Okay. Thank you. Thank you. So, there will be one fact and one opinion in this talk. And the fact is, sorry, I will just let the people in. All right, guys, this already started. Oh, people on the back. This in principle already started. There is nothing on the screen, but that's just a detail. Okay. Thank you. So, this is going to be a little bit different with respect to other talks, mostly because I'm not a member of this community or only marginally interested in the aspect of bacteria physiology and metabolism and ecology. But nevertheless, the purpose of this talk is to provide one fact and one opinion. And the fact is that there exists a coherent, well-developed mathematical and algorithmic framework to address decision problems in complex and dynamically changing environments. Okay. So, this is the fact. And I will not ask myself with the purpose of giving you all the details of this mathematical construction, but just to provide you a sort of map of the landscape of the kind of questions that people ask and find solutions for in different contexts. And the opinion is that this framework to address decision problems in dynamic environments, my opinion is that this could be a very useful language and toolbox to address several questions in microbial physiology and microbial ecology.  So, let's start with something that we would call the theory of decision processes. So, there will be very little mathematics, like I said, but a few ingredients are necessary, but they are very simple and easy to understand. The main concepts that make up a decision process are three. One is called the environment. The other one is called the agent. And what separates the environment from the agent is an interface. And this interface is where all the exchanges between agent and environment take place. And these exchanges take typically two forms. One form is a sensor exchange. So, there is information flowing from the environment to the agent. And there is a second exchange that is a flow of action in which the agent executes actions that have an impact on the environment, might have an impact. So, the environment is characterized by a quantity, a vector, an unspecified object, which characterizes the state of the environment. It might be a very huge dimensional object so far. Okay. Another key thing that I need to tell is that everything takes place in time. So, in the most general case, we are interested in this process doing something over time. So, the counterpart of the state of the environment, which is something external to the agent, is an internal state of the agent, which we labeled by the letter M. The choice of M is not in coincidental because we also mean it to be a memory. It's everything that is inside the agent and plays the role of a memory. I will outline the formal setting very briefly and then sort of we try to understand what kind of things might mean in a biological context. And like I said, the exchanges between the interface and between the environment and the agent occur through two objects. Okay. So, this is called the state. This is called the memory. This will be called observations and this will be called actions. So, given a certain state, an observation can be made. Okay. So, this is the process which we could call sensing and given a memory and an observation, an action is taken. So, this is probably what we would call the decision or the control. As a result of the decision made, the world can go on to a new state as prime at the next instant of time. So, this might be time t and this might happen at time t plus one. At the same time, the observation is encapsulated in the new internal state. Okay. So, this information is processed somehow and together with the action, these are collected and manipulated. There is a computation process taking place here that gives a new internal state of the system and so on and so forth. So, this is a very abstract and high level description which makes sense because this kind of abstraction comes from the merging of different ideas coming from animal behavior, from human behavior, from psychology, from neuroscience, from operations theory that is the theory that tries to give a mathematical description of how factories work or how you do logistics but also from control theory and engineering. Okay. So, it's a sort of way of encompassing all these kinds of approaches into single mathematical language. But, for instance, just to give you an idea, there are two examples that might be sort of, might be resonating with your interest. One is if we think the agent of a cell as a cell, then the environment might be the chemical environment outside the cell. The process by which the environment is sensed might happen through receptors. And these receptors might interact with the internal machinery of the cell, kinases, which respond to the binding of the ligand with the receptor by changing the internal state, changing the phosphorylation level of several downstream effectors. The actions that are taken, the decisions that the cell make, can also affect both the environment and the internal state. And so, also, these are kept, just reason to keep track of them. The reverse situation is, for instance, when you try to manipulate a chemostat or a turbidostat or a bioreactor. In this case, the environment now becomes the cell culture. And the experimenter or the engineer is the agent, which observes certain concentrations or certain levels of optical density. And according to this, might decide to manipulate, keeps records of the observations, changing things in order to, for instance, over time, optimize the yield or... Okay? So, this is the crucial point. You have to ask yourself, can I frame my problem as a decision problem? Yes or no? If it's no, you can go and take a coffee now. If it's yes, to some extent. Then the second exercise is, how do I connect these abstract concepts with the things I'm interested in? And once you succeed, everything else is downhill, more or less. Okay? In the sense that there is, like I said, the framework, which I will try to give you just a glimpse of, that can address questions like, how do I extract information from these things? What are the constraints? What are the things that I can do and cannot do? And more importantly, if I couple this problem with the notion of some objective, how do I optimize my decision-making in view of these objectives? And what I mean, I do optimize is, how do I find optimal solutions and what are the techniques and what can I do in several different conditions? Okay? So, this is the outline. Mathematically, and this will be really the last few questions that I'm going to write, there are some objects that describe these arrows. So, one is a transition probability from the previous state to the new state, given the action. Okay? So, all this framework is cast in terms of naturally stochastic processes. Okay? So, noise is already compassed in the formulation. Second object is how observations are made. Okay? So, what is the probability that I make a certain observation? Why? In my space of observations, which I need to have identified a priori. And how does it depends probabilistically from the state of the external environment. So, if this is a receptor and this is the outside concentration, this y might be the binding state of my receptors, which could be 0, 1, or if it's an allostyric receptors, 0 and whatever. And then, there is another object which is the probability of taking a certain action, a, given the pair m and y. Okay? Which you can think of. I'm making a decision based on the current percept, what I see now, a snapshot of the current station of the environment, and some memory that I collected in the past. Another, just a second, I finish the sentence, and then another example, for instance, in bacteria chemotaxis, you make decisions based on the current concentration and some integral memory of what you have seen in the past, which is expressed in terms of methylation level of the receptor.  