(light airy music) >> Hello, everyone, welcome to theCUBE's presentation of the AWS Startup Showcase, AI and machine learning. "Top Startups Building Generative AI on AWS." This is season three, episode one of the ongoing series covering the exciting startups from the AWS ecosystem, talking about AI machine learning. We have three great guests Bratin Saha, VP, Vice President of Machine Learning and AI Services at Amazon Web Services. Tom Mason, the CTO of Stability AI, and Aidan 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. >> Thank you. >> Thank you. >> So the topic is hype versus reality. So I think we're all on the reality is great, hype is great, but the reality's here. I want to get into it. Generative AI's got all the momentum, it's going mainstream, it's kind of come out of the behind the ropes, it's now mainstream. 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 Code 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 that is where, you know, things like Trainium, things like Inferentia, things like SageMaker come in. And then next is the set of models and then the third is the kind of applications like Code Whisperer and so on. So, you know, it's early days yet, but clearly 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 Services relationship's important for you? >> So yeah, we're primarily committed to making incredible open source foundation models and obviously stable effusions 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, you know, 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 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 kind of general value and 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. Aidan, 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 ChatGPT 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, you know, 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, while I got you there on the 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 ChatGPT. "Oh yeah, it writes my papers for me, does some homework for me." I mean okay, yawn, maybe people say that, (Aidan chuckles) people excited or people are blown away. I mean, it's helped theCUBE out, it helps me, you know, feed up a little bit from my write-ups but it's not always perfect. >> Yeah, at the moment it's like a writing assistant, right? And it's still super early in the technologies 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 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. >> Okay, so I wanted to tee that up early in the segment 'cause 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 to transform 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 Aidan talked about as well as the vision models that stability.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's 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. And then of course software is a big area. You know, generating software, making that, you know, just deploying it in production. Those have been really big areas that we have seen customers start to do. You know, looking at documentation, like instead of you know, searching for stuff and so on, you know, you just have an interactive way, in which you can just look at the documentation for a product. 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. 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, where they work reliably. >> Tom, Aidan, what's your thoughts on this? Where are 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. So one great example of that is HyperWrite. Another one is Jasper. And so for Cohere, 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 block by drafting blog posts, emails, and marketing copy. We also have a global audio streaming platform, which is using us the power of search engine that can comb through podcast transcripts, in a bunch of different languages. Then 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- >> We're doing it right now. We got the language coming in. >> Exactly. >> We'll transcribe this puppy. All right. Tom, on your side, what do you see the- >> Yeah, we're seeing some amazing applications of it and you know, I guess that's partly been, because of the growth in the open source community and 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 Aidan mentioned, who are using stable diffusion for image generation in block creation, content creation. We've got Lensa, you know, which exploded, and is built on top of stable diffusion for fine tuning so people can bring themselves and their pets and you know, everything into the models. So we've now got fine tuned stable diffusion at scale, which is democratized, you know, that process, which is really fun to see your Lensa, you know, exploded. You know, I think it was the largest growing app in the App Store at one point. And lots of other examples like NightCafe and Lexica and Playground. So seeing lots of cool applications. >> 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, they want to get into what you guys see as the challenges that turn into opportunities. How do you see 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 reimagine your customer experience by using generative AI. You know, we talked about Ada, 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 Aidan 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 Aidan mentioned this. You know, you need these models to be up to date. Like you can't have it staying. And in those cases, you augmented 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. >> Yeah. >> 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 the interface that way? 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 APIs. 