And we're back live. We are young talents making way only here on Fink Tech, Hawaii. I'm Andrea Gabrielli. I'm your host. And every Tuesday we keep an eye on the future with our most brilliant school students as we talk about their science projects. But you know, to understand the future and even the present, sometime we need to look at the past. But what if the past is primordial, such as the origin of the solar system and even our planet? Apparently, a meteorite has a lot to say about this. And so joining me today is Chloe Kalani from Mililani High School. Nice to have you here, Chloe. Nice to have you here. Thank you for having me. Thank you. And we also thank Taizen Kikugawa, physics teacher at Mililani High School. And Elizabeth Kumin Shields, who are here with us today. Thank you. Thank you for being here. So Chloe, and what does a meteorite tell you? What sort of stories can it tell us? So meteorites act as like time capsules for scientists to look back on. And what they can tell us because they're about 4.56 billion years old, at least in the case of chondrites, is that the different processes that happened within the solar system and even outside of the solar system that we can observe and look at. Wow. Where can we find them? They are in the solar system. Are they in a specific region of the solar system? So the specific meteorite that I studied, chondrites, come from S-class asteroids. And then they're broken down over time. And then as they go through the Earth's atmosphere, they create their fear. They burn, such as the one we're looking at behind us, yeah. So maybe let's have our first slide up so you can tell us something about what you did as part of your science project. So look at that. What are we looking at here? So the first picture is a picture of my wonderful mentor, Dr. Elizabeth Kuhlman-Shield. She really helped me throughout this entire process using the instruments and helping me find, clarifying information, helping me with the data. The next picture on the top row in the middle is a picture of the inside chamber of the SCM EDS, or the Sky Electron Microscope. This is one of the sensors that you have used as part of your project? Yes. Okay. And then the right is a picture of the sample that I used. Oh, okay. It was called the H6 ordinary chondrite butsura. The bottom left picture is a picture of how we have to set up the sample on the specimen. This is like, that's right, it looks like a plate and so yeah, okay. And then so that was inserted inside of the SCM, and then we can look at it from there. That's the stage that moves. That's right. Okay. We can look at it. And then the last picture is a picture of me on the SCM EDS using the NSS spectral software. Wow, okay. Looking at sample area.  So this is, a meteorite can tell us a lot about the origin of the solar system and as part of your project, I understand that you took this particular meteorite, which is called a chondrite, and you started it using what? What did you use? So I tested with the scanning electron microscope coupled with energy dispersive X-ray spectroscopy, and I compared that data to two previous studies using the inductively coupled plasma mass spectrometer, which is the ICP. These are really, yeah. We're going to have some pictures to explain what these complicated sensors really are, yeah. Which is the ICP-MS and then the electron probe micro analyzer, which is the E-PMA. Okay. So let's have our next slide up so we can see something about, okay, so this is a figure that illustrates, I guess, the different types of meteorites, yeah. So the highlighted sections are the class I specifically focused on. So under the undifferentiated meteorites are the main class of chondrites. And then under those many different types of chondrites, I focused primarily on the ordinary chondrites, which break into H, L and LL, which are described the composition. So H chondrites are mainly composed of heavy metals like magnesium and iron. Okay. Okay. So you focused on these heavy metals for this particular class of meteorites. All right. Let's have our next slide up so we can see, okay, so these are the three complicated sensors that you mentioned earlier. What are the, what can you tell us about these three particular sensors? Yeah. Okay. So the left picture is a picture of the ICP-MS or the inductively coupled plasma mass spectrometer. And mainly what happens in that the sample of the meteorite is dissolved in acid and introduced to the interface as an aerosol or a liquid. So is it, does that mean that the sample is destroyed? It means it's destroyed. Oh, wow. I can't get that sample back. So we can't do any more studying on that particular sample because once we run the sensor, it's gone forever. Yes. Yeah. Okay. What about the one we're looking in the center? The center is the scanning electron microscope with energy dispersive X-ray spectroscopy. That is a non-destructive method. It uses the idea of secondary electrons with a primary beam which makes contact with the sample. And basically through that process it emits a X-ray, a unique X-ray photon for each element and then the EDS is able to pick up and determine the chemical composition. Oh, okay. Yeah. And so we're looking at, there is a third one. That's the electron probe micro analyzer or the EPMA. As you can see in the picture, the SEM and EPMA actually look pretty similar. Like the difference with the EPMA is that it utilizes WDS or wavelength dispersive spectroscopy which is just a different principle than the EDS. Okay. So why did you consider these three sensors? You wanted to measure the sensitivity of each of these sensors and also why did you actually choose these sensors? Well, all three sensors are commonly used to determine the chemical composition of meteorites but they're all used for many different purposes. So I basically looked at the weaknesses and the strength of all three of them. So the strength of ICPMS is that it has very high detection limits. So you can basically guarantee, not guarantee, but basically trust the data that you're getting from it that it would be valid with a very high accuracy. Yeah. And then same with the EPMA, however, with the ICPMS you have to destroy the sample. Destroy it, which is not convenient. But it's not very ideal considering that meteorites, you know, you don't have a lot of them in hand. So the weakness of the EPMA is that it's very time consuming. It requires a lot of calibration for each of the wavelength that you want to find. So it's not a very easy process to use. Okay. However, the SCM addresses both of those issues.  And it's a quick qualitative analysis because it uses the standard list peaks of the EDS. But because it's standard list, we don't really know the precision of the data that we're getting back from it. Okay. And so this is why you decided to see, okay, these sensors. Wow. Okay. So this idea of carrying out this research to see what the limitations and strengths of each of these sensors came from an event which you attended, I believe. Yes. Last year during the summer, I attended a astronomy program called High Star held at UH. And then, so it's basically a camp that taught us like the astronomy basics. But what we did within that program is we did a smaller group project. And then I was partnered with my current mentor, Elizabeth Kuhn-Schild. And then we did a small-scale project where we got to work on the SCM EDS looking at this particular sensor. Simply just trying to identify different types of meteorites. And then from there, I noticed that the data we were getting back from the SCM EDS didn't match the data that we were supposed to be getting as part of just a small activity. And then I started to really question, is this a really precise way of getting our chemical analysis? In these meteorites.  And also you got some help as well for this particular project. And this is what brought you to the state of Hawaii Science and Engineering here. Wow. That's really, you know, it's wonderful. When you get to work with professionals, you can really see what they do and what they study. Let's have our next slide up so we can see more about this project that you did. Okay. So this is, this looks like... So this is the sample that I looked at. It's an H6 ordinary chondrite butsura. This is just under a normal light microscope. And then... A piece of rock that was in space orbiting the sun. Yes. Yeah. Okay. And how do you get these meteorites? Because you mentioned there's not a lot of them. How did you get this particular sample? So I was graciously allowed access to the UH's collection of meteorites. Of meteorites, yeah. And then my mentor was able to provide me a sample. The actual sample. Wow. Okay. And then I just like to address that in the picture it looks really big but in real life that was about half a centimeter. Wow. Okay. So tiny, tiny sample. Okay. Okay. Let's see some more slides that you brought us today. Okay. So what are we looking at here? So this is what I would be seeing on the interface of the computer when I was using the SEM EDS. Yeah. The three, the top row and then the left picture are pictures of the sample areas that I looked at. It's under a mode called backscatter electron. So that displays all the different grays and whites. The white areas are areas of high metals so you would see mostly magnesium and iron there. That's the heavy metal that you mentioned earlier. Okay. Yeah. And then the darker areas are all mostly silicates. Silicates. Okay. Okay. And stuff like that. Well, the bottom right picture would be how I set up each point analysis. So I set up in a 5x5 grid about 50 microns apart from each other. So these are the blue symbols that you put over this picture? Yeah. Okay. The blue symbols. So I took about 25 per each sample area. Okay. I repeated this about 75 times to get over 2000 points. Wow. Okay.  