Technovation: Bridging the Gap with AI Literacy | Intel Technology
(upbeat music) - [Narrator] Welcome to "What That Means with Camille." In this series, Camille asks top technical experts to explain in plain English commonly used terms in their field. Here is Camille Morhardt. - I'm Camille Morhardt. Welcome to today's podcast, "What That Means: AI Literacy in Today's World". I have with me Tara Chklovski, who is CEO and Founder of Technovation.
Welcome to the show, Tara. - Thank you, Camille. I'm excited about this conversation. - I'm really glad to have you on. You've been written up, you're sort of famous, you're well regarded and well known in the land of AI education and including you were recently written up by "Forbes" for your very ambitious goals of educating or reaching 25 million young women in the next 15 years.
Before we get started, maybe you could just let us know what is Technovation? I know it originally was founded with a different name, and then we'll dive into the conversation. - Yeah. I started Technovation, which is called Iridescent almost 18 years ago. And the goal was to bring the world's best technologies and the best education resources to the world's most needy communities, to the most vulnerable populations, and over time it has focused in on girls and women because they are the ones who are... There's no country in the world that has gender equality, and they're the ones who are on the forefront of many, many, many hardships.
And so that's what Technovation is, and over the years we have narrowed into one particular form of programming, which has shown to have like long-term impact, which is a 12 week competition where girls work with mentors from industry, they find a problem in their community and they develop either a mobile app or more recently an AI prototype and actually develop a business plan to take it to market. And this three month experience has shown to have life-changing impact on these girls. And based on that data, we decided we need to bring this to many more girls and young women around the world. And we are partnering with UNICEF to be able to do that.
And our goal is to reach 25 million young women. There is, at the moment, there are only 3 million women in technology who are professionals, and we hope to double that to 6 million by 2030 or so. So yeah, the number of women who are driving innovation in the tech sector dramatically needs to increase, and I think that's a very powerful risk mitigation strategy, especially when it comes to AI. - We were talking about, you know, AI literacy just generally speaking, and maybe even it's more broad than that. Maybe it's like technology literacy in today's world it used to maybe be familiarity with a computer or then, you know, familiarity with the internet or access to it.
Do you have a sense of a definition for, you know, tech innovation and then also just kind of, is there a generally agreed upon definition for technology literacy or AI literacy in today's world? - Yeah, and I think the education world has been grappling with that because it's just, it's more, it's beyond just the memorization of terms. And so I think computational thinking is one interesting lens for us, it's really about going beyond understanding how it works and being able to apply it to complex real world problems. Because that's when you really begin to grapple with the technologies, that's when you really understand its limitations and that's when you really are able to bring your own lived experience and come up with something new. And that's the definition of innovation.
And so for us it's always been not just learn how to code because it's an important skill or anything like that, but it's more about build a mobile app that is solving a real world problem. So it's a tool. So in 2010 it was mobile apps. We started doing AI education in 2016 and we just finished our World Summit last week.
And I think we had about five or six out of the 15 finalist teams who used AI in their solutions. And I was just blown away because it is, they are learning how to use large data sets, they're learning how to have varied sort of representation in their data, and then they're training machine learning models and using all sorts of different sort of algorithms to be able to come up with better higher prediction and higher accuracy. And I would never have imagined that in 2016, like these girls would be able to do this. And girls as young as 11 years old, they're using Kaggle datasets on lung cancer in India so that they can provide better guidance to patients who may be at risk or 'cause so many of these data sets exist now as open data sets.
And so data science is underlying a lot of this. And so it's probably one of the components of the AI literacy, but to me the very exciting part is what are these girls solving with these kinds of building blocks? - Yeah, could you give us a couple more examples? - So one example was to help teenagers assess mental health issues. And so they ran a survey first and they got very few respondents. So then they went and actually found an open data set of a lot of text snippets and they did a sentiment analysis on that. And then they trained their model on if a child were to sort of say how they're feeling, then it would kind of give them guidance and feedback.
And so that was one example of sentiment analysis and figuring out, okay, our current data set is not large enough to have that kind of accuracy. So they went and found one, another one from a team in Canada looks at chest x-rays to look for cardiovascular disease. And so it's called cardiomegaly. I didn't know what that term was. And so then they trained their model on these chest x-rays and came up with a way to predict when or where you are at risk.