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 AI, 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. You know, like Tom was mentioning, a number of companies that are using these models. So once you have it, then you know, you again, use an endpoint API and use it in an application. >> All right, I love the example. I want to ask Tom and Aidan, because like most my experience with Amazon Web Service 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? >> So I can go first. So I mean, we obviously, with AWS working really closely with the SageMaker team, do fantastic platform there for ML training and inference. And you know, going back to your point earlier, you know, where the data is, is hugely important for companies. Many companies bringing their 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 are really excited to work with the SageMaker team over the coming year to bring companies to that platform and make the most of our models. >> Aidan, what's your take on developers? Do they just need to have a team in place, if we want to interface with you guys? Let's say, 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 a barrier even further because it solves problems like data privacy. So I want to underline what Bratin 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 easy to do through SageMaker. >> Yeah. >> But 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 Andrej Karpathy. He was saying like, "It really wasn't on my 2022 list of things to happen that English would become, you know, the most popular programming language." And so the barrier is coming down- >> Yeah. >> Super quickly and it's exciting to see. >> It's going to be awesome for all 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 and I'm convinced it's not yesterday's chat bot, it's not yesterday's AI Ops. It's a whole another ballgame. So I have to ask you guys for the final question before we kick off the company's 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? It could be just getting a win or is it a bigger picture? Bratin 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 their ease, of course to deploy those 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 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? >> Look, I think this, 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. >> Aidan, what's your take? >> Yeah, reiterating Bratin and Tom's point, I think that value in the enterprise and value in market is like a huge, you know, it's the goal that we're striving towards. I also think that, you know, the value to consumers and actual users and the transformation of the surface area of technology to create experiences like ChatGPT 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 another 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. The developers coding the apps, it's a whole another sea change. Reminds me everyone use the "Moneyball" movie as example during the big data wave. Then you know, the value of data. There's a scene in "Moneyball" at the end, where Billy Beane's getting the offer from the Red Sox, then the owner says to the Red Sox, "If every team's not rebuilding their teams based upon your model, there'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. >> Completely Agree >> It'll be a great run. >> Yeah. >> Aidan, 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 Bratin, thanks for coming on representing AWS. And thank you, appreciate for what you do. Thank you. >> Thank you, John. Thank you, Aidan. >> Thank you John. >> Thanks so much. >> Okay, let's kick off season three, episode one. I'm John Furrier, your host. Thanks for watching. (light airy music)

>>from around the globe. It's the Cube with digital coverage of AWS reinvent 2020 Special coverage sponsored by A. W s Global Partner Network. Hello. And welcome back to the cubes Live coverage of AWS reinvent 2020. It's virtual this year. Normally, were there in person doing the interviews, getting the signal from the noise. I'm John for your host. And where the cube virtual Got a great guest here. Aidan McDonald, Program manager, Justice through code the center of justice at the Columbia University. Um, this is a great story, Aiden. Thanks for coming on. Appreciate you taking the time to join me. >>Thanks so much for having me, John. >>So first of all talk about the mission of justice through code. This is such an awesome program. It really is impactful. It's one of those examples where, you know, people want to change the world. This is one. You can actually do it. And with code, take us through the mission. >>Yeah, So I think to understand the mission here, you have to understand a little bit about the problem, right? So the United States has, uh, 5% of the world's population, 25% of the global prison population. When people come home from prison, they're confronted with the reality that it's just very difficult to find jobs right. We have unemployment rates that are stratospherically higher than for the general population. And so, at the core of what we're doing in our mission is really to provide a pathway to career track employment for formerly incarcerated individuals to help support them and their families, and also to begin to change the negative stereotypes that air attached to the formerly incarcerated. >>It's an upwardly mobile mindset growth mindset. Also, there's new skills, always hard to do that, given the environmental conditions, what skills are you guys delivering? Take us through how it works. Give us a feel for kind of the skill sets and what gets what happens. >>Yeah, so we focused the program kind of in two distinct ways. So we have the technical skills aspect of the curriculum and the interpersonal skills. So as far as the technical skills go, we teach a version of a course that's taught to current Columbia MBA students eso that is set up. We teach the fundamentals of programming python, what we call phase one of the program. Then we move on to a P I S and data analysis. And then from there we do a Capstone software project. And for that project, groups of two or more students come together. Really? They conceptualize the design on day execute on building this project. And during that phase, of course, we actually pair students with mentors who are season software engineers from many of the top tech companies in the US and then in terms of the story in terms of the interpersonal skills, um, you know, we really focus on the skills that are necessary to success in the tech workforce s Oh, this is, you know, resumes, cover letters, interviewing skills and also really understanding that for many of our students, they don't have the networks that so maney people are fortunate enough to have that have gone through a traditional educational pathways. We bring in guest speakers from different corporations. Um, and, you know, having the students were quick mentors there really able to start to build that network to support themselves in their career transition when they complete the program. >>You know what's really amazing about what you're doing is and this really is so timing. The timing is perfect. Um, is that with the cloud and the tech scene, where we're at now is you don't you can come out. You can level up pretty quickly with things. In other words, you know, you could have someone go to an Ivy League school and be all the pedigree, and it doesn't matter because the skills now are different. You literally could be a surfing and be a couch potato surfing TV and get online and get an Amazon degree and through educate and and come out, make six figures. I mean, so there is definitely a path