Microsoft Research India Podcast

[Music]Sunayana Sitaram: Our ultimate goal is to build evaluation systems and also other kinds of systems in general where humans and LLMs can work together. We're really trying to get humans to do the evaluation, get LLM's to do the evaluation, use the human data in order to improve the LLM. And then just this continues in a cycle. And the ultimate goal is, send the things to the LLM that it's good at doing and send the rest of the things that the LLM can't do to humans who are like the ultimate authority on the evaluation.Sridhar Vedantham: Welcome to the Microsoft Research India podcast, where we explore cutting-edge research that’s impacting technology and society. I’m your host, Sridhar Vedantham.[Music]Sridhar Vedantham: LLM's are perhaps the hottest topic of discussion in the tech world today. And they're being deployed across domains, geographies, industries and applications. I have an extremely interesting conversation with Sunayana Sitaram, principal researcher at Microsoft Research about LLMs, where they work really well and also challenges that arise when trying to build models with languages that may be under resourced. We also talk about the critical work she and her team are doing in creating state-of-the-art methods to evaluate the performance of LLMs, including those LLMs that are based on Indic languages. RelatedMicrosoft Research India Podcast: More podcasts from MSR IndiaiTunes: Subscribe and listen to new podcasts on iTunesAndroidRSS FeedSpotifyGoogle PodcastsEmail [Music] Sridhar Vedantham: Sunayana, welcome to the podcast.Sunayana Sitaram: Thank you.Sridhar Vedantham: And I'm very excited to have you here because we get to talk about a subject that seems to be top of mind for everybody right now. Which is obviously LLMs.  And what excites me even more is I think, we're going to be talking about LLMs in a way that's slightly different from what the common discourse is today, right?Sunayana Sitaram: That's right.Sridhar Vedantham: OK. So before we jump into it, why don't you give us a little bit of background about yourself and how you came to be at MSR?Sunayana Sitaram: Sure. So it's been eight years now since I came to MSR. I came here as a postdoc after finishing my PhD at Carnegie Mellon. And so yeah, it's been around 15 years now for me in the field, and it's been super exciting, especially the last few years.Sridhar Vedantham: So, I'm guessing that these eight years have been interesting, otherwise we won't be having this conversation. What areas of research, I mean, have you changed course over the years and how is that progressed?Sunayana Sitaram: Yeah, actually, I've been working pretty much on the same thing for the last 15 years or so. So I'll describe how I got started. When I was an undergrad, I actually met the principal of a blind children's school who himself was visually impaired. And he was talking about some of the technologies that he uses in order to be independent. And one of those was using optical character recognition and text to speech in order to take documents or letters that people sent him and have them read out without having to depend on somebody. And he was in Ahmedabad, which is where I grew up. And his native language was Gujarati.  And he was not able to do this for that language. Whereas for English, the tools that he required to be independent were available. And so, he told me like it would be really great if somebody could actually build this kind of system in Gujarati. And that is when it sort of it was like a, you know, aha moment for me. And I decided to take that up as my undergrad project. And ever since then, I've been trying to work on technologies trying to bridge that gap between English and other languages- under resourced languages. And so, since then, I've worked on very related areas. So, my PhD thesis was on text to speech systems for low resource languages. And after I came to MSR I started working on what is called code switching, which is a very common thing that multilinguals all over the world do. So they use multiple languages in the same conversation or sometimes even in the same sentence. And so you know, this was a project called Project Melange that was started here and that really pioneered the code switching work in the research community in NLP. And after that it's been about LLMs and evaluation but again from a multilingual under resource languages standpoint.Sridhar Vedantham: Right. So I have been here for quite a while at MSR myself and one thing that I always heard is that there is this in general, a wide gulf in terms of the resources available for a certain set of languages to do say NLP type work. And the other languages is just the tail, it's a long tail, but the tail just falls off dramatically. So, I wanted you to answer me in a couple of ways. One is, what is the impact that this generally has in the field of NLP itself and in the field of research into language technologies, and what's the resultant impact on LLMs?Sunayana Sitaram: Yeah, that's a great question. So, you know the paradigm has shifted a little bit after LLM's have come into existence. Before this, so this was around say a few years ago, the paradigm would be that you would need what is called unlabeled data. So, that is raw text that you can find on the web, say Wikipedia or something like that, as well as labeled data. So, this is something that a human being has actually sat and labeled for some characteristic of that text, right? So these are the two different kinds of texts that you need if you want to build a text based language model for a particular language. And so there were languages where, you know, you would find quite a lot of data on the web because it was available in the form of documents or social media, etc. for certain languages. But nobody had actually created the labeled resources for those languages, right? So that was the situation a few years ago. And you know the paradigm at that time was to use both these kinds of data in order to build these models, and our lab actually wrote quite a well-regarded paper called, ‘The State and Fate of Linguistic Diversity and Inclusion’, where they grouped different languages into different classes based on how much data they had labeled, as well as unlabeled.Sridhar Vedantham: Right. Sunayana Sitaram: And it was very clear from that work that, you know only around 7 or 8 languages of the world actually can be considered to be high resource languages which have this kind of data. And most of the languages of the world spoken by millions and millions of speakers don't have these resources. Now with LLMs, the paradigm changed slightly, so there was much less reliance on this labeled data and much more on the vast amount of unlabeled data that exists, say, on the web. And so, you know, we were wondering what would happen with the advent of LLMs now to all of the languages of the world, which ones would be well represented, which ones wouldn't etc. And so that led us to do, you know, the work that we've been doing over the last couple of years. But the story is similar, that even on the web some of these languages dominate and so many of these models have, you know, quite a lot of data from only a small number of languages, while the other languages don't have much representation.Sridhar Vedantham: OK. So, in real terms, in this world of LLMs that we live in today, what kind of impact are we looking at? I mean, when you're talking about inequities and LLMs and in this particular field, what's the kind of impact that we're seeing across society?Sunayana Sitaram: Sure. So when it comes to LLMs and language coverage, what we found from our research is that there are a few languages that LLMs perform really well on. Those languages tend to be high resource languages for which there is a lot of data on the web and they also tend to be languages that are written in the Latin script because of the way the LLMs are designed currently with the tokenization. And the other languages, unfortunately there is a large gap between the performance in English and other languages, and we also see that a lot of capabilities that we see in LLMs in English don't always hold in other languages. So a lot of capabilities, like really good reasoning skills, etc, may only be present in English and a few other languages, and they may not be seen in other languages. And this is also true when you go to smaller models that you see that their language capabilities fall off quite drastically compared to the really large models that we have, like the GPT 4 kind of models. So when it comes to real world impact of this, you know, if you're trying to actually integrate one of these language models into an application and you're trying to use it in a particular language, chances are that you may not get as good performance in many languages compared to English. And this is especially true if you're already used to using these systems in English and you want to use them in a second language. You expect them to have certain capabilities which you've seen in English, and then when you use them in another language, you may not find the same capabilities. So in that sense, I think there's a lot of catching up to do for many languages. And the other issue also is that we don't even know how well these systems perform for most languages of the world because we've only been able to evaluate them on around 50 to 60 or maybe 100 languages. So for the rest of the 6000ish languages of the world, many of which don't even have a written form, most of which are not there on the web. We don't even know whether these language models are, you know, able to do anything in them at all. So I think that is another, you know, big problem that is there currently.Sridhar Vedantham: So, if you want to change the situation where we say that you know even if you're a speaker of a language that might be small, maybe say only two million speakers as oppose

