Data Science, AI & Community | Gabriela de Queiroz

Luke: In this episode of In the Open with Luke and Joe, we are bringing you a conversation with Gabriela de Queiroz. Gabriela is the chief data scientist for AI strategy and innovation at IBM, and she is active in a variety of open source and community organizations. Before we welcome Gabriela, let's say hello to my co- host Joseppe.

Joe Sepi: Hey, Luke. How are you, my friend? How's the weather over there?

Luke: I'm doing great, Joe. Thank you for asking. And the weather here is beautiful. And I must say, I thought August was supposed to be this hot, sweltering month. And somehow it feels like June was hotter than August.

Joe Sepi: Yeah. Yeah. It's been a little bit cooler and nice. It's funny, I look at your background and I just have no clue what the weather is or what's going on over there. But I've got windows, I've got sunshine. It's a beautiful day. Yeah. Happy to be alive.

Luke: Can't argue with that.

Joe Sepi: Yeah.

Luke: Without further ado, let's welcome our guest, Gabriela.

Gabriela de Queiroz: Very good. Awesome. It's Friday.

Joe Sepi: Yes. Happy to be alive on a Friday and a sunny day. How's the weather where you're at?

Gabriela de Queiroz: Right now it's cloudy, but I heard that it's going to be very hot.

Joe Sepi: Okay, good luck. Yeah. You're out west, right? Yeah. Yeah.

Luke: Have you had to deal with any of the smoke issues? Has that been a problem?

Gabriela de Queiroz: Not yet.

Luke: Not yet.

Gabriela de Queiroz: And I'm just waiting and waiting because I know that it's coming, but I don't know when.

Joe Sepi: We've had a little bit of air quality issues out here, I think because of fires in Canada, if I'm not mistaken. Do you know, Luke?

Luke: I know it was a remarkable distance, thousands of miles. The smoke made its way to the East Coast and caused some air quality issues.

Joe Sepi: Crazy stuff. Anyway, Gabriela, why don't you tell us about yourself?

Gabriela de Queiroz: Absolutely, yes. My name is Gabriela de Queiroz and I'm a chief data scientist here at IBM California. And I'm working on the AI Strategy and Innovation. So I work on driving AI adoption across existing and potential customers. And I also lead the outreach strategy across our open source ecosystem and data science community. That's a new role that I just joined a few months ago. So before that, I was a program director working with open source data and AI technologies. And I was leading a team of developers who became the first IBM commuters on projects like TensorFlow, PyTorch, Apache Arrow, and others. And as Luke mentioned before, I'm passionate about making data science available to everybody. And I'm very actively involved in several organizations to force inclusive community. So I founded two organizations, which we have to talk... I know we are going to talk about this a little bit later. One is R- Ladies, and the other one is AI Inclusive. So it's a pleasure to be here with you all.

Luke: Thank you for joining us. It's fascinating how all of this... To me, it's like this Venn diagram of how it aligns, where it's the open source, the community work, but then it ties into these are the foundations of what are making it into enterprise- scale business.

Gabriela de Queiroz: Totally. They all come together. And throughout my career, since they didn't know exactly if it would make an impact in the future, it all now makes sense. And it's so interesting to see how things come together from previous things that you did, from current things that you are doing that's going to impact future things that you are going to be doing. So that's amazing.

Luke: I'd like that. And one of the things we try to talk a lot about on this show is the path of a developer and a career path. And it's interesting to hear that, where it's like, follow that passion, follow the interest. And sometimes you don't know what it means in the moment, but you find a way to make it useful later and to build on it.

Gabriela de Queiroz: Totally. Yeah. I joke that, throughout my career, there are a lot of pieces that was like, I don't know if I'm going to be successful. I don't know if I'm deviating from my career. But I want to do this. It's something that I'm passionate. I really enjoy doing this. And several years later, here I am using the skills that I developed 10, 15, 20 years ago. So let me give you an example. I was a music producer, so I was the one scheduling concerts, going to the newspapers, sharing about the new concerts for the bands. The whole pre- production, setting up everything that they needed, the whole equipment, and the post- production show as well, I used to do that. So there are a lot of communication, making sure that everything was working smoothly, making sure that I'm supporting my band and the team behind. So many of those things I use now as a manager, as a leader, for example. So it's just amazing too, something I did 20 years ago, I still use it. Before, I was like, why am I doing this? I'm totally doing something totally different from the normal career that I was aiming for.

Joe Sepi: Yeah. That's fascinating. And we didn't get into this in any of our prep stuff, so that's amazing. For me, it's really similar too. I've been playing in bands since I was young and organizing shows, and really trying to foster a community, and helping that community to grow and be successful. And it totally applies to the work that I do now in advocacy and just engagement. That's fascinating. And I was going to ask you too. You haven't been with IBM for very long. Folks at IBM run into other IBMers that have been there for decades, literally 30 years and such. So I was going to ask you, what were you doing before you came to IBM? It's interesting to hear about your music stuff. Is there something in- between, and how'd you get here?