Another one from India created earthquake prediction app because in India there isn't an app or in many countries around the world. This weekend we had this massive earthquake in Afghanistan, and so there's no prediction system. And so she's using USGS data and then overlaid it and then use like random forest algorithms to come up with a decision tree to say what is gone, what's the likelihood of something to happen, and then what is a safe exit route? So it connects onto maps and helps you sort of figure out, okay, what should I do when this is happening at this moment? So just very, very, very interesting use cases and that's the power of having a diverse group of people learning these tools and applying it to their problems. So we don't stop at, "oh, learn about how this machine model works, learn about how ChatGPT works," but it's "use it to solve a problem that you're facing." - And the problems that people are solving are often local in their communities or regions.
And can you give us a sense of like where all of these girls are from and you know, how big are the teams? And then also I'm also interested in like, I think you pair them with mentors and so what would like a typical mentor, obviously this is gonna require some kind of technical expertise from a mentor as well to help guide on, you know, size of data sets and whatnot. - Almost 50% of our mentors don't know how to code and they're not technical and they participate in this program because they are learning because we have a free curriculum that walks you through from the very basics of what is AI and is my sort of robot rock or my robot vacuum cleaner and AI or not, right? So I think that all the way to actually developing a functional prototype and understanding what are like good data sets and not, I think the girls come from all over the world. I think just because World Summit is fresh in my mind. One of the teams was from a very remote region in northern Kenya, which is a fishing area and they created an app. It didn't have any AI in it, but basically providing a tide prediction information to fishermen because of climate change. Lots of things are changing and these kinds of basic information is not available to a lot of people in the world.
Roughly 120 countries around the world, and many times I have to look up these countries on the map. One of my favorite alumna who actually has launched actual business that's running is from Caribbean island called Dominica. It has a very small number of people.
I don't wanna get the number wrong, but it doesn't have a university. So she's going to a virtual university, just the whole range. Our big country, of course, US, Canada, India, Nigeria, Mexico, Kenya, Brazil, Chile, Spain, but almost a lot of countries.
And I'm sure we can look up and see if we have girls there. - I wanna come back to the projects and the people, and some of the, you know, concerns too, are we thinking about ethics and privacy? But before we get back into those, I wanna know a little bit more about you because you are, you know, all but dissertation for a PhD in aerospace engineering, and specifically on I think the aerodynamics of bird flight, which is like fascinating I think. And that was from University of Southern California. Tell us like how did you decide to pivot and do this instead? - I think I'd always wanted to be an either a pilot or build airplanes, and especially those inspired by birds because birds are incredibly beautiful and so efficient. And so when I was eight years old, I grew up in a pretty poor area in India and community and family but I had an old copy of the "Popular Mechanics," and it had Paul McCready on the cover and Paul McCready was the father of human powered flight, and he made the first solo partner airplane, he made the first solar powered car, race car. And so I was like, I wanna work in this company.
And so I got a degree in physics and then I got a master's in aerospace at Boston University, and then I slowly made my way to southern California where his company AeroVironment was. And I was in a PhD program under advisor who at that time, was the only person in the United States studying bird flight. And I think I didn't really know how to choose an advisor to be completely honest. And I didn't really know, I didn't know that he had never graduated a student successfully. So I really wanted to study seabirds because they are incredibly efficient, they can go for very long distances.
And I got an internship at AeroVironment and I was working there, I was the only woman on the research team. And at that time they started to slowly switch towards drones. They were the first company to start making drones that went into the Iraq war. And that was so far from what I had imagined. And I didn't wanna work in a big aerospace company and there wasn't a aviation startup industry as there is now. And so it was a real time for me to reset.
And I spent, I don't know, maybe then I've forgotten how many years I was in the PhD program, maybe four or five. And there was no other option and I didn't feel like I wanted to just quickly finish and go get another job. And so it really was like going back to the drawing board of what can I do in the world and what are some problems, big problems that resonate with me and my family has a history, the women have a history of when there's a major change in their lives, they start a school because in India, like, I mean almost every country, like education is an acceptable profession for women. And my grandmother started a school when she was 60 years old, she cut down a tree and made it into one table, one chair, one chair, and opened up a school. And the school is 45 years old now, and thousands and thousands of children have gone through it. And so my mother, she was a doctor in the army, she left the army and then she was running a school.
And so education was definitely a key part in my mind, but as one of the most powerful solutions we know to address inequality and injustice. And I just always feel that where you are born is no credits to you. So, and where you're born determines so much of your life.
And we did nothing to earn it or not earn it. And I think that should not determine a person's fate anymore. And education can change that, internet can change that, and a combination of that can change that. And those were the underpinnings of how I started Iridescent.
