Microsoft Research Podcast - Ideas: Language technologies for everyone with Kalika Bali

Episode Date: April 11, 2024

The new series “Ideas” debuts with guest Kalika Bali. The speech and language tech researcher talks sci-fi and its impact on her career, the design thinking philosophy behind her research, and the... “outrageous idea” she had to work with low-resource languages. Learn more:The State and Fate of Linguistic Diversity and Inclusion in the NLP World | Publication, July 2020Project VeLLM | Project pageKahani: Visual Storytelling | Project pageKahani: Visual Storytelling through Culturally Nuanced Images | Microsoft Research Forum | Episode 1, January 2024Teachers in India help Microsoft Research design AI tool for creating great classroom content | Microsoft Research blog, October 2023Digital Labor: Project Karya | Project pageVillage by village, creating the building blocks for AI tools with work that also educates | Microsoft Source Asia blog, February 2024

Transcript
Discussion (0)
Starting point is 00:00:00 I do think in some sense the pushback that I got for my idea makes me think it was outrageous. I didn't think it was outrageous at all at that time. I thought it was a very reasonable idea, but there was a very solid pushback. And not just from your colleagues, you know, for researchers, publishing papers is important. No one would publish a paper which focused only on, say, Indian languages or low-resource languages. We've come a very long way, even in the research community, on that, right? We kept pushing, pushing, pushing, and now there are tracks, there are workshops, there are conferences which are devoted to multilingual and low-resource languages.
Starting point is 00:00:43 You're listening to Ideas, a Microsoft Research Podcast that dives deep into the world of technology research and the profound questions behind the code. I'm Dr. Gretchen Huizinga. In this series, we'll explore the technologies that are shaping our future and the big ideas that propel them forward.
Starting point is 00:01:10 I'm excited to be live in the booth today with Kalika Bali, a principal researcher at Microsoft Research India. Kalika is working on language technologies that she hopes will bring the benefits of generative AI to under-resourced and underserved language communities around the world. Kalika, it's a pleasure to speak with you today. Welcome to Ideas. Thank you. Thank you, Gretchen. Thank you for having me. So before we dive in on the big ideas behind Kalika Bali's research, let's talk about you for a second. Tell us about your origin story, as it were, and if there is one, what big idea or
Starting point is 00:01:46 animating what-if captured your imagination and inspired you to do what you're doing today? So, you know, I'm a great reader. I started reading well before I was taught in school how to read, and I loved science fiction. I come from a family where reading was very much part of our everyday lives. My dad was a journalist and I read a lot of science fiction growing up. And I also saw a lot of science fiction, you know, movies, Star Trek, everything that I could get hold of uh in india and i remember watching um 2001 space odyssey and there was this hal that spoke he actually communicated but he was a computer yeah and i was just so struck by it i was like this is so. You know, here are computers that can talk. Now, how cool would that be if it would happen in real life?
Starting point is 00:02:50 Yeah. I was not at all aware of what was happening in speech technology, whether it was possible or not possible. But that's something that really got me into it. always like kind of been very curious about languages and how they work and you know how people use different things in languages to express not just meaning not just communicating but you know expressing themselves really so I think it's a combination of how and this curiosity I had about the various ways in which people use languages that got me into what I'm doing now. Okay, so that's an interesting path. And I want to go into that just a little bit. But let me anchor this. How old were you when you saw this talking computer? I was in my early teens.