Okay? Question? No, yes, that's exactly one of the key points, where to place the interface. Okay? So, year y doesn't depend on memory. It's something which arrives there. Everything else is post-processing, if you wish. So, if you desensitize your receptor, that's something that is the effect of memory, but you do it downstream. Okay? So, this part is really placed at the outer level of your information processing. You might make this thing as complex as you want in principle. Of course, then this will impact the way that you are able to do stuff properly. But there's no limitation in this. This object could be a very long vector, which contains all the occupancy levels of all receptors, if you wish. And then how do I encode how many there are? This is your choice about how long this observation vector is. But I can change the state of the cell to have only one receptor, zero receptors, 100 receptors. Okay, fine. In that case, you would have to modify this slightly in the sense that you would have to find a way to back-react on the kind of observations you make. That's your regulation of the number of receptors. That's the point. They don't know how to separate the Y from the M. Yeah, you could tweak the arrows here in a way to include this as well. But for simplicity, I'm sort of drawing the simplest diagram that you can handle, but you could tweak it appropriately. Okay. So, the last thing is this process here by which your new memory is generated possibly stochastically from the previous memory and the new observation that has come into the game. Okay. So, in general, there is a distinction between this part, which is typically assumed to be a property of the external environment. So, it's not something that the agent has sort of control over. Okay. Whereas this part is the part where the agent is susceptible of performing or to be optimization. Okay. So, there is that there is a way to change parametrically these probability distributions. How do I decide things and how do I process the information that I receive in a way to optimize a certain goal? Okay. So, here, this is the first important fork because there are two different approaches now. One is I would call modeling. So, modeling is the agnostic viewpoint in which you say, okay, I don't know what my agent is actually interested in doing. I'm only interested in collecting data about my system. Now, I've identified what all these things mean. I can collect data and I construct by modeling these probability distributions starting from observations. Okay. So, this is the agnostic viewpoint, which was very well expressed by our previous speakers who said I have no idea whatsoever what cells want to optimize. That's it. On the other opposite end of the spectrum, there is the point of view of optimization, which of course is everything but agnostic is a strong a priori bias. It's principled. It says that I have an opinion about what the system is trying to achieve and if I assume that in a sort of top-down approach, and I will show you briefly how to do it mathematically in this case. If I assume that, then this provides me with a principle and then I can turn all the cranks, start all the machinery and come up with predictions of what would be the optimal behavior of the system and then compare it to what I see. Okay. Here, you have to take a stand. What I will discuss in the following is this part. But of course, this first part is also interesting because you could ask yourself questions about, given this framework, how do I extract information about these things in this language through inference, through data analysis, etc. But I will not discuss in this question. Yeah. So when it comes to the agnostic approach, even then you are making some demands of the system, right? You are expecting the system to follow certain rules, for example. And so don't you make a priori assumptions? Yes, of course you do. I mean, so how do you differentiate between the assumptions that you make in an optimization scheme and so far, just I'm seeing a certain behavior and I made assumptions about how to describe this behavior in terms of these objects. And these are strong assumptions and say my memory is made of the phosphoration state. My observations are the binding states. My state is the concentration. Okay, these are clearly strong and eliminating assumptions. But then I'm not making any assumption about whether the system is optimizing this behavior towards any objective. This is an additional step that I have to take. Is there some way to mathematically, let's say, reverse engineer the optimization problem? Answer in 22 minutes. Okay. So if we focus on the optimization principle, we have to add another piece to this picture. That is, what is the objective function that we want to optimize? And the largest part, but not all of the of this decision process theory, it focuses on maximizing an object which is based on another, yet another quantity, which is called a reward. Okay, so at every step of this process, there is another scalar quantity called the reward, which quantifies how good was your decision process in this step. Okay, so it's something that occurs on a very short time horizon from one time step to the next. And the goal of the optimization process is to maximize the expected cumulative reward. So if you are at any given instant of time, given your decisions, there will be a trajectory in this abstract space of external and internal states, and you will be collecting a sum of rewards. Maybe the reward will come only at the very end of your process, many steps ahead. So the fact that you can encode for long-term rewards makes the problem extremely challenging, because short-term rewards, you will behave greedily. But long-term rewards require planning, require some sort of ability to forecast what will happen in the future as a consequence of the actions that we take now. And it's also expected because everything is stochastic, so you have to sort of nail this object down to a real variable, and you take expectation values. In the end, I will also comment on how to go beyond this. Okay, yep. In principle, I should expect also to minimize the variance. This is something I will address in the end. This is a so-called risk-neutral approach, in which you care only about expectation values. I will comment in the end if I ever write there about how do you deal with variance and uncertainty. Very good. So a few facts about this scheme. So like you mentioned, the way that these objects are constructed, the choice of your state space, the choice of your memory space, how these objects can be parameterized or written represents or represents constraints. So all of this is part of the more general framework of constrained optimization. And of course, constraints will affect the kind of answers that you have. Second remark. You can distinguish policies, decisions in reactive policies depend only on the current observation, and memory-based policies are the more general ones. Reactive policies are in general suboptimal. They achieve much less than what memory-based policy would do, which is sort of a general explanation of why you should have some sort of computation and processing inside the agent. But there's one important exception is that when, if the observations are the states themselves, so if you see in full the external environment, this is called perfect observability, you can have an optimal strategy which depends only on your percepts. Reactive strategies can be optimal. So you can really ignore this. Why is that? Because if you observe the state, then the top part of this diagram is a Markov process per se. So you don't need to keep memory if, since your state is already predicting everything about the future. But this is a very special case. In this particular case, your optimal policy is also pure. In the sense that in every state, there is one specific optimal action that solves the problem, a single one. Whereas on the contrary, if the observation is only partial, the general solution of this problem depends on the memory, and now it is encoded for information. And also, typically, you can have random strategies and be optimal. So this is a very important general message that whenever the observation of the external environment is partial and you cannot base your strategy on the full state of the environment on the Markov state, then there can emerge random strategies that are optimal. Question? I was thinking about the part of maximizing the expected cumulative reward. So if you're accumulating actually the reward a long time, so you need to keep a track of when you started doing that. There are different classes of problems. There are problems in which there is an explicit time dependence, in which, of course, what you will do will depend exactly on the time at which you start your process, that would be a non-autonomous system. But there are situations in which the system is stationary, statistically stationary, in which the decision policy will not depend on the instant of time. Both of these can be addressed by these techniques with different Okay. Other remarks that I'm done with this comment so far? Yes. Okay. So how does one find the optimal strategies? Once this problem is defined, there is a very well-defined mathematical question. Find what is the best way of acting in this setting? And you could do this by keeping the memory fixed. You can do it by including the memory as part of your optimization process. But nevertheless, there is a clear, defined question of optimizing over some set of parameters that describe these position probabilities in order to achieve the best outcome in terms of cumulative rewards. And when you do this, there's actually a further classification to make, which I maybe I'll try to keep it here, if I manage. So you can divide very different situations in terms of two different components to your problem, which are roughly expressing your level of epistemic knowledge, a priority, if you wish, and your level of observability. So what is the epistemic knowledge? Is how much my agent knows about these things? So does the agent have a accurate model of what happens outside? Or it does not? This model of the external environment in higher organisms might be something that comes from evolution, might be hardwired in the genetics, or might be something that has been learned