Basically, you know, weeks or? Once or twice a week for about six hours or two months. Wow. Okay. So this is a lot of, you know, work and time that you had to. Okay. Wow. Okay. Okay. Let's see some more slides. Okay. So these are your results? Some graphs? So this would be the raw data, I would say. So each of those points that you saw, the blue points, the one graph corresponds to each of those one points. So this would be the peak analysis. It counts the amount of peaks of each element. And then from there it can, the software is able to determine the weight percentage. So here each peak basically represents a particular mineral or element that you can find into this meteorite. Okay. Okay. Wow. So we're really learning a lot about the stories that a meteorite can tell us and this research that you carried out comparing three particular sensors to, you know, get these stories. And so now it is time to take a break, but we're going to be back soon to learn more about this amazing meteorites and particles of rocks that you can find in the solar system with Chloe Kalani here at Young Talent's Making Way on Think Tech Hawaii. We'll be back soon. I'm Pete McGinnis-Mark and every Monday at one o'clock I'm the host of Think Tech Hawaii's Research in Manoa. And at that program we bring to you a whole range of new scientific results from the university ranging from everything from exploring the solar system to looking at the earth from space, going underwater, talking about earthquakes and volcanoes and other things which have a direct relevance not only to Hawaii, but also to our economy. So please try and join me one o'clock on a Monday afternoon to Think Tech Hawaii's Research in Manoa. And see you then. Good afternoon. My name is Howard Wigg. I am the proud host of Code Green, a program on Think Tech Hawaii. We show at three o'clock in the afternoon every other Monday. My guests are specialists from here and the mainland on energy efficiency, which means you do more for less electricity and you're generally safer and more comfortable while you're keeping dollars in your pocket. And we're back live. We are Young Talent's Making Way here on Think Tech Hawaii. And today we're talking about meteorites with Chloe Kalani from Mililani High School. Thank you for being with us today. Thank you. So this experiment, you selected this particular class of meteorites called chondrites. You got a sample and you compared what three sensors can tell us about this particular sample. So what have you learned as part of this project? Like personal? No, about the meteorite, about the three sensors. So overall, the overall conclusion was that SEM-EDS is a feasible method for looking at major elements, which are the elements that make up the bulk of the sample. However, when you want to look at trace elements or elements that are below 1,000 parts per million, it's really not an ideal method to look at those elements. You would want to more go towards the ICPMS or the EPMA. Another interesting finding I found was that the SEM-EDS for major elements didn't tend to overestimate or underestimate any of the elements that I looked at. They were all nicely in the middle between the EPMA and the ICPMS ranges. Looking at the trace elements, the SEM-EDS data was nowhere. If you look at the slide. Let's see some more slides so you can tell us more about these results. Yeah. Okay, let's see the next one. Okay. These are the major elements and the trace elements you're mentioning about. Graphs. Okay. Let's see the next one. Yeah. Okay. So here. So this would be an example of a major element I sampled. This would be a silicon. The silicon, yeah. So the red line would represent the ICPMS, which would be the upper bound of my range. The blue line would be the EPMA, which is the lower. And then the SEM you can see is just in the middle between both of them. And that was normally the trend for all of my major elements. Okay. So, since typically the ICPMS or the EPMA are accepted values of the meteorite composition, you could determine that SEM-EDS would be feasible for looking at major elements. This is amazing that you had this idea of actually comparing these three sensors, considering one destroys the sample. And so it's not very good to carry out research or something. The other one takes forever. It's very long to actually carry out these studies. And the third one is the one that you actually came up with this idea of testing and see what kind of analysis, the precision as well for these sensors can see. So you went to the state of Hawaii Science and Engineering Fair with this project, with this idea. How was it? What kind of experience personally have you gained over there? I attended districts. And then from there it was really fun just trying to explain the project to a lot of other people, especially like the judges and just the normal observers. Oh, the answer does as well. And this is something very interesting because it's something that could also be, could also, could this process your idea of using this particular sensor to look at meteorites without taking forever and destroying the sample? Could it be used for other purposes as well? Or maybe some forensic analysis or? The SCM-EDS is also widely used in a lot of other fields like forensics and meteorites as well. Meteorites as well, yeah. Material science. As you proved. Material science. And then like food science. Wow. So a lot of potential applications as well. Wow. Okay. So why did you get passionate about meteorites and stars and these particular sensors? Well, I've always really had an interest in geology and astronomy. So meteorites are like a nice combination of the two. You put them together. Wow. Okay. So like to understand the characteristics of the meteorite and the different components that go into it, it requires understanding chemistry and geology and all those things. But understanding what it means in a larger context in the universe, you would have to understand astronomy. So I thought it was a really nice balance between them both. It's amazing. It really is. It really is. And I understand that as part of your duties at Mililani High School, you also are the president of the Science Club at Mililani High School. Wow. So what kind of activities do you do as part of this club at your school? So the main thing that the Mililani Science Club participates in is a competition known as Science Olympiad. Wow. It's a national competition, but so far we compete statewide. It's basically like an Olympics for many different types of branches of science. For science. Olympics of science. Wow. I like that idea. Yeah. And then so as a club, we compete as a team, but each individual competes in a different event, in a different branch of science. So do you do meteorites? So my event would be rocks and minerals. Rocks and minerals. Astronomy. And astronomy as well, okay. So this is another event, which is other than the state of Hawaii Science and Engineering Fair. So we can see that Mililani High School is really involved with all these activities and everything. How often do you meet with this Science Club at Mililani High School? So competition season normally would start around September. So from there on, we would meet about once or twice every two weeks, just going over general information, as well as we have study sessions every Saturday from eight to twelve. Eight to twelve. Okay. So that's a, I mean, it's a, yeah, I am. We just come in and like review the concepts or study for the tests or tests. How does that, because we are familiar with the state of Hawaii Science and Engineering Fair and its rules with district fairs and everything, but how does this works? How is the, how is this competition carried out? So if we're competing against other schools. So every school has a Science Club. And so. So we would, so it would usually take place on a whole day and then we would have a schedule that each event, like let's say there are like fifteen different events. So each person would just go into their designated place and then do the, take the test or do the lab. The test and the lab. So you mentioned, you're doing minerals and rocks for this geological point of view. What other areas of science do you, do you have students that, you know, carry out projects for this competition? All different kinds. There's a chemistry event. There's a, looking at cell biology. There's epidemiology. Wow. There's many different kinds, what I'm, what I'm, okay. All different branches of science, material science as well. So what, do you have, so obviously you are passionate about this geology and astronomy. So rocks that you can find in space, these meteorites and these chondrites. Do you have an idea of what you might see yourself in the future? What might be your, you know, future aspirations or something? We're curious here. Think that Hawaii. I'm not quite sure what major I would want to go into, but I definitely would want to go into a science field, go to a college. Into science as well. Wow. Okay. Okay. That's very nice. So maybe we have some time for some more slides that you brought us here today. We can see, these are still more of the magnesium and the elements that we showed. Let's have our next slide up. Okay. So this is the trace elements. The trace elements, yeah. So we mentioned this before, but what are they? Trace elements are myoperational definition of trace elements where all elements that were below 1,000 parts per million or 0.1 weight percentage. So they are elements that are present, but they are very, very rare. Okay. Okay. We have about one minute left in this conversation today. Could you summarize a little bit for us what you did as part of these projects and the importance of it? So I analyzed the chemical elemental composition of a meteorite using three different types of instruments, the ICPMS, the EPMA, and the SCM EDS. And basically what I found was that the SCM EDS is a feasible method of looking at major elements, however not trace elements. And the trace elements, yeah. Or if you would like to have a fast and non-destructive method of testing, SCM is the way to go, however if you want to look at more in-depth and look at trace elements, the ICPMS or the EPMA may be more beneficial to you. And it's important because meteorites are like rare time capsules for us to look at. So if we want to look back at processes of the universe or the solar system, they would be one of your only available specimens to observe. Tools to see. Wow. Thank you so much for coming to us today, Chloe. Thank you. And we thank also our audience. And this is Young Talent's Making Way, only here on Fintech, Hawaii. Next Tuesday at 11 a.m., we will be back for more. Stay tuned.


