Our speaker today is Professor Hao-Chu from University of Texas, Austin. She's going to talk about machine learning for power system operations. Before I start introducing her, let me remind you there is a special seminar next Tuesday. Time is 10 o'clock, 10 o'clock, 11 o'clock. So, if you can make it, I'll recommend you to attend. Our last seminar for this quarter is next Thursday, at the same time. The speaker is from the city of Palo Alto, Utah.  Our speaker today is Professor Hao-Chu. She's currently an assistant professor of electrical and computer engineering at the University of Texas at Austin. She received her bachelor's degrees from Tsinghua University, a master's and PhD degrees from the University of Minnesota, all in electrical engineering. She was a postdoctoral research associate and later became an assistant professor at the University of Illinois at the Barnard's Champagne. Her research focuses on developing algorithms for learning and optimization forms in energy systems. Her current research interests include physics-aware and research-aware machine learning for power system corporations and the design energy management systems. She is the recipient of NSF Career Award and she is also the faculty advisor for three best student papers at the North American Power Supposing. She is a member of the IEEE PES Non-Range Planning Committee and associate editor for IEEE Transactions on Smart Grid. Okay, let's welcome our speaker. Thank you, everybody. Thank you, Cheng Wu, for inviting me and also the introduction. So, yeah, I came to Stanford, I think, five years ago when I was in Illinois. And it's great to be back, unfortunately, virtually, but hopefully, yeah, we will get a chance to have an in-person visit sometime. Yeah, so today I'm going to talk more on problems in power systems operations and particularly how to design machine learning tools that are physics-aware and risk-aware. So I'd like to thank my grad students and also National Science Foundation for the funding support. Yeah, maybe I should go fullscreen. Is this working well? Okay. So we have seen that the use of artificial intelligence and machine learning and technology has brought the development of data analytics in the energy world. And it has been identified to tackle several challenging problems in energy systems related to disaster resiliency, like some kind of anomaly detection, and basically the operation of the grid as well. So this is thanks to the proliferation of various type of data that we have today in the energy systems or the electric power grid in particular. But the question still remains that although the AI and machine learning ML tools are very powerful, there are still some gaps between the problem-specific challenges if we want to apply them to a real-time power system operation. So I'd like to show like three problems that we are currently working on now that how to incorporate domain knowledge from power systems to better design the machine learning or the neural network tools basically. So the first one is related to the market prediction and a lot of interest have been devoted to this problem, but then there is an issue of dimensionality or like the complexity issue. As we know that a large-scale power system has thousands, if not more than a many number of nodes. So the idea we're going to look at is how to incorporate the topology information of the grid to simplify the model of neural network. And the second one is related to the coordination at the grid edge or what traditionally we call distribution systems that we have seen more and more distributed solar, distributed storage, and all these collectively called distributed energy resources that need to be coordinated in a synchronous fashion to support operating condition of the connected distribution system. And when we apply machine learning tools to this problem, there is a concern that when the machine learning models apply on the field, they can cause some adversary operating condition and due to the risk of these tools. And then we will see how to try to reduce the risk associated with these decentralized machine learning models. Last but not least, I just going to quickly show some recent results that we have related to using the same ideas of scalable learning or decentralized learning, but for more like emergency operations during like extreme weather events, when there are multiple failures, how can we use machine learning to quickly restore the operation to normal condition. So I will focus on the first two problems and then the last one will be very nicely connected to the first two as a small extension of our current work. So the first one is on real time market operation. The specific optimization problem for market operations called optimal power flow. So, currently it's solved as an optimization problem using numeric sawers. And you can think of that input is the great condition that the power demand, the topology of the system and then also other parameters. And the solution or the output here is what the dispatchable resource set point or some other type of actions that we can make to a controllable actions to the great resources. So there exists a multitude of optimization sawers or OPF sawers that you can use. And the issue, one of the issues that it can get stuck because it's a non convex problem, or there could become a numeric and also computational concerns, but then in the daily operation basically we fit in like multiple instances of input in the real time OPF solution what is the current situation of the grade on the input side. And then it will get us a set of output in order to send to the generators or the resources for dispatch. So the idea comes here is that since we have all these instances of input and output, can we train a neural network model to predict what's going to be the idea set point of generators or set point of some resources based on specific input. So this neural network model can simplify the online real time computation burden of the OPF solution because the feed forward computation is very fast. So this is the basic idea of using your network for OPF. And as I mentioned is a quickly a growing area and there are a lot of works looking at the DC which is the linearized version or like the AC origin and non linear power flow problem. And people have started using it as like the OPF solution. Oh, sorry, the neural network solution as a warm start so that the AC OPF can converge faster. There are also other versions of stochastic OPF and also like directly connected to the duality and analysis. So our objective here is trying to explore the great topologies because in all of these neural networks, they're generally black box model. They don't consider or content any kind of information about the great topology or the great parameter settings. So our idea here is that by exploiting the topology, we can potentially reduce the complexity of these trends your network models significantly. Okay, so I just be brief here on this formulation of OPF. So thinking about the network model as a graph with n number of nodes here. So the AC OPF original non-linear power flow, OPF power flow problem aims to determine the real power P and reactive power Q injections at every node in order to minimize the total cost of supplying that P solution like the real power supply cost. So there are a bunch of conditions we need to satisfy the first one is power flow balance and this is according to kickoff law. And the second one is the voltage operating limits because we want the voltage to stay close to the rated voltage level. The third and fourth the limits based on the type of generation plans we have what the corresponding limits at every node for the PQ injection last but not least, we also need to satisfy the network line flow limits. So the IJ is the amount of power flow on each transmission line between bus I and bus J and their associated thermal limits for each line as well. So in this sense, at every node, there are inputs that we can stack in into this vector Xi, which includes the PQ limits and also some coefficients for this objective function on the generation cost. Usually it's a piecewise linear or quadratic function depending on the type of generation we have. And this is the number of input at every node. So we have N number of this Xi's. So if we want to do the regular OPF, we will try to predict what is the optimal P and I here. So at every node, we also have the output of optimal P and Q, which means that the number of output also scales linearly with the number of nodes N. So if we have N inputs and N outputs, if we train it using a typical fully connected neural network or FCNN, we know that in each layer, we would expect the number of parameters scale with the product of the number of input and output, which essentially is in the order of N squared. And for a larger system, this is a lot of a lot of parameters that we need to train and then we know that the more complicated and neural network is the more easy we can have issues of stacking like a suboptimal solutions and also the training time computational issue. So, so the idea is to say that, okay, can we incorporate the topology knowledge to simplify the neural network. And as we will see very soon, the topology type of embedded neural network work is called graph neural network or GNN. So people have explored to use the GNN architecture to predict P and Q. However, missing gap here is that to for the GNN network to be very powerful. There needs to be some kind of locality property, which means that the predicted value should be very close if they are in the neighborhood region. Okay, so like say this is the network here so the P and Q at node 10 should be very close to the P and Q on node 8. So that's essentially the locality property. This is not a case for the generation output because the generation output depends on its own cost. So if a generator at node 10 is producing at its higher limit, it doesn't mean that we should also try to utilize fully utilize generator at node 8. So there is a large gap of using the GNN to predict P and Q. Here comes our idea. We realize that although PQ does not satisfy this locality property, the locational marginal price, which is output from the OPS problem specifically from the dualized OPS problem satisfy this locality property. And what is LMP? So let's consider a simpler version of the OPS where we only have power balance and also this land flow limits here. And it's only related to the real power input P. So this is a simple linear constraints here and we can introduce multipliers like larger multipliers for this constraint and then the last constraint. The locational marginal price is essentially the linear combination of all these optimal multipliers, which help us to determine what should be the specific PI set point based on this PI at node I. Okay. So this PI vector is related to the multipliers associated with the land limits according to this matrix S transpose and the matrix S is fully dependent on the graph topology. Specifically, it shows the same egging space as the graph Laplacian B here, which in power system we call the B bus admittance bus matrix, which it is a weighted graph Laplacian, but then it is topology dependent quantity. So, because in real time operations, there are very few congested lines, which means that this multiplier mu difference is mostly sparse. It's only non zero at the congested lines and therefore the LMP PI here is depending on this congested lines fully based on