during development and then put the test into a specific task. Okay. The point is how much of this priority knowledge is available to the agent in the decision process. And this clearly sets a very big difference in how you try to solve this problem and the techniques that you use. When you know a lot about this, this would be the direction in which you say you are working on a model-based setting, because the way you try to optimize this problem, we rely on your knowledge of this. How the impact of how accurate the model is, is a problem in itself, but let's discuss the situation in which the agent has a perfect knowledge of how the world works. Exactly. And this would put us on the far right of this diagram. Otherwise, on this other side, that is what is called the model-free situation. In which at this extreme, you have no idea whatsoever of how the world works. And of course, in order to optimize in this context, you will not be able to rely on a priority knowledge, which you will have to replace with empirical knowledge. Okay. So on this side, it will mostly be a problem of computation. Given this input, the problem of finding optimal policies is the problem of planning. These are the inputs. The outputs are the optimal policies. It's just a problem of computation. On this other end, it's more of a problem of learning. Data come in, and from starting only from data, from experience, you will have to find the best behavior. And typically, this is done by algorithms that reflect the common idea of trial and error.  And in doing so, you construct projections and estimates of what would be your future. If the outcomes are satisfactory or optimistic or pessimistic, you change accordingly. And these algorithms, which are based on this principle, provide a way of optimizing without knowing how the model, how the world works, just by interacting with it. And of course, there's a whole lot of stuff that happens in between when you have partial knowledge about some aspects of the model. So maybe you know how your receptor works, but you don't know how the world changes. How do you deal with these hybrid approaches by a mix of techniques that come from the two extremes? Along these other axes is the notion of observability. At the top, you are in this situation where you truly see the states. This is the perfect observability setting. You work directly with the perfect knowledge of the environment. There is no filter through a sensory system that throws away information. Or if it filters, it does so in a way that it keeps all the relevant information. And here at the very bottom is the situation where the system is totally unobservable. You just move around in the dark. Maybe you can keep track of your own actions, in which case you still can do non-trivial stuff. An example from biology is what is called depth reckoning or inertial navigation. Even without knowing where it is in space, an insect can know how far it was from the initial location as a vector, just by integrating velocities or accelerations along its trajectory. So you don't observe anything outside. You don't see where you are in a map. You just see your own actions. And then by integrating in time, you can know where you are from your starting point. If you don't have access either to observations, contextual observations or actions, then you're totally in the dark and there's little you can do. And that's the bottom of it all. Okay. So there are basically three extreme cases on which everything is built upon in this game. One is the situation in which you are the top right of this diagram. And this is where MDP Markov decision processes live. In this upper corner of the diagram, you have a way to compute the optimal policy given the fact that you know how these two things are. And in particular, since you are perfectly observable, you know that your observations are exactly the states. So in this upper corner, the key tool is an object which is called the Bellman's equation, optimality equation, which per se is a nonlinear vector equation, which you can solve by a variety of techniques. But probably most interestingly for you, there's a way of rephrasing this Bellman's equation through linear programming. So this top right case in which you have perfect observability and perfect knowledge of your model, you can solve by linear programming. And this will give you the optimal actions in every context you find yourself. So what happens if you move down along this axis? So you still know the model, you still know this, but now the observations are partial as it almost always is. Then you enter the realm of what are called the partially observable Markov decision processes. Here you can still solve the optimality by techniques which are more complex. The Bellman's equation in this case becomes, which was a nonlinear vector equation, it becomes a nonlinear functional equation, much harder to solve, but still solvable. The key trick is that in order to solve these PMDPs, you can use Bayesian inference to understand how the memory collects information about the observation. So if you hear, you say that your memory is the belief, which is the probability distribution over states. So rather than knowing where the state is, you have a memory which keeps a probability distribution over what could be your hidden state. Then in this case, PMDPs are just controlled hidden Markov models. So there's a hidden Markov model which is taking place here. This state change, they are hidden, but there are observations coming from these states, and you want to control this problem. In particular, for instance, if you make assumptions about the statistic of these, if these things are all Gaussians, then this boils down to very old stuff in engineering about control of Gaussian processes, so it's a sub case. So in general, if these are your MDPs, for instance, stochastic optimal control is a subclass of this, and optimal control theory is a subclass of this. So for those of you who have some ideas about control theory, for instance, point-tracking principle for solving optimal dynamical system is just a special case of the Bellman equations, which comes from this wider concept of MDPs. So it's in this sense that this kind of framework tries to gather several different approaches into a common language. And in the end, this is the, or the real things happen in which you have limited knowledge about the laws that govern your world and limited observability. So this is what is called the full reinforcement learning problem. And here, of course, the necessity becomes to express all these objects in a way that is sufficiently rich to encode for the structure of your problem and sufficiently flexible to be prone to optimization. So I have zero time to go into this of this, but just to give you an idea, these are what the kind of algorithms that people use to train artificial intelligence to play video games or table games or the game will go. So these objects combine ideas from these different things together in a way that I'm not able to express. But the main message here is that there is a way to systematically overcome the limitations that come from partial observability and partial epistemic knowledge by using data to find the optimization. But what I want to do in the last five minutes that I have, or so, 10 minutes, okay, is to, first of all, to check if I didn't miss anything here, but it doesn't seem the case, to list things that go beyond what I've just described to you. And that are, most of them are already very solid. Some of them are still under development, but I think they bear even more interest to problems in bacterial physiology and ecology to this case. So let me use, probably, again, this part of the slide. So item number one, risk sensitive, okay, so what I forgot to say is that this ensemble of techniques that generalize the decision processes to the situation in which you don't have partial observation and partial knowledge goes under the name of reinforcement learning, mostly for historical reasons. And all the things I will discuss are extension of this basic framework of reinforcement learning. So risk sensitive reinforcement learning is what tries to address the question that this might not be necessarily the object you're interested in, the expected cumulative reward. For the reason that, like it was said before, you're not caring about variances. So if you're confronted, for instance, with two options, okay, in which you get, I don't know, say, one dollar, 10% of the times and $100, sorry, the other way around, $0.90% of the time and $1.10% of the time, and you have confronted with the option in which you're given 10 cents every time.  