>>don't talk mhm, >>Okay, thing is my presentation on coherent nonlinear dynamics and combinatorial optimization. This is going to be a talk to introduce an approach we're taking to the analysis of the performance of coherent using machines. So let me start with a brief introduction to easing optimization. The easing model represents a set of interacting magnetic moments or spins the total energy given by the expression shown at the bottom left of this slide. Here, the signal variables are meditate binary values. The Matrix element J. I. J. Represents the interaction, strength and signed between any pair of spins. I. J and A Chive represents a possible local magnetic field acting on each thing. The easing ground state problem is to find an assignment of binary spin values that achieves the lowest possible value of total energy. And an instance of the easing problem is specified by giving numerical values for the Matrix J in Vector H. Although the easy model originates in physics, we understand the ground state problem to correspond to what would be called quadratic binary optimization in the field of operations research and in fact, in terms of computational complexity theory, it could be established that the easing ground state problem is np complete. Qualitatively speaking, this makes the easing problem a representative sort of hard optimization problem, for which it is expected that the runtime required by any computational algorithm to find exact solutions should, as anatomically scale exponentially with the number of spends and for worst case instances at each end. Of course, there's no reason to believe that the problem instances that actually arrives in practical optimization scenarios are going to be worst case instances. And it's also not generally the case in practical optimization scenarios that we demand absolute optimum solutions. Usually we're more interested in just getting the best solution we can within an affordable cost, where costs may be measured in terms of time, service fees and or energy required for a computation. This focuses great interest on so called heuristic algorithms for the easing problem in other NP complete problems which generally get very good but not guaranteed optimum solutions and run much faster than algorithms that are designed to find absolute Optima. To get some feeling for present day numbers, we can consider the famous traveling salesman problem for which extensive compilations of benchmarking data may be found online. A recent study found that the best known TSP solver required median run times across the Library of Problem instances That scaled is a very steep route exponential for end up to approximately 4500. This gives some indication of the change in runtime scaling for generic as opposed the worst case problem instances. Some of the instances considered in this study were taken from a public library of T SPS derived from real world Veil aside design data. This feels I TSP Library includes instances within ranging from 131 to 744,710 instances from this library with end between 6880 13,584 were first solved just a few years ago in 2017 requiring days of run time and a 48 core to King hurts cluster, while instances with and greater than or equal to 14,233 remain unsolved exactly by any means. Approximate solutions, however, have been found by heuristic methods for all instances in the VLS i TSP library with, for example, a solution within 0.14% of a no lower bound, having been discovered, for instance, with an equal 19,289 requiring approximately two days of run time on a single core of 2.4 gigahertz. Now, if we simple mindedly extrapolate the root exponential scaling from the study up to an equal 4500, we might expect that an exact solver would require something more like a year of run time on the 48 core cluster used for the N equals 13,580 for instance, which shows how much a very small concession on the quality of the solution makes it possible to tackle much larger instances with much lower cost. At the extreme end, the largest TSP ever solved exactly has an equal 85,900. This is an instance derived from 19 eighties VLSI design, and it's required 136 CPU. Years of computation normalized to a single cord, 2.4 gigahertz. But the 24 larger so called world TSP benchmark instance within equals 1,904,711 has been solved approximately within ophthalmology. Gap bounded below 0.474%. Coming back to the general. Practical concerns have applied optimization. We may note that a recent meta study analyzed the performance of no fewer than 37 heuristic algorithms for Max cut and quadratic pioneer optimization problems and found the performance sort and found that different heuristics work best for different problem instances selected from a large scale heterogeneous test bed with some evidence but cryptic structure in terms of what types of problem instances were best solved by any given heuristic. Indeed, their their reasons to believe that these results from Mexico and quadratic binary optimization reflected general principle of performance complementarity among heuristic optimization algorithms in the practice of solving heart optimization problems there. The cerise is a critical pre processing issue of trying to guess which of a number of available good heuristic algorithms should be chosen to tackle a given problem. Instance, assuming that any one of them would incur high costs to run on a large problem, instances incidence, making an astute choice of heuristic is a crucial part of maximizing overall performance. Unfortunately, we still have very little conceptual insight about what makes a specific problem instance, good or bad for any given heuristic optimization algorithm. This has certainly been pinpointed by researchers in the field is a circumstance that must be addressed. So adding this all up, we see that a critical frontier for cutting edge academic research involves both the development of novel heuristic algorithms that deliver better performance, with lower cost on classes of problem instances that are underserved by existing approaches, as well as fundamental research to provide deep conceptual insight into what makes a given problem in, since easy or hard for such algorithms. In fact, these days, as we talk about the end of Moore's law and speculate about a so called second quantum revolution, it's natural to talk not only about novel algorithms for conventional CPUs but also about highly customized special purpose hardware architectures on which we may run entirely unconventional algorithms for combinatorial optimization such as easing problem. So against that backdrop, I'd like to use my remaining time to introduce our work on analysis of coherent using machine architectures and associate ID optimization algorithms. These machines, in general, are a novel class of information processing architectures for solving combinatorial optimization problems by embedding them in the dynamics of analog, physical or cyber physical systems, in contrast to both MAWR traditional engineering approaches that build using machines using conventional electron ICS and more radical proposals that would require large scale quantum entanglement. The emerging paradigm of coherent easing machines leverages coherent nonlinear dynamics in photonic or Opto electronic platforms to enable near term construction of large scale prototypes that leverage post Simoes information dynamics, the general structure of of current CM systems has shown in the figure on the right. The role of the easing spins is played by a train of optical pulses circulating around a fiber optical storage ring. A beam splitter inserted in the ring is used to periodically sample the amplitude of every optical pulse, and the measurement results are continually read into a refugee A, which uses them to compute perturbations to be applied to each pulse by a synchronized optical injections. These perturbations, air engineered to implement the spin, spin coupling and local magnetic field terms of the easing Hamiltonian, corresponding to a linear part of the CME Dynamics, a synchronously pumped parametric amplifier denoted here as PPL and Wave Guide adds a crucial nonlinear component to the CIA and Dynamics as well. In the basic CM algorithm, the pump power starts very low and has gradually increased at low pump powers. The amplitude of the easing spin pulses behaviors continuous, complex variables. Who Israel parts which can be positive or negative, play the role of play the role of soft or perhaps mean field spins once the pump, our crosses the threshold for parametric self oscillation. In the optical fiber ring, however, the attitudes of the easing spin pulses become effectively Qantas ized into binary values while the pump power is being ramped up. The F P J subsystem continuously applies its measurement based feedback. Implementation of the using Hamiltonian terms, the interplay of the linear rised using dynamics implemented by the F P G A and the threshold conversation dynamics provided by the sink pumped Parametric amplifier result in the final state of the optical optical pulse amplitude at the end of the pump ramp that could be read as a binary strain, giving a proposed solution of the easing ground state problem. This method of solving easing problem seems quite different from a conventional algorithm that runs entirely on a digital computer as a crucial aspect of the computation is performed physically by the analog, continuous, coherent, nonlinear dynamics of the optical degrees of freedom. In our efforts to analyze CIA and performance, we have therefore turned to the tools of dynamical systems theory, namely, a study of modifications, the evolution of critical points and apologies of hetero clinic orbits and basins of attraction. We conjecture that such analysis can provide fundamental insight into what makes certain optimization instances hard or easy for coherent using machines and hope that our approach can lead to both improvements of the course, the AM algorithm and a pre processing rubric for rapidly assessing the CME suitability of new instances. Okay, to provide a bit of intuition about how this all works, it may help to consider the threshold dynamics of just one or two optical parametric oscillators in the CME architecture just described. We can think of each of the pulse time slots circulating around the fiber ring, as are presenting an independent Opio. We can think of a single Opio degree of freedom as a single, resonant optical node that experiences linear dissipation, do toe out coupling loss and gain in a pump. Nonlinear crystal has shown in the diagram on the upper left of this slide as the pump power is increased from zero. As in the CME algorithm, the non linear game is initially to low toe overcome linear dissipation, and the Opio field remains in a near vacuum state at a critical threshold. Value gain. Equal participation in the Popeo undergoes a sort of lazing transition, and the study states of the OPIO above this threshold are essentially coherent states. There are actually two possible values of the Opio career in amplitude and any given above threshold pump power which are equal in magnitude but opposite in phase when the OPI across the special diet basically chooses one of the two possible phases randomly, resulting in the generation of a single bit of information. If we consider to uncoupled, Opio has shown in the upper right diagram pumped it exactly the same power at all times. Then, as the pump power has increased through threshold, each Opio will independently choose the phase and thus to random bits are generated for any number of uncoupled. Oppose the threshold power per opio is unchanged from the single Opio case. Now, however, consider a scenario in which the two appeals air, coupled to each other by a mutual injection of their out coupled fields has shown in the diagram on the lower right. One can imagine that depending on the sign of the coupling parameter Alfa, when one Opio is lazing, it will inject a perturbation into the other that may interfere either constructively or destructively, with the feel that it is trying to generate by its own lazing process. As a result, when came easily showed that for Alfa positive, there's an effective ferro magnetic coupling between the two Opio fields and their collective oscillation threshold is lowered from that of the independent Opio case. But on Lee for the two collective oscillation modes in which the two Opio phases are the same for Alfa Negative, the collective oscillation threshold is lowered on Lee for the configurations in which the Opio phases air opposite. So then, looking at how Alfa is related to the J. I. J matrix of the easing spin coupling Hamiltonian, it follows that we could use this simplistic to a p o. C. I am to solve the ground state problem of a fair magnetic or anti ferro magnetic ankles to easing model simply by increasing the pump power from zero and observing what phase relation occurs as the two appeals first start delays. Clearly, we can imagine generalizing this story toe larger, and however the story doesn't stay is clean and simple for all larger problem instances. And to find a more complicated example, we only need to go to n equals four for some choices of J J for n equals, for the story remains simple. Like the n equals two case. The figure on the upper left of this slide shows the energy of various critical points for a non frustrated and equals, for instance, in which the first bifurcated critical point that is the one that I forget to the lowest pump value a. Uh, this first bifurcated critical point flows as symptomatically into the lowest energy easing solution and the figure on the upper right. However, the first bifurcated critical point flows to a very good but sub optimal minimum at large pump power. The global minimum is actually given by a distinct critical critical point that first appears at a higher pump power and is not automatically connected to the origin. The basic C am algorithm is thus not able to find this global minimum. Such non ideal behaviors needs to become more confident. Larger end for the n equals 20 instance, showing the lower plots where the lower right plot is just a zoom into a region of the lower left lot. It can be seen that the global minimum corresponds to a critical point that first appears out of pump parameter, a around 0.16 at some distance from the idiomatic trajectory of the origin. That's curious to note that in both of these small and examples, however, the critical point corresponding to the global minimum appears relatively close to the idiomatic projector of the origin as compared to the most of the other local minima that appear. We're currently working to characterize the face portrait topology between the global minimum in the antibiotic trajectory of the origin, taking clues as to how the basic C am algorithm could be generalized to search for non idiomatic trajectories that jump to the global minimum during the pump ramp. Of course, n equals 20 is still too small to be of interest for practical optimization applications. But the advantage of beginning with the study of small instances is that we're able reliably to determine their global minima and to see how they relate to the 80 about trajectory of the origin in the basic C am algorithm. In the smaller and limit, we can also analyze fully quantum mechanical models of Syrian dynamics. But that's a topic for future talks. Um, existing large scale prototypes are pushing into the range of in equals 10 to the 4 10 to 5 to six. So our ultimate objective in theoretical analysis really has to be to try to say something about CIA and dynamics and regime of much larger in our initial approach to characterizing CIA and behavior in the large in regime relies on the use of random matrix theory, and this connects to prior research on spin classes, SK models and the tap equations etcetera. At present, we're focusing on statistical characterization of the CIA ingredient descent landscape, including the evolution of critical points in their Eigen value spectra. As the pump power is gradually increased. We're investigating, for example, whether there could be some way to exploit differences in the relative stability of the global minimum versus other local minima. We're also working to understand the deleterious or potentially beneficial effects of non ideologies, such as a symmetry in the implemented these and couplings. Looking one step ahead, we plan to move next in the direction of considering more realistic classes of problem instances such as quadratic, binary optimization with constraints. Eso In closing, I should acknowledge people who did the hard work on these things that I've shown eso. My group, including graduate students Ed winning, Daniel Wennberg, Tatsuya Nagamoto and Atsushi Yamamura, have been working in close collaboration with Syria Ganguly, Marty Fair and Amir Safarini Nini, all of us within the Department of Applied Physics at Stanford University. On also in collaboration with the Oshima Moto over at NTT 55 research labs, Onda should acknowledge funding support from the NSF by the Coherent Easing Machines Expedition in computing, also from NTT five research labs, Army Research Office and Exxon Mobil. Uh, that's it. Thanks very much. >>Mhm e >>t research and the Oshie for putting together this program and also the opportunity to speak here. My name is Al Gore ism or Andy and I'm from Caltech, and today I'm going to tell you about the work that we have been doing on networks off optical parametric oscillators and how we have been using them for icing machines and how we're pushing them toward Cornum photonics to acknowledge my team at Caltech, which is now eight graduate students and five researcher and postdocs as well as collaborators from all over the world, including entity research and also the funding from different places, including entity. So this talk is primarily about networks of resonate er's, and these networks are everywhere from nature. For instance, the brain, which is a network of oscillators all the way to optics and photonics and some of the biggest examples or metal materials, which is an array of small resonate er's. And we're recently the field of technological photonics, which is trying thio implement a lot of the technological behaviors of models in the condensed matter, physics in photonics and if you want to extend it even further, some