Gabriela de Queiroz: Yeah, absolutely. So I've been with IBM for three years, not very long. And it's so interesting because, yes, we do see people that have been with IBM for so long. So, before IBM, I was working in different startups here in the Bay Area. So before joining IBM, for about six years, five years, I was working in different startups, doing more data science, hands- on work, and then leading a team, building a team. And before that, I was back in Brazil where I am originally from. Back in Brazil, going back the years, I was working inside a university as a researcher. So I was working with research on air pollution and how the air pollution affects people's health. So I was using statistics applied to public health. So I was there for several years within the research environment. Before that, that's when I mentioned I was working with music for about five years with different singers, different bands. I don't play instruments even though I do love instruments. I know a little bit of harmonica. I do have five different ones, but I don't know the theory behind music. I don't know much. One day I'll go back and learn more, but I do love a harmonica. And even before that, I was a private tutor. As soon as I finished, or I was in the last year of my high school years back in Brazil, I started becoming or being a private tutor. So I would teach students math, chemistry, and physics. And I did that also for several years. It's one of the things that I'm most passionate about, which started back in the days as a way for me to share what I know, to teach others, to share my knowledge and so on.

Joe Sepi: Yeah. It's really interesting how all of that stuff ties together. I find, looking back on my trajectory, lots of similar things that converge and lead to where you are now. It's really interesting.

Luke: I can't believe that we didn't pre- plan talking about air quality and that it just randomly came up in the intro. And it turns out you were an air quality researcher.

Joe Sepi: Cool. I'm curious about the open source stuff that you have done and are doing currently. Tell me more about your open source work.

Gabriela de Queiroz: Yeah. So I've been involved with open source in different ways. For several years, through R- Ladies, I've been involved more on the community side. So let me go back a bit. R is a programming language that is used by data scientists, data analysts. Anyone dealing with data probably knows what R is. It's similar to Python. A lot of people use Python, R, both. I started using R when I was doing research back in the days, the air pollution research. And I was more like a consumer. I was using the language, not much contributing back to the language, but I was already involved with the open source language. And then in 2012 is when I founded R- Ladies, and then I got more into the open source, which is interesting because people think that contributing to open source means contributing code, which is not the only way for you to contribute. There are several ways around that you can do, things that you can do that is very important as a way to contribute to open source. So, community, for example, is one of them. Going back to the open source work, then I moved to IBM. When I joined IBM, I was the open source group, the Open Hack open source group. So the team and myself, we had two fields. We were either creating open source projects and making it available to the community or we were contributing to existing open source projects that were created by different organizations, different companies, or foundations. But we had these two paths that we did in the open source. And yes, since we are on this topic, let me talk about two projects because those two projects that we created, and we talked about this before, is the Model Asset eXchange and the Data Asset eXchange. So one of the goals of our organization was to democratize AI. We know that AI, deep learning, it's so complicated, especially if you are a beginner, especially if you have no idea how things work in the backend. So then one of our goals was to make deep learning more accessible for people. For the Model Asset eXchange, for example, we would get some state- of- the- art deep- learning models, and we would wrap in a Docker container using a framework. So the only thing that you needed to use or consume your deep- learning model was to install Docker and get it running. So we would surface APIs that you could use in your application and consume. And on top of that, the thing that we are always thinking about is every time we create our open source project, we have to think about usability. We have to think about who is going to be using these projects. So we are not only delivering projects, but we are thinking about the usability, the documentation, examples, and also going to conferences and talking about the projects through talks, tutorials, workshops, etc.

Joe Sepi: Yeah, that's really interesting. And I'm curious how it's going. I feel like this space is still young in terms of the broader audience, consumers. I know data and AI has been happening for a while, of course, but I think in terms of the general public, general tech public, it still feels fairly new. How does it seem like it's going, the open source work that's been happening?

Gabriela de Queiroz: I would say it's going full motion, not only from the IBM side but of the whole community. I still think we have a gap in the open source, especially the data and AI side, because we don't have enough people working on it, for example. In general, open source projects, we see developers being burned out because they have been working so heavily on this side project. Usually, it's a side project. So there's a huge gap of contribution, people contributing back, and some investment that also companies are doing to their open source project, which is something that we should talk about. Because, for example, at IBM, and not a lot of people know, we have so many developers that they are getting paid to work full time in open source projects. That's their work. They are giving back to the community through contributions, through code, through documentation, through even the community as I was mentioning before.