So where does this ingredient comes from? Is that because in one case you have some variance, some dispersion about your outcomes, and the other case you have not. And how do you account for that? You account for that by extending this setting, and when I mean extending, I mean that this part will still part of a subclass of this to the concept of risk sensitive reinforcement learning. For instance, what you set up to optimize is, for instance, you might want to maximize, this is one of the possible mathematical translation of this idea. Maximize an object which is just 1 over a alpha of the logarithm of the expected value of the exponential of alpha, and these are the sum of rewards. Okay, so why is this a measure of risk sensitivity? Because if your alpha is positive, you will be very much interested in situations where you get a lot of rewards, and you don't care much when you get punished a lot. So alpha larger than zero will be favoring a risk seeking behavior, but alpha less than zero will be a risk averse. And if you take the limit of alpha 10 into zero, you get exactly the risk neutral case. Now this is interesting in several respects. One respect formal is that this bears a similarity to notions like free energy in physics. But the second and probably most important thing is that this object is the exponential of something which is accumulated over time, so it bears a clear formal resemblance to notions like growth rate. You don't see how it could be because this includes notion about the probability distribution. Yes, but these are random quantities, right? They vary depending on your policy. Yes, but I could still say I call my new reward e to the alpha sum of the rewards. But this object will become a product of things over time, whereas this before it was a sum. There is a multiplicative nature to this object that is not present in the purely. So why is that not allowed? No, I'm saying it is allowed. It's a different problem. It has different solutions. If you use this parameter to tune your problem of decision making in which you add the varying outcomes or stable outcomes with this, you would come to different conclusions depending on alpha and you would favor risk averse or risk sensitive. All else being unchanged. Distributional rewards is always the same. Your objective is changing, but you cannot re-parameterize one problem to the other. These solutions are radically different later. Okay, so second extension, adversarial. So in this description, you're trying to get the best out of a given environment. So this is fixed. Now suppose you want to formulate the same problem against a class of environments. And these environments might be trying to rig the game in the sense that they might choose in which state to be depending on the actions you make. And you want to sort of insulate yourself with respect to this variability of the environment, which could be a strategic variability. So another way to say this is you want to ensure certain robustness of your response because you want to be optimal against the class of environments. And this is what technically is called adversarial reinforcement learning. And there are algorithms for this, which integrate and expand the kind of algorithms that are used in the classical single environment reinforcement learning. The note that I want to make with respect to adversarial reinforcement learning is that there is a class of these algorithms of adversarial reinforcement learning algorithms that formally map into the replicator dynamics. So it seems at least superficially that when you deal with an unknown environment and you have to sort of choose a strategy that does its best against this class of environments in a subclass of problems, you find that this can be mapped formally into dynamics which we know makes sense from evolutionary slash ecology standpoint, which is sort of tantalizing remark. Other extensions. Multi-agent reinforcement learning. So this is against an extension of this, because this was sort of a game against an environment. This is a game against other agents, against or with other agents. Might be cooperative, non-cooperative. So in this extension, there are several agents and each agent has its own reward function, which depends on the environment and what the other agents do. So in this sense, multi-agent reinforcement learning is an extension to game theory. So that you can provably show that there is a subclass of these multi-agent problems, which gives you the setting of game theory, matrix game theory, state-dependent game theory, repeated games in case you incorporate for memory, etc. Question? Yeah. So maybe I missed this, but what I understood is that you go from state S to S prime because of the action A. Might possibly depend, but the environment could change independently of your actions. It's a possibility. This also includes for the possibility that you are making changes to the environment, but it's perfectly okay if you consider a subclass of problems in which it goes by itself or it's even stationary. All these are subclasses of them. And specifically, when you use techniques from the single agent and you let the agents play one against each other, this is exactly the kind of techniques of self-play that are being used in the applications that I was mentioning before. Another item, multi-objective. So since the reward is a scalar function, here you have a single objective, but in many situations you would like to know how the system behaves with respect to vectorial objectives, which of course, in general, do not allow you to find an optimal solution unless you're able to sort of order these vectors in some way. Otherwise, if you cannot order them, then you have all the world of multi-objective optimization with Pareto volunteers and etc. And all of these has been explained to reinforcement learning as well. One common technique is the one that is the most obvious, that is to scalarize your vector of rewards. You combine them together with coefficients and out of a vector you make a scalar. And all the coefficients that appear in this scalar are expressing the trade-offs between the various things that you want to optimize. So you can, once you scalarize a multi-objective problem, you can use it with the same techniques here. And then you can study how it behaves in the function of the trade-offs parameters. Okay, so these also might have applications. The key point is that you can do, depending on your system, you could do this analytically or you could do it in a data-based way, which is sort of the upshot of the reinforcement learning part of the decision-making process. And then last but not least, there is a thing which is called inverse reinforcement learning, which precisely asks the question that you would expect. If I observe a certain behavior, can I infer from my observations what was the reward, what was the objective? So this is a very ambitious question, as you can imagine. It's a sort of something that we naturally ask when we see a behavior, okay, but what was the purpose of that? This is an ill-posed question, in the sense that for any given behavior, optimal or not optimal, but even if it's optimal, there might be different reward structures that give the same result. Okay, there are several examples that you can think of, but it's clear that there is an infinite possibility of choosing the rewards, which sort of collapse onto the same kind of optimal behavior. So how do you get out of this ill-posedness? Well, you make a recourse to concepts like the Occam razor. So when you ask yourself, okay, but what is the simplest possible structure of the reward that would explain this behavior? So if you combine this idea with the maximum entropy approach, then you have effective algorithms that are able to disentangle the rewards. So typically what you do, you start with a vector of possible rewards. Okay, so my system cares about growth rate, but it also cares about cost, metabolic cost, but it also cares about time to completion, but it also cares, and I compile a list of them. Then I scalarize them, just the way I do in multi-objective optimization, and then I run my maximum entropy inverse reinforcement learning approach to sort out what would be the best explanation of all this trade-off coefficients. So starting from data, there is a way, a systematic way of answering to this kind of questions using this. And with that, I think I'm done. Happy to take questions. There is time for a couple of fast questions. Thanks. This was very nice getting a refresher on control again. I was wondering, is it feasible to think of sort of a cell in these different states that it can have as sort of an MPC on reinforcement, that the objectives, depending on what I have and my memory, actually updates the objectives continuously over time? Okay, yeah. So this is yet another chapter, which pertains to another thing which is called broadly speaking reward shaping. So in several applications, it's been shown that you can improve a lot learning and performance in these algorithms by adding additional terms to the reward, which might favor, for instance, exploratory behavior or curiosity-driven behavior. It comes under different names. So yes, then short answer is yes. It's an art, rather than a science, to sort of introduce the kind of reward shaping that favor your process of learning and optimization without destroying the structure of the problem. I was just wondering what was the opinion part that you mentioned in the beginning? That you should care about it. Okay. Then you make your own. All right. If there are no more questions, let's thank Antoni again. All right, good. So we are one hour late.


