In this episode, Alison Wade and Jessie Shternshus chat with Davar Ardalan, a Woman Who Vows to Improve Cultural Intelligence. Davar is the Founder and Storyteller in Chief of IVOW, an early-stage startup specializing in AI-driven cultural content, aiming to make navigating the global marketplace easier via cultural analysis. She is also the former Deputy Director of the White House Presidential Innovation Fellowship Program in Washington D.C. Before this, she was a public broadcasting journalist for two decades at NPR News, where she designed stories anchored in multiculturalism and steeped in historical context.
Alison Wade: You’re listening to Women Who Change Tech, the podcast that gives you access to women who are contributing, inspiring, trailblazing and disrupting the business of technology. We believe that when women inspire other women, amazing things happen. I’m your host, Allison Wade,
Jessie Shternshus: And I am your host, Jessie Shternshus and we are connecting women around the world to share ideas that help us thrive and advance in our personal lives and in our careers.
Alison Wade: Be sure to share this podcast with your friends rate and subscribe to the podcast on iTunes and help us bring Women Who Change Tech into the lives of more professionals.
Jessie Shternshus: Hold on to your seats as we give you a dose of inspiration from some of the most talented and creative women who are shifting the face of technology.
Alison Wade: Good morning. Jessie, how are you?
Jessie Shternshus: I’m doing good. How are you doing Alison?
Alison Wade: Oh, I’m doing great. I am just, you know, our last podcast we did with Cindy Peterson. So I’m sitting here looking through my instinctive drive results and your instinctive drive results and how we work together and collaborate. And this stuff is so fascinating, isn’t it?
Jessie Shternshus: It is. I thought it was pretty spot on and like, hmm either somebody read my diary, or this thing works. What did you think?
Alison Wade: I was quite shocked actually at how accurate it was. And it was, it was quite an affirmation for me because I feel like I’ve done a pretty good job of creating my world in order to you know, match what drives me. I mean, it’s taken a long time, mind you. It’s been a long time in the making, but I really feel like the things I do are in line with my drives. How about you?
Jessie Shternshus: Yeah, I felt that way too. And what I really liked about it is the part where it tells you kind of how to work with other people and so I thought it was neat that we could look at the way that we should collaborate with each other so that we don’t get on each other’s nerves because you know we do that all the time. We’re constantly getting on each other’s nerves me and you. So it’s good to have that breakdown. Just kidding. So it’s good you know, so now I know how to get on your nerves even more.
Alison Wade: Now you really have the targeted tools to get on my nerves.
Jessie Shternshus: I can really push your buttons. So thank you Cindy for that. Just kidding.
Alison Wade: Jessie and I are very similar. In our two of our drives. We scored the exact same score which I thought was so interesting. We both scored really high and improvisation. Weird, right? Who knew. Who knew we were sisters from another mother.
Jessie Shternshus: Weird. With the name like the Improv Effect for my business. So strange.
Alison Wade: I know, I thought about that. I’m like It’s just as well Jessie scored high in improv because what if she didn’t?
Jessie Shternshus: That’d be a real bummer.
Alison Wade: It’d be like a career change!
Jessie Shternshus: Switch it up.
Alison Wade: Well, speaking of instinctive drives and amazing people, I can’t wait for you to meet Davar today. I mean, she is a woman who is so driven to her passion of what she’s doing in creating a cultural revolution inside of AI to speak to how information is propagated about cultures and women and culturally appropriate statistics that people can use in their businesses and I can’t wait for you to talk to her.
Jessie Shternshus: I’m really looking forward to hearing what she has to say.
Alison Wade: Let’s introduce her. I want to introduce everyone to Davar Ardalan, a woman who vows to improve cultural intelligence. She is the Founder and Storyteller in Chief of IVOW, an early stage startup specializing in AI driven cultural content, aiming to make navigating the global marketplace easier via cultural analysis. She’s also the former Deputy Director of the White House Presidential Innovation fellowship program in Washington DC. Prior to this, she was a public broadcasting journalist for two decades at NPR News, where she designed stories anchored in multiculturalism and steeped in historical context. Davar recently delivered a keynote at the STARWEST Testing Conference on Storytelling in the Age of AI. I had the pleasure of meeting Davar when she delivered that keynote. And now I know you’re gonna have the pleasure hearing today. The work she’s doing is incredibly interesting. And she’s just a wonderful human. Welcome, Davar.
