If the pro-Israel position was based on truth and morality, it wouldn't depend on lies and slander. If your position is based on truth and morality, you don't need to make up lies to defend it, and you don't need to hurl false accusations at those who disagree with it. If your position was based on truth and morality, you wouldn't need to circulate bogus atrocity propaganda about decapitated babies, babies cooked in ovens, and murdered pregnant women. If your position was based on truth and morality, you wouldn't have to circulate lies claiming actual dead Palestinian babies in Gaza are plastic dolls. If your position was based on truth and morality, you wouldn't have to falsely present phony audio clips as intercepted Hamas communications and claim they exonerate you of war crime allegations. If your position was based on truth and morality, you wouldn't have to circulate lies and propaganda about the hospitals you plan to attack being secret Hamas headquarters. If your position was based on truth and morality, your official government social media accounts wouldn't have to keep deleting posts after getting caught circulating lie after lie. If your position was based on truth and morality, you wouldn't have to circulate amazingly cringy propaganda videos like Israeli children singing about how great it will be to destroy Gaza, and Israeli women doing yoga over the pictures of Israeli hostages. If your position was based on truth and morality, you wouldn't find yourself engaged in bizarre mental contortions trying to pretend history began on October 7th, while sweeping all the mountains of murder and abuse which led up to it under the carpet. If your position was based on truth and morality, you wouldn't have to keep pointing at something that happened months ago to defend what you've been doing in the present moment in all the days since. If your position was based on truth and morality, you wouldn't need to resort to hurling false accusations of anti-Semitism at those who criticize your side instead of producing robust counter-arguments. If your position was based on truth and morality, you wouldn't need to resort to accusing those who disagree with you of supporting terrorism and serving Hamas instead of defending your position like a normal adult. If your position was based on truth and morality, you wouldn't need to lie and claim that long-standing pro-Palestine slogans are actually calls for the genocide of Jews to try and get them silenced. If your position was based on truth and morality, you wouldn't feel the need to censor and silence everyone who disagrees with you online, on college campuses, and on all platforms of major influence. If your position was based on truth and morality, you would get curious and do some self-examination when young people overwhelmingly reject that position, instead of insisting that the young people are the problem and trying to kill TikTok and cancel demonstrations. If your position was based on truth and morality, you would be defending it with facts, logic, and rational argumentation instead of vitriol, online troll mobs, and incendiary false accusations. If your position is based on truth and morality, you can behave in a truthful and moral way when defending it, instead of doing the exact opposite.


Hi everyone. Happy Wednesday. I'm Joe for jaspyscasebrakes.com with a quick little mega box right here 2018 Donner's Optic football mega box Given away eight spots in that 2017 contenders optic football break Patrick Mahomes hunting right there. So we're gonna do the The break itself first and then we'll give away those spots Big thanks everybody right here And there's all the teams right there. Let's roll it. Let's randomize it One and a four five times for names and teams one two three four and Once more right? Yeah, fifth and final time We got Joshua down to Joshua two different Josh's Yeah, Dennis, that's why they're so pricey one and a four five times for the teams one two three four and And fifth and final time Jaguar is down to the Packers now no trade windows in these little filler breaks. We just want to Just want to get right to who's gonna win those spots That's the crucial part of this break right here. So here's the first half of the list And there's the second half of the list Thank you for giving this a shot. Let's get all this on one. Why is it not all on one screen? Slide this down just a tad. Yeah, there we go So that's all on one screen. Let's sort by column B Just print and rip just by Matt's here Here's the final print out. Let's see what we got. This would be 2018 would be Josh Josh Allen, I think Josh Allen The Marjax and rookie class from now the safety. We're gonna find a whole new rookie class Tomorrow which would be pretty fun. I wonder if there's gonna be some surprises There's always a surprise or two in the draft, right? All right, so here's the rookie threads relic 14 out of 50 Dante Pettis Oh, yeah, big trade today Teddy Bridgewater going into the Denver Broncos maybe battling Drew Locke for a job But from the report I read Allegedly, they're still they're still not out on a quarterback. They still could get a quarterback. There's Durham smith So the Pettis will go to Kyle and the Niners and Durham smith will go to Joshua E and The Miami Dolphins Here's Josh Allen right there. Nice Who has Buffalo? Buffalo Matthew Sheeran with the Buffalo Bills nice You get the bronze rated rookie Josh Allen now out of out of curiosity I wonder what that goes for these days 2018 Donner's optic Josh Allen rated rookie bronze. That's might be too many too many search results right there But still it should be nice we've got wow a PSA 10 Sold for nine hundred dollars on the 15th. What about raw? I think Josh Allen 2018 optic one number 154 bronze prism rated rookie card 250 bucks raw That's not bad And it's not bad at all now Still an opportunity to win some 2017 spots as well. So here's some new dice. Here's a new list Let's roll it. Let's randomize that list one and a four Five times it'll be top eight After five good luck one two three four and Fifth and final time after five Now from nine on down sad times Sorry Joshua, sorry everybody right here, but I appreciate you trying Happy times for the top eight. You're in that 2017 contenders optic football break my home's hunting Terry Joshua Matthew Kyle Joshua Joshua Joshua Scott Joshua Edelitz There you go, and there you have it. I'm Joe for jazby's case breaks calm. Thanks for giving this a shot Congrats again to the winners. I'll see you for that break a little bit later tonight. Bye. Bye


Thank you. So good afternoon. This is a talk about machine learning. How many people have been following machine learning at all? Okay, great. So this talk is going to be more about what's going on, why it should be important to you, a little bit about how you might dive in yourself and why you might want to and how it's applicable to networking and importantly what to watch out for. Because what I will say is we're kind of at the beginning of this for networking and you'll see, I have some editorial comments on this but the hype cycle is pretty intense right now. So how many people have seen this picture or a picture like this? So what happened here was this guy in the code, oh by the way, I'll show you where the code is for almost everything that's here and you can have the PowerPoint or the PDF so you don't need to write it down. And if you don't find it somewhere on the website for these guys, just drop me an email and I'll show you where it all is. But it's pointing right there. Okay, so anyway, this guy made what's called a generative model. That's a machine learning model that tries to learn the core part of whatever it's being trained on and then make more instances that are somehow the same. So this was, you know, this was trained on Van Gogh's Starry Night and others and then the guy took a picture of the Golden Gate Bridge and said make me something that looks like this. So this is sort of the Golden Gate Bridge rendered as a Van Gogh. But this gives you an idea of what you might be able to do with the generative model. Before I dive into this, I'll just tell you that we built generative models that model the sequences that the protocol sequences that TCP and other protocols do and have them generate slight variants on those sequences. Some of them worked, some of them didn't. So this talk isn't really about Siri or anything like that, but I did want to say that speech recognition has really, really hit a milestone which is sort of like above 95% accuracy. So that's why you have Amazon Echo and Google Home and things like this. And this is Andrew Ning. They had a thing, he was at Baidu for a while. He had been at, he's a professor at Stanford, had been at Google. But this is a picture of their thing that's bi-directional recurrent neural network. If you want to know what that is, we'll talk about that later. That's a whole other hour of things. And this is how Google Translate, how many people use Google Translate? I use it all the time. It's great. Well, it's an instance of neural machine translation. So a special kind of neural network that has some memory like a tape. That's why they call it neural machine translation. And it has this idea of zero shot learning. So some of these things, it was never trained on, right? It actually created this thing itself, which they're calling interlingua, which is an internal representation of what the language is. So as long as you have that for one side or the other, you can just connect them up. So this is zero shot, meaning zero instances to train this thing. And then here's one other one. This is the sequence of sequence learning. This isn't actually a Google product, but was done at Google. So it learns sequences and then can decode them from one language to another. It's pretty impressive. Okay. So it's not about any of that. So before we dive in here, this is what this talk is not about. AGI, artificial general intelligence. How many people have heard of it? This is Terminator. We're not building terminators here, right? We're not building sentient beings. We're not building any of that stuff. This is sometimes called strong AI. It's really interesting. There's an open source project. There's many of them, actually. This is Ben Gertzels, OpenCog. Might take a look at it. It's kind of interesting. This is sort of at the nexus of neuroscience, machine learning, AI, and other things. What we are going to talk about is machine learning, which is sort of a methodology for solving engineering problems. Now, it might be used by AGI's because the model you might use to solve some of these problems might look like some kind of either biological or neuroscience kind of model, but that's not the goal. So, for example, a goal here would be image recognition. And sometimes you'll hear this referred to as weak or narrow AI. So that's what we're going to talk about. So goals, briefly review what's about, kind of talk about what we can do in networking. What's practical today? What are the challenges, both sociological and technological, because there's lots of both. And what are our opportunities? And I'll try to go easy on the math, although it's really all about math. But if you want to learn more about that, look on my website. I've written up a lot of stuff for my team there. And then there's so much material on the network, you'll never get through all of it. So I just gave a few quotes here. A year ago at the ONUG, this guy, Bill Cochran, at Sequoia Capital, he just offered this on a panel I was on about storage. And he just said, you might be surprised to know that what's going to drive innovation in an enterprise in public cloud is machine learning. A year ago, he just offered that. I found that interesting. And then last year, I think it was at the Red Hat Summit, Chris and Dave Ward were on the cube. And Chris just said this, you know, so what does all this mean?  