AI transcript
0:00:10 [MUSIC]
0:00:13 Hello, and welcome to the NVIDIA AI podcast.
0:00:15 I’m your host, Noah Kravitz.
0:00:21 On October 17th of 2024, NVIDIA was honored to host the Technovation World Summit
0:00:24 finalist pitch and awards ceremony in Santa Clara, California.
0:00:28 The summit featured more than 50 young innovators from around the world,
0:00:30 pitching tech solutions for sustainable development goals.
0:00:33 And it was a celebration of what happens when the creativity and
0:00:37 resilience of girls meets the transformative power of technology.
0:00:41 Here to talk about the World Summit, Technovation, and
0:00:44 the incredible work young women are doing across the globe to tackle our
0:00:47 biggest problems are Tara Choklovsky and Anshita Sani.
0:00:53 Tara is the founder of Technovation and a repeat guest on the podcast,
0:00:56 which I’m terribly excited about, we’ll get into that in a second.
0:01:01 And Anshita is an alumna of Technovation and currently works as a member of
0:01:06 the technical staff at Open AI, an AI company some of you might have heard of.
0:01:09 Tara, Anshita, thank you so much for taking the time to join the podcast.
0:01:12 Welcome, congratulations on the awards summit.
0:01:14 That was a few months ago.
0:01:16 So I hope your new years are off to a great start and welcome.
0:01:17 >> Thank you, Noah.
0:01:21 NVIDIA has been such a long-term supporter of Technovation that I’m so
0:01:25 happy to be back and so happy to share our stories along with.
0:01:27 An alumna we’re super proud of, Anshita.
0:01:31 >> Well, I am, like I said, Tara, I’m thrilled to get to talk to you again.
0:01:35 And I’m equally thrilled to meet you, Anshita, and to hear about the work you’ve
0:01:35 been doing.
0:01:37 But let’s start, Tara.
0:01:40 You were on the show about five years ago, Time Flies.
0:01:44 At that time, you were the founder of a nonprofit called Iridescent.
0:01:47 You were described, we use this quote from somebody else, not our words.
0:01:50 But you were being described as a pioneer empowering the incredible tech girls of
0:01:51 the future.
0:01:52 Seems like prescient words.
0:01:57 What have you been up to since then and how did it all lead to now to starting
0:01:59 Technovation and us getting to talk to you again?
0:02:00 >> Yeah, thank you.
0:02:03 I think I sort of see Iridescent initially.
0:02:07 That was the name of the organization, the nonprofit that I started almost 19 years
0:02:12 ago with the goal to empower underrepresented communities with the
0:02:18 skills that they could be the innovators of technologies that solve the problems
0:02:22 they face so they didn’t have to wait for a savior.
0:02:28 And so I think when NVIDIA first supported us back in 2018, I could sense
0:02:33 that AI was going to be the most powerful revolutionary force.
0:02:37 And it was interesting, like we started to do that actually in 2016.
0:02:42 And I was talking to people and I was like, we need to revamp our curriculum to
0:02:43 focus on AI.
0:02:47 And nobody was talking about that, especially not in the education space.
0:02:51 And everybody would be like AI and underserved communities don’t go in
0:02:53 the same sentence.
0:02:57 And NVIDIA was one of the first companies to actually pay attention to that.
0:03:01 And they said that, okay, we will fund this program.
0:03:06 And so we launched the first AI family challenge back in 2018, 2019.
0:03:09 And it was the first global AI education program in the world.
0:03:16 And everybody, there was so much negative feedback that I got that this will not work.
0:03:17 People don’t care about it.
0:03:21 And we actually opened it up and we went into communities.
0:03:26 I saw we had a refugee community in Somalia participate in the AI family challenge.
0:03:30 We had low income communities from Michigan participate families.
0:03:33 And they all came to me and they were saying that, thank you for teaching us
0:03:37 about how these things work because we see it in our phones, we see it everywhere.
0:03:41 But nobody ever bothered to tell us about how it works.
0:03:44 And with NVIDIA, we did these three design challenges.
0:03:49 That was literally in 2019 on how a self-driving car works, how a neural network works,
0:03:53 and how parallel processing works, like using hands-on experiments.
0:03:56 Those videos are still some of our top videos of all time.
0:04:03 Because guess what, neural networks, people actually know what LLMs stand for and all that kind of stuff.
0:04:08 So I’m just so excited that NVIDIA had such a huge role to play in pioneering.
0:04:11 Not just AI, but also AI education.
0:04:15 And then when we were looking at the data of what works, what doesn’t work,
0:04:19 the program that was really coming to the forefront that has long-term impact on
0:04:24 participants’ identity was this technology model of girls going through an accelerator.
0:04:29 A three-month accelerator where you find problems that you care about and actually build AI solutions
0:04:34 or mobile app solutions and pitch to investors and have a demo day.
0:04:39 And the whole experience of working in a team, being the founder, is just transformational.
0:04:44 And so we changed the name of the organization from iridescent to technology.
0:04:47 We cut all our other programs and just focused on this accelerator.
0:04:52 And then everybody else started to recognize AI was a thing.
0:04:57 And then in 2022 when ChatGPT came out in November 2022.
0:05:02 So we were the first education organization to remote and we developed a whole curriculum
0:05:06 for our girls to say, this is how you use ChatGPT to help with identification,
0:05:08 with help with developing your code.
0:05:15 And then we had thousands of girls in December 2022 using ChatGPT to sort of come up with,
0:05:16 help them in their accelerator journey.
0:05:18 And since then, of course, I took.
0:05:24 I’m just remembering so vividly that this was around the time or was the time when a lot of
0:05:29 school districts and educators, and I mean, no shade in saying this, were really struggling.
0:05:30 Well, what do we do with this?
0:05:33 We need to ban it so we can figure out how people won’t cheat.
0:05:37 And here you are saying, no, no, no, no, let’s people want to know how this works.
0:05:37 Let’s embrace it.
0:05:38 Let’s use it.
0:05:39 And yeah, absolutely.
0:05:44 So, you know, I remember going to a local school district here in Silicon Valley and telling
0:05:48 them, OK, you’ve got to use ChatGPT and they were like, what is this?
0:05:52 And so they’re like, oh, it blows their mind.
0:05:54 And they’re like, we need to get permission from.
0:05:56 I’m like, it’ll be too late, right?
0:06:00 Get this, have the girls use it and then you can deal with the consequences later on.
0:06:03 But anyway, so and then I mean, there’s more and more information.
0:06:06 About how AI is going to revolutionize the world, right?
0:06:11 And so I was like, the problem of not having enough people developing these
0:06:15 technologies is only going to get bigger because these technologies are moving so fast.
0:06:19 And so we launched the AI Forward Alliance with UNICEF as a core partner.
0:06:24 And the goal was, and the goal still is to empower 25 million innovators, AI
0:06:28 innovators, and especially females, because guess what?
0:06:32 Like globally, there are only, I think, 18 million tech professionals in the world.
0:06:35 Only 14 million men and only 4 million women.
0:06:37 I do this all the time in the show.
0:06:38 And but it’s genuine.
0:06:39 I’m just not a big stats person.
0:06:43 So I apologize, but unfortunately, the 14 four split doesn’t surprise me.
0:06:45 It’s yeah, we need to work on that.
0:06:47 Yeah, goes without saying.
0:06:48 But is that only 18 million?
0:06:50 Wow. OK. Yeah, it’s less than half a percent.
0:06:55 So the reason is so I did this study with the ILO workforce data and we went country
0:06:57 by country by country, whichever country has data.
0:07:01 And I think the reality is like, I mean, if you look around, maybe not in Silicon Valley,
0:07:05 but if you look around, most people are employed in the service sector in the world.
0:07:09 Right. And so people who can actually build technology, they’re very few.
0:07:13 And so I think if countries need to progress economically, they actually
0:07:14 need to have a bigger talent base.
0:07:16 And where are you going to get your talent base?
0:07:17 Happen to the one that’s not part of it.
0:07:21 Right. So so that’s that’s that’s where the AI forward alliances.
0:07:24 And last year, we were just so we had like record numbers.
0:07:28 Like I think we had like 35,000 girls participate in the Technovation Program.
0:07:32 And in the finalists, it was awesome to be able to bring them to Nvidia.
0:07:36 I was and I’m now feeling it again, so bummed that I couldn’t make it down in person.
0:07:38 But by all accounts, it was a terrific event.
0:07:42 Do you want to tell us about some of tell us about the summit?
0:07:44 Do you want to get into some of the pitches?
0:07:46 Yeah, I can. It’s just interesting, right?
0:07:50 Like when you give technology to people, especially young people,
0:07:53 that’s when you see like the limits of the points of breakage, right?
0:07:56 Track technology, but also where innovation comes.
0:08:01 And I think what I’ve always seen is like young people, especially girls,
0:08:05 because you underestimate them all the time, they just have such interesting ideas.
0:08:06 So I’ll give an example.
0:08:11 In 2012, I had a team of girls come up and create a mobile app
0:08:14 that was around focusing and mental health.
0:08:18 This was 13 years before this whole mental health and wellness.
0:08:20 And I could not understand that.
0:08:24 And I was like, why on earth would you want an app that is blocking other apps?
0:08:28 Because this increases productivity and I actually give them negative feedback.
0:08:32 OK, because I was it was so against what I was seeing.
0:08:36 Yeah, but they were like, no, this is really messing with my productivity
0:08:39 because I’m not so addictive and it’s messing with how I feel.
0:08:41 And I was like, wow, right?
0:08:44 Like this was literally 13 years before the adults caught up.
0:08:46 And so it’s a similar thing like this year.