of the implementations off quantum computing are technically networks of quantum oscillators. So we started thinking about these things in the context of icing machines, which is based on the icing problem, which is based on the icing model, which is the simple summation over the spins and spins can be their upward down and the couplings is given by the JJ. And the icing problem is, if you know J I J. What is the spin configuration that gives you the ground state? And this problem is shown to be an MP high problem. So it's computational e important because it's a representative of the MP problems on NPR. Problems are important because first, their heart and standard computers if you use a brute force algorithm and they're everywhere on the application side. That's why there is this demand for making a machine that can target these problems, and hopefully it can provide some meaningful computational benefit compared to the standard digital computers. So I've been building these icing machines based on this building block, which is a degenerate optical parametric. Oscillator on what it is is resonator with non linearity in it, and we pump these resonate er's and we generate the signal at half the frequency of the pump. One vote on a pump splits into two identical photons of signal, and they have some very interesting phase of frequency locking behaviors. And if you look at the phase locking behavior, you realize that you can actually have two possible phase states as the escalation result of these Opio which are off by pie, and that's one of the important characteristics of them. So I want to emphasize a little more on that and I have this mechanical analogy which are basically two simple pendulum. But there are parametric oscillators because I'm going to modulate the parameter of them in this video, which is the length of the string on by that modulation, which is that will make a pump. I'm gonna make a muscular. That'll make a signal which is half the frequency of the pump. And I have two of them to show you that they can acquire these face states so they're still facing frequency lock to the pump. But it can also lead in either the zero pie face states on. The idea is to use this binary phase to represent the binary icing spin. So each opio is going to represent spin, which can be either is your pie or up or down. And to implement the network of these resonate er's, we use the time off blood scheme, and the idea is that we put impulses in the cavity. These pulses air separated by the repetition period that you put in or t r. And you can think about these pulses in one resonator, xaz and temporarily separated synthetic resonate Er's if you want a couple of these resonator is to each other, and now you can introduce these delays, each of which is a multiple of TR. If you look at the shortest delay it couples resonator wanted to 2 to 3 and so on. If you look at the second delay, which is two times a rotation period, the couple's 123 and so on. And if you have and minus one delay lines, then you can have any potential couplings among these synthetic resonate er's. And if I can introduce these modulators in those delay lines so that I can strength, I can control the strength and the phase of these couplings at the right time. Then I can have a program will all toe all connected network in this time off like scheme, and the whole physical size of the system scales linearly with the number of pulses. So the idea of opium based icing machine is didn't having these o pos, each of them can be either zero pie and I can arbitrarily connect them to each other. And then I start with programming this machine to a given icing problem by just setting the couplings and setting the controllers in each of those delight lines. So now I have a network which represents an icing problem. Then the icing problem maps to finding the face state that satisfy maximum number of coupling constraints. And the way it happens is that the icing Hamiltonian maps to the linear loss of the network. And if I start adding gain by just putting pump into the network, then the OPI ohs are expected to oscillate in the lowest, lowest lost state. And, uh and we have been doing these in the past, uh, six or seven years and I'm just going to quickly show you the transition, especially what happened in the first implementation, which was using a free space optical system and then the guided wave implementation in 2016 and the measurement feedback idea which led to increasing the size and doing actual computation with these machines. So I just want to make this distinction here that, um, the first implementation was an all optical interaction. We also had an unequal 16 implementation. And then we transition to this measurement feedback idea, which I'll tell you quickly what it iss on. There's still a lot of ongoing work, especially on the entity side, to make larger machines using the measurement feedback. But I'm gonna mostly focused on the all optical networks and how we're using all optical networks to go beyond simulation of icing Hamiltonian both in the linear and non linear side and also how we're working on miniaturization of these Opio networks. So the first experiment, which was the four opium machine, it was a free space implementation and this is the actual picture off the machine and we implemented a small and it calls for Mexico problem on the machine. So one problem for one experiment and we ran the machine 1000 times, we looked at the state and we always saw it oscillate in one of these, um, ground states of the icing laboratoria. So then the measurement feedback idea was to replace those couplings and the controller with the simulator. So we basically simulated all those coherent interactions on on FB g. A. And we replicated the coherent pulse with respect to all those measurements. And then we injected it back into the cavity and on the near to you still remain. So it still is a non. They're dynamical system, but the linear side is all simulated. So there are lots of questions about if this system is preserving important information or not, or if it's gonna behave better. Computational wars. And that's still ah, lot of ongoing studies. But nevertheless, the reason that this implementation was very interesting is that you don't need the end minus one delight lines so you can just use one. Then you can implement a large machine, and then you can run several thousands of problems in the machine, and then you can compare the performance from the computational perspective Looks so I'm gonna split this idea of opium based icing machine into two parts. One is the linear part, which is if you take out the non linearity out of the resonator and just think about the connections. You can think about this as a simple matrix multiplication scheme. And that's basically what gives you the icing Hambletonian modeling. So the optical laws of this network corresponds to the icing Hamiltonian. And if I just want to show you the example of the n equals for experiment on all those face states and the history Graham that we saw, you can actually calculate the laws of each of those states because all those interferences in the beam splitters and the delay lines are going to give you a different losses. And then you will see that the ground states corresponds to the lowest laws of the actual optical network. If you add the non linearity, the simple way of thinking about what the non linearity does is that it provides to gain, and then you start bringing up the gain so that it hits the loss. Then you go through the game saturation or the threshold which is going to give you this phase bifurcation. So you go either to zero the pie face state. And the expectation is that Theis, the network oscillates in the lowest possible state, the lowest possible loss state. There are some challenges associated with this intensity Durban face transition, which I'm going to briefly talk about. I'm also going to tell you about other types of non aerodynamics that we're looking at on the non air side of these networks. So if you just think about the linear network, we're actually interested in looking at some technological behaviors in these networks. And the difference between looking at the technological behaviors and the icing uh, machine is that now, First of all, we're looking at the type of Hamilton Ian's that are a little different than the icing Hamilton. And one of the biggest difference is is that most of these technological Hamilton Ian's that require breaking the time reversal symmetry, meaning that you go from one spin to in the one side to another side and you get one phase. And if you go back where you get a different phase, and the other thing is that we're not just interested in finding the ground state, we're actually now interesting and looking at all sorts of states and looking at the dynamics and the behaviors of all these states in the network. So we started with the simplest implementation, of course, which is a one d chain of thes resonate, er's, which corresponds to a so called ssh model. In the technological work, we get the similar energy to los mapping and now we can actually look at the band structure on. This is an actual measurement that we get with this associate model and you see how it reasonably how How? Well, it actually follows the prediction and the theory. One of the interesting things about the time multiplexing implementation is that now you have the flexibility of changing the network as you are running the machine. And that's something unique about this time multiplex implementation so that we can actually look at the dynamics. And one example that we have looked at is we can actually go through the transition off going from top A logical to the to the standard nontrivial. I'm sorry to the trivial behavior of the network. You can then look at the edge states and you can also see the trivial and states and the technological at states actually showing up in this network. We have just recently implement on a two D, uh, network with Harper Hofstadter model and when you don't have the results here. But we're one of the other important characteristic of time multiplexing is that you can go to higher and higher dimensions and keeping that flexibility and dynamics, and we can also think about adding non linearity both in a classical and quantum regimes, which is going to give us a lot of exotic, no classical and quantum, non innate behaviors in these networks. Yeah, So I told you about the linear side. Mostly let me just switch gears and talk about the nonlinear side of the network. And the biggest thing that I talked about so far in the icing machine is this face transition that threshold. So the low threshold we have squeezed state in these. Oh, pios, if you increase the pump, we go through this intensity driven phase transition and then we got the face stays above threshold. And this is basically the mechanism off the computation in these O pos, which is through this phase transition below to above threshold. So one of the characteristics of this phase transition is that below threshold, you expect to see quantum states above threshold. You expect to see more classical states or coherent states, and that's basically corresponding to the intensity off the driving pump. So it's really hard to imagine that it can go above threshold. Or you can have this friends transition happen in the all in the quantum regime. And there are also some challenges associated with the intensity homogeneity off the network, which, for example, is if one opioid starts oscillating and then its intensity goes really high. Then it's going to ruin this collective decision making off the network because of the intensity driven face transition nature. So So the question is, can we look at other phase transitions? Can we utilize them for both computing? And also can we bring them to the quantum regime on? I'm going to specifically talk about the face transition in the spectral domain, which is the transition from the so called degenerate regime, which is what I mostly talked about to the non degenerate regime, which happens by just tuning the phase of the cavity. And what is interesting is that this phase transition corresponds to a distinct phase noise behavior. So in the degenerate regime, which we call it the order state, you're gonna have the phase being locked to the phase of the pump. As I talked about non degenerate regime. However, the phase is the phase is mostly dominated by the quantum diffusion. Off the off the phase, which is limited by the so called shallow towns limit, and you can see that transition from the general to non degenerate, which also has distinct symmetry differences. And this transition corresponds to a symmetry breaking in the non degenerate case. The signal can acquire any of those phases on the circle, so it has a you one symmetry. Okay, and if you go to the degenerate case, then that symmetry is broken and you only have zero pie face days I will look at. So now the question is can utilize this phase transition, which is a face driven phase transition, and can we use it for similar computational scheme? So that's one of the questions that were also thinking about. And it's not just this face transition is not just important for computing. It's also interesting from the sensing potentials and this face transition, you can easily bring it below threshold and just operated in the quantum regime. Either Gaussian or non Gaussian. If you make a network of Opio is now, we can see all sorts off more complicated and more interesting phase transitions in the spectral domain. One of them is the first order phase transition, which you get by just coupling to Opio, and that's a very abrupt face transition and compared to the to the single Opio phase transition. And if you do the couplings right, you can actually get a lot of non her mission dynamics and exceptional points, which are actually very interesting to explore both in the classical and quantum regime. And I should also mention that you can think about the cup links to be also nonlinear couplings. And that's another behavior that you can see, especially in the nonlinear in the non degenerate regime. So with that, I basically told you about these Opio networks, how we can think about the linear scheme and the linear behaviors and how we can think about the rich, nonlinear dynamics and non linear behaviors both in the classical and quantum regime. I want to switch gear and tell you a little bit about the miniaturization of these Opio networks. And of course, the motivation is if you look at the electron ICS and what we had 60 or 70 years ago with vacuum tube and how we transition from relatively small scale computers in the order of thousands of nonlinear elements to billions of non elements where we are now with the optics is probably very similar to 70 years ago, which is a table talk implementation. And the question is, how can we utilize nano photonics? I'm gonna just briefly show you the two directions on that which we're working on. One is based on lithium Diabate, and the other is based on even a smaller resonate er's could you? So the work on Nana Photonic lithium naive. It was started in collaboration with Harvard Marko Loncar, and also might affair at Stanford. And, uh, we could show that you can do the periodic polling in the phenomenon of it and get all sorts of very highly nonlinear processes happening in this net. Photonic periodically polls if, um Diabate. And now we're working on building. Opio was based on that kind of photonic the film Diabate. And these air some some examples of the devices that we have been building in the past few months, which I'm not gonna tell you more about. But the O. P. O. S. And the Opio Networks are in the works. And that's not the only way of making large networks. Um, but also I want to point out that The reason that these Nana photonic goblins are actually exciting is not just because you can make a large networks and it can make him compact in a in a small footprint. They also provide some opportunities in terms of the operation regime. On one of them is about making cat states and Opio, which is, can we have the quantum superposition of the zero pie states that I talked about and the Net a photonic within? I've It provides some opportunities to actually get closer to that regime because of the spatial temporal confinement that you can get in these wave guides. So we're doing some theory on that. We're confident that the type of non linearity two losses that it can get with these platforms are actually much higher than what you can get with other platform their existing platforms and to go even smaller. We have been asking the question off. What is the smallest possible Opio that you can make? Then you can think about really wavelength scale type, resonate er's and adding the chi to non linearity and see how and when you can get the Opio to operate. And recently, in collaboration with us see, we have been actually USC and Creole. We have demonstrated that you can use nano lasers and get some spin Hamilton and implementations on those networks. So if you can build the a P. O s, we know that there is a path for implementing Opio Networks on on such a nano scale. So we have looked at these calculations and we try to estimate the threshold of a pos. Let's say for me resonator and it turns out that it can actually be even lower than the type of bulk Pip Llano Pos that we have been building in the past 50 years or so. So we're working on the experiments and we're hoping that we can actually make even larger and larger scale Opio networks. So let me summarize the talk I told you about the opium networks and our work that has been going on on icing machines and the measurement feedback. And I told you about the ongoing work on the all optical implementations both on the linear side and also on the nonlinear behaviors. And I also told you a little bit about the efforts on miniaturization and going to the to the Nano scale. So with that, I would like Thio >>three from the University of Tokyo. Before I thought that would like to thank you showing all the stuff of entity for the invitation and the organization of this online meeting and also would like to say that it has been very exciting to see the growth of this new film lab. And I'm happy to share with you today of some of the recent works that have been done either by me or by character