Podcast- HyWay: Enabling Mingling in the Hybrid WorldAjay Manchepalli: One thing we have learned is that, you know as they say, necessity is the mother of invention. This is a great example of that because it's not that we didn't have remote people before. And it's not that we didn't have technology to support something like this. But we have had this Black Swan moment with COVID, which required us to be not in the same physical location at all time and that accelerated the adoption of digital technologies. You can build all the technology you want. But having it at the right time and right place matters the most.[Music]Sridhar Vedantham: Welcome to the Microsoft Research India podcast, where we explore cutting-edge research that’s impacting technology and society. I’m your host, Sridhar Vedantham.[Music]Sridhar Vedantham: The COVID pandemic forced most of us into a new paradigm of work from home and a number of tools to cater to remote work became popular. However, the post pandemic environment has seen interesting scenarios with some people preferring to continue to work from home, some people preferring to return full time to work and a number of people adopting something in between. This hybrid work environment exists today in the workplace as well as in other scenarios such as events. While tools such as Microsoft Teams do extremely well in supporting scheduled and agenda driven work meetings, there is need for a tool that supports a mix of virtual and in-person gatherings in an informal or semi-structured work environment, such as in hallways or at water coolers. In this edition of the podcast, I speak to Venkat Padmanabhan, Deputy MD (Deputy Managing Director) of MSR India and Ajay Manchepalli. Principal Research Program Manager, about a project called HyWay. HyWay’s a system to support unstructured and semi structured hybrid and informal interactions between groups of in-person and remote participants.Venkat Padmanabhan is Deputy Managing Director at Microsoft Research India in Bengaluru. He was previously with Microsoft Research Redmond, USA for nearly 9 years. Venkat’s research interests are broadly in networked and mobile computing systems, and his work over the years has led to highly-cited papers and paper awards, technology transfers within Microsoft, and also industry impact. He has received several awards and recognitions, including the Shanti Swarup Bhatnagar Prize in 2016, four test-of-time paper awards from ACM SIGMOBILE, ACM SIGMM, and ACM SenSys, and several best paper awards. He was also among those recognized with the SIGCOMM Networking Systems Award 2020, for contributions to the ns family of network simulators. Venkat holds a B.Tech. from IIT Delhi (from where he received the Distinguished Alumnus award in 2018) and an M.S. and a Ph.D. from UC Berkeley, all in Computer Science, and has been elected a Fellow of the INAE, the IEEE, and the ACM. He is an adjunct professor at the Indian Institute of Science and was previously an affiliate faculty member at the University of Washington. He can be reached online at http://research.microsoft.com/~padmanab/.Ajay Manchepalli, as a Research Program Manager, works with researchers across Microsoft Research India, bridging Research innovations to real-world scenarios. He received his Master’s degree in Computer Science from Temple University where he focused on Database Systems. After his Masters, Ajay spent his next 10 years shipping SQL Server products and managing their early adopter customer programs.For more information about the HyWay project, click HyWay - Microsoft Research.For more information about the Microsoft Research India click here.RelatedMicrosoft Research India Podcast: More podcasts from MSR IndiaiTunes: Subscribe and listen to new podcasts on iTunesAndroidRSS FeedSpotifyGoogle PodcastsEmail Transcript[Music]Sridhar Vedantham: So, Venkat and Ajay, welcome to the podcast.Venkat Padmanabhan: Good to be here again.Ajay Manchepalli: Yeah, likewise.Sridhar Vedantham: Yeah, both of you guys have been here before, right?Venkat Padmanabhan: Yeah, it's my second time.Sridhar Vedantham: OK.Ajay Manchepalli: Same here.Sridhar Vedantham: Great! So anyway, we wanted to talk today about this project called HyWay, which, unlike the way the name sounds, is not related to one of your earlier projects which was called HAMS, which actually had to do with road safety. So, tell us a bit about what HyWay is all about and especially where the name comes from?Venkat Padmanabhan: Right. Yeah. So, HyWay, we spell it as H Y W A Y. It's short for hybrid hallway. It's really about hybrid interaction. What we mean by that is interaction between people who are physically present in a location- think of a conference venue or an office floor- and people who are remote. So that's where hybrid comes from, and it's really about sort of enabling informal mingling style, chitchat kind of interaction in such settings, which perhaps other platforms don't quite support.Sridhar Vedantham: OK. And why come up with this project at all? I mean there are plenty of other solutions and products and ways to talk to people that already are out there. So why do we really need something new?Venkat Padmanabhan: Yeah, yeah. So maybe I can give you a little bit of background on this. I think in the very early days of the pandemic, I think in March or April of 2020, you know, all of us were locked up in our respective homes. And obviously there were tools like Teams at Microsoft and equivalent ones like Zoom and so on elsewhere, that allowed people to stay connected and participate in work meetings and so on. But it was very clear very soon that what's missing is these informal interactions, bumping into someone in the hallway and just chatting with them. That kind of interaction was pretty much nonexistent because, you know, if you think of something like a Teams call or, you know, Zoom call, any of those, it's a very sanitized environment, right? If, let's say the three of us are on a Teams call, no one else in the world knows we are meeting, and no one else in the world can overhear us or be, you know, have an opportunity to join us unless they're explicitly invited. So, we said, OK, you know, we want to sort of make these meeting porous, not have these hard boundaries. And that was the starting point. And then as the months went along, we realized that, hey, the world is not going to be just remote all the while. You know people are going to come back to the office and come back to having face-to-face meetings. And so how do you sort of marry the convenience of remote, with the richer experience of being in-person, and so that's where hybrid comes in. And that's something that in our experience, existing tools, including the new tools that came up in the pandemic, don't support. There are tools that do all virtual experiences. But there is nothing that we have seen that does hybrid the way we are trying to do in HyWay.Sridhar Vedantham: Right. So, I wanted to go back to something you just said earlier and basically, when you use the term porous, right, and what does that actually mean? Because like you said, the paradigm in which we are used to generally conducting meetings is that it's a closed, sanitized environment. So, what exactly do we mean by porosity and if you are in a meeting environment, why do you even want porosity?Venkat Padmanabhan: OK. Maybe I can give an initial answer then maybe Ajay can add. I think we're not saying every meeting is going to be porous, just to be clear, right. You know when you have a closed-door meeting and you know, maybe you're talking about sensitive things, you don't want porosity, right? You want to sort of maintain the privacy and the sanctity of that environment, but when you are trying to enable mingling in a, say, conference setting where you’re sort of bumping into people, joining a conversation, and while you're having the conversation, you overhear some other conversation or you see someone else and you want to go there. There we think something like porosity and other elements of the design of HyWay, which we can get to in a moment, allow for awareness, right? Essentially, allow you to be aware of what else is going on and give you that opportunity to potentially join other conversations. So that's where we think