubber cover on the back so it is going to be waterproof the charge point is right on the bottom and it has a usb connector point for your smartphone or your smart devices the tripod can be mounted right on top just connect it here and by using the button this button right here and compressing it simultaneously you can leverage it left and right once you have it in a set position just open the legs up and then you have a base that can allow you to project your spotlight or if you're stuck on the side of the road you can just put the spotlight and turn on the emergency beacon it'll look like this absolute worst case scenario if you don't have the tripod on you or if you're in too much of a rush you can just put it down like this or like this and the beacon can still work as there is a little bit of a lip on the lens the front cover is not going to get scratched but be careful while using it preferably put it down like this because it has the river mounting on top if you're going camping or if you're going hiking or if you're going for a bike ride you can connect the harness that is included right on the bottom like so and it is adjustable it's not a small strap so you can just adjust the length of it and as you can see it is pretty long so you can put it on the side of the shoulder and have the flashlight just hanging down and it is pretty neat that the mounting point is on the bottom that with the flashlight can be looking straight up for charging the light just open it up and plug it into the charging point right here and it has a snug fit or you can plug it into the wall outlet and note the charging brick does have a LED indicator that will turn on once you plug it in right now you can see it's pitch black but if I turn the light on you can see the tree clearly and it has three different settings and it lights it up just like daylight this is another view right now you cannot see anything then the light on and everything is clear if you go camping the side locked in function of this light can come in handy as you can see it brightens up the entire place and even from a distance you can see everything clearly now the amazing feature of this light is if you can have the spotlight on and the side light on at the same time for extra brightness additionally you have two different brightness settings and once you turn the light on and you hold down the button it turns into a strobe light that can be extremely helpful in an emergency situation and can potentially save your life and alert authorities and similar to the nightlight function the spotlight can also function along with the emergency light beacon thing and if you look at the spotlight if you hold down the button it can turn into a strobe which can definitely come in handy a must-have survival item for anyone that is planning to go out camping and as you can see we based on the night shots the light output and the and the uses of this flashlight, laltine, emergency light are limitless thank you so much for watching take care bye


Everybody, thank you for joining us as we go over the highlights of the challenge week for the Rhode Island Genealogical Society. And I'm hoping everybody can see my screen. Great. Okay. So, for those who may not know. Let me take you over to the help page for the wiki tree challenge. The wiki tree challenges are wiki trees largest ongoing community event. We're volunteers work on the growing the connections for the seven ancestors provided our guest seven days. So it's a full week of us all working together to to grow out those connections. So it starts with this forum post the community forum, where we start people volunteer and we start collaborating after the week starts. We use the G to D post. We have there's a post for each a post answer for each of the ancestors, and for people who are focused on one of the ancestors that can collaborate together on that post answer. And then also we have what's called a free space page. That's all about the challenge. So this is your free space page for the challenge number 18 and I've got the links for everything in the chat. The forum post is also linked here on the space page. We have the page we have the starting profiles, and then further down the page. There's the fun part of the page tells us tells us what you found. This is where people report in all the different information that what they found on for the week. So you can see all the different locations in this section from Armenia to Scotland and 12 different states of the United States. This is what we've worked on profiles for. It's really a lot of fun to, you know, we start in Rhode Island, and very quickly we go way out into the world, all over the place. So that's a lot of fun. The next section is our interesting finds. So as we find interesting stories. We report them here. And as you'll see in the slideshow we're going to share. Next step, we also do what's called a survey and the members vote on the top seven. There's also section on military profiles. So if there are ancestor profiles that get created or get connected to your ancestor profiles. We note them here. So there's some interesting stories that are around the military or different wars that occurred. So you can learn more about about those ancestors there. And if we scroll down just a little bit further there's a section on free space pages. As you can see there's one here that a member created, and it's all about Ephraim Codner and his Rhode Island origins. So it goes really more into depth about it. So that'll be something for members of your society to maybe check out and learn more about what that was all about. And also at the top of the page of this free space page is the Scorsi, if you're interested in that. And you can see all the different members that part took 42 members worked on profiles. They did all different kinds of things. Some people focused on writing biographies, and some people focused on adding more profiles or connections, so all different kinds of work can be done. Some people are more interested in writing the biography or finding sources. There's definitely something for everybody. All right, so next I'm going to go ahead and start the slide show that Aeon has put together for us. So next we're going to go through the ancestor profiles. One of the profiles that we have is for Christina Bannister. Christina was a noted abolitionist, hairdresser and wig maker. You can see her listed as Hair Doctoress. So her profile is really fully fleshed out. You can really