the topology or the graph Laplacian. And that is why it satisfy nicely this locality property that we are looking for when we apply graph neural network. So, just to show you some real world price map, this is the the Texas ERCOT LMP real time LMP value. And so it's a control plot. You can see that around this east area, which are usually the load centers, Dallas, Austin, Houston, the price is much higher than like the producer or like more remote area of West Texas. And, and when this price definitely shows this locality property because it's very concentrated based on the geographic area. Similarly, this is the California ISO LMP where you also see this locality when the price, which can go as high as $150 in the in the in the Bay area, I believe here. And well, for the rest of California, the price is much lower around $50 power, I believe. So, basically, the LMP allows us to explore the GNN or the graph neural network model to do the prediction. So, very quickly on what is essentially when we look at the input, which is that every node has this Xi. So I have N of this Xi here, and every layer of GNN considers includes to filter a W here, which is based on the topology. So this W IJ entry is none zero whenever there is a connected line. Okay, and then the other part of the filter is the age. This is like a typical neural network filter. So there's no sparsity there. It allows us to explore the high dimensional mapping very effectively. But then the benefits of using GNN when we have a sparse graph comes with W. And this is our result here in the typical power network, the number of lines actually in the order of the number of buses. So it's usually like two or three times the number of nodes in the system. And because of that, the number of parameters in the GNN layer or the number of parameters, none zero parameters in W scales linearly with the number of nodes N. So in every layer of the graph neural net, we have a linear or the number of parameters is in the linear region with the number of nodes. And this is a big decrease from fully connected neural network with this N scale. Because we utilize the topology and specifically what we are doing here is to utilize the locality property of LMP, we're able to make the GNN very suitable for the prediction in real time OPF. And we, as a result, we also attend this complexity reduction from N square to N. So we can use that for predicting the prices or LMP. So basically, this LMP prediction work has been considered in the past, but then it's mostly based on statistical learning or SVM approach, but then not using like a neural network in the past. So the full like decision rules or the train of variables for the GNN based LMP prediction is to say that, okay, if I have this input matrix from every node, and then I train this neural network, specific by parameter theta here, I can get the prediction of the LMP at every location, which is pie hat here. And using the LMP because it's the dual variable or multipliers that I can determine the optimal primer variables, which is the dispatch real power solution. And I can also form the land flow area well in the system. And then in the basic version, we can consider just trying to match the LMP value between the output value, the actual value and then the predictive value trying to minimize the error of the two. So if we want to introduce some kind of regularization, we can also use this land flow because we can completely determine the land flow if I know the predictive pie value. So I can also introduce this regularization term to reduce the land flow violations and this will be shown to lead to better performance later on. So this is the basic idea of our recent conference paper in linked here. So in addition to that we have considered several extensions. And the idea is to do congested land classification, and the idea is very similar because the contrast line contrasting pattern also has this topology dependency. So it enables us to also use GNN to classify whether a line is congested or not. For classification type of tasks, we have used like cross entropy type of loss. And also, after a few layers of GNN, we also have a final fully connected layer just to try to predict this zero one value for this binary classification task. So here are some results. So we have tested it on like a small system 180 node and also a bigger one 2382 node system. For the small one we consider the non-linear ACOPF and also the big one is the simplified linear DCOPF. So the type of metrics that we're considering here basically the LMP prediction arrow. So the top figure here is the LMP prediction arrow, the normalized L2 arrow. And we also compare the effects when we try to introduce this land flow limit because one very important operating constraint in the OPF problem is the land flow limit satisfaction. So we want to see that whether this accurate predicted LMP value can also try to avoid any possible land flow limit violation. So we consider three solutions to propose GNN for LMP prediction or a genetic fully connected neural network and another simplified fully connected neural network. But they still have the same complexity order as FCNN is called graph informed DNN, but then it is complexity order is still the same as a regular FCNN. So we consider the original version also the regularized version when we regularize the LMP prediction arrow with the land flow limits. So here are the results here on the top for the LMP prediction and the red one here is the proposed GNN and then the green one is the fully connected and the blue one is also a similar fully connected neural network. And we can see that in terms of LMP prediction performance is the proposed GNN is very close to the FCNN or the other variation of FCNN. If we use the feasibility regularization, and this is the circle one here, the arrow actually could decrease very visible if only using the LMP loss function. So definitely having the feasibility regularization is very helpful. So the bottom one here is with the land flow limited violation. So for the small case we didn't see much difference in the land flow limited violation is at a very small level of 1% of violation. But for the bigger case, we do see a very significant difference in the land flow limit violation. Actually using the feasibility regularization, we are able to reduce the land flow limit violation to a very small value. In addition, it seems that the regular, the fully connected neural network has some kind of overfitting issue because of the high number of parameters. And then we do see that the land flow violation is much higher between the fully connected neural network and also the proposed GNN. So, as we mentioned, one of the futures of GNN is in terms of reducing the number of parameters, and this is also verified by comparing the number of parameters existed in each model. You can see a huge difference in terms of it's like the order of magnitude different between fully connected in your network and then the proposed GNN model. And that's why we can better fit the training data with less effects of over parameterization. We have applied that to predicting congested lines and is also for the small system AC power flow and also the bigger system of DC power flow. So we use the recall, which is essentially the true positive rate and also the F1 function, which is the balance between true positive rate and forced positive rate to try to see evaluate the performance between these three neural network models. And by and large performs the other one, the other two, thanks to its reduced complexity as you can see here that it has the highest recall and F1 score here. And interestingly, this we again observe that the effects of over parameterization that for the fully connected neural network, the, or the variation of fully connected neural network. In the larger system is performance is much worse than the sink reduced complexity GNN model. So definitely having less number of parameters to feed can help with the GNN and help with the neural network over parameterization problem. So I will just quickly go over this slide because I think there are some questions, but after here I will stop for question for the question. So here is another attractive future of GNN. So we know that in real time operation, there could be some a lot of variations of the line status, sometimes due to weather events or some kind of unexpected fault events. The line can go out of service and this is what we typically call a line outage. So when the topology changes, a regular black box neural network model needs to be returned whenever there's some topology variation. But because the GM architecture already incorporates topology, it can quickly adapt or what we call like transfer learning paradigm, it can quickly transfer to the new topology. Even if there's some differences in the system connectivity. So we have tested idea we use this originally nominal topology GNN and then we apply it to some a trench topology by selecting randomly like a few lines to disconnect them. And we want to see how this original nominal GNN work with this new topology. Very interestingly, like the fitting arrow is still very close to the original nominal level with a few exceptions that this for certain line combination. It may have changed the system mark the LMP pattern significantly. So there is a large arrow, but then for most of the contingency or topology change cases, this arrow are very similar. And if we go use the nominal GNN to retrain it and for this new topology, it turns out that it's very fast. It only needs like three to five I poke and then all of this new topology cases can quickly converge to a small arrow. So the GMM model is very easy to adapt it to a new topology connectivity condition or adapted to a new operating condition. So this is very interesting to us and we are currently trying to understand better why this is the case. And we suspect this is because even though when there are some small topology perturbation, it does not change the subspace of the underlying graph model very significantly. And that's why we can adapt the original pretrained GMM model for the nominal case to this new topology very quickly. So with that, I think this is for the first part of the talk. If there are some questions I can answer now. Yeah, I just had a question about the the axis you are like a, I believe it was like an error. Yes. Yeah, the normalize L2 error. I was just trying to understand what that means in terms of like presented deviation from the actual values. Yeah, so, so we're comparing the arrow of predicting the pie, the LMP value, and then I just normalize it by the vector, the actual vectors norm. So it's like a 10% like here is like in like 10% of arrow, effectively. I see. Okay, that makes sense. Yeah. Hi, I got a question. I'm not sure whether you can hear me. Yeah, I can hear you. Okay, nice. Yeah, so just a quick clarification. So for this location or marginal price. So we're predicting it so that we can optimize the power flow. Is that how this model is working? Okay. And when we are optimizing the power flow, what are we optimizing for is that distance travel losses. Yeah, great question. So, in for conventional generations is this cost. So it's like the depending on the type of few that we're using to supply this tool to power this generator basically so. But then if there's some kind of demand flexibility. So this is one thing I didn't talk very specifically if they're like the demand response resources. It can