Starting point is 00:03:35 Okay. And so at that time, did you have any conception that... No, you know, there weren't computers around me when I was growing up. We saw, you know, some at school, you know, people coded in BASIC. Right. And we heard about them a lot, but I hadn't seen one since I was in high school. Okay, so there's this inception moment, an aha moment of that little spark. And then you kind of drifted away from the computer side of it. And tell us about how you went from there to that. So that's actually a very funny story
Starting point is 00:04:17 because I actually wanted to study chemistry. I was really fascinated by how these, you know, molecular parts rotate around each other and you know we can't even tell where an electron is etc it sounded like really fun and cool so I actually studied chemistry but then I was actually going to pick up the admission form for my sister who wanted to study in this university and or no she wanted to take an exam for her master's and I went there I picked up the form and I said this is a cool place I would love to study here and then I started looking at everything like you know what can I apply for here and something called linguistics
Starting point is 00:05:00 came up and I had no idea what linguistics was. So I went to the British Library, got a thin book on introduction to linguistics, and it sounded fun. And I took the exam, and then, as they say, that was history. Then I just got into it. I mean, so much has happened in between then and now, and I think we'll kind of get there. But I do want you to connect the larger dot from how you got from linguistics to Microsoft Research as a computer scientist. So I actually started teaching at the University of South Pacific as a linguistics faculty in Fiji. And I was very interested in acoustics of speech sounds etc etc that's what I was teaching and then there was a speech company in Belgium that was looking to start some work in Indian
Starting point is 00:05:53 languages and they contacted me and at that time you needed people who knew about languages to build language technology especially people who knew about phonetics, acoustics for a speech technology. And that's how I got into it. And then, you know, I just went from startups to companies and then Microsoft Research 18 years ago, almost 18 years ago. Wow. Okay. I would love to actually talk to you about all that time, but we don't have time because I have a lot more things to talk to you about technology-wise. But I do want to know, you know, how would you describe the ideas behind your overarching research philosophy not, besides Hal 9000, who's fictional, and any seminal papers that sort of got you interested in that along the way? So since I was really into speech, Ken Stevens, who was a professor who sadly is no longer with us anymore at MIT, was a big influence. He kind of had this whole idea of how speech is produced.
Starting point is 00:07:08 And, you know, the first time I was exposed to the whole idea of the mathematics behind the speech. And I think he influenced me a lot on the speech side of things. For the language side of things, you know my professor um in um india professor anvita abhi um you know she's a padma sri like a she's been awarded by the indian government for her work in um you know very obscure endangered languages you know she kind of gave me a feel for what languages are and why they are important and why it's important to save them and not let them die away. So I think I would say both of them. But what really got me into wanting to work with Indian language technology in a big way was I was working in
Starting point is 00:08:00 Belgium, I was working in London, and I saw the beginning of how technology is kind of, you know, making things easier, exciting, there's cool technology available for English, for French, for German. to people who have no access, right? It actually mattered because here are people who may not be very literate and therefore not be able to use technology in the way we know it, but they can talk and they can speak and they should be able to access technology by doing that. Right. Okay. So just real quickly, that was then. What have you seen change in that time and how profoundly have the ideas evolved? working in language technology, mainly for Indian languages, but even for other languages, it was all a rule based system. So everybody had to create all these rules that then were, you know, responsible for building or like making that technology work. But then just at that time, you know, all the statistical systems and methodologies came into being. So we had hidden Markov models,
Starting point is 00:09:25 you know, doing their thing in speech, and it was all about a lot of data. But that data still had to be procured in a certain way, labeled, annotated, it was still a very long and resource intensive process. Now with generative AI, the thing that I am excited about is we have a very powerful tool. And yes, it requires a lot of data, then have it tagged for part of speech, then, you know, have it tagged for sentiment, have it tagged for this, have it tagged for that, and then only can I think of building anything. So it just shortens that timeline so much and it's very exciting. Right.