0:08:51 We had such interesting solutions where we had some Vietnam,
0:08:56 which are about preserving their cultural heritage through lullabies
0:08:58 and putting them into large language models.
0:09:01 Also sort of Vietnamese facial expressions,
0:09:04 because there are a lot of the facial expression recognition systems
0:09:06 don’t recognize Vietnamese facial expressions.
0:09:09 So they did their own training of data sets.
0:09:12 And so I see sort of the technology community as like
0:09:17 bringing last mile data to large language models or large models
0:09:20 and helping them become more representative of the world.
0:09:23 As we record this, the episode that just went live
0:09:27 was an episode with the founders of a company called GUI AI,
0:09:31 and they are doing conceptually, to me, very similar things and,
0:09:36 you know, feeding localized data around populations, communities,
0:09:39 specific problems, challenges to be solved into LLMs
0:09:43 to create these tools to actually serve the users in those places.
0:09:47 Just the whole idea of, you know, you’re wherever you’re from,
0:09:50 wherever you live, your community’s knowledge, your language,
0:09:53 your history, your stories, your best practices of doing the things
0:09:57 that you do to thrive and survive, you know, that’s just the most important data.
0:10:02 And so being able to use that positively to, you know,
0:10:04 so I think it’s an area that I’m hoping gets more and more exposure
0:10:07 because it’s really, I mean, it’s vital, it’s important.
0:10:09 And it raises the quality for everyone, right?
0:10:11 Because the large language model becomes better.
0:10:14 Just it’s not boring anymore, it doesn’t show in this corporate.
0:10:15 Yes. Yes.
0:10:18 I think I remember one long researcher was saying his wife
0:10:20 calls chatty-pity-chatty boy.
0:10:23 Oh, I’m sorry I took that one.
0:10:25 But I think those are the things that we need to change, right?
0:10:28 And I think that’s what this community is thinking to change, so.
0:10:32 I’m speaking today with Tara Chaklowski and Sheeta Saini.
0:10:36 We were just speaking with Tara about her organization Technovation
0:10:39 and the origin story from when we had Tara.
0:10:42 I didn’t say this earlier, I should have, but it’s episode 85.
0:10:46 If you want to go back in the archives and listen to Tara’s first appearance
0:10:49 on the show, Teaching Families to Embrace AI.
0:10:53 And we’ve been talking now about Technovation and the origin story
0:10:58 and the mission of it to, you know, really unlock the innovators of that we need today.
0:11:03 We certainly need going forward in the future and girls, females, women in particular.
0:11:08 And so now and Sheeta would love to hear your story of how you got involved with Technovation.
0:11:12 You don’t have to be the sole spokesperson for the Technovation experience,
0:11:16 but I really delighted to have you here with us today and would love to hear your story.
0:11:20 Yeah, for sure. So I started getting involved in Technovation through,
0:11:23 I think just serendipity, really lucky to have found the program.
0:11:27 But I had just taken my first computer science class in high school.
0:11:30 That was just AP computer science.
0:11:34 And we had done a lot of fun projects in that class that sort of showed me what coding meant,
0:11:40 what it means to create a program using, you know, just your laptop and your fingers.
0:11:44 And so from there, I was thinking, all right, this is really cool,
0:11:46 but what can I actually do with this?
0:11:48 Right? We made programs like Boggle and stuff like that.
0:11:53 But I wanted to see if I could do something actually useful and in the real world with it.
0:11:56 And so I was looking around for different opportunities to that end.
0:12:01 And that’s how I came across Technovation and that whole mission of empowering girls
0:12:05 to solve problems in their community was something that really resonated with me.
0:12:09 And so I actually ended up starting a club in my high school Technovation Club.
0:12:13 And we were just bringing together a bunch of girls from across our high school
0:12:17 that were interested in this program and interested in finding a problem to solve.
0:12:21 And so one of the first problems that I tackled through Technovation
0:12:25 actually ended up being really personal to me and really personal to the situation
0:12:28 that was going on in my high school at the time that was vaping addiction.
0:12:33 And so this was happening at my high school, I think maybe 2018, 2019.
0:12:37 And in fact, I think the situation became so bad
0:12:40 that we actually had the doors taken off of the bathrooms at my high school.
0:12:44 And so when I sat down at Technovation to think about problems
0:12:47 going on in my community with a group of friends, we said, look,
0:12:48 this is going on right around us.
0:12:53 What can we do with computer science with technology to help this situation?
0:12:59 And so we ended up going through a lot of research from different Johns Hopkins studies.
0:13:02 We had mentors actually from Microsoft that helped us
0:13:04 through the process of building this app.
0:13:09 And I think at the end of it, it’s just really empowering to have that concept of,
0:13:13 hey, like Tara said, I can look at these problems and I can solve them.
0:13:17 I don’t have to wait for somebody else to jump in and solve this problem.
0:13:19 And so that’s what’s got involved with Technovation.
0:13:22 Amazing. I have to know, though, what was the app?
0:13:24 What was the solution that you came up with?
0:13:26 Yeah, so we, again, looking at those different research studies,
0:13:29 we came up with sort of a couple of different approaches.
0:13:31 So I shouldn’t say solution.
0:13:33 What’s the approach? That’s the question. Yeah.
0:13:36 Yeah, there was a couple of different features that we had in our app.
0:13:37 One thing was pretty simple.
0:13:41 It was a craving control timer, which actually was to be remarkably effective.
0:13:45 So setting a 10 minute timer when you have the urge to pick up a vape
0:13:49 or pick up some addictive thing is very helpful to curb that craving.
0:13:53 We would also take a user’s profile of interesting hobbies,
0:13:57 other things that they like to do and suggest those at the time of the craving
0:14:01 control timer. Right. So you’re just like to rail the craving into something positive.
0:14:03 Yeah, so simple, but it’s so hard for humans.
0:14:07 Yeah, yeah, I think I still need that to write with my phone.
0:14:12 So yeah. And then another aspect that we had was trying to create
0:14:15 a personalized quitting timeline that you could visualize along a graph.
0:14:19 We had another chat system so that you could chat with your support system
0:14:20 that was, you know, helping you through this process.
0:14:23 I think addiction is not just something that you can solve in an app.
0:14:27 It’s something where you need people, you know, a support system in community.
0:14:29 So. So a chat system hooked up to humans.
0:14:31 Yes, exactly. Yeah, amazing.
0:14:34 Yeah, with your friends, they would be on the app as well to, you know, got it.
0:14:36 OK, right, right, right.
0:14:38 That sounds amazing and not to diminish it all.
0:14:40 But you’re still in high school at that point.
0:14:42 Now I make sure we have time to keep going forward.
0:14:46 So that was your first Technovation experience, building that up incredible.
0:14:50 And so you’re in high school, you’re 14, 15, something like that.
0:14:52 Maybe how does the story progress?
0:14:56 Right. So I think, again, after Technovation, it was I think that was sort
0:14:59 of the aha moment in computer science for me, where it wasn’t some gimmick.
0:15:02 It’s it’s something that people are using every day to solve these problems.
0:15:05 And so I wanted to go further down that path.
0:15:08 And especially I was interested that in that time in these health things,
0:15:11 after after building this vaping app, I was wondering what what are other problems
0:15:14 that I can go after in this, you know, mobile health care space.
0:15:17 Yes, well, I ended up going out for an opportunity that
0:15:21 well, I never thought I would land it, but it was this research internship
0:15:24 at the University of Washington in Seattle, in Seattle. Yeah.
0:15:27 I’ll go Huskies, my wife’s an alum.
0:15:29 Oh, really? OK. Yeah, I love you, Doug.
0:15:32 And so, yeah, so I applied to this opportunity at the ubiquitous computing
0:15:36 lab, it was called at the University of Washington, and they had a lot of
0:15:39 different projects around solving what seemed to me at the time really big,
0:15:43 complex problems with the help of computer science or just a mobile phone
0:15:44 or something like that.
0:15:47 And so the project that I ended up working on that summer at that lab
0:15:54 after Technovation was essentially being able to detect, count and classify
0:15:57 different exercises with just a phone’s microphone.
0:15:59 And so that sounds kind of hard to wrap up.
0:16:04 So people doing physical exercise, but you’re using the mic, not the camera,
0:16:08 just the mic to detect what they’re doing and then do something with that information.
0:16:10 Exactly, right. That sounds super cool.
0:16:14 Yeah, it was I think it was really cool from a technological perspective,
0:16:16 but also for who we were solving the problem for.
0:16:19 Before you get into that, can I just ask, because again, I got it,
0:16:21 it’s the NVIDIA podcast I got asked.
0:16:25 So were you like bouncing sound waves off from the microphone or?
0:16:30 Yeah, exactly. The whole detection and everything just came from like the Doppler effect.
0:16:31 Yeah, yeah, yeah. Yeah, that’s so cool.
0:16:35 When you’re doing exercises, the waves just reflect back to the phone in a different way.
0:16:40 And so if we just play a really high frequency sound, we can’t even hear it, right?
0:16:43 But the phone is able to pick up the reflection of the sound back to the phone.
0:16:48 And so from there, you can actually see really interesting patterns in the sound waves to see,
0:16:52 like, for example, doing like arm raises is different than doing bicep curls.
0:16:52 And those show up.
0:16:58 Yeah. And so we were solving this problem for some populations
0:17:03 that aren’t typically addressed by normal trackers like the Fitbit or something like that.
0:17:08 And so, you know, obese, disabled populations are often not able to use a Fitbit
0:17:13 for exercise tracking because they’re just prescribed different exercises by their doctors.
0:17:17 For example, like I mentioned, the bicep curls with cans or something like that,
0:17:20 or elderly populations as well.