porosity is really important. It's not like it's something that we are advocating for all meetings.Ajay Manchepalli: One way to think about this is if you are in a physical space and you want to have a meeting with somebody on a specific topic. You pick a conference room, and you get together and it's a closed-door conversation. However, when you're at a workplace or any location for that matter, you tend to have informal conversations, right? So where you're just standing by the water cooler or you're standing in the hallway and you want to have discussions. And at that point in time, what you realize is that, even though you're having conversations with people, there are people nearby that you can see, and you can overhear their conversations. It's a very natural setting. However, if you're remote and you're missing out on those conversations, how do you bring them into play, right? And where it is not predefined or a planned conversation and you're just gonna happen to see someone or happen to hear someone and join in. And what we talk about is a natural porous nature of air and we are trying to simulate something similar in our system.Sridhar Vedantham: OK. So, it's kind of trying to mimic an actual real life physical interaction kind of setting where you can kind of combine some degree of formality and informality.Ajay Manchepalli: Correct! And many of these platforms like Teams or Zoom and things like that, it is built on this notion of virtual presence, so you could be anywhere, and you could join and have discussions. However, our concept is more aligned with, how do I get to participate in a physical space? So,

Episode 013 | June 14, 2022Road safety is a very serious public health issue across the world. Estimates put the traffic related death toll at approximately 1.35 million fatalities every year, and the World Health Organization ranks road injuries in the top 10 leading causes of death globally. This raises the question- can we do anything to improve road safety? In this podcast, I speak to Venkat Padmanabhan, Deputy Managing Director of Microsoft Research India and Akshay Nambi, Principal Researcher at MSR India. Venkat and Akshay talk about a research project called Harnessing Automobiles for Safety, or HAMS. The project seeks to use low-cost sensing devices to construct a virtual harness for vehicles that can help monitor the state of the driver and how the vehicle is being driven in the context of the road environment it is in. We talk about the motivation behind HAMS, its evolution, its deployment in the real world and the impact it is already having, as well as their future plans.Venkat Padmanabhan is Deputy Managing Director at Microsoft Research India in Bengaluru. He was previously with Microsoft Research Redmond, USA for nearly 9 years. Venkat’s research interests are broadly in networked and mobile computing systems, and his work over the years has led to highly-cited papers and paper awards, technology transfers within Microsoft, and also industry impact. He has received several awards and recognitions, including the Shanti Swarup Bhatnagar Prize in 2016, four test-of-time paper awards from ACM SIGMOBILE, ACM SIGMM, and ACM SenSys, and several best paper awards. He was also among those recognized with the SIGCOMM Networking Systems Award 2020, for contributions to the ns family of network simulators. Venkat holds a B.Tech. from IIT Delhi (from where he received the Distinguished Alumnus award in 2018) and an M.S. and a Ph.D. from UC Berkeley, all in Computer Science, and has been elected a Fellow of the INAE, the IEEE, and the ACM. He is an adjunct professor at the Indian Institute of Science and was previously an affiliate faculty member at the University of Washington. He can be reached online at http://research.microsoft.com/~padmanab/.Akshay Nambi is a Principal Researcher at Microsoft Research India. His research interests lie at the intersection of Systems and Technology for Emerging Markets broadly in the areas of AI, IoT, and Edge Computing. He is particularly interested in building affordable, reliable, and scalable IoT devices to address various societal challenges. His recent projects are focused on improving data quality in low-cost IoT sensors and enhancing performance of DNNs on resource-constrained edge devices. Previously, he spent two years at Microsoft Research as a post-doctoral scholar and he has completed his PhD from the Delft University of Technology (TUDelft) in the Netherlands.More information on the HAMS project is here: HAMS: Harnessing AutoMobiles for Safety - Microsoft ResearchFor more information about the Microsoft Research India click here.RelatedMicrosoft Research India Podcast: More podcasts from MSR IndiaiTunes: Subscribe and listen to new podcasts on iTunesAndroidRSS FeedSpotifyGoogle PodcastsEmailTranscriptVenkat Padmanabhan: There's hundreds of thousands of deaths and many more injuries happening in the country every year because of road accidents. And of course it's a global problem and the global problem is even bigger. The state of license testing is as that by some estimates of public reports, over 50% of license are issued without a test or a proper test. So we believe a system like HAMS that improves the integrity of the testing process has huge potential to make a positive difference.[Music]Sridhar Vedantham: Welcome to the Microsoft Research India podcast, where we explore cutting-edge research that’s impacting technology and society. I’m your host, Sridhar Vedantham.[Music]Sridhar Vedantham: Venkat and Akshay welcome to the podcast. I think this is going to be quite an interesting one.Venkat Padmanabhan: Hello Sridhar, nice to be here.Akshay Nambi: Yeah, Hello Sridhar, nice to be here.Sridhar Vedantham: And Akshay is of course officially a veteran of the podcast now since it's your second time.Akshay Nambi: Yes, but the first time in person so looking forward to it.Sridhar Vedantham: Yes, in fact I am looking forward to this too. It's great to do these things in person instead of sitting virtually and not being able to connect physically at all.Akshay Nambi: Definitely.Sridhar Vedantham: Cool, so we're going to be talking about a project that Venkat and you are working on, and this is something called HAMS. To start with, can you tell us what HAMS means or what it stands for, and a very brief introduction into the project itself?Venkat Padmanabhan: Sure, I can take a crack at it. HAMS stands for Harnessing Automobiles for Safety. In a nutshell, it's a system that uses a smartphone to monitor a driver and their driving, with a view to improving safety. So we look at things like the state of the driver, where they're looking, whether they're distracted, and so on. That’s sort of looking at the driver. But we also look at the driving environment, because we think, to truly attack the problem of safety, you need to have both the internal context inside the vehicle as well as the external context. So that's the sort of brief description of what HAMS tries to do.Sridhar Vedantham: Ok. So, you spoke about a couple of things here, right? One is the safety aspect of, you know, driving both internal and external. When you're talking about this, can you be more concise? And especially, how did this kind of consideration feed into, say, the motivation or the inspiration behind HAMS?Akshay Nambi: Yeah, so as you know, road safety is a major concern, not just in India globally, right? And when you look at the factors affecting roads safety, there is the vehicle, there's the infrastructure and the driver. And majority of the instance today focus on the driver. For instance, the key factors affecting road safety includes over speeding, driving without seatbelts, drowsy driving, drunken driving. All centering around the driver. And that kind of started that was motivating towards looking at the driver more carefully, which is where we build the system HAMS, which focuses on monitoring the driver and also how he's driving.Sridhar Vedantham: And India in particular has an extremely high rate of deaths per year, right, in terms of in terms of roads accidents.Akshay Nambi: Yes, it's on the top list. In fact, around 80,000 to 1.5 lakh people die every year according to the stats from the government. Yeah, it's an alarming thing and hopefully we are doing baby steps to improve that.Venkat Padmanabhan: In fact, if I may add to that, if you look at the causes of death, not just road accidents, diseases and so on, road accidents are in the top 10. And if you look at the younger population, you know people under 35 or 40, it's perhaps in the top two or three. So it is a public health issue as well.Sridhar Vedantham: And that's scary. Ok, so how does this project actually work? I mean, the technology and the research that you guys developed and the research that's gone into it. Talk to us a little bit about that.Venkat Padmanabhan: Sure yeah, let me actually wind back, maybe 10-15 years to sort of when we first started on this journey, and then talk more specifically about HAMS and what's happened more recently. Smartphones, as you know, have been around for maybe 15 years. A