Hello, everyone. Welcome to the talk. My name is Karan and I'm a data scientist in the AI Center of Excellence at Red Hat. So, let me start by giving you a brief overview of what we're going to talk about. So, first I'll briefly mention like some of the challenges that we face with data storage at scale. Then I'll mention one possible way in which machine learning can help mitigate some of these issues and maybe the title of the talk gives away what this way is. Then I'll show you a brief demo on how we can use the failure prediction stuff today because all the stuff that I'm going to talk about today is upstream and you can check out all of that. And finally, I'll talk about how the open source community that you guys can get involved in the project. So, what was the motivation for this project? Just by a show of hands, how many people here are data scientists? Anyone? No? Engineers? Software engineers? Alright, architects? Okay, sweet. So, how many people work on getting something useful out of data or provisioning data for others to use? Okay, some. So, essentially the thing is most projects these days involve around doing something useful out of data and making it efficiently available to others. So, and these projects usually involve a lot of data and because data is so central to these projects and because it can literally drive like company decisions, you want to make sure that this data is not corrupted and not lost. So, with that in mind, most of the solid solutions like SAF or maybe some variants of RAID, they use replicas or redundancies to ensure that you get that kind of fault tolerance. But when you're dealing with data that is at a large scale like hundreds of terabytes or petabytes and beyond, then doing this can get really inefficient or maybe even expensive. So, the idea was instead of that, is it possible to monitor the state of our hardware somehow and then identify by some sort of magic that, hey, now is the time when something is going to go wrong. And then based on that information, you proactively add another redundancy to basically solve the problem before it even occurs. So, when you would do that, you'll get a bunch of benefits like you'll have a more efficient resource utilization because you'll only use storage as and when needed. You'll have an increase in the fault tolerance because the probability of losing data is generally related to all of the machines failing and if you add one more machine in sequence, then basically you can improve it by up to an order of a magnitude. Another thing is you will get an increase in the performance because now you have less instances of failure recovery to deal with, so it'll be a more performance. And finally, it will be easier on your pockets and it's going to save you some money. So, the way that we choose to do the prediction of when a hard drive is going to fail, we use machine learning as our predictor tool. So, to build any kind of machine learning model, there is one key ingredient that you need and that is the data. So, what kind of data can we use to learn what does failure mean, what does OK healthy drive mean? So, for this project, we use the smart metrics for our data. And these are essentially a set of attributes that tell you things like read error rate or temperature and so on and so forth. So, it's sort of like the vitals of the hard drive and you check the pulse of the hard drive to say that whether it's OK or not. And the way that you can get these metrics is by using a tool called smartmon tools. So, you can just install it by EM installs smartmon tools or something. And basically how it works is you can just type Sparkadel and then the name of the device for which you want to get the smart metrics. So, when you type that, when you run it, it's going to spit out a list of all the metrics and that's going to tell you how the hard drive is doing. So, that is the data that we use because most of the modern hard drives expose this kind of data. So, it's readily available and more than that, when you're training machine learning model, you need like a lot of data. Like just using one or two instances is generally not enough when training at least a supervised machine learning model. So, the great thing about this was that someone out there had already done the hard work of collecting all of this data for their hard drives. So, Backblaze is I think a cloud backup company that basically collected smart metrics data for their hard drives over a span of like six, seven years or so. And they put it in a very nice format in a CSV format and it sort of looks like this. So, they have like the date for which they collected the metric, the serial number of the hard drive and then the list of all the smart metrics. So, that's great because they have it in a nice format and it's open source and it's readily available for use. And even more than that, what's really great about this is that it also comes with failure labels. So, they took their data and in addition to the smart metrics, they also said that hey, this is the day that the hard drive did not fail. So, zero means it did not fail and one means that it failed. So, if this hard drive with the serial number foo failed on January 3, then this will have a label one. So, this is really useful because now it unlocks like a wide variety of machine learning models that we can try in the supervised domain. So, yeah, so that's the data set that we go with. So, instead of using the raw data set, we tweak it a little bit and we pre-process it. So, the first thing that we do is, so if you notice here, there's only two kinds of labels like zero and one, like fail or no fail, right? So, even if I have the best model in the world that is like super accurate, super precise, that can only tell me that my hard drive is going to fail today. And that itself may not be super useful. You might make it work, but it won't make your life easier. Like imagine if a doctor tells you that, hey, you're going to die today. Maybe at lunch, maybe at dinner, who knows? So, that's not very useful information. So, instead of using like a zero one label, a better way is to use a more fine grained labels. So, you have normal that is like a hard drive has at least six weeks or more to live. Then there's one called warning, which says that the hard drive is going to fail within six weeks. So, it's not super urgent, but keep an eye on it. And then there's critical or bad. So, that means that this hard drive is going to fail very soon. Do something. And of course, if none of that work out, if the model fails to detect, it's not going to crash your system. It's going to just say there's insufficient data or unknown output. So, this is the format that is more useful. And also that's the way the SEF ecosystem is set up for the prediction module. So, that's why we decide to go with this kind of labeling. So, we take the original data that used to look like this. And then based on when the hard drive failed, like using this label and the date, we essentially change the labels that we want to predict on. So, we make this into warning, good or bad. So, that becomes our raw dataset. So, this is great. Like we have our raw dataset up and running. And now we want to build the model on it. But as most data scientists, I think will tell you that you cannot just take the raw data and simply shove it into a model and train the model to do something with it. So, the most of the hard work or the smart work lies in taking the data and making it into a form that is more meaningful to train on. That gives you meaningful outputs. And another thing is that you want to design a pipeline that can accommodate training this kind of data. So, in our use case, let me show you the kind of training setup that we have. So, there was a bunch of things that we have to consider while setting up the training pipeline and the pre-processing pipeline. And some of these for this particular project were the dataset size. So, the back place company has been collecting their data for like a long time. And like approximately one year of data corresponds to roughly 10 gigabytes of CSVs. So, if your resource starved or yeah, if your resource starved, then this is not a great dataset size to work with and it might give you problems. So, you might still be able to do it, but it will be super slow and inefficient. So, one way and probably the most obvious way to deal with this was to change the way that it is stored. So, as it is, the back place dataset comes as CSVs. And that is pretty to look at, but it's not the most efficient storage format. So, we converted to Parquet. That's for faster read writes. And if you're using like a deep learning model like PyTorch or TensorFlow, then instead of using Parquet or CSVs, you can just serialize the data and use like the .pt for PyTorch and similarly for TensorFlow as well. So, essentially make sure that you can do the reads, writes and efficiently. Another thing that we had to do was because the data is so big, the compute power may or may not be enough, especially when you're working in Python and you have libraries like Pandas. So, a library like Pandas is mostly single core based, so that would not give you very efficient, like that would not be very efficient and it would take some time, especially when you have such a large dataset. So, we used a library called Dask and that essentially takes your data and all the tasks that you run on it, the preprocessing, the cleaning and everything, it essentially divides it into workers and runs it over all the cores of the system. And if you have multiple machines, you can also schedule it to run over multiple machines. So, that was a very important design choice. And next, perhaps the more important one is the class imbalance. So, what I mean by this is in most systems, like failure or anomalies is probably not going to happen as frequently as things being okay. And while that's a good thing, like from the perspective of the world and society, et cetera, as a data scientist that kind of annoys you a little bit because this means that you will not have enough data to show your model what failure means. So, if I have a million data planes, maybe for this case you have maybe 100, 200 or something like that drives with failure and the rest of them are working okay. So, it's going to be highly imbalanced and biased. So, to deal with that, there's two main things that we can do. One is to down sample the abundant class. So, if I have 50 million working hard drives and only 10,000 failed hard drives, then what I'll do is out of the 50 million, I pick only a smaller subset like maybe 10 million or so that are well representative of all those 50 million data planes. So, that's one way to down sample the abundant class. And another way is to over sample the minority class. So, and this may and may not work for all models, but essentially you can use the same data point from the minority class again for each batch or something like that. So, these two things were what we did for the class imbalance. And finally, something that has to do with metrics. So, smart metrics as it turns out can be very manufacturer specific. So, for instance, you can have a Seagate hard drive that exposes smart 51, let's say, but a Toshiba hard drive doesn't have that at all. So, if your model uses only smart 51 or something, then it will work only for Seagate and not for all the other manufacturers out there. Or maybe it is the case that what one smart metric means for one manufacturer might mean something else for another manufacturer. So, let's say smart 51 being at a value 50 means like a Seagate drive is doing great, like it's the best drive in the world. But the same value for Toshiba hard drive, let's say, could mean that it's basically trying for help. So, there's a lot of variation in the interpretation and definitions of smart metrics. So, to deal with that, we thought because these metrics and because the data is so specific to the metrics, we should also make the models specific to the manufacturers. So, what I mean by that is basically we have one model to predict stuff for Seagate, one model for Toshiba, one for HGST and so on and so forth. And this is still kind of doable because there aren't as many manufacturers in the world. So, this