We're here at NAMM 2018 in Anaheim and we've been waiting. George and I have been talking about a product But you might be familiar with the Sentron's Mikeport Pro Which a lot of you are probably still using because they're a great product, but we're talking with Michael Goodman, Michael The president of Sentron's and they have a new product that we've been waiting for the rollout for some time The mixer face Tell us about it Yeah, thank you for stopping by then. We're very excited to have you here. We love our supporters in the voiceover community We love the work you guys do and Obviously, we're always envious in how you can speak and how we can't speak This product right here mixer face R4 is our newest member of the mobile recording interface family What this product does is it connects to a phone and it turns your phone into a mobile recording studio So a door essentially right if I pick a phone here You can connect the mixer face to a phone. They're about the same size and Then now you if you have a door or some kind of software on the phone like for example twisted wave Which I know is were used a lot or some other software You can do all of your recording on the phone in post processing on the phone This is what's different from a typical situation when you use a recording interface and you record to a computer or even if you use You know a location recorder of which there's many different models What should you have to do with location record is you have to take out the SD card at the end of the session Move it into a computer then open up a post production Session and it takes a lot of time. It's not ideal So this allows you to do everything in essentially one session because you can record to the phone You can do all of your editing all your process post processing and send that recording off to FTP or email etc, right and if essentially you're done you can finish Post-processing in the back of the cab. We're saying you know on the way back from the gig and you're done This thing is very lightweight. It connects to Android phones iPhones computers Mac and Windows, right? It has an eight-hour battery I will turn it off here. You just push this button very similar to you know some of our other products This is your battery indicator. So it shows you how much batteries is still left and Then it's got to high quality Mike freeze with high Z inputs and high pass filter In case you're doing an interview maybe outside and there's some wind noise that you need to cut out We use that a lot, right? So these Mike Mike breeze are very low noise low distortion. There's two controls These are essentially zero latency monitoring control which is not available to Mike for throw But so many people were asking for them We added them in this lets you adjust the balance of your own voice or the sound that's coming back from the computer So if you're narrating to a music bed or you know some pre-existing track This allows you to balance how much of that or you're doing Skype a Skype guy in a Skype session You hear less of them more of you exactly this is inputs three and four which is also amazing for some music applications But also perhaps for voiceover you can feed and a second Stereotrack into this thing and essentially narrate over that as well So you can create a little monitor mix for yourself headphone output line level output and most importantly built-in recorder Oh There's a Micro SD card that you put in and then this recorder records in the wave file at 1648 Which is perfect for you know most work that you do out there So you can actually record to two places you can record to an iPhone or iPad a computer etc And you can in parallel create a backup safety copy inside of this machine, right? Does it also act as a USB interface? It does act as a USB interface so when you record to the phone you're using the USB interface portion of it But at the same time there's a second tape machine. I'm going to use the old term, right? That's inside there. It's a backup It's a backup exactly. It's a fail-safe recording mechanism We also have a third recording mechanism in a way of analog output jack So you can record to camera if you're doing some camera work, right? Sure, and then you can technically print three different mixes, which is amazing, right price point on something like this We're currently thinking of a price point of retail price of $299 Without the recorder there's going to be a model without the recorder in case you just want to print to To the phone. That's great. And then the recorder will add about 50 bucks We're also looking at a couple of XY Microphones that we're going to be announcing as well Those are prototypes yet and they will add another 50 bucks to the price of the product depending on the kind of customer Somebody may want that somebody may not want that I think most of the voiceover customers will still want to use this like they use the mic port pro meaning a short cable and A professional microphone that defines your sound. I think that's going to be the most popular use case I think the backup recording feature in there is super valuable for people that do audiobooks or really long form I get an email every couple of weeks I hit stop on my doll or whatever and I lost my track or I had clicks and pops because my hard drive If that was recording in the background you'd save a huge amount of frustration feeling out of have that backup recording Thank you We believe that you know people are people people make mistakes it is it this gives you a piece of mind and I would appreciate if I was doing a session and I had a backup track Absolutely congratulations. Thank you very much George. We're we're very happy to see this finally all right Yes, looking forward to shipping a whole bunch of these very soon How soon is it coming out about two months two months already? Thanks for being with us. Thank you very much then. Thanks Michael. Thank you for having me


Because we got to deal with who's social in the team. Then we got to go home like, what's up, nigga? All right. Don't boss talk one on one. One on one. Here we go. I was raised in there. We get was there. I love San Diego. It's so beautiful. I like it better than L.A. I'm sorry. I love it better than L.A. Because the beach is two different. The beach is a country. You want to go to the beach and hang out? Yes, I like the beach there. Clean, white sand beach. Yeah, the beach is in L.A. Got a little urine on it. I remember the first time I went to L.A. And you know, you're looking for the beaches. And me from Jamaica, I'm looking for Jamaican beaches in L.A. I've been dealing with this for a long time. So I'm in my swimsuit jumping and I'm ready to jump in the water. And I'm like, I don't know. I realize why people be wearing them suits. It is so freezing cold. And I'm looking at everybody on the beach. Like, why is everybody laying on the beach and clothes? Oh, no. It's a beach. But it's some people that have jumped in there. I've seen them. What? They call white people.  They would jump in that day. It's crazy.  But in San Diego, you can actually go there and get in the water. Water's cold, too, but sometimes it's warm. No, because I went in the water in San Diego. It was really, really nice. Yeah. I love that atmosphere. You don't know what time the season. We always go, like, July, August. Yeah, it's not bad. There's some murky water down there, too, but it's not like Jamaica and, you know, all the Antigua, all that, it ain't like that. Out of all the places you've visited, what's the most beautiful place you've ever seen? It's got to be between Bora Bora and South Africa. What is it about those two places that you love? Well, Bora Bora, you can literally see the bottom of the ocean. Really? Yeah.   You look over the side, and depending on where you are, how deep it is, and you can see, like, the sandy bottom. Wow, really? Like sharks, I asked some of my friends, we went out, we were out there, and these ones who decided they wanted to go scuba diving, never took a damn- Lesson? Lesson.  They swam with the feeding sharks. He's the happiest shark ever. I would never. I'd be scared.  Oh, no, trust me. Yeah, I was like, wow. They're friendly sharks. They said the shark is full. You like to stay over there? Okay, were your friends white or black? They white.  I was like, boy, I said, somebody got to live to tell this. They don't believe me, they don't think I killed y'all. I mean, man, so acting, man, how'd you get into acting? Bro, you, I love your movies, man. Anytime you in there, man, I'd be like, this nigga, you're crazy like me. You gotta be crazy.  I watched one last night, I didn't even know you were gonna be in there. Which one was it? It was She Hoop with Nick Cannon, and Chris Roe was in there and stuff like that. I was like, look at y'all. Yeah, yeah. His cop was retrieved from me, my nigga. Well, that's Bora Bora, we filming that. Oh, really? Okay. Ooh, that was a good, I loved it, bro. Like. Yeah, to be a part of that is awesome. Yeah, it gotta be. Yeah, that's like, we were out there when Obama was elected. Okay. So it was me and God damn it. What's her name? She played my ex-wife. Oh, yeah, the one that was on Sisters.  Yeah, that. She directs BMF right now. Yeah. Oh, I didn't know that. Yeah. For real? She's a producer on it, too. She beats mode.  I'm picturing her face right now.  I'm gonna go. Did she have a twin sister? Yeah, she does. Yeah, yeah, yeah. She does. Yeah, Tasha. Yeah, y'all trusted Tasha out there. Okay, is she exactly how she is on the show?   I love her. She's been crazy. I love those personalities. She's never changed. She's the same old Tasha.  