Okay. So I'll just warn you guys right now. You want to be careful when people start talking about how great their machine learning stuff is. Because we're kind of at the top of the hype cycle, I'm going to just use the Gartner kind of terminology here. But you know, you are there. And if you look at that, that URL on the bottom, I guess it'll be on the bottom, your left or whatever. Machine learning and AI in particular has failed spectacularly three times already. So just be careful, be skeptical. And in this game, what it comes down to is show me the math, show me the code, show me the data and let me reproduce it. Otherwise, it didn't happen. Right? So, you know, be skeptical. Okay, what's happening? Alright, that's interesting. So for some reason we're kind of oops. Okay, that's a little better. Okay, we were there. So be careful. But this this is all happening at warp speed really happening fast. And this picture is showing you sort of our version of the how many people know about up and to the right. Ever heard that? All during the late 90s and early 2000s, we we lamented the fact that the routing table was growing so fast, and we couldn't do anything about it. And the curves were rose up and to the right. And that's what's happening here. It's all up and to the right. So here's the observation I want you guys to walk away with on this is basically the machine learning has a long history of open source predates things like the open source movements that we've been seeing here. And it's it's everywhere. Everything's open sourced. This is just a picture of deep mind universe or open AI universe deep mind opened their open source their lab all of the stuff is being open sourced. So we have open data. We have open source code. We have open data. We have open models. We have open science really. And all of these things are getting integrated together in this idea of democratizing AI. Now that's a ways off. But the idea of openness is really there. And so when you see breakthroughs in machine learning, what you'll see what you'll notice is that there are published descriptions and results, at least on archive. How do people know about archive? So archives are places where you usually publish scientific papers before they go into a journal or other peer reviewed publication. So get this. So the X in the archive is actually the Greek letter chi. So it's archive. Get it archive. So archive.org. And you'll see okay so see papers there. You'll see a diverse set of open source applications, growing communities just like we have a networking all kinds of them. And we'll see public private partnerships. This is the vector Institute in Toronto and the vector Institute guys. Now, importantly, if you don't see this stuff, ask some questions. Because probably not real. Here's an example of where it went wrong. So Apple wasn't allowed and we're not talking about Siri, but Apple wasn't allowed to publish their research, or involve or get involved with any of these communities. And as a result, Siri really fell away behind. And then last year at NEPS neural information processing systems, they were they announced that Oh, we're going to let our people publish our research. Because they realized that without that, this is not a job for one in one entity, right? So it's interesting to see what's going on. But be aware that if you're not seeing this and you hear things like, you know, Oh, well, our network thing is uses AI or uses machine learning. Ask the question. That's cool. How does it work? And if people can't answer how it works, either you're talking to the wrong people, or something else, and you can guess what I think the something else is. So all of this is, you know, things are just going unbelievably fast in machine learning. You know, I was around at the beginning of the internet in the late 80s and early 90s. And this is way faster. It's really amazing what's happening here. We're behind in networking, I'll explain why in a second. But these are some graphs about different things that you might look at, like the number of papers published in conferences or a number of papers that refer to different kinds of models. And again, they're all up and to the right. So how am I doing that? By the way, if you have questions or comments or anything else, just interrupt me. I don't know if we have, oh, yeah, we have microphones. So just get up and interrupt me. It's fine. Okay, so here's what I think the agenda I'm going to try to get through. I have way too many slides, by the way. So I'm going to pick and choose a little bit. But you know, what can we do today? And what are our challenges? What's the state of the art of machine learning for networking? And then I put this at the bottom here. There's code and different things there if you're interested in learning more about this. So here's one thing we did. We tried to combine SDN and machine learning.  And basically what we did was we just took data out of a SDN controller and did inferencing on it. And we built a system that looks like this. You know, which is, this is, by the way, this is just the typical machine learning sort of loop.  So this kind of model is what you're going to probably see in one sense or another. There's a feedback loop that's not actually shown here, and I'll show you that in a second. We didn't draw it in here because the feedback loops in development and it would have made the picture really impossible to understand. There's hard coded logic in here, too, by the way, for the time being. And that's the SDN controller itself. Why is that an issue? You might be able to learn what the SDN controller is going to be supposed to do. In fact, Jeff Dean said that, Jeff Dean is like Google guy, and he was responsible for a lot of the stuff at Google at scales. He said, I wouldn't write code for that anymore. I would make it learn it. So a lot of this stuff that we write code for now is going to be learned in the future. So, okay, we're here talking about this because there's been like this massive amount of success in the machine learning space, but where? Okay, so first off, perceptual tasks. I kind of made this turn up because term up because it's kind of how it feels to me. You know, these are things like vision, natural language processing, robotics, but also new domains like medicine, finance, other ones like that. So this thing on the right is just a convolutional neural network, which is sort of the where you get this variance on this is where you get state-of-the-art performance on image and object recognition. These are a few of the things. By the way, I put a lot more stuff in here than we can possibly talk about, but I wanted it to be in there for you guys if in case later you want to look at it. There's been a lot of work on statistical approaches. This is Bayes' theorem or Bayes' rule, kind of the basic way we understand the world around us. We use this a lot in machine learning. How many people have heard of anomaly detection? Okay, so this is kind of good. So this is kind of a technique for doing binary classification. So, good, bad. So why? Why has this all happened? Remember, I said AI has failed many times before. Well, the main reason is that these three fellows made, actually it's Jeff Hinton on the left. Jan and Yoshua were actually his students or were post-docs at one point in his lab. But in around 2006, there were technological breakthroughs that allowed for training of these deeper networks, so deep learning became possible. The other thing was that compute is pretty ubiquitous in the cloud these days. Back in the 80s when these people were starting to work on this, compute was literally millions of times too slow. And the largest models I've seen right now have a trillion parameters, a trillion parameters, and they get trained on 8K GPUs, not 8K cores, 8K GPUs. So compute. The second, the third thing is data. The advent of the hyper scales or the web scales have, you know, provided unprecedented source of data, and these deep neural networks are really data hungry. They need a lot of data. And then the fourth thing, maybe which is kind of more, a little bit newer, but now is becoming a more, more dominant feature is talent pipelines are starting to fill up. In other words, there's people who are getting education who can actually do this stuff. Before there were very few, now there are more. And this is a picture of Jurgen Schmidhuber. If you ever get to hear him talk, definitely hear him talk, he's hilarious, and he has a different view than these three fellows on the right do of how all this evolves. So there's a little bit of nice controversy there. Excuse me. Okay, but what about the network space? How many people have done machine learning for networking? Okay, two. That's a lot, actually. So why have we seen little progress? Well, I'll focus on the data aspects of this and talent pipelines. I'll talk a little bit about anomaly detection, too. Okay, so first off in the data thing, the first thing is is that standardized data sets, and I don't need standardized like ITF. What I mean is data sets that are available to everybody that have known properties. Standardized data sets just have been crucial in the development and success of machine learning. Like Andrew said, data is the rocket fuel of machine learning. And this allows for direct comparison of learning and inference algorithms. So, for example, if we both train on the same data set, and I get, I don't know, 10% error and you get 5% error, you did better than I did. And I can compare that, right? Right now, we don't have this in networking. But the result of this has really been a steady ratcheting down to superhuman levels on a lot of tasks. Superhuman levels, what does that mean?  It's going to be about 5% to 7%. These things are below 5%. So they're superhuman. So, we have nothing like this for networking. And there's a lot of reasons for this. Many or most of the data sets are kind of toy. That means small and not representative. They're noisy and normalized. They're just network data is dirty. I'm sure anybody who's worked with network data can commiserate with that. Many data sets are proprietary. Carriers think that their data is a big part of their value proposition. So do the hyperscales. A lot of data is not IID independent and identically distributed. So most of our data sources are also representations of network data that are hard to use for machine learning. And there's another thing. There's no standard way of combining them. So I have now I have logs or maybe syslogs or something like that. And I have flow data. How do I combine those in a way that's that you could replicate or you could compare.  