2B Bolder Podcast : Career Insights for the Next Generation of Women in Business & Tech

#122 Ria Cheruvu AI Architect, ML Engineer and Data Scientist, Industry Speaker, and Instructor

• Ria Cheruvu • Season 6 • Episode 122

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In episode #122, discover the inspiring journey of Ria Cheruvu, a prodigious AI architect at Intel, who challenges the status quo with her groundbreaking work from a young age. Ria's incredible story takes us through her accelerated academic achievements and dedication to security, privacy, and fairness in AI systems. We explore her passion for the convergence of neuroscience and cognitive computing and her advocacy for women in STEM, showcasing how she is shaping the future of technology with her innovative mindset.

Ria shares her inspiring journey as a young AI architect at Intel. She offers insights into her career path, the importance of mentorship, and the evolving landscape of AI. She encourages women in tech to overcome challenges, embrace growth, and leverage community support while exploring opportunities in this transformative field.

Here are some topics covered:

• Ria's journey from a high school prodigy to an AI architect at Intel
• The significance of mentorship and community in overcoming challenges
• Exploring AI's intersection with neuroscience and technology
• Ria's focus on security, privacy, and fairness in AI systems
• Encouragement for young women to pursue careers in STEM
• The necessity of communication, confidence, and rest as key skills
• Recommended resources for learning about AI
• The potential of AI to reshape career opportunities and ethical considerations

Tune in to gain a deeper understanding of building a career in AI, where both technical and non-technical skills are essential.   

AI resources for AI enthusiasts:

Ria’s Profile linkedin.com/in/ria-cheruvu-54348a173

Websites

 Leaders to follow

Ria’s courses 

Learn more about AnitaB.org 

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When you subscribe to the podcast, you are supporting our work's mission, allowing us to continue highlighting successful women in a variety of careers to inspire others helping pay our wonderful editor, Chris, and helping me in paying our hosting expenses. 🎉

Speaker 1:

Hi there, my name is Mary Kiloalea. Welcome to the To Be Bolder podcast providing career insights for the next generation of women in business and tech. To Be Bolder was created out of my love for technology and marketing, my desire to bring together like-minded women and my hope to be a great role model and source of inspiration for my two girls and other young women like you, encouraging you guys to show up and to be bolder and to know that anything you guys dream of it's totally possible. So sit back, relax and enjoy the conversation. Hi, thanks for tuning in.

Speaker 1:

Ai is one of the hottest technologies in the world today and a major driver of career growth and opportunity. Our guest today is a young woman with exceptional talent. Rhea Cheruvu is an AI architect at Intel, machine learning engineer, data scientist, industry speaker and an instructor. Rhea holds a master's degree in data science and a bachelor's in computer science from Harvard University. She's been featured in numerous publications on AI, holds multiple patents and has spoken at prestigious events such as the Women in Data Science Conference, tedx and other top industry events. Rhea is passionate about the importance of open source communities, women in STEM and contributing in disruptive technology spaces. Her area of expertise includes solutions for security and privacy in machine learning. Her area of expertise includes solutions for security and privacy in machine learning, fairness, explainable and responsible AI systems, uncertain AI, reinforcement learning and computational models of intelligence. In addition to her technical work, rhea is a published poet, children's book author and a neuroscience enthusiast. Rhea, thank you so much for being here.

Speaker 2:

It is such an honor to have you on the show. Thank you, Mary. I'm so excited to be here today and have a great conversation with you too.

Speaker 1:

Awesome. Okay, well, you have achieved so much at a young age, and so here's some facts. I don't know if our listeners know, but I read that you graduated from high school at 11, became the youngest ALB graduate in Harvard history and started working at Intel at 14. That is incredible. Can you share your journey and what it was like to land your job at 14 at Intel?

Speaker 2:

Oh, thank you so much, and definitely I think it was a fantastic journey that was filled with lots of great moments and helping hands too. So I graduated high school when I was 11, as just mentioned and you know it was basically a lot of the kind of planning, I'd say in the forethought that my mom put forward as part of my curriculum. She was my learning coach and there with me every step of the way and, you know, basically helped me design a journey that was accelerated, that was meaningful and that was also really kind of exciting, to kind of pursue challenges. And then, you know, after that, I enrolled as part of, you know, the computer science programs at my at the university and basically, you know, started there.