bit longer maybe. And when smartphones started emerging in the mid 2000s and late 2000s, we got quite interested in the possibility of using a smartphone as a sensor for, you know, road monitoring, driving monitoring and so on. And we built a system here at Microsoft Research India back in 2007-08, it's called Nericell, where we used a leading-edge smartphone of that era to do sensing. But it turned out that the hardware then was quite limited in its capabilities in terms of sensors, even accelerometer was not there. We had to pair an external accelerometer and so on. And so the ability for us to scale that system and really have interesting things come out of it was quite limited. Fast forward, about 10 years, not only did smartphone hardware get much better, AI and machine learning models that could process this information became much better and among the new sensors in the newer edge smartphones or the cameras, the front camera and the back camera. And machine learning models for computer vision have made tremendous progress. So that combination allowed us to do far more interesting things than we were able to back then. Maybe Akshay can talk a bit more about the specific AI models and so on that we built.Akshay Nambi: Yeah, so if you compare the systems in the past to HAMS, what was missing was the context. In the past, systems like what Venkat mentioned- Nericell, right, it was correcting the sensor data, but it was lacking context. For example, it could tell did the driver did this rash braking or not, but it could not tell, did he do it because somebody jumped in front of the vehicle, or was he distracted? These cameras new smartphones have can provide this context, which makes these systems much more capable and can provide valuable insights. And in terms of specific technology itself, we go with commodity smartphones, which have multiple cameras today. The front camera looking at the driver, the back camera looking at the road, and we have built numerous AI models to track the driver state, which includes driver fatigue and driver gaze, where the driver is actually looking. And also with the back camera we look at how the driver is driving with respect to the environment. That is, is he over speeding, is he driving on the wrong side of the road and so on.Sridhar Vedantham: So, this is all happening in real time.Akshay Nambi: The system can support both real time and also offline processing. And as y

Episode 012 | May 30, 2022Neeraj Kayal: It’s just a matter of time before we figure out how computers can themselves learn like humans do. Just human babies, they have an amazing ability to learn by observing things around them. And currently, despite all the progress, computers don't have that much ability. But I just think it's a matter of time before we figure that out, some sort of general artificial intelligence.Sridhar Vedantham: Welcome to the MSR India podcast. In this podcast, Ravishankar Krishnaswamy, a researcher at the MSR India lab, speaks to Neeraj Kayal. Neeraj is also a researcher at MSR India and works on problems related to or at the intersection of Computational Complexity and Algebra, Number Theory and Geometry. He has received multiple recognitions through his career, including the Distinguished Alumnus award from IIT Kanpur, the Gödel prize and the Fulkerson Prize. Neeraj received the Young Scientist Award from the Indian National Science Academy (INSA) in 2012 and the Infosys Prize in Mathematical Sciences in 2021. Ravi talks to Neeraj about how he became interested in this area of computer science and his journey till now.For more information about the Microsoft Research India click here.RelatedMicrosoft Research India Podcast: More podcasts from MSR IndiaiTunes: Subscribe and listen to new podcasts on iTunesAndroidRSS FeedSpotifyGoogle PodcastsEmailTranscriptRavi Krishnaswamy: Hi Neeraj, how are you doing? It's great to see you after two years of working from home.Neeraj Kayal: Hi Ravi, yeah thank you.Thank you for having me here and it's great to be back with all the colleagues in office.Ravi Krishnaswamy: First of all, congratulations on the Infosys prize and it's an amazing achievement.And it's a great privilege for all of us to have you as a colleague here.So, congratulations on that.Neeraj Kayal: Thank you.Ravi Krishnaswamy: Yeah, so maybe we can get started on the podcast. So, you work in complexity theory, which is I guess one extreme of, I mean, it's very theoretical end of the spectrum in computer science almost bordering mathematics. So hopefully by the end of this podcast we can, uh, I mean, convince the audience that there's more to it than intellectual curiosity. Before that right, let me ask you about how you got into theoretical computer science and the kind of problems that you work on. So, could you maybe tell us a bit about your background and how you got interested into this subject?Neeraj Kayal: Yeah, so in high school I was doing well in maths in general and I also wrote some computer programs to play some board games, like a generalized version of Tic Tac Toe where you have a bigger board, say 20 by 20, and you try to place five things in the row, column, or diagonal continuously and then I started thinking about how could a computer learn to play or improve itself in such a game? So, I tried some things and didn't get very far with that, but at that time I was pretty convinced that one day computers will be able to really learn like humans do. I didn't see how that will happen, but I was sure of it and I just wanted to be in computer science to eventually work on such things. But in college in the second year of my undergrad, I enrolled for a course in cryptography taught by Manindra Agrawal at IIT Kanpur and then the course started off with some initial things which are like fairly predictable that something called symmetric key cryptosystems where, essentially it says that let's say we two want to have a private conversation, but anyone else can listen to us. So how do we have a private conversation? Well, if we knew a language, a secret language which no one else did, then we could easily just converse in that language, and no one will understand this. And so, this is made a little more formal in this symmetric key cryptosystem. And then, one day, Manindra ended one of the lectures with the following problem: but now suppose we did not know a secret language. Then we just know English, and everyone knows English and then how do we talk privately when everyone can hear us? I thought about it for a few days. It seemed completely impossible. And then Manindra told us about these wonderful cryptosystems, called the Diffie Hellman cryptosystem and the RSA cryptosystem where they achieved this and it was very surprising. And the key thing that these cryptosystems use is something that lies at the heart of computer science, a big mystery still even to this day at the heart of computer science. There are these problems which we believe are hard for computers to solve in the following sense, that even if a computer takes a very long amount of time, if we give it a fairly long amount of time, a reasonable amount of time it cannot solve it. But if we give it time like till the end of the universe, it can in principle solve such problems. So that got me interested into which problems are hard and can we prove they are actually hard or not? And to this day, we don't know that.Ravi Krishnaswamy: So, I'm guessing that you're talking about the factoring problem, right?Neeraj Kayal: Yes, factoring is one of the big ones here. And the RSA cryptosystem uses factoring.Ravi Krishnaswamy: So, it's actually very interesting, right? You started out by trying to show that some of these problems are very, very hard, but I think, looking back, your first research paper, which happens to be a breakthrough work in itself, is in showing that a certain problem is actually easier to solve. Then we had originally thought right so, it is this seminal work on showing that primality testing can be solved in deterministic polynomial time. I mean, that's an amazing feat and you had worked on this paper with your collaborators as an undergrad, right?Neeraj Kayal: Yes.Ravi Krishnaswamy: Yeah, that's an incredible achievement. So maybe to motivate others who are in undergrad and who have an interest and inclination in such topics, could you maybe share us some story on how you got working in that problem and what sort of led you to this spark that eventually got you to this breakthrough result?Neeraj Kayal: So, my advisor Manindra, who also was the professor who taught us cryptography - he had been working on this problem for a long time and there were already algorithms that existed which are very good in practice- very very fast in practice, but they had this small chance that they might give the wrong answer. The chance was so small that practically it did not matter, but still as a mathematical challenge, it remained whether we could remove that small chance of error, and that's what the problem was about. So, Manindra had this approach and he had worked with other students also- some of our seniors- on