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This is what Lotus Tostron looks like. A man complaining about fatty meat. Welcome to 2020. All right guys, welcome back to the channel. If you're new, my name is Bobby. Guys, let's face it, veganism lies in the dirt. You shouldn't beat a dead horse. You shouldn't kick somebody when he's dumb. But thinking back when veganism was in its glory days, on its peak and vegan gains was fantasizing about stomping babies to death.  The vegans made sure to debunk and destroy everybody that didn't agree with their religion. So we're going to proceed one reaction video at the time until veganism dies. Hey guys, welcome back to another When I Eat in a Day vlog vegan couple edition. Why do they all look the same, man? We're late for breakfast. We have been waking up kind of early lately, early for us and doing exercise. You can spot them from miles away. Obviously they are vegans. Look at this blue hair, red hair, all the colorful tattoos, vegans. First thing in the morning, we've been alternating like weightlifting and some form of cardio. So Eric will jog. I'll like jog, walk. On that note, the excessive amounts of exercise that are needed to keep up some level of fitness are ridiculous whilst being on a vegan diet. The value return margin is absolutely minuscule. They are working out every day doing cardio lifting weights looking like this. It is better in the morning. It's nice and breezy and cool. So feminine. I'm talking about the boyfriend. I came back actually and I jogged to the store, picked up a couple of ingredients for breakfast and got back, took a shower and what's going on with her hand. And we are about to make a scramble. So we are deviating from our daily smoothie bowl today because I was craving something savoury. There it is. We just saw this one in a natural vegans fridge. I got some more just egg, got some veggies. This is some shiitake mushrooms. We made ramen the other day. So we had a couple leftovers, got some spinach, some scallions. Eric actually grew these. Oh, I did. Those are scallions. Yeah, we've been replant. So feminized. It's really crazy to see what the vegan diet does to men. In the white parts of scallions in our backyard and they've been springing up. That's why they smell so good. They're very, very pungent. Very pungent, very strong. Yeah. And then we've got some bell pepper and some little tomatoes. I got a really ripe avocado and then I discovered some cream cheese in the back of our fridge yesterday. We did like an impromptu. It's always the same. A whole food plant based diet can reverse heart disease. Yeah, sure. Look at this. It is egg replacements, cheese replacements every single time. Man, what is going on with humanity? Why don't you see this? How can you be so blind? All those veggies won't nourish you. So what you want is dairy and eggs.  To fridge clean out because we are moving quite soon. So, well, we probably still have more stuff than other humans, but we did get rid of a lot of stuff. Then I also picked up some Dave's killer bagels to have with the cream cheese. So let's scramble. Let's look at this. You have the oxalate written greens. You have the goitrogenic nitrates and you have some mushrooms. None of this will sustain you. None of this will give you energy. None of this will give you building blocks for your body. So in order to get some protein, you have to pay a company that produces just egg. It is so just, just for everyone.  You are spending three times the amount and you are supporting plastic. But yeah, I guess if you don't get a straw at Starbucks, you will save the planet with your soy latte. Put salsa on mine. We have this super spicy. It's pretty good though. We actually cut it with, yeah, we had some leftover mild salsa and we combined because it's really, really hot. It's really, really hot. I'll take everything except for the tomatoes. I'm going to throw the tomatoes in like at the very last minute. This here will give you heart disease. Again, it is advertising to gullible idiots. They call it earth balance. Yeah, if the label says so, I must be captain planet. I'm going to save the world with earth balance. This is nothing but candola oil. This is nothing but plant oils. This is nothing but omega sixes. This is nothing but heart disease in a box. They can get warmed through but they won't like, this isn't great. Just use butter. Please, like your mama did. We still have a couple tubs of vegan butter left over from our vegan butter taste test over a month, like two months ago. Now we're trying to use that before the move. Sorry for how dark this is. The side of our kitchen is not well lit. Yeah, just add our egg. Macro shot. Okay, yet again, veganism is based on what? Of course, you have different angles to look at that subject. So it could be ethical.  Okay, I understand that. However, keep in mind that if you eat bread, you are killing millions of rodents.  Second point, health, right? It's either the ethics or the health or the environment. What's up with the health? You are eating vegetable oils. Everybody agrees that this will give you heart disease. Everybody agrees that wheat is probably not that great. So now we established that this is not healthy and that this is not ethical. How about the environment? How about the environment? If you want to have crops, you have to deforest this planet and you have to create monocultures.  Again, veganism is nothing but a sad joke.  Oh, yeah. Oh, yeah. It's mostly vegetables, which is kind of what I call them. Yeah, but it's not. It's mostly processed gunk. That's what I was going for. For health. For health. Mostly vegetables and bagel. Yeah. Let's just not talk about the chemical gunk in a plastic bottle. Cutting up this avocado right here. All right. Breakfast is served.  So, so good. Eric put butter. It's always so, so good. Can we just appreciate what happens when Sarah puts hers together? Yeah. And I put mine together. The difference. Look how much worse mine looks. It looks beautiful. You just started eating it. It looks like a boy did it because a boy did it. You seem very boyish. Cut up the entire avocado even though I didn't know Sarah didn't want to use the entire thing because sometimes- Now we're committed. Now we're locked in. If we don't eat it all right now, it's going to go rancid.  What's going on with your hand? I really would like to know. A little, a little fanning. That vegan life. Eating, starving, watching more vegan recipes. Amazing. This is like the crispy garlic one. So good. So good. Amazing. And I'm going to buy more once we're moved in. It's just like oil infused with chilies in this case, like little crispy bits of garlic. I don't know how it stays crispy in the oil, but it's like, it's so good. Highly recommend. We need a so good counter. It's over 9,000! Hey guys, we are back for lunch. I just prepped all the ingredients to make a cold noodle salad. I've been trying my best to have some kind of salad every day for lunch or if not for lunch then for dinner just because I want to make sure I'm getting in. Lots of vegetables. Just because somebody told you that vegetables are healthy. Meanwhile, we have carnivores that eat no vegetables whatsoever and are healthier than you. How come? Well, I've been on a healthy eating cake. So, feels good. No, you haven't been. You've been on a brainwashed vegan cake. If you would be on a health cake, you would eat meat. That's a bunch of veggies here. I have some cucumber that I cut into matchsticks. I used my mandolin to julienne a couple carrots. Got some, actually a lot of cilantro because I love cilantro. Listen guys, there's nothing wrong with eating a salad here and there. I have nothing against the vegetables. But this whole bowl that would feed a family has not even one percent of the nutrition of raw liver. You would just have to nibble on a couple of pieces of raw liver and you would get more vitamins in than this whole bowl. Think about it. Got some scallions and then the other half of the red bell pepper that we used in our scramble this morning. Slice that up and then this is brown rice, my fun noodles and I just boiled them and they've been chipped. Brown rice is actually worse for you. Ancient civilizations have understood that stripping off the shell is healthier for you. You get rid of the anti-nutrients when you polish the rice. White rice is healthier than brown rice. I'm just gonna kind of wing it with the dressing. Gotta get in there with the hands I think. It's the best way to mix noodles. What is going on with her hands again? But anyways, this cold noodle salad, why would you do that? If you're eating noodles, at least make a stir fry. Actually, let's use the pot because I have this tendency to pick a mixing vessel that's always just one or two sizes too small. I am currently in the process of editing another cookbook review. I tested a bunch of recipes from the Mississippi Vegan Cookbook and they're reviewing each other's cookbooks. I was for tamari in one of his recipes just to make him super umami. Umami or savoriness is one of the five basic tastes together with sweetness, sourness, bitterness and saltiness. It has been described as savory and as characteristic of broths and cooked meats. Go figure. I've never really cooked with tamari that much but I definitely have a newfound appreciation for it. I always kind of assumed it was just like interchangeable with soy sauce which it is to some extent. Everything is trying to replicate animal meats. Say it with me. Vegans love meat. I'm definitely appreciating like the ways that they're different of tamari these days really so. Yes, spice is missing. It's not the meat. I can just make sure we don't have a lime floating around in one of our actually going to do a little bit of garland. Sure, it's ready. It's just a ball of carbs but it's ready. Now look how many ingredients you need to make noodles taste good.   Now I think it's perfect. I really like the same combination of like veggies and dressing with the Japchae noodles. They're like sweet potato or yam noodles that you get in the Asian section. So sad. You're going to be so hungry after this. Completing our noodles. I really wanted to top these with roasted peanuts so I picked some out of this trail mix to chop up. I just feel like it's necessary so. I agree. We get a little sprinkle here. As the person who didn't have to put in the effort to chop them up, I agree. Okay and then I got some more scallions and some extra lime wedges just in key. It's mostly decorative. Let's be real. You know, I was wondering it's 5.20 now. Is this a late lunch and we're going to have a really late dinner later or is this a really early dinner and we're going to have. Oh man, are you really the boyfriend or the best friend? Dinner later. Dinner number one. I like dinner number one. Yeah, it's all a matter of perspective. Oh yeah, I put the remaining quarter of the avocado on Eric's right there. Gotta get them healthy fats in. I'm hungry. Those healthy fats. Yet again, who told you why are avocados healthy fats? How do you know? How come that our body is made out of cholesterol? How come that fiber ferments and becomes butyrate, which is a short chain fatty acid found in butter? How come that the longest living populations have higher cholesterol? Questions, so many questions. So healthy. This is exactly what I wanted. Something healthy. I was going to put some tofu in. I don't know what we're having for dinner and we might have tofu for dinner, so I wanted to. I'm good, right? It's amazing. It's amazing sis. It's so fresh. Sure. You want to make something? We had Chipotle yesterday and it was not a good experience for us. There was a piece of steak in one of our bowls. I put it in my mouth and I bit down and I was like, that's not sofritas.   Oh no, not the bad fat.  Like if it was just a piece of steak, I think I could handle like just okay, take it out of your mouth, put it down, that was gross. But because it was so fatty, it really, really disturbed me. It was so, so gross. So gross. This is what low testosterone looks like. A man complaining about fatty meat. Welcome to 2020. I'll put it down, that was gross. But because it was so fatty, it really, really disturbed me. It was so, so gross. It's really disturbing.  