0:17:23 And so we wanted to create this tracker to solve this problem for people
0:17:26 that current solutions just weren’t accounting for.
0:17:29 And yeah, we ended up building this really cool app.
0:17:33 We did a research study where a bunch of us, including myself, went in
0:17:35 and we were doing a bunch of these exercises to collect data.
0:17:37 And that was my first introduction to AI.
0:17:41 We built a classification system that could classify, I think,
0:17:45 around 14 or 15 different exercises at pretty high accuracy, 80 to 85 percent.
0:17:49 And we were also able to count the number of those exercises.
0:17:52 So you get this really holistic tracker where you don’t have to select
0:17:55 the exercise that you’re doing. It’ll write a note. Yeah. Yeah.
0:17:58 So this was your first exposure to AI per se.
0:18:00 That’s such an umbrella term these days, but that’s fine.
0:18:03 Were you intimidated?
0:18:08 Yeah, I really did not know what AI meant or that you could just implement it with,
0:18:11 you know, maybe a hundred, a little over a hundred lines of code, right?
0:18:13 Like that’s that is a crazy concept.
0:18:14 Yeah, yeah, yeah.
0:18:18 System that actually learns with just, you know, by yourself.
0:18:21 And then what was the process of actually, I mean, you said, you know,
0:18:22 one of the things, right?
0:18:24 You’re like, it’s so few lines of code and it’s doing the stuff.
0:18:29 Once you got going, once it was sort of, you know, somebody in the program,
0:18:32 the program itself, whatever it was, kind of showed you how it worked, right?
0:18:33 Took you behind the curtain.
0:18:36 What then? Was it steep learning curve?
0:18:37 Was it sort of natural? Was it?
0:18:41 I’m just kind of curious of the perspective, you know, because when I was in high school,
0:18:43 I took AP Computer Science in high school.
0:18:47 And I think I wrote code on like with a pencil on like one of those exam books
0:18:49 for like my AP final, right?
0:18:52 So different world and imagining like trust my eye.
0:18:54 No, different world for sure.
0:18:56 I think there was a little bit of a steep learning curve for me.
0:19:00 I think that that idea of something, a computer learning was just something
0:19:02 that was so foreign to me, right?
0:19:03 Is yeah, no, it’s still it.
0:19:05 Yeah, yeah, exactly.
0:19:07 It still is. But that’s to you to me.
0:19:10 I think the idea of, you know, how much data we need to train
0:19:13 of a reasonably confident model was was really surprising to me.
0:19:16 You know, we had so many people come in and do so many different repetitions
0:19:20 in these exercises and needed all that for the system to be able to learn.
0:19:23 And so I think that was really an interesting moment for me because I realized,
0:19:25 you know, OK, yeah, I guess that makes sense.
0:19:29 If you have so many different examples of what’s wrong and what’s right,
0:19:33 maybe it makes sense that a computer can learn to identify those patterns
0:19:35 and and predict them in the future.
0:19:37 And so those are sort of some of the initial learnings I had
0:19:39 that sort of helped me wrap my head around this concept.
0:19:42 But I think now, of course, that something from a classification system
0:19:44 to what we’re doing today is is crazy.
0:19:49 Oops. Yeah. So so not to do that time leap here, but for the sake of the episode.
0:19:52 So this project, the project with the exercise tracker,
0:19:55 you were still in high school and undergrad.
0:19:56 Yes, I was in high school.
0:19:59 That was my right after my first experience. Right.
0:20:00 Right after activation. OK.
0:20:03 And so then you studied computer science in college.
0:20:05 Yeah, yeah. I think after all these experiences,
0:20:07 it just really felt like natural path for sure.
0:20:11 Yep. And did you have a focus on machine learning or I mean, what in 60 seconds?
0:20:13 What’s computer science in college like these days?
0:20:17 There’s still a lot of the core education around data structures,
0:20:19 architecture, so I had a lot of exposure to different things.
0:20:24 My honors thesis ended up being around AI and actually an open AI model.
0:20:27 The clip model was what I was working with for image retrieval.
0:20:30 And so that was sort of my focus towards the end of college.
0:20:31 Yeah. Gotcha. OK.
0:20:33 And how long ago did you graduate?
0:20:36 Just last May, so it’s been like a little less than a year.
0:20:37 Yeah. Wow. All right.
0:20:40 So in the workforce, where are you up to now?
0:20:44 Technovation, all these experiences have led to you’re in the thick of it.
0:20:45 You’re working at open AI.
0:20:47 I am working at open AI.
0:20:48 Yes, I’m an engineer on chat.
0:20:50 GPT growth at open AI.
0:20:52 And so it’s a really cool job.
0:20:55 It’s all about our growing, our free and paid user base.
0:20:57 And that ends up being really broad in practice,
0:21:00 but it is a really, really cool job to be at.
0:21:02 I don’t know. It’s so new.
0:21:05 But in a way, I think that makes your your perspective really interesting.
0:21:08 How similar, how different is it now, you know,
0:21:11 doing the stuff, computer science and research and engineering
0:21:15 at a job versus, you know, all of these educational experiences
0:21:16 kind of kind of leading up to it.
0:21:18 Is it is it vastly different?
0:21:22 Does it feel like the next logical step sort of on the on the journey of,
0:21:25 you know, kind of familiar, lots new, but learning and growing?
0:21:27 What’s it what’s it like being out in the workforce?
0:21:30 I think it’s sort of required a mind mindset shift for me.
0:21:33 I think when it comes to college and internships,
0:21:36 there is sort of this concept of some end goal, right?
0:21:38 For so in college, it’s like, let me get through these classes
0:21:40 and I can graduate. I want to learn a lot.
0:21:41 I want to meet my friends.
0:21:43 I want to meet mentors and things like that.
0:21:46 But I think when it comes to working in the industry for me,
0:21:49 the mindset shift was now I’m thinking about what is the impact
0:21:50 that I can have on the world, right?
0:21:52 And how can I maximize that?
0:21:55 And before that, that that was sort of in the back of my head,
0:21:56 but not not really at the front.
0:21:59 And so now it’s sort of what I’m thinking about all day, every day, right?
0:22:01 I don’t have homework and assignments
0:22:05 and all these things going on to to distract me from that from that goal.
0:22:07 And so I think that shift was really interesting.
0:22:12 And that was especially why I was interested in in a team like ChatGBT
0:22:13 growth at OpenAI.
0:22:17 I think one of the things that is really important to me is making sure
0:22:21 that it’s not just people in Silicon Valley using ChatGBT to help their daily lives.
0:22:25 It’s people in, you know, corners of the world where maybe they don’t have
0:22:29 a super powerful mobile phone that can even support an internet connection
0:22:33 for ChatGBT, but maybe they can message ChatGBT through WhatsApp
0:22:37 and get the messages that and and get help and information
0:22:38 and whatever it is that can help them in that way.
0:22:42 And so I think that that sort of shift in mindset from from college,
0:22:45 from learning to working has really helped me shape my goals
0:22:48 and and just feel passionate about about our mission
0:22:50 and what we’re doing with with the product like ChatGBT.
0:22:51 That’s amazing.
0:22:54 I mean, it’s not it’s not my place to say if this makes sense,
0:22:56 but that’s an asset to the world to have that mindset.
0:22:58 So that’s fantastic.
0:23:03 What advice would you give to girls in high school and college
0:23:07 who, you know, are just into the field or interested in computer science
0:23:10 or maybe, you know, the teenager who’s thinking about it
0:23:13 and getting their first exposure to to working with some of the tools?
0:23:15 Oh, this is kind of cool. Where can I go with this?
0:23:18 Any advice? Any maybe learnings from your journey?
0:23:21 Yeah, I think for my journey, I think one of the things I can say
0:23:24 is building confidence early and not being scared of failure.
0:23:28 I think I probably never would have discovered AI if I hadn’t had
0:23:31 I guess the audacity to go after that first research in a new chip
0:23:33 and say, hey, maybe this is a room I belong in.
0:23:36 And, you know, that was not something I felt when I was applying.
0:23:40 I was talking about this boggle program that I built in my computer science class.
0:23:44 I was like, how am I about to jump from this to searching in a lab
0:23:45 and building things for real people?
0:23:49 But I think having that courage to go out and say, I can do this.
0:23:52 That this is an opportunity that is for me as much as it is for anyone else
0:23:55 is something that’s really important to help you to find your path.
0:23:58 You open doors that you never knew were even there.
0:24:01 I think along with that, what goes into building confidence
0:24:05 is finding mentors and a community that can support you in in these things.
0:24:08 And so for me, that came through Technovation, right?
0:24:12 Even now in the Bay Area when I’m back, that community is still there supporting me.
0:24:17 And, you know, just meeting amazing other alumni in the Bay Area was such a surprise for me.
0:24:21 I didn’t know that it would last, that the community would last long for me.
0:24:23 Communities, women, engineers in college, right?
0:24:26 Meeting meeting other people that are doing similar things to you
0:24:28 is the most inspirational thing that you can find, right?
0:24:30 It’s right. If they can do it, I can do it too.
0:24:35 Yeah. And mentors, of course, as well, so many helpful mentors along my journey
0:24:38 had helped me figure out what’s right for me and, you know, wrap my head around
0:24:40 moving from college, going into the industry.
0:24:43 And I think that’s really important to to have some people that have been
0:24:46 through the same things that you’re going through and getting advice
0:24:50 or just even just talking through what you’re going through.
0:24:52 Yeah, absolutely. What is Wiser AI?
0:24:57 Yeah, so Wiser AI is an organization, loosely, that I’ve recently been working on.