also be incorporated into a cost here. So it's like, what is the cost if I want to tell this load slightly. So it's essentially like economic cost. Okay, and does this cost account for let's say we have transmission over greater distance we have greater losses. Does it account for that that cost. In the AC opf. That's a great question. So in the AC opf the losses are embedded into the power flow equation or the constraint here. So, yes, it will account for the potential losses on the lines. Yeah. But I thank you. Yeah, our, our current. Our current power companies using GNNs for LLMP prediction. Great question. No, they don't. So, so far, the focus is still on developing like superior optimization solver to solve it. So it's just based on the instance to solve it. Yeah. And then so how does GNN compare to you talked about SVMs and other ML techniques, how does GNN compare to other techniques and I assume that it means be difficult to get trading data that is appropriate in the GNN format, or the pre processing was taken a long time. Actually, it's not too much different if we want to apply like either GNN or SVM, but then definitely we have observed that the SVM like has the known issue that if we want to go to a nonlinear prediction task and then we have to run into the problem of choosing like kernels. And this kind of issues. So it's not always very easy to tune up like SVM for the LMP prediction. But in terms of pre processing, so we actually use the, the actual pie or the LMP was produced by the optimization solver in the offline setting. So, so it, so, yeah, we don't need to like take additional effort because it will already, it will automatically give us like the LMP at every location. Okay, and then I fully address your question. Yeah, that makes sense. And I guess my question is to follow up, given that GNNs are some sort of a black box, have the power companies that you've worked with expected you guys to provide some sort of interpretability to these models. And if so, what are the next steps in figuring what what is actually going on under the hood. Yeah, yeah, great question. So I think definitely like these kind of analysis when we go to like adaptive the topology adaptivity, like this is something that we would need for practical implementation. So, I guess the link here is that this W matrix or this graph filter W here is based on the topology. So, in some way it explained to us that if there is a high demand in one location, how it could affect what's the like the biggest impact to other locations LMP. So there are some kind of connection that we know like usually when there are like a high power demand in one location, it could cause congestion and accordingly it will increase the cost of supplying electricity in certain locations. So there are some connection because of this topology based graph filter. But yeah, we haven't like fully try to establish that yet. Yeah, that's a great point. Yeah. Yeah, I think I'm having a second part. So, so we have the second part is most on the distribution grace site. So essentially, we want to use the same idea to do coordination or co-optimization of the great edge resources. It's like a distribution system or what we call great edge where you have controllable devices like PV photovoltaic inverters that can supply what we call reactive power to great to improve the operation. So this is a similar type of OPF problem, but then a key challenge here is that we don't have a very frequent communication. So at every node itself, it can quickly measure its own local quantity, but there is no like a very frequent communication between a centralized location with this distributed PV. And this is the major operating challenge in distribution system. So people have thought about using similar ideas of learning for optimization because this is also a special instance of OPF problem. And there are several papers around this area. So one big issue in this problem specifically is that the operating objective is related to the voltage as we were seeing soon. And a lot of problems have not considered that what if the result and solution will violate that voltage limit. So our focus is trying to address this strategic risk related to this voltage limit violation. How can we incorporate certain type of objective into the loss function to better improve the worst case voltage violation performance. So just to quickly go on the problem itself. So, yeah, so you can see that there are high similarities between this problem of the real time market problem, although it is only in a very small geographic area like a neighborhood power grade. And because we are looking into maintaining the voltage everywhere in the system. So more of the interest here is to change the queue, the reactive power provided by this distributed PV such that the voltage is within the limits, upper lower limits. And then the objective here is related can be cast as the losses is in the system as as mentioned earlier. So we can use some kind of linearization tools to deal with the the model here and then if we linearize the powerful model that is actually a very nice convex quadratic program with linear constraints. So the more challenge here is that to solve this problem in a centralized fashion that I have to collect information everywhere from this distributed PV in a very fast fashion, because this PV output can change in seconds time scale. But currently the kind of communication that we have between the end users or the remote PV and then the central controller can only afford like a 15 minutes one one one time every 15 minutes. So it's impossible to solve this problem in a centralized fashion. So people have thought about of using this similar neural network idea. So say that the control center try to train this all collects the operating condition for multiple instances and solve for the optimal solution at every PV individually. And then instead of training a total neural network for the whole system, we train a scalable one that at every node and we are only trying to use the local data. So the local measurement data can be available in a very fast time scale. So we only use the local data trying to predict the local solution. So, of course, there is some sub up on a more sub-operability issue because we are not allowing for full feedback and this is a known issue in distributed control that there are concerns of like optimality per se. But then we can still do it by using like the non-linear transfer transfer capability of neural networks. And similarly, gene architecture can be applied as well. So we just use the same filter everywhere in the node. So, as mentioned earlier, the loss function that we would use nominally is only taking every sample k here in an equal fashion like a mean square arrow. So it's averaged over all the sample k. So, the issue here is that there even though if we look at this is the distribution of the sample losses is typically in this kind of shape with most of the samples concentrated in a smaller region. But then there could be like the entail can be really long and these are like the worst case samples. So if we only take the average across the samples, we're trying to minimize at this level. It does not help us to mitigate this worst case losses. So very famous metric risk, risk measure is called the conditional value at risk of seawall in robust optimization, which is to say that we consider the losses caused by this worst case samples. So, given a significance level alpha, we average the losses across the top alpha number of samples. So, so our idea here is that in addition to fit this MSE arrow, we can also try to incorporate the seawall risk measure with the regularization parameter. And this can help us to also reduce the worst case performance. So we can consider the worst case performance of predicting the queue solution. We can also consider the seawall for the worst case voltage limit and violation. So one thing that we have done is to address the computation issue. So typically seawall is very popularly used because it can preserve convexity of the model. But we know that neural networks is typically non convex. Although there are some recent results trying to generalize seawall's property to this general non convex function that satisfies PL condition. But then we might not be worried about the convexity per se, but then the key challenge is in the computation side. This is because that when we try to compute or train the seawall's regularized objective, one issue we have is that it only depends on the worst case samples. So say like top 10%. If originally I have 1000 samples, I can only have 100 to compute the seawall loss. And in particular, when we use more than machine learning tools like a stochastic gradient or mini batch methods, it can further reduces the number of samples that we can use to compute this gradient. So there is a high concern about the statistical fidelity issue when we try to compute a gradient of seawall when we do gradient descent. So we have a small idea here to just address it from a memory standpoint. And this is through like a selection step. So basically we when we use like a mini batch type of method for learning a seawall and regularized objective. In a typical mini batch, we randomly generate a subset of samples in this mini batch BI. And then in a typical gradient descent, we will just use the gradient based on this mini batch BI. So to tackle the issue with the statistical significance related to the seawall objective, we don't use every mini batch here. We only select the mini batch when if it has this worst case samples. And what is the criterion for that? We set a threshold gamma alpha here. And if the seawall value for this mini batch is higher than the threshold, we will use it to compute a gradient and do one step of gradient descent. If the seawall value for this mini batch is very small, it means that it doesn't contain a lot of worst case samples. So we will disregard this mini batch. And this is a very simple step to implement, but it turns out to be useful to save on the computation time when we try to compute the gradient for the seawall objective. So here are some results for the six nodes. 