Starting point is 00:10:24 As an ex-English teacher, which I don't think there is such a thing as an ex-English teacher, you're always silently correcting someone's grammar. Just what you said about tagging parts of speech as what they are, right? And that, I used to teach that. And then you start to think, how would you translate that for a machine? So fascinating. So Kalika, you have said that your choice of career was accidental and you've alluded to sort of the fortuitous things that happened along the way, but that linguistics is one subject that goes from absolute science to absolute philosophy. Can you unpack that a little bit more and how this idea impacted your work in language technology? Yeah, so if you think
Starting point is 00:11:05 about it, you know, language has a physical aspect, right? We move our various speech organs in certain way, our ears are constructed in a certain way, there is a physics of it where when I speak there are sound waves, right, which are going into your ear, and that's being interpreted. So, you know, if you think about that, that's like an absolute science behind it, right? But then when you come to the structure of language, you know, the syntax, like you're an English teacher, so you know this really well, that, you know, there's semantics, there's, you know, morphology, how are words formed, how are sentences formed. And that's like a very abstract kind of method that allows us to put, you know, meaningful sentences out there. But then there's this other part of how language
Starting point is 00:12:01 works in society, right? The way I talk to my mother would be probably very different to the way I'm talking to you. It will be very different from the way I talk to my friends at a very basic level, right? The way in India I would greet someone older to me would be very different from the way I would greet somebody here because here it's like much less formal and that, you know, age hierarchy is probably less right if i did the same thing in india i would be considered the rudest creature right ever so and then you know you go into the whole philosophy uh psycholinguistics part what happens in our brains um you know when we are speaking because language is controlled by various parts of our brain right and then you go
Starting point is 00:12:46 to the pure philosophy part like why how does language even occur why do we name things the way we name things you know why do we have a language of thought um you know what language are we thinking in so so it really does cover the entire gamut of language, like from science to philosophy. Yeah, as I said before, when we were talking out there, my mother-in-law was from Holland. And every time she did math or adding, she would do it in Dutch. Would she be speaking in English?
Starting point is 00:13:23 And then she'd go over here and count in Dutch out loud. And it's like, yeah, your brain switches back and forth. This is so exciting to me. I had no idea how much I would love this podcast. So much of your research is centered on this big idea called design thinking, and it's got a whole discipline in universities around the world. And you've talked about using something you call the 4D process for your work. Could you explain that process and how it plays out in the research you do with the communities you serve? Yeah. So we have kind of adapted this. My ex-colleague, Monoji Chaudhary, and I kind of came up with this whole thing about 4D thinking, which is essentially discover, design, develop, and deploy, right? And when we are working with,
Starting point is 00:14:12 especially with marginalized or low-resource language communities, the very basic thing we have to do is discover because we cannot go with, you know, our own ideas and perceptions about what is required. And I can give you a very good example of this, right? You know, most of us as researchers and technologists, when we think of language technology, we are thinking about machine translation. We're thinking about speech recognition. We're thinking about state of the art technology.
Starting point is 00:14:38 And here we were talking to a community that spoke the language, Idu Mishumi, which is a very small community in northeast of India. And we were talking about, you know, we can do this, we can do that. And they just turned to us and said, what we really want is a mobile digital dictionary. Wow. Right. And, you know, if you don't talk, if you don't observe, if you're not open to what the community's needs might be, then you'll miss that. You'll miss the real thing that will make a difference to that community. So that's the discover part. The design part, again, you have to design with the community.
Starting point is 00:15:20 You cannot go and design a system that they are unable to use properly. And again, another very good example, one of the people I know, he gave me this very good example of why you have to think even at the architecture level when you're designing such things. A lot of applications in India and around the world require your telephone number for verification. Now, for women, it might be a safety issue. They might not want to give this telephone number. Or in India, many women might not even have a telephone, like a mobile number, right? So how do you think of other ways in which they can verify, right? So that's the design part. The develop and the deploy part kind of go hand in hand because I think it's a very iterative process. You develop quickly, you put it out there, allow it to fail and, you know,
Starting point is 00:16:19 iterate, iterate. Yeah. So that's like the kind of design thinking that we have. Yeah. I see that happening in accessibility technology areas too, as well as language. Yeah. And, you know, working with the communities very quickly, you become really humble. Sure. There's a lot of humility in me now, you know, though I have progressed in my career and you know supposedly become wiser I am much more humble about what I know and what I can do than I was when I started
Starting point is 00:16:55 off you know I love that well one thing I want to talk to you about that has intrigued me there's a thing that happens in India where you mix languages. You speak both Hindi and English at the same time. And you think, oh, you speak English, but it's like, no, there's words I don't understand in that. What do you call that? And how did that drive your interest? I mean, that was kind of an early on kind of thing in your work, right? Talk about that. So that's called code mixing or code switching. The only linguistic difference is code mixing happens within a sentence and code switching means one sentence in one language in another. Oh, really? Yeah. So but this is like not just India.