Speaker 2:

And after I had graduated with my undergraduate degree in computer science, I joined Intel as an intern and at that time one of my mentors helped onboard me into the process.

Speaker 2:

So I basically interviewed with three different teams that were all related to AI and I had the opportunity to, you know if gotten a go from all three teams which I'm grateful I did to basically select the team that I was interested in and I went ahead and selected a team on deep learning and architecture. And then again I'm so grateful to my mentor. He's the one who kind of completed all the onboarding forms and the documents and things like that to help me get my first internship at 14 at Intel right, which was super exciting. Right before that I actually had an academic internship at Yale at the Clinic of Neuroscience Imaging Center, so I had some experience with you know what it means to kind of be in that environment and, to you know, start having deliverables and pursuing them in the CS space. But working at Intel has definitely changed my career and my life for the best way. So that's a little bit about me and my journey.

Speaker 1:

That's incredible, and I don't know what the other options were, but for you to have had the foresight, to know that AI was going to explode and be so relevant, and what a great career path you chose early on, is amazing.

Speaker 2:

Thank you. Great career path you chose early on is amazing, thank you. And again, I think full credit goes to my parents and also mentors for that one, mary, because both of my parents are computer scientists by trade and have worked in a lot of different, really interesting roles. Right, my mother was really on the data analysis side and now she has, you know, her degree in philosophy and does a lot of work on that side. And then my dad is kind of more on the security side, so very specialized and embedded, and, you know, firmware, et cetera.

Speaker 2:

So one of the things that my mom and I were very interested in while I was growing up, and something she imbibed in me, is kind of an interest in neuroscience, which is a really fascinating still to this day, and that naturally kind of led to an interest in neuroscience, which is a really fascinating still to this day, and that naturally kind of led to an interest in AI.

Speaker 2:

Right, it's the perfect blend of disciplines between understanding neuroscience and then using AI as a way to kind of mimic neuroscience. So that's kind of how it started, and then it just kept bubbling over. And now, at this point in time, I've been at Intel for six years. I've been working in AI for about eight years, counting the time that I was working on projects during my degree and as part of my internship at Yale. I'm returning to a point in my career where I'm excited to get back potentially into neuroscience and psychology. So I think it's a really nice kind of roundup of using AI and tying it together with cognitive computing and neuroscience, and I really have to thank my parents, my mom especially and also my mentors, who were, you know, right at the get-go, were saying you know, ai is a booming field. This is the perfect place to kind of transform and explore challenges and solve them.

Speaker 1:

That's so fascinating. How have you navigated the challenges of being the youngest person in many business settings or academic settings as well?

Speaker 2:

Yeah, I mean, I think there's. I'm personally blessed to not have seen a lot of significant challenges or challenges at all when it comes to discrimination and things like that. I've never kind of experienced that at Intel, which I'm very grateful for, and also at Harvard too, so I think it was a pretty seamless experience. Everyone is always very welcoming, makes space to kind of explore ideas and you know, basically again, it's when I say challenge you know pushing back and forth on assumptions and being able to kind of push the envelope right and keep innovating on better things and, you know, creating better solutions. So I definitely say that very grateful for for that kind of pocket or envelope of, of having the opportunity to explore those things.

Speaker 2:

Of course there's general workplace hiccups that happen at any point in time and I think, again, you know I am kind of back to relying on my community and the folks that I can always reach out to and ask for advice and and help and you know, connect with to understand, hey, what is the next best step for me to take in my career, right? Or what can I do to overcome this particular hiccup or hurdle? Or maybe, you know, lack of interest, right? Or maybe a need to get challenged further, right? So I think that it's a community has been a really big aspect for me personally to get past that.

Speaker 1:

That's such a good thing to call out for the younger people entering the workforce. Many wait to build their community, but having it at the get-go adds so much clarity or guidance. I guess you could say AI covers so many areas. Tell us about your primary areas of focus and interest within AI. I know I mentioned a few in the intro, but why you chose those and what leads the passion there.