here. It's not like it's a slog. It's not like it's a huge leap, so the timing is perfect. We're seeing that across the board. There's more empty jobs, opening cybersecurity, cloud computing administration and with land in all these cool services, it's just gonna get easier. We hear that we see that clearly. What are some of the examples can you share of the graduates? What have they gone on to do? You mentioned some of the big tech companies. Take us through that that tipping point when the success kicks in. >>What s so you know, as I mentioned, one of the really integral parts of our program is this mentorship, right? So students finished the program. They often continue to work on their final projects, um, in conjunction with their mentors and then really focused during that time period on developing the skill sets that they'll need to have entering into junior level software development roles a tech companies For some of our students, this means, um, they've actually found out through the course of the class that they prefer front end web development, and they start working on JavaScript and full stack. And a few of our students have gone on to work it a or enter into apprenticeships that major tech companies, um, in those roles. And then we also have students who are focused on continuing in their development of their technical skill set with Python s. So we have some students who have actually entered into the Columbia University I t department on a big project. They're working on other students that have worked with freelance Web development agencies and projects really have a very diverse, talented group of students. And so from that we see that Everybody has different interests and definitely no one specific pathway but many successful pathways. >>How is Amazon Web services helping you guys? They contributing? They're giving you credits. What's their role here? >>Yeah, so they've provided kind of their expertise and support to the program. Just really excited to be collaborating with them on really looking at, How do we take this program to scale? Right. So we know that this is a problem that affect so many Americans, right? There's 77 million Americans currently with a criminal record. And so, um, you know, with the barriers to employment that come from having been incarcerated, I came to this work because I spent four years incarcerated for my own involvement in the marijuana industry in California Prior toe legalization. And so, you know, I saw a kind of these challenges, right? Firsthand of what it's like to try to get a job. And so, you know, we're just very invested in collaborating with AWS again. Thio bring this program to scale so we can really help uplift the communities that have been impacted by mass incarceration. >>It's interesting you talk about your personal experience, talk about this stigma that comes with that and how this breaks through that stigma. And this is really not only is a self esteem issues up this Israel, you could make more money. You have a career and literally the difference between going down or up is huge. Talk about the stigma and how this program changes the lives of the individual. >>Yeah, I think one important thing Thio consider hearing before understanding is this statement right? Is that unemployment or employment should say is the number one predictor of recidivism. Right? So we see that for people that have really jobs, they don't go back to prison on DSO. You know, we're just so invested in working on that and in terms of the stigma, uh, you know, it's just so prevalent, right? I can think through myself. Before I had going thio to prison, I had started to businesses. I was actually accepted. Thio go to Columbia University when I got out and I would apply the landscaping jobs, couldn't get to the final round, and the job offer would be rescinded, right? I mean, just this automatic sense of this person is not to be trusted because they have a history of incarceration. And so what we're really working on doing with our students is first redefining what people think it's possible, right? I saw this myself coming home from prison. The constant messaging is your life is over. You're never going to accomplish anything of meaning and so just kind of accept your lot on DSO. At first, we really focus on that with students in terms of sharing stories of success. Other people that we know that have taken this pathway on been really looking at providing leadership development. So when our students do enter into these companies, they're really able to service leaders and for people to understand that while you may have these assumptions because of depictions of people that have been incarcerated in the media, the end of they formerly incarcerated people, our brothers, sisters, family members and really deserve a chance in life. >>Yeah, And I got to say, you know, as someone who loves technology and been, uh, computer science when his early days, you know, there was a ladder, you have to have a requisite level now. I mean, you literally could be six weeks in and be fluent on Cloud Computing Administration as three bucket configurations. I mean, there are so many things that so many opportunities if you have some intelligence and some drive you're in, I mean, it's just Z pretty right? It's right there. It's great. It's attainable. It's not a fantasy, it's it's doable. And programs like yours are awesome. My hat's off to you for doing that. Thanks for sharing. >>Definitely. Thank you so much for having me >>final question before we go, How does people get involved? Can you share a minute? Give a plug for what you guys are doing? How do I get involved? How do I give support? Take a minute to >>get? Definitely. I mean, I think at the core like the most important thing that anybody can dio right is to look within the organizations that they work and work at and find out what your fair chance hiring practices are and see if if there's an opportunity to hire our students or other formerly incarcerated students. E think it also were very engaged, as I mentioned in our mentorship program s so people can confined US center for Justice that, uh, Colombia dot e d u on board, you know reach out, tow us about the mentorship program and really begin toe talk about this and share the stories of those who have succeeded and provide support Thio other people that will be returning home. >>All right. And thank you very much. Just a fur coat. Check it out. Columbia University 18 McDonald, Program manager. Thanks for joining us. I'm John for here in the Cube Cube Coverage Cube. Virtual coverage of reinvent 2020. Thanks for watching.