We are back. It's been a couple of hours. Right now I'm going to try to make vegan protein bars. I really, really like the go macro bars. This is a very long video. I'm going to skip ahead. If you couldn't tell I'm hungry, just why I'm. Do you want it? Oh, believe me, I can tell.    It spikes and then it crashes. You get hungry again. The most satiating macronutrient is protein. Again, ask yourself, why? Let's cut out a little piece so we can try them with the chocolate chips. That's good. Not exact. Reminiscent of. I like everything is reminiscent of. I like that. I'm totally the only protein that I actually like. Yeah. Good job. Professional chef. Hey guys. Hey guys. For full transparency's sake, this is the following day. I fully intended to make this dinner for the vlog. We got lazy. We got Taco Bell. Life gone away. That's the answer pot. This is the vegan life. Don't support the animal cruelty. But when it comes down to Taco Bell and a vegan option, no worries. When it comes down to Burger King and a vegan option, no worries. Where do you think your money goes? But so this tofu has been being pressed for almost a full day. Tofu. Now this explains his behavior. Impressed for almost a full day. Ito estrogens. There's a lot of extra firm tofu. And two? Yeah. So I thought it's funny because I feel like one vlog is never enough. Two vlogs is too many. I don't think some weird witchcraft happening there. But I figured we're going to make this crispy. We're going to bake it. This is truly what happens when you have too much comfort.  And therefore, they are looking for a deeper meaning. They think they're going to find this within veganism. The next thing you know, you have blue hair and you're eating tofu. We're going to bake it and the corn starch gets really nice and crispy. So I'm going to put you in charge of this. Okay. Corn starch and soy. Super healthy. I've got a couple tablespoons of corn starch. You can use like tapioca or like potato starch, arrow starch, anything. You could use all purpose flour to if you want. And I think it's time to fast forward again. I've texted with Chris about that idea. Leave us in the comments. Why do you speak like this, man? Recommend us people that we should collab with other YouTubers and we'll try to reach out. Is it Brian Turner Cook? Yeah. Yeah. I just recently started following Brian. I just recently started following Brian. Where did you follow him to? Amount of minced ginger here. That smells so good. We just stir fried our veggies till they were tender. Add our sauce, let it thicken. Think about a Chinese buffet. This reminds me a little bit of it. You can pick whatever you want. You have beef, you have pork, you have chicken, you have fish, you have noodles, you have rice and you have those veggies. So now I'm not saying that you won't pick them ever, but how much will you eat oven? Everybody knows. If you want to get your value, you want to get enough bang for your buck, you will end up eating a ton of meat. You will eat a little bit of those veggies. This is true intuitive eating. So how come that we are drawn to meat? How come that we wouldn't eat copious amounts of veggies? Again, another question for you. Today, I'm not going to make statements. It's really about questioning your ideology, your religion, please. And I cover it for a couple of extra minutes. Well, he let our tofu get nice and crispy. Oh, Steve. And by the way, I used to eat very, very similar to this. This is your vegan protein. The vegetables are your vegan vitamins. And now you need some sort of starch. This is what they tried to sell you. This is a whole food plant based diet. Here's our finished product. There we go. The rice. Added some sesame seed and some scallions. It looks really good. It smells really good. I'm excited to eat this. Hey, it's a different day, clearly. But I just wanted to hop back in. And thank you so much for watching today's video. I truly appreciate you guys so much. There's gonna be more videos coming soon, as soon as we've moved and everything settled back down. Hey, guys, I appreciate you so much. Thank you so much for watching, guys. Please click subscribe, guys. Guys, let's be honest, man. She looks drained. She looks tired, and she already has this blurry filter on her camera. She tries to hide her skin. I can see right through it. I'm editing videos. I know how it works. This woman is drained. She is anemic. She is starving. It is always the same with veganism. Why do you do it to yourself? As I said, it comes from a comfortable state. It comes from being spoiled. You guys have it too good. Go to Africa. Go to Indonesia. See where people don't have enough to eat. See how those people have to survive. What they do to survive and what the most praised foods are. They're nutritionally dense animal foods and not tofu or noodles or vegetables. Stop torturing yourself in 2020. All right, guys, but this is it enough for one video. Long enough as it is. We're going to cut it off here. If you enjoyed it, leave it a thumbs up. If you haven't subscribed already, guys, please do so. If you want to support the channel, all the links are in the description box for you. And as always, may God bless you all and send brain to the vegans. Much love and peace.


The Nigerian Bar Association's section on public interest and development law has faltered the procedure that led to the removal of Petro-Shapsody Nigeria, saying that the federal government should have instituted palliative measures to elevate the harshness of the subsidy removal before making the announcement. Monday, Ubanid, the chairman of NBS Pedal, stated this in a news conference yesterday in a kerja to Herald, the forthcoming annual conference with the theme post-election Nigeria, the judiciary in the eye of the staff. The aircraft said to be for Air Nigeria was merely a chartered flight from a topian airline to who would wing Nigerians.  This matter is a litmus test for the new administration of President Bola Tinibu, especially if he, as the president, wants to be seen as desirous of making sure that he combats corruption in the politics.


Hi guys I'm Rob Riches and on today's video for supplementsworld.com I'll be taking a look at the ingredient Inositol. For more information on Inositol and many other ingredients be sure to check out more at supplementsworld.com Inositol is a pseudovitamin occurring naturally in both plants and animals. Now pseudovitamin means that it's not an essential vitamin or mineral yet it's still vital in the body. Inositol is a carbohydrate source with a chemical structure similar to glucose and is characterized by a sweet sugary taste. It's used in the structural formation of eukaryotic cells and it's an important component of structural lipids and phosphates. Its primary function is to keep fats from accumulating in the body especially the liver and it's found high levels in the brain and heart working with neurotransmitters to circulate messages. Food sources of Inositol include oranges and cantaloupe. As far as benefits Inositol promotes better sleep quality by maintaining levels of serotonin and communicating with GABA receptors in the brain. It also works as a substitute for GABA when it's depleted helping with the treatment of anxiety symptoms. When combined with choline it can accelerate liver detoxification, increase blood circulation, aid weight loss and work towards treating insulin resistance. Dosages depend on the condition being treated. For anxiety relief 2,000 milligrams taken twice daily is the recommended dose. Add in an extra 1,000 milligrams a week until the max dose of 18,000 milligrams is reached or your anxiety symptoms decrease. For better sleep take half to 2 grams of Inositol two hours before you go to bed and do this daily for up to a month. Now for more information on Inositol and many other ingredients check out supplementsworld.com and if you haven't already do subscribe to our channel. I'm Rob Riches, thanks for watching, we'll see you soon.


Hello everyone, I'm really happy to be here today. I'm Ricardo, today we'll be talking about training and optimization of our transformers and we'll talk a bit about the platforms we have at CERN for this kind of workloads and also a real physics use case for once with a real physicist as well, so I don't have to pretend that I know what I'm talking about today. So hi everyone, my name is Maxen Trager and I am supposed to be the real physicist but unfortunately my talk is mostly going to be about how to apply machine learning for a very specific use case in our experiment. Yeah and I'm Ricardo, I lead the platforms infrastructure team at CERN. I'm also a member of the technical oversight committee at the CNCF and the recently formed end user technical advisory board as well. So I'll start by giving a very brief overview of what CERN is. It's the European organization for particle physics. It's been there for several decades and our flagship project is the Large Hadron Collider which is a very large particle accelerator that has 27 kilometers in perimeter and it's 100 meters underground. We accelerate protons to very close to the speed of light and we make them collided specific points where we built these experiments but because a video is better than a thousand words I'll try to play this very quickly so you can see we accelerate two beams of protons on this very large ring. We have these four experiments where we make these collisions and what I want to highlight here is that these collisions and these machines act as very large cameras and we produce a lot of data. So I'll stop here because this is where it gets interesting is that we are generating petabytes of data per second and traditionally to handle this on the nanoseconds we've been building custom electronics that will filter this data most of this data the very very large amount of data we are generating in using custom electronics first and then the very few percentage that is left then we use very large computing farms which traditionally are also CPU based and then from here we finally get to an amount of data that we can store in our data centers and then reprocess and give the physicists for analysis. So this is kind of important to put some context into this talk because it explains a transition that is happening which is all this custom hardware all this custom electronics we got used to develop are now possible to be replaced with GPUs and other accelerators and machine learning algorithms and this is a huge simplification of our infrastructure. So Maxans will talk a little bit about one of the experiments. So as I mentioned I'm part of the atlas experiments and you have a nice photo showing you the scale of the of the detector with a human just so that it's more understandable. So the point of the atlas experiment is to study physics at the fundamental scale and at the fundamental scales physics is described by particles which are fundamental elementary elements that you can't subdivide as far as we know so far and they all listed here into the cycles are showing you the different particles and you have particles that make matter the outer ring and then you have particles that make the interactions themselves the inner rings and as you can see there's one at the center it's the Brutangloth X boson or more commonly called just a X boson or the goddammit particle. So the point of the LHC and atlas was to discover this particle which was done in 2012 but we still have to measure a lot of the properties and the user case I'm going to show is an advanced use of AI so that we can measure some rare the case of it and this is obviously a huge collaboration given just the size of the detector the size of the data set to see through so we are about 6,000 members scattered across 216 institutes and more than 40 countries now and so I'm going to give you a user case by the end of the talk. Yeah so now we'll break kind of the talk into two parts I'll start by talking about infrastructure and focus on the journey we had to get here and also the challenges we are facing and then Maxence will talk about the physics use case at the end so if we look at what we've been building at CERN and we've given many many talks over the years at this conference and others we had a long journey to get to where we are we started looking at cloud native and Kubernetes back in 2016 where swarm was also a possibility being considered we even added mesos at some point and we eventually got to production service and then all the work we've been doing since then is about integration