60 nodes or six PV nodes in a 123 node system. And the decision here is to determine on this reactive power queue. So, as I mentioned earlier, we use this scalable neural network. So each individual neural network at each PV will only use the local information like power output and also the incident land flow to determine what is the optimal queue solution locally. So we compare all the effects of using the seawall in the regularization. So the red one is the standard MSE objective, and the green one is using the seawall and also using our proposed mini batch selection idea. And then the blue one is just the seawall with the regular, does not do any mini batch selection, the regular gradient descent. So this is the arrow, the normalized arrow at every node here. And we can see that when we incorporate the seawall risk related to predicting the queue solution here, it does not change much on this prediction performance. And the reason here is that the prediction is already pretty good for the queue value. So at every node, we can predict the queue value very accurately. So it does not make too much difference if we want to mitigate the worst case prediction performance. However, we do see that we're using the seawall value or the seawall regularization, it accelerates the computation in some sense. So when we use the seawall regularization also introduced this mini batch selection algorithm. It can reduce the computation time by like 20-30%. And especially that this mini batch has also reduced time for each per iPod computation when we have the seawall cost. So you may wonder like what was the point to have this seawall. So the risk is not on predicting the queue solution. As I said, the queue is accurately predicted. So it does not change the queue prediction arrow very much. However, if we consider the seawall for the voltage violation. So remember this queue decisions are supposed to maintain the system voltage to be close to 1.0 per unit or the rated voltage level. So therefore, if we plot out the distribution of the voltage deviation, the normalized voltage deviation from the nominal 1.0. You can see that if I use the original MSE or even the optimal one from the optimal solution, the tail can go over 0.05. And this is the threshold that we can allow for a typical operation. So this without considering the risk of this worst case samples in terms of the voltage performance, we can tend to have very large or the worst case voltage violation would be higher. But if we introduce the regularization for the risk of voltage deviation, it can effectively reduce the worst case voltage deviation here to be and make it to be lower than 0.05. So, if we have some issues with the worst case performance is very effective to use the seawall risk module here. And we can see the similar level of computational improvement here in the right table. Actually, the improvement is even more compared to before that we have seen like almost 40% of reduction of computation time. Yeah, so with that, I think we have seen how the seawall risk can help us address the worst case voltage deviation performance. So we have applied the same ideas to another task, which may be more relevant to both of California and Texas residents as we have seen one more extreme weather events, or what we call like emergency operating conditions. So when there are like natural disasters related to winter storm or unprecedented level of low temperature or wildfires, it can damage the physical infrastructure, the grid infrastructure. So we have also used similar machine learning ideas to enable faster response to utilize this emergency response resources like dispatchable load or change the topology of the grade. And this scalable neural network model can help us to attend this solution in a quick and also safe manner. So this is an idea that we have used your network or machine learning to try to determine what is the optimal low shading decision from the control without the intervention of the control center. So moving from centralized to decentralized low shading paradigm. I don't think I have the time to talk about it, but essentially it's the same idea that we want to enable each individual node without knowing what's happening in a global scale, but then just use its local measurement data and then use a pre trained optimal decision rule to figure out what is the best corrective action in terms of load reduction. So this year ongoing work. Yeah, we probably can come back and talk about it in a different time. Okay, so in summary, we have seen two plus a short small one applications of how to use machine learning on your network in operating power grid. We specifically looking at how to incorporate physics knowledge about the grade. For example, the topology information here to simplify the model reduce the number of parameters in the neural network model and address the issue of over parameterization for example. So we have seen how to introduce risk measure to address the potential voltage violation issue when we deploy this decentralized neural network on the field. We want to make sure that their resultant solution does not cause a lot of voltage violation. Yeah, so we have these similar ideas using your network to learn this fast response actions for each individual resources and this is still ongoing work and we have a lot of potential extensions for all of them. So with that, I would like to conclude the talk and just want to re emphasize that there are a lot of opportunities that we can use machine learning when the grade is transitioning to a different operation paradigm, when we need to consider resilience. So that the fast computation or the fast fit forward computation capability of neural net is very convenient for that when we have dynamic resources or some kind of new type of resources that we don't have the exact model of them. There could be new opportunities of using more like a model free learning for these problems. So with that, thank you for for your attention and if there are some more questions happy to take as well. I got a question. Yeah, so this is on the second solution your distributed distributed energy resources. So just to clarify, you are trying to predict what is the optimal output of each distributed energy resource right so I'm just wondering we actually have the policy and the technology to control kind of the output of this individual distributed energy resource. Because this is basically telling each house how much solar power they can, in a sense, sell to the grid right. Yeah, so so it's, it's an ancillary service reactive power so they may not directly sell the the real power output from the PV but then they do sell some kind of power to the grade. So, currently, so that's a great quote. So currently, without knowing these global information they would do a simple like PI, or like a linear scaling rule so they would just react linearly to like the local condition. So, and a new network based decision rules, enable it to explore the nonlinear operating conditions, and then construct is nonlinear decision is globally optimal solution so that's essentially why we want to use a nonlinear decision rule here. Got it. And for this current solution whereby you say to use the linear PI that is done by the distribution grid right. That is by each individual resource locally too. Okay, and who's enforcing this. Is it like the distribution grid operator that's enforcing this or is it our transmission system operator. Yeah, so it's indirectly by the distribution grade. Oh, it's kind of like a, like a kind of pre conflict into the hot well so they can fix that. Oh, each of these note they would react is react to the local condition in this way. So, yes, it is according to the standards. But it's not like directly commended by the distribution operator. It is only like using it is pre programmed, but then in the real time operation or real time control is only takes the local input to compute the corresponding queue solution. Got it. Thanks a lot. Any more questions. Okay, let's thank our speaker. Thank you very much.


Live from the Wynn Resort in Las Vegas, it's theCUBE, covering .next conference 2016. Brought to you by Nutanix. Now here are your hosts, Dave Vellante and Stu Miniman. Welcome back everybody, Sunil Podias here. He's the Chief Product Officer, Chief Product Development Officer at Nutanix. Host of the keynote this morning and Benny Gill, Chief Architect at Nutanix. Welcome back, good to see you guys. Thank you, likewise. Been a great week so far. We had the analyst day yesterday. We got the Kool-Aid injection, which was great. And then really... Did you have some alcohol injection? Last night, it was good. And then the keynotes this morning, like you were saying, Sunil, how did you top last year? You announced a hypervisor, but how do you feel? Yeah, it's good actually. Obviously the feedback so far has been really good in terms of resonating with the audience in general. But I think the way we look at this we've come a long way, even in the last year, since we announced Acropolis Hypervisor, to the point that I think it's become clear what our clarity and long-term vision is, which is essentially provide the Amazon-like experience for the enterprise while enabling seamless mobility between enterprise workloads and public cloud workloads. Just so that the true North is going to be ensuring that our enterprise customers can consume the right workload on the right cloud. So Benny, one of the things about that Amazon experience is how frequently they release new features. We go to re-invent and before breakfast they've announced 20 new features, right? So the architecture of the product has to allow for very fast release cycles. Sunil was talking about the speed of release cycles. What is it about the architecture that allows you to do that? I think you have to architect first the foundational layers very carefully. For example, we looked ahead into what we wanted to build and started with ZooKeeper, Cassandra, and we have already paid the debts of building web scale early on. So now we are at a point where we have all the building blocks. Say you want to build a virtualization platform, what do you need? You need a database. So we don't have to go use a MySQL or some other database. We already have Cassandra. If you want to run a clustered environment, we already have leadership action and all that. So fundamentally the building blocks are there. Now, building new services are like Lego blocks. You just put them together and build them. And that's why you see we are innovating really fast because all the Lego blocks are actually there. So when you think about the lead, you clearly have a lead. Can we establish, we all agree you got a lead on the competition? Yes. Is that fair? We could debate about the extent of that lead. But now everybody's pivoting to hyper-converged infrastructure. And they're taking all your lines and using them in their marketing. Okay, but then you peel back the skin and you say, okay, there's more meat on the bone here than you see on the others. That said, they're throwing resources at it like crazy. They're throwing more resources at it. Allow the competition to catch up or is it this sort of small team still wins approach? Well, so it's about, see, whenever you're in a rush, you'll probably do, you know, take shortcuts, right? We had the luxury of taking our own time. Forget your fault. Right? Yeah. So, you know, you take your time, architect it right. Nobody was on our backs and say, you know, you're losing the market. We were actually there. We didn't know whether the market existed. So we built it right. So therefore it's never a good position to be in where you're actually catching up. Otherwise you'll, you know, you'll be left behind. So throwing resources is not really the solution here. It's about taking the time to build the right platform. Yeah. And I think that's one dimension of if I can call it sustainable differentiation is the luxury of doing the right thing. Because this is a architectural shift. It's not a incremental feature shift, right? The other thing is also it's the luxury of not having an incumbent base that has to be replatformed. That's right. As a business opportunity, right? So we don't have necessarily a large base of customers and a profitable margin that we have to hold ourselves accountable to. We can go full on to essentially replatform the data center in the right architectural model, right? So it's between both the luxury of having knowing competency as well as the luxury of taking the time to build the right product. I think are going to extend our lead for quite a while. So I want to make an observation and have you guys comment. So when you talk to the SaaS companies like Salesforce or even or a service now, the cloud model is transformative clearly. You talk to Amazon, the cloud model is transformative. You talk to traditional infrastructure companies. They talk cloud, but it's really not quite there. Howard showed some slides from a Gartner survey today. I liked it very much. Are you doing private cloud? Oh yeah, we're doing private cloud.  