is the design plan that we decided to go with. So, at this point, we have the pipeline sort of set up and we want to take the data and start feeding it into the pipeline. So, one of the most important parts of trading the model is to featureize the data that you have. So, first of all, you have to clean the data because like in the wild, the data is going to have a lot of holes. It's going to have inconsistent values and things like that. So, I'm sort of just hand waving it right now but like that's a big chunk of the process of training a model. So, you have to first clean the data set. Once you have a clean data, we selected only a small subset of the metrics to work with because there's a lot of smart metrics out there. And so, with that kind of high dimensions, it becomes very resource intensive to train the model. And even so, even more than that, it becomes more difficult to find models to train on because the concept of distance in high dimension is not very well defined for some metrics. So, having a lot of features can be a problem. So, that's why what we did was we read some previous research, we read on the internet about the domain. And we also looked at some of the analysis that Backplace had done. And based on all of this information, we took a small subset of the features that we want to train on. So, now once we have the features, we want it to go beyond just using the features as is. So, as it turned out in the CEPH, this prediction module, we have up to six days of like exactly six days of data available at inference time. So, instead of looking at just today's data to predict the health, why not look at the sequence? Why not look at the whole six days of data and look at the behavior of how it changed over time? So, we tried to characterize like this behavior over the six days over time. So, essentially for each time point, we have like a window and we look at like the six days before it. And we tried to characterize how it was. So, we look at the mean value over the last six days, the standard deviation of the metric and the coefficient of variation. That's sort of like just a ratio of the two. So, instead of looking at just a metric, just a number, you look at the behavior of that number over a period of time. So, now we have our clean data. We have down sampled the number of features. We have down sampled the data and we have featureized all our metrics. And at this point, we are sort of at the stage where we can just start feeding it to different kinds of models and try to see how it does. So, this part is what I think is the easiest part of machine learning to actually fit the model. Because here is, so this doesn't involve that much work from the data scientist side necessarily. Because this is just something like from a library, you just call model.fit or something like that. So, that's the easier part. So, for this particular task, we tried a bunch of models starting with like the defecto or like the go-to models for this. So, things like SVMs or decision trees and random forests. And then we also tried some fancier models that build beyond this. So, something like a XG boost, gradient boosted decision tree. So, it's like a tree but fancier, made in a fancier way. We also slightly explored deep learning techniques. So, something called long short-term memory. And yeah, but we didn't go too much deep into it and in a minute it will become clear why. So, with all our experiments, we found that what worked the best within what was possible was just a random forest. So, a random forest is essentially a collection of decision trees. So, a decision tree is essentially a set of questions the model asks to arrive at a conclusion. So, like is smart metric 10 less than 5? Yes, then go here. Is smart metric 50 more than 70? Yeah, go here. And then sort of trickles down into a decision. So, that's hence what is called a decision tree. And a collection of those is a random forest which we found to work the best with some limitations. So, the limitation that I have been talking about that what prevented us from trying other or going after more fancier Python libraries and things like that is that at the end of the day you want this model to be integrated upstream, right? You don't want it to just sit in some repository and sit there and look pretty. So, the thing was with upstream integration, not all Python packages work well for that. There's a limited set of packages that we can use to build models that can easily integrate with what already exists in this ecosystem. So, because of that we didn't experiment too much with things like deep learning and so on because it's a little bit more difficult to get it upstream. So, within that drill we found the random forest things to work the best. And yeah, so one more thing is the results at least from my personal opinion it's not something that is out of the world. It's definitely, it can definitely get better, but it outperforms the sample model that is already upstream. So, it's already an improvement over what we already have. So, that's one key win and another key win was that all of this, like all the data set, the model making and everything is completely open source as opposed to the one that is upstream which is sort of like a black box to us. So, with that said, there's always scope for improvement on the results. So, speaking of results, let's speak of results. So, usually when you have such an imbalanced data set and an imbalanced problem of failure when you have one class dominating the data set and a very small percentage forming the rest of it, you cannot use accuracy directly as your performance measure. So, we use, you use something other than that called recall and precision which I'll get to in a minute. And so, the recall is basically that of the data points or of the hard drives that were actually good or that were actually bad, how many did my model correctly identify as being good or being bad? So, in that respect, we were pretty close to profit store and in some case better than profit store. And yeah, so this is done on a subset of data that neither of the models has seen before. So, it's as fair as it can get with the data that is already available to us. So, that was our metrics on this. And another metric that we used to evaluate the model was precision. So, when my model says that, hey, this hard drive is going to fail, then how likely is it that that hard drive is actually going to fail? How precise is my model? So, in that respect, our models were almost always better than the profit store models, like you can see here. And in some cases, like the warning class, it significantly outperformed the profit store models. And the thing that is something to highlight here is that it worked especially very good with the warning and the fail, the bad class. So, essentially, it does very well where it matters the most. So, with that said, I want to show a small demo on like how you can get this set up on your self cluster, how you can use a predictor model and run it today. Is this sort of visible at the back? I think it won't, I don't think it will actually go full size, it will just add the black. But, oh well. Awesome. Contributions. All right, so here, first thing that I was just cloned the repo as is upstream. And because I was testing it out on my local machine, I did not, obviously my local machine is not a self cluster. So, self comes with a utility called VSTART and that essentially builds a deployment fake cluster for you to test out your developments on. So, the first few lines is just me setting up the fake cluster for testing, just make your build and then make restart. So, that's all you have to do, just two lines of code. And then it's going to go through making it, but I'm not going to go through it because it takes a very long time and this is a small talk. So, once it's ready, you want to start the cluster locally. So, before that, they also have a script called VSTART.sh and we just have to call it and that's going to set up a local cluster for you to test out. So, this also takes like a minute, but it's a super useful utility too. Thank God for the skip button. Okay, so now we have the local cluster set up for testing and the first thing that we want to do is to enable the disposition module. So, that's just SEV manager module enable and that will enable the disposition whether it's professed or whether you use these models. This is sort of a requirement for you to set up. So, one set is set up, you want to type, yeah. So, SEV has two kinds of this prediction available. One is locally, so it uses the local models that it has in the system to predict. And another one is cloud that essentially takes all the smart metrics and sends it to a server and then that server runs the prediction and gives you the result. The models that we made are only available locally and not on the cloud at the moment. So, the first thing that you want to do is ensure that it's running the disposition locally. Right, so that's there. So, once that's active. So, the next thing that you want to do is although we are confident in the models, we don't impose that on the user. So, by default, the models that are going to be used are the ones that are already upstream, the profit store models. But if you should you want to change the models that you use, you can just set a config variable in SEV that is predictor underscore model. So, we can set that model to Red Hat for the lack of a better name, which was Red Hat. And then once that is set, it's going to use the models that we built and then use predictions from those. So, that is pretty much all the setup that you need. So, once you have that set up, you're free to run all the predictions and have fun with it. And the way to do that is, so, SEV and then disc, is it disc predict or device life expectancy predict. And then the name of your device or the name of the hard drive that you want to run the prediction on or something like that.  It's unknown. So, you're already here, you're back where you started. But I tried both the models and both the models give the same prediction, which is unknown. And maybe it's not so much interesting running locally. But yeah, it's out there. You can do it and hope that it works. So, that is the end of the demo.  So, it also has a script called stop.sh, just run it. And yeah, I think that was all for the demo. All right. So, as you probably saw, it's a great model, but it's definitely not the end goal that we want to have. And in fact, at least as I see it, it's more of a starting point from where you want to go. And the goal of the project was to enable development on this and iterate again and again on the models. So, let's talk about how you can contribute to this. So, a core part of the project was a bunch of notebooks, Jupyter notebooks. And if you're not familiar with these, it's a format in which you can run, in which you have these code blocks, which have Python code, along with some fancy outputs, pictures, and things like that. So, we have notebooks that are designed for every step of the training pipeline. So, you have one for exploring the data, getting used to it, and getting a feel of what it's actually like. Then there's a notebook for cleaning up the data, which is a very, or at least in this case, it's a very hard thing to do. So, there's a notebook for that, and then you can take the data, clean it, and then there's a notebook for pre-processing that clean data, and featureizing, extracting the features, and then feeding it to the model. And then finally, there's also some notebooks that take all of this and put it into one thing. Even beyond that, we have something called a Kaggle Setup. So, my colleague Michael Clifford took the effort of taking all of that notebooks and essentially packaging it very nicely into this one Kaggle kernel, and essentially what that does is introduces the problem very gently, if you're a first user, and then tells you, describes the problem, describes what kind of models you could use, and basically handholds and takes you through the entire process. So, if you want to quickly go through this, there's like a nice description, the feature selection, and the missing data handling that we do in the repo put very nicely in this. Once you go through