Joe Sepi: Yeah. That's interesting. I take every opportunity I can to talk about that. IBM dedicates a lot of resources and money and people to the work in open source. Yeah. Yeah. We see this as well in the space that I operate in, the JS, JavaScript space, and OpenJS Foundation, trying to get other companies to be able to dedicate not just people but money and resources and attention to the work that we need to do. One area in particular that we're talking about is security. We've got security fixes that need to get done right away, and we can't just wait till some developer has the time on the weekend or something to contribute. So that's something we're focused on. I don't know if you're seeing anything similar where you're at.

Gabriela de Queiroz: We see that there is sometimes a gap. And sometimes the other piece is, which is interesting, a lot of products in general, not only IBM products, they use open source in the backend. And then there are some features that they need or some bugs that they encounter that they need to go back to the open source project and say, " Hey, there is a bug here" or, " We really need this feature." So then what do you do if you don't have enough people working on the open source project? It's delaying the products or it's creating headaches for the product owners because they cannot move or go beyond the bug because there is no one working on this, and you depend or rely on open source projects. So it's such a huge deal because we all are consumers and users. Everybody uses the open source in some way or the other.

Joe Sepi: Yeah. Whether you know it or not. I'm in a bunch of meetings, internal and external. And thinking about the internal ones, we're often just meeting regularly to just talk about these things like what's going on, what should we be concerned about? Are there areas that we need to spend a little time or add some extra people to try and cover? Is that project that we've been concerned about moving into open governance or do we have something to worry about there? We're always trying to be prepared and ready to do what we need to do because we do build a lot of our products and platforms on this open source.

Gabriela de Queiroz: Yeah. No, absolutely. And talking about open governance, it's another interesting topic. And I'm going to mention something that we were working on, the whole inaudible the AI phase that we as IBMers, we work very heavily on. There were three projects, actually, four projects that we donated last year to the Linux Foundation. So now it's under the open governance. There are four main projects. One is the AIF360, so AI Fairness 360, which is a set of tools for you to mitigate bias in your machine learning pipeline. The other one is about explainability. And if you want to go to the URL, you just change AIF to AIX. So it's the same URL. So it's about explainability. So that's the second project. The third project is around FactSheet. It's another project that we donated as well. The fourth project is ART, Adversarial Robustness Toolkit. I didn't have all these URLs ready. Poor Luke is trying to find all the URLs that I'm saying now, but we can share on the channel later. And the other one is ART. So all of them now, it's under the Linux Foundation. And there are other projects that we created inside IBM that we donated to the Linux Foundation, for example. There are many other examples that probably Joe knows about that IBM also donated to a foundation or open governance.

Joe Sepi: Yeah. There are so many, whether it's the Apache Software Foundation or the Linux Foundation or what have you, Eclipse. So much work that we do on a regular basis. And I do find that open governance is something we really should be talking more about. I've done a lot of talks in the past about just contributing to open source and getting involved and stuff, but I've been talking more and more about open governance and why that's important and why that's a good place to get involved and certainly be aware of and get involved overall. Speaking of talks, I know one of the things we talked about, you give a lot of talks, huh, Gabriela?

Gabriela de Queiroz: I do. And I love it. I say it's a way for me to increase my resilience. It's the way that I get energized, especially if there is a week where, oh my god, I'm so tired, I'm so tired. Then there is the talk and then everything shifts. So, for me, it's a way to getting back this energy. And it's just something that I really like doing it. And I also say that, luckily, IBM is very supportive. And one of the things that draw me to join IBM is the support for you to being out there to talk, to travel, to share your knowledge. And I do that very constantly. Just to give you an example, yesterday I gave a keynote on this big conference called Bioconductor. And then today I'm here talking to you. Go find a way to find the space in my agenda to go and give a talk. And last year, because everything was online, it was easier for me to be on the stage more often because I didn't have to travel. Because traveling can be a little bit tricky, especially if you have a family and you have to be on a plane and away from the family. So tricky. But then, because everything is online right now, I've been trying to do as much as I can to take advantage of the online environment. The other thing is now I'm able to reach and talk to people that I would never have this opportunity before because I would not either have time to travel to that place or because it was just impossible for me to be there. So now I'm able to connect with different communities because we are doing all these online conferences, meetups, and events.

Luke: That's really interesting. And hearing a lot of the events that are starting to be planned in person now are going to be hybrid events. So it seems like this part of what we've been doing during the pandemia is going to stick for a while at least. And you're right, it's great because it democratizes it because not everybody can afford or has the time to go to the conference, but they should be able to access that information.

Gabriela de Queiroz: Absolutely. And I love that, the whole democratization. I do love that because now everybody can go and watch. But I have to say I do miss in- person events. I do miss being there, feeling the energy of people live, going to stage live. I do miss that piece.

Joe Sepi: Yeah, absolutely. And I think we spend so much time on our computers, when you have an event on your computer, you're like, oh, all right, I might go make a sandwich too. I don't know.