Speaker 2:

Yeah, so I think there's a couple of disciplines in AI that really speak to me personally. So I think it's changed over the years. When I first kind of started off my career path, mary, I think it was really focused on again that connection to neuroscience, right. So a lot of it was on statistical data analysis, like of fMRI images, and then the other part of it is something that I still kind of smile about when I think back about something called neural cryptography, which was a really interesting blend of using neural networks and security algorithms, and I hope that the field gets revived. It was a very niche field that you know. It's still only a little attention is given to it. But I'm sure as AI models popularity and complexity increases, we'll get back to using neural networks, for you know encryption protocols and other elements, and quantum computing is definitely going to accelerate that right. But that's kind of where it started.

Speaker 2:

In the middle of my journey I kind of, at Intel, got introduced the idea of the end-to-end stack, right.

Speaker 2:

How do AI models and in general, you know the things that we interface with work from the chip level all the way to? You know the front-end web interface level, like how voice assistants are working in smartphones or how our laptop and blur our background, right, and all of these really fun, interesting facets of the technology. And then I'd say after that, sorry, it's just, you know, a progression, but there's also kind of interesting technologies around, you know, cognitive computing and reinforcement learning, the idea that AI models can scale up and learn and, you know, get feedback from their environment. And that's led me to my current interest today around human-centered AI and AI that is capable of learning and adapting to different environments, right. So I'd say that's kind of been my progression of interests and areas of AI and that allows a lot of AI developers and programmers to touch all of these different areas from, you know, basic foundational neural networks and machine learning models and data analysis, all the way to these complex paradigms.

Speaker 1:

It's so fascinating. As an influencer and an advocate for AI. What do you hope to teach others most about this field, because you know there's so many things worrying people about AI today. What is it that you hope to, I guess, have an impact on the people that follow you?

Speaker 2:

the people that follow you. Yeah, I mean, I think there's two facets to that. The first one is that it's easy to get started and it is a great kind of opportunity and a field to get into, even though the hype may, you know, die down. I know now, all of a sudden, at least across LinkedIn and other social media networks, the new boom is starting to become quantum computing, with Google's announcement, right. So a little bit of the focus has shifted from AI to quantum and some hybrid things there.

Speaker 2:

But regardless of where the hype goes, I think that there's a lot of value in the AI technology space for just building really cool applications that are smart and intelligent and reactive. So there's an increasing need for young talent that is just willing to break assumptions about the technology and start to say, hey, what can I do with this? Right? And then I think the second aspect of that that I want to be more vocal about and, I think, be able to represent better as I grow in my career, is that community aspect as well.

Speaker 2:

Mary, you know that we discussed it earlier, right? I think it's just that general idea of you know what it takes to be a, a leader in tech, and what are some of the decisions you have to make, because you're going to get a lot of criticism from folks that you don't know for making decisions that you may know are right or be different than what a colleague is going to deliver, because it's customized or, ideally, it's customized to your interests and to your passion and problem statement, right? So I think that those are the two things that I'm continuously learning about, and I, you know, I always, almost every other day, I ask my mom for her advice on these, cause it's just so important as a young person in tech to figure out what's my next step, what do I do that can make a difference. So I would encourage folks of my generation and of all generations right, I think it's a really interesting set of questions to ask ourselves as we continue to grow in AI.

Speaker 1:

Well, I think the fact that you're asking yourself what difference can I make is by far the biggest thing that anyone can do. What advice do you have for women who are trying to find their voice and build confidence in their careers?

Speaker 2:

I'd say the number one thing to recognize is what it takes to be an expert. I think is not typically what we think it is. I've had a lot of conversations with women and colleagues in the space right, who wanted to learn, for example, data science and data analysis, which is a topic, a specialization I got my master's in. So, you know, when you get a degree, it's generally known that, hey, you know, you're an expert in this space. But I think that things are changing now, right, Regardless of certifications and degrees, right, internally, to build a confidence, to be able to speak about something you need to recognize when you're comfortable with calling yourself as an expert and it's not going to come at the standards that society may put out, right, Because, again, you know to be an expert.