Starting point is 00:17:34 This is a very, very common feature of multilingual societies all over the world. So it's not multilingual individuals, but at the societal level, when you have multilingualism, then, you know, this is a marker of multilingualism. But code mixing particularly means that you have to be fluent in both languages to actually code mix, right? You have to have a certain amount of fluency in both languages. And there are various reasons why people do this you know it's been studied by psychologists and linguists for a long time and for most people like me multilingual people that's the language we dream we think about that's the language we talk to our siblings and friends in right and for us it's like just natural we just keep flipping between the two languages for a variety of reasons.
Starting point is 00:18:26 We might do it for emphasis. We might do it for humor. We might just decide, OK, I'm going to pick this from this. The brain decides I'm going to pick this from this language and this. So the reason we got interested in like looking into code mixing was that when we are saying that we want humans to be able to interact with machines in their most natural language then by some estimates half the world speaks like this right so we have to be able to understand exactly how they speak and you know be able to process and understand their language,
Starting point is 00:19:06 which is code mixed. Well, it seems like the human brain can pick this up and process it fairly quickly and easily, especially if it knows many languages. For a machine, it would be much more difficult. It is. So initially it was really difficult because, you know, the way we created systems was one language at a time. Right. Right. And it's not about having an English engine and a Hindi engine available. It doesn't work that way.
Starting point is 00:19:38 So you really need something that, you know, is able to tackle the languages together. And in some theories theories this is almost considered a language of its own because it's not like you're randomly mixing there is a structure to oh is there yeah where you can where you can't um gotcha you know so there is a structure a grammar you can say of code mixing so uh we went after that We kind of created tools which could generate grammatically viable code mix sentences given parallel data, etc. So, yeah, it takes effort to do it. But again, right now, because the generative AI models have at their disposal, you know, so many languages and at least like theoretically can work in many, many, many languages, you know, code mixing might be an easier problem to solve right now. Right.
Starting point is 00:20:31 Okay. So we're talking mostly about widely used languages and you're very concerned right now on this idea of low resource languages. So unpack what you mean by low-resource and what's missing from the communities that speak those languages. Yeah. So when we say low-resource languages, we typically mean that languages do not have, say, digital resources, linguistic resources, language resources that would enable technology building. It doesn't mean that the communities themselves are impoverished in culture or linguistic richness, etc. Right. But the reason why these communities do not have a lot of language resources, linguistic resources, digital resources, most of the time it is because they're also marginalized in other ways, social and economic marginalization. And these are,
Starting point is 00:21:26 if you look at them, they're not, I mean, of course, some of them are tiny, but when we say low-resource communities, we're talking about really big numbers. Oh, really? Yeah. So one of the languages that I have worked with, language communities that I have worked with, speak a language called Gondi, which is like a Dravarian language that is spoken in like a south indian language that is spoken in north central north area um it's a tribal language um and it's uh got around three million speakers oh wow that's like more than welsh yeah right but because socio-politically they have been or economically they have been marginalized they do not have the resources to build technologies and you know when we say
Starting point is 00:22:13 empower everyone yeah and we only empower the top tier um i don't think we fulfill our ambition to empower everyone. And like I said earlier, for these communities, all the technology that we have, digital tools that we have access to, they really matter for them. So, for example, a lot of government schemes or the forest reserve laws are provided, say, in Hindi. If they are provided in Gondi, these people have a real idea of what they can do. Similarly for education, you know, there are books and books and books in Hindi. There's no book available for Gondi. So how is the next generation even going to learn the language? Right.