0:24:59 It’s it’s more of an initiative.
0:25:02 Just I think what I noticed when I first started working in AI,
0:25:07 even in research and college was we have done so much work to sort of uplift
0:25:08 women in technology.
0:25:12 And there was a big focus, I think, on on that gap when I was in high school
0:25:13 and even middle school.
0:25:15 And I was really seeing feeling that around me.
0:25:20 But as I got into AI and research, I was noticing these are all really,
0:25:21 really male dominated rooms.
0:25:25 And for some reason, I just wasn’t finding as many people talking about
0:25:28 women in AI and underrepresented folks in AI.
0:25:33 And when I first landed the opportunity at Open AI, there was a lot of chatter
0:25:36 about, you know, how quickly AI is moving, you know, the exponential curve
0:25:40 at which these models are performing on these really, really powerful benchmarks.
0:25:44 And that really got me thinking, if we’re building this really powerful
0:25:48 technology that is meant to benefit everyone, how are we going to build that
0:25:52 if we don’t have everyone’s voices involved in developing the system?
0:25:57 And so Wiser AI is an initiative targeted towards that and in bringing
0:26:00 more underrepresented voices in AI, specifically women.
0:26:03 One of the things that I’m really interested in doing with this initiative
0:26:05 is supporting underrepresented founders in AI.
0:26:09 I think that’s a really interesting space where the progress in terms
0:26:13 of like female founders and, you know, the percentage of females founders
0:26:18 supported by investments or even just the percentage of like all women founded
0:26:20 companies is really, really surprisingly low.
0:26:23 I think that was an interest of mine at one point, right, becoming a founder.
0:26:26 And it’s it’s really surprising to see that that’s still.
0:26:32 Yeah, it’s it’s so unreflective of the actual, you know, population
0:26:34 of people walking around the planet. It’s yeah.
0:26:35 And so you started Wiser.
0:26:37 Yes. Yeah. Yeah.
0:26:38 Bringing together a team right now.
0:26:41 I think one of the really big things that we want to do this year and sort
0:26:45 of the big target for the organization this year is to actually hold a
0:26:49 conference, bringing together underrepresented founders in AI, seeking
0:26:52 people that can talk about what it means to build responsible AI and how
0:26:54 we can incorporate that into our systems.
0:26:58 And yeah, just just helping bring together a community that can support each other.
0:27:03 Amazing. So, Tara, I want to sort of welcome you back for this kind
0:27:06 of last sort of wrap up thing and ask both of you.
0:27:09 And I feel like this is one of those questions that like shouldn’t need
0:27:13 to be asked out loud and answered because it should be obvious, but it’s not.
0:27:16 Why is is the work you’re doing so important?
0:27:21 And why is it so important to work for equitable and inclusive AI right now?
0:27:25 You’ve talked about it throughout the episode, but to kind of put a point
0:27:28 on it and really bring it to bear, why is this so important right now?
0:27:33 As I was saying, like the large language model has been built on the majority,
0:27:37 right, like the majority language on the Internet, which may not
0:27:39 necessarily reflect the reality.
0:27:44 And so you actually get boring results because it’s the generic
0:27:46 very, very the lowest baseline, right?
0:27:52 And so what’s the fastest way to make more innovative products, right?
0:27:53 Is to open it up, right?
0:27:55 And I think there could be many ways of doing it.
0:28:00 I think one obvious way is look, 70 percent of the developers
0:28:04 are of a particular background and from a particular cognitive perspective,
0:28:08 right? And so when you have different people around the room, I mean,
0:28:12 it just, it came from India last night, and it’s fascinating, right?
0:28:15 Like just going to a different place and understanding how people
0:28:19 in different cultures think it just increases your sort of scope, right?
0:28:21 So it’s just basic stuff.
0:28:26 So I’m not big on business axioms per se, but isn’t there just a basic guideline
0:28:29 of sort of product development that, you know, the more that what you’re developing,
0:28:32 the more that people building the thing sort of reflect the people who are going
0:28:36 to be using it, the better off you’re likely, you know, the thing you’re
0:28:37 making is likely to be now.
0:28:41 Absolutely. And the market is beyond the U.S., right?
0:28:44 No, I mean, this is, this is everything you’re building for the world.
0:28:48 So to me, it’s just a very basic common sense argument that you want
0:28:51 a diverse group of people innovating.
0:28:57 And I will say that the challenge is the big one because women represent
0:29:00 I think the largest group of people that have been historically
0:29:03 discriminated against as a whole, right?
0:29:07 So I think 151 countries legally discriminate against women.
0:29:10 And so social norms are very deeply rooted, right?
0:29:13 And I think men and women are extremely different.
0:29:17 So I think that the conversation can be very nuanced and subtle.
0:29:22 But from an economic point of view, I think you are missing out if 50%
0:29:27 of the population isn’t represented in your software development teams.
0:29:30 You know, it’s not enough to say we have equal representation
0:29:32 in sales or HR or whatever.
0:29:34 I think it has to be in the software development team.
0:29:39 So I think everybody will benefit when the products are less boring.
0:29:44 Yep. Anshita, in many ways, this whole conversation, particularly you and I
0:29:47 speaking, you talking about your work in the past several minutes, you know,
0:29:51 address this question, but specific to talking about the importance of working
0:29:54 for Equitable Inclusive AI right now, anything you’d like to add?
0:30:00 Yeah, I think overall we’ve already seen the effects of building bias systems.
0:30:03 In systems like predictive policing or in health care.
0:30:07 We know there’s been many studies about how this affects
0:30:09 disproportionately affects underrepresented populations.
0:30:14 I think at the rate at which AI is developing right now, these systems
0:30:18 are going to be everywhere, not just in health care, but also in finance,
0:30:19 hiring everywhere.
0:30:23 And so I think it’s just we’re at a crucial point where if we don’t
0:30:26 start doing that work towards making these systems inclusive, when they’re
0:30:28 everywhere, it’s hard to take that back.
0:30:30 I think it needs to happen now.
0:30:35 For listeners who want to learn more, want to get involved,
0:30:39 maybe want to see if they can join Technovation, any and all the above,
0:30:44 all the great work you’re both doing, where can folks go online to learn more?
0:30:47 Again, I’ll plug Episode 85, Tara, your first appearance on the pod.
0:30:51 But for more information about Technovation, Tara, where can people look?
0:30:54 Yeah, I would say the Technovation season is ongoing right now and you’re
0:30:59 actively looking for mentors, so and you don’t need to have a technical
0:31:02 background to be a mentor, because what you need to bring to the table is
0:31:07 just this courage to say, I don’t know, let’s go find out together.
0:31:08 Because guess what?
0:31:11 You probably don’t know because these things are changing so fast, but it’s
0:31:15 an incredible experience where you’re project managing and cheerleading
0:31:18 a team of girls to actually build an AI prototype within three months.
0:31:23 So it’s an amazing way to actually go through an accelerator yourself.
0:31:27 We’ve had stories where mentors have applied to my combinator and gotten in
0:31:30 after they went through Technovation because Technovation gave the mentors
0:31:32 the courage to start their own business.
0:31:36 So I’d say 95 percent of the time when girls match with mentors,
0:31:37 they finish the full program.
0:31:41 So mentors are the one of the most important pillars for our program.
0:31:43 So please sign up at Technovation.org.
0:31:45 Technovation.org?
0:31:45 Yeah.
0:31:46 OK, great.
0:31:48 And Chita, is there wiser?
0:31:50 Can people find out more about wiser online?
0:31:54 Yes, and I’m actively looking for women to work with to organize this
0:31:56 conference that we have a goal of having this year.
0:31:57 Do you have a location in mind?
0:32:00 It might have to be around the Bay Area just because of production,
0:32:04 like maybe in the Bay Area, but it’s still still working on a location.
0:32:06 Honestly, so open to any suggestions.
0:32:10 But we would love to have more talented, amazing, passionate women to work with.
0:32:16 They could reach out directly or wiser.ai.org is the website.
0:32:17 Excellent.
0:32:19 Well, again, thank you both so much, Chita and Chita,
0:32:23 for taking the time to come on the podcast and share the work you’re doing.
0:32:28 It’s just really great, inspiring work to learn about and for people to get involved with.
0:32:32 And, you know, anybody out there who’s considering maybe being a mentor,
0:32:34 if you’ve never had that experience, I haven’t worked with Technovation,
0:32:38 but I’ve done other related things and it’s the best.
0:32:41 And not only that, but you get to go through an accelerator yourself.
0:32:42 It’s kind of hard to beat, right?
0:32:43 Let’s do it again.
0:32:44 Let’s catch up down the line.
0:32:48 But it was an absolute pleasure to meet you and Chita and all the best to both of you
0:32:49 and the work you’re doing.
0:32:50 Thank you, Noah.
0:32:51 This was a pleasure.
0:32:52 Yeah, thank you so much, Noah.
0:32:53 And thank you, Tara.
0:32:56 It’s just so inspiring to hear about the work that you’re continuing to do
0:32:58 and knowing how much of an impact it’s already had on me.
0:32:59 So thank you.
0:33:00 That made my day, right?
0:33:03 Like, listening to alumna and saying, OK, all the hard work
0:33:05 and your vision of improving the world.
0:33:07 Like, that’s what we work every day for, right?
0:33:11 So I wouldn’t have the energy to do what we do in the battles to fight
0:33:13 if it weren’t for the alumna, so it’s mutual.
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0:00:13 Hello, and welcome to the NVIDIA AI podcast.
0:00:15 I’m your host, Noah Kravitz.
0:00:21 On October 17th of 2024, NVIDIA was honored to host the Technovation World Summit
0:00:24 finalist pitch and awards ceremony in Santa Clara, California.