Well, we're starting a bit of a pullback. It's kind of real tough to see how deep the water is on this, although you could almost make it a black hole. Some of the numbers I have say that we could hit 3,800 by Friday on the S&P cash or down in that area. I'm not thinking that's a high priority. I think probably 1 in 10. 3,900, probably a much better number for a target. And why we might get that, we might have a little bit of a bounce coming in tomorrow. We're starting to see at least some of those trend numbers that I like to look at coming down into the lower oversold levels. But especially during options, expiration week, it's not uncommon to see the Wednesday, if it's down to see the Thursday up a little bit and then see the Friday back down. There's a lot of congeal, what is it, conniving going on as options expire and they want to push them around a little bit. Of course the other thing would be to check are we getting kind of the volume that we're talking about or looking about. And at least so far today, let me find it here, it is fairly light, about 6.3 billion shares. So the first thing we have to do is test yesterday's low in the spies or in the S&P, whichever one you want to look at. That's just a hair lower. What is that like 9, excuse me, 394. And I'll have to think about it 394 and a half, I think somewhere around in there. So we're only about a buck above that on the spies. And you know, maybe a 10th percent lower in the S&P 500 to test out. So if we don't break this we don't break this kind of 395 area on the spies figure that we're probably headed down one more tick. Tonight we'll be talking on the show about Cisco and of course target and low earnings today. The reality is that navigating financial markets can be risky. Markets can be chaotic and difficult to understand.


Live from Las Vegas, it's theCUBE. Covering Discover 2016, Las Vegas. Brought to you by Hewlett Packard Enterprise. Now, here's your host, Dave Vellante. Welcome back to Discover 2016 everybody, HPE's big customer event. This is theCUBE, we go out to the events, we extract the signal from the noise. Kyle Green is back, he's the director of Enterprise Storage Solutions at Hewlett Packard Enterprise and he's joined by Jim Thomas, the director of IT operations at Pella Windows. Gentlemen, welcome to theCUBE. Kyle, welcome back. Good to see you. Thank you. So Pella Windows, tell us, what's the company all about? All right, Pella is actually a privately owned window door company, headquartered in Pella, Iowa. One of the leading manufacturers of windows and doors in North America. Strong, strong heritage, we just celebrated 90 years in business, making an incredibly good product. Great people at the company, small town environment, so just a great company to work with. I think I have a Pella door. Well, I think we can sign you up for more. But I don't have Pella windows and I'm sorry about that, I'm not happy with my windows. Well, we can take care of that. Yeah, well, I'm not sure I can take care of that, but that was a big project. Okay, so your role as director of IT ops, maybe describe that a little bit. Sure, I have responsibility for IT operations, infrastructure and information security. So we have a unique situation. I think Pella will define strategic relationships differently than what you would historically. So the way we go to market and the way we attack IT is through strategic partners, HP being one of them. We are pretty much all in as long as it can meet our core fundamentals, we would rather commit to a partner in a relationship. And we get back to that relationship and folks, that's why we do this. And in reality is we get so much more out of it. But as we talk through this, we have several things over the years where we've been actively engaged with HP or another partner as well. This is an interesting topic because there's an age old discussion within the IT world is do you just sort of beat your supplier down for the best price or do you try to partner and get more value? Kyle, I mean, you must have this discussion with customers all the time. And of course, we live in this world where prices are always dropping. Everybody's doing trying to do more with less so you can understand that. But how does this conversation go? Jim's a great example of how we'd like it to go. And that is, we get to customers regardless. You may not, Jim's not the largest customer we have. But Jim is strategic to us because he uses many of the things we do strategically and he counts on us to help him with his business. He's like many customers though. He can't resist or turn down an opportunity to explore a cost effective solution from other vendors. But he weighs that against the relationship which is what we'd love people to do as they imagine. Jim sits on many of our advisory councils and again, plays an important role in our strategy and what we're trying to do to solve other customers like Jim, big or small. And he plays a very important role there. But it's human nature, right? We all want a deal, right? Sure. You want the best deal, the best discount. So how do you get through that and how do you quantify the value to your business of a partnership where you may not always have the lowest price but there's other value? The good news about Pella is being around 90 years, we're not worried about next quarter. We're doing things to the family that's engaged in the business, they weren't around for another 90 years. So how do you do that? You make long-term investments. So my investments with my partners, although they'll view it the other way round, they pay back greater than the better price I'll get. My team is extremely good at delivering and executing. So when we sign up with a promotion, we have delivered. And so the reality is over time, I get far more back than we put in. And whether it is through professional services, relationships, when I'm in a bind, I can call folks in the organization that I have deeper contacts that could help me out. So it's astounding how much we get back and it's so hard for me sometimes to understand why people don't choose to invest that. I could go buy the cheapest and fight for the cheapest price every time but the value is the long-term approach. Talk some more about that.  Is that an example and other others that you can share? It's not just that, it's also an engagement at another level. I mean, whether if it happens to be storage and we're talking about three par, we can talk about what are we trying to solve as a business? I rate somewhere down here on the dollars I spend as the top customers at HP. But I rate up here as am I willing to talk about where we've had success. So we'll talk about the issues we're seeing, we'll talk about the challenges in our business and we'll talk about how that matters and where we go forward. So it's really about, I'll say, paying it forward. So sort of you're in a situation where you're creating more value than you're extracting. Absolutely. And you're getting the benefit of that. So we'll talk about what's driving your business, what's going on in the window? Well the good news is the housing market's back. There was a five year period there that if you can ever want to watch new housing starts go from roughly 2 million down to 300,000, not the most comfortable time. Now we have a replacement, we have commercials, so we stayed strong and being privately held, it is a strong company on core foundation. So really there. But the housing market's coming back, we're strong in the commercial space, continuing to grow that area, looking at things like home automation. So shades in your home, you start talking about automation around doors, locked, unlocked, windows, open, closed. So a lot of things that are really happening there. Our goal is we've historically been an innovator in our industry when it continued to be there. So we're continuing to find ways to deliver both to our traditional products and how we enhance those forward. So is there smartness coming into the home, data coming into your business in a big way? It will be. The challenge is trying to work through the landscape in the maturing markets of home automation. You have security companies, home automation services, how do you fit and meld within the ecosystems, but still provide the unique value of your product in those solutions. You seem to be very in tune with your business, maybe more so than most, you know, smaller, relatively smaller IT shops. Is that the cultural thing? Is that the nature of the privately held company? Part of it's I'm old. You've been around for a while. Okay, I've been with the company 18 years and have enjoyed every minute, but the reality is I have an opportunity to engage throughout the business. I have an opportunity to engage with my partners. And so numerous teams I set in across the business has really enabled me to have a lot of insight. It's just been a great ride. Yeah, so you're looking to the future, your company's looking to the future. I mean, today I presume pretty small is the percentage of homes that are actually instrumented, but when do you