Gabriela de Queiroz: Do you know what I've been doing lately? Well, a while now. Some of the presentations that I do online, I have a standup desk, so I do talk standing up because it emulates that I'm on stage, which I can walk a little bit more. I can have more gestures. I really talk with my hands, but I can have more gestures, more body movements. So I do try to be standing up to emulate the same energy that we do when we are in person.

Joe Sepi: Yeah. That's smart. I wish I had a standing desk. I would do that as well. And I was just thinking too, to tie a few of the things together that we're talking about, IBM and the work that they do in open source, and also thinking about research and inventions and things like that. And then also just people being at IBM a long time. I knew this guy, Ray, who I had just met at a coffee shop, and turns out he worked for IBM for 40 years. And he invented the ECG, the electrocardiogram, the original one in the'70s.

Gabriela de Queiroz: Oh, wow.

Joe Sepi: He did it for IBM, invented it there, and then traveled the world talking to people about it and sharing the work there. It's really fascinating. IBM has been doing this for a long time. What we're doing today, it's a long time at IBM.

Gabriela de Queiroz: I didn't know that, so today I learned.

Luke: I have a thought about this too. Doing the podcast and being an advocate, I've gotten to meet so many people across IBM. And I actually just released on the IBM Developer Podcast channel this week an interview with Kevin Roche, who is a 37 or 38- year IBMer. He's a physicist. He designs all of the apparatus and the automation out at Almaden, which is the San Jose research facility. And he took me on... I went on a tour right before pandemic hit. It is like you're in a spaceship. It's buzzing and popping and all of this stuff. And I had no idea, but IBM is the innovator in spintronics, which is, essentially, 3D printing of individual layers of atoms inside these big vacuum chambers. It's not semiconductors. It's metals and insulators. And then it moves through this factory inside of, essentially, a space vacuum. And then it prints individual atoms. And then because they're individual atoms, they interact on a quantum level. This is how IBM revolutionized the hard drive in the 1990s. They're able to, essentially, print microelectronics at that level in that same laboratory. If you've ever seen A Boy and His Atom, it's a film made with actual atoms. It's a stop- motion animation made with atoms. They did that as well. IBM invented the electron tunneling microscope. They do all kinds of pure research that normally only happens at the university level. But the difference is now we're able to fund that... what do they call it? White- space research. And maybe we sit on it for a while, but it's much easier for us to take it to product than it is for it to happen in the research university and take it to product. We really stand alone in pure scientific research that makes itself to products. There's almost no companies in the world, there may be very few that do the kind of research we do.

Gabriela de Queiroz: No, absolutely. It amazed me every day, it still amazes me every day, all the innovation that we are doing inside IBM. Every day there is something like this, you were saying, Luke, or what Joe was saying. There is always something, like a story or something that someone invented inside IBM. Or someone comes to me and says, " Oh, this was invented by IBM." I'm like, " Really? I had no idea." There is always something. I would say that we inaudible in different aspects every day, different pieces. We are always doing innovative stuff.

Joe Sepi: Yeah, for sure. This was just before the pandemic too. I was at the research center with one of our other colleagues. He's like, " Oh, I want to introduce you to this guy. He wrote the first garbage collector for whatever platform." I was like, oh, that's fascinating. You just walk around and meet folks at IBM who have done interesting things. Everybody's got a story. It's really fascinating. This might be a good spot, Luke, to take a moment and do some plugging of some of the other things that we were discussing earlier.

Luke: Absolutely. I had to unmute myself. I was about to speak and then I caught it. Thank you for tuning in to In the Open. You can always find our latest shows as well as all the past episodes on ibm. biz In the Open. So we're a livestream show. We do it twice a month, but all of it also gets published as a podcast. But all of our podcasts are actually here, developer. ibm. com/ podcasts. So we just launched a new one this week I want to tell you about. Z application platform talks. So this is what's running on mainframes, not the mainframe itself and not the applications, but the platforms that you could run applications on mainframes. And if you're not familiar with this, it's a fascinating space. Mainframes are still incredibly vital to the world. This is another one of those rabbit holes we could go down. Mainframes are just remarkable pieces of machinery. Some of them have been running for decades, literally decades. They can hot- swap, update. If you're into anything, if you like cars, you'll like mainframes. If you like music gear, you'll like mainframes. It's fascinating stuff. So yeah, you can check out all of our podcasts on IBM Developer Podcasts. And I should also mention we're about to launch a data and AI podcast with Gabriela. So you're going to be hearing a lot more from her on our channels, and that will be coming up in a few weeks. I will post some information about when that will be on our site. But yeah, please tune in. And as always, developer. ibm.com. We have all kinds of content there. We've got blogs. We've got videos. We have code patterns. We have learning paths. There's all sorts of wonderful content there. So please check that out.