I love her. I love that personality. She's been the same. I'll be a detective. She's doing a great job directing because she was a teaching actor.  Keep just going up.  I'm rooting for her. I mean, she's... That's awesome. Crazy. But the thing I can say about it is you know, yeah, you're really good at these parts about acting that husband that just left. You know? That just gets out of it. You talked about that the other night. I'm like, oh, that nigga did that for real in the movie. So let's keep coming back. I was practicing.  So did you have to prepare any for them roles? Well, it is or it's not. I play a more square guy. Sure do, yeah. So I don't play it phase on. Got a little more edge to it. Yeah.  I don't know what I'm doing. Like when she... I don't know what... Me and the director was like, I was like, he's like, well, what would you do if your wife left and you're like, I'll let it go, you're fucked. He's like, no, phase on. We got to go. We got to... We got to act like we care. So he was like, she's laughing at it. She's gone, guys. Ha! She's gone. That was... I got to get her. I was like... So he's more of a square guy.  He's a person. Yeah, but you... And it's, I've been that guy, the one or two black guys there, or the one black guy in the midst of that whole, the white, the one black guy, it's just one, maybe two. Maybe two, but that's it. I've been that in Carpet America, so when I see it, I can relate, man.  Because we got to deal with... I don't know, who's social in the team? What's with social here? Then we got to go home like, what's up, nigga? All right! You got to deal with both sides of the fence. They start to deal with that side. Yeah, yeah. But the Robin Harris, acting as Robin Harris, I wrote that down to ask you about that. The baby's kids, man. How was that, man? It was awesome, because... The voice, right? Yeah, yeah, because they were planning to do a live version. Okay. And then Robin passed. Yeah, I remember that. And then I talked to Warranty and Huddlin, and then he was like, man, because I was doing that in my act, because I knew Robin. Yeah.  So everybody knew I... You knew the voice? Yeah, they knew, I did it because I would... I'd be on stage teasing Robin with it. And that was one way of hanging with him on stage, because he was a beast on stage. Yeah, yeah. Back then, he was having, I'm gonna say it was Robin Harris, Damien Waynes, Keen and Ivy Waynes, Robert Townsend, Martin Lawrence. Well, that's haven't talent.  That was just one night lineup. He had to hang with that. Yeah, we on boss talk one-on-one, one-on-one. Yeah, we gon' talk...


Hi, I'm Sandrine, head educator at Pruneus and today I want to talk about the Max Gloss. So the Max Gloss is a time-saving gloss, so it says you can work faster. When you apply the Max Gloss, you have to apply it to five fingers at the same time. If you are afraid that the gloss could run into the cuticles, you can also work four fingers at one time and then the thumb separately. So the curing times is 60 seconds for the light and 30 seconds full for the smart light. So there is absolutely no need to cure it more than the required timing. The Max Gloss will shine perfectly with the right timing. If you are curing more than the required timing or you are working finger by finger, after that your client at home can face some yellowing problem. And you don't want that and you want your client to be happy. So stay close to the correct timing. Also, the Max Gloss has a sticky layer after curing. And to remove the sticky layer, you have to use the non-aceton polish remover. And don't forget to use enough product. So this was the do's and don'ts about the Max Gloss. I hope you enjoy it and I wish you a lot of success with this wonderful gloss.


Hello and welcome to Newsclick. Today we're going to discuss the spread of COVID-19 in India and particularly its resurgence in cities like Delhi. So both these aspects are there. A second round of increase that is now currently visible but also it's spread throughout the country. When you look at the total number of COVID-19 patients, active cases around the world, total number of people who have been, till now, who have been infected and shown symptoms of the disease or what would we call identified cases. India is very rapidly catching up with the United States. The United States has been the front runner in the epidemic. It ignored initially the epidemic and it has paid the price of that because it's really spread quite far and wide in the United States from certain states which are affected earlier to now newer and newer areas. If we look at India, India is now number two but on the number of new cases and we have been saying this for quite some time, we have been number one for a long time now. Three countries are on the top of the list in terms of new cases. One is of course India which is almost touching 100,000 now. So within a day or so, it seems the new cases numbers will be more than 100,000. There is no flattening of discovery and we'll discuss this just a little later. The US is about 38,800 odd cases and Brazil is about 40,000 cases. So these are the three which stand out in the world from all the other countries who are all below 10,000, maybe one or two or above 10,000. I think we have Argentina which is about 11,000 cases but Spain is about 10,000 cases now but all others are way behind these three countries. So these are the countries which are today shaping the way pandemic is developing in the world and particularly in the case of India, we have the further issue that you have resurgence in certain areas. By the way, that's also something that's taking place in Europe. Countries which have controlled the epidemic earlier like Spain, Italy, France are seeing renewed resurgence of cases. So that's something to worry about there but it is nothing on the scale we are seeing for instance in towns like, in cities like Delhi where the numbers have gone down somewhat in the middle but it's come up again. We'll discuss more of this later. But if you look at the picture it's very clear that India is in a very critical state and we are at the moment seeing only the expansion of the epidemic, both geographically and also more numbers in cities like Delhi and Pune. Let's look at now the detailed charts that we use click on this has and we'd like to start with new cases. And if you look at new cases, you will see that some some of the states had flattened and gone down, but all of the states now except barring one or two seems to be an upward curve again. Here is Delhi, the numbers are rising and we look at it when you look at the cities charts as well. But if we look at for instance UP, it's continuously rising. If we look at Tamil Nadu, however, there is some flattening that has taken place. That's something we need to watch. And if we take for instance, places like Gujarat, they seem to have flattened earlier. And they still seem to show a certain degree of flattening. So we is a mixed bag as yet some states are rising. Some states are flat. Some states did go down for a little while as Delhi did, but has now started to rise again. So these are some of the picture. There are some of the issues that we need to take up in more detail. If we come to the India map, we don't have a district wise map as yet. But if you look at the India map, then you will see on our map chart that we have on our side, you will see that it is now spread to almost all the states. And states above 10,000 are the bulk of the Indian states. The bulk of the Indian states now show the epidemic COVID-19 cases have reached more than 10,000 in terms of active cases. We're not talking of total cases, which are of course larger. So more than 10,000 active cases are there in every state. If you look at total cases, it's clear that bulk of the Indian states are above 20,000. This is the number of total cases. And there are a significant number of those who are above 3,20,000. Maharashtra, Karnataka, Andhra Pradesh and Tamil Nadu. All of them are above 3,20,000. So not a nice picture that we have seen. We had to look at the district level figures earlier in our show on Monday. And we had seen that it is spread to almost all districts. Very few districts today are out of the grip of the COVID-19 pandemic. So this is the large picture that we have that it has spread quite far in different districts. And therefore, the pandemic spread is now much wider and much deeper in the country than earlier. It's not a question of a few places, a few localities in cities and towns, but it is now quite widespread. And we do not seem to see any slackening of the growth of the epidemic. In fact, this is one of the things we had commented earlier that India is one of the few countries which in spite of a draconian lockdown did not show any slowing down of the epidemic. We had argued that this is because of lockdown was done prematurely without any preparation and later on it led to large scale migration and therefore the purpose of the lockdown itself was defeated. And that's because it was looked upon as if it's a large curfew, large law and order problem to be met by section 144 curfew and not by the measures which needed to be prepared in advance for lockdown to really succeed. We had a failed lockdown and we have spread of the disease and now it's really spread all over the country unless we have a collective approach with the government and the people working together. And the government would be really the central and the state governments. We will not really work out what is to be done at the moment. Let's look at the cities, for instance. So if you look at Delhi, it is now running