guys sit back and look at the sort of your strategic plans? Do you see that as a real tailwind for you going forward? We think it's going to grow slowly. I mean, home automation is it's, you've got that do it for me group and the do it yourself group. And they're just kind of picking up at different paces. New construction, do it for me, put it in when it's in place. For us, it's learning how to behave. We're a traditional manufacturer. Now we're talking about consumer electronics when you're talking some of this. So it's a different game. So we're being very cautious because our brand is critical to us. We've built a reputation around our brand and we want to make sure that we protect, guard and deliver on that brand promise. So there's a lot of going into it. Kyle, from HPE's perspective, how did the relationship with Pella Window evolve? I mean, it's interesting. It's a relatively small company, Jim was saying. Well, we're down here on the spend, but we're up here on the service levels that we're getting and the value that we're getting. How's that all work? Well, I think it goes back to the things Meg talks about. It's this idea economy today, right? It started traditional IT's purchase, right? There was a need for storage, right? There's a need for servers and we courted Jim like many other vendors would. But as that relationship grew, now you're back to what's next. And Jim has ideas, but he's got a strong brand and we feel like that's something HPE can bring to the table is looking out for your brand or helping you protect against that unpredictability, if you will, in the market. But you're leading that charge as well and dragging us with you and that's where the synergy comes into play. It's not about the servers or the storage anymore. Although those are things he buys and uses in the business. It's about how to protect his brand, how to help him grow the business as to what's next. And we don't understand his business, he does. So collaboratively, we really get a lot out of the partnership there. So what does the plumbing look like? Take us inside, paint a picture of what's underneath. Well, actually, the core, the heart of what we're running is HPE's Superdome X. We actually, although my team refuses to call that, we still call it by the code named Dragonhawk. We had shirts done. We actually had them on last year. Nice Hawaiian shirts. So very unique. We had one of the first 100 units that came out of production actually said in our data centers, we tested and evaluated it. It runs the core of our ERP database. And our product, you would think windows and doors are not incredibly complex, but when you consider the fact that you can order any window on any quarter size increment, different glass options, different colors, different, we estimate somewhere in a combination of eight octillion. So incredibly, we have to generate a bill of material for every window ordered. We don't pull from bill of materials, we create one. So the demand on the processing, the storage. So we've got the HPE Superdome X. We're now back ending it. Currently, I'm back ending it with actually a competitor's flash storage. But we are now looking very aggressively at three-par. We have a three-par in our test dev environment. Four years ago, it was the right decision for us. We needed the speed, we needed the performance. We just couldn't get what we needed four years ago. That landscape has radically changed. I think the HP storage has come along. Their leading performance is there. The software has matured significantly. They're obviously leading in that space. We've got one unit in our test dev environment. We're looking at replacing our production environment with three-par sometime in the next six months. You're talking about an all-flash system or a head-flash. We are all-flash today. And actually, I was one of the folks that would really talk about all-flash, at least for my critical databases, four years ago. That was very unique four years ago. It was. Well, I'll tell you, we were right there. Whether we predicted back in 2009, that by 2014, the high spin-speed disks would be dead. Now, we were a little early, but back then, that was pretty bold. People tip that we were crazy. Sounds like you were crazy, too. Oh, I'm still crazy. We're good there. Okay, so what else is going on in your infrastructure? So Oracle Database, is that what you're running? We run Oracle's ERP Suite on HP hardware. I think our challenge is continuing to go forward is how do we consolidate and pull? I've got what will eventually face us around consumer data. What do we do? How do we handle that? Where are we going to store it? If we store it, obviously folks are very concerned about their privacy and making sure that we handle that. We've got things around shop floor automation as we continue to get smarter factories that will generate far more of their video. We're looking at solutions right now that would, how do we facilitate our in-field service? So how do we make things in the field easier if a consumer is calling us with a service issue? How do we solve it quicker? Better information. If we have to make a call, we have to be there with the right parts, the right time with them. So how do we get that? So we're looking at how do we even leverage video from that capability? So rapidly changing a lot of fronts. Interesting, a lot going on. And you run security as well? Are you the CISO or the factory of the CISO? Yeah, we have smaller titles than that, so yes. How big is the IT organization? How many people? We have a couple hundred IT folks. Okay, so it's substantial and it's not a tiny shop, but you're in charge of security. Do you, what's the conversation with the board about security or the owners? You know, we've had a very good, our board is extremely strong. It's about a risk management platform though. It's about understanding your risk portfolio. So when we look at who we are, we do know that I don't have a ton of credit card information. So we're on the lowest level of PCI compliance because most of our transactions are not handled and we handle that very clearly. We are privately held, so I don't have a lot of government assistance or SEC assistance, those types of things, and I have very little healthcare, okay? And not surprisingly, not a huge amount of trade secrets, but we still have the responsibility that the business has to run. So we take our risk base and we've actually followed the SANS model and we're really attacking it at the right level of investment for what we're doing. So we're picking and choosing partners. We've leveraged a couple of other partners. We work closely with Verizon on making sure that we're doing the right things. We're talking a little bit with the HP security team, but really trying to make the right investments for our risk portfolio. Right, I love that answer. I mean, because a guy in your position has got to talk to the board or at least communicate what you did. Sounds like you got a pretty clear perspective on that. Last question, what are you going to take away from HPE Discover 2016? What are you going to bring back to Pella? You know, it was a great trip out here. For me, it was fun, but the reality is, it's relationships. With what we do, I have folks that are far smarter than I am that work for me, and that's a really good thing. But for me, it's about building the relationships. So I had not had a strong relationships over the last couple of years with the storage team that I wanted to. Had some great opportunities, spent some time with some of the storage team. I really understand where they're going, what they're doing, the product groups, the other area, but also the rest of the organization. So it's just continuing to build our relationship. It's important for me to stay relevant in HP's eyes, and the only way we do that is, we tell them what we need, we tell them what we're doing, and we deliver on what we tell them we're going to do. All right, Kyle, we'll give you the last word. Put the bumper sticker on Discover 2016. Discover 2016's been terrific, and I think it's echoed the market adoption that we're getting. So not only from our customers like Jim, but the market share data that continues to pour out where we're taking market share from our competitors. And it just tells us that we're on the right track. It validates the enthusiasm and our challenges to get that word out and get in front of more and more people. And Jim fortunately plays a big role in that with us. Even though he may not be the biggest one in our, you know, the portfolio, he's extremely valuable in helping us shape our message and get out to folks. All right, Jim and Kyle, thanks very much for coming to the queue. Appreciate it. Thank you. All right, that's a wrap here. Keep it right here, we'll be back after this short break, go to CrowdChat.net slash HPE Discover. You'll see essentially a transcript in the CrowdChat of all the CUBE interviews. Go to siliconangle.tv, siliconangle.com, wikibon.com for all the research. Keep it right there. This is theCUBE, we're live from Las Vegas. We'll be right back.