Speaker 2:

Let's say, in computer science, maybe you need to know a bunch of programming languages and be able to be a super efficient coder, right, you can manage all of these tasks right. But maybe that's not what an expert means in, again, your interest area, your problem field, or what it means to you, right? So I think identifying the boundary at which you believe that you, you know, have the expertise you need to communicate and to strongly represent yourself is really critical. It doesn't mean setting the bar lower for ourselves or too high so that we can never achieve it right, but it's that balance.

Speaker 1:

Yeah, no, I completely agree, and I think you know. One thing that I've I've heard from many women is just, you know, not having that fear to to raise your hand in a meeting and to speak up, even though you know you may not be that so-called expert, your view and why you're in that room matters. So to not be quiet and sit in the corner is, you know, one action that each woman could take.

Speaker 2:

Exactly. I completely agree with that, and I'd say that that action and the idea of implementing it makes you an expert in certain domains. Right, Because it means that not only do you understand a subject or a topic enough to be able to voice something about it or ask a question, but you're actually taking action and doing something about it, and I've noticed that you know, especially in the corporate world, and I would love to get your thoughts on this too that's kind of what it takes to be a leader, from my understanding, which is actually doing something about. You know a topic or an area, asking questions, and you know. That's how you start making a difference.

Speaker 1:

Absolutely no. I found my time in corporate that one when you work remote, be on camera so that people can see you and see that you're engaged and be engaged but then also to ask those questions and maybe you know you might know that answer partially, but for the people in the room that are less willing to ask those questions, I also took on that kind of advocacy role in my time. Absolutely, who have been and I think you touched on this earlier but beyond your parents who have been most influential in your life?

Speaker 2:

Yeah, I mean I can definitely reference so many different mentors and teachers and professors over the years. Steve, too, is the primary kind of mentor who onboarded me to Intel and helped me with my internship process, and at Intel I've had the pleasure of being mentored by, and having communications and networking with, so many brilliant leading women in this space. Lama Nachman, who leads Intel's responsible AI efforts, is one of the amazing women who's kind of a trailblazer in these efforts. Also Huma Abidi, who has now left Intel, but she and her team are always kind of have been a shining star during their time at Intel and also, you know, continue to be like there's a long lasting legacy of brilliance and technological innovation that I always look up to. And so many other fantastic colleagues.

Speaker 2:

Dr Hal Blumenfeld from Yale University, who kind of first, you know, helped me learn about the details of an internship right, as I mentioned earlier it still is, you know, plays a really key role in kind of inspiring me to to kind of keep shooting for interesting new ideas and although, you know, sometimes you're not that much in touch with certain folks just because of time right, I think they're always kind of part of your network or you're always able to reach out to them, which I'm so incredibly grateful for. So I'd definitely say these are some of the few folks that are kind of very, very close. There's also a lot of fantastic you know VPs at Intel as well that I've had the pleasure of networking with as part of conferences, right like Pallavi Mohanjan and others, and they're really fantastic women who are just always there, right and ready to reach out and, of course, brilliant leaders.

Speaker 1:

That's fantastic and I love that. You've had external influencers and mentors along the way, and I certainly don't want to discount your parents, because it starts with parenting. You know the positive influence, so what do you think they did right to nurture the love of learning in you?

Speaker 2:

I think I mean I've reflected on it a lot as I've been growing up. I think one of the fundamental things is you know, my parents themselves have a love for learning, so it was kind of like a monkey, see, monkey do, which is what I like to call it.

Speaker 2:

But it's just the general idea and I mean you know, again, I think that there's a lot of areas where I find myself stumbling to, where I kind of look up to, you know, my mom and dad and see how they react to things right, and how they get excited about, you know, learning new topics, new areas and getting past those. You know those problems or those roadblocks and always being excited to kind of explore the new things. I mean you know those problems or those roadblocks and always being excited to kind of explore the new things. I mean you know my mom and I talk about this sometimes and you know when she was reading books to me when I was little, like her excitement around, you know, flipping the pages and looking at the new things. That's kind of what I think got imprinted onto me.

Speaker 2:

So anytime I'm not feeling excited about something, I go and read it by her, honestly so and you know, I see, you know, am I, you know, having a little bit more of a lower vibe right towards it? Does that kind of match where I want to be right? Because you know there's not. There's always kind of an opportunity for us to maybe take a step back when there's an opportunity instead of taking a step forward, because we're not really sure if that's right for us or not. So I think, having her as a mentor or guide that you can rely on and say, hey, you know, is this exciting right? Do you see the value in this? Or, you know, do you see value in you know another area I think that that's been absolutely crucial for me personally.