that, so like just making one function that does all of that previous steps for all other instances of the dataset or other manufacturers. And once you have that, there's like some simple heuristics that he wrote that helps you better understand the data and compare how machine learning models would have done against a simple prediction, like return one. So, that's all of this part. And of course, it has a lot of pretty graphs. And yeah, so finally, we compare the ML models with some simple heuristics and see how it does. So, that's all up here in this repository, and it helps you basically get set up with the problem and like the name suggests, getting started. And finally, if you're a deep learning enthusiast or like me, we also have made a set of tools, utilities, dataset classes that essentially help you get set up with this problem, but in a deep learning context. So now it opens like a whole new door of things that you can try on. So, what this includes is like a set of tools to take the raw dataset and make it into a format that is better to work with PyTorch. So again, this is going to be very PyTorch specific. Because of personal bias, but at the moment, this is what's available. And essentially it takes the data, makes it available in a format that's easier to use with PyTorch, and it has some dataset classes that you can use for very easily loading data and then training model. So, to give you a very brief overview. So, all you have to do is like from datasets import dataset and then, excuse me, give it the path to where the dataset is stored and it's going to do the splitting and everything for you. And yeah, and then in this line you define which dataset you want to use. So train dataset equals this dataset that I imported from here. And yeah, that's pretty much it that you need to load the data. And once you have this data loaded in like three lines of code, you can go ahead and define the model. And please don't define it like this. This is just a toy model. It will do very poorly if you run it, if at all. So yeah, you can define the model very simply. And then all you need to do is write a for loop, like for sequence in train order, prediction equals predict. So in like very few lines of code you can get started with this problem in a deep learning context and go have fun with it. With that said, I do want to mention one thing that because the dataset is so highly biased because there's like 99% data is of the successful or like working drives and only 1% is of the failing drives. So deep learning models usually don't do very well on that kind of data, at least not when you use a very simplistic loss function. So this may or may not give you great results if you use it as is with the default loss functions and everything. But with this basically you don't have to worry about getting the data and getting it set up. You can focus on things that matter more, I guess. So yeah, so whether you're a data scientist who wants to go deep into the problem, we have notebooks for that. Whether you're just getting started, we want an easy path to the problem. We have a Kaggle kernel for that, too. And if you're a deep learning person, we have that set up for you, too. So yeah, that's pretty much all I wanted to talk about today. And yeah, I'll be happy to take any questions. Yes. The general gist of it, for me at least, is that smart is a very bad predictor for failure. It's a very good signal to tell you, yes, I already failed. I see. And what I've noticed is a good predictor of failure is when the performance of the drive is going down. Like, a time for the drive requires multiple times of additional delay, like if you have a 700 RPM drive. If it takes like 40 and then 21, like 36 milliseconds to reach a given sector. And if that happens for multiple sectors, then you know that that drive really will fail in a soon-basic. I see. Do you have any plans to add this kind of monitoring and also data for self so that it can have better prediction? As you saw, there are around 30% failure for that prediction. Sorry to interrupt, but I'm really happy to succumb to that. Because everybody in self support has seen a lot of customer drives that before failure when they get really slow. Like you have slow ops just on normal SD. And this is a very good indicator that the drive will fail. But we had a lot of cases where there was not ever seen a smart on the control or something. It's still the drive that's already quite slow. Yeah, so did everyone hear the question? It's kind of too long to repeat. Okay, so to answer your question first, I think, but I may be wrong, smart metrics do have one of the metrics that measures the spin rate. Correct me if I'm wrong, but I think there's a metric that measures how fast the thing is spinning. Yeah. Normally, if you are reading just sequentially, you get blocks by blocks in under millisecond time. If the hard drive needs to read the block again, because the CRC correction failed or something like that, then you will see a multi-millisecond delay when he did that to that sector. So that's the predictor for the drive really has a problem because it doesn't give you the data. Oh, I see. Can you say the last part again? It doesn't give you the data? If you ask this to give you the data, you are asking a sector after a sector. You will get the sector spinning one millisecond, one millisecond, half a millisecond, one millisecond and so on. Yeah. Okay, I get it. So I don't think there is... So at least with this data set, I don't think they have that information and I don't know if how you could get that metric, essentially, because we were not doing the data collection itself. Part of the difference is you can't... Yes, I agree. But the thing was we didn't actually do the data collection part. We were using the data that people have already collected and open sourced. Yeah, that's my question. Do you plan to add that? Yes. So I personally don't know if SEF is planning on doing that. So I work in the AICOE. So I'm not necessarily on the SEF team. So I don't know if they have plans to do that, but that is a very good suggestion and I'll probably trickle up to the people. Yeah, thank you. I just follow up the challenge. One of the things I wanted to do was enable working with data like this. So that place here was the only publicly available data set where we could do something like this and SEF adds this prediction. It was already built in and you replace it with an open source data set. That's basically the first step and then we put the feedback from the community or guys that are actually running large entrusters and then play with that data set and then you come up with a better way. When it's the first punch or hold into this problem, somebody told me that basically serial numbers are the best way to predict what the driver will pay. So you see monitoring that batch and just moving forward with the others on that batch. But the office needed one of all serial numbers. Maybe that was the best way to do it. Yeah. So the question was are you planning on adding the number of LBAs written or the number of bytes written as a feature on the data set, right? Yeah. Yeah. So we tried half of what you said. So we tried to look at, I think you're mentioning power on hours, right? That's the lifetime of the, like how long the drive has been on for. So in one of the notebooks that I showed before, we looked at that metric for fail versus working drives and we basically found no difference in like when they fail. In fact, like if you run one of the notebooks, you will see that most metrics, how they work is they're usually like doing okay, okay, okay. And then suddenly they fail. So we tried looking at that and we looked at the power on hours too, but it did not provide any significant value. But that being said, we did not look at power on hours by day. So I'm trying to think if the model would, Yeah, exactly. So let's go on. If that's more, that would be more of the people to SSDs than to string it up, string it up. So that's an interesting point because we've got all these type of specific variations. It's thinking about this spindle, it's grasping at a point, you might see a flash drive storage which doesn't emit a single smart drive, also the smart lock is completely empty or doesn't receive any new entry. But the flash is accessible very soon because the garbage collection and the devices, the internet, so you don't have any evidence of who are egos or something like that. It used to come with their slow access. Yeah. So, yeah, sure. Just before you go, so about the LBA is written, right? So I think smart 241 or something, that is exactly that number of LBA is written. So that is already in the, yeah. Oh, yes. Yeah, it's on the ratio. Yeah, we did not do that. So I'm obviously not the domain expert, so I didn't know if that would be okay to do or what features should I be taking ratio of or playing around with. So did not add that and just took the metrics as is and featureized it. But yeah, maybe what you're doing could be a more powerful indicator. And the second question. So this was essentially written as a model to be used in the SEF ecosystem. But at the end of the day, it's just a model. So it's just a serialized pickle file. So if you can run Python on the system that you want to use this on, then you can essentially, yeah, so you can take the model, feed it the same data that we are feeding. And if the data is same, then yeah, the model will work. Yeah, any other questions? Of course. You know, I'm a father. You had a split second power outage and the firmware couldn't quite help with it and the device went offline and never came back after racing. We set that up to the vendor, they do their, you know, black magic and return. It's completely okay. That kind of thing because I wasn't poor. This is, of course, only a very small subset. I hope that, right? Because the fingers were quite hot. So are you planning to go all that also? Which goes back to the general framework I already had, like feeding in that vendor information. You won't probably get that kind of vendor information that will help ease the chance of outage. Can you say the last part of the last thing, though? Yeah, the last thing was, you know, vendor information.  I just don't want to play with power and it went offline and never came back. Even though the physical media was intact. You see that kind of information needed to be fed into the system also along the lines of, you know, TPW and you know, so at the moment, because the models are manufacturer-specific, I'm not sure how you would feed, so that's already kind of meta-data. That's already kind of like an assumption that model has to do only with that manufacturer. So that should already be able to deal with what you're saying. Like if that happens specific to vendors, to manufacturers, then that model should be able to deal with that. Does that make sense? Sure. Yeah, I'll be happy to. That is true. The second model. Yeah, the second model. The second model. The model has been trained in this deploy. Can you add it to the model? So, I'm not sure. So there's two answers to your question. So one is does it get data, field data from the customers? And the answer to that, I'm again not sure of because I'm not sure what the SEF team is planning for their instances. So I'm not sure if it gets from the customers, but as long as, as far as the retraining goes, yes, the models do need to be retrained and there is no automated way of doing that right now. Like you're just required, like someone to fetch new data from back place every quarter and then retrain it. Exactly, yeah. The SEF team do that on a community basis and say what's the better things about collecting that from the customer side, but it's still in the really early stages punching the first holes into SEF that we can work on that data and collect that data and setting up systems that will collect it from a community standpoint. So far it's just people's head and we need to implement it so that anybody who is working in the SEF community can drive that forward. Basically, our team tries to bring AI tools to the IT industry and storage also just an example in the virtual side of it. All right. Any more questions? Going once, going twice. Okay.