Are you doing self-service? And then it just goes down to 7%. You guys seem to be very close, at least as part of your true North, to actually delivering that type of cloud experience, cloud inspired experience you've been calling it on-prem. Is that a fair observation? And how close are you actually to that? Yeah, I mean, I think, I'll comment a bit and I'll have Benny chime in. I think, you know, as we've laid out, we have these three acts of the company. Hyperconversion is actually our first act. As of last year, we've transitioned from actually, you know, quote unquote, morphing the lines between virtualization and compute and storage while the rest of the industry is still focused on the first act, right? And as part of this year's announcements, we've taken our first official leap into what I would call consumer grade consumption and operations in the form of self-service, in the form of like multi-hypervisor management, a variety of operations that are multi-cloud. So I think we are in act one of the cloud innings of this company, just from a relative state of mind of where we're looking at, right? So maybe you can comment a little bit on the technologies. You know, one of the key things in cloud is first of all, it should be scale out, it should be elastic. So all that capabilities we have, where customers can pay as they grow and quickly add and remove resources, that's one, right? The other is that consumption should be frictionless. That's where we announced just today, you know, the self-service capability. So once you have frictionless consumption, that sort of mimics the benefits of cloud while you get the control that the enterprise really wants, they want to control where the data is, they want to control the costs, all that goodness was already there. So I think we'll end up in a position where the enterprise cloud, as we're talking about, might appear more meaningful and powerful for the enterprise because now they get a platform where they have control, they can also look at a future where this can control a hybrid environment, your private and the public, and allow you to do app mobility between the two. Yeah, so one of the things that's interesting to look at Nutanix is there's very few things in IT that are sticky anymore. You know, if I look at, you know, when Nutanix first came out, it was like, well, customers have their server of choice. It's like, most of your customers, first few years you were selling, didn't really care. I mean, Super Micro in there, tried Quantifer a little bit, you know, you do have OEM relationships for customers that really want it, but that doesn't matter. I know your hope is that, you know, the hypervisor becomes a feature and, you know, differentiates itself, but what will make Nutanix sticky in the environment? Because boy, if it's invisible, don't I just forget about it after a little while? It's kind of that dichotomy. Yeah, I mean, I think, again, I'll have been asked with Chairman. So for us, I think our true stickiness is in the experience of operating our infrastructure. The fact that they don't have to operate it, they don't have to touch our infrastructure in itself is our core competency. That sounds a little oxymoron-ish, but the fact that we have built so much intelligence in our core fabric that our management plane is so dead-simple, versus having to actually build all this complexity in the management team, that you need 50 experts or we experts or whatever it is to actually go manage the infrastructure, right? So I think our long-term differentiation and stickiness will come from the fact that by being simple, we change the lifestyle of our end users. And once you're addicted to that lifestyle, it's very hard to kind of come back to another lifestyle. Yeah, in today's day and age, it's not necessarily the features, but the interface is what people are loyal to. Like Uber is an interface, right? Apple is an interface. If that interface changes, they might lose their user base, right? But if the features come and go, performance is up and down, it's possibly okay. So that is where we are focused heavily. We've invested quite a lot and we believe that we have a straight shot making that great interface that'll be differentiated for a long time. Yeah, I think an interesting point on that is in the keynote this morning, the biggest applause came when you talked about really the multi-hypervisor management to come in a future release, Asterix. So again, that's an example of the same features, but the interface is different and people love that interface, right? Well, it's certainly the case with Amazon, right? I mean, that's been pretty sticky. Yeah, I mean, the Amazon command, and one interesting thing I wanted to highlight even though when we say it's Amazon-inspired or Amazon-like, there's actually two aspects of Amazon. There's the AWS aspect of Amazon, which is how you build infrastructure to be consumed on demand, which is start small, start quickly, pay as you go. But the other dimension is not as explicit, but it's actually, frankly, for us, more differentiating long-term, which is the Amazon.com experience. The retail side, yeah. A one-click. Imagine every portion of your enterprise being consumed as one-click operations. And that, to us, is the secret behind the long-term strategy. One-click operation. You love one-click. You're addicted to one-click. You'll pay more for Amazon to go to one-click, right? Just for the convenience. It's a value to a consumer. To us, actually, long-term, frankly, if we can actually, while delivering cloud-dining infrastructure, but really sustain that consumer-grade interface, experience, the whole stack, I think that's going to be the true Amazon-like experience that we want. So one of the real challenges we see out there today is customers are trying to figure out where do applications live and there's some applications that have pieces on-premises versus in the public cloud. You talk about self-service. You talk about the interface. How do you work through that piece of it? How are you going to help customers? You guys don't own the applications, so why are you going to be an important piece of helping customers with this environment? Right, I mean, certain applications are more suited to the public cloud. They're elastic and we see a lot of big data applications going to the cloud. Some of it is driven because the applications that they're doing analytics on are on the cloud, so it's easy to do analytics on top of that data there. Essentially, it's about, first of all, how much it's going to cost and where. So that's one thing, whether customers want to go with the CAPEX model or the OPEX model, that makes them decide. We believe that the same set of apps can run on both sides in the long term. It would be just a business decision as to where you want to run it, also security and what's the comfort there, and also speed of light. I mean, at the end of the day, customers would say this app has to be local to my users and, you know, so those things will play out. Yeah, makes sense. Give us an example of that. Give us one click, but give us some other examples of that experience. Yeah, I mean, I think, look, we just call it under the umbrella of one click, but imagine the lifecycle of an operator's experience, right? First time install, in terms of how fast can you take the box out, rack and stack it, much like cloud, which is all you just have to go to a portal to set things up. While there is some physical aspects to rack and stacking, the fact that you can configure stuff, provision stuff, get VMs provision in minutes without having to get like 15 guys involved, that's like the first step of what we call one click provisioning. The more important as you go down the lifecycle, it becomes more visceral, which is one of the examples that I use all the time is like the first month that I was with Nutanix, couple of weeks later I fly to State of Washington, one of the large customers, the government customer, talks about existing last traditional storage visualization customer, and the architect stops me in the middle of the presentation, goes, well, wait a second. Well, do you remember when we used to call Mary and their wives and say, look, on Friday night, we got to take off center two hours, come to work, spend, you know, Friday, Saturday, Sunday upgrading my order management system, get the storage guy, visualization guy, networking guy involved. Well, guess what? Same system, running on eight nodes in Nutanix. Last Thursday at three o'clock we did an upgrade, it was done at three o' eight. Something simple, something unsexy like that, mundane, but it truly elevates the operational experience of those people, right? So one click upgrades is another example. One click planning, you know, we talked about that today in terms of, okay, every time we know there's a level of uneasiness as to what is the impact on infrastructure. What if the system can tell you, based on your application, you know, requirements, what the impact on your infrastructure is, and all, doing all of that in a one click planning way. So there's, I think, there's like three or four such things that are abstracted under the term one click, which essentially mean, you know, consumer grade experiences, right? So those are all things that I think as we go around the fact that you can do one click hypervisor conversion. Essentially it's not just the dueling portion, but the fact that, you know, we call it the $2 million Nutanix mug, right?   So there's a variety of those things that go into it. That was old enough to remember the Amdahl coffee cup. Oh, is that right? Yeah, so Amdahl salespeople would give the coffee cup for the IBM mainframe, right? Oh, is that right? Discount, so it's... Got it, got it, got it. Anyway, so it's the optionality, it's the optionality. Amdahl was an old mainframe company for those of you who are young folks that may not remember that. Yeah.  When you go public and you have that 90 day shot clock, you guys seem to be a pretty thoughtful management team. You're hungry, but humble. Of course, IPOs have this personal financial game, but there's also corporate, there's exposure to public markets. Do you ever worry about a change in that culture? How do you, because culture's so important to Nutanix. Yeah, I think, yeah. How are you maintaining that? Again, I believe the culture starts at the top, really, in any organization. So as long as we are clear at the top that we are building this company for the long haul, we want to make the right decisions even after IPO, I think we'll be good. All right, Sunil, I'll give you the last word. We've got to wrap, get to the keynotes. The experience here, dot next. Put a wrap on it. In addition to this awesome interview here, no, I think, look, I think we're on a long journey here. And I think we're still in the early to mid innings of helping replat from the data center. We're having a lot of fun doing it, and we're here for the long haul.  So I'm looking forward to it. Biddy and Sunil, thanks very much for coming to theCUBE. It was great to see you. Thank you. And thanks for having us here. Thank you. All right, keep it right there, the wrap and the keynotes, right after this word.