So if you're interested, take a look. There are various different kinds, not all network data. Talent pipelines. So this one, when I talk to network people, they kind of, I don't know, there's a there's a kind of a little bit of defensiveness about this. So if you get that, raise your hand. So I'll know. But essentially machine learning is math. It's in a form of applied mathematics. And it's multidisciplinary empirical science, which sort of means you use a lot of different domains and you try stuff. Network engineers like us, we typically don't have the background that include the kinds of mathematical training and experience that really are necessary here. So this is just a picture of the things you'd expect, linear algebra, probability theory, differential calculus, and stuff like that. And that's what all this is built on. So if you're going to get a network guy or network person to do machine learning, it requires those skills and others. And the most important one is the ability to rapidly learn new concepts, because the whole machine learning space is moving so fast. But you know, here's a list of some things. But the main result here that I want to leave you with is that there's a serious skills gap between what we do in our daily lives and what we need to know if we want to be successful in applying this stuff. So think about this a little bit. And this URL at the bottom is an interview Andrew Nen gave to Harvard Business Review while he was talking about this. And essentially, I just cut this out of it. But again, you see that data and talent are the gating factors. So we have to, we have a challenge. Said another way, it's all about math. I said that already. This particular stuff is from Facebook. There's a really great set of videos there that describe how this all works in non-technical ways. Do you have a question? Oh, go for it. Do you have a mic? Can you? Yeah, great. Test, test. So before you move on, because you said to interrupt you, I'm sure I have a question about both data and talent. So on the data side, do you think that you get the most bang for your buck right now going with supervised learning and trying to tackle very specific individual problems or going for something like tensor or deep neural net or something like that and trying to just take raw data and have it generate? And then the second one, as far as talent, do you think that it's the same that it was probably say five years ago when if you didn't understand, you know, Gauss and like looking at linear algebra and stuff like that. Now that we have scikit-learn, you know, Theano, all of that, like get people to be consumers of tools that will do all the math in the back end for them. Like, do you think that we're at a point now where even if you're not a number cruncher, you could still make some amazing things happen? Yeah, great question. Okay, so let me answer the first one first. So I think you're asking about supervised versus unsupervised learning, right? Okay, supervised learning means the data of the form x, y, x is the input, y is the answer. So, and I'll show you in a second, but in the case of say image data, I would say here's an image and the label, that's the y, says cat or whatever it is. And then what you do is in supervised learning is you make a prediction about what's in the picture, say, and then you compare it to what they tell you it is. So I make some prediction that says dog, but it's really cat. Dog and cat are represented as vectors. I can do arithmetic on it and calculate the error, use that error to improve my model. So that's the supervised learning part of it. Unsupervised learning doesn't have those labels. And unsupervised learning in the neural network space is cutting edge. It's a lot harder. Now there are kinds of unsupervised learning that have been around for decades like k-means and things like that that are very useful and I'll show you a little bit of that later. So, but I would, what I would say is that the vast majority of all of the fantastic results you've seen from machine learning have been in supervised learning. Does that answer that? Yeah? Okay, the second one, can we, can we just be consumers of this stuff? Right now the answer to that is yeah, if you don't want to do anything at scale, but only at things don't, the only thing that matters is being able to do it at scale, because if it doesn't matter, if you don't have to be at scale it's easy. So right now it still takes these skills. To build a, to build a, you know, to build a big inference model that can work at scale, still takes all of this skill. You still have to know how this stuff works. I will say that things like, you know, TensorFlow, scikit learn, all these millions of, there's a new, by the way there's a new framework every week for this stuff. So one of the jokes in the whole area is, oh well, how long is it going to take you to convert your code into the new framework, because you do it every week, right? So, but still it still requires those skills. Trying to watch the time a little bit. So we will become consumers of it, and that's what this idea of democratizing AI is about, by the way, is making it consumable in the way you're describing it. It's just not there yet. Okay, so in the real world, essentially this is the way it works. You have some real-world system, it generates some data, we gather that data, we might visualize it by k-means or something like that. We build a model, we hand the model to the, to the whoever's gonna evaluate it. They take a new data, new piece of data, and run it through the model, that's called inference. We see what it does, and we just loop. And if it, if it does well, we just go, we're done. And if it doesn't do well, we refine the model. Now, here's the, well here's the scientific method applied to it. Essentially, you got to use case, you know, we want to be able to detect anomalies and say the way that VNFs are being deployed. You get a hypothesis, oh, I can collect this data and use an auto encoder, I'll show you what that is in a second.  And you actually need automation inside this ability to do automation if you want to do this effectively, right? So this loop right here needs to be automated. And we did it with StackStorm, but you could do it with, you know, any other, any other workflow automation. But you have to be able to automate it and it has to go fast. So I'm just saying that this is, TJ Watson said this, it's basically fail fast, right? Fail a lot and fail fast. So let's see. This is, I'm just going to quickly go through this because I want to mention this to you guys is that a lot of this is data plumbing, you know, a lot of your time is spent building systems. Now this has a serious implication, and I hope I have a slide on this. Yeah. So here's a picture of a typical ML stack. This is Spark. And you can see it has all the typical, you know, typical functionality that you need to get this done. And Spark's nice, right? So I rearranged all this stuff because I wanted to kind of look at how much was ML code. And it's actually this. It's not that much. It's very dense, but it's not that much code compared to the rest of the system. What this means is that these systems get technical debt at the system level really quickly. So there are other problems in just maintaining a system that does this. I'll give you an example. We had some containers. We wanted to run Jupyter inside the containers, but because of the way Jupyter is implemented, it's hard to use GPUs from inside Jupyter inside a container. And it hacked around that, right? Now how many people are using that container and do they know that code is there? Probably not very many. Okay, so let's just talk briefly. Let's see what kind of time I have left. Let's talk briefly about what anomaly detection is because this is kind of a core thing. Anomaly detection might be, like I said, maybe a VNF is behaving unusually or maybe it's a security thing. There are unusual flows or there are unusual packets or something like that. So what it's all about really is you want a group, everything that's normal into one group, and then learn the anomalies. So on the right, there's sort of a binary classification. So you just, okay, there's a group of things and now I got these other points. How far away from all the other ones are there? And this just gives me anomaly. And you could do that, you could just split that again and have multi-classes, but it's easier to think about it in just one class, but without loss of generality. So the approach that you take when you're trying to build this is you construct a nominal model of the baseline, you know, where all of those points are that are in the circle. Think about how you might do that. And then you assume that data that's far from that are generated by a different process, the anomaly. And so there's a hitch here, right? And the hitch is that the evaluation of far is something you have to define outside of what you learn. So you got to tune that. That's a tunable parameter. Now there are lots of ways to do this. Clustering, I mentioned that, K-means. That picture there is what you get out of K-means. It shows you, oh, my data has three kind of main things. That can be very useful for when you're exploring. Principal components analysis, auto encoders, I'll show you that in a second. But there are lots of ways to do this. Oh, I already talked about this, but basically that's, you know, you have a host. You want to be able to do all this stuff with it. So I want to just show you this. And thanks for the, I guess, from TensorFlow, whatever I was using to TensorFlow 1010, they changed the names of subtract and multiply or something. And somebody fixed that for me, because I hadn't looked at it. But check this out. This is kind of an interesting way to think about this. Okay, so MNIST is one of these standard data sets I was talking about. It's 28 by 28 grayscale handwritten images. There's 55,000 of them. And they're in different kinds of configurations. So, and by the way, the labels are what you use for supervised learning. But I'm going to use an auto encoder, which is unsupervised. By the way, we can analyze network data in the same way. This is just a long, what I did here was I just flattened it into a long string of bits where each bit is kind of like a feature. So you could have a flow record. That's what's below there. So I'm using MNIST mostly because you can easily visualize what's going on. In our case, instead of, you know, the input being these vectors that have a bit or 8 bits if it's grayscale, it'll be a vector of counters or various KPIs or things like that, depending on how you design your experiment. So here's a way to recognize it. So what we want to do here is we want to recognize whether or not a 28 by 28 bit image is part of the MNIST data set. We're not going to try to classify what kind it is. That takes a harder network or a little bit more complex network called a convolutional network. But here's the basic network. You have the input, that's the image. You squish it down into a fewer number of bits in the hidden layer, and then you blow it back up. And then you compare what you blew it back up into the input, and that defines the error, and then you try to minimize that error. And that's all you're doing, right? So this is a form of unsupervised learning. So here's what the numbers look like. So the input 785, that's 784, that's 28 by 28. I just flattened it with NumPy, a simple thing to do. I made 100 hidden layers Y. I just, you know, picked a parameter out of the error, and then I blew it back up into what it was. And then I computed the error. So you know what, I'm going to pass on this because we're not going to have enough time, but let's just notice that if you have that simple network I just showed you, that thing has 784 times 100 plus 100 times 784 parameters, about 156,800 parameters. Where are you going to run that in your network? Think about that. And this is tiny, right? The big networks have a trillion. This is 100,000 or a couple of 100,000. We're going to skip this. All kinds of cool stuff. This is how it really works. This is the code you need to do it in TensorFlow. That's it. So, and there's a Jupyter notebook there at that URL, and you can just look at the code and kind of mess with the parameters and see what it does. It's kind of interesting. But rather than talk about this, let's look at what it does. So here's the output from this thing. If I give it one example, the reconstruction, the thing that it blew back up into looks like that kind of, you know, what is that noise on your TV? Why do you think it looks like that? Anybody have an idea? Okay, it's because I initialize the weights that the thing uses to compute what it is randomly. So right now those are just random numbers. And then if I give it 10 on the right there, I guess that's on your left, it starts looking like something, right? And then finally, and finally, finally it starts looking like the real numbers. So on the right where there's a thousand examples, actually now if you give this thing a 28 by 28 bitmap that's not one of these handwritten images, it'll have high reconstruction error, this little equation on the left, it'll get on your right, it'll get high reconstruction error and you'll know it's not one of those. So that did an anomaly detection, right? It told me that this is not one of those, it's somehow different. It's an anomaly, right?  