This is the Danny K. Show with Harry James and his music makers, presented by Paps Blue Ribbon. 33 fine brews blended into one great beer. Gas neighbors for your entertainment, Paps Blue Ribbon presents Eve Arden, Lionel Sander, Bob Jullison, the outstanding music of America's top band, Harry James and his music makers, first really Ken Niles and Danny K. The opening night of Danny K's new playhouse, and in those frantic few moments just before curtain time, all is confusion and excitement. The jury is warming up. The fans are putting the scenery into place. The actors are running through their parts. The orchestra is in the pit, filling up. Quiet, quiet, quiet. Now let's run through that number again. Okay? One, two. What happened to me on opening night? Why, what's the matter, Danny? Well, look at me, Eve. I'm bent over double. I can't straighten up. What? Well, look at me. I'm bent over like a jackknife. Oh. Now hold still a second. Don't move. Now unhook your suspenders from your garters. Gosh, Eve, I don't know what I'd do without you. I know what I'd do without you. I'd live to a ripe old age. Now look, Danny. Don't be nervous. Relax. Relax? Relax? Who says I'm not relaxed? I'm perfectly relaxed. What makes you think I'm not relaxed? Relax? I'm calm? Cool? You take it easy. It's only opening night. What can happen? Yes, you're absolutely right, Eve. What can happen? What can happen? Oh, a lot of things can happen. Now, nothing will happen. I've checked everything. The costume. Oh, they won't fit. The scenery. It'll fall down. The lights. Oh, the lights. The lights. I knew they would do it. They'll go out. And if they go out, we'll have to do the play in the dark. And if we do the play in the dark, no one can see us. Maybe it's better that way. Yeah. But if they can't see us, they won't know when we're going. Yeah. But if they can't see us, they won't know when we start. And if they don't know when we start, they won't know when we're finished. And if they don't know when we're finished, they won't know when to go home. And if they don't go home, I'll be breaking the curfew. If I break the curfew. Danny, will you please calm yourself? Oh, calm myself. Yes, calm myself. I'll do it. I'll calm myself. I said calm myself. Oh, oh! What's the matter now? Oh, I forgot what I'm supposed to say. What's my opening line, Eve? Well, you walk on the stage, through the kitchen door, and say, Ellen, I love you. Yes. Yes, I remember now. I love you. Earless, I love you. What is that? I don't know, Eve. I don't know what happened. No, all of a sudden, something happened. I can't help it. Whenever I think of that opening night audience, my throat tightens up. Now, forget about the audience. Take the line again. Earless, I love you. Let us go before a juicess of the purse and we'll make his burden grim. I cannot marry you. I am in love with Jack. Jack? Come on, Danny. Let's run through it again. All right. It's no use, Eve. I'm as tight as a drum. I just can't. I, I... Eve, Eve, Eve. What's the matter? It's gone now. I'm, I've got laryngitis. Oh, Eve, Eve, Eve. Woo, woo, woo, woo. Danny, what's the matter with you? I've laryngitis. Laryngitis? That's nothing but nerves. I can cure that in a minute. You can? How? Well, the first thing you've got to do is stop whispering. Just saying your normal voice. Hello, Lionel. How are you? I've got laryngitis. I've got to do anything with Danny tonight. He's just a bundle of nerves. Naves? Naves are controlled by the brain. I've got no nerves. Why? No coaching from the audience, please. Oh, what about me? I have laryngitis. I can't talk. Well, you'll just have to talk to your tonsils. Talk to my tonsils? Sure, Danny. You see, I read a book. You read? Well, sure. Sometimes what I read will move my lips even. Remarkable. Now, the doctor that wrote this book says that the only way to overcome nervousness is to talk to different parts of your body and get them to relax.  All is forgiven. Miss Arden, I shall ignore both your in and your end, though. Pretty difficult to overlook that. Now, look, Danny. Suppose I want to relax my feet. The first thing I have to do is have a talk with my big toe. He's the boss. I say to my big toe, Tom, I say, why are you so nervous?  You haven't acted like this since the last time I got a hot one. Now, stop wiggling and relax. Go to sleep. There. Toes to sleep? Yeah. Out like a light. Look at that. Evie, it worked. Gee, Lionel, the doctor who wrote that book certainly knows his stuff. I bet he could straighten out my nerves in a minute. When can I meet him, Lionel? Oh, you can't meet the doctor now, Danny. Why not? He just had a nervous breakdown. This is Ken Niles in the lobby of the Danny K Playhouse. There's a tremendous crowd here tonight. Everybody who isn't anybody in Hollywood is here. The excitement of an important premiere is in the air, and everybody's talking of just one thing, the new Playhouse. Listen. Got a match? Yeah, got a cigarette? Nope. Yes, this promises to be the most glittering of all Hollywood opening nights. And speaking of opening nights, every night is opening night for a bottle of Pat's Blue Ribbon beer. The crowd is surging into the theater, and already the first 33 rows are filled to capacity. And speaking of 33, remember Pat's Blue Ribbon is blended from never less than 33 fine brews. Full flavor blended as only Pat's can do it to achieve its cherry sparkle and tang. It's gold and rich color. Good evening, Mr. Niles. Yeah, good evening. Folks, that was one of Hollywood's greatest columnists and critics. And speaking of great, when you taste that delicious appetizing goodness of Pat's Blue Ribbon, you'll say this really is a great beer. Curtain call. Well, there's the curtain call. And speaking of calls, wherever smart people gather, Pat's gets the call. For they know you can order it with confidence and serve it with pride. For no matter where you go, there is no finer beer. No finer blend. Then Pat's Blue Ribbon. How's everything looking? Oh, wonderful, Danny. Oh, well, any celebrity show up yet? No, but hey, look at that big car pulling up. I'll bet that belongs to some famous Hollywood star. Hey, look, he's getting out. Maybe it's Gary Cooper or Kerry Grant. Well, let's go over and see. Yeah. Oh. Jealouson. For a minute, we thought you were Kerry Grant. Oh, that's silly. I'm much more the Humphrey Bogart type. Well, I'm glad to see that you came to my opening, Jealouson. Oh, I'm a first nighter from way back. You are? You betcha. I thought you betcha. Hey, Mr. K. What play are you doing tonight? Well, we're doing a Russian play, my father wrote to me when he was nine weeks old. He was a child prodigy. Oh, what's the name of it? Well, the Russian title is Cipi drippi, cipi drippi lapamponi.  Jack na Cipi, Jack cedrak na Cipi drippi, Jack cedrak cedroni, na Cipi drippi lapamponi. Oh that's wonderful. But what does it mean? Well, it means that once upon a time there were three Chinese boys. Jack, Jack cedrak, Jack cedrak cedroni. And there were three Chinese girls. Cipi, Cipi drippi, Cipi drippi lapamponi.  Then they all got married. Jack married Cipi, Jack cedrak married Cipi drippi, Jack cedrak cedroni, married Cipi drippi lapamponi. Very happy. That's a very tender story. What does it mean? Once upon a time there were three Chinese boys. Jack, Jack cedrak, Jack cedrak.  Harry's about to play the overture. Oh that's fine, what's he playing? I wish I knew. Oh, doesn't anybody know what's going on around here? The curtain is going up in a minute. Danny, that's the name of the song and Kitty Callan's going to sing it. Oh, thank you for it. The curtain's about to go up. You all set to eat? Lionel, how about you, Harry? I'm all set, Danny. I am James Butler. I murdered my master because he was cruel to me.   Oh, that's fine, Harry, that's fine.    Danny, Ken Niles is out front greeting the first nighter. Well, good friends, we welcome you all to the opening of Danny Kay's new playhouse. Tonight we bring you an epic of Hollywood moviemaking starring Danny Kay as Donovitch Kayov, the great Russian impresario. Curtain? Music. The scene opens, the great Donovitch Kayov is just entering his private office. Where is my secretary? Right here, Mr. Kayov. Take some notes here, I got an absolutely sensational idea for a beautiful catastrophe. I'm ready. Quick, take notes. I'm ready. Too late! My brains have too much under mind, wait. Wait, I have a second idea for an absolutely sensational motion picture. I have seen close-up of men, long beard, razor in hand. What do you guys call it? Objective Bormashave. All my product. Bad humanity, that salmon you had last night must have disagreed with you. What? Who is salmon to disagree with me? Take it easy, Chief, it was only an idea. Well, who are you to get ideas? I am the genius. Never forget this. Which reminds me, I forgot my coat in a restaurant. The secretary, the brown derby on the phone. Here you are, sir. What took you so long? Hello, brown derby. Would you do me a favor? Would you please go across the street to Joe's cafeteria and get my coat? Secretary, topen up your tea, Prater. I have for you a very long shorthand. A super detective story in technical color. A detective story? Absolutely. I will call it Meet Me in Louis. Saint? For my stars, I will have Lorry in black ball and Humphrey Bugle. Can you get him, Chief? Can I get him? Of course not. But with Danimic K.O. Nothing is possible. What is next? There's an actor outside waiting to see you. An actor? All right, show him in. This way, please.  James the butthole. I killed my master because he was cool to me, so I killed him.  Well, Mr. K.O. How do you like that? I can tell you in one word. Well? That is not the word. Can I make pictures with actors like this? How can you make pictures without money?  Who is frog? What are you talking about? Yesterday, I put big deposit in bank. 6,000 clams. But that is not good enough for them. They want money. Wait just a moment. I have it. That girl. What girl? The girl I meet last night. Little Iwaneska high pockets. She is very rich. Very crazy about me. Also very rich. Yes.  