Welcome everyone back to another episode of Anabaptist Perspectives. Today I'm here with Verlin Miller, who's my father-in-law.  And I want to talk to you about that a little bit today. Sure. Good to be here. Good to have you here. So the last number of years, you've worked with a business advising team, and primarily you're working with Anabaptist businesses. And so that gives you an interesting view into kind of a cross-section of our Anabaptist circles. Can you just tell us in brief what you do, and we'll go from there. Sure. So work with a business advising team, and what that means is we work with companies that come to us for a variety of reasons. Typically there's about three things that people really want from us when they come in the businesses. A stereotypical company that will come to us is somebody who is really good with their hands.  They're good with that part of the business, but they don't like the office part. They don't like the bookkeeping. They don't understand the numbers. They just don't want to do it. And so it's a necessary thing. They have to have a level of understanding to make wise decisions. And so that's a core piece of what happens. But it really leads into other parts of business management and ownership as well, including just needing someone to talk to. There's a lot of people out there who own businesses. And for whatever reason, they're not comfortable talking to their brother or their dad or someone in the church. Maybe they're embarrassed. Maybe they're scared. And they really find it helpful just to have a third party, confidential, somebody who's used to looking at numbers and talking through business things. And we do a lot of that where we're just talking ideas. We're talking about the what ifs. And some of that comes out of the numbers because going through those ideas is very critical. And you got to have good information. And some of that comes from the numbers. So then the third piece is that we do a lot of work with relationships. Now, if you think about many businesses, you've got relationships and partnerships, customers, vendors, employees. And we end up talking about a lot of things in those areas as well. So our team is working with about 100 companies right now. The heart of our program is monthly meetings. That's mostly done remotely over the phone and with the computer. And we just talk through the ongoing business operation that they have. So I really view my job as a teaching job, as a walk alongside me kind of guy, an advisor. And then there's other days where it's almost like counseling. We've got to figure out how to resolve some kind of a problem. I've really enjoyed working with people. It's been a great experience working with business owners. Probably 95% or more of our companies that we work with are in the conservative Anabaptist world. And so we find that enjoyable having that shared values and understanding of the people we're working with. So I mentioned in my last question about this cross-section of the Anabaptist circles that you are exposed to through the business world. So as you've been in this job the last number of years, what are some things that surprised you about these businesses in good and bad ways? The view I have best is of the companies that come to us for help. And I recognize that there's people outside of that that this wouldn't be an accurate representation. But that spectrum of people that we help is anywhere from companies who are struggling all the way up through very successful, profitable companies who come to us for other reasons. They don't need help on profit. They maybe just need a third party to talk through some ideas. Maybe they need planning. Maybe they're looking at future projections and they just want to understand how to go through that process of making a wise decision. So we see a broad range. But what surprised me? Two things I think of. The one is just the level of generosity that's there. You know, looking in, before I was a business advisor and I knew people, I had friends and you kind of know things but you don't really know them and you surmise. So I was in that boat. When I got more into seeing the workings and some of the insides of the companies and their finances, I have been very blessed at the generosity that is there. Now that doesn't mean that I've seen it everywhere. I think that there's room for growth there. But there are a lot of very generous conservative, anti-Baptist business owners and it's because of them that many of our conservative anti-Baptist ministries are funded at the level that they are. That was a very pleasant surprise. One that wasn't quite so pleasant is the level of debt that some of these companies have. And as you think about it, I guess it's understandable. The amount of capital that is needed for some of these ventures, unless they had a lot of capital coming in as a young person, they had to get the financing somewhere. It's not always a commercial loan. Sometimes it's someone else in the community or family that's helping to finance that. So I was surprised that even some companies that we would view as more mature, they've been around a while, they still have a significant debt obligation that they're dealing with. And that's a piece I'm not really intending to present it positively or negatively, it's just there. And I think it's helpful for people to understand that there is a lot of risk, there is a lot of capital investment and it goes way beyond just what we think of the business world into the farming community and some of the other ventures that we have, maybe even just the properties that we own. You mentioned these businesses that are doing well and give a lot and fund a lot of charity or mission type projects. How do you see business furthering the gospel in the kingdom of God? And after you speak to that or as you speak to that, maybe give a little insight into how that affects your view and what you do as an advisor as you seek to speak into those situations. Great question. One that everybody ought to wrestle with, whether you're a business owner or not, I think people in the Christian community ought to, we ought to get our heads wrapped around this and to think about business properly. Can it be used to advance God's kingdom? Absolutely. If you think about kingdom activities, whether it's ministry of serving other people in the church, whether it's going on a ministry thing to lost people, publications, maybe it's something as simple as stopping and visiting your neighbor. If you go through many of those activities, in fact I'm going to say all of them, you can find an element of this, somewhere funding was required for that. And at some point somebody had to make money and contribute. So now at the business level, how do we look at that within our communities? Well, that's obviously a place where more, a higher level of funding can be generated and can go to kingdom work. So I would back up and say, businesses as we operate them as followers of Jesus, they should reflect to other people what everything in life does as a Christian. It should show the world and the people around us, this is how it looks when a business owner follows Jesus. And I think it can be a huge impact and just here's a Christian business owner functioning in my community. But then beyond that there is the monetary aspect of it as well that actually can contribute to the funding of these different ministries and local churches, whatever those needs are, maybe charity as well. So as a business advisor, you're emphasizing to some extent at least stewardship. Absolutely. Yes, one of the things that happens in, and we have different levels of engagement, but the core of what we do is these monthly meetings. And as a part of that, the advisors will go through little articles, we call them a call to stewardship. And they're not very long, a few minutes long, but it's a reminder at the beginning of every meeting that we are accountable to the Lord. Everything we have in one way or another has been given to us and at some point I'm going to give an account to him for how responsibly or irresponsibly I handled that opportunity. So what does that do for me as an advisor? Well, we make no bones about it. We advise from a Christian perspective. And it doesn't mean that you have to be a Christian company to come to us. At this point, I think all of them are. But if a company comes to us, that is the perspective from which we operate and advise and work with them and think about decisions and vision. So what we say as a team is that our goal is to encourage and assist business owners to free up resources for the kingdom of God. And we say that fairly broadly because that can go a lot of different ways. But that's what we really want to do, to decrease the stress of business ownership, increase the level of contribution that can be made to God's kingdom. It's not overt in every discussion, but it's this undergirding principle that's there in all of our interactions as we interact with the different companies that come to us. I hear you talking about a lot of things that business can enable. So let's just talk a little bit about those positives. What are some positives that you see, maybe trends even in the Christian business circles, that we should affirm and continue to cultivate? I've mentioned a couple of them already. So the generosity was one of them, and I'm blessed to see that. And I'm not privy to what all business owners do with their funding or with the funds that they take home. It's not all done through the companies. But they will do that through their personal finances. Very encouraged by that. There's a lot of people who want to give. One of the things that was concerned of me starting into this job was I knew I would not be fulfilled if all I was helping people do was become wealthy. That seems rather meaningless to me. And what I discovered and was told this would be the case and it's true is that actually that level of greed and materialism within the business owner community that we work with, that tends to be a smaller segment than the segment of people who are wanting to contribute and have the mindset of I want to give. I'm really blessed by that as I see that attitude. And then the other thing goes along with that. I mentioned the call to stewardship articles that we do. We actually get a lot of positive feedback on that and a lot of people resonate with those biblical principles and they identify with it. They aspire to that. And so there again I'm just blessed with that whole picture of it's not just a bunch of greedy materialistic people although sadly I'm sure that does describe some business owners. But we have a lot of people who really care about the kingdom of God and the way that they operate their businesses in a healthy function. So that's a blessing to me. It's one of the positives. Another one is the personnel development. I see a lot of business owners who care about the people around them and I think of one situation with an employee where the business owner knew it was going to be a shorter term employment with the young person. I think he was I don't know 14 or 15 when he came to work for him and he just knew it wasn't going to be long term. He had other interests. But he approached it with the express purpose of I want this young man to be a better person when he leaves than when he got here. And so I see that not always that overtly is that particular business did it but a lot of care and concern about what's happening. There was an employee who was having difficulty at work and the owner wasn't quite sure how this was going to work out. This young man came to the Lord and he's a new man and he was a completely different employee and that was really what the business owner was hoping for. He didn't know how this was going to work out but I see that level of concern and care there for the employees and it kind of goes into a broader thing that many of these people feel a degree of responsibility. I'm providing employment. I'm helping people make a living in my faith community in the broader community. I'm contributing to their well-being and there's that sense of care and responsibility that they feel as they operate their companies. Let's flip that around now. We've talked about some positives but you also mentioned some people have greed and any time you have a degree of success or wealth there are negative tendencies that that can creep in. Can you speak to some of the negatives that you've maybe seen in what you're doing? One of the things I would see and this is not always negative. Sometimes it's just what is and it's kind of a tension that we have to learn to manage and to live with but that's the thing of time commitment and so it's not unique just to business owners but it is definitely there.  