with our internal systems so things like storage and our self systems and other storage systems we have in house and then usability and ease of deployment we get up secret management things like flux and Argo then allowing heterogeneous clusters with node groups auto scaling auto healing load balancers and then a very strong focus on dissemination internally mostly to get people used to handle this kind of applications and also security which is something we put a lot of focus on and finally one key key aspect is ensuring business continuity in disaster recovery and we've been building on all the features that have been offered by by our tools so this has been a long journey but it got us to to quite far and you can see here we use these tools but a lot more but from from the top you can see the webinars we organize internally and you see two different kinds of things we are offering you can see the services so here at the top you have how certain runs critical application service on Kubernetes and this is really the critical systems on the campus the everyday services we need to support the campus with 10,000 people and then we also see the other side which is more the physics analysis and you can see here analysis reproducibility with Rihanna and Kubernetes this is where the physics actually happens and then the base computing power we need Ruchio here moving Ruchio to production in Kubernetes Ruchio is the system that is responsible for in the Atlas experiment moving data around and this are pretty large requirements so if we look at Ruchio for example I have two plots on the top there you can see we are any random week at CERN we are moving over 50 petabytes of data around and you can see transferings of 7 petabytes of data as well and then on the capacity you can see here an experiment that was done by the Atlas experiment using public clouds where they tried to scale out to in this case GCP and they managed to run for several days with over 80,000 cores of spot instances so we have a lot of experience we built all this infrastructure that people got used to so when we start looking at machine learning in AI it's only obvious to try to build on it and we started looking and Kubeflow was a good option it builds on all the principles we had in-house already it kind of doesn't build anything new it gets a lot of components working together in a way that can help the end users and it's it's been quite successful in answering all our needs which is things from data preparation to some sort of fast iteration using notebooks or other means then scaling out to discrete training and hyperparameter optimization and then model storage and serving I won't go into details on this but you have the QR code of previous talks that we gave in this area so what I will focus is we have this infrastructure what are the challenges that we have today and these are challenges that are both on the stack but also on the usage of the of the resources so the first the first challenge we have and I don't know how many people are running their on-premises infrastructure or just relying on external cloud providers but if you're running your infrastructure at least for us this has been a huge challenge the pattern of usage of this hardware is very different from what we call our traditional CPU workloads the needs for power and for cooling increase dramatically if we look at this plot here we can see that the needs for power in the current generations but especially if we look at the future generations that are coming kind of limit a lot the density we can have and at the same time people are you are requesting this this kind of density not only the density in the single node but they are also asking for things that were traditionally only needed by HPC centers and supercomputers things like fast fast networking interconnects infinite band and friends all of this comes with a huge request for power and cooling so if we look at that diagram there we can see if we have four GPUs per node with the interconnects internally we are already putting quite a lot of demand on the power and cooling required for each rack if we start talking about fast interconnects between the nodes then things get even more complicated the second one is hardware evolution and this doesn't seem like it's a calming down we got used to sort of stable increase in terms of evolution of hardware around CPUs but suddenly we started having GPUs coming into the scene and the the rate of increase is much higher so we can see here the predicted the next generations for NVIDIA and we see what they are optimizing for so they are clearly targeting things like LLMs which are not necessarily the main use cases we have internally for machine learning but what this means is that people are following the trend when technology allows you to do something you start building new use cases and this is what we are seeing in the house the fact that you can have such large models in the GPU and such power in a single GPU means that people are considering what I was showing at beginning of having this custom electronics for the very fast filtering in the detectors this can now be replaced potentially by more commodity hardware with GPUs and other accelerators so these use cases are coming at the same time people will want to have the new fancy GPUs from our side because they're extremely expensive we want to make them last longer so we are already pushing the lifetime of this kind of hardware from five years which is our standard to eight years while people want to have a much faster turnaround because this is what the public cloud providers are giving them and then what this means is that we have to make the best of our internal infrastructure but we also want to offer the the more advanced use cases the hardware they need so what this means is that we probably or we are going hybrid clearly and this is to fit both the needs but also the costs for this specific use cases and the very large delivery times we are facing today and we could say this will be a hype that disappears but we already saw this with Bitcoin and now we have another hype maybe there will be a new a new one so it doesn't seem like this kind of infrastructure is going away going hybrid brings a lot of new challenges and luckily we have some quite some experience with this the challenges the needs the new needs and requirements for network and storage over the last 20 years we built something that you can see there on the top right which is the grid computing infrastructure for the LHC these are 200 different centers around the world that we connected with very high throughput links so we know how to distribute the data we know how to do the workload scheduling to put the workloads where the data is or move the data when appropriate if we look at the cloud native stack though there are things that are missing we don't have the primitives yet completely there for advanced scheduling things like cues things like co-scheduling and we have we don't have an easy way to handle multicluster the management of multicluster is there the scheduling across multicluster is not there so this is something I find extremely interesting to focus on right now projects like you and the new features they've been adding around multi queue are really interesting and if you look at other projects like volcano or Armada I can see that they will start building on this base layer as well what is really interesting here is that if we solve this problem and we are focusing on this mostly because of jnai we are actually going to solve a lot of problems for HPC centers that have been using traditional tools like slurm for scheduling and suddenly they start being able maybe to offer a pure cloud native API to their users this is a huge simplification and for us this would mean that we can use the same API on premises public cloud providers and targeting HPC centers as well so this is a something that will be very interesting to follow in the next year or so and the last one I have and this is came while discussing with Maxans for this talk is that we probably need to start focusing on the Python for our C++ infrastructure if you have anything ever to do with high energy physics you probably heard of the root framework which is a data analysis framework developed at CERN it's written in C++ and there's a lot of people that are exporting C++ that love it and wouldn't change C++ for anything else but there's a lot of other people that do not want to handle the complexity of C++ so there's a layer called pyroot that kind of simplifies the life of a data physics analysis physicist quite a lot so when we start looking at our stack we see constantly when we offer tools like Kubeflow that people say okay my job is pending I have no idea what that means I don't know how to debug it I run the kubectl command that is in the documentation but I really don't know what I'm doing so maybe the abstraction is required to kind of bridge this gap and then also for HPC there's this this lack of knowledge to answer HPC questions when you're running very large workloads on a kind of batch like way there are things that you want answered to be able to do your job properly things like how long is my workload going to take to complete or when will real resources be available for my workload to run cloud native stack wasn't necessarily designed for this kind of workloads batch workloads it was more for the service oriented type of workloads and maybe we need to like take a step back and see what primitives were missing to be able to answer these kind of questions and with this I pass to Maxans to continue with a real use case all right so yeah sorry so go to the use case so as I mentioned like one of the things we really interested in Atlas is to find Higgs boson and you have a nice visualization of certain events in Atlas this is a real event in which you can see one fundamental particle called immune that is emanated from the main event and also you can distinguish these two bluish cone which are more visible on this side which is a Higgs boson decaying to two types of quarks called bees and we want to be able to identify these quarks in our detector you can see that this leaves a very complicated signature definitely possible to analyze humanly so we do need a detailed analysis techniques and what we want to be able to do more specifically is classification of these be quarks versus other types of quarks which are seasoned light primarily so and of course we want to do that because we want to study the Higgs because in the end this is the physics experiment and we want to make prudence on the theory the best way to find these be quarks be jets is to use machine learning for the because they give the best accuracy obviously and obviously to get the best accuracy you need the best performing machine learning model so you need to optimize your hyper parameters which is very costly and this talk is this part of the talk really is about this it's about a framework to improve the hyper parameter optimization using kubflow and a technique from the literature so on our side we've been not only focusing on the hyper parameters of course we've also been trying other architecture and I'm just showing this plot so that you have a bit of an idea of the history of the project so we're doing B versus C versus L classification which every time they quarks and you can see how the models performance evolves for the year so this is at the fee B efficiency identification efficiency is showing you in green the C rejection and in blue the light rejection where the rejection is the inverse of the misclassification efficiency and you can see that through the years adopting more advanced machine learning from like a deep neural network to including a recurrent neural network to using a deep set base architecture using graph attention and finally using gen two which is a transformer base model I'm gonna mostly talk about we managed to have this really nice improvement in in performance and this is very important for us because these are very difficult and rare events to find so we literally sifting through to find like the needle in the haystack even though the haystack is much bigger and all of these models are trained in a similar way they use combined type of inputs which combines different types of physics input so that they can output the probability for each for jet to