0:00:28 The summit featured more than 50 young innovators from around the world,
0:00:30 pitching tech solutions for sustainable development goals.
0:00:33 And it was a celebration of what happens when the creativity and
0:00:37 resilience of girls meets the transformative power of technology.
0:00:41 Here to talk about the World Summit, Technovation, and
0:00:44 the incredible work young women are doing across the globe to tackle our
0:00:47 biggest problems are Tara Choklovsky and Anshita Sani.
0:00:53 Tara is the founder of Technovation and a repeat guest on the podcast,
0:00:56 which I’m terribly excited about, we’ll get into that in a second.
0:01:01 And Anshita is an alumna of Technovation and currently works as a member of
0:01:06 the technical staff at Open AI, an AI company some of you might have heard of.
0:01:09 Tara, Anshita, thank you so much for taking the time to join the podcast.
0:01:12 Welcome, congratulations on the awards summit.
0:01:14 That was a few months ago.
0:01:16 So I hope your new years are off to a great start and welcome.
0:01:17 >> Thank you, Noah.
0:01:21 NVIDIA has been such a long-term supporter of Technovation that I’m so
0:01:25 happy to be back and so happy to share our stories along with.
0:01:27 An alumna we’re super proud of, Anshita.
0:01:31 >> Well, I am, like I said, Tara, I’m thrilled to get to talk to you again.
0:01:35 And I’m equally thrilled to meet you, Anshita, and to hear about the work you’ve
0:01:35 been doing.
0:01:37 But let’s start, Tara.
0:01:40 You were on the show about five years ago, Time Flies.
0:01:44 At that time, you were the founder of a nonprofit called Iridescent.
0:01:47 You were described, we use this quote from somebody else, not our words.
0:01:50 But you were being described as a pioneer empowering the incredible tech girls of
0:01:51 the future.
0:01:52 Seems like prescient words.
0:01:57 What have you been up to since then and how did it all lead to now to starting
0:01:59 Technovation and us getting to talk to you again?
0:02:00 >> Yeah, thank you.
0:02:03 I think I sort of see Iridescent initially.
0:02:07 That was the name of the organization, the nonprofit that I started almost 19 years
0:02:12 ago with the goal to empower underrepresented communities with the
0:02:18 skills that they could be the innovators of technologies that solve the problems
0:02:22 they face so they didn’t have to wait for a savior.
0:02:28 And so I think when NVIDIA first supported us back in 2018, I could sense
0:02:33 that AI was going to be the most powerful revolutionary force.
0:02:37 And it was interesting, like we started to do that actually in 2016.
0:02:42 And I was talking to people and I was like, we need to revamp our curriculum to
0:02:43 focus on AI.
0:02:47 And nobody was talking about that, especially not in the education space.
0:02:51 And everybody would be like AI and underserved communities don’t go in
0:02:53 the same sentence.
0:02:57 And NVIDIA was one of the first companies to actually pay attention to that.
0:03:01 And they said that, okay, we will fund this program.
0:03:06 And so we launched the first AI family challenge back in 2018, 2019.
0:03:09 And it was the first global AI education program in the world.
0:03:16 And everybody, there was so much negative feedback that I got that this will not work.
0:03:17 People don’t care about it.
0:03:21 And we actually opened it up and we went into communities.
0:03:26 I saw we had a refugee community in Somalia participate in the AI family challenge.
0:03:30 We had low income communities from Michigan participate families.
0:03:33 And they all came to me and they were saying that, thank you for teaching us
0:03:37 about how these things work because we see it in our phones, we see it everywhere.
0:03:41 But nobody ever bothered to tell us about how it works.
0:03:44 And with NVIDIA, we did these three design challenges.
0:03:49 That was literally in 2019 on how a self-driving car works, how a neural network works,
0:03:53 and how parallel processing works, like using hands-on experiments.
0:03:56 Those videos are still some of our top videos of all time.
0:04:03 Because guess what, neural networks, people actually know what LLMs stand for and all that kind of stuff.
0:04:08 So I’m just so excited that NVIDIA had such a huge role to play in pioneering.
0:04:11 Not just AI, but also AI education.
0:04:15 And then when we were looking at the data of what works, what doesn’t work,
0:04:19 the program that was really coming to the forefront that has long-term impact on
0:04:24 participants’ identity was this technology model of girls going through an accelerator.
0:04:29 A three-month accelerator where you find problems that you care about and actually build AI solutions
0:04:34 or mobile app solutions and pitch to investors and have a demo day.
0:04:39 And the whole experience of working in a team, being the founder, is just transformational.
0:04:44 And so we changed the name of the organization from iridescent to technology.
0:04:47 We cut all our other programs and just focused on this accelerator.
0:04:52 And then everybody else started to recognize AI was a thing.
0:04:57 And then in 2022 when ChatGPT came out in November 2022.
0:05:02 So we were the first education organization to remote and we developed a whole curriculum
0:05:06 for our girls to say, this is how you use ChatGPT to help with identification,
0:05:08 with help with developing your code.
0:05:15 And then we had thousands of girls in December 2022 using ChatGPT to sort of come up with,
0:05:16 help them in their accelerator journey.
0:05:18 And since then, of course, I took.
0:05:24 I’m just remembering so vividly that this was around the time or was the time when a lot of
0:05:29 school districts and educators, and I mean, no shade in saying this, were really struggling.
0:05:30 Well, what do we do with this?
0:05:33 We need to ban it so we can figure out how people won’t cheat.
0:05:37 And here you are saying, no, no, no, no, let’s people want to know how this works.
0:05:37 Let’s embrace it.
0:05:38 Let’s use it.
0:05:39 And yeah, absolutely.
0:05:44 So, you know, I remember going to a local school district here in Silicon Valley and telling
0:05:48 them, OK, you’ve got to use ChatGPT and they were like, what is this?
0:05:52 And so they’re like, oh, it blows their mind.
0:05:54 And they’re like, we need to get permission from.
0:05:56 I’m like, it’ll be too late, right?
0:06:00 Get this, have the girls use it and then you can deal with the consequences later on.
0:06:03 But anyway, so and then I mean, there’s more and more information.
0:06:06 About how AI is going to revolutionize the world, right?
0:06:11 And so I was like, the problem of not having enough people developing these
0:06:15 technologies is only going to get bigger because these technologies are moving so fast.
0:06:19 And so we launched the AI Forward Alliance with UNICEF as a core partner.
0:06:24 And the goal was, and the goal still is to empower 25 million innovators, AI
0:06:28 innovators, and especially females, because guess what?
0:06:32 Like globally, there are only, I think, 18 million tech professionals in the world.
0:06:35 Only 14 million men and only 4 million women.
0:06:37 I do this all the time in the show.
0:06:38 And but it’s genuine.
0:06:39 I’m just not a big stats person.
0:06:43 So I apologize, but unfortunately, the 14 four split doesn’t surprise me.
0:06:45 It’s yeah, we need to work on that.
0:06:47 Yeah, goes without saying.
0:06:48 But is that only 18 million?
0:06:50 Wow. OK. Yeah, it’s less than half a percent.
0:06:55 So the reason is so I did this study with the ILO workforce data and we went country
0:06:57 by country by country, whichever country has data.
0:07:01 And I think the reality is like, I mean, if you look around, maybe not in Silicon Valley,
0:07:05 but if you look around, most people are employed in the service sector in the world.
0:07:09 Right. And so people who can actually build technology, they’re very few.
0:07:13 And so I think if countries need to progress economically, they actually
0:07:14 need to have a bigger talent base.
0:07:16 And where are you going to get your talent base?
0:07:17 Happen to the one that’s not part of it.
0:07:21 Right. So so that’s that’s that’s where the AI forward alliances.
0:07:24 And last year, we were just so we had like record numbers.
0:07:28 Like I think we had like 35,000 girls participate in the Technovation Program.
0:07:32 And in the finalists, it was awesome to be able to bring them to Nvidia.
0:07:36 I was and I’m now feeling it again, so bummed that I couldn’t make it down in person.
0:07:38 But by all accounts, it was a terrific event.
0:07:42 Do you want to tell us about some of tell us about the summit?
0:07:44 Do you want to get into some of the pitches?
0:07:46 Yeah, I can. It’s just interesting, right?
0:07:50 Like when you give technology to people, especially young people,
0:07:53 that’s when you see like the limits of the points of breakage, right?
0:07:56 Track technology, but also where innovation comes.
0:08:01 And I think what I’ve always seen is like young people, especially girls,
0:08:05 because you underestimate them all the time, they just have such interesting ideas.
0:08:06 So I’ll give an example.
0:08:11 In 2012, I had a team of girls come up and create a mobile app
0:08:14 that was around focusing and mental health.
0:08:18 This was 13 years before this whole mental health and wellness.
0:08:20 And I could not understand that.
0:08:24 And I was like, why on earth would you want an app that is blocking other apps?
0:08:28 Because this increases productivity and I actually give them negative feedback.
0:08:32 OK, because I was it was so against what I was seeing.
0:08:36 Yeah, but they were like, no, this is really messing with my productivity
0:08:39 because I’m not so addictive and it’s messing with how I feel.
0:08:41 And I was like, wow, right?
0:08:44 Like this was literally 13 years before the adults caught up.
0:08:46 And so it’s a similar thing like this year.
0:08:51 We had such interesting solutions where we had some Vietnam,
0:08:56 which are about preserving their cultural heritage through lullabies
0:08:58 and putting them into large language models.
0:09:01 Also sort of Vietnamese facial expressions,
0:09:04 because there are a lot of the facial expression recognition systems
0:09:06 don’t recognize Vietnamese facial expressions.