it, and in that course, he came up with a conjecture. And then when we joined, me and my colleague Nitin, we joined this project , we came across this conjecture and my first reaction was that the conjecture is false. So, I tried to write a program which would find a counterexample and I thought we would be done in a few days-Just find that counterexample and the project would be over. So, I wrote a program- it will train for some time, didn't find a counterexample, so I decided to parallelize it. A huge number of machines in the computer center in IIT Kanpur started looking for that counterexample. And then to my surprise, we still couldn't find the counterexample. So there seemed to be something to it. Something seemed to be happening there which we didn't understand, and in trying to sort of prove that conjecture, we managed to prove some sort of weaker statement which sufficed for obtaining the polynomial time algorithm to test if a number is prime or not. But it was not the original conjecture itself. Many days after this result came out, we met a mathematician called Hendrik Lenstra who had worked on primality testing, and we told him about this conjecture. And after a few days he got back to us and it showed that if you assume some number theoretic conjecture is true, which we really really believe, it's true.Ravi Krishnaswamy: Ok, I see. So, the original conjecture, which you hoped to prove true is false, but the weaker conjecture was actually true, you proved it to be true, and that was enough for your eventual application.Neeraj Kayal: Yes, so in some sense we are very lucky that in trying to prove something false we managed to prove something useful.Ravi Krishnaswamy: Yeah, I mean it's a fascinating story, right? All the experiments that you ran pointed you towards proving it, and then you actually went and proved it. If you had found, I imagine what would have happened if you found a counterexample at that time, right?Neeraj Kayal: So yeah, Hendrix proof was very interesting. He showed that modulo this number theory conjecture a counterexample existed. But it would have to be very, very large and that's why you couldn't find it. So, he explained it beautifully.Ravi Krishnaswamy: Yeah, thanks for that story Neeraj. So. I guess from then on you've been working in complexity theory, right?Neeraj Kayal: That's right, yeah.Ravi Krishnaswamy: So, for me at least, the Holy Grail in complexity theory that I've often encountered or seen is the P versus NP problem, which many of us might know. But you've been working on a very equally important, but a very close cousin of the problem, which is called the VP versus VNP problem, right? So, I'm going to take a stab at explaining what I understand of the problem. So, correct me whenever I'm wrong. So, you are interested in trying to understand the complexity of expressing polynomials using small circuits. So, for example, if you have a polynomial of the form X ^2 + Y ^2 + 2 XY, you could represent it as a small circuit which has a few addition operations and a few multiplication operations like you could express it as X ^2 + Y ^2 + 2 XY itself. Or you could express it as (X + Y)^2. Which may have a smaller representation in terms of a circuit. So,

Episode 011 | January 18, 2022Keratoconus is a severe eye disease that affects the cornea, causing it to become weak and develop a conical bulge. Keratoconus, if undiagnosed and entreated, can lead to partial or complete blindness in people affected by it. However, the equipment needed to diagnose keratoconus is expensive and non-portable, which makes early detection of keratoconus inaccessible to large populations in low and middle income countries. This makes it a leading cause for partial or complete blindness amongst such populations. Doctors from Sankara Eye Hospital, Bengaluru and researchers from Microsoft Research India have been working together to develop SmartKC, a low-cost and portable diagnostic system that can enable early detection and mitigation of keratoconus. Join us as we speak to Dr. Kaushik Murali from Sankara Eye Hospital and Dr. Mohit Jain from Microsoft Research India.Dr. Kaushik Murali, President Medical Administration, Quality & Education, Sankara Eye Foundation India (Sri Kanchi Kamakoti Medical Trust) which is among the largest structured community eye hospital network in India, (www.sankaraeye.com) with an objective of providing world class eye care with a social impact. A paediatric ophthalmologist, Dr. Kaushik has completed a General Management Programme and is an alumnus of Insead. He has done a course on Strategic Management of Non Profits at the Harvard Business School. He has been certified in infection control, risk management for health care and digital disruption. He is a member of Scalabl, a global community promoting entrepreneurship.  Dr. Kaushik is a member of the Scientific Committee of Vision 2020, the Right to Sight India. He is currently involved in collaborative research projects among others with the University of Bonn & Microsoft.Dr. Kaushik has received many recognitions, key among them being the Bernadotte Foundation for Children's Eyecare Travel Grant, Mother Teresa Social Leadership Scholarship ,International Eye Health Hero, All India Ophthalmological Society best research, International Association for Prevention of Blindness (IAPB) Eye Health Hero, Indian Journal of Ophthalmology Certificate of Merit. Beyond the medical world, he is part of the National Management Team of Young Indians – Confederation of Indian Industry (CII). He represented India at G20 Young Entrepreneur Alliance 2018 at Argentina and led the Indian delegation for the Inaugural India- Israel Young Leaders Forum in 2019. More recently, he led the first citizen’s cohort for a workshop on Strategic Leadership at LBSNAA (Lal Bahadur Shastri National Academy of Administration). Mohit Jain is a Senior Researcher in the Technology and Empowerment (TEM) group at Microsoft Research India. His research interests lie at the intersection of Human Computer Interaction and Artificial Intelligence. Currently, he focuses on developing end-to-end systems providing low-cost smartphone-based patient diagnostic solutions for critical diseases. Over the past decade, he has worked on technological solutions for the developing regions focusing on health, accessibility, education, sustainability, and agriculture.He received his Ph.D. in Computer Science & Engineering from the University of Washington, focusing on extending interactivity, accessibility and security of conversational systems. While pursuing his Ph.D., he also worked as a Senior Research Engineer in the Cognitive IoT team at IBM Research India. Prior to that, he graduated with a Masters in Computer Science from the University of Toronto, and a Bachelors in Information and Communication Technology from DA-IICT.For more information about the SmartKC project, and for project related code, click here.For more information about the Microsoft Research India click here.RelatedMicrosoft Research India Podcast: More podcasts from MSR IndiaiTunes: Subscribe and listen to new podcasts on iTunesAndroidRSS FeedSpotifyGoogle PodcastsEmailTranscript  Dr. Murali Kaushik: Sitting in an eye hospital, often we have ideas, but we have no clue whom to ask. But honestly, now we know that there is a team at MSR that we can reach out to saying that hey, here is a problem, we think this warrants attention. Do you think you guys can solve it? And we found that works really well. So, this kind of a collaboration is, I think, a phenomenal impact that this project has brought together, and we hope that together we will be able to come up with few more solutions that can align with our founders’ dream of eliminating needless blindness from India. [Music]Sridhar Vedantham: Welcome to the Microsoft Research India podcast, where we explore cutting-edge research that’s impacting technology and society. I’m your host, Sridhar Vedantham.[Music]Sridhar Vedantham: Keratoconus is a severe eye disease that affects the cornea, causing it to become weak and develop a conical bulge. Keratoconus, if undiagnosed and entreated, can lead to partial or complete blindness in people affected by it. However, the equipment needed to diagnose keratoconus is expensive and non-portable, which makes early detection of keratoconus inaccessible to large populations in low and middle income countries. This makes it a leading cause for partial or complete blindness amongst such populations. Doctors from Sankara Eye Hospital, Bengaluru and researchers from Microsoft Research India have been working together to develop SmartKC, a low-cost and portable diagnostic system that can enable early detection and mitigation of keratoconus. Join us as we speak to Dr. Kaushik Murali from Sankara Eye Hospital and Dr. Mohit Jain from Microsoft Research India. [Music] Sridhar Vedantham: So, Dr. Kaushik and Mohit, welcome to the podcast.  Mohit Jain: Hi, Sridhar. Dr. Kaushik Murali: Hi Sridhar, pleasure to be here.  