Starting point is 00:23:04 And there are many many languages which are low resource in fact you know we did a study sometime in 2020 i think we published this paper on linguistic diversity and there we saw that you know we divided languages in five categories and the top most which have all the resources to build every possible technology, have only five languages, right? And more than half of the world's languages are at the bottom. So it is a big problem. Yeah. Let's talk about some of the specific technologies you're working on. And I want to go from platform to project, because you've got a big idea in a platform you call Vellum. Yes.
Starting point is 00:23:46 Talk about that. So Vellum, which actually means jaggery, the sweet, sugary jaggery in Tamil, one of the languages in India. Let me interject that it's not Vellum like the paper or what you're talking about. It's capital V, little e, and then LLM, which stands for large language model. So universal, the V comes from there. Empowerment, E comes from there, through large language models. Got it. Okay. But you shorten it to Vellum. Yeah. Okay. So go.
Starting point is 00:24:19 So the thing with Vellum is that a bunch of us got together just when this whole GPT was released, etc. We have a very strong group that works on technologies for empowerment in the India Lab, Microsoft Research India. And we got together to see what it is that we can do now that we have access to such a strong and powerful tool. And we started thinking of the work that we've been doing, which is to, you know, build these technologies for specific areas in specific languages, specific demographies. So we kind of put all that knowledge and all that experience we had and thought of like, how can we scale that really across everything that we do. So Vellum at its base you know takes a GPT like LLM you know as a horizontal across everything.
Starting point is 00:25:18 On top of it we have again horizontals of machine learning of multilingual tools and processes which allow us to take the outputs from say GPT like things and adapt it to different languages or you know some different kind of domain etc. And then we have verticals on top of it which allow people to build specific applications. Let me just go back and say, GPT, I think most of our audience will know that that stands for generative pre-trained transformer models. But just so we have that for anyone who doesn't know, let's anchor that. So Vellum basically was an enabling platform on which to build specific technologies that would solve problems in a vertical application. Yes. And because it's a platform, we're also working on tools that are needed across domains as well as tools that are needed for specific domains.
Starting point is 00:26:21 Okay. So let's talk about some of the specifics because we could get into the weeds on the tools that everybody needs. But I like the ideas that you're working on and the specific needs that you're meeting, the felt need thing that gets an idea going. So talk about this project that you've worked on called Kahani. Could you explain what that is and how it works? It's really interesting to me. So Kahani actually is about storytelling, culturally appropriate storytelling with spectacular images as well as like textual story. So visual storytelling? Visual storytelling with the text. So this actually started when my colleague Sameer Sehgal, he was trying to use generative AI to create stories for his daughter.
Starting point is 00:27:08 And he discovered that, you know, things are not very culturally appropriate. So I'll give you an example that, you know, if you want to take Frozen and take it to like the South Indian state of Kerala, you'll have the beaches of Kerala, you'll have even have the coconut trees. But then you will have this blonde princess in a princess gown who's there, right? So that's where we started discussing this. And we kind of started talking about how can we create visuals that are anchored on text of a story that's culturally appropriate. So when we are talking about, say, Little Red Riding Hood, if we ask a generative AI model, okay, that I want the story of Little Red Riding Hood,
Starting point is 00:27:54 but in an Indian context, it does a fantastic job. It actually gives you a very nice story, which, you know, just reflects the Red Riding Hood story into an Indian context. But the images don't really match at all. So that's where the whole Kahani thing started. And we did a hackathon project on that. And then a lot of people got interested.