We have a duo in the studio they go by the name ReInvention so we just want to know what they are reinventing, what they having stuff for us today and we just get to know them. Yuu wussh! Yuu wussh! Rha bo ra bo rad! Rha bo ra. Rha bo ra ... 2a 2a wa 2a wa 2a. Yuu wussh! Mina ito badaman, Yesmina na samanga wussh, ReInvention. Waagwan, waagwan. Okay. Yes, nandimetokai group inetwa re-invention. It's a movement. Mm-hmm. Most definitely. Dota Mefika Apa, Y254. Yeah. Thank you for hosting us. You're welcome. Definitely. Mm-hmm. Bayam bayam bayam bayam. Tipsy Apa. Red red wine. Your bills killer. Letter bills to the finance. Niko nazo mingi. Letter, letter, letter, letter zote. I think anyways. Zote. Yes, zote. So, to run after shows. After after, we'll run by EKBC. Allah Allah Allah. I think sayi, nina huna dreams languza kunda dubai, they are coming through. Yeah, yeah. I can see them. I have an interest in a trip to Dubai. Thanks to Tipsy. Yeah. Tipsy, am I re-invention? Re-invention. Njota or you? See Tipsy, re-invention. Re-invention. Okay, fine. Actually, see we are leaving the dream. You know what I'm saying? Ataka kunja studio kuku proposiya. Yeah. So, huna huna city kuku studio. Mm-hmm. Living yatu kubeba rings. Muse muse muse. Muse muse. O muse, o muse. No, no, no. No ring. No ring. No ring. No ring. No ring. No ring. Oh my God. Oh my God. Nisa kanda mahiwa. Allah. Allah. Yeah. Anyway, so, am siji tohanzi kwa Tipsy. So, kindly tell us a bit about re-invention. How the re-invention came about. And what are we re-inventing here? Yeah, so re-invention movement. I mean, Bada man na mese, man. It's movement, eh? Yeah. So, see it me come. See no other as day when I tried to get up and go home again? I mean, get up and again? artist Put his key. Yeah. Me zikietu early FinejudGet angel But when you put your key Vas MRH. batili wa swana gigi bamba yeah yeah that's the way so bad man tell me about how he came to be mil kwa mabaeshi ii ma bro how did we usana samanga atiwa tuna fana ya actually ii ma ii saw your first photo ii thought your brother mil kwa mga ya batiki tuenia hii kwa tuligro pamoja we have seen each other grow from a very young age Baka mahali penye tu mefika Mziki tu liyanda kufanya pamoja This year, actually last year do tu lukotinongia This year tu kawani haji Wacha tu rodi studio to release kitu But most definitely re-invention Two meter ka a long way back And that's why we have the chemistry between us Most definitely It's like a relationship Kama penye tu lukotinagusu It's a little over It's a little over It's a little over You don't feel the love I feel it That's what I'm saying Juna jia nistori interesting We just see this chemistry Batman is talking about On one of the songs that goes by the name DJ Nona ka uta left But nisa Yes, and there you go That was re-invention We are trying to mix old school classes And you know this new school vibe Kus me enye by the way In that song nimeskia Quite a lot of that kind of old school music I used to like Now before we listen to the song There was a story tu lukotinambiawa But tu miambiawa re-invention In a relationship There is kuna mwenya kuna Tu patiwizi dynamics But it's true that is how Ado is supposed to be What is the story Batman I mean Singing Mziki is all about a relationship So a relationship Yatuni a relationship A new school and old school Sisi kama wasani Tu me kachini tu kasa mani haji Lasima tu transform i mziki ya kenya I move to the next level Kuna kitu mi nibamba sana na kenya muna du Muna support So that is what re-invention is all about Yes That is the movement Tipsi Being together And then kuna time likuwa pamoja And then you went separate ways And then you came back together Of course kukuwa pamoja Kufanya kaziwa tuwa wili kuna disagreements Kilam doko na opinioni ya ke Kilam doko na opinioni ya ke iskizue Menahando lage kukuwa kama group As a duo And pushing away all this Small issues that come with Working together I think Sini mbogi moja auta aika Krosa tiwato na pigana sijiwa Na butana sijiwi ni ni Sini wa say tu hi kachini Tu ki agrit na fanya kituflani Na jo of course kungana izo Mi na takahi Mi na takahi lakini Tu me kama long way that Kisiki ya badaman kuna kitu anafili Yondo anafili Kuna kitu ikon na niyangu sana Tu na henda naio Mi hambun Levo na tu mihazakuak So understand that Time pass But let's talk about Your newest release Ayaya badman Tell us a bit about that And what was your thought process while Working on Ayaya Ayaya was our second jam Tu konangoma zingine mingi sana ko studio Tuna kungana team of guys Wanyu wana kachini wana samani aze This is the next song to release So the second song we wanted to do Tu lukon na takahi kwen naio feel good vibe So Ayaya ni feel good vibe Mi kama DJ but now a notch higher Tu lukon na sama kwa tu patiawa say something better Da ni nyutu lukotumua patiaw before Oki skia Ayaya una skia ni Ni kali Oki skia Ayaya Yuki tu ni kali sana Most definitely Ume se mam konangoma mingi sana Kwa studio zingine zia fanywa What's the plan with them Kuna album, kuna EP, na kuja Any collaborations kuna mutumna wakna M, get started with Ok kuna EP, kuna album Collaborates na come along the way With some of your best Artist in Kenya And stuff like that But most of them Ni solo project Your invention Ni kise maio Nizapele kwa Ni kuru di home Kwa matia waze Nukuna waze Waki skia di me jari buku Na uno moja kwa pa Kwa pa evinda Hai manida Niki jari bu Niki jari bu Na smile tu na heo Nia hezi ni smile Una smile Kwa gini beefika manida Nuna po nia je Bo spalo ni chawmane Laki ni haitu jachawmane Kwa gini tawam bini social media Ma liwa tawana Niz expect the next project Nia yeminatoa And when are we expecting something new Nia un tachi abadama Mbindo mbindo am social media Mbindo ma na mbindo mbindo Mbindo mbindo mbindo Mbindo mbindo mbindo Iqa nii naka taji maji nano jwa Sui pointi mii wei Mki nii zati plate pa le social media Facebook, IG, twitter Tik top, mii nii zati plate kama reinvention KI Reinvention KI Nwa tu plate pa le wa nwa tu follow Tu ne engage pale 1 to 1 tu tu na kung wangana giveaways mingiki kama This week tu kona challenge ya aya dance Tinii tu, ay aya dance challenge Kiki dance po ho ayo nga maitu umpeya Naka patiya, Maini