It comes with the turf. You're stuck with it and you're responsible to make it happen. So sometimes that business just becomes all-consuming. It's really enjoyable and gratifying to me when we can help a company go through that I'll call it a pain point where you're having a growth you've got responsibilities and you can help develop the company and develop systems to a place where it's sustainable. Sometimes short-term businesses will go through phases long-term. It's not sustainable but we can push through it for a short time and so I think the key there is calling people to be aware and intentional of is this business managing me or am I managing it? And it can be a struggle but it is one that they can be worked with and often can be improved and brought to a healthy level. Another one is just responsible management of debt. We have seen a broad spectrum of attitudes towards financing and I recognize that particularly in business there's just a tremendous amount of capital that's required to start up and most of us do not have that kind of capital and in order for there to be that kind of a business venture there has to be some kind of financing and so this isn't really intended to be a discussion if that's right or wrong or wherever that comes. It's more an observation of attitudes that I've observed where some people are just very, very comfortable with a very, very large debt load. The question we have to deal with as advisors then, okay, so you have it, how do we service that debt load and meet those obligations and sometimes that can be a very difficult, lengthy process of helping pull out of that one. So just the negative I would see is when we don't have that healthy things. Many people are cautious. Some people are not cautious enough and it's one of those things. I was surprised at how many I would have said would have been more comfortable with extremely high risk than what I would have expected. So another one that I've seen on the negative side is people who are controlled by fear. So there's healthy fear that we ought to have and then there's a debilitating fear that keeps us from doing the right things sometimes or from making wise decisions. So some examples of that would be fear of dealing with a problem in the company, maybe with an employee, maybe even with a customer but any kind of a problem area. It's like I don't want to touch that. I'm scared of what will happen. I'm scared of losing business. I'm scared of losing an employee. Really as we can identify those and begin to remove emotion there again, it doesn't have to be a negative. In the big picture we can flip that around. We can take what should be a valid caution. We talk through it. We gather information and go to fact-based decisions more than emotion and that's what fear does to us is it keeps us on the emotion side. Another question I have is you're working with businesses that a lot of them it sounds like have numerous employees. They're not just one or two man shops. They have a number of employees. How can that be a mutually beneficial relationship and how can that be more than just somebody coming in and punching a time clock and going home? How can there be more mission or purpose behind that? You're speaking to the business owner and instilling value or cultivating values there that are hopefully kingdom focused. How does that trickle down? One of the things that we do with some companies as they want us to is we'll do team building events where we try to help them understand care and just understanding the personalities that are present and how people function and learning to interact well on a personal relationship. For the business owner, what can he do? May I start with a phrase that I've heard in the business community which says this, and I wish I'd know who to attribute it to. I don't know who said it, but they said people don't quit their jobs. They quit their managers. And I think that that is often the case as of observed people working and what happens in those workplace environments. What determines if an employee is really happy there and will stay there long term is much more than compensation. That matters. They've got to be compensated. But it's down the list a little ways. And ahead of that, you have things like job satisfaction, personal satisfaction of I'm contributing in a good way. There's any number of things that come into play where they just enjoy being there. The workplace is a friendly environment. And I'm going to say that there's got to be some appropriate boundaries. We don't go to work and it's just all fun and games and this is a wonderful place to goof off. But it's got to be a place where we can come and enjoy and feel satisfaction in working there. So for employees and business owners, how can that be mutually beneficial? Well, one is the owner needs to be good at communicating that appreciation. You're doing a great job. You've helped out in this way verbalizing some of those things just on a healthy communication. Another thing that we see often working well is what I'll call an incentive pay system in which people are compensated for helping the company do well. And it really promotes teamwork and it motivates them. They know I can help improve my level of compensation when I help the company succeed. The company is going to kick some of that back to me and it's mutually beneficial. But there again, if that was the only piece that was there it wouldn't work. It's the bigger picture of people like to come to work. They enjoy what they do. It's a good fit for them and they're contributing well to what's happening at the company. Maybe another simple way of saying it is to be able to communicate to them that he cares about them as a person that they're not just a number another cog in his business machine that keeps turning out products and hopefully making money. The business owner can communicate that level of care and concern for what's happening in their lives in a very healthy, helpful way. So fostering a sense of teamwork and even maybe through a centralized pay system a sense of ownership and contributing to the overall cause can be impactful. Have you seen any companies put any sort of giving program in place for their employees to match giving to charities or anything like that? Or have some sort of public goal as a company of, hey, let's try to hit this target and share? I have seen some of that for some companies that's not they've not gotten to the level where that's a good option. But yeah, I have been involved in some of those discussions where what's a good way to structure this? How can we encourage our employees in fact one business owner I was familiar with he actually said, I'd like to give them more and what he was wrestling with in his mind was he'd like to give them more so they can give more and then the discussion was, okay, how do we balance if we give it to them, it's up to them what they do with it, but yeah that heart of figuring out how can we give together is definitely present with some companies. I think it's one of those things that's maybe more unspoken than structured like you're talking about, but that is definitely present than many people that I work with. Well, very good. Those are some things that are inspiring and things that are a challenge to those of us who have been involved in business and maybe are. Do you have anything else to say to anybody who's listening or watching this? I'm blessed by what I see in the Christian business community. Are there things we can improve? Absolutely. But there's a lot of good there and I want to foster and encourage that for people to keep on serving the Lord and letting their businesses be light and showing the world around them and here's what it looks like when Jesus is in charge of a business owner's life. Just basic integrity and being a person of your word goes so far in today's world. Thank you for sharing a bit of insight into your work and the view you have of our Anabaptist circles and the businesses that are there and how they can be part of God's kingdom and be a tool. They're not just a large wealth generating selfish corporation. It can be much, much more than that. So thanks for your time and for sharing your thoughts. And thank you to each one who's joined us for this episode. If you want to see more content like this, you can go to our website, www.anabaptistperspectives.org. We'll see you next time.


Hi, my name is Rene Condon and I'm running for the Faculty Convener of Engineering and Computing. I'm Ava Hayden and I'm running for Entering and Computing Faculty Rep. I'm Connor McCullough and I'm running for Faculty Rep of Engineering and Computing. I'm running because I have a passion for student representation, I love helping students and I want to bring more to DCSU. I'm running because I'm probably the first female convener in the faculty, that's the main reason. Other reasons are to promote STEM, to have better engagement with the SU and also to promote the societies within the faculty. I'm running because I've been a class rep for the last year and it's been a really interesting experience. I've got to meet lots of different people and lots of different backgrounds and lots of different hobbies and interests. It's just been really interesting to see that kind of thing and I kind of want to take to the next level and do more parts for the bigger part of the SU again. Within the engineering faculty there has been a few issues from what I've experienced in my first year here, such as having say five exams in a row which isn't, according to some people isn't within the regulations so I'm hoping to fix that for next year. Also that the allocation for labs is very inefficient and it's not really prioritized. People who say have assignments were 20% and they need a lab space to actually do it and they can't go down to the library because all the software isn't on there. So I'm hoping to just optimize the lab allocation, make it more efficient and then somehow prioritize some lab spaces. To be honest in the engineering faculty we're the forgotten faculty I think with the SU so I'd like to see the SU engage with us more, kind of cater the events more to a time that suits the engineers and also to see the engineers engage more with the SU as well. One of the things I've noticed a lot is that in general for a lot of students they can't actually attend a lot of the events we have. A lot of them live either off campus at home or just simply don't necessarily enjoy some of the events we have, like a lot of them are centered around alcohol and a lot of people don't, a bit surprised at the sheer volume of people that don't actually like that and it's interesting just to see that and I kind of feel there needs to be more tailored for that group of people and just more things to engage more students is a big point for me. I hope to work with the head of school, head of school so people like Rory O'Connor to actually do these, put these things in place and then work with module coordinators who actually help set out the timetable and then if they have say labs where it's not full but they don't want any people in there we need to stop that so that say fourth years and third years who are actually contributing towards their degree have somewhere to do their work and they are pushed out into a packed lab such as LG25. First of all obviously kind of whoever is running the SU next year to be able to sit down with them, talk through, make sure our voice is heard that look we're in class 9 to 6 every day there's a lot of events that aren't really catered for us. I'd also like to see Engineering this week, part of the SU column there so that would be one of the main things I'd be looking at doing and also just general promotion of STEM which is Science, Psychology, Engineering and Maths around campus just so people are aware that we're not just stuck in the engineering building all the time. Well I have a lot of different ideas of what I want to do and one of the main things is I was thinking of organising a mock version of the Olympic Games I was thinking to call it maybe Frackle the Games or something like that and it would just have a load of different random events there would be some sporting and some kind of maybe related to the courses and then there would also be like complete and other pistic events just to spice it up a little so hopefully either way I hope to do something along the line to that at some point. The dream job, let's say just working in my field, like in any top company in my field because I absolutely love computer science and it's just, it is my dream job to be honest. Okay, that's really good. I'd actually love to be an engineer in the ISS at the space station because you see the cool photographs that they take from space so that would be a pretty cool job to have for whatever six months or a year. Well, an inventor is kind of the thing I'm aimed towards for engineering but it's not necessarily an exact path to it but that would be kind of the thing I'm aimed at purely because I want to help make a change I know that's as stereotypical as it gets but like just something to help contribute to society I guess is in some way shape or form a happy one.