Starting point is 00:28:19 It's an ongoing project. So I won't say that it's out there yet. But we're very excited about it because think of it, we can actually create stories for children, you know, which is what we started with. But we can create so much more media, so much more culturally appropriate storytelling, which is not necessarily targeted at children. So that's what Kahani is about. Okay. And I saw a demo of it that your colleague did for Research Forum here. And there was an image of a girl, it was beautiful. And then there was a mask of some kind. What was that? So the mask is called Nazarbattu, which is
Starting point is 00:29:00 actually, you have these masks which are supposed to drive away the evil eye so that's what the mask was about it's a very indian thing you know when you build a nice house you put one on top of it so that the envious glances are like kept at bay so yeah so that's what it was um and was there some issue of the generative AI not really understanding what that was? No, it didn't understand what it was. So then can you fix that and make it more culturally aware? So that's what we're trying to do for the image thing. So we have another project on cultural awareness where we are looking at understanding how much generative AI knows about other cultures.
Starting point is 00:29:44 So that's a simultaneous project that's happening. But in Kahani, a lot of it is like trying to get reference images, you know, into the system, into the system and trying to anchor on that. So, and we're not going to talk about that project, I don't think, but how do you assess whether an AI knows? By just asking it, by prompting and seeing what happens? Yeah, yeah. So in another project, what we did was we asked humans to play a game to get cultural artifacts from them. The problem with asking humans what cultural artifacts are important to them is we don't think of like things as culture right this is food this is who we are this is my food like you know it's not culturally important artifact this is how i
Starting point is 00:30:33 greet my parents it's not like culturally so it's just like fish swimming in water you don't see the water exactly so we gamified this thing and we were able to get certain cultural artifacts and we tried to get generative AI models to tell us about the same artifacts. And it didn't do too well. But that's why it's research. Yes. You try, you iterate, you try again. Cool. As I mentioned earlier, I was a high school English teacher and an English major.
Starting point is 00:31:05 I'm not correcting your grammar because it's fantastic. Thank you. But as a former educator, one of the projects I felt was really compelling that you're working on is called Shiksha. It's a co-pilot in education. Yes. Tell our audience about this. So this is actually our proof of concept for the vellum platform since almost all of us were interested in education we decided to go for education as the first use case that we're going to work on
Starting point is 00:31:33 and actually it was a considered decision to go target teachers instead of students i mean you must have seen a lot of work being done on taking generative AI to students, right? But we feel that, you know, teachers are necessary to teach because they're not just giving you information about the subject. They're giving you skills to learn, which hopefully will stay with you for a lifetime, right? And if we enable teachers, they will enable so many hundreds of students one teacher can enable thousands of students right over her career so instead of like going and targeting students if we make it possible for teachers to do their jobs more effectively or like you know help them get over the problems they have,
Starting point is 00:32:27 then we are actually creating an ecosystem where things will scale really fast, really quickly. And in India, you know, this is especially true because the government has actually come up with some digital resources for teachers to use. But there's a lot more that can be done. So we interviewed about 100 plus teachers across different parts of the country. And this is the, you know, discover part. And we found out that lesson plans are a big headache. Yes, they are.
Starting point is 00:33:02 Can confirm. Yeah. And they spend a lot of time doing lesson plans because they're required to create a lesson plan for every class they teach. Sure. With learning outcomes. Exactly. All of it. All of it.
Starting point is 00:33:15 So that's where we, you know, zeroed in on how to make it easier for teachers to create lesson plans. And that's what the Shiksha project is about. You know, there is an enrollment process where the teachers say what subject they're teaching, what classes they're teaching, what boards, because they're different boards of education, which have different syllabus. So all that. But after that, it takes less than seven minutes for a teacher to create an entire lesson plan for a particular topic you know class assignments class activities home assignments homework everything like the whole thing in seven minutes and these teachers have the ability to go and correct it like it's an interactive thing so you know they might say i think this activity is too difficult
Starting point is 00:34:06 for my students yeah can i have like an easier one or can i change this to this so it allows them to interactively personalize modify the plan that's put out and i i find that really exciting um and we've tested this with the shikshana Foundation, which works with teachers in India. We've tested this with them. The teachers are very excited. And now Shikshana wants to scale it to other schools as well. My first question is, where were you when I was teaching, Kalika? There was no generative AI.