at about 4,300 odd numbers right now. In the last, this is a seven days average. If you take a single day rise, Delhi saw roughly 4,300 number of new cases. This has happened in a matter of 20, 25 days that we have seen the figure change from something like 1100 to 4,300. So one of the sharpest rises in numbers that Delhi has seen. So this does create a problem that is it going to grow? Yes, why? Because the unlockdown now has meant that the metros are going to start starting. There's also talk about bars, cinema halls being made open. Already, Delhi markets, if you go there, you will find the crowds are there. If you go to the roads, you will see roads are quite full. So it's clear that Delhi, as it returns to normalcy, you have a sudden spike of cases again. And it sort of synchronizes with the unlockdown procedures we have followed. We have not succeeded in our so-called microcontainment by which people who are confined to their homes when they're ill, if they're very sick, they're taken to hospitals. So this isolation of the people, this has not succeeded. And it's not surprising because given the kind of density of population in Indian cities, this is very, very unlikely to succeed as simply as it is being thought of. If you put people who are sick, let them remain at home, they'll infect the families. They need other support people to come and support them. They will get infected. This is what seems to be happening, that we have cascading infections that are taking place. At least the good statistic which is still there, even though the numbers are high, both in Mumbai and Delhi, and Delhi particularly because it's showing a rising graph as of now, what we have is the active cases still remain relatively low. And if the active cases remain low as it is currently, then what happens simply is that at least your hospitals are able to cope with the kind of patient that may be coming into the hospitals. So the fact that if you have 20, 25,000 people may stretch the hospital system because at least 20, 25% of them can be seriously ill, maybe 15% go to the hospitals. But that is not a very big pressure in a city like Delhi or Mumbai, which can take the pressure of say 2,000, 3,000 people coming for COVID-19 treatment to the hospitals. So that's we are well able to take care of this hospital demand that may surge as a consequence. But if it continues for very long, this surge, then it is possible the numbers would need hospitalization would rise and we would have consequently problems. So that is something to watch, but at least the hospitals at the moment do not seem that they're likely to be overwhelmed. So the coming back to the issue, what is to be done? That's a key issue. What we see missing completely in the current scenario is that any response by either the central government or a concerted response by the central and the state governments, what is to be done? In fact, as of now, under the Disaster Management Act, all the powers are still centralized in the central government. We have also issued under the Ministry of Home, various notifications. It's also interesting, all these notifications are coming from Ministry of Home Affairs. It is still the nodal ministry for the epidemic is still home department, which is the police, essentially. The government, central government has said we'll directly talk to the district administration. Well, again, the approach is a top down administrative approach using the police by enlarge to try and control the epidemic. And the police is not equipped to do it. And it needs a much, much more inclusive approach of the communities of the people of the local administration and the state government. It seems to be happening. But it is amazing that the health minister, the health ministry, the health officials are missing. They don't appear anywhere. The prime minister who came in early declared lockdown, 18 days, Mahabharata war will be over, 21 days. The Corona war will be over. It's not completely missing. We don't hear of him talking about the COVID-19 again. And even about the economy, which there is a real crisis because unless you can address the problem of the epidemic, you are not going to be able to solve the problem of the economy either. If 4300 people become in ill just in one day in Delhi, which is what the tests show and the actual numbers could be higher, then it's very unlikely you'd reopen the city in any economic sense of the term. So you will still see people not consuming, not going to markets for consumption, except absolutely necessary consumption. People getting people to come and do stuff at home may not happen. So you are going to see a very big hit of the informal economy, which is already taking place. Necessities will continue, but that's only a small fraction of the economy. So under these conditions, we would have expected the central government and the ministry of health, particularly take the lead and say, what is what are we going to do? This hasn't worked. What is working? What is not working? And let's get everybody together and see how we can beat back the epidemic. At the moment, we seem to have given up. If we look at what the government is doing, it's talking a little bit of the economy. It's talking about defense, northern borders, and it seems that the attention is, at least for the media, is entirely on one particular case of an unfortunate young man, a very promising film career, who, by all accounts, seems to have committed suicide, but that has become the media sensation. And we have a complete witch hunt going on in the media in an attempt to take the attention away completely from the seriousness of the epidemic in the country, and also the seriousness of the economy. Coming back to the other issue, which is vaccines. We're all waiting for vaccines. We have discussed vaccines a number of times in our various shows. It's very simple. Yes, vaccines will control the disease. There's no question that we're all convinced it will control the disease. But the first set of vaccines which may come may be moderately successful. They will need to be tweaked, improved, before we have really much better vaccines available. But the first line of defense is going to the first vaccines that appear on the market. Now here is the catch. Austro-Zeneca, which is supposed to be the front runner, which is called the Oxford vaccine, has had one particular case. Probably the second time it is happening in the trials for Austro-Zeneca, that person has shown some adverse reaction, which could be the vaccine, which should be something else. That's yet to be established. The trials have stopped for the time being, and Serum Institute, which wanted to continue the trials, has also now suspended the trials after the Drug Authority in India told them to do so. So there is a bit of a question mark on the front runner, but let's also agree that all vaccine trials have to go through this. There will be unexplained illness which need to be checked, and if it is completely unconnected to the vaccine, the vaccine trial will again continue. So this is not surprising. This is apparently what happens in all vaccine trials, and that's why you have large phase three trials just to address whether there are such cases because of the vaccine. So I wouldn't think that is much cause to worry, but the second cause to worry, and that's really something we should start thinking about, that it seems that almost 8 to 9 billion doses of vaccines have been blocked by what would be called the rich countries, and they have blocked three times their population, each of them. So they have not just blocked for themselves, but they've also blocked three sets of vaccines, different sets of vaccines to cover their beds. So the US has, I think, procured already placed orders, not procured 900 doses of such vaccines from different manufacturers. Other countries are also following suit, notably the European Union and Australia has also joined that recently. In this context, will the rest of the world get the vaccines? Will they get vaccines in time? Will they get vaccines at a cost which is reasonable? Will they get access to the most successful vaccines? They're all open questions. India is lucky. It has at least three major vaccine manufacturers in the country. Sedum Institute is the largest generic vaccine manufacturer in the world. So we have some leeway, we have some leverage over the vaccine manufacturers. At least a part of the manufacture should be available to the Indian people. But are we doing anything to scale up the vaccine manufacturing by investing in more infrastructure for producing vaccines? What about the cold chain? We have done polio vaccines. Yes, that is true. But nevertheless the numbers were much smaller per year. Now we have to vaccinate 1.3 billion Indian people. And a lot of these vaccines, in fact, most of the first run of vaccines are going to require a cold chain. So are we doing anything about that? These are all questions. I'm not saying the government isn't doing anything. But what it is doing is not visible to us. There seems to be a shout of secrecy around both the government policies, how to combat the epidemic right now, the phase that we are in, or about the vaccines when they become available. So I think these are all open questions we have. And we need to ask the government. We need political parties. We need to have other media organizations ask the government. What is the plan on these two counts? And let's not think it's epidemic versus the economy. That way lies neither addressing the economy nor addressing the epidemic. I'll stop here. Do keep watching NewsClick. Do visit our website.