So, came running out of time. So how much of this has been applied to networking? It already says not too much. Anybody have any ideas of why not? Okay, let's see if we can figure out. Because you know, it would be obvious that we'd want to be able to have this capability in networking, but we don't really have it yet. So the first one is the community challenges I was talking about. Sort of a lack of understanding, we don't quite have the understanding of how progress is made in machine learning. The hype cycle thing is crippling right now. Skill sets are a problem. Proprietary data sets. So these kind of community challenges. We have all these diverse types of network data. And no obvious or consistent way to combine them. Incomplete data sets, all kinds of things like that. So data's not so good. Skill sets, we talked about different types of models for different data. Yeah, that's still an area of active investigation. And this is one that I kind of like to outline is that there's no real useful theory of what a network is. And useful, I used the word useful because it's not useful for machine learning. There's all kinds of network theories like, you know, queuing theory and all different kinds of things, but they're not useful for us. So if you consider the problem of object recognition or something like that, it's been so successful because there's a theory of how vision works and that theory of how vision works is implemented in these convolutional nets. Yeah, so the one other thing I wanted to mention here is transfer learning. So transfer learning means I train a model on one data set and then I can add more things to it and have the union of both of those things. We need this for networking because if you can't get transfer learning, then every network is a one off and that's going to be crippling. We want to have general purpose infrastructure for networking. So let's see. Oh, I talked about this on packet pushers if you're interested there, so you're all. Okay, so I want to just get to the end of this so we can have some Q&A. This is stuff that, you know, it's happening and networking all over the place. And, okay, so why the slow uptake? I mentioned this, community challenges. Most ML systems today use very old, decades old technology, not all, but most. A lot of them are unsupervised learning, things like K-means, et cetera, and other kinds of clustering. There's all kinds of ways of doing this and no usable theory of networking. Editorial comments, everything's ML today. It's kind of like cloud or SDN, you know. It's like remember when SDN started everything that wasn't SDN was and, you know, and as I mentioned, if you do have breakthroughs, things that are really working are new, you'll see it. And, you know, most systems today are these big data platforms, many of these around. Ad hoc statistical techniques, some hardware sometimes, you know, like things like packet brokers and things like that. And a healthy dose of marketing and hype. And I'm kind of concerned about that because that's, I think that's slowing us down. Let's see. How many people remember open washing? Ever heard that term? Okay, in the early days of like open daylight and things like that. And when I was working in the Linux Foundation, I learned about this and open washing just means you call your thing open source and then it's got all the good benefits of open source, right? Well, now there's ML washing. You just call it ML, whatever it is. Okay. And we also need to learn from our environment. So this, I'm not going to go through this because I want to get to the last page of this. If I can find it. Okay, so here's, I'll give you a summary, lots of network data, right?  But it's not standardized. It's not labeled. It's proprietary, all these kind of things that make it hard, hard to use. There's millions of open source frameworks, cloud APIs, all this kind of stuff. I didn't even put all the ones that are here and every week or every week, there's a new one. But we're still dominated by ML washing and platforms, clustering, hype, this kind of thing. Skills gap is still persisting. And as I said to the gentleman over there, it still takes experience to build these deep neural networks and find the architecture and models. It still takes skill to prevent overfitting. I can explain what that is. And it still takes these kinds of skills to make this work at scale. But even with all of that, you're going to see this in every facet of networking. In fact, a lot of the things that we do today are going to be done like Jeff Dean said, by learning. So what do we need to do? We need to find a useful theory of networking. If you want to know more about that, talk to me. We need public data sets. We need to hone up our skill sets. We need to apply state-of-the-art machine learning algorithms so we can't be stuck back in the 80s, 90s, 2000s. We need explainable end-to-end systems. I know that many people talk to me today about the fact that in these neural nets, you don't really know what they're doing. That's a problem because network operators aren't going to like that. We need to learn control and not prediction. We have to learn from our environment in an ongoing way. Yeah, there's all kinds of issues there. Okay, so here's the thing I wanted to get to. So if you want to jump into this right now, it's a great time because first off, there's so much material that's available to help you learn and there's so much infrastructure that you can use so you don't have to reinvent the wheel, like the gentleman over there was saying, for everything. But there's so many mysteries and so many things that you might like to just try to understand and push the whole thing forward, whether it be networking or otherwise. The first one, I'll just talk about briefly is back propagation. This is the way we train these networks. It's a simple algorithm. I could explain it to you in less than an hour and you would totally understand it. The question is, how can this simple thing create these powerful models? It's a mystery. And on top of that, there's such a thing as adversarial images or in examples where what you can do is perturb, like in the image case, you can perturb the image in such a way that you can't, as a human, detect their perturbation, but they will deterministically misclassify. What is going on there? Here's a picture. The thing thinks it's a panda before their perturbation. Now it thinks it's something else. But you can't tell the difference. What is going on there? There's these new multi-player game things where you take two neural networks and you have them play against each other and train one another. This is incredibly powerful happening right now. By the way, I did this for Network. I built what's called a generative adversarial network for our network data to see if what I could do is have it learn what a transport protocol was, and it did right away. It learned TCP Reno right away. I mentioned this. Neural networks, what are they exactly doing? It turns out that this picture is a visualization of the activations of the first level of a convolutional neural network. These are called Gabor filters. They just are edge detection and orientation. But above that, it's hard to know. Here's another interesting one. These guys train this network on the sentiment data from Yahoo. And they found out that one of these neurons, one of these neural neurons could tell whether or not the sentiment was positive or negative. How did that happen? There's a pointer there. This is the sentiment neuron. What does this even work? Remember I told you in our case, the 28 by 28 grayscale, so that's 784. Suppose there were eight bits of grayscale. Sometimes there's more. But that would be 256 to the 784 possible images of that size. Some just gigantic number. The model I showed you had 157,000, 156,800,000. So if you divide 156,8 by 256 to the 784, that's zero for all intents and purposes. So how does that simple code find these? It's kind of a mystery. And there's so many others. But this is a great time to jump into this. Networking area is going to explode with this stuff as soon as we break some of these problems. And you guys are all in the right, everybody's in the right place to get this to take advantage of all of this and look at what the next generation of automation is going to be about. Let's see what else I got. I think I'm out of time. I'm out of time, right? By the way, if anybody wants these slides or has questions on any of this stuff, just find me or drop me an email or whatever. You're welcome to the slides. You're welcome to use them for whatever purpose you have.


A disturbing analysis of mortality and morbidity was recently published in the Journal of Gerontology. Are we living longer? Yes. But are those extra healthy years? No, and it's worse than that. We're actually living fewer healthy years than we used to. A 20-year-old woman in 1998 could expect to live about 60 more years, whereas a 20-year-old in 2006 could look forward to 61 more years. So we gained a year. Great! Same with men. That 20-year-old in the 90s though would only live about 10 or 11 of those years with a serious disease, whereas closer to now it's more like 12 or 13 with a serious disease. So we live a year longer, but we come down with a serious disease, like stroke, cancer, diabetes, two years earlier. So it's like one step forward, two steps back. They also measured one's ability to function. In the study, you were considered disabled if you couldn't walk a quarter mile, couldn't walk up 10 steps, couldn't stand or sit for two hours without having to lie down, or couldn't stand, bend, or kneel without using special equipment. Using those criteria, we live one year longer, but in less than just one decade we not only have more years with serious disease, but more years unable to function. So we're living longer in sickness, not in health, a longer lifespan, but shorter health span.