be of each quark and then we use this we build discriminant based on this chord that later on analysis can use to do the data analysis they all train on Monte Carlo simulated data but they calibrated on real data to account for some mismodeling effects there and so the model I'm going to focus on is the gene to model which is a transformer base model which we could describe as a multi-model multi-task model it's multi-model in the physics sense that it combines different types of physics input which for us is kind of nice because it combines varied things we use historically to treat in very different ways we used to have a one network that would deal with one type of information and another network that would build use another one and the nice thing with this single architecture that is combining the different type of input it's much simpler to maintain and upgrade and do we study such as hyperparameter visualization or when we change the entire software stack to reconstruct several sub-elements and it gives us the simplicity with the state of the art performance really the usual trend in I think energy learning to have one large huge network able to do several stuff at once and really able to do several stuff at once because it's multi-task and you can see this from the fact it's not just predicting the flavor so the class it's also predicting other physics physically relevant information that we know from expert knowledge so it's a way to put expert knowledge into the design and it's working really well with the only caveat that is quite resource intensive for us to train it takes us roughly two to three a 100 GPUs an entire week of training to do one full training due to the size of the data set mostly and the problem is we are not a large tech company and most people in our collaboration do not have access to a high performance cluster with the sufficient amount of GPUs to be able to contribute to this sort of project it also makes it prohibitively large for us to do hyperparameter optimization just given the scale of one training and the fact that you have to iterate so really this talk is about you know how we could democratize this and obviously this is the cupola conference so we thought that this would be like a nice way to do this for several reasons so first the nice reason is that this you know container orchestration engine characteristic of cupola it's quite suitable with our framework we we always keep our code on get snap repository and it's quite already well adopted to to make them build buildable into executable images so using the CI the continuous integration tools of a GitLab so far it's very easy to integrate this with this KT framework in this case because it's about hyperparameter optimization that is provided by CERN in this this website here which is the server that's in sort of the flow and on our side more specifically so we just have our code on the GitLab which is executable and we have our data accessible through an S3 like data storage that we can mount more locally so that when we train the speed of loading in the input output is higher and one thing we really like with this approach is that we can actually use your guys work which means that we get for example access to automation learning agreements which we normally not use so it really gives us easily access to a lot of our development in this side and I just want to highlight like what we think are the key points that really makes it interesting for us so first of all it's this open source and active community that really continuously develop these tools which means that we don't have to do it ourselves which is not our expertise on the Atlas side more of an expertise in that on the sense that of course for us it's reviewed as a multi-platform and very flexible framework meaning that we don't really we're not really so dependent on one server we could really move if we need to to a private cloud provider if we need to massively scale suddenly and you know for a single one-off project so really gives us an optimized resource usage also it means that we could more easily share hardware resource with the other experiments because at the moment the thing is quite fragmented and we do not work with other experiments to share hardware which is not ideal because we would like to have a huge GPU clusters and it would be easier if we were to share them with the other experiments and again one strong point of us is that it's accessible to everyone so it really democratize access to machine learning heavy projects in our experiment and has a nice side benefits of having visualization when you do hyperparameter optimization with you you're probably familiar with all right but this is not enough of course because it just it's a nice framework to run something but it doesn't mean that the thing you're running is simpler you can use only stopping from hyperparameter optimization but we still need to reduce the computational complexity so that we able to do hyperparameter optimization of our transformer and this is why the talk slightly changes direction and goes into a technique because there's a technique in the literature to be able to do the hyperparameter optimization at a lower cost for some of the hyperparameters and this has to do with the parametrization of the network and here you have like a classical deep neural network and what I mean by parametrization really is the way you initialize the weights so the way you sample them from a random distribution function in this case Gaussian but really doesn't matter so much what really matters is that the variance is inversely proportional to the size of the input of the layer and the learning rate classically for all of the weights in the networks are the same effort like the master learning rate so this would be a standard parametrization a parametrization and one way to actually move into a nicer parametrization for the project I'm going to mention is to adopt this maximum maximal update parametrization from the literature and I'm just highlighting the key difference with the standard one the key difference being that the output layer variance is scaled down by the input dimension of this layer and the learning rates of the hidden and the output layers after the scaled on to and this is this actually makes sense in a way like if you think of a network with a specific width the output layer would be like the more opaque layer as if you were to think of a cross section of the ocean the surface is most of the sun but the bottom really doesn't see so much of the sun and here the sun would be the the loss function and the bottom would be the input layer so this has the effect of making the output side of the network a bit more transparent so that learning goes into depth and this is actually provable and I'm going to go into this later but I'm just going to give you like the two key conclusions from this maximum update parametrization is it has this effect that the updates of the activations become roughly independent of the size of the width of the layer so just to clarify the width is the number of units per layer so the really the transverse of the dimension not the depth and also it has this nice provable theoretical property that it's the maximal update one in the sense that it's as big as an update you could get for each layer without leading to instabilities and there's a way to see this which is quite cool and come from this paper on on the left which is to take the simplest network you can think of which is one input x has through two layers an input layer u with n dimension and an output layer v with n dimension two so here n would be the width on the left you have the sp k's and on the right you have the mvp k's and you can see like some of the key difference differences at initialization the way the they sampled and also the way the output layer gets updated after one gradient step and if you look at what this layer what what this network computes the f of x after one gradient update on the sp side you have this nicely term theta u transpose u which by the low of large numbers will scale not with the input x but with the dimension of the layer n um so this means that this would not be with independence in a way what on the right side thanks to the modification to the rules of the parameterization you can see that this term is correctly scaled on by n so that the scaling is with the input instead of with the dimension and this can be this can be actually viewed in a network so this is showing you a gene to like architecture trained for shown at three different time steps the initialization time step and one and two training steps the first row is sp and the second row is mu p and every time each plot is showing you for different dimensions um the sum of the absolute value of the pre activation weights of the different layers and each layer is its own curve and you can see that the on on the sp side things quickly get unstable after a few um steps of uh of learning for very large bit and on the mu p side however things stay stable so really it scales nicely across width and uh it's the maximum update in the sense that it stays stable and um this actually leads to a few properties that are quite nice and relevant to hyper parameter optimization the first one is your trainings are stable from the learning rate want to do so that's actually already quite nice but also a wider model is always going to be more performance than a less wide one and that is on the training loss you may over overfit but this is a side that i'm not going to touch here so wider always better so it also makes no architecture search easier you just take a model that is as wide as you can afford and finally and this is the key point here is that you get similar performance i key across different width which means that for the mu p side on the left scanning for one hyper parameter here which is the maximum value of the learning rate scheduler and showing the validation loss on the on the x-axis you can see that they share the same best hyper parameter so that's quite nice that means that if if you want to be efficient at this you scan oh sorry i should have said but so these three curves and the yellow one is a transformer we are then bending width of width 64 and the red one is with 128 while the purple one is 256 so the thing that's nice with the fact that they all share the same best hyper parameter is that you could use the small one which is much easier to train to find the best hyper parameter for the full width one while on the SP side you have no such quarantine first even for in this case it seems to roughly work but also you get this really unstable behavior when you get a learning rate that's too high which is again due to the fact the standard parameterization models are not resistant to the width scaling so this is quite nice actually and it leads to a algorithm to do the hyper parameter optimization that's come from this paper which is called new transfer where you just basically do the hyper parameter optimization on the small model and you directly transfer that to the full model so that you don't you have to you can avoid like the complexity of training that several times and we've tried this in Atlas and what we've tried is because we have a required compute limited again we try to actually optimize two parameters the maximal learning rate in the initial value of the learning rate scheduler and this is shown here with the same types of plot as the previous slide just different plots show you different initial value of the learning rate so it's very efficient for us to actually so this argument makes it very efficient because if you just focus on the mupe side which is the bottom row you could have directly found the best one from the small model and it really avoids like the very costly large model training also it makes the model in general stable and also it makes the model in general more performant than the equivalent SP side thanks to this maximal update in this transparency behavior so we think it's actually quite a nice framework to combine with Kubeflow so we from the Kubeflow side what we really expect is this you know natural multiplatform execution so that as a user you don't really have to think where you're going to run it's quite portable and flexible so if you know how to use it on on the third one you can very easily move to a private cloud one you