Sridhar Vedantham: It's our pleasure to host you, Doctor Kaushik, and for me this is going to be a really interesting podcast for a couple of reasons. One is that the topic itself is kind of so far afield from what I normally here at Microsoft Research and the second is I think you're the first guest we are having on the podcast who's actually not part of MSR, so basically a collaborator. So, this is really exciting for me. So let me jump right into this. We're going to be talking about something called keratoconus, so could you educate us a little bit as to what keratoconus actually is and what its impact is?  Dr. Kaushik Murali: So, imagine that you were a 14-year-old who was essentially near sighted. You wore glasses and you were able to see. But with passing time, your vision became more distorted rather than being blurred, which is what you would have expected if just your minus power kept increasing, especially for distance. And to add to your misery, you started seeing more glare and more halos at nighttime. Words that you started to read had shadows around them or even started to look doubled. This essentially is the world of a person with Keratoconus. Literally it means cone shaped. Keratoconus is a condition of the cornea, which is the transparent front part of the eye, similar to your watch glass, where instead of it normally retaining its dome shape, it is characterized by progressive thinning and weakening of the central part, what we call as a stroma, and this makes the cornea take on a conical shape. In a few, this can actually even progress beyond what I describe, where the central cornea overtime becomes scarred and the person could no longer be corrected, with just optical devices like a glass or a contact lens but may actually end up requiring a corneal transplant.   Sridhar Vedantham: I see, and what are the causes for this?  Dr. Kaushik Murali: So there have been very many causes that have been attributed, so it's thought to be multifactorial. So, this again makes it a little tricky in terms of us not being able to prevent the condition, so to speak. But multiple risk factors are known. Ultraviolet exposure, chronic allergies; habitual eye rubber is thought to be more prone for this. Essentially, you end up seeing it more during the pubertal age group, and more in men.  Sridhar Vedantham: I see. And how widespread is this problem, really? Because frankly, I'm of course as lay a person as you can get, and I hadn't really heard of eye disease called keratoconus until I spoke to Mohit at some point and then of course after reading papers and so on. But what is the extent of the issue and is it really that widespread a problem? Dr. Kaushik Murali: So, unlike most other conditions, there is no real population-based survey where we have screened every household to arrive at numbers. But largely, we base our estimation on small surveys that have been done across different parts of the world. Based on this, we estimate that it is approximately affecting about one in 2000 individuals. So, in the US, for example, it is thought to affect almost about 55 people in about 100,000, who had been diagnosed with keratoconus. But in countries like India, it is thought to be more widespread. So there was actually a survey in central India where they found almost 2300 people out of 100,000 people being affected with keratoconus. So, the numbers are quite large. And again, all of this could be underestimated simply because we don't have enough ability to screen. And what makes this number even scarier is this is a disease that typically affects people between the age group of 10 and 25. So, once they're affected and they’re progressively going to have their vision come down, they're going to spend most of their protective years not being able to see clearly.  Sridhar Vedantham: OK, that is kind of scary.  Mohit Jain: I would just like to add to that is that there is actually a combination of demographics, genetic and weather condition which makes India a really good host for this disease. So, apparently Indian population tend to have a thinner and steeper cornea to begin with and moreover the hot and humid cli

Episode 010 | September 28, 2021Artificial intelligence, Machine Learning, Deep Learning, and Deep Neural Networks are today critical to the success of many industries. But they are also extremely compute intensive and expensive to run in terms of both time and cost, and resource constraints can even slow down the pace of innovation. Join us as we speak to Muthian Sivathanu, Partner Research Manager at Microsoft Research India, about the work he and his colleagues are doing to enable optimal utilization of existing infrastructure to significantly reduce the cost of AI.Muthian's interests lie broadly in the space of large-scale distributed systems, storage, and systems for deep learning, blockchains, and information retrieval.Prior to joining Microsoft Research, he worked at Google for about 10 years, with a large part of the work focused on building key infrastructure powering Google web search — in particular, the query engine for web search. Muthian obtained his Ph.D from University of Wisconsin Madison in 2005 in the area of file and storage systems, and a B.E. from CEG, Anna University, in 2000.For more information about the Microsoft Research India click here.RelatedMicrosoft Research India Podcast: More podcasts from MSR IndiaiTunes: Subscribe and listen to new podcasts on iTunesAndroidRSS FeedSpotifyGoogle PodcastsEmail TranscriptMuthian Sivathanu: Continued innovation in systems and efficiency and costs are going to be crucial to drive the next generation of AI advances, right. And the last 10 years have been huge for deep learning and AI and primary reason for that has been the significant advance in both hardware in terms of emergence of GPUs and so on, as well as software infrastructure to actually parallelize jobs, run large distributed jobs efficiently and so on. And if you think about the theory of deep learning, people knew about backpropagation about neural networks 25 years ago. And we largely use very similar techniques today. But why have they really taken off in the last 10 years? The main catalyst has been sort of advancement in systems. And if you look at the trajectory of current deep learning models, the rate at which they are growing larger and larger, systems innovation will continue to be the bottleneck in sort of determining the next generation of advancement in AI.[Music]Sridhar Vedantham: Welcome to the Microsoft Research India podcast, where we explore cutting-edge research that’s impacting technology and society. I’m your host, Sridhar Vedantham.[Music]Sridhar Vedantham: Artificial intelligence, Machine Learning, Deep Learning, and Deep Neural Networks are today critical to the success of many industries. But they are also extremely compute intensive and expensive to run in terms of both time and cost, and resource constraints can even slow down the pace of innovation. Join us as we speak to Muthian Sivathanu, Partner Research Manager at Microsoft Research India, about the work he and his colleagues are doing to enable optimal utilization of existing infrastructure to significantly reduce the cost of AI.[Music]Sridhar Vedantham: So Muthian, welcome to the podcast and thanks for making the time for this.Muthian Sivathanu: Thanks Sridhar, pleasure to be here.Sridhar Vedantham: And what I'm really looking forward to, given that we seem to be in some kind of final stages of the pandemic, is to actually be able to meet you face to face again after a long time. Unfortunately, we've had to again do a remote podcast which isn't all that much fun.Muthian Sivathanu: Right, right. Yeah, I'm looking forward to the time when we can actually do this again in office.Sridhar Vedantham: Yeah. Ok, so let me jump right into this. You know we keep hearing about things like AI and deep learning and deep neural networks and so on and so forth. What's very interesting in all of this is that we kind of tend to hear about the end product of all this, which is kind of, you know, what actually impacts businesses, what impacts consumers, what impacts the health care industry, for example, right, in terms of AI. It's a little bit of a mystery, I think to a lot of people as to how all this works, because... what goes on behind the scenes to actually make AI work is generally not talked about. Muthian Sivathanu: Yeah.Sridhar Vedantham: So, before we get into the meat of the podcast you just want to speak a little bit about what goes on in the background.Muthian Sivathanu: Sure. So, machine learning, Sridhar, as you know, and deep learning in particular, is essentially about learning patterns from data, right, and deep learning system is fed a lot of training examples, examples of input and output, and then it automatically learns a model that fits that data, right. And this is typically called the training phase. So, training phase is where it takes data builds a model how to fit. Now what is interesting is, once this model is built, which was really meant to fit the training data, the model is really good at answering queries on data that it had never seen before, and this is where it becomes useful. These models are built in