0:09:09 So they did their own training of data sets.
0:09:12 And so I see sort of the technology community as like
0:09:17 bringing last mile data to large language models or large models
0:09:20 and helping them become more representative of the world.
0:09:23 As we record this, the episode that just went live
0:09:27 was an episode with the founders of a company called GUI AI,
0:09:31 and they are doing conceptually, to me, very similar things and,
0:09:36 you know, feeding localized data around populations, communities,
0:09:39 specific problems, challenges to be solved into LLMs
0:09:43 to create these tools to actually serve the users in those places.
0:09:47 Just the whole idea of, you know, you’re wherever you’re from,
0:09:50 wherever you live, your community’s knowledge, your language,
0:09:53 your history, your stories, your best practices of doing the things
0:09:57 that you do to thrive and survive, you know, that’s just the most important data.
0:10:02 And so being able to use that positively to, you know,
0:10:04 so I think it’s an area that I’m hoping gets more and more exposure
0:10:07 because it’s really, I mean, it’s vital, it’s important.
0:10:09 And it raises the quality for everyone, right?
0:10:11 Because the large language model becomes better.
0:10:14 Just it’s not boring anymore, it doesn’t show in this corporate.
0:10:15 Yes. Yes.
0:10:18 I think I remember one long researcher was saying his wife
0:10:20 calls chatty-pity-chatty boy.
0:10:23 Oh, I’m sorry I took that one.
0:10:25 But I think those are the things that we need to change, right?
0:10:28 And I think that’s what this community is thinking to change, so.
0:10:32 I’m speaking today with Tara Chaklowski and Sheeta Saini.
0:10:36 We were just speaking with Tara about her organization Technovation
0:10:39 and the origin story from when we had Tara.
0:10:42 I didn’t say this earlier, I should have, but it’s episode 85.
0:10:46 If you want to go back in the archives and listen to Tara’s first appearance
0:10:49 on the show, Teaching Families to Embrace AI.
0:10:53 And we’ve been talking now about Technovation and the origin story
0:10:58 and the mission of it to, you know, really unlock the innovators of that we need today.
0:11:03 We certainly need going forward in the future and girls, females, women in particular.
0:11:08 And so now and Sheeta would love to hear your story of how you got involved with Technovation.
0:11:12 You don’t have to be the sole spokesperson for the Technovation experience,
0:11:16 but I really delighted to have you here with us today and would love to hear your story.
0:11:20 Yeah, for sure. So I started getting involved in Technovation through,
0:11:23 I think just serendipity, really lucky to have found the program.
0:11:27 But I had just taken my first computer science class in high school.
0:11:30 That was just AP computer science.
0:11:34 And we had done a lot of fun projects in that class that sort of showed me what coding meant,
0:11:40 what it means to create a program using, you know, just your laptop and your fingers.
0:11:44 And so from there, I was thinking, all right, this is really cool,
0:11:46 but what can I actually do with this?
0:11:48 Right? We made programs like Boggle and stuff like that.
0:11:53 But I wanted to see if I could do something actually useful and in the real world with it.
0:11:56 And so I was looking around for different opportunities to that end.
0:12:01 And that’s how I came across Technovation and that whole mission of empowering girls
0:12:05 to solve problems in their community was something that really resonated with me.
0:12:09 And so I actually ended up starting a club in my high school Technovation Club.
0:12:13 And we were just bringing together a bunch of girls from across our high school
0:12:17 that were interested in this program and interested in finding a problem to solve.
0:12:21 And so one of the first problems that I tackled through Technovation
0:12:25 actually ended up being really personal to me and really personal to the situation
0:12:28 that was going on in my high school at the time that was vaping addiction.
0:12:33 And so this was happening at my high school, I think maybe 2018, 2019.
0:12:37 And in fact, I think the situation became so bad
0:12:40 that we actually had the doors taken off of the bathrooms at my high school.
0:12:44 And so when I sat down at Technovation to think about problems
0:12:47 going on in my community with a group of friends, we said, look,
0:12:48 this is going on right around us.
0:12:53 What can we do with computer science with technology to help this situation?
0:12:59 And so we ended up going through a lot of research from different Johns Hopkins studies.
0:13:02 We had mentors actually from Microsoft that helped us
0:13:04 through the process of building this app.
0:13:09 And I think at the end of it, it’s just really empowering to have that concept of,
0:13:13 hey, like Tara said, I can look at these problems and I can solve them.
0:13:17 I don’t have to wait for somebody else to jump in and solve this problem.
0:13:19 And so that’s what’s got involved with Technovation.
0:13:22 Amazing. I have to know, though, what was the app?
0:13:24 What was the solution that you came up with?
0:13:26 Yeah, so we, again, looking at those different research studies,
0:13:29 we came up with sort of a couple of different approaches.
0:13:31 So I shouldn’t say solution.
0:13:33 What’s the approach? That’s the question. Yeah.
0:13:36 Yeah, there was a couple of different features that we had in our app.
0:13:37 One thing was pretty simple.
0:13:41 It was a craving control timer, which actually was to be remarkably effective.
0:13:45 So setting a 10 minute timer when you have the urge to pick up a vape
0:13:49 or pick up some addictive thing is very helpful to curb that craving.
0:13:53 We would also take a user’s profile of interesting hobbies,
0:13:57 other things that they like to do and suggest those at the time of the craving
0:14:01 control timer. Right. So you’re just like to rail the craving into something positive.
0:14:03 Yeah, so simple, but it’s so hard for humans.
0:14:07 Yeah, yeah, I think I still need that to write with my phone.
0:14:12 So yeah. And then another aspect that we had was trying to create
0:14:15 a personalized quitting timeline that you could visualize along a graph.
0:14:19 We had another chat system so that you could chat with your support system
0:14:20 that was, you know, helping you through this process.
0:14:23 I think addiction is not just something that you can solve in an app.
0:14:27 It’s something where you need people, you know, a support system in community.
0:14:29 So. So a chat system hooked up to humans.
0:14:31 Yes, exactly. Yeah, amazing.
0:14:34 Yeah, with your friends, they would be on the app as well to, you know, got it.
0:14:36 OK, right, right, right.
0:14:38 That sounds amazing and not to diminish it all.
0:14:40 But you’re still in high school at that point.
0:14:42 Now I make sure we have time to keep going forward.
0:14:46 So that was your first Technovation experience, building that up incredible.
0:14:50 And so you’re in high school, you’re 14, 15, something like that.
0:14:52 Maybe how does the story progress?
0:14:56 Right. So I think, again, after Technovation, it was I think that was sort
0:14:59 of the aha moment in computer science for me, where it wasn’t some gimmick.
0:15:02 It’s it’s something that people are using every day to solve these problems.
0:15:05 And so I wanted to go further down that path.
0:15:08 And especially I was interested that in that time in these health things,
0:15:11 after after building this vaping app, I was wondering what what are other problems
0:15:14 that I can go after in this, you know, mobile health care space.
0:15:17 Yes, well, I ended up going out for an opportunity that
0:15:21 well, I never thought I would land it, but it was this research internship
0:15:24 at the University of Washington in Seattle, in Seattle. Yeah.
0:15:27 I’ll go Huskies, my wife’s an alum.
0:15:29 Oh, really? OK. Yeah, I love you, Doug.
0:15:32 And so, yeah, so I applied to this opportunity at the ubiquitous computing
0:15:36 lab, it was called at the University of Washington, and they had a lot of
0:15:39 different projects around solving what seemed to me at the time really big,
0:15:43 complex problems with the help of computer science or just a mobile phone
0:15:44 or something like that.
0:15:47 And so the project that I ended up working on that summer at that lab
0:15:54 after Technovation was essentially being able to detect, count and classify
0:15:57 different exercises with just a phone’s microphone.
0:15:59 And so that sounds kind of hard to wrap up.
0:16:04 So people doing physical exercise, but you’re using the mic, not the camera,
0:16:08 just the mic to detect what they’re doing and then do something with that information.
0:16:10 Exactly, right. That sounds super cool.
0:16:14 Yeah, it was I think it was really cool from a technological perspective,
0:16:16 but also for who we were solving the problem for.
0:16:19 Before you get into that, can I just ask, because again, I got it,
0:16:21 it’s the NVIDIA podcast I got asked.
0:16:25 So were you like bouncing sound waves off from the microphone or?
0:16:30 Yeah, exactly. The whole detection and everything just came from like the Doppler effect.
0:16:31 Yeah, yeah, yeah. Yeah, that’s so cool.
0:16:35 When you’re doing exercises, the waves just reflect back to the phone in a different way.
0:16:40 And so if we just play a really high frequency sound, we can’t even hear it, right?
0:16:43 But the phone is able to pick up the reflection of the sound back to the phone.
0:16:48 And so from there, you can actually see really interesting patterns in the sound waves to see,
0:16:52 like, for example, doing like arm raises is different than doing bicep curls.
0:16:52 And those show up.
0:16:58 Yeah. And so we were solving this problem for some populations
0:17:03 that aren’t typically addressed by normal trackers like the Fitbit or something like that.
0:17:08 And so, you know, obese, disabled populations are often not able to use a Fitbit
0:17:13 for exercise tracking because they’re just prescribed different exercises by their doctors.
0:17:17 For example, like I mentioned, the bicep curls with cans or something like that,
0:17:20 or elderly populations as well.
0:17:23 And so we wanted to create this tracker to solve this problem for people
0:17:26 that current solutions just weren’t accounting for.
0:17:29 And yeah, we ended up building this really cool app.