Joe Sepi: And I'll just say too real quickly. On this show, we just lined up a few more guests that I'm pretty excited about. I don't know if we want to share any of that, but yeah. What's next? I think James Snell is going to talk to us, who actually used to work at IBM. He went to NearForm. And then I moved from a role I was in to the team that he had left. I'm excited to talk with him about the work he's doing at Linux Foundation Health, another kind of org under the LF. And they do COVID tracking and passport stuff. The work that they're doing has been adopted by a number of countries in the EU and such. We're going to talk with Robin Ginn, the executive director of the OpenJS Foundation. I'm really excited to talk with her. And she's, I think, going to have some stuff to announce and talk about there. And who else? There's somebody else that we had booked recently as well.

Luke: Parul Singh. She is a lead developer at Red Hat and does a lot with innovation. And I've met her through some of the stuff that's happening around quantum and getting quantum onto the cloud. She's going to be a fascinating guest.

Joe Sepi: Yes. We got some good stuff coming up. Great. Now, back to our regularly scheduled program.

Gabriela de Queiroz: Let me interrupt you here because we are talking about innovation. And the work that I'm doing now is around innovation as well. Well, not a different type of innovation, but we were talking about IBM innovation. So one of the things that I feel and I notice is we have a lot of innovation inside IBM coming from different parts of IBM, from research, from systems, from design, from open source, from anywhere. And I still think that we are not doing as good as we should in terms of facing those innovations to the general public; customers, clients, developers, to data scientists, anyone pretty much. So what my team is doing now, we are getting all the innovation inside IBM right now coming from research, and we are surfacing those innovations to life again to the general community. So, pretty soon we are going to be seeing things coming out of inaudible where we are going to be facing this type of innovation. It could be through experience, it can be through notebooks, APIs, etc.

Joe Sepi: I'm curious, how are you going to surface them? If you can get into it, what's your plan to raise awareness of that sort of work?

Gabriela de Queiroz: I can't say yet. inaudible. It's coming, but I cannot say much.

Joe Sepi: That's exciting. Are you focusing on your particular space like data and AI or going beyond that as well?

Gabriela de Queiroz: Yes. I'm focusing, at first, in two pillars. So it's around language, anything around language, NLP. And the other pillar is inaudible the AI piece that we have. So those are the two main pillars that I'm focusing in right now.

Joe Sepi: Is there anything that you can talk about that you might focus on first or in terms of particular innovations? I'm trying to suss anything out of you.

Gabriela de Queiroz: No, I can't. I can't. Hopefully, we'll see something by the end of the year.

Joe Sepi: Consider us a resource. We'd be happy to talk more about it as things develop.

Luke: Yeah. Obviously, we'll put some of this onto the new data and AI podcast, but I'd like to maybe talk around it a little bit, not the specifics, but what you had just said. And I think advocacy does a great job or has a great platform for being able to talk about these things that it's not really a marketing or an advertising message. So much of what we do is confidential because it's client- based. And then after it's done, we can maybe tell a success story from the client. And maybe open source is why it's really a sweet spot where we can start to talk about these things and promote it. And I would say, with AI, we were mentioning it earlier, there's the hype cycles that we've all seen from Gartner reports, where you get this peak of inflated expectations and then the trough of disillusionment, and then it comes into the plateau of productivity. And I would say, with AI especially, I think we're in that trough of disillusionment now where there's a little bit of cynicism because there is a lot of hype and over- promising, and it's made to be some sort of magic, when in reality, we see that it's not easy, it's hard work. You've got to not only do the work to do the thing, but you got to make sure that fairness and biases is considered, and who are your users, and what is the data. So it's not an easy thing. And what's so crazy, a message I want to get across about IBM is that because we're functioning at such enterprise scale, we have so much data. We have these real problems of our own as well as with clients that we're solving. So that's where it's hard to tell that story, and I'm excited for you to do it. But we are the real deal when it comes to implementing the real AI, not the hype AI. I'm obviously a company man and a fan, so that's why I'm saying this. But it's fascinating that we're going to get to slice and see that because I know that it's the truth and it's like, how do we tell that story?

Gabriela de Queiroz: Absolutely, yes, you got it. Absolutely. And we should leverage also the developer advocates should be helping us facing that. And I see also there is a gap, especially if you talk about research, there is a gap between the things that come out of research, which is usually papers. It's very academic. And then you have product. It's something that it's like scale, you can deploy, you can use in production. But there is a gap here in- between, which is where I see the open source and the other things come in, in helping facing the in- between. And also, not everything that's coming out of research is going to be added to the product. A good number is going to be added to the product. But a lot of things are going to be here in research. And we should be seeing that, we should be facing that, because that is very interesting in value as well.