of Hong Kong. Honest Group indicates the title of my talk is a neuro more fic in silica simulator for the communities in machine. And here is the outline I would like to make the case that the simulation in digital Tektronix of the CME can be useful for the better understanding or improving its function principles by new job introducing some ideas from neural networks. This is what I will discuss in the first part and then it will show some proof of concept of the game and performance that can be obtained using dissimulation in the second part and the protection of the performance that can be achieved using a very large chaos simulator in the third part and finally talk about future plans. So first, let me start by comparing recently proposed izing machines using this table there is elected from recent natural tronics paper from the village Park hard people, and this comparison shows that there's always a trade off between energy efficiency, speed and scalability that depends on the physical implementation. So in red, here are the limitation of each of the servers hardware on, interestingly, the F p G, a based systems such as a producer, digital, another uh Toshiba beautification machine or a recently proposed restricted Bozeman machine, FPD A by a group in Berkeley. They offer a good compromise between speed and scalability. And this is why, despite the unique advantage that some of these older hardware have trust as the currency proposition in Fox, CBS or the energy efficiency off memory Sisters uh P. J. O are still an attractive platform for building large organizing machines in the near future. The reason for the good performance of Refugee A is not so much that they operate at the high frequency. No, there are particular in use, efficient, but rather that the physical wiring off its elements can be reconfigured in a way that limits the funding human bottleneck, larger, funny and phenols and the long propagation video information within the system. In this respect, the LPGA is They are interesting from the perspective off the physics off complex systems, but then the physics of the actions on the photos. So to put the performance of these various hardware and perspective, we can look at the competition of bringing the brain the brain complete, using billions of neurons using only 20 watts of power and operates. It's a very theoretically slow, if we can see and so this impressive characteristic, they motivate us to try to investigate. What kind of new inspired principles be useful for designing better izing machines? The idea of this research project in the future collaboration it's to temporary alleviates the limitations that are intrinsic to the realization of an optical cortex in machine shown in the top panel here. By designing a large care simulator in silicone in the bottom here that can be used for digesting the better organization principles of the CIA and this talk, I will talk about three neuro inspired principles that are the symmetry of connections, neural dynamics orphan chaotic because of symmetry, is interconnectivity the infrastructure? No. Next talks are not composed of the reputation of always the same types of non environments of the neurons, but there is a local structure that is repeated. So here's the schematic of the micro column in the cortex. And lastly, the Iraqi co organization of connectivity connectivity is organizing a tree structure in the brain. So here you see a representation of the Iraqi and organization of the monkey cerebral cortex. So how can these principles we used to improve the performance of the icing machines? And it's in sequence stimulation. So, first about the two of principles of the estimate Trian Rico structure. We know that the classical approximation of the car testing machine, which is the ground toe, the rate based on your networks. So in the case of the icing machines, uh, the okay, Scott approximation can be obtained using the trump active in your position, for example, so the times of both of the system they are, they can be described by the following ordinary differential equations on in which, in case of see, I am the X, I represent the in phase component of one GOP Oh, Theo f represents the monitor optical parts, the district optical Parametric amplification and some of the good I JoJo extra represent the coupling, which is done in the case of the measure of feedback coupling cm using oh, more than detection and refugee A and then injection off the cooking time and eso this dynamics in both cases of CNN in your networks, they can be written as the grand set of a potential function V, and this written here, and this potential functionally includes the rising Maccagnan. So this is why it's natural to use this type of, uh, dynamics to solve the icing problem in which the Omega I J or the eyes in coping and the H is the extension of the icing and attorney in India and expect so. Not that this potential function can only be defined if the Omega I j. R. A. Symmetric. So the well known problem of this approach is that this potential function V that we obtain is very non convicts at low temperature, and also one strategy is to gradually deformed this landscape, using so many in process. But there is no theorem. Unfortunately, that granted conventions to the global minimum of There's even Tony and using this approach. And so this is why we propose, uh, to introduce a macro structures of the system where one analog spin or one D O. P. O is replaced by a pair off one another spin and one error, according viable. And the addition of this chemical structure introduces a symmetry in the system, which in terms induces chaotic dynamics, a chaotic search rather than a learning process for searching for the ground state of the icing. Every 20 within this massacre structure the role of the er variable eyes to control the amplitude off the analog spins toe force. The amplitude of the expense toe become equal to certain target amplitude a uh and, uh, and this is done by modulating the strength off the icing complaints or see the the error variable E I multiply the icing complaint here in the dynamics off air d o p. O. On then the dynamics. The whole dynamics described by this coupled equations because the e I do not necessarily take away the same value for the different. I thesis introduces a symmetry in the system, which in turn creates security dynamics, which I'm sure here for solving certain current size off, um, escape problem, Uh, in which the X I are shown here and the i r from here and the value of the icing energy showing the bottom plots. You see this Celtics search that visit various local minima of the as Newtonian and eventually finds the global minimum? Um, it can be shown that this modulation off the target opportunity can be used to destabilize all the local minima off the icing evertonians so that we're gonna do not get stuck in any of them. On more over the other types of attractors I can eventually appear, such as limits I contractors, Okot contractors. They can also be destabilized using the motivation of the target and Batuta. And so we have proposed in the past two different moderation of the target amateur. The first one is a modulation that ensure the uh 100 reproduction rate of the system to become positive on this forbids the creation off any nontrivial tractors. And but in this work, I will talk about another moderation or arrested moderation which is given here. That works, uh, as well as this first uh, moderation, but is easy to be implemented on refugee. So this couple of the question that represent becoming the stimulation of the cortex in machine with some error correction they can be implemented especially efficiently on an F B. G. And here I show the time that it takes to simulate three system and also in red. You see, at the time that it takes to simulate the X I term the EI term, the dot product and the rising Hamiltonian for a system with 500 spins and Iraq Spain's equivalent to 500 g. O. P. S. So >>in >>f b d a. The nonlinear dynamics which, according to the digital optical Parametric amplification that the Opa off the CME can be computed in only 13 clock cycles at 300 yards. So which corresponds to about 0.1 microseconds. And this is Toby, uh, compared to what can be achieved in the measurements back O C. M. In which, if we want to get 500 timer chip Xia Pios with the one she got repetition rate through the obstacle nine narrative. Uh, then way would require 0.5 microseconds toe do this so the submission in F B J can be at least as fast as ah one g repression. Uh, replicate pulsed laser CIA Um, then the DOT product that appears in this differential equation can be completed in 43 clock cycles. That's to say, one microseconds at 15 years. So I pieced for pouring sizes that are larger than 500 speeds. The dot product becomes clearly the bottleneck, and this can be seen by looking at the the skating off the time the numbers of clock cycles a text to compute either the non in your optical parts or the dog products, respect to the problem size. And And if we had infinite amount of resources and PGA to simulate the dynamics, then the non illogical post can could be done in the old one. On the mattress Vector product could be done in the low carrot off, located off scales as a look at it off and and while the guide off end. Because computing the dot product involves assuming all the terms in the product, which is done by a nephew, GE by another tree, which heights scarce logarithmic any with the size of the system. But This is in the case if we had an infinite amount of resources on the LPGA food, but for dealing for larger problems off more than 100 spins. Usually we need to decompose the metrics into ah, smaller blocks with the block side that are not you here. And then the scaling becomes funny, non inner parts linear in the end, over you and for the products in the end of EU square eso typically for low NF pdf cheap PGA you the block size off this matrix is typically about 100. So clearly way want to make you as large as possible in order to maintain this scanning in a log event for the numbers of clock cycles needed to compute the product rather than this and square that occurs if we decompose the metrics into smaller blocks. But the difficulty in, uh, having this larger blocks eyes that having another tree very large Haider tree introduces a large finding and finance and long distance start a path within the refugee. So the solution to get higher performance for a simulator of the contest in machine eyes to get rid of this bottleneck for the dot product by increasing the size of this at the tree. And this can be done by organizing your critique the electrical components within the LPGA in order which is shown here in this, uh, right panel here in order to minimize the finding finance of the system and to minimize the long distance that a path in the in the fpt So I'm not going to the details of how this is implemented LPGA. But just to give you a idea off why the Iraqi Yahiko organization off the system becomes the extremely important toe get good performance for similar organizing machine. So instead of instead of getting into the details of the mpg implementation, I would like to give some few benchmark results off this simulator, uh, off the that that was used as a proof of concept for this idea which is can be found in this archive paper here and here. I should results for solving escape problems. Free connected person, randomly person minus one spring last problems and we sure, as we use as a metric the numbers of the mattress Victor products since it's the bottleneck of the computation, uh, to get the optimal solution of this escape problem with the Nina successful BT against the problem size here and and in red here, this propose FDJ implementation and in ah blue is the numbers of retrospective product that are necessary for the C. I am without error correction to solve this escape programs and in green here for noisy means in an evening which is, uh, behavior with similar to the Cartesian mission. Uh, and so clearly you see that the scaring off the numbers of matrix vector product necessary to solve this problem scales with a better exponents than this other approaches. So So So that's interesting feature of the system and next we can see what is the real time to solution to solve this SK instances eso in the last six years, the time institution in seconds to find a grand state of risk. Instances remain answers probability for different state of the art hardware. So in red is the F B g. A presentation proposing this paper and then the other curve represent Ah, brick a local search in in orange and silver lining in purple, for example. And so you see that the scaring off this purpose simulator is is rather good, and that for larger plant sizes we can get orders of magnitude faster than the state of the art approaches. Moreover, the relatively good scanning off the time to search in respect to problem size uh, they indicate that the FPD implementation would be faster than risk. Other recently proposed izing machine, such as the hope you know, natural complimented on memories distance that is very fast for small problem size in blue here, which is very fast for small problem size. But which scanning is not good on the same thing for the restricted Bosman machine. Implementing a PGA proposed by some group in Broken Recently Again, which is very fast for small parliament sizes but which canning is bad so that a dis worse than the proposed approach so that we can expect that for programs size is larger than 1000 spins. The proposed, of course, would be the faster one. Let me jump toe this other slide and another confirmation that the scheme scales well that you can find the maximum cut values off benchmark sets. The G sets better candidates that have been previously found by any other algorithms, so they are the best known could values to best of our knowledge. And, um or so which is shown in this paper table here in particular, the instances, uh, 14 and 15 of this G set can be We can find better converse than previously known, and we can find this can vary is 100 times faster than the state of the art algorithm and CP to do this which is a very common Kasich. It s not that getting this a good result on the G sets, they do not require ah, particular hard tuning of the parameters. So the tuning issuing here is very simple. It it just depends on the degree off connectivity within each graph. And so this good results on the set indicate that the proposed approach would be a good not only at solving escape problems in this problems, but all the types off graph sizing problems on Mexican province in communities. So given that the performance off the design depends on the height of this other tree, we can try to maximize the height of this other tree on a large F p g a onda and carefully routing the components within the P G A and and we can draw some projections of what type of performance we can achieve in the near future based on the, uh, implementation that we are currently working. So here you see projection for the time to solution way, then next property for solving this escape programs respect to the prime assize. And here, compared to different with such publicizing machines, particularly the digital. And, you know, 42 is shown in the green here, the green line without that's and, uh and we should two different, uh, hypothesis for this productions either that the time to solution scales as exponential off n or that the time of social skills as expression of square root off. So it seems, according to the data, that time solution scares more as an expression of square root of and also we can be sure on this and this production show that we probably can solve prime escape problem of science 2000 spins, uh, to find the rial ground state of this problem with 99 success ability in about 10 seconds, which is much faster than all the other proposed approaches. So one of the future plans for this current is in machine simulator. So the first thing is that we would like to make dissimulation closer to the rial, uh, GOP oh, optical system in particular for a first step to get closer to the system of a measurement back. See, I am. And to do this what is, uh, simulate Herbal on the p a is this quantum, uh, condoms Goshen model that is proposed described in this paper and proposed by people in the in the Entity group. And so the idea of this model is that instead of having the very simple or these and have shown previously, it includes paired all these that take into account on me the mean off the awesome leverage off the, uh, European face component, but also their violence s so that we can take into account more quantum effects off the g o p. O, such as the squeezing. And then we plan toe, make the simulator open access for the members to run their instances on the system. There will be a first version in September that will be just based on the simple common line access for the simulator and in which will have just a classic or approximation of the system. We don't know Sturm, binary weights and museum in term, but then will propose a second version that would extend the current arising machine to Iraq off F p g. A, in which we will add the more refined models truncated, ignoring the bottom Goshen model they just talked about on the support in which he valued waits for the rising problems and support the cement. So we will announce later when this is available and and far right is working >>hard comes from Universal down today in physics department, and I'd like to thank the organizers for their kind invitation to participate in this very interesting and promising workshop. Also like to say that I look forward to collaborations with with a file lab and Yoshi and collaborators on the topics of this world. So today I'll briefly talk about our attempt to understand the fundamental limits off another continues time computing, at least from the point off you off bullion satisfy ability, problem solving, using ordinary differential equations. But I think the issues that we raise, um, during this occasion actually apply to other other approaches on a log approaches as well and into other problems as well. I think everyone here knows what Dorien satisfy ability. Problems are, um, you have boolean variables. You have em clauses. Each of disjunction of collaterals literally is a variable, or it's, uh, negation. And