- Thank you for sharing. That's really interesting. Do you feel like there's an element of technology now, and maybe you're gonna say AI or maybe you're gonna say large language models, I'm not sure because it allows coding to occur just in language as opposed to having to, you know, learn, and I shouldn't say new because a lot of people would have to learn English in order to use some of the LLMs. But is there yet a great kind of equalizer or potential bridge for this digital divide? I mean, the internet is sort of like a double-edged sword, I suppose.
What is your take on whether there's a new or an emerging concept in technology that might really help make it matter less where you were born, kind of your opportunities? - I think the people focused strategies are really the key there. I think I'm not a very big fan of talking about the digital divide. And the reason is that I think it's a very solvable problem and it is one of the solvable problems, or it is one of the things that you are making the most progress on.
On the UN 2030 agenda, there are 36 indicators and there are only three indicators that we are on track as a world. And those three indicators are access to electricity, access to internet, and access to mobile phones, nothing else. People will get access to mobile phones because guess what, it's in the best interest of corporations to do so, and for the consumer it provides immediate value. So it's not a very hard problem. I think the harder problem is things like, which are people focused and that are not as glamorous or sexy or...
And the silver bullet part where you say, okay, these number of people need to get access to phones and then somebody cuts a check and you get the phone. So the harder problem is, well, you need to teach, train all these teachers who then need to go and have these kind of more interesting ways of teaching these children. And then you have to assess the impact and follow up and make sure there's no corruption. So I think the focusing on bringing these powerful technologies to girls is a very, very, very important strategy for addressing inequality today. And I think one of the problems is that too often girls' education is limited to primary and secondary education, and people think in sequential, "oh, the girls cannot, why are you thinking about bringing, teaching them about AI when they don't even know they've not finished whatever, like the sixth grade or seventh grade?" And I love what Seymour Pepper used to say, they would be a world where a three year old could interact with a computer and ask and say, show me a picture of a bear without even knowing how to read or write. And that totally happens now.
So there's no reason to have a sequential way of saying, "oh, girls need to go through primary and secondary before learning about computer science." Too often many girls focused initiatives and organizations are run by women themselves who are tech phobic because they went through a lot of the same systems about, "oh we shouldn't," so they're not as interested or excited about bringing AI into this. AI feels very scary.
So I think then you have a vicious cycle where you're not bringing in these kinds of new technologies and new ways of learning and thinking into these very vulnerable populations. - No, it's interesting. And I also wonder like, let's talk about bringing AI then into education and what are some of the reasons people are afraid of it? Because surely some of those are valid, right? I mean, how are you addressing, you know, teaching or helping people understand or interact with technology that has big implications for privacy and ethics? You know, how are you bringing those components in or are you purposefully not bringing them in and saying like, this is a technology and you know, that kind of thing has to be handled elsewhere. How are you dealing with that? - I think just education, like being very open about it. And that's why we have a very detailed three month curriculum that teaches the adults really honestly, the girls are not worried about these things, but we teach them exactly about how a large language model works.
How do you train a model, what does a good data set look like? What's wrong with this and not... I think education is the only way to combat fear. And I've heard too much of the same kind of argument. "Here are all the risks." Well, yeah, I mean every kind of technology has risks.
The only way to combat that is to change the makeup and format of the teams that are behind this and to make sure you have a very broad set of people who understand how this works. - So they're asking the right questions and they're using these technologies in the right way. It's not gonna stop and it's not gonna slow down.
So you better catch up. - You mentioned that a lot of the girls are looking at open data sets so that they can, you know, build models with enough data to make a difference. So what is your perspective on that? Like whether, you know, I've kind of heard multiple perspectives with different people I talked to about, you know, how much data sets and there's a privacy concern. Obviously anything released would be in theory, anonymized or you know, some kind of privacy, differential privacy or something used in conjunction with it. But what is your take on data being made open as long as it's done anonymously? Versus data remaining sort of within the control and purview of those who collect it? - To be completely sort of clear, the girls, they go through this program once mostly, and when they create a prototype, they're still in school even when they win thousands of dollars for the winning app or, they're still in school.
So we give the funding not to launch their business, but for them to continue their learning and deepen and most of them don't continue building their app. And then many of them come back year after year. And as you come back, you get a deeper understanding of the problem or a deeper understanding of the tools. And that's when you begin to like, "Oh my goodness, I never thought of this, I never thought of that."
So we have quite a robust set of curriculum, but they're not fully processing every part of it. Like that's why you study English for so many years or grammar or literature or whatever, and you keep peeling the layers of the onion. In terms of open data, I think all of us are also grappling with that. What does it mean to own something when maybe a lot of people have contributed to it and knowingly, or not knowingly, but I don't know. I think again, as an educator, I think our job is to present the problem and to help girls think about it.