Speaker 1:

AI has such a broad potential, how do you see it impacting career opportunities specifically, and what roles do you foresee being created or eliminated in the short or long term?

Speaker 2:

It's a great question. I think AI and the job market has so many interesting conversations and corollaries. On one end, there's the talent for CS students and, in general, any area that's intersecting. You know applications right and computers right. I know friends in aviation and in healthcare, et cetera, and all of their fields are kind of getting impacted by AI, either directly or indirectly, as part of, maybe, professors telling them to integrate it as part of their projects or being incorporated in courses. So I think skilling up for the AI revolution is an incredible idea and also very accessible with a lot of the resources sitting on the internet, a lot of the free courses and material, and then if you have the budget to pay right, the certifications, the degrees, the pathways, depending on the credentials and the jobs and the roles that you're reaching for, there's an immense opportunity on that front. On the other front, there's the general idea of disruption right with AI and the job market. The other front, there's the general idea of disruption right with AI and the job market, and I know it's a very hotly debated topic.

Speaker 2:

So, to put it kind of very lightly, I'm personally an advocate for you know, human-centered AI-related developments and algorithms and models right, this idea that you know, on one end, we're encouraging AI models and the innovation there, but we're also being very careful about what exactly is the role of human in being able to kind of participate right in the algorithm development or in the feedback of the algorithm, right and, you know, creating an environment, basically, where both parties are kind of being able to interchange and then provide inputs and outputs.

Speaker 2:

Again, it's, I think, an incredibly nuanced topic, from everything from like autonomous vehicles, where maybe you want your vehicle to do fully self-driving so you can focus on phone call, or, you know, managing, you know, family members in the back of the car, right, and again it depends on the perspective, right. Or if it's, you know, again, a hybrid right, where you don't want an AI model that's automatically screening your resume to miss it just because you missed a couple of keywords, right? So I think you know a lot of key nuances and I think that's where the human-centered AI technology definition really comes into play.

Speaker 1:

Are there AI tools that you would recommend people like, more non-technical people use, Like? I know there's ChatGPT, but do you recommend people start using those tools today to accelerate their brand? You know, getting like. I think that's one thing that I try to teach people because I use it within my own business, and so what are your thoughts on using ChatGPT to work on your personal brand, and what other tools would you recommend?

Speaker 2:

I mean, I definitely agree with it, and my response may have been a little bit different, personally, a couple months ago compared to now, but I've personally started to use tools like ChatchBT, claude, gemini I'm still just starting to use Gemini's as a little bit different but Copilot and others for personal brand development. The best thing I think about AI tools that are kind of writing oriented or text generation, if we want to use the right word, is that they can kind of communicate about your personal brand in a way that's very objective and fact-based that we may not be able to convey on paper, right? So, again, I'll take an example of my mom, because I love her and we always have these really great conversations, so I apologize if I keep reusing them, but she just started a small business where we live in Arizona, and one of the key things is to write your bio, right, you want it to be catchy, something small, right? And Chachi PT did a fantastic job of taking a list of accomplishments and summarizing it into something that's impactful, something she's confident about, right?

Speaker 2:

But it's kind of challenging to write on your own, especially when you don't know, maybe, how to articulate your work, because it happens, I think, to all of us. So I definitely recommend the use of these tools for branding. Of course there's other ones like text-to-speech right, if you want to just kind of say some things out loud and you want a model to kind of transcribe and take care of it for you. And even diagramming tools, right. Ai diagramming tools can be really helpful in just trying to put your vision on paper or like on a PowerPoint or something like that, to kind of get started with, you know, defining what you want to do. So I definitely recommend it.

Speaker 1:

Yeah, it's amazing that all the different tools out there I mean really, if you think it, it's probably out there in some type of app already, so it's just knowing what to look for and going to those tools and educating yourself. You probably look it up on YouTube and get a lesson. Yeah, absolutely Go ahead.