Ah, good evening, my little scallion. You are so lovely. You are so beautiful. I would like to run barefoot through your hair. I am James the butthole. All right, I'll run through your moustache. Don Quibble. Oh, Donovich, it's just too, too charming of you to have dropped in. I think it's fun to barge in on people. I am always happy to drop in on a barge like you. Ah, my little cabbage. You are so beautiful. If I could only capture your beauty and candles. If I only had my brush and my weasel. Ah, every time I look into your eyes, my heart goes flop-chicka-flop-chicka. Oh, Donovich, let me hold me in your arms while I sing to you the love song of my native country or chest health. Help me to make such a metronome. I think that the great Donovich chaos loves poor little me. Poor? You are poor? Papa controls the estate. But never mind Papa and his money. Kiss me. Step aside, I kiss Papa. Easy as that, Donovich. Papa is extremely frugal. You mean your father squeezes nickels? Oh, he isn't as bad as that. Would you care for a glass of buffalo milk? Really, Donovich, I must tell you this. What is it? Papa is utterly heart... Just a second, that's Papa. Oh, pardon me. Papa is utterly heartless and ruthless. He's a cold man. Out of my way, my little droshka. I'm going in to see your father. Papa, papa. Ah, there you are, Mr. High Pockets. No way.  I am nasty. Are you making me? Spare me one moment, please, I beg of you. I am Donovich chaos, the impresario. I write, act, direct, produce, and Saturdays I play short stuff for Pasadena Bloomer Girls. I am a genius. And I have a letter signed by me to prove it. State your business. I am a very busy man and also very tough. All right, I will get down to brass tactics. I need one million dollars. No, more. Five hundred thousand. Okay, you talk me into it. What are you going to do with the money I give you? I am going to produce a great epic. A modern master-post based on the story of Little Red... Little Red Riding Jacket. I call it, For whom the wolf howls. Jay, that sounds fetching. But before I give you the money, there's one proviso I must make. There is always one proviso. Unfortunately, not, not lately. Yes, I insist that my son have the leading role. Only I have one condition when I do this. Can he act? Oh, he is a great actor. I'll call him in. All right, son, act. I am James the butler. I killed my master because he was so cruel to me. Out of this! Fair enough. Rather than use him as an actor, I abdicate from the picture business. I return to the ballet. The ballet? Oh, are you a dancer too? Am I a dancer? I was born to be a dancer. And when the music plays, the ants and my fans do touristy things. And what is the answer? Why is this condition? Because when I was born, I was born in fifth position. I enter the world with an oop. LA. Page two month, three weeks. I was calling on my ballet. Other children say da-da. And mother. When I opened my mouth, came out Pavlova. So I just the chassis and I plead to play it till I was the corpse. The corpse de ballet, was then I met Pavlova. Uh-huh. What an elevation! In such an extension She was dancing in Big Syracuse Ballet with a part of the famous Syracuse. I spell a Syracuse, but no mind of his own. In this ballet he is walking on tightrope, 100 feet in the air, when suddenly somebody yells, Swing it! I spell a Syracuse, but no impulses. There was Pavlova without a part right in the middle of heritage. Everybody's in great confusion. But not me. I am unconscious. A greatly sympathetic Pavlova told me what to do to rise from Corpse Ballet to public dancer number 2. It's a very simple method, there is really nothing to it. Whatever everyone else is doing, you don't do it. They are up, you are down.   They go east, you go west, that is where the sun shines best. Pavlova! I got out of Siegme to dance in the healthy prince. It is bitter cold night. And for costumes we are wearing strings of vitamin pills.  So I say to myself, what is more important? Open your morning. Make all appearance of that flannel underwear. Next morning I am hailed by the critics as the first hot ballet dancer. But then on, when Paul Hake starts a faking wedding, he's making with the shaken apple drape a salsa paper on the floors. Then who's the man they call on when they cannot get the Paul? I'm the man in the red flannel drawers.  Then to scare the bull and carmen, they just send a three-alarm for the man in the red flannel drawers. That leeching, mass-inducing, faking, pretzver go to bober, to man of a luban of a u-san of a Pavlova. He grefsky, or she grefsky, or patefsky, but of course, I came to America to make my 54 farewell debuts. But between my farewell and my debut, what has happened to the dance? They're now dancing with angle. They're now dancing with barefoot. They're now dancing with significance. Martha Graham. Martha Graham and her six little crackers are performing monster ballet called diesel engine number 45. Martha Graham is playing part of the diesel engine.  Dwight Mann is making guest appearances flywheel. The ballet star. Pavlova. The wonderful things we used to do. I could do them again if I only had you. But how can you do when you ain't got with me? Pavlova. Oh yeah, you lionel? I'm a sucker for all. I'm cultural culture. Well, I'm glad to see you enjoy the finer things, lionel. I just ooze refinements. I'm a patron of the arts, a patron of the opera, and a patron of the cinema. How about beer? Patron of Pat's Blue Ribbon. Oh, that's fine, lionel. All people who appreciate the finer things always choose Pat's Blue Ribbon because it's a blended beer. Full flavor blended from never less than 33 fine brews. This process of full flavor blending ensures the even goodness and sparkling quality that sets this famous beer apart. Yes, Pat's Blue Ribbon has all the qualities that particular people look for in a beer. It's the most tingling, appetizing flavor that ever flowed from a bottle. So order it with confidence and serve it with pride. For no matter where you go, there is no finer beer, no finer blend than Pat's Blue Ribbon. This program was directed by Dick Mack and brought to you by the Pat's Brewing Company in Milwaukee with confidence. You are here to know. This is E.D.S. Trigolumbia Broadcasting System.


Hello everyone. I am Ambassador Gina Abercrombie-Win Stanley, the State Department's first standalone Chief Diversity and Inclusion Officer. Before I share my thoughts on why it's so important to diversify our diplomatic corps, I first want to thank the organizers of this year's conference. This is the 13th HBCU Annual Foreign Policy Conference and it is one of the State Department's most important outreach events of the year. It is an honor to be with all of you today. As you've heard throughout the program, we are eager to recruit HBCU graduates to join the State Department as both foreign and civil service officers. We need your talent, your excellence, and the diverse lived experiences that you bring to the table. Secretary Blinken has said repeatedly, America's diversity is our greatest national security asset and I couldn't agree more. As we navigate the challenges of the 21st century, a once-in-a-generation pandemic, erosions to democracy here, at home, and abroad, and a climate crisis that threatens our collective security, we need all hands on deck. This means creating a State Department that reflects the rich diversity of our nation. A more inclusive workforce will ensure our foreign policy is stronger, smarter, and more effective. Throughout my career, I've had a motto that guides my work. It's divided into three parts. Intentionality, transparency, and accountability. The first is intentionality. We need to understand the importance of DEIA and the pursuit in everything we do. It means asking with intention who is at the table, and more importantly, who is not, and is the table accessible to all? In June of last year, President Biden underscored the importance of DEIA by issuing a White House executive order calling on all federal agencies to develop multi-year action plans that advance equity, inclusion, and accessibility in concrete ways. The Department's Strategic Plan is being finalized now and will be released in March. This bold blueprint will lay the groundwork to build a State Department that not only recruits but works to retain diverse talent so that everyone has an equal chance of success, no matter who you are or where you come from. The second pillar is transparency. And the foundation of our transparency efforts at the State Department is grounded in data. And by data, I mean disaggregated data. One of the first actions I took on when my office was established was to launch a DEIA data working group. We recently developed a workforce demographic baseline report which will be updated annually and allow us to measure our future progress. And lastly, and perhaps most importantly, it is accountability. Accountability is the heart and soul of DEIA work. It means building a culture of courage and allyship, where we speak truth to power and lift one another up. Many of you may have seen that earlier this month we honored Ambassador Terrence Todman. For those who don't know his story, in 1957, when Ambassador Todman was just starting his career in the Foreign Service, he noticed something was not right. As a black diplomat, he could not eat in public due to segregation laws in Virginia. Rather than stay silent, he spoke up, and his principal dissent and courage in the face of injustice led to the department integrating our dining facilities. Ambassador Todman went on to become a six-time ambassador, the second highest number of ambassadorial appointments in the State Department's history. To my State Department colleagues watching here in D.C. and around the world, my call to action is to you specifically. There are various HBCU graduates staffed within your bureau, and we want to keep them motivated, engaged, and empowered to go the distance in our organization. I encourage you to coordinate with fellow HBCU alumni to engage your alma maters, as well as employee affinity groups such as Blacks and Government and the Thursday Luncheon Group. These organizations are in place to support the advancement and development of our colleagues from African-American and Black communities. I hope you enjoyed this year's programming and hang on a bit longer to join us during the after-hours session from the yard to the embassy, featuring diplomats who leverage their student leadership skills as public servants.