One of the things that comes up very, very frequently is initially the girls would want to patent the idea. And I was like, "Guess what? You are here because people have donated their money. Corporations have funded this program, thousands of volunteers are volunteering their hours.
This program wouldn't exist without generosity and kindness. And the right thing to do is to take your intellectual property and bring it back into the world and make it open so others can build on it." That's the viewpoint and philosophy that I hope the girls are walking away with where rather than "I going to keep my idea," because there are plenty of great ideas. The hard work is in the execution. And I think maybe like the main thing is don't hurt anybody.
And we have a pretty detailed ethics curriculum, but some of these questions are very tough and I know a lot of these companies haven't figured that out either. So the right thing to do is to discuss and to learn. - What are some of the like most interesting questions that you get or that either the girls ask or that the companies ask or that, you know, NGOs are asking like in this space, what are people still sort of perplexed about or wondering how to approach in technical education, AI education? - People are responding in kind of a fearful mode where let's just create like a very detailed curriculum and give it to students.
And I think that, I don't think that's the right approach. UNESCO did a pretty deep report of state of AI education last year and there are about 16 countries in the world that have adopted AI education as part of the national curriculum. And all of it is just a bunch of facts, right? Or if anything, it links to Andrew Ng's course on machine learning now, which kid would follow that course and get anything out of it? The key to think about is not from a place of defense or of fear, but more like how can we empower students and young people to use these very powerful technologies to tackle these enormous problems that we are handing to them. We are not, we are giving them climate change and like severe inequality and not that many solutions.
And I think AI is one of the positives. I'm not a complete like rosy picture painter here, but I just think that we underestimate children. I see this all the time, and girls tell me this all the time, that "my parents never thought I was capable of this" because they never thought that their daughter could get up on stage and talk about the AI model that she built. And she's only eight.
So I think that that to me shows that we are severely underestimating young children and we shouldn't just stop at teaching them about how it works, but actually challenge them to say, okay, use these tools. - Do you think that large language models specifically are changing the game as opposed to other kinds of AI programming that could occur? - We were one of the first organizations to use ChatGPT in the first week of December last year because as soon as it came out I was like, oh my goodness, like this is gonna have so many implications. And so we rapidly created a whole series of resources and pushed it out into the community and we got back quite a bit of data because we were able to act so quickly. And what we found was that most of the teams, of course were using it for ideation and coming up with like new solutions. But the second most common usage was translation, which was very interesting and not something that we had expected. And our curriculums actually use block-based language coding platforms.
And what I was surprising was so many teams use that to ask for coding advice and at that point I was like, how are they using this for blog based? But I guess it was teaching them what should be the algorithm like so I think then I think we see this all the time, right? Like users surprise the inventors of the platform. But I think that coding will become easier and easier. The need for computational thinking and the need for understanding how these models work are critical because the tool itself is only a tool and that tool still needs to be deployed into the real world.
And only when you understand how it works, do you understand the negative implications or the positive implications. - And right now you are open, actually your organization is open to so accepting new applicants or can you just describe a little bit if people are interested? - We launched yesterday, it was the International Day of the Girl and we welcome anyone who has a daughter, a girl who's teaching students to join. You can go to technovationchallenge.org and sign up to be a mentor, to be a coach, to be a judge. We need lots and lots of volunteers if you wanna be a club ambassador, all of these take different amounts of time. And then of course for girls, it is the organization and the program that has research to show that it dramatically changes a girl's sense of self-confidence, her problem solving skills, her abilities to work in a team, her ability to innovate all of the skills that the future needs.
- And when the girls get together in teams, do you sort of help form the team? Like a girl gives a submission, gives her age, gives her interest, are they like teams from around the world or pocketed together? - Absolutely. So I think the app that I was mentioning about mental health one, some of the girls are in the US and then one team member is in the Philippines. And so yeah, so you can find based on your geographic location who's looking for a team. And then you can also connect with mentors similarly where you can see by expertise or time zone. And yeah, Technovation is unforgettable experience, so I highly recommend everyone to try it. - Thank you very much, Tara Chklovski, CEO and Founder of Technovation.
Really appreciate your time today. - Thank you Camille. Those were good questions.
(gentle music) - [Narrator] Never miss an episode of "What That Means with Camille" by following us here on YouTube. Or search for InTechnology wherever you get your podcasts. - [Narrator] The views and opinions expressed are those of the guests and author and do not necessarily reflect the official policy or position of Intel Corporation.
2023-10-30 08:44