various domains. It could be for recognizing an image for converting speech to text, and so on, right. And what has in particular happened over the last 10 or so years is that there has been significant advancement both on the theory side of machine learning, which is, new algorithms, new model structures that do a better job at fitting the input data to a generalizable model as well as rapid innovation in systems infrastructure which actually enable the model to sort of do its work, which is very compute intensive, in a way that's actually scalable that's actually feasible economically, cost effective and so on.Sridhar Vedantham: OK, Muthian, so it sounds like there's a lot of compute actually required to make things like AI and ML happen. Can you give me a sense of what kind of resources or how intensive the resource requirement is?Muthian Sivathanu: Yeah. So the resource usage in a machine learning model is a direct function of how many parameters it has, so the more complex the data set, the larger the model gets, and correspondingly requires more compute resources, right. To give you an idea, the early machine learning models which perform simple tasks like recognizing digits and so on, they could run on a single server machine in a few hours, but models now, just over the last two years, for example, the size of the largest model that's useful that state of the art, that achieves state of the art accuracy has grown by nearly three orders of magnitude, right. And what that means is today to train these models you need thousands and thousands of servers and that's infeasible. Also, accelerators or GPUs have really taken over the last 6-7 years and GPUs. A single V-100 GPU today, a Volta GPU from NVIDIA can run about 140 trillion operations per second. And you need several hundreds of them to actually train a model like this. And they run for months together to train a 175 billion model, which is called GPT 3 recently, you need on the order of thousands of such GPUs and it still takes a month.Sridhar Vedantham: A month, that's sounds like a humongous amount of time. Muthian Sivathanu: Exactly, right? So that's why I think just as I told you how the advance in the theory of machine learning in terms of new algorithms, new model structures, and so on have been crucial to the recent advance in the relevance in practical utility of deep learning.Equally important has been this advancement in systems, right, because given this huge explosion of compute demands that these workloads place, we need fundamental innovation in systems to actually keep pace, to actually make sure that you can train them in reasonable time, you can actually do that with reasonable cost.Sridhar Vedantham: Right. Ok, so you know for a long time, I was generally under the impression that if you wanted to run bigger and bigger models and bigger jobs, essentially you had to throw more hardware at it because at one point hardware was cheap. But I guess that kind of applies only to the CPU kind of scenario, whereas the GPU scenario tends to become really expensive, right?Muthian Sivathanu: Yep, yeah.Sridhar Vedantham: Ok, so in which case, when there is basically some kind of a limit being imposed because of the cost of GPUs, how does one actually go about tackling this problem of scale?Muthian Sivathanu: Yeah, so the high-level problem ends up being, you have limited resources, so let's say you can view this in two perspectives, right. One is from the perspective of a machine learning developer or a machine learning researcher, who wants to build a model to accomplish a particular task right. So, from the perspective of the user, there are two things you need. A, you want to iterate really fast, right, because deep learning, incidentally, is this special category of machine learning, where the exploration is largely by trial and error. So, if you want to know which model actually works which parameters, or which hyperparameter set actually gives you the best accuracy, the only way to really know for sure is to train the model to completion, measure accuracy, and then you would know which model is better, right. So, as you can see, the iteration time, the time to train a model to run inference on it directly impacts the rate of progress you can achieve. The second aspect that the machine learning researcher cares about is cost. You want to do it without spending a lot of dollar cost.Sridhar Vedantham: Right.Muthian Sivathanu: Now from the perspective of let's say a cloud provider who runs this, huge farm of GPUs and then offers this as a service for researchers, for users to run machine learning models, their objective function is cost, right. So, to support a given workload you need to support it with as minimal GPUs as possible. Or in other words, if you have a certain amount of GPU capacity, you want to maximize the

Episode 009 | June 15, 2021 The Internet of Things has been around for a few years now and many businesses and organizations depend on data from these systems to make critical decisions. At the same time, it is also well recognized that this data- even up to 40% of it- can be spurious, and this obviously can have a tremendously negative impact on an organizations’ decision making. But is there a way to evaluate if the sensors in a network are actually working properly and that the data generated by them are above a defined quality threshold? Join us as we speak to Dr Akshay Nambi and Ajay Manchepalli, both from Microsoft Research India, about their innovative work on making sure that IoT data is dependable and verified, truly enabling organizations to make the right decisions. Akshay Nambi is a Senior Researcher at Microsoft Research India. His research interests lie at the intersection of Systems and Technology for Emerging Markets broadly in the areas of AI, IoT, and Edge Computing. He is particularly interested in building affordable, reliable, and scalable IoT devices to address various societal challenges. His recent projects are focused on improving data quality in low-cost IoT sensors and enhancing performance of DNNs on resource-constrained edge devices. Previously, he spent two years at Microsoft Research as a post-doctoral scholar and he has completed his PhD from the Delft University of Technology (TUDelft) in the Netherlands.  Ajay Manchepalli, as a Research Program Manager, works with researchers across Microsoft Research India, bridging Research innovations to real-world scenarios. He received his Master’s degree in Computer Science from Temple University where he focused on Database Systems. After his Masters, Ajay spent his next 10 years shipping SQL Server products and managing their early adopter customer programs. For more information about the Microsoft Research India click here. Related Microsoft Research India Podcast: More podcasts from MSR India iTunes: Subscribe and listen to new podcasts on iTunes Android RSS Feed Spotify Google Podcasts Email Transcript Ajay Manchepalli: The interesting thing that we observed in all these scenarios is how the entire industry is trusting data, and using this data to make business decisions, and they don't have a reliable way to say whether the data is valid or not. That was mind boggling. You're calling data as the new oil, we are deploying these things, and we're collecting the data and making business decisions, and you're not even sure if that data that you've made your decision on is valid. To us it came as a surprise that there wasn't enough already done to solve these challenges and that in some sense was the inspiration to go figure out what it is that we can do to empower these people, because at the end of the day, your decision is only as good as the data. [Music] Sridhar Vedantham: Welcome to the Microsoft Research India podcast, where we explore cutting-edge research that’s impacting technology and society. I’m your host, Sridhar Vedantham. [Music] The Internet of Things has been around for a few years now and many businesses and organizations depend on data from these systems to make critical decisions. At the same time, it is also well recognized that this data- even up to 40% of it- can be spurious, and this obviously can have a tremendously negative impact on an organizations’ decision making. But is there a way to evaluate if the sensors in a network are actually working properly and that the data generated by them are above a defined quality threshold? Join us as we speak to Dr Akshay Nambi and Ajay Manchepalli, both from Microsoft Research India, about their innovative work on making sure that IoT data is dependable and verified, truly enabling organizations to make the right decisions. [Music] Sridhar Vedantham: So, Akshay and Ajay, welcome to the podcast. It's great to have you guys here. Akshay Nambi: Good evening Sridhar. Thank you for having me here. Ajay Manchepalli: Oh, I'm excited as well. Sridhar Vedantham: Cool, and I'm really keen to get this underway because this is a topic that's quite interesting to everybody, you know. When we talk about things like IoT in particular, this has been a term that's been around for quite a while, for many years now and