Davar Ardalan: Thank you so much, Alison, and Jessie.
Jessie Shternshus: So happy you’re here.
Davar Ardalan: Thank you.
Alison Wade: Yeah, we are really happy you’re here. It’s so wonderful to have you. I’m so glad that you got to meet Jessie today. It’s always exciting when people from different areas of my life get to meet, and I know you guys are gonna love talking to each other today.
Davar Ardalan: Thank you. Awesome.
Alison Wade: You have like such an incredibly interesting background. Can you go ahead and tell us about your background and how you got to where you are today?
Davar Ardalan: Yeah, absolutely. So I’d love to share more about my experience at NPR News, so I was a Senior Producer there for many years, as you indicated, and when I would close my eyes to write a story, I would imagine, I’m writing a story for one person, but in fact, I had to reach 30 million people. And so as I looked at the future of AI, it felt like it was very similar to writing a radio story where you have to be able to touch a user, one user, but you have to make sure you can scale. And so so much of my experience as a storyteller at NPR, is really helping fuel some of the creative products that we’re creating at IVOW AI. And I’m actually talking to you from Washington, DC, in an incubator where I just started. So it’s really exciting because I’m surrounded by a lot of entrepreneurs right here and a lot of advisors and meeting a lot of investors, so it’s a really fun time to be talking to you.
Alison Wade: That’s wonderful. And I love that you used to be NPR and that all of your stories were anchored in this multiculturalism and historical context. And now you’ve taken that to a totally different platform, which is AI and when I when I first met you, one of the things that you explained to me about the way AI is working is that these images that are out there are getting tagged and getting reused and getting propagated by AI and if we’re not careful to intervene, the world could look, have one voice, one color, one everything and could become very stereotypical. So one of your goals is to inject more context and more multiculturalism into the world of AI, right?
Davar Ardalan: Yeah, absolutely! And you know, what’s really important is that there’s actually profit in this because brands are looking for new ways to reach people in a more culturally relevant way. And so our AI platform actually enhances the cultural fluency of brands and their chatbots. What this means is that we have the nucleus of our technologies, a culture graph that’s ingesting public information around festivals, food, music, arts in various zip codes and regions around the world, and through a display of a world map, you can actually see where events are happening and brands can reach people in completely new ways. And the way the AI works is that let’s say you’re trying to book a hotel with Hawaiian Airlines or a travel and you want to be there during the time that there’s a hula competition. And you’re not the chatbot isn’t necessarily going to know what hula means. But as soon as that they’re connected with our cultural engine, the chatbot will be able to understand what hula is and be able to send the customer a link to some of the festivals that are happening with that particular hula in mind. So really gives you a sense of how injecting cultural information into AI narratives will make number one, it more culturally inclusive and number two, a much more interesting experience for customers.
Jessie Shternshus: So is that something that it kind of couldn’t do before?
Davar Ardalan: Yes, so right now, actually, brands are having a lot of issues with creating personalized content. There’s a lot of recent studies around marketers wanting to customize their campaigns and in a recent report coming out of Europe, by Qualifio 83% of marketers surveyed said that their biggest challenge is creating personalized content. And this is at a time when conversational AI’s are becoming ubiquitous and what I mean these are the speech based first assistance, the chatbots, the AI powered customer support services. And so currently, the brands are lacking the tools to have a sophisticated engagement with customers with richness of culture, right? And so obviously diminishes the return on automation investment and weakens their ability to deliver a meaningful engagement and experience
Jessie Shternshus: In why, why does this matter to you? Like, what makes you get up every morning and get you excited to make these kinds of changes?