Starting point is 00:34:41 No. In fact, we just discovered the fax machine when I started teaching. Oh, that dates me. You know, back to what you said about teachers being instrumental in the lives of their students, you know, we can remember our favorite teachers, our best teachers. We don't remember a machine. And what you've done with this is to embody the absolute sort of pinnacle of what AI can do, which is to be the collaborator, the assistant, the augmenter, and the helper, so that the teacher can do that inspirational, connective tissue job with the students without having to like sacrifice the rest of their life making
Starting point is 00:35:25 little plants and grading papers. Oh my gosh. Okay, on the positive side, we've just talked about what this work proposes and how it's good, but I always like to dig a little bit into the potential unintended consequences and what could possibly go wrong if, in fact, you got everything right. So I'll anchor this in another example. When GPT models first came out, the first reaction came from educators. It feels like we're in a bit of a paradigm shift, like we were when the calculator and the internet even came out. It's like, how do we process this? So I want to go philosophically here and talk about how you foresee us adopting and moving forward with generative AI in education writ large.
Starting point is 00:36:10 Yeah, I think this is a question that troubles a lot of us and not just in education, but in all spheres that generative AI is. Art, writing, journalism. Absolutely. And I think the way I kind of think about it in my head is it's a tool. At the end of it, it is a tool. It's a very powerful tool. But it is a tool. And humans must always have the agency over it. And we need to come up as a society, you know, we need to come up with the norms of using the tool. And if you think about it, you know, internet, taking internet as an example, there is a lot of harm that internet has propagated, like the darknet and all the other stuff that happens right but on the whole there are regulations but they're also an actual consensus around what constitutes the positive use of internet right nobody says that for example deep fakes are good good. So so so we have to come from there and think about what kind of regulations we need to have in place, what kind of consensus we need to have in place, what's missing.
Starting point is 00:37:34 Right. Another project that has been around and it isn't necessarily on top of Vellum, but it's it's called CAR. And you call it a social impact organization that serves not just one purpose, but three. Talk about that. Oh, Karya is my favorite. So Karya started as a research project within Microsoft Research India. And this was the brainchild, again, of my colleagues. I have like some of the most amazing colleagues too that I work with called Vivek Shashadri and Vivek wanted to create you know digital work for people who do not have access to such work so he wanted to go to the rural communities to people who belong to slightly lower socio-economic demographies and provide work like micro tasks kind of work a gig work to them and he was doing this and then we started talking and I said you know we need so much data for all these languages and all these different tasks and that could be like a really cool thing to try on Karya and that's where it all started my involvement with Karya
Starting point is 00:38:47 which is still pretty strong and Karya then became such a stable project that Microsoft Research India spun it out so it's now its own standalone startup right now like a social enterprise and they work on providing digital work they work on providing skills like upskilling they work on awareness like you know making people aware of certain social financial other such trainings so what's been most amazing is that karya has been able to essentially collect data for AI in the most ethical way possible. They pay their workers a little over the minimal wage. They also have something called data ownership practice, where the data that is created by, say, me, I have some sort of ownership on it.
Starting point is 00:39:46 So what that means is that every time Karya sells a data set, royalty comes back to the workers. Hey, we need to scale this out. Okay, so to give a concrete example, the three purposes would be educational, financial, on their end, and data collection, which would ultimately support a low resource language by having digital assets. Absolutely. So you could give somebody something to read in their language that would educate them in the process. They would get paid to do it. And then you would have this data. Yes.
Starting point is 00:40:21 Okay, so cool. So simple. Like I said, it's my favorite project. I get that. I totally get that. And they have been winning awards and things all over for the work that they're doing right now. And I am very involved in one project with them, which is to do with gender intentional AI or gender intentional data sets for AI for Indian languages. And that's really crucial because, you know, we talk about gender bias and data sets, etc. But all that understanding comes from a very Western perspective and for languages like English, etc.