Hello there! This video won't be a tutorial or anything like that, I just want to share some thoughts with you. So if you want a step by step tutorial, this won't be the video for you, you can check out my other videos, but in this one I actually want to share some thoughts that I have during the open jam, so let's get it started. If you want to be a professional game designer, you can't rely on your willpower and motivation or inspiration to create your games, right?  I mean, I see a lot of people talking about, hey, I am not motivated today, so I won't do this work, I won't do this job, I won't do a piece of art today because I lack inspiration or I lack motivation for that. And I was thinking about that for a while because this weekend I was participating in the open jam, the open jam 2018, which I was very happy for, I was sharing every stuff that I knew about that. It was a very cool game jam because it is a game jam about open source and using open source tools and you all know that I love this and I love using free and open source software. So I was very happy about that, but during the meantime of the game jam I had some problems of my personal life coming all together at the same time and I really got this motivated. I lost all my motivation and all my inspiration and all my drive to make a game for the open jam. So I realized how fragile this is, right? This is very unstable. I mean if we rely on our emotions to fulfill a work, we don't have control on that, right? I think we don't have control on our emotions, at least not to the point that we can say okay, at this time I will enter in a motivated, full motivated state of mind in which I will make a very awesome piece of game here. I don't think this is actually on our control, right? We actually use inspiration and motivation as resources, but they shouldn't be, right?  You can actually buy motivation or buy inspiration. You can get inspiration from others like, hey man, can I loan some inspiration from you? How much would you charge for this amount of motivation? You can rely on that. This is very a matter. Sometimes you will be inspired, sometimes you won't be, and this is very unstable. So did you ever stop to think about how fragile this is? What will happen when you are seriously making some work from a client or for another studio and or for an investor? I mean if you are in a studio and you are working for investors, I mean what will you say for them when you are not motivated? Hey man, sorry about your spending your money, but I am not motivated enough so I won't do this job anymore or let's say you reach the deadline and they are on a meeting for seeing what you developed and you just came out on the room and say, hey guys I have nothing to show because I was not motivated and that's all, sorry and to the next time. No, you won't say that. Can you see how beautiful this is? So I started to think that I can rely on that. I can make games professionally if I rely on my state of mind, on my emotions. This won't happen. So I started to think about for a while and I remember that a lot of the designers I follow, not game designers specifically but designers in general, they all have a design process. I see that from Ren from Inflex and from Chris Doe from The Future and they talk about this design process, this methodology that you can state for you to follow some steps and if you follow these steps you will have something to work with at the end. So no matter your state of mind because I know that something that makes us not finish stuff I guess relies so much on motivation and inspiration is the fact that sometimes we don't know what to do next and if we are motivated we can do a lot of stuff and try out a lot of stuff because you know you are motivated so you have a lot of energy to work with but if you are not motivated you won't do anything because you don't know what you should do next. This is the topic of this video. What is a design process? A design process is basically you stating some steps or some tactics that you follow to reach a desired result. So in my case would be a game or at least a prototype but this video is not about my design process because I don't have one and this is actually what made me not having a good entry for the open gem in 2018 I made a very poor entry I could have made a better entry for the open gem my entry was about six hours long from start to finish including the two hours I spent streaming so I actually developed for like four hours which is not even 5% of the game gem time span the open gem was 80 hours long and I spent four hours working on my game because I didn't I mean this is my excuse for this but I know that is very mature you mean I mean my excuse is I wasn't motivated but the real thing is that I'm not professional enough to make a game to have a process to make a game without being without relying on my emotions yet so this is the topic of this video you should establish your design process so you make sure that every time you need to make a game every time you need to deliver a game be it for a client be it for investors be it for the public I mean your consumers you have a step by step process which if you follow it and you iterate on it you will have a game a good game at the end I mean no it doesn't matter the quality of the first iteration because the design process is just about having something at the end and make sure actually to have iteration on your design process because this will be very important because you follow the process let's say twice or three or as many times as you need to actually achieve your final product your game but you know that if you follow this product this this tactics you will have something at the end and if you input your result on the next iteration it will become better and better and better so that's it I know that this isn't the usual content of the channel but I felt like this is very important and valuable to share so I decided to share this thoughts with you if this is a very obvious topic to talk about I mean did you already have a design process and it was just me and my materialism that find this useful please share with me your thoughts share share this in the comments because I would love to know what you think about this what you think about taking the next step into being a professional been working with games in a professional way also if you find this valuable to you leave a thumbs up so I can keep digging in these topics I mean the topics of being an indie game developer not only tutorials and stuff like that but the whole set of being a game developer don't forget to subscribe and turn on notifications this way you get immediate access on once I release my content this video was brought to you by my beloved patrons and it went out three days early for them so if you will find this useful as well check out the perks and reward my patrons have by checking out my patreon so that's it thank you so much for watching keep developing and until the next time


By the way, I'm on Bass Road. It's my type of road. What's going on guys to the next day? I uh, I got kind of caught up doing stuff yesterday So I really didn't get to finish the video, but I do want to do something with this video like I'm gonna do something different It's gonna be a lot of fishing in this video. Thank God Because I think I'm gonna film something different today, but hold on let me show you something. Look at all these boxes Sadly, this is not gonna be in this video It's gonna be in a different video, but I'll make sure I'll make sure everything works I'll I'm going to show you guys everything in these boxes. Just gonna be a different episode with that being said I Filmed a lot in Savannah. All right. I filmed a lot of just fishing. Honestly, I didn't really find that much All right, I call off there's probably a 30 fish in the past like three or four days there on that pond I Got like 10 catches on film. I didn't film too much So I filmed all that and it's kind of different days throwing together So I'm gonna throw those in to this video. Hope you guys enjoy them. I'm gonna play them right at my house I'm pretty pumped and excited about it I'm about to go out here. Hopefully catch some fish. You know every day I've been catching fish and I haven't really been filming so today I'm actually gonna take the camera. I'll take the chest cam I'll take the GoPro out actually film me fishing today. I think that's gonna go really well I think I'm hoping I'll catch a lot of fish I'm kind of I kind of already know the pond out on how those fish are sitting Why those fish are sitting there and I'll kind of go in the depth on that as well Hopefully we'll go out there and wax some fish.  It's cloudy. I'm a little bit of wind Perfect conditions. Perfect. It's not too hot right now. It's feeling pretty good So I'm gonna tie my poles on right now And we'll get out there First I'm gonna tie on a drop shot and Yeah, just ignore what I have on right now. Just ignore it. All right I got something at eye cast and it's a little like night bobber type deal All right, just just go with it I need to go to Walmart and actually pick up some more fishing line Like light line for my drop shot and also some drop shot weights if they have them I highly doubt They have them but if they do that would be great Because I've been catching a majority of my fish when I'm in a majority I mean 90 95 percent of my fish out here on a drop shot, which doesn't surprise me at all You know drop shots very finesse um Especially when you go out these hot summer days, you know, you can't really beat a drop shot and I kind of got back into drop shotting and finesse fishing. So, you know, why not do it? I was pretty stubborn for a while.  I'll be willing to bet you guys anything That this bait right here is going to catch more fish Out on this pond today didn't anything I'm going to throw. I'll be willing to bet anything So like I said, I don't have any drop shot weights So I'm actually using split shots Which is kind of funny Um Because I know I haven't even used a split shot since I was younger and I actually had some in my truck I think one time I went cat fishing or something. I don't know. I had some in my truck and Honestly, I got to make it work. I've been making it work and they've been eating it Literally, it's just honestly a good way for you. I mean a lot of people, you know when they get fish in there like I just want to You know, they want to go the inexpensive route because they're on a budget Totally get that a hundred percent um There's nothing wrong with this to be honest. I mean, yeah I can get a drop shot weight and be much better and look better a little bit of everything but This whole split shot thing's been working out past a couple of days and I ain't complaining Just got the drop shot tied on I'm gonna stop talking. Let's go ahead and get out here and try to catch some fish That's a bass He ain't bad Little tiny guy guys first one of the day I'll hear a chance right now.  I've got like three bites right there.   He's still big Give him up here He's still two and a half pounds He ate it good too. Look at that guys. That's that's pretty fish Nothing wrong with that. I'll tell you it's bigger for a minute. Pretty fish guys. Let's get a release on her Well, let's just found this About the size of my You know how to do this, right? Oh, yeah, that's what I do too, so I'm going behind the gills Tiny is doing this the tiniest one I've caught out of here.  Oh, is that a good one? What happened? Where's that tree? That's a better one Here go guys little one drop shot. It's a lot better I actually caught him off a little tree. He's pretty thick Pretty healthy fish Hey come cast it come cast over here Dude, he made me Whoa, dude. He would tapped it You talk about tapping it, dude He about slapped it and tapped it right there. He's Chance, I don't know what to say. Oh, he looks way bigger out there Hey, I think I got one more Are these in my pocket? Oh another little one.   That's a good one dude. That's a good one right there.  I just like to end off this video and thank you guys so much for the support Um, yeah, I filmed a little bit when I was in Savannah and just some fish catches and stuff So I wanted to throw it into a video and just put some fishing for right now since I'm actually still at nationals and uh Don't really have too much footage that I can post so overall Thank you guys for the support if you enjoyed the video Please hit the like button Please subscribe and hit the little bell and be sure to leave a comment below and uh new videos every tuesday thursday and sunday And i'll catch you guys Never wait about what they attain if i'm living then i really can't complain can't contain who you really are I know my true color