Speaker 2:

please, no, go ahead, go ahead, please, no, go ahead, I'd say.

Speaker 2:

The one tip that I've learned regarding finding the right tools though because again, there's a lot of hype out there, a lot of tools that kind of get shoved in our face sometimes is whatever is kind of generally more popular, I'd say, within the community, is definitely something to go for first right.

Speaker 2:

For example, I think that you know these text generation tools like Plot and ChatGPT are very popular, and that's where we kind of see the most value. There are these smaller applications, just as you mentioned, mary right, for you know, again, like I mentioned, text-to-speech transcription or diagramming, but there's so many different applications that have popped up there. So I always go first towards the ones that are more popular and once I've kind of examined them and understood the way that they work further, then I kind of dive into ones that are, you know, a little bit less used, less popular, maybe don't have that good of a you know, videos or documentation or guidance around them, because you kind of ease yourself into the entry of what the tool looks like and what to expect. So you're not settling for less right, you get the expectations and the quality of the tool that you want to work with. So that would be my recommendation for non-technical folks getting started with AI tools.

Speaker 1:

That's awesome because I think there's so many people out there that are looking and needing and wanting to pivot in their career, so knowing where they can start to embrace the technology, because during interviews, if they can speak to the fact that they are using AI or are adaptable or have that growth mindset around AI, I think that's a selling point, because most companies are integrating AI in one way or another these days. Yes, but for those who are already technical you know, because many women in tech listen to this what do you have advice-wise for? Maybe careers that they can go into that kind of accelerate their growth?

Speaker 2:

Okay, I think it depends kind of the way that I've started on it and I have encouraged my friends and colleagues to look at it. If you're interested in AI and data science in general is to kind of think about the end to end stack of a problem statement you're interested in. That's how it started for me. I was interested in autonomous vehicles as an example, and then the other neuroscience side of things. But taking autonomous vehicles as an example, there is everything from the sensor data fusion that's installed on the cars, like LiDAR sensors, all the way down to the chip level, embedded computing right and algorithms that you run there, and then the AI models in the interface that are doing object detection, pedestrian intent estimation, right.

Speaker 2:

I think that anchoring on the specific algorithms that seem interesting and the questions that we get like how is this AI model in this car able to detect that somebody is walking next to it, or how was it able to create a 3D model right Each of those own questions opens up an entirely new career path, I would say, or not even a path, but an interesting specialization and a set of skills and tools and knowledge you can gain, for example, on the object detection path, which is very popular in the AI space, right, it opens up this idea of you know, using different toolkits like YOLO algorithms, right From Ultralytics and other types of you know libraries and capabilities around you know, founding box detection and optimization and false positives and failure analysis, right so, and you can create projects and tailor a resume and then start to apply for jobs, let's say, in the computer vision engineer or deep learning engineer or AI engineer space, whereas if you're interested in the chatbot space, right, you're looking at large language model, foundational model definition, ai agents, right, lane chain, lama index, a lot of those toolkits and you kind of target your focus there, learn the skills, add it to your resume and then keep moving forward.

Speaker 2:

That would be my recommendation from what I've learned to kind of keep up to date with the rapidly changing pace of the tools and to kind of get those skills on our resume and start looking for jobs that really interest us.

Speaker 1:

That's fantastic, because not only did you talk about kind of the path in which someone could go about doing that, but you brought up a really good point and that is, I think, being drawn to what you're interested in. And then kind of reverse engineering how do I get into that space and educate myself? And then what did I do to learn, to learn that? And then communicating that in your resume.

Speaker 2:

I would definitely say that that's the approach to go, and in some cases you learn that kind of the painful way where you get involved in a project that you think is going to look really good on your resume and then a couple of days in you realize there's not a lot of good documentation, there's not a lot of, maybe, good communication, just because teams are working on different things.

Speaker 2:

It's hard to intercept, but at the end of the day, outside of all of the excuses and I've done this personally you kind of realize that you're not interested in it really that much and the interest that you felt going in was about the potential but not about the implementation.