0:17:33 We did a research study where a bunch of us, including myself, went in
0:17:35 and we were doing a bunch of these exercises to collect data.
0:17:37 And that was my first introduction to AI.
0:17:41 We built a classification system that could classify, I think,
0:17:45 around 14 or 15 different exercises at pretty high accuracy, 80 to 85 percent.
0:17:49 And we were also able to count the number of those exercises.
0:17:52 So you get this really holistic tracker where you don’t have to select
0:17:55 the exercise that you’re doing. It’ll write a note. Yeah. Yeah.
0:17:58 So this was your first exposure to AI per se.
0:18:00 That’s such an umbrella term these days, but that’s fine.
0:18:03 Were you intimidated?
0:18:08 Yeah, I really did not know what AI meant or that you could just implement it with,
0:18:11 you know, maybe a hundred, a little over a hundred lines of code, right?
0:18:13 Like that’s that is a crazy concept.
0:18:14 Yeah, yeah, yeah.
0:18:18 System that actually learns with just, you know, by yourself.
0:18:21 And then what was the process of actually, I mean, you said, you know,
0:18:22 one of the things, right?
0:18:24 You’re like, it’s so few lines of code and it’s doing the stuff.
0:18:29 Once you got going, once it was sort of, you know, somebody in the program,
0:18:32 the program itself, whatever it was, kind of showed you how it worked, right?
0:18:33 Took you behind the curtain.
0:18:36 What then? Was it steep learning curve?
0:18:37 Was it sort of natural? Was it?
0:18:41 I’m just kind of curious of the perspective, you know, because when I was in high school,
0:18:43 I took AP Computer Science in high school.
0:18:47 And I think I wrote code on like with a pencil on like one of those exam books
0:18:49 for like my AP final, right?
0:18:52 So different world and imagining like trust my eye.
0:18:54 No, different world for sure.
0:18:56 I think there was a little bit of a steep learning curve for me.
0:19:00 I think that that idea of something, a computer learning was just something
0:19:02 that was so foreign to me, right?
0:19:03 Is yeah, no, it’s still it.
0:19:05 Yeah, yeah, exactly.
0:19:07 It still is. But that’s to you to me.
0:19:10 I think the idea of, you know, how much data we need to train
0:19:13 of a reasonably confident model was was really surprising to me.
0:19:16 You know, we had so many people come in and do so many different repetitions
0:19:20 in these exercises and needed all that for the system to be able to learn.
0:19:23 And so I think that was really an interesting moment for me because I realized,
0:19:25 you know, OK, yeah, I guess that makes sense.
0:19:29 If you have so many different examples of what’s wrong and what’s right,
0:19:33 maybe it makes sense that a computer can learn to identify those patterns
0:19:35 and and predict them in the future.
0:19:37 And so those are sort of some of the initial learnings I had
0:19:39 that sort of helped me wrap my head around this concept.
0:19:42 But I think now, of course, that something from a classification system
0:19:44 to what we’re doing today is is crazy.
0:19:49 Oops. Yeah. So so not to do that time leap here, but for the sake of the episode.
0:19:52 So this project, the project with the exercise tracker,
0:19:55 you were still in high school and undergrad.
0:19:56 Yes, I was in high school.
0:19:59 That was my right after my first experience. Right.
0:20:00 Right after activation. OK.
0:20:03 And so then you studied computer science in college.
0:20:05 Yeah, yeah. I think after all these experiences,
0:20:07 it just really felt like natural path for sure.
0:20:11 Yep. And did you have a focus on machine learning or I mean, what in 60 seconds?
0:20:13 What’s computer science in college like these days?
0:20:17 There’s still a lot of the core education around data structures,
0:20:19 architecture, so I had a lot of exposure to different things.
0:20:24 My honors thesis ended up being around AI and actually an open AI model.
0:20:27 The clip model was what I was working with for image retrieval.
0:20:30 And so that was sort of my focus towards the end of college.
0:20:31 Yeah. Gotcha. OK.
0:20:33 And how long ago did you graduate?
0:20:36 Just last May, so it’s been like a little less than a year.
0:20:37 Yeah. Wow. All right.
0:20:40 So in the workforce, where are you up to now?
0:20:44 Technovation, all these experiences have led to you’re in the thick of it.
0:20:45 You’re working at open AI.
0:20:47 I am working at open AI.
0:20:48 Yes, I’m an engineer on chat.
0:20:50 GPT growth at open AI.
0:20:52 And so it’s a really cool job.
0:20:55 It’s all about our growing, our free and paid user base.
0:20:57 And that ends up being really broad in practice,
0:21:00 but it is a really, really cool job to be at.
0:21:02 I don’t know. It’s so new.
0:21:05 But in a way, I think that makes your your perspective really interesting.
0:21:08 How similar, how different is it now, you know,
0:21:11 doing the stuff, computer science and research and engineering
0:21:15 at a job versus, you know, all of these educational experiences
0:21:16 kind of kind of leading up to it.
0:21:18 Is it is it vastly different?
0:21:22 Does it feel like the next logical step sort of on the on the journey of,
0:21:25 you know, kind of familiar, lots new, but learning and growing?
0:21:27 What’s it what’s it like being out in the workforce?
0:21:30 I think it’s sort of required a mind mindset shift for me.
0:21:33 I think when it comes to college and internships,
0:21:36 there is sort of this concept of some end goal, right?
0:21:38 For so in college, it’s like, let me get through these classes
0:21:40 and I can graduate. I want to learn a lot.
0:21:41 I want to meet my friends.
0:21:43 I want to meet mentors and things like that.
0:21:46 But I think when it comes to working in the industry for me,
0:21:49 the mindset shift was now I’m thinking about what is the impact
0:21:50 that I can have on the world, right?
0:21:52 And how can I maximize that?
0:21:55 And before that, that that was sort of in the back of my head,
0:21:56 but not not really at the front.
0:21:59 And so now it’s sort of what I’m thinking about all day, every day, right?
0:22:01 I don’t have homework and assignments
0:22:05 and all these things going on to to distract me from that from that goal.
0:22:07 And so I think that shift was really interesting.
0:22:12 And that was especially why I was interested in in a team like ChatGBT
0:22:13 growth at OpenAI.
0:22:17 I think one of the things that is really important to me is making sure
0:22:21 that it’s not just people in Silicon Valley using ChatGBT to help their daily lives.
0:22:25 It’s people in, you know, corners of the world where maybe they don’t have
0:22:29 a super powerful mobile phone that can even support an internet connection
0:22:33 for ChatGBT, but maybe they can message ChatGBT through WhatsApp
0:22:37 and get the messages that and and get help and information
0:22:38 and whatever it is that can help them in that way.
0:22:42 And so I think that that sort of shift in mindset from from college,
0:22:45 from learning to working has really helped me shape my goals
0:22:48 and and just feel passionate about about our mission
0:22:50 and what we’re doing with with the product like ChatGBT.
0:22:51 That’s amazing.
0:22:54 I mean, it’s not it’s not my place to say if this makes sense,
0:22:56 but that’s an asset to the world to have that mindset.
0:22:58 So that’s fantastic.
0:23:03 What advice would you give to girls in high school and college
0:23:07 who, you know, are just into the field or interested in computer science
0:23:10 or maybe, you know, the teenager who’s thinking about it
0:23:13 and getting their first exposure to to working with some of the tools?
0:23:15 Oh, this is kind of cool. Where can I go with this?
0:23:18 Any advice? Any maybe learnings from your journey?
0:23:21 Yeah, I think for my journey, I think one of the things I can say
0:23:24 is building confidence early and not being scared of failure.
0:23:28 I think I probably never would have discovered AI if I hadn’t had
0:23:31 I guess the audacity to go after that first research in a new chip
0:23:33 and say, hey, maybe this is a room I belong in.
0:23:36 And, you know, that was not something I felt when I was applying.
0:23:40 I was talking about this boggle program that I built in my computer science class.
0:23:44 I was like, how am I about to jump from this to searching in a lab
0:23:45 and building things for real people?
0:23:49 But I think having that courage to go out and say, I can do this.
0:23:52 That this is an opportunity that is for me as much as it is for anyone else
0:23:55 is something that’s really important to help you to find your path.
0:23:58 You open doors that you never knew were even there.
0:24:01 I think along with that, what goes into building confidence
0:24:05 is finding mentors and a community that can support you in in these things.
0:24:08 And so for me, that came through Technovation, right?
0:24:12 Even now in the Bay Area when I’m back, that community is still there supporting me.
0:24:17 And, you know, just meeting amazing other alumni in the Bay Area was such a surprise for me.
0:24:21 I didn’t know that it would last, that the community would last long for me.
0:24:23 Communities, women, engineers in college, right?
0:24:26 Meeting meeting other people that are doing similar things to you
0:24:28 is the most inspirational thing that you can find, right?
0:24:30 It’s right. If they can do it, I can do it too.
0:24:35 Yeah. And mentors, of course, as well, so many helpful mentors along my journey
0:24:38 had helped me figure out what’s right for me and, you know, wrap my head around
0:24:40 moving from college, going into the industry.
0:24:43 And I think that’s really important to to have some people that have been
0:24:46 through the same things that you’re going through and getting advice
0:24:50 or just even just talking through what you’re going through.
0:24:52 Yeah, absolutely. What is Wiser AI?
0:24:57 Yeah, so Wiser AI is an organization, loosely, that I’ve recently been working on.
0:24:59 It’s it’s more of an initiative.
0:25:02 Just I think what I noticed when I first started working in AI,
0:25:07 even in research and college was we have done so much work to sort of uplift
0:25:08 women in technology.
0:25:12 And there was a big focus, I think, on on that gap when I was in high school
0:25:13 and even middle school.