Joe Sepi: Yeah, absolutely. I couldn't agree more. And I think Luke and I have talked about doing more work around this to try and surface research and innovation and also the historical stuff. We've been around for a hundred years, a little more than that. There's so much there we could be talking about and diving into. I'd love it.

Gabriela de Queiroz: Exactly. And we are the one that does the most amount of patents every year. So the innovation is there.

Joe Sepi: Yeah. I think this is a virtue as well in that we don't toot our own horn a lot, but we should toot it a little bit more than we are, yes, which is a good thing, but we should definitely let people know what we're doing. But I guess that's part of what we do, right? I'm curious. I know you were talking a little bit about stuff that you can't talk about, but what other things are you excited about? And where do you see things developing and growing and flourishing in the space that you're working?

Gabriela de Queiroz: Let me first talk about something that we are doing. And it was a project that I inherited, and now it's happening, which is not going to be related to the things in a way that I'll be doing from now on, but it's something I'm excited about, which is this hackathon that we are doing with the University of Florida. The registration is open, it's free. And you have access to mentors, to cloud and AI mentors. You have access to domain experts. And the whole hackathon is going to be around climate change. And it's not going to be a one- weekend hackathon. It's going to be something that you can develop throughout several weeks. So the deadline to submit your project is end of October. And there is a 100, 000 prize pool. So we have several prizes in different categories. And also, you have access to IBM technologies. Every time you register to the hackathon, you get$ 200 cloud credit. You use IBM products, data and AI products like Watson Studio, Watson Discovery, Watson Assistant, and so on. So I'm very excited about this hackathon that we are doing that is live. So if anyone that is listening to us is interested in joining us, go to the website. And think about this. Also, the way that I think about... and I get asked all the time, especially by data scientists, is, " Great, I want to move to data science or I don't have an experience in data science. And every time I apply to a job, people ask for experience, but I don't have it." And I'm like, " Okay, so why don't you join this hackathon," for example? And from that hackathon, you can use the projects, the things that you are creating, as a way to showcase your knowledge, your skills. So you can get the project that you are going to develop through doing this hackathon and you can put it on your GitHub profile and create a data science portfolio. So that's a good way for people to create a portfolio. So that's the thing that we are doing right now.

Joe Sepi: That's exciting. I'll also add too that I've found, and I encourage people to do these sorts of things, is that you get an opportunity to network too. You may pair up with other people. You may interact with other people. Certainly, in person, it's a little bit easier, but it's a great opportunity to network. I don't know, Luke, we used to do events and hackathons in the city. And his name is escaping me now, but there was one guy who was at a lot of the events, super nice guy. And, lo and behold, I see he enters the Call for Code and ends up winning Project OWL, I think. Was that the one?

Gabriela de Queiroz: It could be OWL.

Joe Sepi: Yeah. Yeah. I was like, " Hey, we used to have him at our events." Super nice guy, really smart. Look at the work that he's doing now. It's being successful. So yeah, it's a great place to network and just really get some experience and build some stuff. And that's important.

Gabriela de Queiroz: Totally. And you have access to mentors. You are going to have mentors throughout the time helping you not only on the project creation but also answering any question that you have. The community around... Again, going back to the community, once you register to the hackathon, you have access to the whole community so you can ask questions. So it's a lot of things that you get out of this, which is free. The price is a huge deal. All the other things around are as important as the price, I would say.

Joe Sepi: Yeah. And mentoring isn't just code. It's thinking through your thoughts and how will this play out, and how will people use it, all the other things that go along with building something, for sure.

Gabriela de Queiroz: Yeah, absolutely. I'm very excited to see what's coming out of this.

Luke: I would just echo and reaffirm everything you both said and also say that it really is, and we saw this in New York City, something that appeals to everybody, from the student to the senior folks. I had folks I remember would come that were running their own companies. And they're like, " I come to these to stay fresh, to find out what's going on, to stay current, to even scope for talent at the events." It's a beautiful community event that, you're right, it creates this silo where it's like, hey, this is a little sprint we're going to do here. And if you're newer side of your career or a student, it's a great way to get that real experience, figure out how to work with folks. And if you have something to offer and give, now you can plug in as a mentor. It really is. I'm a huge fan. I echo everything I just heard.

Gabriela de Queiroz: Totally. And as a hiring manager myself, I would be looking around what people are doing. There is a lot of eyes during this hackathon, so it's a great place for you to be in.

Joe Sepi: And I'm going to put this up here too just to encourage folks. This person is only 12 years old and building stuff. So yeah, keep at it, get into some of the hackathons, find some mentors, and have fun and build stuff.

Gabriela de Queiroz: That's amazing. That's amazing to be that early around technology.

Joe Sepi: I know. I wish I had started that early. I love it.

Gabriela de Queiroz: Same.