>>from around the globe. >>It's the Cube with digital coverage of AWS reinvent 2020 sponsored by Intel and AWS. Yeah. Hello and welcome back to the cubes. Live coverage of AWS reinvent 2020. It's virtual this year. Normally, were there in person doing the interviews, getting the signal from the noise. I'm Sean for your host. And where the cube virtual Got a great guest here. Aidan McDonald, Program manager, Justice through code, the center of justice at the Columbia University. Um, this is a great story, Aiden. Thanks for coming on. Appreciate you taking the time to join me. >>Thanks so much for having me, John. >>So first of all, talk about the mission of justice through code. This is such an awesome program. It really is impactful. It's one of those examples where, you know, people want to change the world. This is one. You can actually do it. And with code, take us through the mission. >>Yeah, so I think to understand the mission here, you have to understand a little bit about the problem, right? So the United States has 5% of the world's population, 25% of the global prison population when people come home from prison, they're confronted with the reality that it's just very difficult to find jobs right. We have unemployment rates that are stratospherically higher than for the general population. And so, at the core of what we're doing in our mission is really to provide a pathway to career track employment for formerly incarcerated individuals to help support them and their families, and also to begin to change the negative stereotypes that air attached to the formerly incarcerated. >>It's an upwardly mobile mindset growth mindset. Also, there's new skills always hard to do that right. Given the environmental conditions. What skills are you guys delivering? Take us through how it works. Give us a feel for kind of the skill sets and what gets what happens. >>Yeah, so we focused the program kind of in two distinct ways. So we have the technical skills aspect of the curriculum and the interpersonal skills. Soas faras. The technical skills go. We teach a version of a course that's taught to current Columbia MBA students eso that is set up. We teach the fundamentals of programming python in what we call phase one of the program. Then we move on to a P I s and data analysis. And then from there we do a Capstone software project. And for that project, groups of two or more students come together. Really? They conceptualize the design on day execute on building this project. And during that phase, of course, we actually pair students with mentors who are season software engineers from many of the top tech companies in the US And then in terms of the story in terms of the interpersonal skills, um, you know, we really focus on the skills that are necessary to success in the tech workforce s Oh, this is, you know, resumes, cover letters, interviewing skills and also really understanding that for many of our students, they don't have the networks that so maney people are fortunate enough to have that have gone through a traditional educational pathway. So we bring in guest speakers from different corporations. Um, and you know, having the students work with mentors there really able to start to build that network to support themselves in their career transition when they complete the program. >>You know what's really amazing about what you're doing is, and this really is so timing The timing is perfect. Um, is that with the cloud and the tech scene, where we're at now is you don't you can come out. You can level up pretty quickly with things. In other words, you know, you could have someone go to an Ivy League school and be all the pedigree, and it doesn't matter because the skills now are different. You literally could be a surfing and be a couch potato surfing TV and get online and get an Amazon degree and through educate and and come out, make six figures. I mean, so there is definitely a path here. It's not like it's a slog. It's not like it's a huge leap, so the timing is perfect. We're seeing that across the board. There's more empty jobs, opening cybersecurity, cloud computing administration, and with land in all these cool services, it's just gonna get easier. We hear that we see that clearly. What are some of the examples can you share of the graduates? What have they gone on to do? You mentioned some of the big tech companies take us through that, that tipping point when the success kicks in? >>Yeah, so you know, as I mentioned one of the really integral parts of our program. Is this mentorship? Right? So students finished the program. They often continue to work on their final projects, um, in conjunction with their mentors and then really focused during that time period on developing the skill sets that they'll need to have entering into junior level software development roles a tech companies For some of our students, this means, um, they've actually found out through the course of the class that they prefer front end web development and they start working on JavaScript and full stack. And a few of our students have gone on to work it a or enter into apprenticeships that major tech companies, um, in those roles. And then we also have students who are focused on continuing in their development of their technical skill set