Davar Ardalan: Yeah, well, when I was at NPR and working in public broadcasting, I think every one of my big mandates was growing the audience beyond the radio. So you’re looking at all the places where new audiences are coming in. And then at the time it was Twitter and Facebook, and this was before they were taboo. This was when digital platforms were new places of engagement. And so I was able to successfully create new conversations and even new news programs that were really heavily relied on reaching audiences in new ways. And when I looked at the future of automation, I saw incredible opportunities and some of the same challenges that exist in public broadcasting where we don’t really have the voice of the public, we need to be better at making sure that we’re more inclusive, even in public broadcasting. I saw that the challenges with automation are going to be even worse unless we pause right now and we find a way to think about beneficial AI, beneficial artificial intelligence that is going to allow us to have the richness of global voices, as Alison mentioned earlier, also be present.
Jessie Shternshus: What is beneficial AI?
Davar Ardalan: So beneficial AI is actually something that Stephen Hawking said before he passed away. He said that, so much of what’s happening in the world of artificial intelligence is moving really fast, around smart cars and smart cities and facial recognition. But then we actually have to pause and look at the science and the data behind all of these commerce, these commercial products that are out there, because in fact, the data sets are very biased. So as you know, you’ve read about all the ways that facial recognition is biased. And that’s what beneficial AI means. Beneficial AI means can we please stop and for a second, look at the databases behind these products and solutions, and see if there’s a way that we can enhance them with better data, more data, and also make them obviously more inclusive and relevant to more people, not just the very elite.
Alison Wade: That’s incredible. So since you’ve, I know you had an original vision when you started, do you want to sort of talk about your original vision and then what’s changed as you’ve been going through this process about the way you see that you’re going to be able to implement this in the world. I mean, I love that there’s a full profit model here, because I think that’s so important moving things forward in this world. So it’s amazing that you can do both of those things, affect culture, do some deep inclusion, but also that there’s a profitability model here.
Davar Ardalan: Yeah. And that’s a really good question because when I started two years ago, I guess, researching this space, I held a summit at Morgan State University, which is a historically black college. And it’s just in Baltimore, which is just like 20 minutes from where I live. But I actually paid to bring many of the world’s foremost pioneer experts on artificial intelligence and culture and storytelling to be in the same room with people from the United Nations, with people in academia and in computer science, because I really wanted this to be an exercise to understand like, where is this technology right now, what are researchers in AI and storytelling even working on? And what could be the potential commercial options to bring this to scale. So I spent a good year and a half, looking at the AI for good side of things, until I it registered, how we would be able to actually insert this for brands because we also saw in the news that brands were having problems with the AI that actually wasn’t sufficient, didn’t have enough sufficient dat aset. And so they were having their own gaps and situations with AI’s and it felt like it was a really good opportunity to bring a solution to the table. Many of the AI scientists that I have been cultivating in my network are being amazing advisors of mine, and I rely on them and it’s been really cool to see them continue with me on the journey. Having said this, we’ll talk about this in a minute but we’re also focusing on creating new data sets and so at the same time that we’re building a start up around brands and creating more cultural fluency and chatbots. We also are creating new data sets, which we can talk about, because I don’t think anyone will take us seriously to say that we’re cultural intelligence and AI startup, unless we also are creating new data sets which we’re crowdsourcing.
Alison Wade: Yeah. That’s incredible. So tell us a little bit about, you know, how women are affected culturally by AI as well. And then that would lead into good conversation about the the algorithms that you are doing for women in the data set that you’re creating there.
Davar Ardalan: Yeah, absolutely. So currently, there’s no comprehensive algorithm to create AI ready data around the narratives of women. And the reason this is important, I’ll tell you is I was in Geneva at an AI summit last May and one of the people I had invited I brought a delegation of 20 AI storytellers and traditional storytellers and leaders in conversational AI to join me in Geneva, one of them was Mariana Lin, who by the way, you will love meeting one day, we have to connect with each other. So Mariana was the Global Director at Apple, and she actually was the writer for Siri. So she created the first narratives for Siri and we were sitting there together in Geneva on a panel talking about how these conversational interfaces need to become more culturally aware. And Mariana was like Davar, one of the first data sets you need to do is about women, has to be about women. Because think about how many women in mythology and legend have inspired humanity across the centuries. And AI needs to know about these women because we have to form the future spaces that we do using artificial intelligence with some wisdom to understand what has come before. And so that has led to some of the work that we’re doing with the data set challenges and women.
Alison Wade: Total goosebumps hearing you talk about that. That’s incredible. Yeah, so tell us about the bias that comes with with AI in these algorithms and that how you’re working on helping to prevent bias.