Good morning, everyone. It's my great pleasure to be here. I've been working in the field of tectonic modeling and surface dynamics is not a total stranger in tectonic modeling field, but still I'm pretty much new to serious surface dynamics research.  Instead, I'll be a lot less ambitious and I'll take this opportunity to introduce to you a tool that I developed for tectonic modeling, which can potentially be useful for surface dynamics research. So here's the outline of my talk. First, I give an overview of the code snack, and then I'll give you some examples of tectonic models with surface processes included. Assuming that you are all familiar with erosion in organic environment, I chose a couple of examples from extensional environments, and at the end I'll give you an quick preview of ongoing project that is trying to couple one surface modeling code with the tectonic modeling code. And I'll discuss how snack can fit into that project. More often than not, I have to worry about losing audience because of too much math, but this CSDMS meeting must be one of the exceptions, and so I put up the equation slide first without being apologetic. I don't know, maybe I should apologize that there's too little math or too low-level math. Anyways, so snack is an acronym for St. Germain Analysis of Continua. St. Germain is the name of software framework on which snack is built. So name itself doesn't have much meaning, but LSD sounds friendly. The governing equation of snack is the momentum balance equation given in the form of the principle of virtual power. It is the use of this integrated form that rendered the snack comparable to finite element method. In snack, the spatial domain is discretized into hexahedron elements, and each hexahedron is subdivided into two different sets of five tetrahedron. So the basic unit of computation or the element type is the linear tetrahedron. But the mesh is organized in this hierarchical and redundant way to ensure a symmetric response. The final form of discretized momentum equation is nothing more than the Newton's second law of motion. So the time rate of change of momentum at a given global node L is the sum of surface force and body force, which are also the sum of all the contributing tetrahedron around that global node L. So once you get the net force acting on a node on a node, then acceleration is time-integrated to get velocity and update grid point. The one advantage of explicit formulation is that there is no need for nonlinear iteration. Because the time integration is explicit, all the updated quantities, velocity and grid point location, is depending only on the previous time steps quantities. And velocity and node locations are updated through leapfrog method. And it's obvious that description of motion is Lagrangian, in which grid points move with material. It's nice to have dynamic formulation because in the nonlinear problems we don't have to worry about convergence issue. However, to get a static or quasi-static solution, we need to damp out the inertia. That means we need to include a certain dissipative process. And SNAC uses so-called local damping, such that before we update the velocity, the actual net force on the node is damped by a certain damping force. And the damping force is determined as fixed proportion of the net force itself, but it's acting on the negative, the opposite direction to the current velocity. So if you look at this very simple 1D damped oscillator, it might sound ridiculous to go through this dynamic relaxation scheme to get a solution. Because you simply divide this gravitational Mg with spring constant K. But in multi-degrees of freedom problem, the spring constant K becomes very large sparse matrix, stiff matrix, and inverting it is not trivial at all. So this dynamic relaxation scheme is particularly useful for nonlinear problems. Another issue with dynamic formulation is that the time integration is not always stable, and the time step size is governed by so-called Quran condition. So if we have a kilometer resolution, and if we consider the typical P wave velocity in the crust, then time scale is easily becomes less than second. And that is prohibitive to cover millions or tens of million years of model time. One way to go around that limitation is to adjust the P wave velocity. So if we convert the usual definition of P wave velocity from stiffness matrix, sorry, stiffness divided by density, but if we volume integrate them, we can have corresponding stiffness divided by a certain mass. And we adjust this value of mass, we call it scaled mass, then we can have a lot smaller P wave velocity than physical value, then we can have sufficiently large time step. So the actual gravitational mass is not touched, so the problem itself doesn't change, only the dynamic relaxation step is affected. The constitutive model employed in SNAC is elasto-viscoplastic. And actually it's a linear combination of elastic, viscous, and plastic units. This model is very flexible. For instance, if we have a very high viscosity, then the material, whole material becomes essentially plastic material, for example, more Coulomb. And if yield stress is said to be extremely high, then whole material becomes essentially Maxwell viscoelastic. So if we have a temperature dependent viscosity, then burrito ductile transition will be naturally determined by a given temperature field. And if we have yield stress that is a function of a certain internal variable, then we can have strain hardening or softening, which is required for a strain localization phenomena. SNAC is an MPI parallel code. So here I'm showing some data from performance tests. So on the right, the plot shows work clock time for three different problem size as a function of number of cores. And on the right, it's a corresponding speed-up data. For the smallest problem size, the performance degrades pretty quickly. So if you look at the speed-up, it actually decreases when number of cores goes beyond 1,000. It is because the overhead of MPI operations quickly dominate the overall numerical cost when the local domain size is too small. But if we increase the problem size, then performance stays pretty good. It's very close to ideal value up to core number of 4,096. In terms of efficiency, it stays above 70%. Now we've covered the main features of SNAC. I'll give you a demo simulation to actually see how it works. So the example I brought here is oblique rifting problem. This is a model setting from Sandbox analog model. So on box filled with sand is considered as a block of crust, brittle crust, and it's pulled on two sides. And inside the domain, we have a rift zone, which has some obliquity to the extensional direction. And inside the rift zone, it can be strained, but outside of it, all the box remains as moves as rigid blocks. And on the right, you can see the surface deformation pattern after some extension. To set up a comparable model with SNAC, but in a real continental rifting scale, I set up a domain with the size of 100 by 100 kilometers and 20 kilometers thick. So this is a brittle crust block, and I'm pulling this block on two sides. As in Sandbox model, I define a rifting zone which can be strained, but outside of it, there's no gradient of velocity. And at the same time, I put some weak seed only within the rifting zone so that faults can develop out of those seeds. So this image shows the pattern of accumulated plastic strain after some extension. This is the animation that shows how actually the fault system develops. So you can see initial plastic seeds, and as extension goes on, you can see that faults grow out of one of those seeds, and they try to connect to each other. So the bottom box shows accumulated plastic strain distribution, and the top warped surface is the exaggerated topography. So the maximum relief from the highest to lowest point is about one kilometer. So let me just run the animation again. So individual faults are making pretty much a perpendicular direction to the extension. However, the interconnected structures are followed the predefined rifting zone. In that sense, it's pretty much consistent with Sandbox model. So in summary, snag is a 3D parallel Lagrangian code for elasto-viscule plastic material. It has been benchmarked and is decently documented. It's an open source being distributed through the website of computational infrastructure for geodynamics. And it's been linked to CSDMS website too. Now let's move on to a more important issue of how tectonic and surface processes interact with each other. So erosion in a convergent origin is now an iconic problem demonstrating those two processes interact in a non-trivial way. So this picture is from a paper by Willet, Giger 99, and it shows that pro or withdrawal wedge erosion can lead to completely different patterns of exclamation. So again, assuming that all of you are familiar with this type of interaction, I chose a couple of projects in the opposite setting. Opposite in a sense that we'll be looking at extension of tectonics rather than compression of one, and then sedimentation rather than erosion will be the process that adds complexity. So first example is the rifting style with and without sedimentation. It has been suggested that hot and thick content across when it's extended, then it will go through a wide rifting. Wide rifting meaning distribution of extension is pretty much uniform over a wide region. However, if the extended region is covered by sediments, then the style of rifting changes to narrow or focused rifting. Bialas and Bach showed this using a numerical model. So they set up a thick continental crust, which is about 50 kilometers thick and about three times wider. And if you look at the temperature field, it is hotter than the surroundings because it has a more abundant radiogenic heat. And as expected, the numerical model shows that without sedimentation, the rifting system evolves with a wide rifting style. So after about five million years of extension, which corresponded to 100 kilometers of extension, you can see from this phase field and temperature field that extended region has pretty much uniform thickness. And at the same time, if you look at the strain weight field, the two ends, two boundaries of the extending region is always the site of maximum deformation. However, you get completely different response when you start adding sediments. So on top of the extending region, this green stuff represents some sediment layer. So after about, again, 100 kilometers of extension, if you look at the strain weight field, now there is only one side of active deformation. And if you look at the temperature field, it has clearly asymmetric thickness variation. In other words, the whole system is now going through focused rifting. And the reason is that the thick blanket of sediments increased boundary layer thickness and the system will try to take advantage of non-uniform strength. In other words, the weakest part will only the weakest part survive and takes up all the extension. And another example I'm going to show is about rider blocks.  So it's a little tricky concept just described by words. So I'm going to show an animation. This animation captures the process of rider block formation. So this is 10 kilometer thick crust and 100 kilometer wide and is being pulled on two sides. And being supported by invested fluid on the bottom. So let me run it again. Normal fold develops and it rotates quickly. And as it rotates, it locks and then new fold forms. And at the same time, rider blocks form just one more time. So these blocks are called rider blocks. They are passively riding the foot wall and being transported. So here is a generic mechanism for rider block formation. So first you have new fold that follows so-called optimal angle. But as offset on the fold increases, the crust layers isostatically bend and therefore fold rotates and eventually it locks up. And if it cannot take any more offset, then the crust will develop a new fold at a higher angle than currently locked fold. Then a piece of crust that is between these two folds becomes a rider block. According to this conceptual model, we can infer two conditions for rider block formation. First we can say that a fold is too weak, then the fold is never going to lock up even if it becomes a low angle. And at the same time, if fold is strong enough if there is nothing on the hanging wall side, in other words, if there is no sedimentary infill in this rifted valley, then rider blocks wouldn't form. So if we turn these arguments around, then the presence of rider blocks implies that fold is strong enough and there should be enough amount of sedimentary infill. And these two conditions for rider block formation has very interesting implication. So I'm now showing you oceanic core complex like its continental counterpart in an oceanic core complex, mid-crustal material is exposed on the surface through a large ups and normal fold. So this specific core complex is at about 30 degrees north along mid-Atlantic ridge. And this is an Atlantis transform and it's right north of it. So it's called Atlantis massive. So if you look at the more detailed bathymetry map, this whole block is called Atlantis