with Python s. So we have some students who have actually entered into the Columbia University I T department on a big project. They're working on other students that have worked with freelance Web development agencies and projects, um, really have a very diverse, talented group of students. And so from that we see that everybody has different interests and definitely no one specific pathway, but many successful pathways. >>How is Amazon Web services helping you guys? They contributing? They're giving you credits. What's their role here? >>Yeah, so they've provided kind of their expertise and support to the program. Just really excited to be collaborating with them on really looking at, How do we take this program to scale? Right. So we know that this is a problem that affect so many Americans, right? There's 77 million Americans currently with a criminal record. And so, um, you know, with the barriers to employment that come from having been incarcerated, I came to this work because I spent four years incarcerated for my own involvement in the marijuana industry in California Prior toe legalization. And so, you know, I saw kind of these challenges right firsthand of what it's like to try to get a job. And so, you know, we're just very invested in collaborating with AWS again. Thio bring this program to scale so we can really help uplift the communities that have been impacted by mass incarceration. >>It's interesting you talk about your personal experience, talk about this stigma that comes with that and how this breaks through that stigma and this is really not only is a self esteem issues up this Israel, you could make more money. You have a career and literally the difference between going down or up is huge. Talk about the stigma and how this program changes the lives of the individual. >>Yeah, I think one important thing Thio consider hearing before understanding is this statement, right? Is that, um, unemployment or employment should say is the number one predictor of recidivism. Right. So we see that for people that have really jobs, they don't go back to prison on dso Um you know, we're just so invested in working on that and in terms of the stigma, um, you know, it's just so prevalent, right? I could think through myself. Before I had gone thio to prison, I had started to businesses. I was actually accepted. Thio go to Columbia University when I got out and I would apply the landscaping jobs, couldn't get to the final round, and the job offer would be rescinded, right? I mean, it's just this automatic sense of this person is not to be trusted because they have a history of incarceration And so what we're really working on doing with our students is first redefining what people think it's possible, right? I saw this myself coming home from prison. The constant messaging is your life is over. You're never going to accomplish anything of meaning and so just kind of accept your lot on DSO. At first, we really focus on that with students in terms of sharing stories of success. Other people that we know that have taken this pathway on been really looking at providing leadership development. So when our students do enter into these companies, they're really able to service leaders and for people to understand that while you may have these assumptions because of depictions of people that have been incarcerated in the media, the end of they formerly incarcerated people, our brothers, sisters, family members and really deserve a chance in life. >>Yeah, And I got to say, you know, as someone who loves technology and been, uh, computer science when his early days, you know, there was a ladder, you have to have a requisite level now. I mean, you literally could be six weeks in and be fluent on Cloud Computing Administration as three bucket configurations. I mean, there are so many things that so many opportunities if you have some intelligence and some drive you're in, I mean, it's just Z pretty right? It's right there. It's great. It's attainable. It's not a fantasy, it's it's doable. And programs like yours are awesome. My hat's off to you for doing that. Thanks for sharing. >>Definitely. Thank you so much for having me >>final question Before we go, How does people get involved? Can you share a minute? Give a plug for what you guys are doing? How do I get involved? How do I give support? Take a minute to >>get? Definitely. I mean, I think at the core like the most important thing that anybody can dio right is to look within the organizations that they work and work at and find out what your fair chance hiring practices are and see if if there's an opportunity to hire our students or other formerly incarcerated students. E think also were very engaged, as I mentioned in our mentorship program s so people can confined US center for Justice that, um, Colombia dot e d u on bond, you know, reach out, tow us about the mentorship program and really begin toe talk about this and share the stories of those who have succeeded and provide support Thio other people that will be returning home. >>All right. And thank you very much. Just a fur coat. Check it out. Columbia University 18 McDonald, Program manager. Thanks for joining us. I'm John for here in the Cube Cube Coverage Cube. Virtual coverage of reinvent 2020. Thanks for watching.