Hello, everyone. Welcome to theCUBE's presentation of the AWS Startup Showcase AI and Machine Learning. The top startups building generative AI on AWS. This is season three, episode one of the ongoing series covering exciting startups from the AWS ecosystem to talk about AI and machine learning. We have three great guests, Broughton Saas, VP, Vice President of Machine Learning and AI service at Amazon Web Services. Tom Mason, the CTO of Stability AI and Aiden Gomez, CEO and co-founder of Cohere. Two practitioners doing startups and AWS. Gentlemen, thank you for opening up this session, this episode. Thanks for coming on. Thank you. So the topic is hype versus reality. So I think we're all on the, the reality is great. Hype is great, but the reality is here. I want to get into it. Generative AI has got all the momentum. It's going mainstream. It's kind of come out of the, it's come out of the behind the ropes. It's now mainstream. And we saw the success of ChatGPT opens up everyone's eyes, but there's so much more going on. Let's jump in and get your early perspectives on what should people be talking about right now. What are you guys working on? We'll start with AWS. What's the big focus right now for you guys as you come into this market that's highly active, highly hyped up, but people see value right out of the gate. You know, we have been working on Generative AI for some time. In fact, last year we released Cold Whisperer, which is about using Generative AI for software development. And a number of customers are using it and getting real value out of it. So Generative AI is now something that's mainstream that can be used by enterprise users. And we have also been partnering with a number of other companies. So, you know, stability.ai, we've been partnering with them a lot. We want to be partnering with other companies as well. In seeing how we do three things, you know, first is providing the most efficient infrastructure for Generative AI.  And then the next is the set of models. And then the 30s, the kind of applications like Cold Whisperer and so on. So, you know, it's early days yet, but really there's a lot of amazing capabilities that will come out and something that, you know, our customers are starting to pay a lot of attention to. Tom, talk about your company and what your focus is and why the Amazon web service of relationship is important for you. So yeah, we're primarily committed to making incredible open source foundation models and obviously stable diffusion has been our kind of first big model there, which we trained all on AWS. We've been working with them over the last year and a half to develop obviously a big cluster and bring all that compute to training these models at scale, which has been a really successful partnership and we're excited to take it further this year as we develop commercial strategy of the business and build out the ability for enterprise customers to come and get all the value from these models that we think they can get. So we're really excited about the future. We've got hugely exciting pipeline for this year with new modalities and video models and wonderful things and trying to solve images for once and for all and get the general value proposition correct for customers. So it's a really exciting time and very honored to be part of it. It's great to see some of your customers doing so well out there. Congratulations to your team. Appreciate that. Aiden, let's get into what you guys do. What does Cohere do? What are you excited about right now? Yeah, so Cohere builds large language models, which are the backbone of applications like chat GPT and GPT-3. We're extremely focused on solving the issues with adoption for enterprise. So it's great that you can make a super flashy demo for consumers, but it takes a lot to actually get it into billion user products and large global enterprises. So about six months ago, we released our command models, which are some of the best that exist for large language models. And in December, we released our multilingual text understanding models and that's on over a hundred different languages. And it's trained on authentic data directly from native speakers. And so we're super excited to continue pushing this into enterprise and solving those barriers for adoption, making this transformation a reality. Just real quick, well, I got you there on these new products coming out. Where are we in the progress? People see some of the new stuff out there right now. There's so much more headroom. Can you just scope out in your mind what that looks like? Like from a headroom standpoint, okay, we see chat GPT, oh yeah, writes my papers for me, does some homework for me, I'm okay, yawn, maybe. People say that, people excited. Or people have blown away.  Yeah, at the moment, it's like a writing assistant, right? And it's still super early in the technology's trajectory. I think it's fascinating and it's interesting, but its impact is still really limited. I think in the next year, like within the next eight months, we're going to see some major changes. You've already seen like the very first hints of that with stuff like Bing Chat, where you augment these dialogue models with an external knowledge base. So now the models can be kept up to date to the millisecond, right? Because they can search the web and they can see events that happened a millisecond ago. But that's still limited in the sense that when you ask the question, what can these models actually do? Well, they can just write text back at you. That's the extent of what they can do. And so the real project, the real effort that I think we're all working towards is actually taking action. So what happens when you give these models the ability to use tools, to use APIs? What can they do when they can actually affect change out in the real world beyond just streaming text back at the user? I think that's the really exciting piece. I mean, so I wanted to tee that up early in the segment because I want to get into the customer applications. We're seeing early adopters come in using the technology because they have a lot of data. They have a lot of large language model opportunities. And then there's a big fast follower wave coming behind it. I call that the people who are going to jump in the pool early and get into it. They might not be advanced. Can you guys share what customer applications are being used with large language and vision models today and how they're using it, transforming that on the early adopter side? And how is that a tell sign of what's to come? You know, one of the things we have been seeing both with the text models that Edan talked about as well as the vision models that are stability that AI does Tom is customers are really using it to change the way you interact with information. You know, one example of a customer that we have is someone who is kind of using that to query customer conversations and ask questions like, you know, what was the customer issue? How did we solve it? And trying to get those kinds of insights that was previously much harder to do.   Those have been really big areas that we have seen customers start to do.  And, you know, all of this goes to where we need to take the technology. One of which is, you know, the models have to be there but they have to work reliably in a production setting at scale with privacy, with security. And, you know, making sure all of this is happening is going to be really key. And that is what, you know, we at AWS are looking to do which is work with partners like stability and others and in the open source and really take all of these and make them available at scale to customers when they work reliably. Tom Aiden, what's your thoughts on this? Were customers landing on this first use cases or set of low hanging fruit use cases or applications? Yeah, so I think like the first group of adopters that really found product market fit were the copywriting companies. And so one great example of that is Hyperwrite, another one is Jasper. And so for Coher, that's the tip of the iceberg. Like there's a very long tail of usage from a bunch of different applications. Hyperwrite is one of our customers they help beat writer's blog by drafting blog posts, emails and marketing copy. We also have a global audio streaming platform which is using us to power a search engine that can come through podcast transcripts in a bunch of different languages than a global apparel brand which is using us to transform how they interact with their customers through a virtual assistant, two dozen global news outlets who are using us for news summarization. So really like these large language models they can be deployed all over the place into every single industry sector. Language is everywhere. It's hard to think of any company on earth that doesn't use language. So it's very, very important. Well, we're doing it, we're doing it right now. We got the linguists coming in with transcribe this puppy. All right. Tom, on your side, what do you see? Yeah, we're seeing some amazing applications of it. And I guess that's partly been because of the growth in the open source community. And then some of these applications have come from there that are then triggering this secondary wave of innovation which is coming a lot from, you know, controllability and explainability of the model. But we've got companies like, you know, Jasper which Aiden mentioned to using stable diffusion for image generation in blog creation, content creation. We've got Lenzer, you know, which exploded and just built on top of stable fusion for fine tuning so people can bring themselves and their pets and, you know, everything into the model. So we've now got fine-tuned