the goal is to find an assignment to the variable, such that order clauses are true. This is a decision type problem from the MP class, which means you can checking polynomial time for satisfy ability off any assignment. And the three set is empty, complete with K three a larger, which means an efficient trees. That's over, uh, implies an efficient source for all the problems in the empty class, because all the problems in the empty class can be reduced in Polian on real time to reset. As a matter of fact, you can reduce the NP complete problems into each other. You can go from three set to set backing or two maximum dependent set, which is a set packing in graph theoretic notions or terms toe the icing graphs. A problem decision version. This is useful, and you're comparing different approaches, working on different kinds of problems when not all the closest can be satisfied. You're looking at the accusation version offset, uh called Max Set. And the goal here is to find assignment that satisfies the maximum number of clauses. And this is from the NPR class. In terms of applications. If we had inefficient sets over or np complete problems over, it was literally, positively influenced. Thousands off problems and applications in industry and and science. I'm not going to read this, but this this, of course, gives a strong motivation toe work on this kind of problems. Now our approach to set solving involves embedding the problem in a continuous space, and you use all the east to do that. So instead of working zeros and ones, we work with minus one across once, and we allow the corresponding variables toe change continuously between the two bounds. We formulate the problem with the help of a close metrics. If if a if a close, uh, does not contain a variable or its negation. The corresponding matrix element is zero. If it contains the variable in positive, for which one contains the variable in a gated for Mitt's negative one, and then we use this to formulate this products caused quote, close violation functions one for every clause, Uh, which really, continuously between zero and one. And they're zero if and only if the clause itself is true. Uh, then we form the define in order to define a dynamic such dynamics in this and dimensional hyper cube where the search happens and if they exist, solutions. They're sitting in some of the corners of this hyper cube. So we define this, uh, energy potential or landscape function shown here in a way that this is zero if and only if all the clauses all the kmc zero or the clauses off satisfied keeping these auxiliary variables a EMS always positive. And therefore, what you do here is a dynamics that is a essentially ingredient descend on this potential energy landscape. If you were to keep all the M's constant that it would get stuck in some local minimum. However, what we do here is we couple it with the dynamics we cooperated the clothes violation functions as shown here. And if he didn't have this am here just just the chaos. For example, you have essentially what case you have positive feedback. You have increasing variable. Uh, but in that case, you still get stuck would still behave will still find. So she is better than the constant version but still would get stuck only when you put here this a m which makes the dynamics in in this variable exponential like uh, only then it keeps searching until he finds a solution on deer is a reason for that. I'm not going toe talk about here, but essentially boils down toe performing a Grady and descend on a globally time barren landscape. And this is what works. Now I'm gonna talk about good or bad and maybe the ugly. Uh, this is, uh, this is What's good is that it's a hyperbolic dynamical system, which means that if you take any domain in the search space that doesn't have a solution in it or any socially than the number of trajectories in it decays exponentially quickly. And the decay rate is a characteristic in variant characteristic off the dynamics itself. Dynamical systems called the escape right the inverse off that is the time scale in which you find solutions by this by this dynamical system, and you can see here some song trajectories that are Kelty because it's it's no linear, but it's transient, chaotic. Give their sources, of course, because eventually knowledge to the solution. Now, in terms of performance here, what you show for a bunch off, um, constraint densities defined by M overran the ratio between closes toe variables for random, said Problems is random. Chris had problems, and they as its function off n And we look at money toward the wartime, the wall clock time and it behaves quite value behaves Azat party nominally until you actually he to reach the set on set transition where the hardest problems are found. But what's more interesting is if you monitor the continuous time t the performance in terms off the A narrow, continuous Time t because that seems to be a polynomial. And the way we show that is, we consider, uh, random case that random three set for a fixed constraint density Onda. We hear what you show here. Is that the right of the trash hold that it's really hard and, uh, the money through the fraction of problems that we have not been able to solve it. We select thousands of problems at that constraint ratio and resolve them without algorithm, and we monitor the fractional problems that have not yet been solved by continuous 90. And this, as you see these decays exponentially different. Educate rates for different system sizes, and in this spot shows that is dedicated behaves polynomial, or actually as a power law. So if you combine these two, you find that the time needed to solve all problems except maybe appear traction off them scales foreign or merely with the problem size. So you have paranormal, continuous time complexity. And this is also true for other types of very hard constraints and sexual problems such as exact cover, because you can always transform them into three set as we discussed before, Ramsey coloring and and on these problems, even algorithms like survey propagation will will fail. But this doesn't mean that P equals NP because what you have first of all, if you were toe implement these equations in a device whose behavior is described by these, uh, the keys. Then, of course, T the continue style variable becomes a physical work off. Time on that will be polynomial is scaling, but you have another other variables. Oxidative variables, which structured in an exponential manner. So if they represent currents or voltages in your realization and it would be an exponential cost Al Qaeda. But this is some kind of trade between time and energy, while I know how toe generate energy or I don't know how to generate time. But I know how to generate energy so it could use for it. But there's other issues as well, especially if you're trying toe do this son and digital machine but also happens. Problems happen appear. Other problems appear on in physical devices as well as we discuss later. So if you implement this in GPU, you can. Then you can get in order off to magnitude. Speed up. And you can also modify this to solve Max sad problems. Uh, quite efficiently. You are competitive with the best heuristic solvers. This is a weather problems. In 2016 Max set competition eso so this this is this is definitely this seems like a good approach, but there's off course interesting limitations, I would say interesting, because it kind of makes you think about what it means and how you can exploit this thes observations in understanding better on a low continues time complexity. If you monitored the discrete number the number of discrete steps. Don't buy the room, Dakota integrator. When you solve this on a digital machine, you're using some kind of integrator. Um and you're using the same approach. But now you measure the number off problems you haven't sold by given number of this kid, uh, steps taken by the integrator. You find out you have exponential, discrete time, complexity and, of course, thistles. A problem. And if you look closely, what happens even though the analog mathematical trajectory, that's the record here. If you monitor what happens in discrete time, uh, the integrator frustrates very little. So this is like, you know, third or for the disposition, but fluctuates like crazy. So it really is like the intervention frees us out. And this is because of the phenomenon of stiffness that are I'll talk a little bit a more about little bit layer eso. >>You know, it might look >>like an integration issue on digital machines that you could improve and could definitely improve. But actually issues bigger than that. It's It's deeper than that, because on a digital machine there is no time energy conversion. So the outside variables are efficiently representing a digital machine. So there's no exponential fluctuating current of wattage in your computer when you do this. Eso If it is not equal NP then the exponential time, complexity or exponential costs complexity has to hit you somewhere. And this is how um, but, you know, one would be tempted to think maybe this wouldn't be an issue in a analog device, and to some extent is true on our devices can be ordered to maintain faster, but they also suffer from their own problems because he not gonna be affect. That classes soldiers as well. So, indeed, if you look at other systems like Mirandizing machine measurement feedback, probably talk on the grass or selected networks. They're all hinge on some kind off our ability to control your variables in arbitrary, high precision and a certain networks you want toe read out across frequencies in case off CM's. You required identical and program because which is hard to keep, and they kind of fluctuate away from one another, shift away from one another. And if you control that, of course that you can control the performance. So actually one can ask if whether or not this is a universal bottleneck and it seems so aside, I will argue next. Um, we can recall a fundamental result by by showing harder in reaction Target from 1978. Who says that it's a purely computer science proof that if you are able toe, compute the addition multiplication division off riel variables with infinite precision, then you could solve any complete problems in polynomial time. It doesn't actually proposals all where he just chose mathematically that this would be the case. Now, of course, in Real warned, you have also precision. So the next question is, how does that affect the competition about problems? This is what you're after. Lots of precision means information also, or entropy production. Eso what you're really looking at the relationship between hardness and cost of computing off a problem. Uh, and according to Sean Hagar, there's this left branch which in principle could be polynomial time. But the question whether or not this is achievable that is not achievable, but something more cheerful. That's on the right hand side. There's always going to be some information loss, so mental degeneration that could keep you away from possibly from point normal time. So this is what we like to understand, and this information laws the source off. This is not just always I will argue, uh, in any physical system, but it's also off algorithm nature, so that is a questionable area or approach. But China gets results. Security theoretical. No, actual solar is proposed. So we can ask, you know, just theoretically get out off. Curiosity would in principle be such soldiers because it is not proposing a soldier with such properties. In principle, if if you want to look mathematically precisely what the solar does would have the right properties on, I argue. Yes, I don't have a mathematical proof, but I have some arguments that that would be the case. And this is the case for actually our city there solver that if you could calculate its trajectory in a loss this way, then it would be, uh, would solve epic complete problems in polynomial continuous time. Now, as a matter of fact, this a bit more difficult question, because time in all these can be re scared however you want. So what? Burns says that you actually have to measure the length of the trajectory, which is a new variant off the dynamical system or property dynamical system, not off its parameters ization. And we did that. So Suba Corral, my student did that first, improving on the stiffness off the problem off the integrations, using implicit solvers and some smart tricks such that you actually are closer to the actual trajectory and using the same approach. You know what fraction off problems you can solve? We did not give the length of the trajectory. You find that it is putting on nearly scaling the problem sites we have putting on your skin complexity. That means that our solar is both Polly length and, as it is, defined it also poorly time analog solver. But if you look at as a discreet algorithm, if you measure the discrete steps on a digital machine, it is an exponential solver. And the reason is because off all these stiffness, every integrator has tow truck it digitizing truncate the equations, and what it has to do is to keep the integration between the so called stability region for for that scheme, and you have to keep this product within a grimace of Jacoby in and the step size read in this region. If you use explicit methods. You want to stay within this region? Uh, but what happens that some off the Eigen values grow fast for Steve problems, and then you're you're forced to reduce that t so the product stays in this bonded domain, which means that now you have to you're forced to take smaller and smaller times, So you're you're freezing out the integration and what I will show you. That's the case. Now you can move to increase its soldiers, which is which is a tree. In this case, you have to make domain is actually on the outside. But what happens in this case is some of the Eigen values of the Jacobean, also, for six systems, start to move to zero. As they're moving to zero, they're going to enter this instability region, so your soul is going to try to keep it out, so it's going to increase the data T. But if you increase that to increase the truncation hours, so you get randomized, uh, in the large search space, so it's it's really not, uh, not going to work out. Now, one can sort off introduce a theory or language to discuss computational and are computational complexity, using the language from dynamical systems theory. But basically I I don't have time to go into this, but you have for heart problems. Security object the chaotic satellite Ouch! In the middle of the search space somewhere, and that dictates how the dynamics happens and variant properties off the dynamics. Of course, off that saddle is what the targets performance and many things, so a new, important measure that we find that it's also helpful in describing thesis. Another complexity is the so called called Makarov, or metric entropy and basically what this does in an intuitive A eyes, uh, to describe the rate at which the uncertainty containing the insignificant digits off a trajectory in the back, the flow towards the significant ones as you lose information because off arrows being, uh grown or are developed in tow. Larger errors in an exponential at an exponential rate because you have positively up north spawning. But this is an in variant property. It's the property of the set of all. This is not how you compute them, and it's really the interesting create off accuracy philosopher dynamical system. A zay said that you have in such a high dimensional that I'm consistent were positive and negatively upon of exponents. Aziz Many The total is the dimension of space and user dimension, the number off unstable manifold dimensions and as Saddam was stable, manifold direction. And there's an interesting and I think, important passion, equality, equality called the passion, equality that connect the information theoretic aspect the rate off information loss with the geometric rate of which trajectory separate minus kappa, which is the escape rate that I already talked about. Now one can actually prove a simple theorems like back off the envelope calculation. The idea here is that you know the rate at which the largest rated, which closely started trajectory separate from one another. So now you can say that, uh, that is fine, as long as my trajectory finds the solution before the projective separate too quickly. In that case, I can have the hope that if I start from some region off the face base, several close early started trajectories, they kind of go into the same solution orphaned and and that's that's That's this upper bound of this limit, and it is really showing that it has to be. It's an exponentially small number. What? It depends on the end dependence off the exponents right here, which combines information loss rate and the social time performance. So these, if this exponents here or that has a large independence or river linear independence, then you then you really have to start, uh, trajectories exponentially closer to one another in orderto end up in the same order. So this is sort off like the direction that you're going in tow, and this formulation is applicable toe all dynamical systems, uh, deterministic dynamical systems. And I think we can We can expand this further because, uh, there is, ah, way off getting the expression for the escaped rate in terms off n the number of variables from cycle expansions that I don't have time to talk about. What? It's kind of like a program that you can try toe pursuit, and this is it. So the conclusions I think of self explanatory I think there is a lot of future in in, uh, in an allo. Continue start computing. Um, they can be efficient by orders of magnitude and digital ones in solving empty heart problems because, first of all, many of the systems you like the phone line and bottleneck. There's parallelism involved, and and you can also have a large spectrum or continues time, time dynamical algorithms than discrete ones. And you know. But we also have to be mindful off. What are the possibility of what are the limits? And 11 open question is very important. Open question is, you know, what are these limits? Is there some kind off no go theory? And that tells you that you can never perform better than this limit or that limit? And I think that's that's the exciting part toe to derive thes thes this levian 10.