Hi, I'm Rusty Komori, and this is Beyond the Lines on Think Tech, Hawaii. I was the head coach of the Punahou Boys varsity tennis team for 22 years, and we were fortunate to win 22 consecutive state championships. My book Beyond the Lines is about leadership, creating a superior culture of excellence, and finding greatness, which is what this show is all about. My special guest today is a man of great character, a former highly respected judge known for being tough but fair on crime, and a candidate for Honolulu prosecutor. He is Judge Steve Aum, and today we are going beyond the courtroom. Hey, Judge Steve, welcome to the show. Thank you for having me, Rusty. It's great to be here. I know you grew up in Manoa and Kaimuki. Tell me about what schools and sports you played in. Well, both of my parents were professors at UH, so my brother Robbie and I went to the lab school from preschool on, and then I went for two years to the University of Hawaii, then went to transfer to the University of Oregon, which was a good move.  Judge Steve, what's the first job you ever had that you got paid money for? Dole Canary. After my sophomore year in high school, I joined, I think, the other 10,000 high school students and going to Dole Canary. And I still remember the first thing I ever bought was a clock radio from Shirokia with my first paycheck. Did you, I heard that you were a taxi driver as well, too.  And in high school, I played every sport that University Lab school had virtually. And then after I graduated from high school that summer, I was working nights at Dole. I needed a sport, so I went down to Kalakaua Wreck to learn how to fight. And I did some amateur boxing from there. Ironically, the first time I went to Oahu Prison was for a fight. Wow. You mentioned your wife, Haunani. Can you tell me more about your wife and your son? Right. My wife, Haunani, she joined the prosecutor's office as well. And she was there for nine years. She was then an administrative prosecutor with DCCA. She then became an administrative law judge. And she did that for a decade. So we've had shared interest during that time. Our son, Chris, is now living in the Washington, DC area. He's a writer. That's what's in his heart. And he's very good. I think he'd make a great lawyer, but he has no interest in that as far as we can tell. And Judge Steve, what is your connection to President Bill Clinton? Well, I was a division head at the Honolulu Prosecutor's Office. And the recommendation for who to be the United States attorney comes from the senior senators. And Senator Inouye was interested in a lot of things, but law enforcement wasn't at the top of the list. And so they looked around for a couple of years. And then he asked the Hoy State Bar Association, who would you recommend? They recommended me. And so I didn't know Clinton. I wasn't involved in politics. I was a local guy working at the training police officers, training deputy prosecutors. And the last case I did at the Honolulu Prosecutor's Office was the murder of a police officer. So I then got tapped to be the United States attorney. And the police were thrilled. Wow, that's interesting to see how that all started. And then in 2001, you became a judge. And you quickly had a great reputation of being tough but fair on crime. Can you tell me more about how you were tough on crime? Well, I think part of it was I was the first career prosecutor to be appointed to circuit court in Honolulu.  But my heart was in it. My commitment was in it. So I was a felony team captain. I was head of district and family court. And then when I became US attorney, I was tough but fair there too. So when I became a judge, I had support from the defense bar. I had support from the prosecution. And on the bench, the truly violent and dangerous, the ones who absolutely won't stop stealing, should be sent to prison at sentencing. But fortunately, that's only about 30% or 35% of the defendants at sentencing. That means the vast majority, 65%, 70%, can and should be placed on probation. And we have good strategies to do that. And I was a real proponent of treating people that are appropriate for it compassionately and help them deal with their drug and alcohol and mental health problems. So that's where the balance comes in, doing both sides. Yeah, balance is key. And you also founded the HOPE probation. Can you tell me about that too? Well, the very first week on the criminal calendar, I took Marine Milk's place. And I would see these motions to revoke probation, because most people get placed on probation at sentencing. And we have really good probation officers. They're all social workers from UAH. They really care. But they didn't have any mechanism to have any quick responses if guys screwed up on probation. And so I thought to myself, there's got to be a better way to do it. And I thought, how did my wife and I raise our son, who was 15 at the time, but when he was a little kid? And how were we raised? And if your parents love you, they care about you. But if you do something wrong, they do something about it immediately. And that's how you tie together our bad choice of behavior with a consequence. So I thought if we could give a swift, certain, consistent, and proportionate jail sanction, usually very short, a couple of days, then this might help our defendants succeed. So I worked with Shirley Noye at the probation department, and then all of the criminal justice partners to create hope probation. That's such a great program that you did. It's so necessary. And I want to ask you, Judge Steve, about your thoughts on how some prisoners are being released from prison because of the coronavirus. And then as soon as they get out, they're committing crimes. Well, I think nationally, prisons really became a hotspot in many communities. So the Supreme Court, other people, the public defenders here, were really worried about that happening here. Even though we have a very low infection rate as a state, they were worried about that. So they asked the folks to get involved in doing that. I was really happy that the initial request was just to release people categorically. And that request has turned down. And the special master said, no, it should be a case-by-case basis. And many people got released just because that's the way jails work. People finished their short sentences there. And just so people understand, jail is pretrial confinement, or a place where people do time as a violation of probation. That's OCCC. Prison is multiple-year sentences. So some people were getting released anyway. I think the Honolulu Police Department did a great job of trying to issue citations rather than arrests when they could. Delayed arrests on some other cases so the inflow into the jail was greatly reduced. Then through this process, the prosecution and defense could decide could they agree on releasing some people. And if they couldn't, then it went before a judge to make that call. So it was a combination of factors that did it. But I don't think anyone in the business was surprised that when you release some guys who are in jail, some of them are going to commit crimes. I was concerned when hope people were released, because hope is a model where you have a consequence for every violation. And they need to have that consequence to keep learning about it.  So the other thing I think the public doesn't quite realize is people are not really being supervised these days. There are no in-person interviews. If people are released, there's no drug testing going on. I'm happy to see that they've taken a pause now. They've taken a break. Supreme Court says this experiment, this part of it is over, and we can try to carry on. I think we also should be looking at the Department of Public Safety to do social distancing inside, do testing of every prisoner and every guard and every civilian employee. So we can find out for sure what the situation is. Judge Steve, what are your thoughts about how the Black Lives movement will change the criminal justice system? Well, I think it'll probably change it more depending on the city and depending on the culture of the police department, because nobody's perfect. We have a very big police department. I think it's 2,000 uniforms. But the culture is not killing Black people here. And the police officer would be the first to tell you that. Every once in a while, you're going to get a police officer in that bigger group who behaves inappropriately, criminally, and they should be prosecuted. And when I was the United States Attorney, we did that. We prosecuted police officers for corruption. We prosecuted a police officer for brutality, for beating up prisoners. And he went away to federal prison for more than five years. And it's the sergeants at HPD that really are the backbone and the ones that set the tone on the street. So I think our police department, for all the problems you're going to have with that bigger unit, does a good job overall. And they don't have that culture. At the same time, if there are inappropriate things done and you see it in the paper every once in a while, those folks need to be prosecuted and they need to have consequences for it so the public can be reassured that whether it's a police officer or a prosecutor or anybody else, if they break the law, they're going to be held accountable. Yeah, accountability is key. And now that you're a candidate for Honolulu prosecutor, what will you do to restore trust in the prosecutor's office? Well, I think you've hit the nail on the head that this campaign is all about restoring trust to the prosecutor's office. I don't think the public has trust there. I don't think the police department does. And part of it will be creating a culture of doing justice, not winning cases. And there's the great US Supreme Court case, Berger versus the United States, that when I was a supervisor of the district and family court division, where people first come into the office, I would hand that out to all the deputies. We would discuss that these aren't words only. The defense attorney's job, and there's several running for prosecutor, their job is to help their individual client. It's not to reform the system. It's not to try to change. Their job is to help their client. And if I was charged with the crime, I would really want to have somebody devoted solely to helping my case. The prosecutors are in a different position. As this case said, you're to strike hard blows but not foul ones. So you've got to create that culture. At the same time, you've got to train the deputies to be ethical and effective trial attorneys. I did that when I was head of district court. I will do that again if I get in. So the deputies are trained. They've got to be encouraged. I've got a whole raft of people volunteering to come back to the office and help do training if I win. And then they'll be more confident and they can create plea agreements from a position of strength. And at the same time, it's meeting with all the other law enforcement partners to say I'm here to partner with you. Nobody, my parents taught me to treat everybody with courtesy and respect. But also nobody does anything by themselves. It's always working with others. And if you have the right leadership, if you have a spirit of collaboration and you work hard, you can get almost anything accomplished. So I will be a prosecutor who wants to work with the attorney general's office, work with the US attorney's office, work with the courts in trying to make Honolulu the safest place it can be and to treat people fairly.  