read all about her. They did a lot of great work on writing up her profile. So in the occupation sections that they have here, they've really listed out what she's listed at. And I really thought it was interesting that her salons were a popular meeting place for abolitionists. You can see they've got the little tax assessments for Boston for her. It's a really interesting information how they broke it out. So you can see two at the top of the page. We have what's called projects and wiki tree where they're focused on different subjects or maybe locations. So she is part of the U.S. Black Heritage Project. So that's what this box is up here at the top, noting that she's part of U.S. Black History. And we have a great picture of her here too. Great. All right. And the next profile that we'll go through is this Ephraim Codner. So I just want to point out on this profile. That wiki tree is a great place to have your ancestor information because not only can you write out this biography the story of their life, you can have a section, which we have here, you can see here is research notes. You can use this section to work through information from various members or there may be conflict between sources you can kind of outline that here and work together with other members to collaborate and iron out which what the correct way to go is based on those sources. Also here you can see that a member has uploaded a colonial land record. And if we go to that image, you can see that you can attach multiple people to the images. Or even if there is a space page, you can attach a space page. For instance, you could use if you've scanned a will in. You can upload the image and you can attach it to a space page where you've done the transcription. So it's just a way to use the different resources on wiki tree. The next profile is for Easton. I just want to show you here how you can see quickly if somebody is connected to the global tree. Some of these profiles were only had a few connections when the challenge started. So they weren't connected to the tree. So a quick way you can tell if somebody's connected is if you scroll down to the bottom of the profile, you can see right here it says that Easton is 15 degrees from our profiles of the week. Sandra Day O'Connor, 21 degrees from Jane Birken. So that's a quick way to just check to see if they're connected to the tree. You can also check your connection to this person at the bottom here. Next profile, I was really interested in this one, Mary Elizabeth Laravie. She is the called the queen of baseball. So she made history as the first female player to play against major league players in 1922. So I thought that was very interesting. And they did a great job working of her profile. Another great read. The next profile is for Charles Henry short. In Charles profile, you can see he's been added to what we call a category. If you scroll down to the bottom of the page again for his profile through the sources, memories and comments matches emerges down here at the bottom. You can see there's a category, the different categories, and one of the categories is for weavers. If you look up that category, you can see different if they're sub categories which there are here. We can also see that there are 360 ancestor profiles that are part of this category. And one thing you can do in any category, which is one of my favorite things about wiki tree is you can go here and click this my connections button. If you click that, it will show you any weavers in the category that you have connections to. So this first box shows you your ancestors and cousins that are categorized as weavers, and then any that are just connections through marriage. So that was one of the things that drew me to wiki tree was being able to check your connections everybody wants to connect to that global family tree. And Samuel is our next ancestor, he's the father of American industrial revolution. Wiki Trees did a wonderful job on this profile. It's really well written biography with images in the biography, just really great example of how that can be formatted out to look really great. And the last profile ancestor profile is for Walter Norris Williams on Walter's profile. I just want to turn on the wiki tree browser extension just to kind of highlight that briefly. It's a browser extension and it just allows you to dial in how you want to view or use wiki tree. So I'm going to turn that on. And I'm going to refresh the page. And right away, you see here at the top, the 17 degrees. So right away with a glance we can tell he's connected to the tree, and he's 17 degrees away from me, because I'm logged in. So that's a lot of fun. So I go back over to one of the other profiles really quickly. Open up. Yeah, so Easton Cole, open up Easton Cole's profile, you can also see, in addition to being 14 degrees away he's my sixth cousin six times removed. And the relationship is through. If there are multiple ways are connected to them. Then it shows you all those if you open that up. So that's just a really one of the few ways. I mean there are many options within the wiki tree browser extension, those are just a few little highlights of that extension. All right, so we're going to go back to the slide show. So wiki treeers did a great job adding families to these ancestors and for some getting them connected to the tree. The largest increase was 2278 profiles are now within seven degrees of Easton Cole, and the total increase in connections for the week was 4638. So that's really exciting. The closest connection between the seven ancestors was between Ephraim and Easton, they're within 11 degrees from each other and you can see here what where that, how that works out.  