Joe Sepi: I love it. Very cool.

Luke: You were interested in technology, Joe. It was just the guitar. It was maybe hundreds or thousands of years old. I don't know.

Joe Sepi: Yeah. We don't have to get into my story, but I played music I'll say briefly and just was always like, oh, music will provide a career for me. And what ended up happening is our drummer got a job in tech and brought a laptop home. And I was like, oh, what's this computer thing? Oh, how does this work? And then that's how music actually did provide a path for me into technology. So it's interesting.

Gabriela de Queiroz: Yeah. I have to say I'm very late to the technology. I didn't grow up with computers. I didn't program until I was probably early 30s, in my 30s. Yes. 29.

Joe Sepi: Yeah. I'm similar. I was maybe mid to late 20s. I even took a class in college and was like, " Meh, whatever." I think the professor was really boring. And I was like, " Meh." But it's interesting to hear about this and I think to share with people too that there are a lot of folks I know who are thinking about switching careers and getting into tech. You can do it at any age. And I think going to these sort of hackathons and just building stuff, taking some classes perhaps, or just... I did a lot of stuff self- taught. If you find a path, it can be accessible and can be rewarding. And I love the detective aspect of it. If something's not working, you're like, okay, I got to figure this out. Let's start simple and break it down and figure out where the problem lies. It's fun.

Gabriela de Queiroz: Yeah, absolutely. I would say it's never too late to do what you want.

Joe Sepi: Yeah.

Luke: I also would say we had a question about where the data and AI podcast will be. And I shared this in the chat, but we did a season one last year. And now we're going to relaunch season two. And you'll be able to find it here on the IBM Developer Podcast site.

Joe Sepi: Cool. We have maybe another 10 minutes or so. What else do you want to talk about, Gabriela? I want to make sure we get all of your stuff in here.

Gabriela de Queiroz: AI Inclusive. Because it's something that I've been invested since 2019. It is still on my day- to- day life. AI Inclusive is another organization that I created. I created this one in 2019, where the mission is to increase the representation and participation of minority groups in AI. Why did I decide to create another organization given that R- Ladies was up and running very successfully? Let me go back in time a bit. Since 2018, I was involved with AI technologies. And one of the things that I noticed is there is a lot of discussions around discrimination, bias, all the implications of the algorithms. And I noticed that the conversation was very centered in the US and China, for example. And then we were seeing all these problems, discrimination, especially with underrepresented minorities. And I'm like, this is going to affect not the people in the US only, but the people in those other countries that they are not aware of what is happening. So I was in this state where I need to do something. So then I'm like, why don't I create something similar to R- Ladies where I can go and create a community where we can now share and learn about... First, create awareness of the implications of AI so people is aware. Because for them, the majority of people, they thought that AI was something very futuristic that was not around their lives, which it is indeed. It is around your life every day, all the time, even though you don't notice. So the first piece is around bringing awareness about the problems. And then the second piece is we know that the teams behind AI, the teams that are creating all these algorithms, they are not diverse at all. So how can we change the landscape of the teams? How can we change the landscape of AI? We need to include more people, more diverse people into these teams. Okay. So let's empower those communities, those people to enter into the AI field. And how can we do that? Opportunities, education, for example. So, AI Inclusive. So we have these two pieces, so awareness and empowerment, knowledge, opportunities. In the past two and a half years, we've been focusing on providing scholarships to the community. We now have around 11, 000 members. And we have been partnering with different boot camps, schools to offer scholarships to the community. So we awarded already 1, 000 members with different scholarships from online boot camps to membership for online classes and so on. So if anyone is interested in joining the community, here's a URL. We also do a lot of events. We have events every month where we talk about AI technologies, where we talk about ethics in AI, where we talk about how to get into AI, how to get into data science. And then we have the whole social media piece where we share also knowledge, we share about what is happening in the world, what is coming up for us in terms of scholarships.

Joe Sepi: That's really amazing and awesome. And thank you for doing it. It really is something that we need to be focused on and working on. Like you said, so many people in the space are less diverse, I guess you'd say. And whether they mean it or not, there's implicit bias that comes along with that. It's not necessarily that people are bad actors always, but you just need more diverse voices at the table to get more diverse output. So I'm curious, if folks are interested in learning more and getting more involved, what would that look like? Whether they are interested in just attending and being involved. And what would it mean for someone who wants to do more? And maybe leadership isn't the right word, but if they wanted to be more active, like a chapter or what have you. Tell me more about how folks can get involved.