Davar Ardalan: Sure. So one of our advisors is Aprajita Mathur, who you know very well, she’s a very well known tester, senior software tester, and she’s a advisor with us and she is in bioinformatics field which really looks at clinical trials and research to be able to help save lives, and in general, not just necessarily in the work that she’s doing but across the healthcare industry. The data sets are diverse enough, which means that when medical, the medical field are trying to create solutions, cancer solutions, and their data sets are only around white men. So the products that they create are not going to be beneficial to somebody who is Southeast Asian, because it’s not going to work. So for her, it’s very very clear why we need data sets and the fact that data sets are bias because every time in the field of healthcare, you’re creating new products, and you’re seeing that the DNA doesn’t is not necessarily going to match, it becomes really very obvious why there’s issues. The others, things you’ve already heard about, obviously, around facial recognition or some of the first automatic cars, self driving cars, didn’t have enough data around African American or people with darker skin, in which case, some of the first accidents that happened were with people of color because there weren’t enough data sets around them when they were testing.
Alison Wade: Yeah, it’s just amazing. I was at dinner with Jennifer Bonine and her crew in in Toronto and they were showing the images that they’re showing their AI bots that are is like, is this a croissant? Or is this a chihuahua? Like, this is like visual recognition and then it really tunes you in to the subtlety of what is what is the the bot going to recognize? And what are they going to tag it as when they’re recognizing it and it’s quite amazing.
Davar Ardalan: Yeah, absolutely.
Alison Wade: The subtleties of culture, and, and people’s physical features and things like that, and women’s voices and all those things are just way bigger than that. That’s just amazing.
Davar Ardalan: Yeah. So one of one of the first data sets that we’re crowdsourcing is to devise a method to create a data set to provide historical context for the preservation of culture with an emphasis on women. And the second is obviously to create an algorithm that’s reproducible, so allowing AI to become more knowledgeable and more sensitive to the incredible diversity of the human experience, and not have it just be again in the realm of the developers who started all of this work in Silicon Valley, which, by the way, love them. Thanks for doing everything that you did. But again, let’s not move so fast that we’re not allowed to create systems that are going to actually understand the depth of human contributions, especially the contributions of women.
Jessie Shternshus: What’s your best way to get the word out to find people to do this kind of work?
Davar Ardalan: Yes. So regarding our crowdsourcing campaign, right now, we’re actually in fundraising mode. We thought if 5,000 people gave us $25, we would be able to do this right away. We also have a founding friend sponsorship at $1,000. And in general, the timeline for this is to be crowdfunding right now. Then announcing what we’re doing in Geneva at the AI summit there in May 2020. And then in Fall, actually having it go live, which would mean that we have the next six months to be able to put the word out and get people to help us. Yeah.
Alison Wade: Well, that is great. Well, when you as soon as you’re ready, we will put up something so that people can be proud of that 5,000 people donating $25 to get this off the ground because to me, this is so important and so close to my heart, and really an important thing that we can help build here.
Davar Ardalan: Thank you. Thank you so much. Yes. And, again, your community, Alison has been so incredibly supportive. And I just want to say that when I came to STARWEST in this Fall, and I spoke at Disneyland on the future of artificial intelligence, culture, and storytelling, it was incredibly surreal to see the enormous amounts of senior leaders in the testing community, QA community who were very fascinated by what I was doing. Not only that, but they actually are sponsoring our data set challenge. They’re sponsoring the work, early work that I’m doing on cultural AI. And again, this is just like the network that you created that has embraced me. And so I’m feeling incredibly grateful that you understand the significance of this. And if you think about the testing community, briefly, to me, the testing community is the last line of defense when it comes to AI products and solutions. And so, testers, the community of testers are very diverse, in which case, they should be able to also inform the future of these products to be more culturally relevant, because so many of them come from different backgrounds themselves.
Alison Wade: Yeah. And, you know, if you get a really good tester who understands context, that’s going to be very important to them. So that’s amazing. Well, can you talk a little bit about AI Girls, I know that you’ve done some work. You’re also you’ve got a group of you started this little organization. I don’t know if you started AI Girls or how AI Girls came into being, but I just love this concept that you are helping young women out there understand AI and understand what their role in it is.