stable diffusion at scale which is kind of democratized, you know, that process which is really fun to see. Lenzer, you know, exploded, you know, I think it was the largest-growing app in the app store at one point. And lots of examples like Nutcafe and Lexicur and Playground. So we're seeing lots of cool applications. So much, so much applications. We'll probably be a customer for all you guys we'll definitely talk after. But the challenges are there for people adopting. I want to get into what you guys see as the challenges that turn into opportunities. How do you see that the customers adopting generative AI applications? For example, we have massive amounts of transcripts timed up to all the videos. I don't even know what to do. Do I just, do I code my API there? So everyone has this problem. Every vertical has these use cases. What are the challenges for people getting into this and adopting these applications? Is it figuring out what to do first? Or is it a technical setup? Do they stand up stuff? They just go to Amazon? What do you guys see as the challenges? I think, you know, the first thing is coming up with where you think you're going to re-imagine your customer experience by using generative AI. And, you know, we talked about AI down and Tom talked about a number of these ones. And, you know, you pick up one or two of these to get that robust. And then once you have them, you know, we have models and we'll have more models on AWS, these large language models that Ada was talking about. Then you go in and start using these models and testing them out and seeing whether they fit in use case or not. In many situations, like you said, John, our customers want to say, you know, I know you've trained these models on a lot of publicly available data, but I want to be able to customize it for my use cases because, you know, there's some knowledge that I have created and I want to be able to use that. And then in many cases, and I think Ada mentioned this, you know, you need these models to be up to date. Like you can't have it stayed. And in those cases, you augment it with a knowledge base. You know, you have to make sure that these models are not hallucinating. And so you need to be able to do the right kind of responsible AI checks. So, you know, you start with a particular use case and there are a lot of them. Then, you know, you can come to AWS and then look at one of the many models we have. And, you know, we are going to have more models for other modalities as well. And then, you know, play around with the models. We have a playground kind of thing where you can test these models on some data. And then you can probably, you will probably want to bring your own data, customize it to your own needs, do some of the testing to make sure that the model is giving the right output, and then just deploy it. And, you know, we have a lot of tools to make this easy for our customers. How should people think about large language models? Because do they think about it as something that they tap into with their IP, or their data, or is it a large language model that they apply into their system? Is it interface that way? What is the, what's the, what's the interaction look like? In many situations, you can use these models out of the box. But, in typical, in most of the other situations, you will want to customize it with your own data or with your own expectations. So the typical use case would be, you know, these are models are exposed through API. So the typical use case would be, you know, you're using these APIs a little bit for testing and getting familiar. And then there will be an API that will allow you to train this model further on your data. So you use that or, you know, make sure you augmented the knowledge base. So then you use those APIs to customize the model and then just deploy it in an application. And, you know, like Tom was mentioning, a number of companies that are using these models. So once you have it, then, you know, you're going to use an endpoint API and use it in an application. All right. I love, I love the example. I want to ask Tom and Aiden, because like most my experience with Amazon Web Services in 2007, I would stand up in EC2, put my code on there, play around. If it didn't work out, I'd shut it down. Is that a similar dynamic we're going to see with the machine learning where developers just kind of log in and stand up infrastructure and play around and then have a cloud-like experience? Si, I can get first. So we're obviously with AWS working really closely with the SageMaker team. They do a fantastic platform there for ML training and inference.  You know, many companies, you know, bringing their, bringing the models to their data in AWS on-premise for them is hugely important. Having the models to be, you know, open sources makes them explainable and transparent to the adopters of those models. So, you know, we're really excited to work with SageMaker team over the coming year to bring companies to that platform and make the most of our models. Aiden, what's your take on developers? Do they just need to have a team in place? If we wanted to interface with you guys, is it, can they start learning? What do they got to do to set up? Yeah, so I think for Cohere, our product makes it much, much easier to people, for people to get started and start building. It solves a lot of the productionization problems. But of course with SageMaker, like Tom was saying, I think that lowers the barrier even further because it solves problems like data privacy. So I want to underline what Brutton was saying earlier around when you're fine-tuning or when you're using these models, you don't want your data being incorporated into someone else's model. You don't want it being used for training elsewhere. And so the ability to solve for enterprises, that data privacy and that security guarantee has been hugely important for Cohere. And that's very, very easy to do through SageMaker. But yeah, the barriers for using this technology are coming down super quickly. And so for developers, it's just becoming completely intuitive. I love this, there's this quote from Andre Caparthi. He was saying like, it really wasn't on my 2022 list of things to happen, that English would become the most popular programming language. And so the barrier is coming down just super quickly. And it's exciting to see. It's going to be awesome for the companies here and then we'll do more. We're probably going to see explosion of startups already seeing that, the maps, ecosystem maps, the landscape maps are happening. So this is happening, I'm convinced, it's not yesterday's chatbot. It's not yesterday's AI ops. It's a whole nother ballgame. So I have to ask you guys for the final question before we kick off the company showcasing here. How do you guys gauge success of generative AI applications? Is there a lens to look through and say, okay, how do I see success? And it could be just getting a win or is it a bigger picture? Bratton, we'll start with you. How do you gauge success for generative AI? You know, ultimately it's about bringing business value to our customers and making sure that those customers are able to reimagine their experiences by using generative AI. Now the way to get the ease of course, deploy those models, deploy these models in a safe, effective manner and ensuring that all of the robustness and the security guarantees and the privacy guarantees are all there. And we want to make this, we want to make sure that this transitions from something that's great demos to actual at scale products, which means making them work reliably all of the time, not just some of the time. Tom, what's your gauge for success? I think we're seeing a completely new form of ways to interact with data, to make data intelligent and directly to bring in new revenue streams into business. So if businesses can use our models to leverage that and generate completely new revenue streams and ultimately bring incredible new value to their customers, then that's fantastic. And we hope we can power that revolution. Caden, what's your take? Yeah, reiterating Bretton and Tom's point, I think that value in the enterprise and value in market is like a huge, it's the goal that we're striving towards. I also think that the value to consumers and actual users and the transformation of the surface area of technology to create experiences like chat GTT that are magical. And it's the first time in human history we've been able to talk to something compelling that's not a human. I think that in itself is just extraordinary and so exciting to see. It really brings up a whole nother category of markets, B2B, B2C. It's B2D, business to developer. Because I think this is kind of the big trend. The consumers have to win. Developers coding the apps. It's a whole nother sea change. Everyone uses the Moneyball movie as example during the big data wave and value of data. There's a scene in Moneyball at the end where Billy Bean's getting the offer from the Red Sox and the owner says, if every team's not rebuilding their teams based on your model, they'll be dinosaurs. I think that's the same with AI here. Every company will have to need to think about their business model and how they operate with AI. So it'll be a great run. It'll be a great run. Nathan, Tom, thank you so much for sharing about your experiences at your companies and congratulations on your success. And it's just the beginning. And Bratton, thanks for coming on, representing AWS. And thank you. Appreciate what you do. Thank you. Thank you, John. Thank you, Nathan. Thank you, John. Okay, let's kick off season three, episode one. I'm John Furrier, your host. Thanks for watching.