>>Good morning. Good afternoon. Good evening, everyone. I should thank Entity Research and the Oshie for putting together this program and also the opportunity to speak here. My name is Al Gore ism or Andy and I'm from Caltech. And today I'm going to tell you about the work that we have been doing on networks off optical parametric oscillators and how we have been using them for icing machines and how we're pushing them toward Cornum. Photonics should acknowledge my team at Caltech, which is now eight graduate students and five researcher and postdocs as well as collaborators from all over the world, including entity research and also the funding from different places, including entity. So this talk is primarily about networks of resonate er's and these networks are everywhere from nature. For instance, the brain, which is a network of oscillators all the way to optics and photonics and some of the biggest examples or meta materials, which is an array of small resonate er's. And we're recently the field of technological photonics, which is trying thio implement a lot of the technological behaviors of models in the condensed matter, physics in photonics. And if you want to extend it even further. Some of the implementations off quantum computing are technically networks of quantum oscillators. So we started thinking about these things in the context of icing machines, which is based on the icing problem, which is based on the icing model, which is the simple summation over the spins and spins can be their upward down, and the couplings is given by the G I J. And the icing problem is, if you know J I J. What is the spin configuration that gives you the ground state? And this problem is shown to be an MP high problem. So it's computational e important because it's a representative of the MP problems on NPR. Problems are important because first, their heart in standard computers, if you use a brute force algorithm and they're everywhere on the application side. That's why there is this demand for making a machine that can target these problems and hopefully it can provide some meaningful computational benefit compared to the standard digital computers. So I've been building these icing machines based on this building block, which is a degenerate optical parametric oscillator on what it is is resonator with non linearity in it and we pump these resonate er's and we generate the signal at half the frequency of the pump. One vote on a pump splits into two identical photons of signal, and they have some very interesting phase of frequency locking behaviors. And if you look at the phase locking behavior, you realize that you can actually have two possible face states as the escalation result of these Opio, which are off by pie, and that's one of the important characteristics of them. So I want to emphasize >>a little more on that, and I have this mechanical analogy which are basically two simple pendulum. But there are parametric oscillators because I'm going to modulate the parameter of them in this video, which is the length of the strength on by that modulation, which is that will make a pump. I'm gonna make a muscular. That'll make a signal, which is half the frequency of the pump. >>And I have two of them to show you that they can acquire these face states so they're still face their frequency lock to the pump. But it can also lead in either the zero pie face state on. The idea is to use this binary phase to represent the binary icing spin. So each Opio is going to represent spin, which can be >>either is your pie or up or down, >>and to implement the network of these resonate er's. We use the time off blood scheme, and the idea is that we put impulses in the cavity, these pulses air separated by the repetition period that you put in or t R. And you can think about these pulses in one resonator, xaz and temporarily separated synthetic resonate Er's If you want a couple of these resonator is to each other, and now you can introduce these delays, each of which is a multiple of TR. If you look at the shortest delay it couples resonator wanted to 2 to 3 and so on. If you look at the second delay, which is two times a rotation period, the couple's 123 and so on. If you have any minus one delay lines, then you can have any potential couplings among these synthetic resonate er's. And if I can introduce these modulators in those delay lines so that I can strength, I can control the strength and the phase of these couplings at the right time. Then I can >>have a program. We'll all toe all connected network in this time off like scheme. >>And the whole physical size of the system scales linearly with the number of pulses. So the idea of opium based icing machine is didn't having these o pos. Each of them can be either zero pie, and I can arbitrarily connect them to each other. And then I start with programming this machine to a given icing problem by just setting the couplings and setting the controllers in each of those delight lines. So now I have a network which represents an icing problem thin the icing problem maps to finding the face state that satisfy maximum number of coupling constraints. And the way it happens is that the icing Hamiltonian maps to the linear loss of the network. And if I start adding gain by just putting pump into the network, then the OPI ohs are expected to oscillating the lowest, lowest lost state. And, uh and we have been doing these in the past, uh, six or seven years and I'm just going to quickly show you the transition, especially what happened in the first implementation which was using a free space optical system and then the guided wave implementation in 2016 and the measurement feedback idea which led to increasing the size and doing actual computation with these machines. So I just want to make this distinction here that, um the first implementation was on our optical interaction. We also had an unequal 16 implementation and then we transition to this measurement feedback idea, which I'll tell you quickly what it iss on. There's still a lot of ongoing work, especially on the entity side, to make larger machines using the measurement feedback. But I'm gonna mostly focused on the all optical networks and how we're using all optical networks to go beyond simulation of icing. Hamiltonian is both in the linear and >>nonlinear side and also how we're working on miniaturization of these Opio networks. So >>the first experiment, which was the four Opium machine it was a free space implementation and this is the actual picture of the machine and we implemented a small and it calls for Mexico problem on the machine. So one problem for one experiment and we ran the machine 1000 times, we looked at the state and we always saw it oscillate in one of these, um, ground states of the icing laboratoria. Yeah, so then the measurement feedback idea was to replace those couplings and the controller with the simulator. So we basically simulated all those coherent interactions on on FB g A. And we replicated the coherent pulse with respect to all those measurements. And then we injected it back into the cavity and on the near to you still remain. So it still is a non. They're dynamical system, but the linear side is all simulated. So there are lots of questions about if this system is preserving important information or not, or if it's gonna behave better Computational wars. And that's still ah, lot of ongoing studies. But nevertheless, the reason that this implementation was very interesting is that you don't need the end minus one delight lines so you can just use one, and you can implement a large machine, and then you can run several thousands of problems in the machine, and then you can compare the performance from the computational perspective. Looks so I'm gonna split this idea of opium based icing machine into two parts One is the linear part, which is if you take out the non linearity out of the resonator and just think about the connections. You can think about this as a simple matrix multiplication scheme, and that's basically >>what gives you the icing Hamiltonian model A. So the optical loss of this network corresponds to the icing Hamiltonian. >>And if I just want to show you the example of the n equals for experiment on all those face states and the history Graham that we saw, you can actually calculate the laws of each of those states because all those interferences in the beam splitters and the delay lines are going to give you a different losses. And then you will see that ground states corresponds to the lowest laws of the actual optical network. If you add the non linearity, the simple way of thinking about what the non linearity does is that it provides to gain, and then you start bringing up the gain so that it hits the loss. Then you go through the game saturation or the threshold which is going to give you this phase bifurcation. >>So you go either to zero the pie face state, and the expectation is that this the network oscillates in the lowest possible state, the lowest possible loss state. >>There are some challenges associated with this intensity Durban face transition, which I'm going to briefly talk about. I'm also going to tell you about other types of non their dynamics that we're looking at on the non air side of these networks. So if you just think about the linear network, we're actually interested in looking at some technological behaviors in these networks. And the difference between looking at the technological behaviors and the icing uh, machine is that now, First of all, we're looking at the type of Hamilton Ian's that are a little different than the icing Hamilton. And one of the biggest difference is is that most of these technological Hamilton Ian's that require breaking the time reversal symmetry, meaning that you go from one spin to on the one side to another side and you get one phase. And if you go back where you get a different phase, and the other thing is that we're not just interested in finding the ground state, we're actually now interesting and looking at all sorts of States and looking at the dynamics and the behaviors of all these states in the network. So we started with the simplest implementation, of course, which is a one d chain of thes resonate er's which corresponds to a so called ssh model. In the technological work, we get the similar energy to los mapping. And now we can actually look at the band structure on. This is an actual measurement >>that we get with this associate model and you see how it reasonably how how? Well, it actually follows the prediction and the theory. >>One of the interesting things about the time multiplexing implementation is that now you have the flexibility of changing the network as we were running the machine. And that's something unique about this time multiplex implementation so that we can actually look at the dynamics. And one example >>that we have looked at is we can actually go to the transition off going from top a logical to the to the standard nontrivial. I'm sorry to the trivial behavior of the network. >>You can then look at the edge states and you can also see the trivial and states and the technological at states actually showing up in this network. We have just recently implement on a two D, >>uh, network with Harper Hofstadter model when you don't have the results here. But we're one of the other important characteristic of time multiplexing is that you can go to higher and higher dimensions and keeping that flexibility and dynamics. And we can also think about adding non linearity both in a classical and quantum regimes, which is going to give us a lot of exotic oh, classical and quantum, non innate behaviors in these networks. >>So I told you about the linear side. Mostly let me just switch gears and talk about the nonlinear side of the network. And the biggest thing that I talked about so far in the icing machine is this phase transition, that threshold. So the low threshold we have squeezed state in these Oh, pios, if you increase the pump, we go through this intensity driven phase transition and then we got the face stays above threshold. And this is basically the mechanism off the computation in these O pos, which is through this phase transition below to above threshold. So one of the characteristics of this phase transition is that below threshold, you expect to see quantum states above threshold. You expect to see more classical states or coherent states, and that's basically corresponding to the intensity off the driving pump. So it's really hard to imagine that it can go above threshold. Or you can have this friends transition happen in the all in the quantum regime. And there are also some challenges associated with the intensity homogeneity off the network. Which, for example, is if one Opio starts oscillating and then its intensity goes really high. Then it's going to ruin this collective decision making off the network because of the intensity driven face transition nature. So So the question is, can we look at other phase transitions? Can we utilize them for both computing? And also, can we bring them to the quantum regime on? I'm going to specifically talk about the face transition in the spectral domain, which is the transition from the so called degenerate regime, which is what I mostly talked about to the non degenerate regime, which happens by just tuning the phase of the cavity. And what is interesting is that this phase transition corresponds to a distinct phase noise, behavior So in the degenerate regime, which we call it the order state. You're gonna have the phase being locked to the phase of the pump as I talked about in the non the general regime. However, the phase is the phase is mostly dominated by the quantum diffusion off the off the phase, which is limited by the so called shallow towns limit and you can see that transition from the general to non degenerate, which also has distinct symmetry differences. And this transition corresponds to a symmetry breaking in the non degenerate case. The signal can acquire any of those phases on the circle, so it has a you one symmetry. And if you go to the degenerate case, then that symmetry is broken and you only have zero pie face days I will look at So now the question is can utilize this phase transition, which is a face driven phase transition and can we use it for similar computational scheme? So that's one of the questions that were also thinking about. And it's not just this face transition is not just important for computing. It's also interesting from the sensing potentials and this face transition. You can easily bring it below threshold and just operated in the quantum regime. Either Gaussian or non Gaussian. If you make a network of Opio is now, we can see all sorts of more complicated and more interesting phase transitions in the spectral domain. One of them is the first order phase transition, which you get by just coupling to oppose. And that's a very abrupt face transition and compared to the to the single Opio face transition. And if you do the couplings right, you can actually get a lot of non her mission dynamics and exceptional points, which are actually very interesting to explore both in the classical and quantum regime. And I should also mention that you can think about the cup links to be also nonlinear couplings. And that's another behavior that you can see, especially in the nonlinear in the non degenerate regime. So with that, I basically told you about these Opio networks, how we can think about the linear scheme and the linear behaviors and how we can think about the rich, nonlinear dynamics and non linear behaviors both in the classical and quantum regime. I want to switch gear and tell you a little bit about the miniaturization of these Opio networks. And of course, the motivation is if you look at the electron ICS and >>what we had 60 or 70 years ago with vacuum tube and how we transition from relatively small scale computers in the order of thousands of nonlinear elements to billions of non linear elements, where we are now with the optics is probably very similar to seven years ago, which is a tabletop implementation. >>And the question is, how can we utilize nano photonics? I'm gonna just briefly show you the two directions on that which we're working on. One is based on lithium Diabate, and the other is based on even a smaller resonate er's Did you? So the work on Nana Photonic lithium naive. It was started in collaboration with Harvard Marko Loncar and also might affair at Stanford. And, uh, we could show that you can do the >>periodic polling in the phenomenon of it and get all sorts of very highly non in your process is happening in this net. Photonic periodically polls if, um Diabate >>and now we're working on building. Opio was based on that kind of photonic lithium Diabate and these air some some examples of the devices that we have been building in the past few months, which I'm not gonna tell you more about. But the OPI ohs and the Opio networks are in the works, and that's not the only way of making large networks. But also I want to point out that the reason that these Nana photonic goblins are actually exciting is not just because you can make a large networks and it can make him compact in a in a small footprint, they also provide some opportunities in terms of the operation regime. On one of them is about making cat states in o pos, which is can we have the quantum superposition of >>the zero pie states that I talked about >>and the nano photonics within? I would provide some opportunities to actually get >>closer to that regime because of the spatial temporal confinement that you can get in these wave guides. So we're doing some theory on that. We're confident that the type of non linearity two losses that it can get with these platforms are actually much higher than what you can get with other platform, other existing platforms and to >>go even smaller. We have been asking the question off. What is the smallest possible Opio that you can make? Then you can think about really wavelength scale type resonate er's and adding the chi to non linearity and see how and when you can get the Opio to operate. And recently, in collaboration with us. See, we have been actually USC and Creole. We have demonstrated that you can use nano lasers and get some spin Hamiltonian implementations on those networks. So if you can't build a pos, we know that there is a path for implementing Opio Networks on on such a nano scale. So we have looked at these calculations and we try to >>estimate the threshold of a pos. Let's say for me resonator and it turns out that it can actually be even lower than the type of bulk Pippen O pos that we have been building in the past 50 years or so. >>So we're working on the experiments and we're hoping that we can actually make even larger and larger scale Opio networks. So let me summarize the talk I told you about the opium networks and >>our work that has been going on on icing machines and the >>measurement feedback on I told you about the ongoing work on the all optical implementations both on the linear side and also on the nonlinear behaviors. And I also told you >>a little bit about the efforts on miniaturization and going to the to the nano scale. So with that, I would like Thio stop here and thank you for your attention.