Davar Ardalan: Yes. So I when I was leaving my government position as an innovation specialist, with the Presidential Innovation fellowship program, you know, I was in a leadership role, and there were several women who we work very closely with, and I was on Slack, almost like the day before I left with one of them. And we were like, how the hell are we not gonna see each other like after today? And then I was like, hey, what if I just started AI Girls and so together with her Nina Bianchi, we ended up creating this mixer and the idea is to bring women of many different backgrounds, interdisciplinary backgrounds, civic tech leaders in many different industries who not all of them are AI practitioners, not all of them, even are data practitioners, but they’re all in positions of power, where they will somehow inform either a framework for artificial intelligence, or the future of AI itself. So we come together, and we have like six lightning talk speakers. These are a combination of women who are doing machine learning projects and so they do like three minutes where they show some slides about the work that they’re doing. And then we take one question, so we talked about what do you think the future of AI is? Or why, what do you think about culture and AI? And these allow us to have smaller, more focused conversations, and the women in the room are very influential, and so many amazing connections have happened. So for example, one of our Presidential Innovation Fellows in December saw Jennifer from pink line.ai And she was like, hey, Jennifer, I can take us to Davos if you want and put us on like three panels. And I was like, what? Literally three weeks later, they were in Davos. And yeah, that happens in December.
Alison Wade: Yeah, I got a text from Jennifer saying, “Hey, you know, I’m on my way to Switzerland.” And she sent me a screenshot saying, “I’m talking at the World Economic Forum.” I’m like, “you go, girl!” That’s amazing!
Davar Ardalan: Yeah. So it’s really cool because the women, like I said, they’re in leadership roles, and not all of them, so we’ve also had PhD students in machine learning speak, and even students of computer science, because we want to be able to learn from different generations on what they’re working on, and then how they want to inform this feature, and how we can together promote beneficial AI. Which means that go to your companies, go in the government agencies that you’re working in, and insist that are the data sets diverse? How can we bring more inclusivity in this future? So these are the kinds of things that we hope to foster through these mixers.
Jessie Shternshus: And are the mixers in DC? Or are they done all over the place? Or where are they gonna be done?
Davar Ardalan: So we would love to expand. So right now we’ve done five in the Washington DC area, we’re actually going to do April in Silicon Valley and May in Geneva and Dublin. But as we look at Fall 2020, we definitely are looking for a partner to expand this beyond Washington, DC and these experiences that we’re going to have in Europe.
Jessie Shternshus: And then for people that are involved in it, are they continuing to connect after the meetings? Like do they have a way to continue their conversation throughout the year?
Davar Ardalan: Yeah, so so we are because, you know, we just started and this is really a pilot we’re what we’re doing is we’re taking notes and trying to understand what kind of services we can offer. And so the very basic is oh, can we have a Slack channel? So these are the kinds of things we’re talking about. But also, at every event, when we ask a question, we give people a card, so that they can actually write down what they’re thinking. And these cards are part of an interactive wall, where every time we meet, more and more cards are going on a wall. And this allows people to read and hear what other people are talking about or thinking. And then when we go to Geneva, they actually have created they’re going to be creating an interactive wall, just to put all of our cards in. So that’s really cool.
Jessie Shternshus: That’s cool.
Alison Wade: Yeah, will you tell us the story about we were talking a little bit before we got on to record about the connection that you had with Ken Johnson at Microsoft as a result of coming to STARWEST then he introduced you to someone else who has taken these amazing steps to document a language and how what’s going to be going on with that? I just wanted people to hear that story because I think it’s so fascinating.
Davar Ardalan: Yes. So when I was at STARWEST, at the Mickey Mouse penthouse, I met..
Jessie Shternshus: Say that three times fast.
Alison Wade: Which for our listeners, by the way, we just have to stop and say, I have slept in the Mickey Mouse penthouse. And what is really hilarious about it is when you ring the doorbell, Mickey’s voice comes on and says, “Woohoo, somebody’s is at the door”. I know, it just rolls off the tongue, right?