Speaker 2:

And it can be kind of like a rock that you keep pushing through and you're saying, okay, I do need to do this. But at the end of the day, if there's something more exciting that's catching your attention and you feel like that's something you could easily do, right, maybe it's worth it to take a pause, if you can, from this project. That's kind of, you know, not really your interest point and go and solve some challenges and tackle the frustrations of what you're interested in, what you're drawn to, right, because I think, as engineers, at a certain point. You know we get excited about everything in terms of the potential to do and again make a difference right, but you know that potential can wear down when you hit the implementation roadblocks. So I think it's important to kind of think about and choose an area or look for the opportunities that come to us about things that interest us, that allow us to kind of keep overcoming those hurdles.

Speaker 1:

Shifting gears a bit, speaking of things that you love and that you're drawn to. I think it's fascinating that you wrote a book Forest Mystic. Yes, tell me what was the inspiration behind that. I just love that you wrote a children's book.

Speaker 2:

Thank you, I appreciate it and thank you for breaking it up too. I've always kind of loved poetry since I was a kid. I've stopped it recently but I'm hoping to get back into it and you know the like really cute stories again. You know I have to credit my mom for the idea of the story for this one, because it was basically a small tiny story that we came up with and then we modified the moral of the story. It was actually, you know, based on stories as a kid right that I got into and that I heard, and then we decided to take some pictures from, you know, vacations and trips that we've taken, turn it into a nice cute little poetry book that you know our friends' kids loved it, right, I think neighbors loved it too.

Speaker 2:

I think it's just kind of a small way of exploring an interesting story and I think, interestingly, the moral of the story that I recognize now that I'm older for that book is that you know it's important to kind of it's actually about community, right, it's important to ask for help when you need it and also, you know, to kind of learn from recognizing when help is being given to you and what is genuine and what isn't right. So the hero of the story or one of the protagonists is a prankster who kind of learns that throughout the story and it's just like a very interesting idea of why community is important, why being genuine and kind and friendly is so important to it's so important to kind of grow.

Speaker 1:

I just enjoy talking to you so much. Um what non-technical skills or?

Speaker 2:

attributes have you found most valuable in your career? Um, I know that there's the, the major ones that are mentioned, which is communication, which absolutely is so, so critical, um, that I still work on right. Uh, confidence is another thing that I'm currently working on, which I think it's just around representation, and, mary, I've seen and I learned a lot from our prior conversations, too, from you on this, and you know, I think, just reiterating that it's so, so crucial from my understanding, to kind of have that presence and that you know confidence while you're speaking. I think another key soft skill that I've learned is rest which is in and of itself a soft skill.

Speaker 2:

Right when you're about to give a big speech, a really detailed technical demo, starting on a coding project, or starting to map out your career path or even communicate with stakeholders or anything like that, or start a new side, hobby or project, having that opportunity to take a moment to rest, to kind of introspect briefly before going forward is so critical. It's kind of like that self-talk aspect. So that's, that's the third main soft skill I would emphasize on.

Speaker 1:

That is great Cause I don't think many people bring that up and that is so important for clarity of just the mind. Okay, with so much noise around AI, what are some good resources books or podcasts or influencers that you would recommend people tune into or read for future learning?

Speaker 2:

Yes, I think I also mentioned this in a recent interview with Mashable about these two brilliant, brilliant women leaders and pathfinders in the space. Then Yejin Choi again, I apologize, I don't know if I mispronounced her name, but she is also an incredible kind of leader and a pathfinder in really interesting areas. I think I first kind of got familiarized with her research in the context of moral databases and mapping for AI models, which was really interesting in the context of what's happening in the AI space going forward. So I definitely recommend those two amazing folks. And then there's also Sebastian Braschka, who does an amazing set of technical resources. I'm still trying to find time to be able to go through them all, right. So that's kind of another fantastic, brilliant kind of engineer in mind to follow.

Speaker 2:

There is also another set of resources I'm forgetting the name of the professor who pioneered them, but it's called AI by Hand and I follow the professor and the researcher who does those implementations on LinkedIn. Really a fantastic deep dive down into, you know, the technical mechanisms of AI which are, you know, he makes them super fascinating, super digestible to understand, right, and it's always kind of a joy to even browse through it if you don't have enough time right. So definitely those are the best resources to kind of follow. Again, they're kind of super technical. If you're looking for more high level ones, I definitely think that you know some of these organizations that are doing some good AI research that you know we are interested in, like maybe Microsoft or Google, or you know OpenAI right. Their blogs are really interesting ways to kind of keep subscribed to the latest and greatest right. Again, depending on whether you know you like their implementations, you know you're interested in their toolkits and technologies, that's another really great resource the technical blogs there.