>>from around the globe. It's the cue with digital coverage of active EO data driven 2020. Brought to you by activity. We're back. This is the cubes coverage. Our ongoing coverage of active FiOS data driven. Of course, we've gone virtual this year. Ash. Ashutosh is here. He's the founder, president and CEO of Active Eo. Great to see you again. >>Likewise, They always always good to see you. >>We have We're in a little meet up, You and I in Boston. I always enjoy our conversations. Little did we know that, You know, a few months later, we would only be talking at this type of distance and, uh and of course, it's sad. I mean, a data driven is one of our favorite events is intimate, its customer content driven. The theme this year is you call it the next normal. Some people call it the new abnormal, the next normal. What's that all about? >>I think it's pretty pretty fascinating to see when we walked in in March, all of us were shocked by the effect of this pandemic. And for a while we all scrambled around trying to figure out How do you react to this one, and everybody reacted very differently. But most people have this tendency to think that this is going to be a pretty broom environment with lots of unknown variables, and it is important for us to try to figure out how to get a get our hands on this. By the time we came on. For six weeks into that, almost all of us have figured out this is Ah, this is not something you fight again. This is not something you wait, what, it to go away? But this is one. Did you figure out how to live in and you figured out how to work around it? And that, we believe, is the next long. It's not about trying to create a new abnormal. It's not about creating a new normal, but it's truly one that basically says that is it. That is a way, perhaps packed forward. There's a is a way to create this next normal, and you just figured out how to live with the environment, behalf and the normal outcomes of companies that have done remarkably well as a result of these actions. Fact. If you're being one of them, >>it's quite amazing isn't it? I mean, I've talked to a lot of tech companies, CEOs and their customers, and it's almost like they feel the first reaction was course they cared about their there, their employees and their broader families. Number one number two was many companies, as you know, saw a tailwind, and it initially didn't want to be seen as ambulance chasing. And then, of course, the entrepreneurial spirit kicked in and they said, Okay, we can only control what we can control and tech companies in particular just exceedingly Well, I don't think anybody really predicted that early >>on. Yeah, I, um I think of the heart, We're all human beings, and the first reaction was to take it off. Four constituencies, right? One. Take care of your family. Take it off your community, take care of your employees, take care of your customers. And that was the hardest part. The first 4 to 6 weeks was to figure out How do you do each of those four. Once you figured that part out or you figured out ways to get around to making sure you can take it off those you really found the next mom, you really start forgetting our out of continue to innovate Could, you know to support each of those four constituencies and people have done different things. I know it's amazing how, um, Cuba continues to operate As far as a user is concerned, they're all watching anymore. Yes, we don't have the wonderful desk, and we all get to chat and look in the eye. But the content of the messages asked powerful as what it waas a few months ago. So I'm sure this is how we're all going to figure out how to make through this new next normal >>and digital transformation kind of went from from push to pull. I mean, every conference you go to, they say, Well, look at uber, you know, look at Airbnb and it put up the examples you have to do this to, and then all of sudden the industry dragged you along. Some Curis esta is toe. How and and I guess the other point there is digital means data. We've said that many, many times. If you didn't have a digital strategy during the height of the lock down, you couldn't transact business and still many restaurants is still trying to figure this out, But so how did it affect you and your customers? >>Yeah, it's very interesting. And I we spend a lot of time with several of our customers were managing some of the largest I T organizations. We talk about very interesting phenomena that happened some better beginning of this year. About 20 years ago, we used to worry about this thing called the Digital Divide, those who have access the network and Internet and those who don't. And now there is this beta divide, the divide between organizations that know how to leverage, exploit and absolutely excellent the business using data and those adorable. I think we're seeing this effect so very clearly among organizations that unable to come back and address some of this stuff. And it's fascinating. Yes, we all have the examples off the lights off. People are doing delivery. People are doing retailing, but there are so many little things you're seeing organizations. And just the other day, he had a video from Century Days Is Central Data System, which is helping accelerate Cohen 19 research because it will get copies of the data faster than they would get access to data so that these are just much, much faster. Sometimes you know, several days to a few minutes. It's that that level of effect, it's not just down to some seven. You know, you almost think of it as nice to have, but it's must have life threatening stuff. Essential stuff or just addressing. Korea was running a very pretty in a wonderful article about this supercomputer in That's Doing an Aristo covert 19 and how it's figured out most of these symptoms they're able to figure out by just crunching a ton of data. And almost every one of those symptoms that the computer has predicted Supercomputer is predicted has being accurate. It's about data. It is absolutely about data, which is why I think this is a phenomenal time for companies. Toe Absolutely go change. Make this information about data exploration, data leverage, exploitation. And there's a ton of it all over all around us. >>Yeah, and and part of that digital transformation, the mandate is to really put data at the core. I mean, we've we've certainly seen this with the top market cap companies. They've got dated at the core, and and now, as they say it's it's become a A mandate. And, you know, there's been several things that we've clearly noticed. I mean, you saw the work from home required laptops and, you know, endpoint security and things of that. VD. I made a comeback, and certainly Cloud was there. But I've been struck by the reality of multi Cloud. I was kind of a multi cloud skeptic early on. >>Yeah, >>I said many times I thought it was more of a symptom than it was a strategy, but it's that's completely flipped. Ah, recently in r e t r surveys, we saw multi cloud popping up all over the place. I wonder what you're seeing when you talk to your customers and other CEOs. >>Yeah, So fascinating, though really is the first flower part of sometime in 2018. End of 2018 >>Go right, Yeah, >>the act if you'll go on world, which is a phenomenal way to completely change the way you think about the using object storage in the flower for two years that we saw about 20% of our business. By the end of two years, the beginning of this year, 20% of our business was built on never it in the cloud since March. So that was end of our almost ended the Q one. So now we just limit left you three in six months. We added 12 more percent of the business literally weeded in six months. What we did not do before for 18 months before that, right? Significantly more than what we did for a year and a half before that. And there are really three reasons and we see this old nor again, we have a large customer. We closed in January. Ironically, were deploying out of UK, a very large marketing organization. Got everything deployed, running the they're back up and beyond and a separate data center. And they had a practical problem of not being able to access the second sight literally in the middle of deployment. Mystere that customer, Did you see me Google Cloud? Because they were simply no way for them to continue protecting their data, being able to develop new applications with that data that simply had no access. So there was. This was the number one reason the inability for already physically access, but put their their employees at rest and have before the plow would be the infrastructure. That's number one, so that first of all, drove the reason for the cloud. And then there's a second reason there are practical reasons. And why some clerk platforms that good one working the other ones are not. So where, uh, some other more fuels. And so if I'm an organization that has that spans everything, I've got no power PC and X 86 machine A vm I got container platforms. I got Oracle. They got a C P. There is no single cloud platform that supports all my work loaders efficiently. It's available in all the agents I want. So inevitably I have to go at our different about barefoot. So that's a second practical visa. And then there's a strategic reason. No, when no customer what's really locked into anyone card back at least two. You're gonna go pear more likely? Three. So those are the reasons. And then, interestingly enough, have you were on a panel with as global Cee Io's and in addition to just the usual cloud providers of you all know and love inside the U. S. Across the world, in Europe, in Asia, there's a rise off the regional flower fire. See you take all this factor. So have you got absolute physical necessity? You got practical constraints of what can the club provided support the strategic reasons on why either Because I don't want to be locked into a part for better or because there is a rise off data nationalism that's going on, that people want to keep their data within the country bombs all of these reasons. But the foundations or why multiplier is almost becoming a de facto. It's impossible. What a decent size organization to assume. They were just different on one car ready. >>The big trend we're seeing, I wonder if you could comment. Is this this notion of the data life cycle of the data pipeline? It's a very complex situation for a lot of organizations, their data siloed. We hear that a lot. They have data scientists, data engineers, developers, data quality engineers, just a lot of different constituencies and lines of business. And it's kind of a mess. And so what they're trying to do is bring that together. So they've done that data. Scientists complain they spend all their time wrangling data, but but ultimately the ones that are succeeding to putting data at the core is, we've just been discussing are seeing amazing outcomes by being able to have a single version of the truth, have confidence in that data, create self serve for their for their lines of business and actually reduce the end and cycle times. It's driving your major monetization, whether that's cost cutting or revenue. And I'm curious as to what you're seeing. You guys do a lot of work. Heavy work in Dev ops and hard core database those air key components of that data Lifecycle. Yeah, you're seeing in that regard regarding that data pipeline. >>Yeah, it's a It's a phenomenal point if you really want to go back and exploit data within an organization. If you really want to be a data driven organization, the very first thing you have to do is break down the silos. Ironically, every organization has all the data required to make the decisions they want to. They just can't either get to it or it's so hard to make the silos. That is just not what trying to make it happen. And 10 years ago we set out on this mission rather than keep this individual silos of data. Why don't we flip it open and making it a pipeline, which looks like a data cloud where essentially anybody who's consuming it has access to it based on the governance rules based on the security rules that the operations people have said and based on the kind of format they want to see data. Not everyone even want to see the data in a database. Former, maybe you want the database for my convert CSP for my before you don't analytics And this idea of making data, the new infrastructure, this idea of having the operations people provide this new layer for data, it's finally come to roost. I mean, it's it's fascinating. I was the numbers last quarter. We just finished up. You do now. 45% of our customer base is uses activity or for reuse is the back of data for things that excellent. The business things that make the business move faster, more productive or you will survive. That was the mission. That was what we set out to do 10 years ago. We were talking to an analyst this morning, and now this is question off. You know, it looks like there's a team of backup data being reused, said Yeah, that's kind of what we've been saying for 10 years. Backup cannot be an insurance back up in order to your destination. It has to be something that you could use as an asset and that I think it's finally coming to the point with you can use back up a single source of truth only if you designed it right from the beginning. For that purpose, you cannot just lots of lots of ways to fake it. Make it try to pretend like you're doing it. But that was a trooper was off making date of the new infrastructure, making it a cloud, making it something that is truly an ask. And it's fascinating to see our businesses. You take any of our larger counts and the way they've gone about transforming not just basic backup. India. Yes, we are the world's glasses back up in most Kayla will be our solution. That's that's a starting point. But do we will be used after Devil applications 8, 10 times faster? Ron Analytics, 100 ex pastor. The more data you have, the more people who use data you have, the better this return makeups. >>You know, that is interesting to hear you talk about that because that has been the holy Grail of backup. Was toe go beyond insurance to actually create business value. And you're actually seeing some underlying trends We talked about that data pipeline in one of the areas that is the most interesting is in database, which was so boring for so many years. Ah, and you're seeing new workloads emerge. Take the data warehouse beyond your reporting. Never really lived up to its Ah, it's promise of 360 degree view. You mentioned analytics. That's really starting toe happen. Ah, and it's all about data John, for Used to say that your data is that is the new development kit. You call it the new infrastructure, and it's sort of the same same type of theme. So maybe some of the trends you're seeing in ah in database enoughto talk about that for a little bit and then pick your brains and some other tech like object storage is another one that we've really seen takeoff? >>Yeah. So I think our journey with object story began in 16 4017 as we started or Doctor Cloud platform in response to the user requirements, Uh, we did more like most companies have done and unfortunately continue to do to take the in print product. And then it's smooth under the cloud. And one of the things we saw was there was a fundamental difference off how the design points of flower engineering is all about what they're designed it for object story, that one of those one of those primitives fundamental stories, primitives that the cloud providers actually produced that we know really exploited. There was. It was used as a graveyard for data. It's a replacement for me, please, where data goes to die. And then we look at it really closely and say, Well, this is actually a massively scalable, very low cost storage, but it has some problems. It has an interface that you cannot use with traditional servers. Uh, it has some issues around not being able to read, modify right the data. So it feels like a consuming a lot of stories. So we're going to solve those problems because a good two years to come back with something on world that fundamentally creeds objects the lady like this massive use capable high performer disk? Yes, except it is ridiculously low cost and optimize the capacity. So this finger on world that patented has really become the foundation of how everything in our works without using CPU Ray, that is simply nothing at a lower PCO that if you wanted to basic backup, the, uh, more importantly, use that to do this a massive analytics and you don't know more data warehouse data leaks. It is not a good deal of Lake House aladi. All of these are still silent. All of these are people trying to take some data from somewhere put into one of the new construct and have it being controlled by somebody else. This is artist thing. It's just you just move the silos from some place to another place instead of creating a pipeline. If you want to really create a pipeline object story has been integral part of the pipeline, not a separate bucket by itself. And that's what we did. And same thing with databases, you know, most business, most of the critical business and I was on a daily basis, and the ability to find a way to leverage those. Move them on our leverage in terms of whichever format databases access. Which location or Saxes doesn't know how big it is. Lots of work has gone into trying to figure figure that one out. And we we had some very, very good partners in some of the largest customers who help take the journey with us. I'm pretty much all of the global 2000 accounts you see across the board, but an integral part of a process. >>You mentioned the word journey and triggered a thought. Is your discussion with Robbie, the CEO of of Seeing >>A. It was a customer years. >>Ah, and what he said. I liked what he said. He course he used the term journey. We all do. But he said, You know what? I kind of don't like that term because I want to inject the sense of urgency essentially what he was saying. I want speed, you know, journeys like Okay, kids get in the car, were in a drive across country. We're gonna make some stops. And so, while there's a journey, he also was was really trying to push the organization hard and he talked about culture. Ah, as some of the most difficult things and it goes like many. See, I said, Now the technology is almost the easy part. It's true when it works. Oh, I thought that was a great discussion that you had. What were some of your takeaways >>with thinking? Robbie's is very astute. Ah, I t executive was being around the block for so long and one of the fascinating things, but a asking this question about what's the biggest challenge was just gone through this a couple of times. What is the biggest challenge? Taking an organization as vulnerable as well known A C gate is. I mean, this is a data company. This is This is the heart of the Oliver Half the world's data is on seeing stuff. How are you today was, or company has been around for long in the middle of Silicon Valley and make it into ah into a fast growing transformation company that's responding to the newer challenges. And I thought he was going to come back with Well, you know, I gotta go to the abuses. I picked this technology that techno in. Surely that is exactly what I expected he would end up with. There's nothing through technology in this day and age when you can have an Elon Musk and send a card of Mars. It's not many technologies that we can really solve many covered 19 ism. Next one Do we gotta go solve? Well, frankly, he kid upon the one thing that matters to every company. It is the fundamental culture to create a biased of action. It's a fundamental culture where you have to come back and have a deliverable that moves the ball forward every day, every month, every quarter, as opposed to have this CDs off. Like you said, a journey that say's and we all know this right? People talk about, we're going to do this in face one. We're gonna do this and face to and good food release and face three nothing and what happens Invasive. Nobody gets a number feast. I think he did a great job of saying I fundamentally had to go change the culture that was my biggest take away, and this I've heard this so many times the most effective I D execs wait a transformation. It actually shows in the people that they have. It's not the technology, it's the people. And some. This history is replete with organizations that have done remarkably well, not by leveraging the heck out of the technology, but truly by leveraging the change in the people's mindset. And, of course, that at that point that leverages technology where a proper here. But Robbie's a insightful person, always such a They lied to talk them, said they like for him to have chosen us as a its information technology for him to go pull his data warehouses and completely transformed how I was doing manufacturing across the globe. >>You know, I want to have some color of what you just said because some key keep takeaways that from what you just said, ashes is You know, you're right when you look back at the history of the computer industry used to be very well known processes, but the technology was the big mystery and the and the big risk and you think about with Cove it were it not for Technology Way didn't know what was coming. We were inventing new processes literally every day, every week, every month. It's so technology was pretty well understood. It and enabled that. And when you when you think when we talked earlier about putting data at the core, it was interesting to hear Robbie. He basically said, Yeah, we had a big data team in the U. S. A big tainted TV in Europe. We actually organized around silos and and so you guys played a role you were very respectful about, you know, touting active video with him. You did ask him, You know what role you play, But it is interesting to hear and talk about how he had to address that both culturally. And of course, there's technology underneath to enable that unification of data that silo busting, if you will. And you guys played a role in that. >>Yeah, I always enjoy, um, conversation with folks who have taken a problem, identified what needs to be done and then just get it done. And its That's more fascinating than you. Of course, I video plays a small part in a lot of things, and we're proud to have played a small part in his big initiative, and that's true of know the thousands of customers we talk about. But it's such a fascinating story to have leaders who come back and make this transformation happen, and to understand how they went about making those decisions, how they identified where the problem with these are so hard. We all see them in our own life, right? We see there is a there's a problem, but sometimes it takes a wider don't understand. How do you identify them and what do you have to do and more importantly, actually do it? And so whenever use, whenever I get an opportunity with people like Robbie, I think understanding that there's a way to help, uh, we always make sure that we play our own small part, and we're privileged to be a part of those kinds of journeys. >>Well, I think what's interesting about activity on the company that you created is essentially that. We're talking about the democratisation of data, that whole data pipeline, that discussion, that we had the self service of that data to the lines of business and, you know, you guys clearly play a role there. The multi cloud discussion fits into that. I mean that these air all trends that are tail winds for companies that can that can help sort of you know, flattened the data globe. If you if you will, your final thoughts. >>Yeah, I know you said something that is so much at the heart of every idea Exactly that you're talking to, if they truly is. The fundamental asset that I finally end up with is an organization. The democratization of data. Where I do not lock this into another silo, another platform, another ploughed. Another application has to be part of my foundation design and therefore my ability to use each of this cloud platform for the services they provide. While I and they were to move the data to where I needed to be. That is so critical. So you almost start to think about the one possession and organization now has. And we talked about this with a group of CEOs. They might be some pretty soon. Not too far off, but data stolen asset. I might actually have our data mark data market, just like you. I was stopped working, but I can start to sell my data. You know, imagine a coup in 19. There's so many organization that have so much data, and many of them have contributed to this research because this is an existence of issue. But you can see this turning into a next level. So, yes, we've got activities, will move the data toe one level higher where it's become a foundation construct for the organization. The next part is gonna actually done. This is the one asset would actually monetize someone stuff. And it will be not too long when you need to talk about how there's this new exchange and what's the rate of data for this company? Was, is that company in the future trading options? Who knows is gonna be really interesting. >>Well, I think you're right on this notion of a data. Marketplaces is coming, and it's not not that far away, Blash. It's always great to talk to you. I hope next year a data driven weaken we could be face to face. But I mean, look, this has been we we've dealt with it. It's it's actually created opportunities for us toe to reinvent ourselves. So congratulations on the success that you've had and ah, and thank you for coming on the Cube. >>No, thank you for hosting us and always a big fan off Cube. You guys, you engage with you since early days, and it is fascinating to see how this company has grown. And it's probably many people don't even know how much you've grown behind the seats, technologies and culture that you created yourself. So it's hopefully one day we'll strict the table that I would be another side and asking of our transformation. Digital transformation of Cuban cell >>I would love to. I'd love to do that index again. And thank you, everybody for watching our continuous coverage of active fio data driven keeper Right there. We'll be back with our next guest right after this short break. >>Thank you.