Davar Ardalan: Yeah, so Ken and I talked for an hour, and he was really fascinated about what I’m doing. And he was like, I really want to introduce you to Tracy Monteith of Microsoft, because he is from the Cherokee background, Native American and he’s a Senior Software Engineer and he actually spent 20 years putting the Cherokee language into Microsoft Word. So he is the only Native American. I mean, it’s the only Native American language in Microsoft Word. So as you can imagine, I was just blown away. So Ken introduced me to Tracy and through others in my network, who are also Native American background and AI specialists. I’m actually chair of a session in Geneva in May 2020 on cultural heritage in AI, and Tracy will be joining me and this is again, just through connections that we made in STARWEST. So the panel that we’re doing is called indigenous knowledge and AI. And as you know, indigenous knowledge for the most part exists in the form of oral histories and ancient texts and in original languages and paper archives, and maybe even unstructured Wikipedia entries. So the point of the conversation is how can their digital transformation into AI suitable data sets represent a really important step in making sure that future machines are not just aware of global cultures but understand the patterns around food and nutrition as documented by indigenous nations. So who we have on our panel is Wolfgang Victor Yarlott. And he’s an AI researcher at Florida International University from the crow tribe of Montana, Tracy Monteith of Microsoft and then technologists Chamisa Edmo, who I have been mentoring, and she is a graduate student looking to study, continue computer science work, but she’s also like a full stack developer and she worked for Hanson robotics as a conversational AI writer. So yeah, we’re all coming together. And we’re actually even gonna create a demo for the purpose of this particular summit looking at creating an indigenous knowledge graph. Again, just a small demo to show suitable ways to grow food, sustainable farming and health benefits that come from different tribal traditions. And in terms of Tracy, so obviously, the Cherokee language has been spoken for, according to Tracy something like 3,500 years and been in written form since 1821. And so the idea that there’s millions of documents in existence in the Cherokee archives, and ways that we can use AI technology to unlock these words and these stories and these histories from their ancient ancestors. I mean, that’s what keeps me up at night, but that’s not going to pay my bills. So by the way, Tracy also works at Microsoft. So we understand that there is a dance that has to happen here that yes, there has to be a focus on preserving our cultures, but we have to be smart. And we have to figure out a way to also make it so that, for example, our product culture IQ, what we’re trying to do is identify, analyze, and catalog cultural segments using public data. But then culturally train consumer facing messaging platforms to provide cultural analysis and informed marketing decisions. So that’s kind of the progression of the last couple years of my work now.
Alison Wade: Just amazing, really amazing. I love that this has like a multi that your company has a multi stacked you know, mission and purpose. It’s really incredible. So if our listeners want to get involved in this in some way, somehow the best way for them to stay on top of what’s going on is to follow you on Twitter right? To go to IVOW.com your website, is that correct? I’m just trying to get a hold of if people want to become involved in this in in either a part time or a full time situation. What are the best ways for them to do that?
Davar Ardalan: Yes. So our website Is IVOW.ai, but we’re really actually right now at a critical stage of trying to raise funding for the data set challenge. So if anybody’s interested in helping me spread the word and become like a founding friend, I know it’s $1,000. But if there’s any opportunities for companies who want to be part of creating new data sets that are AI enabled, that would be amazing. And yeah, I’m on Twitter @idavar.
Alison Wade: Excellent. And then also tell us about the webinars that you’re running to teach people how to build data sets and create algorithms.
Davar Ardalan: So yeah, we actually have collaborated with Test Master Academy. And so Test Master Academy, Anna Royzman, she and I met in Dublin a couple years ago at Quest For Quality actually, and the idea of our 2020 webinar series is to deliver like practical advice as we delve into the future of testing and AI. So some of the conversations we’ve already had are on one basic conversation like what even is AI? And why do we need new data sets? And future webinar that we’re doing, for example, on February 18, is all about cyber security and AI. And then we’re going to get into how do you test an algorithm. So we’re building up into introducing the idea of needing new data sets, but then also helping prepare testers as to how to even test an algorithm because none of what we’re doing really is going to be effective unless QA leaders and testers are involved in helping us make sure that the kinds of data or the methodology that we’re creating is going to be viable and that’s why I’m grateful that Aprajita Mathur and Raj Subrameyer is involved and Jennifer, of course, of pinklion.ai.