Speaker 1:

Do you teach as well?

Speaker 2:

Yes, I do teach. I'm super passionate about teaching. I'm actually in the middle of creating a course right now on interview prep for data science and machine learning, but I've taught eight courses so far in the past two years. It's been quite a journey, but I absolutely love instruction.

Speaker 1:

Okay, well, of all those people that you listed, I want to make sure that you and I connect afterwards and I get that list and I'll include it in the show notes and then, when you publish your your newest lesson, I want to make sure I include that, because that sounds fascinating.

Speaker 2:

Thank you, mary, absolutely.

Speaker 1:

So how do you unplug from work and I guess, reboot or recharge yourself?

Speaker 2:

Yeah, I mean I love to hike, especially it's it's kind of beautiful this time of year in Arizona, so, um, lots of I. I'm motivating myself to do more hiking right, and to get more exercise. I also love swimming Uh, it's my favorite sport out of everything that you know and and especially, it's just so wonderful to kind of cool down and to relax, but also gives you a great workout. In addition, reading is really fantastic.

Speaker 2:

I think now the my kind of latest interest has been finding these older books and novels that are kind of, you know, 1960s, 1970s, right, and then just reading them through if you can find them in your library, right, because you know it's so interesting to kind of see how the same concepts that are discussed in those books can still apply today. I think it's just so fascinating, um, with, without you know, zero changes, honestly, um, in terms of the transfer, the concept, so it's really fascinating. And books on psychology and other really cool stuff. Um, and games, of course, love to play games with, uh, with friends and um, digital, physical, it doesn't really matter.

Speaker 1:

That's awesome, okay, so what does to be bolder mean to you?

Speaker 2:

To be bolder means being able to understand and create, I think, a value definition for yourself and I know that sounds a little bit corporate-y, but the general idea that you kind of have value inherently in whatever you do. You kind of bring that to the statement whether you're tired or you're unenergetic or you're at your best and you're thriving, you inherently have value, regardless of everything that's happening in your life and seeing how that kind of grows as you experience different experiences and circumstances and you know whether it takes a setback or a step forward, kind of seeing how that value grows and nurturing it. I think that's what it means to be bolder, which is really seeing that value in ourselves inherently.

Speaker 1:

I love that unique perspective. Where do you see yourself in five years, say, or 10 years?

Speaker 2:

It's a great question. Probably a CEO of a multinational corporation is the goal, yeah, and I'm counting on my network and my community to keep me accountable to it. And you know I've got a lot to learn to get to that point. I think a lot on leadership and you know, again, confidence right and stepping into spaces, but I'm always open to mentorship and to advice and to opportunity. So, yeah, I'm going to shoot for the moon and see where it takes me.

Speaker 1:

Well, I'll buy stock in that company for sure when you're leading it. Thank you, that's amazing. Okay, before I let you go, just some quick, rapid fire fun questions so people can get to know you. And I know this interview has been not focused on all the wonderful things and deep dives that we could take in AI, but I wanted to make sure that I had you on our show just to talk about careers, because I think that's such a good opportunity and you really are inspiring to so many women in STEM and I just I think, if you don't hear it enough, thank you for that, okay, so pizza or pasta, a pasta, dogs or cats, cats, Summer or winter.

Speaker 2:

Winter.

Speaker 1:

Ice cream or cake.

Speaker 2:

Cake.

Speaker 1:

Comedy or drama.

Speaker 2:

Comedy.

Speaker 1:

All right, I'll let you go, but before I do, thank you so much for being here and sharing your story with us.

Speaker 2:

Absolutely. Thank you so much for having me, Mary.

Speaker 1:

Thanks for listening to the episode today. It was really fun chatting with my guest. If you liked our show, please like it and share it with your friends. If you want to learn what we're up to, please go check out our website at 2BBouldercom. That's the number two, little b, bouldercom.