Hi everybody, Joe for jaspyscasebrakes.com coming at you with a one box break of 2020-2021 Panini Immaculate basketball This is break number nine break ten is the last Is the last box of the case that we popped open so one of these are gonna be for break nine And one of those are gonna be for the next break break ten which is already loaded up jaspyscasebrakes.com big Thanks everybody here for getting in on it appreciate it And Brian I think we we missed Think I did not put you in the list for the last break. I apologize about that So we rolled it over to to this one Sorry about that There are the teams right there. Let's go. Let's roll it randomize it two and a six eight times for names and teams one two three four five six seven and Eighth and final time Jason. I have no idea of Jason, but check the break schedule. That'll have all your filler information there Honestly, don't remember Two and a six eight times Right there two and a six eight times for the teams one two three four five six seven and Eighth and final time Jim feeling a lamello in here Got Phoenix down to the Grizzlies Well, that would make That would make Eric very happy because he's got the Hornets in this one boxer So there's Brian with the Suns. There's Eric with the Hornets.  You can't have that player would not be in here. This is a basketball break So let's sort by column B by team And we're going to pause the video. We're going to see if they're actually, no, we're not going to pause the video We're going to flip over to this screen while you're thinking about trades And let's select that die right there We'll go one two three for the top box four five six for the bottom box And one top box So this we will say for break 10 It's almost a random team out there or a picker team out there. It's not a picker team. It's a random team So that'll be for break 10. All right. Now I'm going to pause the video when we come back We're going to see if there's any trades looks like clippers up for trade We're going to see if there's a trades and then we'll have the breaks tick around be right back All right, welcome back Some trade chatter, but in the end no deals done here on a monday Thanks everybody We're getting in the Utah Jazz hanging tough against the Phoenix Suns Utah Jazz are shorthanded. They don't have their a number of their main starters Nine minutes left. They're up by a couple of points So a lot of game left who's that blank card? Just slow play the hits a little bit Good luck. Here we go. First one out of the gate Kemba Walker nine out of 49 Boston Celtics Jeff With the C's We got Mike Miller The standard relic Five out of 49 Jeff with Denver. We got Kira Lewis jr. Jersey numbers relic 19 out of 50 Pelicans Andrew we got furkan corkmas 16 out of 99 for the Sixers Past and present signature this sharp shooter going to Demetrios And we have Caleb Martin 74 out of 99 rookie introductions On-card autograph for the charlotte hornets. That'll be for eric All right, and the last one is going to be jaden mcdaniels 99 out of 99 Two-color jersey and on-card autograph Jeff with the timber wolves Well, we got one box left from the case Maybe we'll find both of these guys as teammates in the uh in the next box So check it out on jaskies case breaks.com. I'm joe. I'll see you next time for the For another immaculate break. Maybe later tonight jaskies case breaks.com. I'll see you. Bye. Bye


Pipelines in the Tarsan service, a small percentage of people, and we look forward to meeting with our new leader. We look forward to talking about these things that matter as a gesture of a way of the future and the direction that we need to go to save humanity is a gift of these solar panels. Green energy, we've seen it all over the world where people are taking initiatives and moving in that direction. To stay with the nation, we're part of the second best-selling wind turbine company in Canada, so we know the jobs that it creates, we know the economic value that it has, and so on behalf of all the people here and all my friends as you see the crowd here, it's a good representation of Canada. We have all nationalities, we have all ages, and on behalf of us we want to send a message saying this is the way. Yeah.


And there was a clip yesterday out of mic'd up that explained that 13 second drive where Travis Kelsey walks over to Patrick Mahomes and goes with Tyree Kill. He goes, Hey, Ted, I'm gonna run here. You just kind of go in and then set him up out. I'll be able to block it for you. Tyree Kill first completion, same exact thing that Travis Kelsey said on the sideline to Patrick Mahomes, time out to gain some yards. Then Travis Kelsey saying, Hey, if they give us that look, the seam, the seam is wide open or whatever. And then before the play, everybody hears obviously now at this point, Mahomes looking, do it, Kelsey, do it, Kelsey. And then he looks and goes, uh-huh. Actually in the mic'd up, runs the seam, picks up the yardage, looks at the clock, gets down on purpose, calls the time out. It's like, okay, so Travis Kelsey's football IQ is on 100,000 trillion next level.


This video is going to use elimination to solve systems. The goal of the elimination method is to add the two equations in the system together so that one of the variables is eliminated. There's our word for the day. That is, the term containing the variables add to zero. This process creates an equation which is one variable that enables us to solve for one of the variables in the system. If we look at this system right here, if we add these two equations in the system, then 3x plus a negative 3x is going to be 0x, so they cancel each other out. And y plus three more y is going to give me 4y, and 5 plus 7 is going to give me 12. Now you can see that we've eliminated one variable, so we're down to a one variable equation that we can solve for. And let's go ahead and solve this one. Divide by four. Now we know that y is equal to three. Now you have two equations, and we know y is equal to three. I just have to plug it back into one of them. This type of equation would be nice because this y has a coefficient of one. So let's try 3x plus, and then I'm going to put in my y, equal to five, and my y is three. So that means I want to subtract three from both sides so I can solve for x. 3x is going to be equal to five minus three is two, and if I divide by three, then I find out that x is equal to two-thirds. So my ordered pair is two-thirds and three. This system has opposite coefficients on the y. I can add these right away because they're going to drop out with opposite coefficients. So 3x plus 2x would give me a total of 5x. This cancels out and gives me zero, and eight plus seven is going to be 15. So if I divide by five, then I know that x is equal to three. Now remember, again, it takes an ordered pair, and all we have is the x. So I have to plug it back into one of the other equations. I can plug it into either one. I'm going to choose to do the second equation because it was a positive y, so I won't have to worry about signs. So we have two times our x, which we found, plus y equal to seven, and our x was three. So two times three is six, plus y is equal to seven, and if we subtract the six from both sides, y will be equal to one. So our ordered pair is x is three, and y is one. Now this system doesn't have opposite coefficients, but it does have opposite signs on both the x and the y. If I choose to get rid of x, then I need to get these two things to be the same. I need to multiply one of my equations. Two is the smaller number, so I would have to multiply this equation by five to get rid of the x's. Let's try that. So my original equation for the first one stays, and then this tells me I'm going to distribute five all the way through, even across the equal sign, which will give me negative 10x. Five times six is plus 30y, and five times 16 is 80. And if I add those two equations, 10x plus a negative 10x gives me zero. Negative 3y plus 30y will be 27y, and one plus 80 will be 81. And if I divide everything by 27, I get y, and 81 and 27, I know they have nine in common. Nine times nine is 81, and nine times three is 27, but it must be that they went in perfectly because nine divided by three is actually three. So now we have y equal three. I have to find the x, plug it into either equation. The bottom equation is a little bit smaller, but it has a nice little negative in here, so you can plug it into the top one if you want. In fact, let's do that. So we do that and we get 10 times x minus three times our y, which is three, is equal to one. So 10x minus nine is equal to one. Add nine to both sides. 