So you can follow the relationship path using the connection to me option, or you can put in a put in if you do connection to me then you can put in somebody else's profile ID to check between two different profiles besides Okay, and then Charles short is 20 degrees away from Ephraim and Samuel Slater both. This is just showing the relationship path for Ephraim and Charles and then here's Charles and Samuel Slater. And we found this really interesting Ephraim was 14 degrees away from Samuel Slater 16 degrees away from Walter Williams 18 degrees away from Kushiana banister. So they're all pretty closely connected actually. Okay, so this is what I was talking about earlier the top seven interesting finds that were voted on by the wiki tree challenge participants. So Hannah Slater is Samuel's wife, and she is among the first American women to receive a pattern for thread. She used a hand spinning wheel to spin it spin cotton into thread and it turned out to be stronger than linen thread which I thought was really interesting my mom's a seamstress. It's interesting to me. And of course Samuel father American Industrial Revolution, he pioneered the American textile industry. And Jackson Andrew Jackson is quoted as saying I understand I understand you have taught us how to spin. So I'm going to talk to you about the tubers I thought this was very interesting to you. He was convicted of having milk in his possession that contained less than 12% solids and less than 3.25 milk, milk, milk fats. I didn't know that was a crime. So that was really interesting. James how is the next one. This one is about the brothers. They did a voyage. They completed it. They were expected home very soon, but they wrecked off the coast of Cape Cod that same year. And all lives were lost. So very sad there. Judge Thomas stir fees are next one. A judge and a poet. So during the US Civil War he was a frequent contributor to papers and it is said his pen was powerful and support of the Union cause. He published also the village picnic and other poems, a collection of poems capturing the beauty and charm of rural life in the mid 19th century. All right. So these next three finds were just ones that you and I had picked out and wanted to share. These are within seven degrees. The first one here is Ruth Slater. And actually we wanted to kind of show about the her and her four sisters. They were very interested had very interesting lives. If you look at her profile. This excerpt that is written out. It was very interesting it says these women except perhaps Ruth were not intellectuals in the modern sense, but they knew history, the classic authors knew something about contemporary trends in music, art and the literature. And most important, they placed a high value on mental activity. I just thought this they have their lives are very interesting. Unfortunately, Ruth's term to drugs, and she remained addicted to cocaine cocaine for the rest of her life. But they all had really very interesting lives. Catherine Slater. Unfortunately died of Brides disease. But again very, very interesting women. Check out that I think there may be a space page in the works about these five sisters. All right, the next one we wanted to point out is Nelson Rockefeller. So, Nelson, this is this is another one of the seven interesting finds. Nelson Rockefeller was born on. In 1908, and he was the vice president under Gerald Ford 49th governor of New York. So we go to his profile is Rockefeller 101 can read more about him. You can see there. All the information about him and there's lots of good pictures. Okay, and the next. This is more about the sisters. These are the four sisters, Ruth Georgia Lydia and hope and Catherine. The next interesting find that, and I wanted to point out was Joseph Stanton, General Stanton. He's seven degrees from F from Codner. He was a soldier in a politician. Let me go ahead and open up his profile here for you. And he has, there's a, they have his home is listed on the national register. It's shown there. And he had partook in the French and Indian war and this revolutionary war. He's a day our Patriot ancestor. And we'll see here there's a section on legacy. So the SS Joseph Stanton is a Liberty ship built in the United States during World War two it was named after him. And then there's a memorial marker on us route one in Charleston, Rhode Island. And there's also general Stanton in in Charleston, Rhode Island. Just very interesting gentleman. Hugh Cole is one that we wanted to point out as well. So he is Easton Cole's fourth great grandfather. He held positions related to the land and highway management involved in numerous legal proceedings. And his home was burned during King Phillips war. So if we go to his profile here. They have a rock marking the old well at the coal house site. And that's pictured here on the side. They've done a really great job of this profile outlining his life and everything about about him. And again just pointing out the research notes section that they've utilized here. And this is what I was talking about earlier that this is, you know, making a statement that there are many publications with accounts about the relationship with King Philip, but no historical records were found to authenticate them. So this is a good way to make your case and make sure it's available for other members to see kind of explaining maybe why something wasn't used or some other point that needs to be collaborated on. All right. So, thank you to everybody who contributed to this week of collaborating on the seven ancestors. I wanted to open it up. Patrick is here with us and young Kathy's here. And I just wanted to point out that we have. He has a bounty points he got 90 bounty points. And Patrick did an awesome job as well. He's right there at the top with 228 profiles added for the short line. So we have some members that just did a lot of work. There were a total of 229 bounty points that were awarded so please, if anybody would like to share. Patrick, I know you started to share earlier about what you were working on. If you want to go ahead and kind of expand a little bit about that now that we're recording it'll be preserved for posterity. It's funny that the person who was chosen for the contest shared the same name as my wife, just kind of a coincidence and I didn't know that going into it that I was working on profiles related to them. Oh, that's awesome. Yeah. That was one of short great granddaughters that had proposed that we we follow them, and then her name was Daniel Danny Graham, or her maiden name and my wife's married name is Danny Graham. So that was just a coincidence. That is. That's great. Does anybody else have anything to share or would, does anybody have any questions. Well, if everybody is ready to close up I guess we're going to close up a little bit early. All right, thank you very much for focusing on the Rhode Island genealogical society. Yeah we had a lot of fun and again the links for everything is are in the chat, and we will be posting the video the highlight video out on our channel. And it will be going out on all forms of social media, so that everybody can get the link for it. All right, well thank you everyone for joining and hope everybody has a great evening. Thank you, but happy new year.