Gabriela de Queiroz: We are mentioning all this is volunteer- based, so we don't get paid. And it's a lot of work. With R- Ladies and now with AI Inclusive, I would say that I would invest, every week, 10, 20 hours of my time on creating the community. So it's a huge deal. And we need volunteers to help us keep the organization running. So if anyone is interested on the more global level, just get in touch with us through email or you can send me a message. If they want to be more on the city, chapter level, they can also send us an email. And because of the pandemic, inaudible that on hold because right now we are not meeting in person. But once we go back, then the chapters are going to be crucial because the only way for you, and from my experience with R- Ladies, the only way that you can change people's lives in certain communities is if you have local representatives, people in the community. Because the only way that we can get to that specific community, very small maybe, or somewhere that we can... The internet maybe is not stable or they don't have computers. The only way is through the local communities. We now have four chapters for AI Inclusive. Once the pandemic is over, we are going to be accelerating this process of creating chapters and providing support and being there for the community.

Joe Sepi: Yeah, that's great. I would encourage anyone who has an interest in this to definitely reach out. Like you said, think about globally but also be planning for locally because I think, in time, hopefully, we can spend time together again. Wouldn't that be great?

Gabriela de Queiroz: Yeah. And it's so interesting. R- Ladies, we heard several stories on how the community was impactful for their careers, for their lives. So there are so many stories on chapter, city- based community was essential for them. Because some places, and I have a few examples, even back in Brazil, we don't have computers, for example. So how do you learn how to program? How can you get into AI if you don't have computers, if you don't have internet? Some places, we are lacking the basics. So how do you change that? So it's a struggle. But if you have this community where, let's say, we have one computer, people can share. There were a few events that I remember, somewhere, I think it was Indonesia, where someone was, on the whiteboards or the blackboards, writing a programming language, but they didn't have a computer yet. They all had cellphones but not a computer. So that was very interesting.

Joe Sepi: Yeah. Wow. That's a good segue too. We have a question here about getting into AI and whether you need more technology or other skills. Maybe you can talk a little bit about that, Gabriela.

Gabriela de Queiroz: Yeah. I'm a huge advocate of other skills, data and technical skills, because we do talk a lot about you need to know this. And especially AI, if you look at the job description, they are going to ask for so many technologies. And how do you highlight yourself, how do you differentiate yourself from the crowd? And I would say that, sure, technology, the tech skills are good, but there are other skills. But people sometimes say the soft skills, which I call essential skills. They are as important as the tech knowledge. The other piece that I want to mention is, again, we have the whole hype around AI, and there are a lot of buzzwords and people talking about deep learning and computer vision. But if you go back to the companies, majority of the companies, they are not there yet. They are still doing... Or the bulk of their work as data scientists or AI developers, the bulk of their work is around querying the data, getting the data, acquiring the data, cleaning the data, make sure the data is in a form that you can use. And one of the challenges that we have seen in, I would say, the new frontier on AI... Because right now we need to load tons of data to create a deep- learning model. So the big challenge right now that we have to... The next big thing is can we create a model using last data? So anyway, all that to say that technical skills are good, but don't forget the other piece, the soft, essential skills that are as good as the tech skill.

Joe Sepi: I like that you call it essential skills. I'm going to borrow that.

Gabriela de Queiroz: Yeah. I don't like the soft because, for me, they are not soft. They are hardcore.

Joe Sepi: Yeah. There's an old saying that computers are easy, people are hard. That's where the soft skills are.

Gabriela de Queiroz: Totally. Being a manager, sure, I love it, but it's one of the hardest jobs in the world because you are dealing with people.

Joe Sepi: Yeah. And that's one of the things I talk about when I give talks about open source is that's a skill that you develop working in open source. You don't share a manager with this person that you can go talk to. You got to figure it out and learn to deal with people and move things forward with a variety of personalities. It's hard. It's not soft skills. It's hard skills.

Gabriela de Queiroz: Yeah, absolutely.

Joe Sepi: That's amazing.

Luke: So I think we're about out of time. This has been a fantastic conversation, though. And I'm looking forward to many more podcasts with you, Gabriela. This is going to be great launching this new data and AI podcast.

Gabriela de Queiroz: I can't wait.

Joe Sepi: That's exciting. Cool. I really enjoyed chatting with you, Gabriela. And let us know when you want to come back on. I know you'll have your own podcast, and that's cool. But if you ever want to come back on and talk about some exciting stuff, I'd love to dig more in.

Gabriela de Queiroz: Yeah, thank you very much. And I have to say that I'm a fan of you too, so let me do my fan moment here and say thank you. Thank you for all the work that you have been doing.

Joe Sepi: Thank you. That's very sweet. I appreciate it. Thank you.

Luke: Yeah. Much appreciated.

Joe Sepi: Yeah.

Gabriela de Queiroz: Awesome.

Joe Sepi: Cool. All right. Thanks, everyone, for tuning in. Thank you, Gabriela. Thank you, Luke. And I guess we'll call it a day, hey?

Gabriela de Queiroz: Awesome. All right. See you later.

Joe Sepi: Bye.