massive. And if you take cross section of topography and also structure interpretation from south to north, then it looks like this. So at the southern tip, it has a very smooth and corrugated surface. But to the north, it has more rugged topography. The interpretation of this difference, a long-rich difference in topography by restaurant Ranero is that as you go north, you get closer to segment, rich segment center where magnetism is more active. That means the whole rift valley is filled with volcanic infill but at the tip of rich segment, magnetism is not as active, so it doesn't have that much of volcanic infill. That means even if fault can be strong enough to have right of blocks, at the southern tip where there is no sedimentary infill, you cannot have right of blocks. But if you get closer to segment center, then there is enough of sedimentary infill and fault is strong enough so that you can have multiple right of blocks. So we've just seen that sedimentation or space filling surface process is also important in tectonic processes. So in 2D settings, sediments are usually assumed that it's transported along the third dimension in and out of plane and as much as we need. So a lot of hand waving is going on there. And as we've seen in the oceanic coral complex problem, some problems are inherently three dimensional. So that leads us to conclude that physically based 3D coupling between tectonic and surface process modeling is desired. There is one ongoing effort led by Federal Opton in JNS Science, New Zealand and Greg Tucker here at University of Colorado. They are trying to couple a tectonic modeling code, the FLAC3D, that is a commercial geotechnical modeling code and child, the surface processing code. So as a pilot model, they set up a 60 by 60 kilometers and 10 kilometers thick domain, which is pushed from the left at a rate of 1 centimeter per year and the whole left half of the domain is actually kinematically constrained and the right half has a frist lip boundary condition. So according to these kinematic boundary conditions, mountain belt will be built in the middle of domain and that topography developed in the code FLAC3D is given to child and child will adjust that topography by erosion and that adjusted topography is given back to FLAC3D and keep going that way. So here is a preliminary result from this coupling project. On the left, the material is assumed highly erodable and on the right, material is less erodable. The result you are supposed to be seeing is that highly erodable material has slightly larger wavelength of topography along the mountain belt and less erodable material has higher frequency topography and at the same time the maximum topography for less erodable material is obviously a lot higher. So this simplistic and preliminary model already shows that this is the power of code coupling. Then the question is can SNAC be used in place of FLAC3D in this type of project? So FLAC3D's 3D SNAC is 3D2. SNAC can handle inelastic deformation which is necessary for build a mountain belt and SNAC can handle non-uniform boundary conditions too. The remaining question is can SNAC be coupled with child? The answer is actually yes but the real question should be asked is how? And SNAC is based on software framework so I believe it's already in a pretty good shape to be incorporated into CMT system. So I'd like to come up with a little more concrete plan for this coupling during this meeting and use of open source like SNAC should be beneficial not only financially but also scientifically because researchers can modify the code for their own purposes. Thank you for your attention. The seeding that you did for the first example of SNAC, how much does that determine the outcome of the ripped geometry, number of rifts, spacing and so on? It's very hard to quantify. So I'm actually trying to calculate collective property like the displacement versus, well the length versus the fault drop ratio or the size distribution. So it can be defined only that in a collective way. So if I change the distribution of initial seeds it will definitely change the pattern but it's not clear whether that collective property will be also changing. Any other questions? Great. Can you tell us a little bit about whether SNAC can go to very high strains? Do you use re-gridding or something like that? Yes, I do, yes. Well to add something there is issue with re-gridding. As you know re-gridding involves numerical diffusion which can be sometimes very serious. So it would be great if you can come up with the better algorithm than the currently implemented SNAC. Again a simple question but when you actually put the sediment, the example that you showed where you filled your basin with sediment, where did that sediment come from? Do you have like rudimentary surface processes that you actually removed material from the block and deposited or you just basically created mass? Short answer is we simply create material as much as we want. But that model actually included very simple like the diffusion style topographic modification but that amount is not totally constrained, not totally constraining the amount of actual sediment we put in the basin. Thank you very much.


My name is Robin Simpson. I work for the Welsh Government in the Health Department. It affected me in 2001 when I was diagnosed with polycystic kidney disease, which is a degenerative disease that you end up with kidney failure. It took about 15 years for it to happen, but three years ago, two and a half years ago, I went on the transplant list and I've been on dialysis for two years now.


legislature years ago to get a ban to put into statute that would allow for a different pricing so that's why I'm curious it comes back to my past activities we'll look into that secondly Emily on your slide it might make jump in quickly on this one you have no new members and I'm curious if that means no new attributed life spirit to the plan or if an existing employer could still add new employees so that's a good question so existing employers could add covered lives so for instance if someone got married who was employed their employer was a member of the association they could be added to the health plan but the association cannot accept any new employer members at this point so if a company approached the association and wanted to join they could not do that so it should be the power could be I don't have control of so questions from the board Tom I have one quick review your question last winter I think it was Agatha and Sean she and put together this very nice chart that kind of showed the cliff that the healthcare app gets in its presentation and this is kind of looking back at the 2019 with some fairly steep angles that confront people should their income go up and just a couple of them are when you're below 400% poverty with all subsidies etc for a couple the low-cost bronze plan is 2.73% of income and then when you cross that line above 400% poverty jumped to 13.8% of income so that's a pretty steep issue and I had asked last February if you folks could kind of put a price tag on the improvements from 400% of poverty to 450 and 450 to 500 so we just get a sense of what that price might be fully understanding that global commitment might fill that because of the waiver restrictions so I'm just asking you know where that stands at Diva and I think what we said this winter was we definitely share the concern about the cliff and that population of the unsubsidized individual market so we would love to chat with you about what it is that would be useful to see I mean I think that we have some ideas about how we could address that group and I know you were looking at something that is kind of a version of expanding the Vermont premium reduction into that group so I think we just need to talk a little bit more about what it would be what would be useful to see if you could do that sooner rather than later I would be sure and I don't think she's here anymore but it will be Ina Backes who's our director of health reform who is available to talk about that open up the public for comments who wasn't sure Sarah if you were scratching your head or raising your hand I have only one comment it's on the chart that you have that shows all the components of the rate change when you have the non claims components you have other and I would write very specifically regulatory or statutory changes that impact the plans or the coverage offer because I think especially after all the discussion is subsequent to that significant component of rate changes so just to be explicit you captured it there but how was it thank you let's see so I want to thank the panel it was very informative thank you very much is there any old business to come before the board seeing none is there a new business to come before the board seeing none is there a motion to adjourn so it's been moved and seconded to adjourn all those in favor signify by saying aye any opposed thank you everyone have a great rest of the day


bombers winging toward Japanese targets on Seidor in the South Pacific, a dramatic close-up of the terrific low-level attacks that soften enemy bases preliminary to amphibious assault. Northeast of New Guinea in the middle of the Bismarck Sea, the Admiralty Islands have been occupied by Japan since the last war. Now Manus, largest of the group, with Los Negros at the tip, is shelled by American warships. Ending on Los Negros, mechanized United States cavalrymen surprised the enemy with a powerful striking force. Begun as a reconnaissance, the operation now develops into full-scale invasion of the island. Cautiously, troops move up against Japanese defending the airfield. By rifle fire and hand grenade, the enemy is exterminated. Field is taken. Jap planes destroyed before they ever left the ground. The matter of ours, the Navy's engineers bring ashore heavy equipment and have the airstrip ready for American planes. Another vital base from which to strike and encircle the Japanese on nearby Rabaul. General Stillwell, fighting in the jungles of Burma, observes wounded being brought into a frontline field hospital. Here, amid some of the world's wildest terrain and under constant fire from Jap snipers, Lieutenant Colonel Gordon Seagrave, called the Burma surgeon, carries on his job of saving human life. Eight full Burmese nurses, seasoned members of Seagrave's staff, have worked with the doctor since the Japanese invasion of Burma in 1942. Burmese and their allies, working hand in hand to save and embattle people. High above the more inaccessible regions of Burma, giant transport planes carry food and equipment to outposts that can be reached only by air. Over the Naga Hills on the Burma-India border, packs are dropped by parachute with amazing accuracy. Outpost, high on a narrow mountain ridge, a small target, but the supplies land right on the spot. Field in the Solomon Islands, Fiji troops fighting with the United Nations forces deep in the interior, must also be served by air transport. By radio, they contact the supply plane. The chutes land almost on top of the camp. No outpost is too remote for these supply trains of the air. Curacao, 40 miles off the coast of Venezuela, turns out to greet a royal visitor, Princess Juliana of the Netherlands. The princess makes an extensive tour of Curacao's 210 square miles. Dutch marines form an honor guard. Plags of the United Nations pass in a review staged for the princess at the sports stadium. Here, many miles from her homeland, Juliana walks again upon free Dutch soil. In the spring of 1944, a Nazi-occupied Europe looks to the skies, for the Allied air offensive is on in earnest. Already its shadow is upon the enemy. From a Britain now bristling with air drones, Allied squadrons are working round the clock. Day and night, their roaring propellers are being heard over Nazi Europe. These are marauders, swift medium bombers capable of speeding at more than 300 miles an hour. Their jobs to hit specific military installations and airports. Steel helmets and steel jackets protect the crews from anti-aircraft fire. For now, they're over the enemy coast, driving into a flat-filled sky. Mission for today, a Nazi airfield in Holland, 500-pound demolition bombs landing directly on the target. In 39 days, British and American air forces have dropped more than 37,000 tons of bombs on German war industries. The offensive has cost the Allies in planes and men. Even now, those flares indicate wounded aboard, and ground crews anxiously watch as their comrades return. Landing gear damaged, this one comes in on its belly and, well done, their losses have been greater than two to one. In 39 days, more than 170,000 American fighting men such as these have been in action over Germany. Nearly 16 mechanized divisions taking the war to the heart of the enemy. Here above the clouds, they make for the French invasion coast for Nazi installations guarding the shoreline. Over the target, it's bombs away. Bombs that are steadily reducing the production of Nazi war supplies.  Bombs that are smashing their factories wherever they may be. This is a sample of the round-the-clock bombing that today is shaking the very foundations of Germany itself.