10x is going to be equal to 10, which means when we divide by 10, we find that x is equal to one. Now I want to do one other thing, but I'm not going to solve the whole problem again because we know what the answer is. But what, let's get rid of this five over here. What if we had wanted to get rid of the y instead of the x? That means I want these three and six to become the same number. Again, three is smaller, and I can make it become a six by multiplying by two. So I would have to multiply by two and distribute it clear through. If you want to set up the system, if you want to solve it on your own later and verify that it's three, one, three is your solution, you could do that. So two times 10x is going to be 20x. Two times negative three y is going to be minus six y. And two times one is going to be equal to two. Don't forget to go across the equal sign. And then the original bottom equation stays. We haven't done anything to it because now when I look at my y's, I have opposite coefficients, and you can add them. I'll add them to get down to unvariable. So 20x plus and negative two x will be 18x. Negative six y plus six y gives you zero, and two plus 16 will be 18. And you can see pretty easily that that x is going to be one. This system doesn't have any opposite coefficients, and it doesn't have any opposite signs anywhere. So we have a little bit more work to do. What we're doing, trying to do, is to get the coefficients to be the same number but opposite signs. I can't make three become four. It's a smaller number, but I can't make it become four. So I'll try my y's, but I can't make two become five either. So this time I have more work than I did before. I'm going to either have to eliminate the x's and find out what three and four have in common, or eliminate the y's and decide what x and two and five have in common. It really doesn't matter. We'll set it up both ways again. If I want to eliminate x, then this top equation, I need four and three are going to have 12 in common. The least common multiple is going to be 12. So I need to multiply this one by three, and that'll give me 12x. But if I multiply that one by three, I need this one to be the opposite, negative 12. So four times three would give me 12, but then I need to make it a negative. And it really doesn't matter which one's negative, but since they were both positive, I had to multiply one equation by a positive and one equation by a negative. So let's set that system up, and then we'll go back and see what it would have been if we eliminated y and then we'll solve. In the top equation, we have 12x plus 6y will be equal to 36, and on the bottom equation, we'll have negative 12x, and negative four times 5y will be minus 20y, and 16 times four, or negative four, is going to be a negative 64. There's that system that we have to add. Now let's go through and figure out if I wanted to get rid of my y's. Well, the LCM in that case is going to be 10. This is for my y's. Let's take care of the negative first this time. To make two become negative 10, I have to multiply by negative five. I have to distribute the negative five in. I have to distribute it everywhere, but especially to that negative, or that 2y. And then to make five become 10, I have to multiply by two, and I'm going to distribute it all the way through. On the top equation, negative five times four is negative 20x, negative five times 2y will be minus 10y, and negative five times 12 is going to be equal to negative 60. And if I take the two times everything, I'll have two times 3x, which will be 6x, plus two times 5y would be 10y, and two times 16 is going to be 32. It doesn't matter which way we did it. You don't have to do it both ways. You're either going to eliminate x, or you're going to eliminate y. And we eliminated x the first time last time, so let's eliminate y the first time here. So we're going to use this system and go to work. Negative 20x plus 6x will be a negative 14x, and my y's cancel out, and negative 60 plus 32 is going to be equal to 28. If I divide by negative 14 on both sides, then I find out that x is equal to negative two. I've got my x, I need a y, I need to go back up to my original equations. And in my original equations, they're about the same kinds of numbers. It really doesn't matter which equation. I'm going to choose to put it into the top equation. Don't ask me why, I just am. So 4 times my x, which I just found, then plus 2y is going to be equal to 12. This is negative 8 plus 2y equal to 12. If I add 8 to both sides, that would give me 2y equals 20. And if I divide by 2, I now find out that y is equal to 10. So my solution is the ordered pair negative 2, 10. Alright, so now for our special cases. We've got a lot to think about here. We've got to think about coefficients. We have to think about signs. This is solving any system. I need to think about coefficients and signs on each variable. So for this x, I don't have opposite signs. And on the y's, I don't have opposite signs. So that's not very helpful. And I don't have the same coefficients. So that's not real helpful either. But I can see that it won't be too bad to get 3 to become 6. And actually I could have 6 become 12 as well. But 3 is a little bit smaller to get to the 6. So I'm going to eliminate my x's. I need this to become a positive 6. Okay, my LCM that I'm going for is 6. And I already have negative 6 down here. So I need to multiply times 2. That'll give me negative 6 though. So if I multiply by a negative 2, now I'll get a positive 6. So if I distribute, that gives me positive 6x. And distribute here, I'll get a positive 12y. And distribute here, I'm going to get a positive 4. This gives me 0x plus 0y. So on this side I have 0. And negative 4 plus 4 is also 0. But remember when it's true, that means we have the same line or infinite solutions. But that's what it looks like when we solve them. We come down with the true statements and all my variables are gone. Come over here to this problem. I can see here that I don't have any opposite signs. And I don't have any coefficients that are the same. But I could make 3 become 9. And I very easily could make this y become a 3y. So again, I this time want to get rid of y. We got rid of x over there, so let's get rid of y this time. If I get rid of y, I'm going for at least common multiple of 3. Well, this one's already a negative 3. So I'm going to take my bottom equation. And I'm going to multiply by, if I only multiply by 3, I still have negative 3y. So I need to multiply by negative 3. Because negative 3 times negative y will be a positive 3y. You're at my problem. This becomes negative 9x. We already said that that was going to be plus 3y. And then when we distribute all the way down to the negative 10, negative 3 times negative 10 is positive 30. Now we add the two equations. And our x's drop out. And our y's drop out. So we have 0 on the left hand side. And 12 plus 30 is 42. And 0 is not equal to 42. So we know that we have no solution. Again, your variables will drop out, but you'll end up with a not true statement with your numbers.


Now exiled supporters of ex-President Laurent Babou have returned after 10 years of exile at the same time as the release of 100 prisoners accused of violence related to the 2020 presidential election was announced. Six supporters of former Ivorian President Laurent Babou, including former party officials, landed in Abizion from Ghana under the auspices of the United Nations High Commissioner for Refugees, UNHCR, in agreement with the Ivorian government in the name of national reconciliation. The ex also included former budget minister Justin Kettinen-Kuhn and Lernberg's younger sister Jeanette Kudur were greeted at the airport by dozens of activists, kept at a safe distance by police as well as journalists.


So a good question is, why are we doing this now? And looking around, you have to acknowledge this is a period of really, really dynamic change. We're settling into whatever's happening after the pandemic. Risks are escalating all around. There's this explosion of conversation around generative AI. Meanwhile, higher education models are being stress tested and often falling short of expectations. So it's the perfect time for us to reassess the difference that EDUCAUSE is making, how we can contribute to the conversation and these dynamic challenges. So really the perfect time for us to take on this work. Our new vision is to inspire the transformation of higher education in service to a greater good. And that's just this wonderful, almost idealistic, soaring, expansive vision. It really fits our community. Our community is expansive and growing and changing. At its heart and soul are the CIOs and the IT professionals that founded us decades ago. But now we include instructional designers and cybersecurity and privacy officers, institutional researchers, librarians, all kinds of other folks working to advance technology across all sectors of higher education. The strength of our community isn't defining ourselves in a way that leaves people out. The strength of our community is all the people that we include. A few highlights from the plan for me. One is that we're moving toward more actionable, practical how-to resources for our members. We know that that's what you've been asking for when you're trying to make the case for change on your campus. In addition to that, we're going to be working to elevate technology and data as a strategic asset on your campus, helping you to use technology and data contribute to that greater good that we're focused on in this plan. Finally, we're going to intensify our efforts to help you assess and mitigate risk on your campus. Such an important and growing concern. And we think that resilience, institutional resilience, is part of what we can do to help in that conversation. And finally, nobody does a better job than EDUCAUSE at bringing together campus stakeholders and industry stakeholders. And we're committed in the years ahead to evolving the higher education marketplace, making it ever more responsive to colleges and universities and learners. So this plan is a multi-year marathon, not a sprint. Actually, it's a sprint and a marathon. We've already are hard at work delivering in 2023. I hope you'll find a way to connect with us and learn more about what we're up to. There's lots of chances for you to engage with EDUCAUSE. If you've been thinking, I should engage with EDUCAUSE more, now is the time to do it. As we're regrouping, reassessing, reimagining our future, we'd love to have you in it.