just it's very nice if you don't have to think about slur and all of this stuff because you people do that and to another extent it means that we have an improved resource usage it means we can more easily share I know the results with the other experiments we can also use resources out there not really use from you know state clouds or national laboratories and stuff like that on the subject of the user case here it really improves the hyper parameter search thanks to this you know auto machine learning algorithm so it's stopping and all of that so it also exposes us to some of the development being happening in the machine learning community also it has this nice thing that you know you can inspect and visualize it also makes it globally simpler to just install things and to serve the model later on and importantly for us it's accessible to everyone in our collaboration so it really democratizes access to these very interesting project for us and finally on the we want to combine this with mu p which is um the this technique which means that a wider model will always be more performing and makes it possible for us to use me transfer which is very useful for example just to give you some numbers the full embedding with models 256 has 2.3 million parameters and it takes 40 minutes for one epochs on two GPUs due to the size of the data set on the small width one which has a tenth for the parameter we can actually do an epoch in 20 minutes with one GPU which means that equal compute we can do four tests of the hyper parameters on the small model for one test of the large one which means we get a far better coverage and i just want to conclude the like reminding that this is quite important like the sort of performance gains we have from the best hyper parameter is equivalent to the sort of performance gain we have from adopting way more complicated architecture changes or physics infused changes and this is shown here with a very elaborate rock curves that's typical of the field where you have the b-jet efficiency on the x-axis and the y-axis are showing you the rejection for lights and c-jets so we really want to be higher there and i showing you this for the best model in green and the worst model in purple from the scan on the right and you can see that in areas where we're interested you really have 20 to 30 percent gain really from just this one parameter change so really something important for us and i think that that's pretty much it for us and thank you for your attention see you again there's time for one question so oh yeah there's a couple but i think we only have time for one so i don't know do i thank you for the talk hi this is abhishek from IBM research and i have two questions both based on kubernetes so one is how do you manage custom hardware with kubernetes and i think you mentioned for quite some time that you use sharing techniques so how does kubernetes help you in sharing your custom hardware or gpus today yeah okay so that's a very quick question so if you look at previous talks we've given uh i think last year at kubcon uh amsterdam we went a bit more in detail so um we've been we wrote if you go to kubernetes.docs.com.ch you will find a series of blog posts about sharing gpus and we tried and benchmarked all sorts of possibilities so we tried anything from using uh pure sharing of the gpus with no uh knowledge between the workloads and the issues with that we also documented quite well how to use nvidia mig and the integration with the gpu operator from nvidia and we've been trying also nps for for for a possibility of sharing as well so basically all our clusters have these capabilities of of partitioning in either physical or logical way all the gpu resources and this is what we expose to the kubflow users we you might give get a full gpu a fraction of a gpu or a totally shared gpu depending on what you're asking for and the availability is the main thing if you ask for a full gpu a full a100 or h100 we don't have that many so you won't get them very fast and people fall back to the second best uh i think there was another part of the question but i don't know how do we manage custom hardware so in most cases for now these are fpgas uh and these are just attached to the nodes and we just pass through in the same way we do for gpus uh so all the gpu access we are giving we are not virtualizing we're just doing pci pass through uh in some cases partitioning it using Kubernetes but for the nodes themselves they they are just doing pci pass through a really cool talk madri from elotal um wondering if you had to pick one key problem in the multi cluster scheduling space that you alluded to what would that be uh i think i think the scheduling is is like what the main the main issue is that there is no notion in the scheduler of multi cluster there are some some efforts to add this to the to the stack uh but there's there's little the scheduler can do right now because it doesn't know about the availability so we tried in the past to kind of go around this so we tried something a few years ago where we implemented a virtual kubelet uh that would basically be an abstraction of a full kubernetes cluster behind so that we could advertise to the scheduler the resources from many clusters and this was something that was picked up by some people but it has drawbacks as well so it is not a perfect situation i would say the ideal thing would be for the scheduler somehow to be able to incorporate the resource availability of multiple clusters uh i don't know exactly how we can do this because availability based scheduling is number one i think so yeah thank you i think that's it okay thank you very much and if you have more questions just reach us


Everyone, welcome to the special CUBE conversation here in Palo Alto, California, CUBE Studios. I'm John Furrier, host of theCUBE, founder of SiliconANGLE Media. We're here for some exclusive news around security audits, blockchain smart contracts, and a hot new startup, Amber Data. We have the chief operating officer, Tong Tong Gong, who's here, chief operating officer, Amber Data. Great to see you. You guys, I've interviewed Sean Douglas, the CEO founder before, and when he was really getting the technology going, amazing progress. We have some exclusive discoveries here. Welcome to theCUBE. Thank you. Thank you. Thanks for having me here. And it's awesome. We've done so much in the past couple of weeks and I'm really excited to announce that we have taken security audits, automated that to be able to provide automated as scale security audits for all the smart contracts on Ethereum through our platform. This has been a huge problem. We've been covering it for the past year with Vidya, but also on the blogs. Ethereum specifically has been the developer chain of choice. People are using Ethereum, it's literally programming on it. And that's what a lot of the Dapps, decentralized apps, which we think are going to be a tsunami of, and we're a big bullish on it. But the problem is, is that everyone rushed with these ICOs and they didn't think about, hey, we better make sure our token generating event works because they got to do a smart contract on that. And then ultimately these marketplaces that will be emerging from the apps through the communities will be a lot of smart contracts as the transaction of choice. This is what is the benefit of token economics. The problem is security.  They've been expensive and there's been a time lag in getting it done. So you got a time factor too slow, too expensive and it was last minute. This has been a huge problem. Are you saying that you solve that problem? Well, yeah, kind of. So give you some stats. There are about 7.8 million to 8 million smart contracts unchanged today. On average, it's about 500, 600 smart contract gets deployed every day into MenNet Ethereum. And so what we've done, we talked to a lot of security teams that's in this space and at the end of the day, everybody use the same tools, set of tools to perform security audits. What we have done is, we programmatically did that so we can run security audits on every smart contract on-chain. So we launched this feature last Friday. What we did is we picked the top 2000 smart contracts based on transaction volume on the MenNet and we performed the audits on those. And last night, yeah, three days later, we performed all 8,000 smart contracts that's been created and deployed in the past 90 days. So the top 2000 active ones and then the 8,000 recently deployed ones, we performed security audits on those. So this is pretty incredible. So I wanted to make sure I get this right because I think if this is the case, this is the first ever automation or DevOps-like approach to smart contract audits and security. So let me just kind of slow down to your mind. So today, most people have to go in and manually look at code reviews or use some tooling to do that.  This has been doing on a console, no, many. Hot Show does that, many more do it. And they're bringing tools to the market there too. But I don't think anyone's actually automated at volume. So you're saying you're automating, ingesting data from the chain. Mm-hmm. We'll analyze the bytecode as well as the source code to identify vulnerabilities and issues, things like integer overflow into their code. And we actually assigned custom, we have our own scoring system to score basically the vulnerability exposure of the smart contract. Okay, so I want to just kind of push back on that because I'm skeptical. So you do byte review. Byte code. And byte code and source code review.  Do you service it automatically? Does that happen automatically? So take me through what you do manually and what happens with the computers. What is automated? It's everything's automated. So we integrated the tools that every security expert use in this space today to run the security audits on the smart contract and the byte code. And then we flag the particular source code and function calls that's flagged with the issue. That's in the code itself. That's in the code itself. And that surface through our website, through our console, and you can actually see it. You can search on any smart contract and the console dashboard will show you the real time live streaming events of your smart contract function calls as well as the model. This is amazing. So, okay, so this means that you can save a lot of time. Love this feature. This is exciting. This is actually the first news I've ever heard of this. So I want to make sure I get it right. How many contracts can you do? How fast does it take? So you mentioned you've ingested on last week stuff off the chain. How many contracts was that? We did last week, 2000. And then up to last night we finished 8,000 smart contract scans. We'll continue to do that for every smart contract. How fast is this? I mean, back when I was learning how to code for the first time back in the old days, you hit press a button, they have a compiler and you got a bug in line, syntax error, there it is. That's normal kind of old school computer science, syntax, compiler, interpreter, whatever you want to call it. It sounds like you're doing something similar. The same kind of speed. It's code review, analysis of the contract security through the tools. How fast is it? I mean, how long does it take to do a review of one smart contract, for instance? I don't know that. I will say minutes. Not days. No, it's not like it goes out and hourglass and it'll check your email, be there. Happens pretty much on the fly. Happens pretty much on the fly, real time. So how many contracts can you guys do in a day? We've done 8,000 in three days, so a lot. And that, we have 10 machine running right now.  Exactly, scale up. Scaling out is easy to do, just go. Our goal is to basically make it very easy for developer to understand the state and health of a smart contract. And then they can go find consultant experts to fix those vulnerabilities and issues. Yeah, this is going to be a rising tide. I think rising tide floats all boats when you have these emerging markets. You move to the next problem and you do, Jeff Frick always says that on theCUBE and he's right. You take away security. You're now enabling this tool for these consultants to actually add more value. Exactly. Is that the focus? Do you guys even know who's going to use this tool yet? Obviously, this is a game changer. I mean, if I'm a data scientist, I love this. But also, I'm a trader. I might want the data. I'm a risk management, audit, compliance person. So, you guys? Yeah, like, I'm a data. One of our missions has always been providing enabling infrastructure, enabling tool sets to allow developer to allow operators to allow the industry, to allow business to adopt blockchain. That's always been our mission.  