That's so necessary. I mean, just for public safety, right? It is. And unfortunately, over the years, the prosecutors have had a knee jerk approach in court of asking for prison for everybody. And that's crying wolf. And the judges do not pay attention as much because it's the same story every time. Or the deputy prosecutor to come into the back in chambers with the defense attorney and say, well, you know our office, I got to ask for prison out there, but I think the guy should be put on probation. Well, what I'm gonna talk to the deputies about is let's identify who are the bad guys, who are the dangerous, violent ones, the ones who won't stop stealing. And then you go all to the map to try to get them in prison. But if somebody is appropriate for probation, ask for probation, that's the right thing to do. Don't worry about CYA, you know, identify it and then talk to your supervisor if you're not sure. You will have more credibility with the judges. And then maybe they'll listen to you more about that because those people should be in prison. Not everybody should get a chance at probation at sentencing. Some people should go to prison right away. Judge Steve, you're also making a commitment to reduce crime in local communities. And I know Chu Lan and Lieutenant Governor Josh Green will absolutely love you for that, right? Absolutely. One of the programs I was most pleased in doing was the Weed and Seed program in Kalihi Palama in Chinatown. And we went and asked the community, what are your biggest crime problems? And they said, we are the experts. I said, yeah, but I live in Kamaki, I don't live in Kalihi. And so they told us what the big crimes were and we addressed those. And they said, you know, I wish we had things for kids to do after school. I wish we had a Head Start program and other things. So I'm really pleased. I was able to take a leadership role in that, but we worked with the prosecutors, we worked with the police department, federal agencies. And together, this whole effort, we reduced crime by 70% in Kalihi Palama in Chinatown over three years. That's from like 7,600 misdemeanors down to 2,400, from 3,000 felonies down to 750. That changed the character of the neighborhood. When we started, local people were afraid to park their cars in Chinatown and go walk. After three years later, they were confident in doing that and a lot of restaurants and bars and hipster joints could open up because the neighborhood was safe now.  And like we did before, expanded to Kakako and Alamoana. And then I'm thinking Waikiki would be next. After that, it would be working with whatever community on the island wanted to try that strategy. Because it takes work, it takes coordination and but I think it's a great way to try to reduce crime and get the businesses and residents involved as well. No, that's hugely significant. And you've already proved that you're capable and able to do it. And we just need that done in every community. And I wanna ask you, Judge Steve, this is the first time that you're campaigning. And how do you like campaigning and meeting a lot of your supporters and new people? Well, it is something new. And as a former judge, as a United States attorney, I tended to avoid social media. There was Ben Villaflor when I went to fight at Kalakaua Wreck, we're the same age. He was a pro, I was an amateur. So, but we became friends. He's the Sergeant in Arms at the Senate, the State Senate these days, and has been for a long time. But the whole thing that I really enjoyed about campaigning is meeting people. There you see a coffee hour and people get to ask me questions. What about this? What about that? And because I've been involved in the criminal justice system for 31 years, it's given me a much better unique perspective. Because I have to say, if you've only worked at the prosecutor's office, you tend to view things, you see only meet police officers and victims.  As a judge, I was able to see the whole picture and realize both roles are important. They're very difficult. They're very different. But we need the whole system to work. And this campaign has been great to meet people from all over the island, hear what their concerns are. A lot of them know me from being a judge or a part of the U.S. attorney. So it's great to reconnect with some of it. And with other ones, it's great just to try to explain how the system works and answer their questions and tell them what I plan to do. Having that, seeing the big picture and having the awareness about all of that is so key. And what endorsements have you received so far, Judge Steve? Well, right off the bat, it started with the Chopo, the police union. And they recognized that, I've been there before, I've been a proven leader, I'm gonna be able to hit the ground running on the first day in office. And then after that, the masons, the laborers, the iron workers, the bricklayers, the teamsters, the ILWU, they recognized, as one of them said, you're the only administrator in the group. Running that office, it's got more than 200 employees, there are 105 lawyers, it's the second largest law firm in the state. You've gotta be able to lead. And I did more than, I did 26 jury trials and I was at the prosecutor's office, including five murder cases. I presided at over 200 jury trials as a judge. So I have the chops to be able to talk to them about it and do training in trial advocacy and in ethics. And I think the combination of it is good. And so I've been in competition with my opponents to get these union endorsements and I'm really happy to see so many of them are lining up behind me because they know I can get the job done. I've got the leadership background and I'm willing to do the tough, make the tough decisions and doing it. It's like for Schopel, I had convicted people that were in the police department because they did wrong and the police officers have no problem with that because they don't want bad guys in the department too. And if a person like beats up a prisoner, he should be prosecuted but he also puts every other officer in the area in the difficult situation because they could get prosecuted for not doing something about it. So running a clean department, having the police department lead, Sue Ballard's doing a good job. They're 99% of the police officers are good. We've got to prosecute or kick out the ones that are not. I totally agree with you. And I want to ask you another important question, Judge Steve, how did you like reading my books? They were great. At times I thought we have a mind meld on this. It really was funny because a lot of it is leading by example, treating everybody the right way, being as open and honest as you can about decision making, giving people a chance to prove themselves and testing them. I mean, this is a tough business. People joined the prosecutor's office after watching CSI or all of these one hour TV programs and they don't understand what being a prosecutor is all about. When I was a judge, I would encourage people, if they wanted to go into litigation, join the public defenders or the prosecutor's office, they'll be in court every day and then they can see whether they like it or not. And a few went to civil and about half of the others went to the prosecutors and the defense. And your books talk about leadership, but it's leading by example, it's giving people a chance, it's being honest with them, it's giving them feedback, whether they want to hear it or not, but it's also encouraging them and stressing the good parts of what they're doing and trying to teach by example and teach by catching them doing good things. And I've done all these things with, and I've tried to do that in every job I've had and I think I've gotten better at it by learning from mistakes over the years. Yeah, it's about making a positive impact and choosing better choices and creating that superior culture of excellence. And I liked when I talked with you the other month about, and we could talk on and on for hours and hours because you have that superior culture of excellence.  Well, I think as, when you're trying new initiatives, that's always gonna be a challenge because change is the hardest thing for people.  So I think we have to use our positions to try to make life better for people.  And I was convinced we would. When it came to hope probation, it was trying to get people to do things the new way. Hope is a lot harder than regular probation. But because on regular probation, what most people don't realize is because they're the only sanction they can give on regular probation really is revoking probation and send somebody to prison for five or 10 years. They tend to give people a lot of chances in hope they get a sanction every single time and learn from it. And I was the rare judge that I'd actually, you know, look at research, look at data, and I think criminal justice decision making should be based on research and data and not on anecdote or hearsay or the dreaded, we've always done it this way.  But the beauty is we have great research from, this is a part from all the anecdotal stuff where people are, so many people's lives have been helped. But we have research from Pepperdine UCLA and NYU showing tremendous success for hope probation. And as a nice addition, we asked them, how did native Hawaiians do in hope? And on regular probation, the failure rate is 26% fail at probation and go to state prison for several years. In hope, it's only 15%. And since in hope, the hope judge supervises more than 2,000 felons at a time, that means hundreds of native Hawaiians have succeeded on probation and not gone to prison because they were in hope, which makes me tremendously happy and satisfied that when we bring everybody together and we work hard and we ignore the naysayers, we can get almost anything done. Yeah, no, that's so good. I loved when I looked into the whole probation and everything that you, how you started it and the impact that it's having. And I wanna ask you, Judge Steve, what do you feel the best leaders do? Well, I think they respect their employees. They're willing to take a chance. They set out their vision for what they wanna do and they talk to their employees or the people that are working with them about why they each are important to getting that job done and getting to the point. And so my parents teaching me about treating people with courtesy and respect that really comes into it here. When I was head of district court, a woman came up to me and she, one of the clerical persons and she said, another clerk is teasing me every day about my accent. And she said, I'm Filipino, I have an accent. And she said, this is making life horrible here at work. And so I met with that other clerk as well as the head of the clerical section and we talked about it. And I told her, this is cruel, it's inappropriate, it stops today. And that woman stopped me in the parking lot of the funeral a few months ago and reminded me of that and said, it did stop the very next day and never came back. Well, that's the kind of thing a leader has to do. You have to care about your people, you have to take care of them and you have to bring them along and encourage them. So we're all going in the same direction and we're part of something bigger than ourselves that does good. And the Honolulu CrossFit here can do a lot of good if it's run right and you have the right leadership. I totally agree with you. And Judge Steve, I want to thank you so much for joining me on the show today and sharing your insights.  Well, thank you very much for having me. It's been a pleasure and keep writing some books. Thank you, Judge Steve. Okay, take care. I'll see you on the campaign trail maybe. And thank you for watching Beyond the Lines on Think Tech Hawaii. For more information, please visit RustyKamori.com and my books are available on Amazon and Barnes & Noble. I hope that Judge Steve and I will inspire you to create your own superior culture of excellence and to find your greatness and help others find theirs. Aloha.