Alison Wade: Yeah, it’s gonna be a really important skill for testers to have. So going forward in their careers as AI, propagates everything we’re doing, it’s going to be a really important skill for them to have. So it’s not only beneficial from, from several different perspective for them learning about it, but be able to have it a skill that they can pull out of their pocket and do. So that’s incredible.
Davar Ardalan: Yeah, absolutely.
Alison Wade: Excellent. So is there anything else you want to tell us about what you’ve got coming up? And we know we touched a lot on different things that are coming up in your, in your future. Is there anything else that you want to tell us about that so people know where to find you and what’s going on with you?
Davar Ardalan: Well, I think that just an anecdote that I like to say is, a year ago, I walked into my granddaughter’s home in Pittsburgh, and she said, Aziz, come and meet my friend, Alexa. And the moment she said that I knew that anything that I create from this day forward is going to be informing the AI’s that she will meet in her lifetime. That it is our responsibility to create products and solutions that our children and our grandchildren can use responsibly and can learn from, not to just use for playing games or asking about the weather, but actually to learn about their ancestry and to learn so much of what we can pass down through generations, in completely new ways that will engage them obviously.
Jessie Shternshus: I love that.
Alison Wade: That makes my heart very happy, in this in this age of a dissemination of fake and false information and constant bias of the way things are getting propagated that really makes my heart happy to hear that. So thank you, thank you for all that you’re doing for AI and for women and your company is amazing. And we wish you every success in this world.
Jessie Shternshus: Thank you for being with us today.
Davar Ardalan: Thank you so much. It was amazing talking to you. And thank you once again for all of your support and making so much of this come true in the last six months, so thank you.
Alison Wade: Thanks Davar. Wow, Jessie, I don’t think I’ve ever heard you so quiet in a podcast before, you were mesmerized.
Jessie Shternshus: I really was. I think the Davar is just incredible. And the work that she’s doing just had my brain going a million miles a minute. It was really, really interesting. I think, between the AI and storytelling and cultural diversity, its just I don’t know, it just has my, has my heart.
Alison Wade: Oh, yeah, me too. I love everything about this and some of the important things that I think she talked today about were the commercial applications for this, right. I mean, this, I want stories retold, I want there to be a lack of bias in AI, all these things, but the fact that you can actually create a commercial application out of this, that people can use in marketing and sensitive marketing to their customers and their clients is going to make this a thing of the future. So I’m so excited for that. So I loved hearing about that. What was some of your favorite parts?
Jessie Shternshus: Yeah, I thought that was interesting too, when she talked about like how some of the brands lacked the tools that they need when she was talking about the Hawaiian Airlines and how that AI could be more culturally sensitive and she was giving a lot of interesting examples. I really liked when she talked about meeting the woman, Mariana from Apple who wrote the narrative for Siri, I thought that was really interesting about how AI needs to be captured from women and their wisdom from history and how we really need that wisdom to be captured so that the women of the future have the women’s history from the past.
Alison Wade: Yeah, that was really interesting. So I think I don’t know about you, but I think the one I want to replay is kind of the definition of what she talked about as beneficial AI. I mean, that, to me is a statement of the future. I don’t know what you thought. And I love that this is something that a concept that Stephen Hawking brought to life before he passed away.
Jessie Shternshus: Yeah, I love that too. Why don’t we listen to that?
Davar Ardalan: Find a way to think about beneficial AI, beneficial artificial intelligence that is going to allow us to have the richness of global voices, as Alison mentioned earlier, also be present.
Jessie Shternshus: What is beneficial AI?
Davar Ardalan: So beneficial AI is actually something that Stephen Hawking said before he passed away. He said that so much of what’s happening in the world of artificial intelligence is moving really fast, around smart cars and smart cities and facial recognition but then we actually have to pause and look at the science and the data behind all of these commerce, these commercial products that are out there. Because in fact the data sets are very biased. So as you know, you’ve read about all the ways that facial recognition is biased. And that’s what beneficial AI means beneficial AI means can we please stop and for a second, look at the data bases behind these products and solutions, and see if there’s a way that we can enhance them with better data, more data, and also make them obviously more inclusive and relevant to more people, not just the very elite.
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