is Think Tech Hawaii. Community matters here. Aloha and welcome to Eyes on Hawaii on Think Tech Hawaii. I'm your host, Carol Cox. Today, my guest is Mr. Dave Moskowitz, community and rights advocate. We will be talking about President Donald Trump's directive that will allow employers to opt out of non-cost birth control and issues religious freedom, directions to override anti-discrimination protection. And what I have here in my hand is the actual presidential executive order promoting free speech and religious liberty. This was provided on the White House website, and I encourage you to go there and see it and look at it for yourself if you have any doubt or we don't want to talk about fake news, but this was recovered from the White House website. Now, there are a number of things that this is quite disturbing because it concerns women issues, it concerns gay rights and many things and all based on—and it gets its start from the religious rights, beliefs and moral and all of those things. We're going to talk about them at length today, and that's why I brought Mr. Dave or invited Dave Moskowitz with me. So, Dave, thank you for joining me. My pleasure. I appreciate you being here. And now, I'm not an expert at this subject matter, but I do have concerns for civil rights and the violations and people discriminating against others while they don't have a right to, but it seems that this directive or these directives do just that, rob people of their rights and subject them to abuse by others. So, let's get into it, and what do you think about this process and what has been presented by these directives? Do you have any real concerns? Actually, it's a major step backwards and an attack on all the progress that has been made by LGBTQ immunity in their protecting their rights of equality and acts of discrimination based on unfair and unjust legislation and this directive in particular will allow people to do whatever they want, even outside quasi-legal. You know, I am—when I was younger, my first time I stepped into Harvey Milt's camera shop in San Francisco in 1977, I believe, and I was active in the anti-gay teacher initiative, which we won thanks to Harvey. So, I've been pretty active in the process and for a long time of work with Cesar Chavez and Robert Kennedy and I was kind of in this day and age to see this kind of directive being given out to people which actually affects everyone, even a regular person in their hiring of, say, a church group that may own like Hobby Lobby and how you behave outside of work, how you conduct yourself, who you sleep with, what you might be doing, even though have no proof. I mean, it really is quite concerning that there's a—they're going to allow people to do whatever they want to do outside of the law and you have no chance of having to kind of challenge back. So, we were bothered by the Lobby-Hobby ruling Burrville versus, in that case, what about that? Was that the start? Was that the shot across the bow? Was that a strong warning to say that the general public best beware if you get the right people in place, such as we have now this administration? One of the things I find quite curious and interesting, we're talking about morals, we're talking about values and religious rights and what have you. Isn't it funny that the Health and Human Service Office is not led by anyone now, Mr. Tom Price? He had to resign. Morals and values and honesty and decency and respect for the law, right? Well, that's one. Jeff Sessions as well, the Attorney General, was one foot out the door, but he felt that his agenda of such as this, I suspect, was more important than his integrity and what he's presented, how he's presented by the President of these United States. Very interesting. You have any observations on that? Well, you know, I just, I was watching Bill Maher last night. You know, he was discussing, even when these, they have this like suicide pack amongst the adults in the room in the White House. And he, one of the questions is, who, behind, when you pull the curtain at the White House, who's really running the White House? Who's dictating these policies? Where is this coming from? Is it from Bannon? Is it, I mean, is it from Tony Perkins? Really? You know, Tony Perkins has a snitch line to turn people in if they're gay, which he set up. I just, I don't understand who's running the show there. If these say, say, if Mattis and Tillerson, all those normal guys go, Kelly, who are we going to have then running these cabinets? Because all the rest of them are now gone. What kind of chaos is this going to descend into when we started getting these like childish kind of, I actually don't think they're childish. I think they're purposeful directives that are being issued to appease his base, maybe, but also give people a lot of, I mean, the Hobby Lobby thing was a Supreme Court decision as legal bearing, so Hobby Lobby can do what they want now. So I think this is a further extension and an attempt to see just how far they can push it. So what concerns me with this and as an observation? When they decide the vendor or the shop owner or the employer determines that you are gay, I ask the questions, what criteria do you use? Is it a standard? Is it prescribed the same method to be used by all people or a circumstance? How does one gauge the sexual orientation of another in these situations? So it sets a dangerous precedent because it's so liberal that it invites, in my opinion, an observation that it invites a pile on discriminatory practices without even wanting to. It says that you now have the power invested in you by the government of these United States of democracy to look at another human being and assess that party of that person and deny them their basic rights. You know, I think that I was just reading a document I got off of Doc USA that day and they're looking for these guidance, these guidelines for this process to be enabled and there is no guidance currently. I don't know that there ever will be any guidance. I think guidance will be purely subjective each and every time. I think this is actually to stir a level of chaos into the legal mix. And as we talked earlier about this being a state, every state, every court system will have a different response to this depending upon who sits in those positions. The primary face of evidence, the evidence that needs to be brought to the floor to be used in adjudicating a matter, it would be who's burden worse. It's me who being felt that I've been denied or discriminated against because of my race or you, for example, because of your orientation or sexually or anyone else. Where does a burden, who places a burden and where does it lie? The victim I think is the one who gets the first right. Can you imagine what evidence do you bring in the court? It's the victim. What evidence? For me, if someone didn't want to patronize or want my patronage and if I was interationally married or whatever, they would say, I don't like that, especially taken in the deep south. They opposed much of that. So it didn't have to be gay. Just simply say, I don't like that.   But thanks to Mr. Trump and Mr. Sessions, it seems to be there now invested in these people. So my big question is, is the religious right right? Oh, no, not at all.  To a court, that's just, I don't know how to wrap my head around. Where do you begin with that? I mean, my partner, who's a buddy, where I don't have a physical relationship, we're companions for 20 years. We're both fathers. We've decided that, you know, maybe at some point we're going to get married for the full legal things that can help us tax-wise and things. But I imagine if we were in another state, besides Hawaii, we could be discriminated against because someone would assume that we are having a physical relationship. So I have to go to court to prove that we're not and we're just friends living together. I mean, there's a lot of that going on out there.  That's really no one's business. Even if your name is Stave and Jim on the cake, that doesn't, that's, to take it one step further is an invasion of privacy. And I think based on, I don't know where you go from there. I mean, this is like a whole new, a whole new landscape that we're seeing of litigation. It's a tinderbox of legal litigation that's going to come down the pike. And everyone, and it's going to probably, I don't know how it's going to, I don't know how it's going to play out in the long run when it comes to Supreme Court. We're relying on Justice Kennedy. He's the last one who seems to have any sense. And what happens when he's gone? Now you spoke about the wizard earlier, who's behind this, or who's orchestrating this, this whole effort. So we, we know that the Obama bill, they've tried to kill it, remove it. They fail. But this is still a continued effort to their on a path, war path to undermine and undercut and overturn this in any way they can. So the question is, who is behind it? Well, there was a quote from Paul Ryan, the speaker of the house. Now we know that by now he should realize that there's some problems with this whole setup. And what did he say? This is a landmark day for religious liberty. The speaker of the house. So, is that a sample or indicator that we have decay in the American process now in this democracy? I've always considered the evangelical right to be American Taliban in a lot of ways. They want to make you, they have a whole set of rules and how women's rights and how women are to be treated and pay equality, you know, workplace equality, gender equality. You know, I think they're taking us back in time to the mid ages to a period where of a dream. And I think actually the thing that with Obama was that then the night he, the night he moved into that White House, I was just in heaven and elated that they would be sleeping in the Lincoln bedroom. And I think it drove a lot of people absolutely nuts who are, oh, our right, I think just blatant, hate-filled people.  And I think there's a lot of discrimination against things that are strange and different and new that comes from a generation that hasn't moved on yet. And I think this, to me, I really hope and I'm dreaming that this is a last gasp of a dying group of people with a dying thought process. And I'm hoping that perhaps when people, these issues needed to come to the forefront and now they are, and we're going to have to face them square on and deal with them. And maybe people will step up to the plate and start taking action to fight this kind of legal or quasi-legal action and directives. Yeah. And the question is, can you be exempted from federal law?  