>> Live from New York City, it's buck you. Here is your host, Jeff Frick. >> Hey, welcome back. I'm Jeff. Rick. We're here at the Cubes. Fifth birthday party. A big date in Icy in Manhattan is part of the big Date. A week. It's got Stratos cough, a dupe world. And, of course, big Aidan. I see. So now having our party, which is always good to have, and I'm joined department X gas. Kerry McFadden from Parodi Research. Carrie. Welcome. Well, thank you very much. So last last we saw he was actually a big data and twenty thirteen, So it's lots changing the year. >> Absolutely, Absolutely. I think the whole hoodoo thing is really taken off. And the thing that interests me the most about show or or the exhibitors at the show is that Bye. You could get a lot of data into Duke, but how do you get it out? How do you make it useful? What do you do with it when you get it out? You know, I said on structure data is structured. Date. Is that a combination? Is it ski Melis? >> All the above all the above, >> right? Exactly. So I think really, that's been on and actually have been Jeff to all the shows, right? Since the beginning, when it was just a new world. Okay, Cube started back. And I think two thousand ten two thousand filling our fifth birthday. Right? So at least at least at least twenty ten. So since then, you've seen, you know, progression off vendors coming in to provide services that actually enable Duke to do more than it does started is kind of a batch oriented type of solution that now, because of these other value added solutions can to really or near real time processing, you can take the data out of it a lot more easily. You can use do basically as a as a repository, right on DH. And a lot of the solutions out there are are evolving to the point where you can, uh, you could basically make a sense of the information, and I think that's a really important rights. Dated information information inside, right? That's where we want to go with this thing. Business decisions made in real time. Which way? Define as in time to do something about it. Right? Right. Yes. Some of the players, I mean, you've got the map. Our guys. You've got the act. Aeon folks that just bought pervasive software. So they've got the Predictive Analytics piece sort of covered. Obviously. That's stone breakers. Old company, you know, a variant of ing gris, right? You've got. Obviously, IBM is a player in this space. With their blue mix and their cloud capabilities and all of their information management pieces, every major vendor is got a piece of is part of the action, if you will. Trying to build something on top of a dupe to make it more useful and make it more valuable. Yeah, the floor was filled with little companies, big companies, and everyone is certainly jumping in. So let me get your prospectus that you've been coming for a lot of years on this thing. Where are we on the journey? How? How? You know, I think we're past the P E O C stage, right? People are getting stuff into production deployments, but it's still early days. You know, the Giants are playing tonight. Go Giants, are we? First inning, third inning, seventh inning. Where are we? I think we're probably in the second or third any second. I think we got a ways to go. And what's the next big hurdle to get us to the next inning. I think one of the problems is this storage issue, right? So you've got this issue of being able to scale out theoretically, exponentially, right? The nice thing about do piss If you need Teo, if you need more space, you just add No J had storage and whatnot, But what happens when you get too much information? You're into the pedal bike, multiple PETA right range now, and most of that data, you know you're not going to access. You may access only two percent of it overtime. I think they're a lot of figures around that. But actually, a wicked bon article that I read recently is very interesting, one called Flake Flake or what they were doing. Flake. I want to make sure he gets a slave by a herd where he said it to me off camera, right? It's a f L a P. It's a combination of flash and tape on DH. Basically, there's a great article on the Wicked Bond site by Wicked Bonds CTO, David's lawyer Okay, and his premises that at some point, relatively soon a cz thie as data grows exponentially into the multiple petabytes ranges and maybe even beyond The thing is gonna get squeezed is the traditional HDD or hardening is spinning disc, right? So tape has become much more, uh, much more resilient. Uh, tape last has a meat time failure of about twenty six or thirty years versus disc, which is about five. And obviously flash is much, much faster, right? Right in some cases don't get into all the nuances of almost feet feet, but flavor going to squeeze out disks and the men think so. And what that'll offer customers is a is a much lower TCO from managing those huge petabytes scale environments and also accessing it at a relatively quick speed. So I think that's that's a piece. It's interesting that the other part that's very interesting to me, Mr Cognitive Computing face. So I was at the no SQL event last week last month in in San Jose, and with that they had a cognitive computing component on DH. I think thie idea of trying to get machines to think more like people building neuro morphing chips to two. It's kind of mimic the way synapses or electricity, electricity in the brain, you know, works how neurons fire and so forth is very interesting. And I think once you Khun Get Dupe is the repository. You've got the data there. But how do you make use of it? And I think that's the challenge. That's going to be, well, paramount the next few years. Exciting days ahead. Well, Gary, thanks for taking a few minutes. We're at the fifth birthday party at the Cube. Were at Big Data and nice jefe. Rick, we're on the ground. Thanks for watching.