We have built APM, we have built the similar mix panel. It's all about providing access to data and to information to allow everyone to have a better understanding, better transparency into the state and health of the blockchain, the state and health of their smart contracts. So that's, you know, in line with what we're doing. So tell me about the scoring thing, because okay, I love this automation. I think that's a game changer. So congratulations. This is the first I've heard of it. I think this might be the first news in the industry out there. How does it work beyond that? What else do you guys do? You ingesting, you adding overlays to it.  I mean, you're ingesting it. You're doing some security audits. Where does it go from there? So we're actually working with the Web3 Data Foundation. So the Web3 Data Foundation is building a decentralized data marketplace to allow everyone in the ecosystem to list, subscribe, consume, distribute, monetize data assets that's generated by blockchain and data that's on blockchain. So what's the URL for that? Web3? Web3data.org. Three the number or three? The three the number. So Web3data.org. Data.org, yeah. Okay, and is that an open community? Is that a foundation? Yes, that's a nonprofit foundation. And I believe they're launching a token, Web3data token, and Amber Data is working with the Web3data Foundation as a launch partner to utilize the data ingestion pipeline we have built and to serve up all the data for everyone to have access to it. Okay, so what's your business model of Amber Data? Are you going to have your own token? Are you going to use the foundation? Is the token holding place? Can you just take us through the relationship of Amber Data with the foundation? Because I mean, I get the foundation, but what you're doing here is you're essentially building IT operations into the blockchain, which is when scaling things with automation, which certainly is only going to get better with more compute and AI and other cool things. So I love that. How do you make money? Is it a token model? Is it just classic? Do you get paid? So what's the relationship? Is the foundation issuing tokens? Do you have your own token? Take us through that. So the Web3data Foundation is the one issuing the token and we are the launch partner. So we're using the blockchain agnostic data ingestion pipeline that we can ingest all the data. And we're building together, building the data marketplace, using smart contract to enable everyone to list, curate, consume, distribute, monetize the data. It's because if you think about it, right? Data on blockchain is just a fraction of the data out there. And as SAP development going on, as a trading application going on, there's a lot of data guns. So it's going to be generated by blockchain as well. And those data is now getting captured, analyzed, and utilized today. And I think today as a trader, investor, or as a developer, people don't have access to this data, have data-driven decision to help them continuously improve whether it's application or investment decisions. So the data marketplace will enable everyone to be able to have that access. And it also might enable more faster acceleration of decentralized applications. Which Fred Kruger and I were talking on Twitter, I mean, Facebook about this, that we think that's the killer app is going to be the tsunami of apps coming. But all these chain problems are out there. So it's a little bit of a resetting going on in the industry. Obviously you see that with some of the pricing and funding and everything, but for the most part, we see a big market coming. So I got to ask you the obvious question from there is which chains are you supporting? You mentioned main net, which is great for Ethereum. What up? Today we're supporting Ethereum main net and Rinkabee, the test net. We also support Aion's main net and test net. We also support Stellar. We're working on EOS and Tron as well. And so we have open source our data collector to allow community to contribute to that. And we'll use both the data token to incentivize the community to contribute, to verify, to enrich the data. So I got to ask you the security question. Maybe this might be for more of the nerds and the geeks built down in the product level, but maybe you can get it is security is huge. So I'm skeptical you're doing scoring. What can you be hacked? What's the security answer to that? Like, well, if she's controlling the score, I might want to spoof the code and take over and say it's okay. The code we get is actually on-chain. It's the code that you deploy on-chain. So good luck spoofing the data on, you know, unblocking. That's the whole point of blocking. So it's already answered. Yeah, so yeah.  Thanks.  All right, so what's the next for you guys?  What's the story? I've been following you guys on Twitter and Telegram. You've been traveling a lot. What's the update on the company? What's the status? So we are, we're a long-term partner of Webster Data Foundation. Right now there's a token sale. We're in the middle of closing our pre-sale. It's a SAFT offering. And we're building and expanding the team as we're speaking. How much are you raising on the SAFT? Can you talk about that or? No. No, okay, you might say the number. Yeah. Yeah, just be careful. It's hard to raise too much. So the foundation and you guys, okay, so how do I want to ask you a personal question? Because we love women in blockchain where the satoshis female shirt or as much as I can. How did you get into this? Because this has been, there's a lot of women coming into blockchain more than people are advertising. So, you know, I'm seeing a lot more women in tech, certainly women in crypto, blockchain and crypto. You guys are doing it like a most of cloud DevOps serious venture here. How did you get into this? What's your story? I've always been a cloud girl. I started my career building utility computing, enterprise grid computing, right? That's when I was 23 years old and working for Axiom in the data center in Arkansas. And I'm the only one that were high heels in data center and get stuck in the vent, you know? So that's, you know, my background. So it's not far stretched from to understand blockchain and the use case of if you talk about distributed computing, distributed storage. So it's very natural for me to, from technology perspective, get into the space. And on personal note is that I really believe decentralization and I believe the change is going to make to our life and our offspring's life in the future. It's real, you think? It's real. It's a real trend. It's here to stay. So what's your vision of blockchain? What are people not getting? Obviously, there's a lot of scams out there that have kind of tainted on the ICO side, but what are people missing? When you talk to people, you have kind of like, oh, I get it. And then people kind of, I don't really see that. What's the main thing are they missing? What's missing? I think it's missing that killer dap to actually get people to realize, oh, it's actually easy to use. I don't have to think about the inner workings and it just works, right? My mom still lives in Beijing. I talk to her on Skype all the time. She's not worrying about TCPIP. She's not worrying about how is this phone not getting encrypted or not encrypted? You know, what's my network bandwidth? She just used the phone and called me, right? Like I'm right next to her. I think I just would develop building the adapts and people don't think about they're using blockchain. They just use it. It's like explaining it to like a parent or someone who's not technical. Hey, how does the internet work? Kind of just type a keyword into the browser or search engine. Instead, it's today, it's more like, hey, you know how BGP works and you know how packets move around? Like it's so hard to explain. So it's got to be easier. Totally agree. Totally agree. Well, Tong, thank you for coming on theCUBE. Appreciate it. Great update, exclusive news. Automation, bringing cloud computing and utility computing. Real geeky stuff to the table here. This is theCUBE conversation. I'm John Furrier with Amber Data, COO, Chief Iron Tong Tong Gong here inside theCUBE. Thanks for watching.


Thank you, mama, for all that you've done, mama, that all this is created by my mother and no one else. And my mother has gone all out for the last four months. She's worked for, I think, 18, 19 hours a day. And I'm extremely grateful to mama. And thank you so much. I would like to thank each and every one of you present here. I know everyone has made it to Jamnagar to make me and Radhika feel special. We are all honored and humbled to have all of you here. I am sorry if we have caused any inconvenience to anyone. Please do forgive us, both the families. So I hope everyone is going to enjoy the coming three days. And I would just like to thank my mother, my father, my sister, and my brother, my sister-in-law, brother-in-law, for making this event so memorable for me and Radhika. I think my family has gone all out to make us feel special. Everyone has been sleeping for less than three hours a day for the last two, three months. And I'm so happy that I can share this joy with everyone out here. I really don't have words to express my gratitude. As many of you know, my life has not always been entirely a bed of roses. I've also experienced the pain of thorns. I've faced many health crises since childhood. But my father and mother have never let me feel that I've suffered. My father and mother have always stood by me. And my father and mother have always made me feel that if I can think, I'll do it. And I think that is what my father and mother mean to me. And I am eternally grateful. Now I'll come to Radhika. I'm 100% the lucky one. There's no doubt about that. I also don't know how I got Radhika. So I am definitely the most luckiest year. Radhika has been, for the last seven years, it feels like I met Radhika yesterday. But every day I fall more and more in love. Like my brother-in-law says that when he used to see my sister, he had volcanoes and fountains going up in his heart. I would say I have earthquakes and tsunami is going in my heart when I see Radhika. So thank you, Radhika, for everything. Viren Ankil, Shaila aunty, Anjali Aman. Thank you for welcoming me into your family with open arms. I am extremely, extremely thankful and grateful for having the patience for the last seven years. Finally, the day is coming. And thank you, Aman. Thank you. I would like to take blessings from my grandmother, Kokila mummy. So as my mom, I think my mom said it, but Kokila mummy comes from Jamnagar. And Kokila mummy has been a big inspiration. My grandmother has been a big inspiration to me. I always take motivation. My grandfather, wherever he is, he'll be blessing me. My nani has taught me how to be religious. She is a Nagar Brahmin. Very proudly, she tells me that I'm a Nagar Brahmin. So she teaches me everything about, I've stayed mostly with my nani when I was young. So she taught me everything. And I have Radhika's nani here. Radhika's Nana is blessing us from top. My Nana is also blessing us from top. I'm sure they'll all be extremely happy. And over the next two days, you're going to see a small part of one Tara, I think, tomorrow and day after. I hope it's as magical for all of you as it is for us. One Tara has been my passion for the last. We started the first rescue center, I think, in 2008 when I was, I don't know, 10, 12 years old. And it's been only possible because of my parents and my family's support. My mother and my father have always supported me and always taught me to serve. And this was my giving back to society. I started off small and then, as my grandfather said, dare to dream. So I dreamt and then I made it a reality. So today it's, tomorrow you'll see, I don't want to talk much about it. I want you all to experience it. And thank you, I don't want, I know that we've already prolonged the show times. I don't want to talk more. I'm going to make my better half speak on my behalf.