Hi, thanks for coming. I'm Tim Smith from GridCentric. And I'm going to be talking about virtual memory streaming and how it enables scale-out virtualization for OpenStack. So GridCentric makes an OpenStack extension called virtual memory streaming. And I'll be going into a bit more detail about what it is exactly later. But what it enables is something we call scale-out virtualization. And by that, we focus on three main things. Being able to rapidly scale virtual infrastructure. So taking a virtualized application and scaling it from a single virtual machine to dozens or hundreds of virtual machines on the order of seconds. That also plays into kind of high performance and getting the most out of your physical infrastructure. And finally, by increasing efficiency and reducing the amount of resources, physical resources, that your virtual infrastructure is taking, effectively lowering your total cost of ownership of your OpenStack cloud. So on the rapid scale front, what I mentioned earlier about taking a virtualized application, something like a web service or a virtual desktop pool, and being able to instantly scale that on demand. So as more users come to your website or as more of your desktop users log into their machines in the morning, being able to instantaneously scale up the virtual infrastructure to be able to handle that load and then being able to shrink it back down on demand as well. So VMS enables this ability to rapidly scale up and down in demand to workload in a time frame that essentially allows you to construct real-time auto-scaled applications. Virtual memory streaming is based on this ability to create what we call a live snapshot or a live image. And the idea is essentially that you take a running virtual machine as a template and create what we call a live snapshot or a live image of it. So this goes beyond just taking a regular disk snapshot. It's also the CPU and memory state of the virtual machine. And that live snapshot, we can essentially launch new virtual machines from that on demand that come up ready to run and ready to service requests. Because these virtual machines are clones of this live snapshot, we're able to do a lot of memory optimization and memory sharing among them. And because they skip the boot process, they're able to skip a lot of the boot-related network in storage I.O. that would normally occur with a full boot cycle. So moving on to the products that we provide. As I mentioned, the main technology we provide is virtual memory streaming or what we call VMS. And it eliminates that boot store I'm associated with booting up virtual machines. So if you run a lot of Windows on your infrastructure or you run a lot of application servers or web servers, that booting them up and getting them to the point where they are ready to perform work takes a lot of time and will actually put a lot of load on your network. I'll be showing a demo video shortly of booting up some Windows machines and how quickly we're able to get them to launch without using a lot of I.O.P.s. Because these virtual machines are launching from these live image snapshots, we're able to efficiently stream in the memory used that their memory footprint on demand. So they don't occupy their complete memory footprint up front. So we're essentially doing copy on write on the memory. One of the features of VMS is that we're fully integrated with Active Directory. So you can bring up Windows VMs. And as you're bringing them up, they can auto register with Active Directory. So you don't need to go through a couple boot cycles.  And you can define metrics for an application that when Reactor sees a large number of users coming in or sees your response time starting to go up, can increase the number of virtual machines on the back end using VMS to quickly bring them up and then scale them back down. So all of this stuff is fully integrated with OpenStack.  We have a lot of OpenStack code that goes by the name of Cobalt that acts as a kind of interface with VMS that sits at the actual hypervisor layer. And we currently support both KVM and Zen hypervisors. So the use cases that VMS is really good for fall into a number of categories. One is your horizontally scalable web services. So these are things like WordPress or Apache or Nginx or Hadoop where the way that you go from getting a little bit of work done to getting a lot of work done is spinning up a bunch of virtual machines. And if you're constantly spinning up virtual machines, you'll find that the hit on your resources is actually big enough that you end up spending more time waiting for your virtual machines to come up than you actually do performing work. So because VMS is really good at being able to quickly spin up virtual machines, and once they're up, they're extremely memory efficient, you get a lot more out of the hardware that you have. Another use case is memory intensive middleware. So if you're running a big Java servlet, these applications typically take minutes to start up and to be able to warm up. They occupy a lot of memory. And so they're very hard to scale normally. But with VMS, you can essentially create a template live image of a VM that's already started, already warmed up, the application's ready to run, and create this live image from that and then be able to spin up new instances of your middleware servers very, very quickly. If you're doing a lot of development and test, especially across multiple platforms and multiple versions of operating systems and, say, browsers or different test configurations, you want to be able to do that efficiently. So you want to be able to do, you want to have a wide variety of environments that you're going to be able to test on. But you don't necessarily want to have them sitting around all the time. So what you want is a way to be able to quickly spin up a large number of virtual machines of one type, do your testing, and then collapse them back down. So VMS, again, because it's very quick to spin up complicated virtual machines, is very good for that use case. And finally, for Windows desktop or Windows server, Windows, when it starts up, performs a lot of IO on your storage devices just to get to the point where it's ready to be logged in. We found that it's somewhere in the order of 20,000 up to around 70,000 boot-related input-output operations. So storage IOPS, just to get to the point where Windows 7 VM is sitting at the login screen. And with VMS, what we do is we just take a single VM and we get it to the point where it's sitting at the login screen, and then we take this snapshot. And then from that point on, you can spin up new desktop VMs very, very quickly. So I'm going to play a short demo video just to show that principle kind of in action. So we've got a live image here created with a Windows 7 desktop. And what we've just done using the regular Nova client, which is going to talk to our extension, is spun up five of these clones. You can see they've got their network addresses and they're in this building state. And meanwhile, in this Horizon plugin, we can see that at the lower part of the screen, our disk operations are remaining low. And our memories, I guess we'll click over to that right now, the operations on our disk are remaining low at around 200 input-output operations per second. Normally, if you're going to spin up five desktops, you would see something around 20,000 to 70,000 input-output operations IOPS per desktop for around somewhere between 100,000 IOPS and 350,000 IOPS. But we're able to get to the point where these five VMs were ready with much, much fewer, somewhere around just around 5% of that. And as you can see, the VMs are ready and you can log into them and so on. Another thing to point out is that the memory usage, for these five VMs, they're two gigabyte VMs that they would have normally occupied around 10 gigabytes of physical memory on our host. And currently, they're sitting around two gigabytes. So just sitting there idling, they're occupying about a fifth. If we're actually to log into these VMs, start running off, start running all our applications, it would still hold steady at probably around four gigabytes total, so around a 60% reduction in memory. Anyhow, that was the demo. And we've got a booth just on the other side of this wall here. If you'd like to come by later and ask us questions or come see a live demo, we'd be happy to show you one. I'm Tim Smith from GridCentric. Again, thank you very much for coming.