>> Announcer: Live from Vancouver, Canada it's theCUBE, covering OpenStack Summit North America 2018. Brought to you by Red Hat, the OpenStack foundation, and its Ecosystem partners. >> Welcome back, I'm Stu Miniman and my cohost John Troyer and you're watching theCUBE's coverage of OpenStack Summit 2018 here in Vancouver. Happy to welcome first-time guest Roland Cabana who is a DevOps Manager at Vault Systems out of Australia, but you come from a little bit more local. Thanks for joining us Roland. >> Thank you, thanks for having me. Yes, I'm actually born and raised in Vancouver, I moved to Australia a couple years ago. I realized the potential in Australian cloud providers, and I've been there ever since. >> Alright, so one of the big things we talk about here at OpenStack of course is, you know, do people really build clouds with this stuff, where does it fit, how is it doing, so a nice lead-in to what does Vault Systems do for the people who aren't aware. >> Definitely, so yes, we do build cloud, a cloud, or many clouds, actually. And Vault Systems provides cloud services infrastructure service to Australian Government. We do that because we are a certified cloud. We are certified to handle unclassified DLM data, and protected data. And what that means is the sensitive information that is gathered for the Australian citizens, and anything to do with big user-space data is actually secured with certain controls set up by the Australian Government. The Australian Government body around this is called ASD, the Australian Signals Directorate, and they release a document called the ISM. And this document actually outlines 1,088 plus controls that dictate how a cloud should operate, how data should be handled inside of Australia. >> Just to step back for a second, I took a quick look at your website, it's not like you're listed as the government OpenStack cloud there. (Roland laughs) Could you give us, where does OpenStack fit into the overall discussion of the identity of the company, what your ultimate end-users think about how they're doing, help us kind of understand where this fits. >> Yeah, for sure, and I mean the journey started long ago when we, actually our CEO, Rupert Taylor-Price, set out to handle a lot of government information, and tried to find this cloud provider that could handle it in the prescribed way that the Australian Signals Directorate needed to handle. So, he went to different vendors, different cloud platforms, and found out that you couldn't actually meet all the controls in this document using a proprietary cloud or using a proprietary platform to plot out your bare-metal hardware. So, eventually he found OpenStack and saw that there was a great opportunity to massage the code and change it, so that it would comply 100% to the Australian Signals Directorate. >> Alright, so the keynote this morning were talking about people that build, people that operate, you've got DevOps in your title, tell us a little about your role in working with OpenStack, specifically, in broader scope of your-- >> For sure, for sure, so in Vault Systems I'm the DevOps Manager, and so what I do, we run through a lot of tests in terms of our infrastructure. So, complying to those controls I had mentioned earlier, going through the rigmarole of making sure that all the different services that are provided on our platform comply to those specific standards, the specific use cases. So, as a DevOps Manger, I handle a lot of the pipelining in terms of where the code goes. I handle a lot of the logistics and operations. And so it actually extends beyond just operation and development, it actually extends into our policies. And so marrying all that stuff together is pretty much my role day-to-day. I have a leg in the infrastructure team with the engineering and I also have a leg in with sort of the solutions architects and how they get feedback from different customers in terms of what we need and how would we architect that so it's safe and secure for government. >> Roland, so since one of your parts of your remit is compliance, would you say that you're DevSecOps? Do you like that one or not? >> Well I guess there's a few more buzzwords, and there's a few more roles I can throw in there but yeah, I guess yes. DevSecOps there's a strong security posture that Vault holds, and we hold it to a higher standard than a lot of the other incumbents or a lot of platform providers, because we are actually very sensitive about how we handle this information for government. So, security's a big portion of it, and I think the company culture internally is actually centered around how we handle the security. A good example of this is, you know, internally we actually have controls about printing, you know, most modern companies today, they print pages, and you know it's an eco thing. It's an eco thing for us too, but at the same time there are controls around printed documents, and how sensitive those things are. And so, our position in the company is if that control exists because Australian Government decides that that's a sensitive matter, let's adopt that in our entire internal ecosystem. >> There was a lot of talk this morning at the keynote both about upgrades, and I'm blanking on the name of the new feature, but also about Zuul and about upgrading OpenStack. You guys are a full Upstream, OpenStack expert cloud provider. How do you deal with upgrades, and what do you think the state of the OpenStack community is in terms of kind of upgrades, and maintenance, and day two kind of stuff? >> Well I'll tell you the truth, the upgrade path for OpenStack is actually quite difficult. I mean, there's a lot of moving parts, a lot of components that you have to be very specific in terms of how you upgrade to the next level. If you're not keeping in step of the next releases, you may fall behind and you can't upgrade, you know, Keystone from a Liberty all the way up to Alcatel, right? You're basically stuck there. And so what we do is we try to figure out what the government needs, what are the features that are required. And, you know, it's also a conversation piece with government, because we don't have certain features in this particular release of OpenStack, it doesn't mean we're not going to support it. We're not going to move to the next version just because it's available, right? There's a lot of security involved in fusing our controls inside our distribution of OpenStack. I guess you can call it a distribution, on our build of OpenStack. But it's all based on a conversation that we start with the government. So, you know, if they need VGPUs for some reason, right, with the Queens release that's coming out, that's a conversation we're starting. And we will build into that functionality as we need it. >> So, does that mean that you have different entities with different versions, and if so, how do you manage all of that? >> Well, okay, so yes that's true. We do have different versions where we have a Liberty release, and we have an Alcatel release, which is predominant in our infrastructure. And that's only because we started with the inception of the Liberty release before our certification process. A lot of the things that we work with government for is how do they progress through this cloud maturity model. And, you know, the forklift and shift is actually a problem when you're talking about releases. But when you're talking about containerization, you're talking about Agile Methodologies and things like that, it's less of a reliance on the version because you now have the ability to respawn that same application, migrate the data, and have everything live as you progress through different cloud platforms. And so, as OpenStack matures, this whole idea of the fast forward idea of getting to the next release, because now they have an integration step, or they have a path to the next version even though you're two or three versions behind, because let's face it, most operators will not go to the latest and greatest, because there's a lot of issues you're going to face there. I mean, not that the software is bad, it's just that early adopters will come with early adopter problems. And, you know, you need that userbase. You need those forum conversations to be able to be safe and secure about, you know, whether or not you can handle those kinds of things. And there's no need for our particular users' user space to have those latest and greatest things unless there is an actual request. >> Roland, you are an IAS provider. How are you handling containers, or requests for containers from your customers? >> Yes, containers is a big topic. There's a lot of maturity happening right now with government, in terms of what a container is, for example, what is orchestration with containers, how does my Legacy application forklift and shift to a container? And so, we're handling it in stages, right, because we're working with government in their maturity. We don't do container services on the platform, but what we do is we open-source a lot of code that allows people to deploy, let's say a terraform file, that creates a Docker Host, you know, and we give them examples. A good segue into what we've just launched last week was our Vault Academy, which we are now training 3,000 government public servants on new cloud technologies. We're not talking about how does an OS work, we're talking about infrastructures, code, we're talking about Kubernetes. We're talking about all these cool, fun things, all the way up to function as a service, right? And those kinds of capabilities is what's going to propel government in Australia moving forward in the future. >> You hit on one of my hot buttons here. So functions as a service, do you have serverless deployed in your environment, or is it an education at this point? >> It's an education at this point. Right now we have customers who would like to have that available as a native service in our cloud, but what we do is we concentrate on the controls and the infrastructure as a service platform first and foremost, just to make sure that it's secure and compliant. Everyone has the ability to deploy functions as a service on their platform, or on their accounts, or on their tenancies, and have that available to them through a different set of APIs. >> Great. There's a whole bunch of open-source versions out there. Is that what they're doing? Do you any preference toward the OpenWhisk, or FN, or you know, Fission, all the different versions that are out there? >> I guess, you know, you can sort of like, you know, pick your racehorse in that regard. Because it's still early days, and I think open to us is pretty much what I've been looking at recently, and it's just a discovery stage at this point. There are more mature customers who are coming in, some partners who are championing different technologies, so the great is that we can make sure our platform is secure and they can build on top of it. >> So you brought up security again, one of the areas I wanted to poke at a little bit is your network. So, it being an IS provider, networking's critical, what are you doing from a networking standpoint is micro-segmentation part of your environment? >> Definitely. So natively to build in our cloud, the functions that we build in our cloud are all around security, obviously. Micro-segmentation's a big part of that, training people in terms of how micro-segmentation works from a forklift and shift perspective. And the network connectivity we have with the government is also a part of this whole model, right? And so, we use technologies like Mellanox, 400G fabric. We're BGP internally, so we're routing through the host, or routing to the host, and we have this... Well so in Australia there's this, there's service from the Department of Finance, they create this idea of an icon network. And what it is, is an actually direct media fiber from the department directly to us. And that means, directly to the edge of our cloud and pipes right through into their tenancy. So essentially what happens is, this is true, true hybrid cloud. I'm not talking about going through gateways and stuff, I'm talking about I speed up an instance in the Vault cloud, and I can ping it from my desktop in my agency. Low latency, submillisecond direct fiber link, up to 100g. >> Do you have certain programmability you're doing in your network? I know lots of service providers, they want to play and get in there, they're using, you know, new operating models. >> Yes, I mean, we're using the... I draw a blank. There's a lot of technologies we're using for network, and the Cumulus Networking OS is what we're using. That allows us to bring it in to our automation team, and actually use more of a DevOps tool to sort of create the deployment from a code perspective instead of having a lot of engineers hardcoding things right on the actual production systems. Which allows us to gate a lot of the changes, which is part of the security posture as well. So, we were doing a lot of network offloading on the ConnectX-5 cards in the data center, we're using cumulus networks for bridging, we're working with Neutron to make sure that we have Neutron routers and making sure that that's secure and it's code reviewed. And, you know, there's a lot of moving parts there as well, and I think from a security standpoint and from a network functionality standpoint, we've come to a happy place in terms of providing the fastest network possible, and also the most secure and safe network as possible. >> Roland, you're working directly with the Upstream OpenStack projects, and it sounds like some others as well. You're not working with a vendor who's packaging it for you or supporting it. So that's a lot of responsibility on you and your team, I'm kind of curious how you work with the OpenStack community, and how you've seen the OpenStack community develop over the years. >> Yeah, so I mean we have a lot of talented people in our company who actually OpenStack as a passion, right? This is what they do, this is what they love. They've come from different companies who worked in OpenStack and have contributed a lot actually, to the community. And actually that segues into how we operate inside culturally in our company. Because if we do work with Upstream code, and it doesn't have anything to do with the security compliance of the Australian Signals Directorate in general, we'd like to Upstream that as much as possible and contribute back the code where it seems fit. Obviously, there's vendor mixes and things we have internally, and that's with the Mellanox and Cumulus stuff, but anything else beyond that is usually contributed up. Our team's actually very supportive of each other, we have network specialists, we have storage specialists. And it's a culture of learning, so there's a lot of synchronizations, a lot of synergies inside the company. And I think that's part to do with the people who make up Vault Systems, and that whole camaraderie is actually propagated through our technology as well. >> One of the big themes of the show this year has been broadening out of what's happening. We talked a little bit about containers already, Edge Computing is a big topic here. Either Edge, or some other areas, what are you looking for next from this ecosystem, or new areas that Vault is looking at poking at? >> Well, I mean, a lot of the exciting things for me personally, I guess, I can't talk to Vault in general, but, 'cause there's a lot of engineers who have their own opinions of what they like to see, but with the Queens release with the VGPUs, something I'd like, that all's great, a long-term release cycle with the OpenStack foundation would be great, or the OpenStack platform would be great. And that's just to keep in step with the next releases to make sure that we have the continuity, even though we're missing one release, there's a jump point. >> Can you actually put a point on that, what that means for you. We talked to Mark Collier a little bit about it this morning but what you're looking and why that's important. >> Well, it comes down to user acceptance, right? So, I mean, let's say you have a new feature or a new project that's integrated through OpenStack. And, you know, some people find out that there's these new functions that are available. There's a lot of testing behind-the-scenes that has to happen before that can be vetted and exposed as part of our infrastructure as a service platform. And so, by the time that you get to the point where you have all the checks and balances, and marrying that next to the Australian controls that we have it's one year, two years, or you know, however it might be. And you know by that time we're at the night of the release and so, you know, you do all that work, you want to make sure that you're not doing that work and refactoring it for the next release when you're ready to go live. And so, having that long-term release is actually what I'm really keen about. Having that point of, that jump point to the latest and greatest. >> Well Roland, I think that's a great point. You know, it used to be we were on the 18 month cycle, OpenStack was more like a six month cycle, so I absolutely understand why this is important that I don't want to be tied to a release when I want to get a new function. >> John: That's right. >> Roland Cabana, thank you the insight into Vault Systems and congrats on all the progress you have made. So for John Troyer, I'm Stu Miniman. Back here with lots more coverage from the OpenStack Summit 2018 in Vancouver, thanks for watching theCUBE. (upbeat music)