Starting point is 00:41:06 They do not translate very well to Indian languages. And in this particular project, we are looking at first how to define gender bias. How do we even get data around gender bias? What does it even mean to say that technology is gender intentional? Right. All right. Well, let's talk a little bit about what I like to call outrageous ideas. And these are the ones that, you know, on the research spectrum from sort of really practical applied research to blue sky, get dismissed or viewed as unrealistic or unattainable. So years ago, here's a little story about you, when you told your tech colleagues that you wanted to work with the world's most marginalized languages, they told you you'd only marginalize yourself. Yes. But you didn't say no. You didn't say no. Two questions. Did you feel like your own idea was outrageous back then? And do you still have anything outrageous yet to accomplish in this plan? I hope so. Yeah, no, I do think in some sense, the pushback that
Starting point is 00:42:14 I got from my idea makes me think it was outrageous. I didn't think it was outrageous at all at that time. I thought it was a very reasonable idea, but there was a very solid pushback. And not just from your colleagues, you know, for researchers, publishing papers is important. No one would publish a paper which focused only on, say, Indian languages or low resource languages. We've come a very long way, even in the research community on that, right? We kept pushing, pushing, pushing. And now there are tracks, there are workshops, there are conferences which are devoted to multilingual and low resource languages.
Starting point is 00:42:54 When I said I wanted to work on Hindi, and Hindi is the biggest language in India, right? And even for that, I was told, why don't you work on German instead? There are lots of people working on German who will solve the problems for German. Nobody's looking at Hindi. I mean, people should work on all the languages, people should work on German, but I don't want to work on German. So there was a lot of pushback then. And I see a little bit of that with the very low resource languages even now.
Starting point is 00:43:27 And I think some people think it's a feel good thing, whereas I think it's not. I think it's a very economically viable, necessary thing to build technology for these communities, for these languages. No one thought Hindi was economically viable 15 years ago, for whatever reason. That floors me. Yeah. But, you know, we're not talking about tens of thousands of people in some of these languages. We're talking about millions. Yeah. I still think that is a job that I need to continue, you know, pushing back on. Do you think that any of that sort of outrageous reaction was due to the fact that the technology wasn't as advanced as it is now and that it might have changed in terms of what we can do?
Starting point is 00:44:16 There was definitely the aspect of technology there that it was just quite difficult and very very resource intensive to build it for languages which did not have resources you know there was a time when we were talking about how to go about doing this and because people in various big tech companies people did not remember a time when for English they had to start data collection from scratch because everyone who was working on say English at that time was building on what people had done years and years ago so they could not even conceptualize that you had to start from scratch for anything right but now with the technology as well I'm quite optimistic and trying to think of how cool it would be to do smaller data collections and fine-tune models specifically and things like that. So I think that the technology is definitely
Starting point is 00:45:14 one big thing. But economics is a big factor too. Well, I'm glad that you said it isn't just the feel-good, but it actually would make economic sense because that's some of the driver behind what technologies get greenlit, as it were. Is there anything outrageous now that you could think of that even to you sounds like, oh, we could never do that? Well, I didn't think hell was outrageous. Back to hell 9000. Yeah. So I don't think of things as outrageous or not. I just think of things as things that need to get done, if that makes any sense. Totally. Yeah. Maybe it's how do we override open the pod bay door, Hal? No, I'm sorry, Dave. I can't do that. Well, as we close, and I'm sad to close because you are so much fun, I want to do a little vision casting, but in reverse. So let's fast forward 20 years and look back. How have the big ideas behind your life's work impacted the world? And how are people better off or different now because of you and the teams that you've worked with? So the way I see it is that people across the board, irrespective of the
Starting point is 00:46:36 language they speak, the communities they belong to, the demographies they represent, can use technology to make their lives, their work better. I know it sounds like really very big and almost too good to be true, but that's what I'm aiming for. Well, Kalika Bali, I'm so grateful I got to talk to you in person. And thanks for taking time out from your busy trip from India to sit down with me and our audience and share your amazing ideas. Thank you so much, Gretchen.

There aren't comments yet for this episode. Click on any sentence in the transcript to leave a comment.