What's going on everybody my name is Nick. I'm the manager here at the surf station and today I'm here to talk to you about our Stoka Hero giveaway. During these hard times we were trying to think of ways that we could do to help the people who have been giving to us so much. With that being said we figured we'd reach out to you to help nominate some people that you might find deserving of a brand new CI happy. After going through all the emails there was one that stood out to us the most and it was a clinical pharmacist at a Ronald Reagan Medical Center in Los Angeles. So this individual was able to not only help in his local community but also when he heard the call from Governor Cuomo in New York that they needed healthcare workers he decided to take his two weeks vacation and fly from LA to New York to blend his services to help fight this pandemic. With that being said this is why we chose Ryan Morgan of Los Angeles to be our winner of our Stoka Hero giveaway. So congratulations Ryan you now have your choice of picking any CI happy that we have in stock so reach out to us let us know what you want we'll be sure to get that right out to you. After going through all these emails we realized that it was too difficult just to pick one person so our owner Tory Strange decided to not only give away just one surfboard but another 10 surfboards for the next 10 weeks. So keep submitting your emails you want to keep keeping you guys stoked so please be sure to just send an email Stoka Hero at surf-station.com and keep submitting your stories keep nominating people that you think deserve a brand new channel Island Surfboard.


Hold on. I'm not sure what you're doing. This is Mr. This is Mr. This is Mr. This is Mr. This is Mr. This is Mr. This is Mr. Go ahead and stroke it. Uh, it's a miracle. He was quite happy. Birdie's drug. No. They can save her. Birdie's drug. How old is he? 28 weeks. And 55 pounds. The bird's shrugged. Now Sam's no better than that. It was the first year when the bird almost beat the brothers of the dead. They were the whole family. Because he'd be quiet and he wasn't shrugged. I ignored this point actually. That's a question no one can answer. What has to happen, Mr. President? If they'd given me a different answer on Charlie and his future, I would have pardoned him. No answer to that. Mr. President, are you monitoring the situation with the Cuban prisoners who are rioting and are you going to do anything? The Attorney General is on that and taking care of that and has proposals to make to them. It's suggestions and things that can't be done. Are you going to promise that they won't be sent back to Cuba, sir? I'm waiting for the Attorney General to come down and make his proposal, too. What did you mean, Attorney General? That's just being cagey. I have to go back home with a surprise. This is a little time-free. Let's go to the Turkey one day. We'll see you at the Turkey one day. Give it a party. I think one Turkey knows a man. If you give him information otherwise...


Hello everybody! Watch this video to find out how to install and configure a postfix mail server with Dial Cod. How to configure the mail server to prevent your mails from falling into the spam folder, how to add DNS entries such as SPF, DKIM and DMorg. Creating a mail server on Linux-based machines could be one of the most important things that every system administrator should do. If this is the first time you configure your server, you're going to face a lot of aspects that have to be considered during setup. After this video, I'll take you through the initial server configuration. We'll discover how to install a postfix mail agent and a DovCod mail server and how to configure them for your emails to circulate properly. Postfix is a free and open-source MTA, which stands for Mail Transfer Agent, that can be used to route and deliver electronic mail on a Linux system. DovCod is a mail-deliver agent created primarily with security in mind. I'll show you how to configure it as an IMAP or POP3 server. In our channel and blog, you will find solutions to any problem from installing an operating system or configuring it to fixing possible bugs and errors or optimizing mobile gadgets. Our specialists will answer any questions you ask in your comments, under the videos or articles. To begin the installation, you'll need to properly configure the Linux Ubuntu server with a fully qualified domain name or FQDN for short. On the server, you need to add a non-root user but with pseudo-privileges and open corresponding ports in your network. I already have a server with required settings and its ports are configured properly, so the only thing left to do is to add a user and start installing mail applications. To add a user, open the terminal by pressing the key shortcut Ctrl Alt T and run this command. Set a new password for this user and leave other values without changes. You can specify any additional information if you need to. Now you have a new user account with standard privileges. However, sometimes you may have to perform administrative tasks as a root user. To avoid having to sign out of your regular account and then sign into a root user account, you can configure the so-called superuser or root privileges for your regular user account.  To add privileges, you need to add a new user into the pseudo-system group by running this command. The next thing to do is to set a true domain name for your server. For this purpose, there is a special command – hostname CTL. After that, you need to add mx and a records for the domain in the DNS control panel. These records will show to the other mail agents that your domain is in charge of delivering electronic mail. Open the DNS control panel and add these records. Here you need to specify your server's public IP address. Now you can begin the installation of a Postfix mail agent. Here is the command you need to use. In the Postfix configuration window that opens, click OK. After that, the system will suggest you to choose mail configuration type, select Internet Site and press Enter. In the next window, type the domain name to be used when sending electronic mail. After a Postfix is installed, it will start automatically and create a configuration file. You can check Postfix version and service status by using the following commands. If you need to change the configuration, use this one. And click OK. Here select Internet Site and press Enter. In the next window, type the domain name to be used when sending electronic mail. Then add the mail recipient. Specify other destinations to accept mail. Force Synchronous updates on mail queue – no. I will leave local network settings without changes. Mailbox size limit – 0. Mail address extension character – plus. Internet protocols to use – all. Noveless check – if your mail server is connected to port 25 by running this command. The result displayed by the system indicates that the connection has been established successfully. To close the connection, type QUIT. Also, you can use a mail application to test the mail service. However, it should be installed first, so run the installation command. Here is the command to use. Specify the email address to send your mail to, subject and contents. And then press CTRL D to send. As you can see, the mail service works and the email was delivered, but it ended up in the spam folder. I'm going to show you what settings should be changed to prevent emails from being classified as spam. After sending the first email, the program will create a file with the user's name in the BAR mail folder. In this file, all incoming and outgoing mail will be recorded. This recording format is known as MBOX. To use the mail-deer format, which divides emails to separate files which are then moved between the directories depending on the user's actions, you need to make some changes to the configuration file. Add this line. Or run this command. In this case, mail will be stored in separate files and can be found by this path. As a result, the mail server is running, you can send and receive mail, but it can hardly do without an opportunity to send mail by SMTP. This protocol is already supported in Postfix, but it has no authentication by default. To add authentication support, you should use Dovcott. As a bonus, you'll get the opportunity to view your mail by POP3 and IMAP protocols. First of all, you need to install the Dovcott service itself. And here is the command to use. After the installation is over, it is recommended to restart the Dovcott service. To configure Dovcott, you should edit the configuration file. To open it, you need one more command. You can choose which protocol to use. It could be POP3, POP3S, secure POP3, IMAP or IMAPS, which is secure IMAP. IMAPS and POP3S are more secure than conventional IMAP and POP3, since they use SSL encryption for their connections. As soon as you have chosen a protocol to use, correct the next line in the file Dovcott.conf.add or edit this line. Press Ctrl-X and then Yes to save it. Enter. Now let's check how POP3 works. Go to a checkup service, enter the data, and click to start the test. As you can see, the test is successful. Alright, we have configured the mail service. Now we need to make sure that the mail doesn't end up in the spam folder. And for that purpose, you should add a few more DNS records. Open the DNS control panel, add SPF, DMARC and DKIM records. To have SPF and DMARC running, all you need is to add their DNS records. But for DKIM to work, it has to be installed in the server. Domain keys identified mail or DKIM for short is an email authentication method designed to detect forged sender addresses in email, known as email spoofing. DKIM enables the user to check if an email was actually sent from the stated domain. And here is the command you need to install the package. Then start it and add it to automatic setup. After that, you should create a certificate with the help of OpenDKIM GenKey. Create a directory for keys using this command. And use another command to generate a key. Here you need to specify your service domain name. In the folder ETC OpenDKIM, two files should appear, with extensions, private, a private key and TXT, a TXT record. Now let's configure DNS. View the contents of the TXT file, copy the contents, switch to DNS control panel and create a TXT record. Assign a name to it and paste the TXT file contents, where mail is selected to name and here is the public key. Now let's make changes to the configuration file. Here you need to command and add a few lines. Add your domain to trusted hosts. Add the domain Ctrl-X, Yes, and Press Enter. Give the path to the key Ctrl-X, Yes, and Press Enter. Now that the record is added, let's check it. Open a D-KIM checkup service in the browser. Type the domain in this field, add the selector name in the other field. As you can see, the service has found my D-KIM record. The record is added. For additional fine tuning, adding trusted hosts, domains, etc., you will have to open the configuration file and add extra settings. Now let's test mail sending again. Ctrl-D. As you can see, the email arrived and this time it was not dumped into the spam folder. Let's reply to this email. Now the mail service works properly. Alright, so we have installed and configured postfix and dovcott, checked how that mail service works and added corresponding DNS records. For more convenient work, you can add a MySQL storage server and a mail client. For example, RoundCube. But that is all for now. Hopefully, this video was useful. Remember to click the Like button and subscribe to our channel. Leave comments to ask questions. Thank you for watching. Good luck.