0:25:15 And I was really seeing feeling that around me.
0:25:20 But as I got into AI and research, I was noticing these are all really,
0:25:21 really male dominated rooms.
0:25:25 And for some reason, I just wasn’t finding as many people talking about
0:25:28 women in AI and underrepresented folks in AI.
0:25:33 And when I first landed the opportunity at Open AI, there was a lot of chatter
0:25:36 about, you know, how quickly AI is moving, you know, the exponential curve
0:25:40 at which these models are performing on these really, really powerful benchmarks.
0:25:44 And that really got me thinking, if we’re building this really powerful
0:25:48 technology that is meant to benefit everyone, how are we going to build that
0:25:52 if we don’t have everyone’s voices involved in developing the system?
0:25:57 And so Wiser AI is an initiative targeted towards that and in bringing
0:26:00 more underrepresented voices in AI, specifically women.
0:26:03 One of the things that I’m really interested in doing with this initiative
0:26:05 is supporting underrepresented founders in AI.
0:26:09 I think that’s a really interesting space where the progress in terms
0:26:13 of like female founders and, you know, the percentage of females founders
0:26:18 supported by investments or even just the percentage of like all women founded
0:26:20 companies is really, really surprisingly low.
0:26:23 I think that was an interest of mine at one point, right, becoming a founder.
0:26:26 And it’s it’s really surprising to see that that’s still.
0:26:32 Yeah, it’s it’s so unreflective of the actual, you know, population
0:26:34 of people walking around the planet. It’s yeah.
0:26:35 And so you started Wiser.
0:26:37 Yes. Yeah. Yeah.
0:26:38 Bringing together a team right now.
0:26:41 I think one of the really big things that we want to do this year and sort
0:26:45 of the big target for the organization this year is to actually hold a
0:26:49 conference, bringing together underrepresented founders in AI, seeking
0:26:52 people that can talk about what it means to build responsible AI and how
0:26:54 we can incorporate that into our systems.
0:26:58 And yeah, just just helping bring together a community that can support each other.
0:27:03 Amazing. So, Tara, I want to sort of welcome you back for this kind
0:27:06 of last sort of wrap up thing and ask both of you.
0:27:09 And I feel like this is one of those questions that like shouldn’t need
0:27:13 to be asked out loud and answered because it should be obvious, but it’s not.
0:27:16 Why is is the work you’re doing so important?
0:27:21 And why is it so important to work for equitable and inclusive AI right now?
0:27:25 You’ve talked about it throughout the episode, but to kind of put a point
0:27:28 on it and really bring it to bear, why is this so important right now?
0:27:33 As I was saying, like the large language model has been built on the majority,
0:27:37 right, like the majority language on the Internet, which may not
0:27:39 necessarily reflect the reality.
0:27:44 And so you actually get boring results because it’s the generic
0:27:46 very, very the lowest baseline, right?
0:27:52 And so what’s the fastest way to make more innovative products, right?
0:27:53 Is to open it up, right?
0:27:55 And I think there could be many ways of doing it.
0:28:00 I think one obvious way is look, 70 percent of the developers
0:28:04 are of a particular background and from a particular cognitive perspective,
0:28:08 right? And so when you have different people around the room, I mean,
0:28:12 it just, it came from India last night, and it’s fascinating, right?
0:28:15 Like just going to a different place and understanding how people
0:28:19 in different cultures think it just increases your sort of scope, right?
0:28:21 So it’s just basic stuff.
0:28:26 So I’m not big on business axioms per se, but isn’t there just a basic guideline
0:28:29 of sort of product development that, you know, the more that what you’re developing,
0:28:32 the more that people building the thing sort of reflect the people who are going
0:28:36 to be using it, the better off you’re likely, you know, the thing you’re
0:28:37 making is likely to be now.
0:28:41 Absolutely. And the market is beyond the U.S., right?
0:28:44 No, I mean, this is, this is everything you’re building for the world.
0:28:48 So to me, it’s just a very basic common sense argument that you want
0:28:51 a diverse group of people innovating.
0:28:57 And I will say that the challenge is the big one because women represent
0:29:00 I think the largest group of people that have been historically
0:29:03 discriminated against as a whole, right?
0:29:07 So I think 151 countries legally discriminate against women.
0:29:10 And so social norms are very deeply rooted, right?
0:29:13 And I think men and women are extremely different.
0:29:17 So I think that the conversation can be very nuanced and subtle.
0:29:22 But from an economic point of view, I think you are missing out if 50%
0:29:27 of the population isn’t represented in your software development teams.
0:29:30 You know, it’s not enough to say we have equal representation
0:29:32 in sales or HR or whatever.
0:29:34 I think it has to be in the software development team.
0:29:39 So I think everybody will benefit when the products are less boring.
0:29:44 Yep. Anshita, in many ways, this whole conversation, particularly you and I
0:29:47 speaking, you talking about your work in the past several minutes, you know,
0:29:51 address this question, but specific to talking about the importance of working
0:29:54 for Equitable Inclusive AI right now, anything you’d like to add?
0:30:00 Yeah, I think overall we’ve already seen the effects of building bias systems.
0:30:03 In systems like predictive policing or in health care.
0:30:07 We know there’s been many studies about how this affects
0:30:09 disproportionately affects underrepresented populations.
0:30:14 I think at the rate at which AI is developing right now, these systems
0:30:18 are going to be everywhere, not just in health care, but also in finance,
0:30:19 hiring everywhere.
0:30:23 And so I think it’s just we’re at a crucial point where if we don’t
0:30:26 start doing that work towards making these systems inclusive, when they’re
0:30:28 everywhere, it’s hard to take that back.
0:30:30 I think it needs to happen now.
0:30:35 For listeners who want to learn more, want to get involved,
0:30:39 maybe want to see if they can join Technovation, any and all the above,
0:30:44 all the great work you’re both doing, where can folks go online to learn more?
0:30:47 Again, I’ll plug Episode 85, Tara, your first appearance on the pod.
0:30:51 But for more information about Technovation, Tara, where can people look?
0:30:54 Yeah, I would say the Technovation season is ongoing right now and you’re
0:30:59 actively looking for mentors, so and you don’t need to have a technical
0:31:02 background to be a mentor, because what you need to bring to the table is
0:31:07 just this courage to say, I don’t know, let’s go find out together.
0:31:08 Because guess what?
0:31:11 You probably don’t know because these things are changing so fast, but it’s
0:31:15 an incredible experience where you’re project managing and cheerleading
0:31:18 a team of girls to actually build an AI prototype within three months.
0:31:23 So it’s an amazing way to actually go through an accelerator yourself.
0:31:27 We’ve had stories where mentors have applied to my combinator and gotten in
0:31:30 after they went through Technovation because Technovation gave the mentors
0:31:32 the courage to start their own business.
0:31:36 So I’d say 95 percent of the time when girls match with mentors,
0:31:37 they finish the full program.
0:31:41 So mentors are the one of the most important pillars for our program.
0:31:43 So please sign up at Technovation.org.
0:31:45 Technovation.org?
0:31:45 Yeah.
0:31:46 OK, great.
0:31:48 And Chita, is there wiser?
0:31:50 Can people find out more about wiser online?
0:31:54 Yes, and I’m actively looking for women to work with to organize this
0:31:56 conference that we have a goal of having this year.
0:31:57 Do you have a location in mind?
0:32:00 It might have to be around the Bay Area just because of production,
0:32:04 like maybe in the Bay Area, but it’s still still working on a location.
0:32:06 Honestly, so open to any suggestions.
0:32:10 But we would love to have more talented, amazing, passionate women to work with.
0:32:16 They could reach out directly or wiser.ai.org is the website.
0:32:17 Excellent.
0:32:19 Well, again, thank you both so much, Chita and Chita,
0:32:23 for taking the time to come on the podcast and share the work you’re doing.
0:32:28 It’s just really great, inspiring work to learn about and for people to get involved with.
0:32:32 And, you know, anybody out there who’s considering maybe being a mentor,
0:32:34 if you’ve never had that experience, I haven’t worked with Technovation,
0:32:38 but I’ve done other related things and it’s the best.
0:32:41 And not only that, but you get to go through an accelerator yourself.
0:32:42 It’s kind of hard to beat, right?
0:32:43 Let’s do it again.
0:32:44 Let’s catch up down the line.
0:32:48 But it was an absolute pleasure to meet you and Chita and all the best to both of you
0:32:49 and the work you’re doing.
0:32:50 Thank you, Noah.
0:32:51 This was a pleasure.
0:32:52 Yeah, thank you so much, Noah.
0:32:53 And thank you, Tara.
0:32:56 It’s just so inspiring to hear about the work that you’re continuing to do
0:32:58 and knowing how much of an impact it’s already had on me.
0:32:59 So thank you.
0:33:00 That made my day, right?
0:33:03 Like, listening to alumna and saying, OK, all the hard work
0:33:05 and your vision of improving the world.
0:33:07 Like, that’s what we work every day for, right?
0:33:11 So I wouldn’t have the energy to do what we do in the battles to fight
0:33:13 if it weren’t for the alumna, so it’s mutual.
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In this episode of the NVIDIA AI Podcast, Tara Chklovski, founder and CEO of Technovation, returns to discuss the importance of inclusive AI. With Anshita Saini, a Technovation alumna and OpenAI staff member, Chklovski explores how Technovation empowers girls through AI education and enhances real-world problem-solving skills. Saini shares her journey from creating an app that helped combat a vaping crisis at her high school to taking on her current role at OpenAI. She also introduces Wiser AI, an initiative she founded to support women and underrepresented voices in AI.