Or does it exempt because of the Obama Act or the Obamacare? At that time, for about six years and all, it provided coverage for birth controls and things of that nature. But now they're saying, no, but it's a funny thing that this the right wants to say, they can't support preventing pregnancies. Their religion does not, religion does not allow that. And then you can't abort your baby. So it leaves, it metals in the rights of women and others. So they don't want you to take birth controls to prevent the birth. And they don't want you to abort. So what does that mean? How does that sit with most people? It creates one of the most harshes. And the way the records show in some of the reports now that I've been reading, it will present a bigger problem later. Oh, they're on the wrong side of history, first of all. But you know, I also am reading here that they haven't, they have transferred directly to the internal, not to enforce the Johnson Amendment, which barred churches and taxes and groups from endorsing political candidates. He has now advised them to not to enforce the Johnson Amendment. So they're donating to these political candidates who become people who make the decisions that will determine the outcome of any legal cases in various states, counties and jurisdictions. I think that that based on basically if you accept federal funds, I don't think in the, I don't know how the Supreme Court is going to rule. It seems like the Supreme Court has become a political tool or is moving in that direction for the next, this last election was so important. And for the next 20, 30 years, if we could lose, this could be very bad. So the way this thing is written and the directives are written and how they were proposed, it's an attack on Obamacare number one. So and they're hell bent on getting rid of it. So now you as a civil rights and all that, who do you appeal to? Do you go to Jeff Sessions, the attorney general and hope that he would represent a right on behalf of the put the public or the people that's being discriminated or denied? No, he's the one that's trying to implement this. So we are really in a position right now that it's here's failure. It's going to be an ongoing legal battle. And we seem to have slowed down their machine. His legislative agenda is dead in the water. And hopefully that because of legal action taken by ACLU and other individuals across the country, that they'll be able to have a long period of time before they can actually, I mean, they're going to affect people right off the bat, left this, but I think it'll be a while. Okay, well, hold that thought and we will take a break. This is eyes on Hawaii think tech Hawaii. I'm your host Carol Cox. We will be right back. This is think tech Hawaii raising public awareness. We have this crazy thing going on today. I was just walking by and all these DJs and producers are set up all around the city. I just walked by and I said, what's happening guys? They told me they're making music. So I did. I'm your host Carol Cox. And my guess is Mr. Dave Moskowitz and Dave is from Waikiki area here and he's an activist and a rights advocate. So we're talking about the new directives that were sent out by President Donald Trump. And Mr. Jeff Sessions, his attorney general, and a couple observations that I made that this it gets a start. It's something's wrong with the whole process. Anyway, Jeff, this is about morals and values and honesty and the fear of God and you know, righteousness. And he had to resign the chief, the director or the head of HHS human health services quit because of a scandal of his abuse. Jeff Sessions was also basically the president wanted to fire him or ask him to resign. But his agenda, he's so driven about this and his morals and values that he stays. And that worries me. What do you think the direction will be in the future is they say people start objecting to people's behavior on the job, say they start refusing women's rights of contraception and turning on people to have a wedding in a church and people take legal action. I mean, to what degree should people respond and how should they respond? Well, the first of all, the danger that this presents is in anything, it can be abused. Then if you have an attorney general who's willingness to get this and implement it and he wrote the status of the Obama care, that presents a problems. So you don't have a police policing the situation that they created. So in other words, we as the general public, not the religious right or not the Christian right or whatever it is that's pushing this and that they're playing to. We're left on our demise. They're on a court to deal with these. And unfortunately, who have the money well, you mentioned earlier, ACLU and it's few other legal women voters might intervene. But why should they have to intervene? When we have this process, we're in a democracy and we should be guided by the Constitution. You have a right to believe in what you believe practice what religion you practice is practice. But you do not have a right to allow it to breathe, bleed over into the lives of others. You just don't have that right, in my opinion. You know, I was watching another show and people are sort of looking at on what it is he's doing, what Trump is doing, Trump is sowing chaos. And he's like a guy who's the producer of a reality show. And every day, it's a new show for him. And I just wonder, what's he showing and doing over here versus what's really going on over there? And you try to find out what the sub subtext what what really their motive is in the long run? Is it to shore up the base? Is it to cause dissension within people who are in opposition? And so what to do? I remember that in the left, back in the 70s, they did a lot of things to split people, divide and conquer tactics. But this this particular case, I think we'll probably end up having a lot of legal challenges. It'll end up having a lot of people on the streets in various towns and this starts to affect them. There'll be a lot of boycotts, you'll see a lot of but there's so much going on right now across the board in the country because of Trump. I don't think people are trying to focus on one particular issue. Do we want to wait until the chaos triggers this? Or do we hope to have a common courtesy to say in respecting the needs of women in the workplace, the need to afford them the right? So because some of them can't afford a pay for birth controls or long term types of birth controls or implants and what have you? What about those? How do we get so far? This debate has been going back and forth. But this is as close as I've ever seen where people are just completely, in my opinion, violated. Women are violated, their rights are violated. The employers can be as conniving as they wish. And that poor innocent woman or those women, some 62 million, they say will be impacted. Or as HHS says 120 million. So something's something People in rural America that are really going to be impacted who don't have the community that we have like we have here in Hawaii in support and action in a progressive attorney general. I'm really concerned about their welfare and people who live out in rural and suburban America. In different states, I find they're going to be at a loss. I really I don't know what's going to occur with this, but a lot of people are going to have to go, you know, I think all good politics or local politics, and that I find that a lot of things are going to be cut, even here on Hawaii, Oahu with the budget, and people are going to have to start funding services more and more from other companies. And I don't know, maybe people just need to leave. If it's leave where Hawaii, no, leave wherever it is that it's being offensive to them. But that that's not the way life is going to the reality.  I'm getting out of here. I move into Hawaii. I was going to say, I'm going to just wiggle my coffin and wake up. But a lot of people don't have that luxury. Yeah. So and that's that's the real danger of this, because it's going to broadly affect everybody. And some can afford it. But this then gives the employer the right to deny you certain medical care, because of the religious stance. There's a legal process you can go through to what degree that's actually effective. I've been lucky with the EOC and courts, but to get the money to get a turn to a higher and attorney to take action. Well, I legal justice groups, Lambo and Lambo legal or whatever. That's another show. The EOC and its youthfulness, it really is not effective. Because if you're creating this massive problem, you're going to have a number of victims who seek to file complaints. But look at how the EOC is staffed. It's not it's inadequately staffed. So all of those things that checks and balances are the safe thing that's that exists that would afford someone the right to go and file a complaint. Either you're going to be fortunate to find someone to follow on a class action. But as an individual, you go to EOC, you might as well be scratching on the door for 20 days and you'll get nothing. Yes. On occasion, some do get results. You got what you describe as a good result. But I think probably in the long run wasn't really a good results based on their history. But they do they hold your case and they write your letter you have a right to sue and they do an investigation. But they are short staff. So why are we basically throwing innocent people to the wolves? We're leaving them out there naked, stripping them of those rights when all that what causes us to people or a few people behind the scene that wants to erode away the Obamacare Act and in doing so they don't care who they hurt. You know, besides the fact that this is an abuse, I think this is also an incitement of sorts and excitement because what I see in the future, especially with the upcoming 2018 primaries, having been an auto activist and active in the anti-war movement, I was an STS stopped the draft week. I think there are going to be large scale demonstrations in cities. There's going to return rapidly. I think they want to encounter have people encounter each other on the streets. And they use this as an attempt to rather themselves to win one more term of the presidency if it were to go that far. Here's my concern. I think it's a political purpose. Here's my concern. We spent the half hour last half hour basically talking about what the religious right and part of the Republican Party. Now, we had an emergency session to hear the rail and its impact and the funding for it by our state legislatures who is democratically in control. So the question is why are they they know this law? Our legislators and our Attorney General know that this law is when why are they waiting to see its negative impacts and not hearing up? Maybe they are, but I've not heard anything public. So why don't our Doug, Doug Chen need a case? Well, we don't know, but we need to know that there are people going to be impacted here. And so therefore, our legislators should be convening some kind of session because there's going to be substantial number of people impacted negative. It's a good question opposed to Attorney General Doug Chen, you know, we're running out of time matter of time. You have anything closed. We got about one less than one minute. There will be a Gay Pride Parade in Waikiki on the 21st. Gay Pride Parade in Waikiki ending in Capulani Park. Come down and see the nice people that are there in Iraq with your community. Well, thank you, Dave. Dave Moskowitz. By sharing our thoughts. Okay, this is about the close of this show and we'll be back. If you are interested in getting on our mailing list, go to thinktecawaii.com. Thank you for joining me on today's show, Eyes on Hawaii on Think Tecawaii. Thanks to Jay Fadale, our executive director and our technical support team, Robert McLean, Ray Sangolon and Nick Sexton. I'll see you again in two weeks. I'm Carol Cox. Aloha.


She came in five minutes before visiting hours were over, and on a Saturday night, just kind of matter of factly, walked in and said, well, we have the reports back. You have pancreatic cancer, stage four, and it's very, very bad. I felt like I had—I'm sorry, can I hold on a minute? My name is Pat Friend, and I'm here today talking about my husband, Bill Friend, who served in the United States Army in Vietnam from 1969 to 1970. We were married for 47 years. He was the love of my life. He was the father of my two children, and just a really good man. Well, my son was born before my husband left for Vietnam, and my daughter was born in 1971, so a year after he returned. Agent Orange was not on my radar when they were little. It became an issue and a topic of discussion after my husband's illness and after he passed away. My daughter had cervical cancer at the age of, I think it was 35, and had a partial hysterectomy as a result of that. I am more worried now about my daughter and her children just because of the exposure and how much we still don't know about what it can cause. I would hope going forward that the military would be more careful and has learned a lesson about what Agent Orange can do to people before they experiment with something else.