nga pa le Nasisi wei nga tu nga left fits So we need to do a TikTok and we need to cut the left foot to make it liftoff We need to do the lip syncing We need to do lip syncing You can do something dramatic in the end You in a phone We create a daily platform We follow the platform so we can do a live one So we can do a live one We have the music channel We need to do a live one We need to do a live one We have the music channel Muzaikiru naza pata pala kwa YouTube channel Easy Concepts Media Definitely Abo kwen dapali wuki subscribe Rudi atena ya Easy Concepts Easy Concepts Media Yes Una subscribe pala, una finya kwa leka baton kwa leka Nye toki release kiki wiki kwa leka bed kwa re Kwa lafusa toki release muziki bi Una kwa wakanda kwen banda Most definitely Okay Patting Shorts has you wind up? Patting Shorts Nuta wakute in kwa lase Ota me kwa nasisi kwa ijani Kina Yuri, kina Big Sam Kenyash Kush Yon bo giyo tanya Iko behind the reinvention Na shukurusana Na piya familiaritu Na familiaria Na badaman Na shukurusana Utu ala utu kam pamoja Utu fanyaiki itu Upi nimi me to support Financiali Kila kitu Emotionali Iza wutuzote Utu kwa padyu enyu So big up to yourself Na producer weitu Wikipondis Alexis on the beat WB Wikipondis Nya wudu na shukurusana Na atambua Maikt na atambua Sana Vanessa Magale Nya wudu na atambua Big up yourself Okay Iza taka patting shorteng Kwanza di kutanku ma DJ Wonten Yokkusiaza mziki ya kenya So most definitely Watu kama DJ na shukurusana Na shukurusana Paisyo enyu Na support local talent And this is the way to go back Mziki ya kenya Lazi me move to the next level And most definitely Na takak shukuru Management, easy concepts media Wana dojo po asana As you can see As you can see That's all for me So thank you so much for coming through guys All the best in your music Thank you Mziki kutupatia Those good vibes Mziki hache And right about now Wacha to skize It is even better than DJ In my opinion So


Hi everybody, Sean with jaspyscasebrakes.com doing a 2020 clearly authentic one box break random teams each 14 spots in this we're gonna double up the list 28 spots for the teams we removed the pirates and also combo the royals and the marlins significant to this break we also are gonna randomize the original 14 names on the list and we're gonna be giving away two tops dynamic dual boxes so we'll be doing that in the same break right here is the original 14 names on the list from Anthony down to Charles or John with last spot mojo gonna double up that list real quick and here is the list of teams from the diamond backs down to the nationals with the combo team of the Kansas City Royals in Miami marlins and no Pittsburgh pirates we're gonna use the same dice roll for both randomizers here we're going 11 times 1 2 3 4 5 6 7 8 9 10 and 11th and final time Jacob down to Matt McCray and 11 times on the teams 1 2 3 4 5 6 7 8 9 10 and 11th and final time Mets down to the Tigers so we've got Jacob Artman with the Mets Jacob Artman with the A's Matt McCray with the Reds Matt McCray with the Dodgers Anthony Venturino with the Rangers John Samuelson with the Giants Matt McCray with the Rays Anthony with the Braves Matt with the Cardinals Matt with the Yankees Darren Redding with the Twins John with the nationals Matt with the Indians Darren with the Red Sox Greg with the Royals and Marlins combo Matt with the Diamondbacks Leonard with the Mariners Matt with the Brewers Leonard with the Phillies Leonard with the Rockies Matt with the Angels Leonard with the Astros Kyle with the Padres Anthony with the Cubs Greg with the White Sox Kyle with the Orioles Anthony with the Blue Jays and Matt with the Tigers and we're gonna sort this by teams quick trade window by the time this gets printed if anybody's here 20 people still in the chat one box break so here's the list one box break clearly authentic no talk about trades at all so we're good to rip we have Mauricio Dubon for the San Francisco Giants clearly authentic rookie auto and San Francisco Giants is John Samuelson last spot mojo congrats now randomize the original list of 14 names for the two box giveaway and we'll have a new dice roll on that so back to random.org original list from Anthony down to John and new dice roll seven times on the dice roll good luck top two names is going are going to get the top two or the two boxes for the tops on demand dynamic duels autos and we'll randomize the boxes with a actual dice roll once we get there so doing it seven times one two three four five six and seventh and final time good luck John Samuelson with the first spot and Anthony Venturino with the second spot now back to here are the two boxes I picked so John Samuelson and Anthony Venturino win the two boxes now as far as John Samuelson is going to get the first box and second box is going to be for Anthony Venturino I'm gonna roll the dice one time one two three is left box four five six is the right box whichever box it randoms for the first time is going to be John Samuelson's box so good luck it went all the way to the back and we can see that that is a six so we're going box on the right for John Samuelson this box here is going to be for Anthony Venturino good luck John you know John when when Joe's gonna just mute your comments jokes can still hurt people John jokes can still hurt so Joe said the auto is a second card so we'll put that right here and these are all going to be base cards it looks like there's oh there are two other cards there I didn't notice that and the dual auto for John Samuelson Cecil Fielder and Moe Vaughn that's pretty nice what a classic right there man and it's good Moe Red Sox Moe that is pretty cool congrats John nice first uh dynamic dual auto there and now I was going to go right back in the box with your now Anthony Venturino with the second box see what we can follow that up with and our dual auto for Anthony wow father-son combo red parallel Craig Bijio and Kevin Bijio and that's number nine out of ten that's a cool card don't see that every day very cool congrats Anthony and guys that was a clearly authentic break with two tops dynamic dual autos uh or boxes given away jaspyscasebreaks.com check out the sides and get take a look and see at our future breaks