Live from San Juan, Puerto Rico. It's the Cube. Covering Blockchain Unbound. Brought to you by, Blockchain Industries. >> Welcome back everyone. This is the Cube's exclusive coverage in Puerto Rico. I'm John Furrier, the co-host of the Cube, co-founder of SiliconANGLE Media. In Puerto Rico for Blockchain Unbound, this is a global conference. Going to the next level in industry migration up and growth, and blockchain, decentralized internet and obviously cryptocurrency, changing the world up and down the stack. I have an industry veteran here. My next guest Salim is founding CEO, Singularity University and author of the best-selling book, Exponential Organizations. He's seen many waves, friend, known him for years. Haven't seen you in a while, you look great. You haven't changed. >> (laughs) The hair has changed a lot. >> (laughs) I've still got mine. Hey great to see you. Bumping into you in Puerto Rico is really compelling because you have a nose for the future, and I've always respected that about you. You have the ability to understand at the root level what's going on but also pull back and see the big picture. Puerto Rico is the center of all the action because the killer wrap in this is money. So money is driving a lot of change, but there's some fundamental infrastructure, stack upgrades going on. Blockchain has been highly discussed, crypto is highly hyped, ICO's are-- Scammers out there but now some legits. What's your take? What's your view right now on the current situation? >> Well I think what's happening with a place like Puerto Rico is. When you get kind of wiped out of the old, you have the chance to leap-frog. When you think about any of our traditional environments, laying down Blockchain technologies, et cetera. It's really, really hard because you have to get the Supreme Court, the Constitution to approve blockchain based land titles, and then you build a stack there from a legal perspective. Here they can basically start from scratch and do it completely from the ground up. Which is what's exciting for everybody here. >> The top story that we've been reporting here is that Puerto Rico is rebooting. The hurricane obviously, I won't say a forcing function, but in general when you get wiped out, that is certainly an opportunity to rebuild. If there's any kind of silver lining in that. >> There's a long history of that. Japan got wiped out during World War II, so did Germany and they rebounded incredibly. We've seen that recently with Rwanda. We do a lot of work in Medillin, in Colombia, and that's just been one of the worst cities in the world, is now the most innovative city in the world. So this is the transition that we've seen a pattern for. >> One of the things I'm really excited about decentralization and blockchain is all the conversations have the same pattern. Efficiency is getting wired into things. So if you see slack in the system or inefficiencies, entrepreneurs are feeling the void. The entrepreneurial eye of the tiger goes that to that opportunity to reset, reduce steps, save time and make things easier. Classic value proposition in these new markets. You run a great university but also author of Exponential Organizations. A lot of people are scared, they're like, "Whoa, hold on. Slow down, this is bullshit, "we're not going to prove it." And then the other half saying, "No this is the future." So you have two competing forces colliding. You have the new guard saying, "We got to do this, this is the future." Old guard saying, "Blocks, Road blocks, blockers" You covered this in your book in a way, so how do you win, who wins? How do you create a win win? >> You can create a win win. What you have to do is leap-frog to the newest, fast as possible. The only question is, how can you get to the new? And the problem that you have is, as you rightly pointed out is. When you try disruptive innovation in any large organization or institution, the immune system attacks. I saw this at Yahoo running Brickhouse. Yahoo is supposedly a super advanced organization, and yet the minute you try to do something really radical, you spend all your time fighting the mother ship. So I've been focusing a lot of time the last few years focused on that particular problem, and we're pretty excited, we believe we've cracked it. >> How does someone crack that code? If I'm Puerto Rico, obviously the government officials are here at Blockchain Unbound. This is not just a tech conference. It's like a tech conference, investor conference, kind of world economic form rolled into one. >> Sure >> There's some serious players here. What's your advice to them? >> So what we do, and let me describe what we do in the private sector and what we do in the public sector. A couple of years ago, the global CI of Procter & Gamble came to me and said, "Hey, we'd like to work with you." And what we typically see is, some executive from a big company will come to Singularity. They'll go back headquarters with their hair on fire going, "Oh my god!" If they're from BMW for example. They go back going, "Drones, autonomous cars, hyperloop, VR." Back in Munich, they'll be given a white coat and some medicine and be put in a corner. "You're too crazy, now stand over there." And that's the tension that you are talking about. And then somebody else will come six months later then they'll do the Silicon Valley tour, then they'll have one of our people go over there, and it takes about three years for the big company to get up to speed, just the C-Suite to get up to speed. Forget transmitting that down. So I was talking to Linda Clement-Holmes and I said, "Look we're about to start this three year dance "I've been thinking about this, "let's shrink it to 10 weeks." So we designed what we now call an ExO Sprint. Which is how you get a leadership, culture and management thinking of a legacy organization, three years ahead in a 10 week process. And the way we do it is, we're in an opening workshop, that's really shock and awe. Freaks out all the incumbent management. And then young leaders and future lieutenants of the business do the thinking of what should come next. And they report back. Some thing about that opening workshop suppresses the immune system, and when the new ideas arrive they don't attack them in the same way. >> It's like a transplant if you will. >> It's like when you do a kidney transplant. You suppress the immune system, right? It's that same idea. So we've now run that like a dozen times. We just finished TD Ameritrade, HP, Visa, Black & Decker, et cetera. We're open-sourcing it. We're writing a manual on how to do it so that anybody can self-provision that process and run it. Because, every one of the Global 5000 has to go through that process with or without us. So then we said, "Okay, could we apply it to the public sector?" Where the existing policy is the immune system. You try and update transportation and you're fighting the taxis. Or education and you're fighting the teacher's unions. We have a 16 week process that we run in cities. We do it through a non-profit called the Fastrack Institute based out of Miami. We've run it four times in Medillin, in Colombia and we just finished four months with the mayor of Miami on the future of transportation. We're talking to the officials here about running a similar process here in Puerto Rico. >> Are they serious about that? Because they throw money at projects, it kind of sits on the vine, dies on the vine. Because there is an accelerated movement right now. I mean, exponential change is here. I'll give you an example. We're seeing and reporting that this digital nation trend is on fire. Suddenly everyone wants digital cities, IoT is out there. But now what cryptocurrency, the money being the killer app. It's flowing everywhere, out of Colombia, out of everywhere. Every country is moving money around with crypto it's easier, faster. So everyone is trying to be the crypto, ICO city. Saw it on Telegram today, France wants to be, Paris wants to be the ICO city. Puerto Rico, Bahrain, Armenia, Estonia. U.K. just signed a deal with Coinbase. What the hell is going on? How do you rationalize this and what do you see as a future of state here? >> Well I think, couple of thoughts. And you're hitting into some of the things I've been thinking about a lot recently. Number one is, that when you have a regulatory blockage, it's a huge economic developing opportunity for anybody that can leap-frog it. Nevada authorized autonomous cars early and now a lot of testing is done there. So the cities that have appreciated-- >> So you're saying regulatory is an opportunity to have a competitive advantage? >> Huge, because look at Zug in Switzerland. Nobody had ever heard of the place. You pass through there on the way to Zermatt. But now it's like a destination that everybody needs to get to because they were earlier. This is the traditional advantage of places like Hong Kong or Dubai or whatever. They're open and they're hungry. So we're going to see a lot of that going on. I think there's a bigger trend though, which is that we're seeing more and more action happen at the city level and very, very little happen at the national or global level. The world is moving too fast today for a big country to keep up. It's all going to happen this next century at the city level. >> Or smaller countries. >> Or small countries. >> So what's going on here at Blockchain Unbound for you? Why are you here? What are you doing? What's your story? >> I have this kind of sprint that we run in the private sector and in the public sector and then a community of about 200 consultants. And I have to pay 200 people in 40 countries and it's and unholy mess. Withholding taxes and concerns around money transfer costs-- >> It's a hassle. >> It's a nightmare. And so I've been thinking about an internal cryptocurrency just to pay our network. All of a sudden now, three or four countries have said, "Hey we want to buy that thing, "to have access to your network." So I've got all this demand over here, and I need to figure out how to design this thing properly. So I've been working with some of the folks like Brock and DNA and others to help think through it. But what I'm really excited about here is that, there's a-- You know what I love is the spectrum of dress. You got the radical, Burning Man, hippie guy, all the way to a three-piece suit. And that diversity is very, very rich and really, real creativity comes from it. This feels like the web in '96, '95. It's just starting, people know there's something really magical. They don't quite know what to do. >> Well what I'm impressed about is that there's no real bad vibe from either sets of groups. There's definitely some posturing, I've noticed some things. Obviously I'm wearing a jacket, so those guys aren't giving me hugs like they're giving Brock a hug. I get that, but the thing is, the coexistence is impressive. I'm not seeing any real mud-slinging, again I didn't like how Brock got handled with John Oliver. I thought that was unacceptable because he's done a lot of good work. I don't know him personally, I've never met him, but I like what he's doing, I like his message. His keynote here, at d10e, was awesome. Really the right messaging, I thought. That's something that I want to get behind and I think everyone should. But he just got trashed. Outside of that, welcoming culture. And they're like, "Hey if you don't like it, "just go somewhere else." They're not giving people a lot of shit for what they do. It's really accepting on all sides. >> Here's my take on the whole decentralization thing. We run the world today on a series of very top down hierarchical structures. The corporation, the military industrial complex, Judeo-Christian religions, et cetera. That are very hierarchical-- Designed for managing scarcity, right? We're moving the world very, very quickly to abundance. We now have an abundance of information, we'll soon have an abundance of energy, we'll soon have an abundance of money, et cetera. And when you do these new structures, you need very decentralized structures. Burning Man, the maker movement, the open-source movement, et cetera. It's a very nurturing, participatory, female type of archetype and we're moving very quickly to that. What we're seeing in the world today is the tension going from A to B. >> And also when you have that next level, you usually have entrepreneurs and sponsorships. People who sponsor entrepreneurs the promotion side of it, PR and that starts the industry. Then when it hits that level it's like, "Wow it's going to the next level." Then it gets capital markets to come in. Then you have new stake holders coming in now with government officials. This thing is just rocket-shipping big time. >> Yes >> And so, that's going to change the dynamics. Your thoughts and reaction to that dynamic. >> Completely, for example... When we do these public sprints we end up usually with a decentralized architecture that needs to built. For example, we're working with the justice system in Colombia. And the Supreme Court has asked us to come in and re-do the entire justice system. Now you think about all the court filings and court dates, and briefs, and papers all should be digitized and put on a blockchain type structure because it's all public filing. We have an opportunity to completely re-do that stack and then make that available to the rest of the world. I think that trend is irreversible for anything that previously had centered-- I mean, most government services are yes, ratifying this and ratifying that. They all disappear. >> Well Salim, I want to tap your brain for a second. Since you're here, get it out there, I want to throw a problem at you, quick real time riff with you. So one of the things that I've been thinking about is obviously look at what cloud computing did, no one saw Amazon web services early, except some of the insiders like us. Who saw it's easy to host and build a data center. "I have no money, I'm a start-up or whatever." You use AWS, EC2 and S3... They were misunderstood, now it's clear what they're doing. But that generated the DevOps movement. So question for you is, I want to riff with you on is, "Okay that created programmable infrastructure, "the notion of server-less now going mainstream." Meaning, I don't have to talk about the server, I need resource so I can just make software, make it happen. That's flipped around the old model, where it used to be the network would dictate to the applications what they could do. How is that DevOps ethos, certainly it's driven by open-source, get applied to this cryptocurrency? Because now you have blockchain, cryptocurrency, ICO is kind of an application if you will, capital market. How does that model get flipped? Is there a DevOps model, a blockchain ops model, where the decentralized apps are programming the blockchain? Because the plumbing is the moving chain right now. You got, Hashgraph's got traction, then you got Etherium, Lightning's just got 2.5 million dollars. I mean, anyone who's technical knows it's a moving train in the plumbing. But the business logic is pretty well-defined. I'm like, "I want to innovate this process. "I'm going to eliminate the efficiency." So this dynamic. Does the business model drive infrastructure? Does the plumbing drive the business model? Your thoughts on this new dynamic and how that plays out. >> I suspect you and in violent agreement here. It's always going to be lead by the business model because you need something to act as the power of pull to pull the thing along, right? The real reason for the success of Etherium right now is all the ICOs and it was a money driven thing. Today we're going to see these new stacks, now we're on version three of these new types of stacks coming along, and I think they're all looking for a business model. Once we find some new killer ops for this decentralized structure, then you'll see things happen. But the business model is where it's at. >> So basically I agree with you. I think we're on the same page here. But then advice would be to the entrepreneurs, don't fret about the infrastructure, just nail your business model because the switching cost might not be as high as you think. Where in the old days, when we grew up, you made a bad technical assess and you're out of business. So it's kind of flipped around. >> Yeah, just hearing about this term, atomic swaps. Where you can just, essentially once you have a tokenized structure, you can just move it to something else pretty quickly. Therefore, all the effort should be on that. I think finding the really compelling use cases for this world is going to be fascinating to see. >> So software-defined money, software-defined business, software defined society is coming. >> Yes >> Okay, software defined, that's the world Salim thanks for coming on, sharing your awesome expert opinon. Congratulations on your awesome book. How many countries is your book, Exponential Organizations-- >> It's now about a quarter of a million copies in 15 languages. >> Required reading in all MBA programs, and the C-Suite. Congratulations, it's like the TANEx Engineering that Mark Dandriso put out. A whole new paradigm of management is happening. Digital transformation. >> We now have the ability to scale an organization structure as fast as we can scale technology. >> Blockchain you know, the nature of the firm was all about having people in one spot. So centralized, you can manage stuff. Now with blockchain you have a decentralized organization. That's your new book, the Decentralized Organization. >> Although, I'm not sure I have another book in me. >> There's a book out there for somebody, Decentralized Organizations. Salim, thank you for joining us. The Cube here, I'm John Furrier the co-host. Day two coverage of Blockchain Unbound more coverage after this short break. (electronic music)