we've heard a lot about the benefits that IoT can bring to us as a society or as a community, or as people at an individual level. Now you guys have been talking about something called Dependable IoT. So, what exactly is Dependable IoT and what does it bring to the IoT space? Ajay Manchepalli: Yeah, IoT is one area we have seen that is exponentially growing. I mean, if you look at the number of devices that are being deployed it's going into the billions and most of the industries are now relying on this data to make their business decisions. And so, when they go about doing this, we have, with our own experience, we have seen that there are a lot of challenges that comes in play when you're dealing with IoT devices. These are deployed in far off locations, remote locations and in harsh weather conditions, and all of these things can lead to reliability issues with these devices. In fact, the CTO of GE Digital mentioned that, you know, about 40% of all the data they see from these IoT devices are spurious, and even KPMG had a report saying that you know over 80% of CEOs are concerned about the quality of data that they're basing their decisions on. And we observed that in our own deployments early on, and that's when we realized that there is, there is a fundamental requirement to ensure that the data that is being collected is actually good data, because all these decisions are being based on the data. And since data is the new oil, we are basically focusing on, ok, what is it that we can do to help these businesses know whether the data they're consuming is valid or not and that starts at the source of the truth, which is the sensors and the sensor devices. And so Akshay has built this technology that enables you to understand whether the sensors are working fine or not. Sridhar Vedantham: So, 40% of data coming from sensors being spurious sounds a little frightening, especially when we are saying that you know businesses and other organizations base a whole lot of the decisions on the data they're getting, right? Ajay Manchepalli: Absolutely. Sridhar Vedantham: Akshay, was there anything you wanted to add to this? Akshay Nambi: Yeah, so if you see, reliability and security are the two big barriers in limiting the true potential of IoT, right? And over the past few years you would have seen IoT community, including Microsoft, made significant progress to improve security aspects of IoT. However, techniques to determine data quality and sensor health remain quite limited. Like security, sensor reliability and data quality are fundamental to realize the true potential of IoT which is the focus of our project- Dependable IoT. Sridhar Vedantham: Ok, so you know, once again, we've heard these terms like IoT for many years now. Just to kind of demonstrate what the two of you have been speaking about in terms of various aspects or various scenarios in which IoT can be deployed, could you give me a couple of examples where IoT use is widespread? Akshay Nambi: Right, so let me give an example of air pollution monitoring. So, air pollution is a major concern worldwide, and governments are looking for ways to collect fine grained data to identify and curb pollution. So, to do this, low-cost sensors are being used to monitor pollution levels. There have been deployed in numerous places on moving vehicles to capture the pollution levels accurately. The challenge with these sensors are that these are prone to failures, mainly due to the harsh environments in which they are deployed. For example, imagine a pollution sensor is measuring high pollution values right at a particular location. And given air pollution is such a local phenomenon, it's impossible to tell if this sensor data is an anomaly or a valid data without having any additional contextual information or sensor redundancy. And due to these reliability challenges the validity and viability of these low-cost sensors have been questioned by various users. Sridhar Vedantham: Ok, so it sounds kind of strange to me that sensors are being deployed all over the place now and you know, frankly, we all carry sensors on ourselves, right, all the time. Our phones have multiple sensors built into them and so on. But when you talk about sensors breaking down or being faulty or not providing the right kind of data back to the users, what causes these kind of things? I mean, I know you said in the context of, say, air pollution type sensors, you know it could be harsh environments and so on, but what are other reasons for, because of which the sensors could fail or sensor data could be faulty? Akshay Nambi: Great question, so sensors can go bad for numerous reasons, right? This could be due to sensor defect or damage. Think of a soil moisture sensor deployed in agricultural farm being run over by a tractor. Or it could be sensor drift due to wear and tear of sensing components, sensor calibration, human error and also environmental factors, like dust and humidity. And the challenge is, in all these cases, right, the sensors do not stop sending data but still continues to keep sending some data which is garbage or dirty, right? And the key challenge is it is nontrivial to detect if a remote sensor is working or faulty because of the following reasons. First a faulty sensor can mimic a non-faulty sensor data which is very hard to now distinguish. Second, to detect sensor faults, you can use sensor redundancy which becomes very expensive. Third, the cost and logistics to send a technician to figure out the fault is expensive and also very cumbersome. Finally, time series algorithms like anomaly detectors are not re

  Episode 008 | April 20, 2021 Microsoft Research India is constantly exploring how research can enable new technologies that positively impact the lives of people while also opening new frontiers in computer science and technology itself. In this podcast we speak to Dr. Sriram Rajamani, distinguished scientist and Managing Director of the Microsoft Research India Lab. We talk about some of the projects in the lab that are making fundamental changes to the computing at Internet scale, computing at the edge and the role he thinks technology should play in the future to ensure digital fairness and inclusion. Sriram also talks to us about a variety of things his own journey as a researcher, how the lab has changed from the time he joined it years ago, and his vision for the lab. Sriram’s research interests are in designing, building and analyzing computer systems in a principled manner. Over the years he has worked on various topics including Hardware and Software Verification, Type Systems, Language Design, Distributed Systems, Security and Privacy. His current research interest is in combining Program Synthesis and Machine Learning. Together with Tom Ball, he was awarded the CAV 2011 Award for “contributions to software model checking, specifically the development of the SLAM/SDV software model checker that successfully demonstrated computer-aided verification techniques on real programs.” Sriram was elected ACM Fellow in 2015 for contributions to software analysis and defect detection, and Fellow of  Indian National Academy of Engineering in 2016. Sriram was general chair for POPL 2015 in India, and was program Co-Chair for CAV 2005. He co-founded the Mysore Park Series, and the ISEC conference series in India. He serves on the CACM editorial board as co-chair for special regional sections, to bring computing innovations from around the world to CACM. Sriram has a PhD from UC Berkeley, MS from University of Virginia and BEng from College of Engineering, Guindy, all with specialization in Computer Science. In 2020, he was named as a Distinguished Alumnus by College of Engineering, Guindy. For more information about the Microsoft Research India click here. Related Microsoft Research India Podcast: More podcasts from MSR India iTunes: Subscribe and listen to new podcasts on iTunes Android RSS Feed Spotify Google Podcasts Email Transcript Sriram Rajamani: We are not like an ivory tower lab. You know we are not a lab that just writes papers. We are a lab that has our hands and feet, dirty, we sort of get ourselves dirty sort of get in there, you know, we test our assumptions, see whether it works, learn from them and in that sense actually the problems that we work on are a lot more real than a purely academic environment. [Music] Sridhar Vedantham: Welcome to the Microsoft Research India podcast, where we explore cutting-edge research that’s impacting technology and society. I’m your host, Sridhar Vedantham. [Music] Sridhar Vedantham: Microsoft Research India is constantly exploring how research can enable new technologies that positively impact the lives of people while also opening new frontiers in computer science and technology itself. In this podcast we speak to Dr. Sriram Rajamani, distinguished scientist and Managing Director of the Microsoft Research India Lab. We talk about some of the projects in the lab that are making fundamental changes to computing at Internet scale, computing at the edge and the role he thinks technology should play in the future to ensure digital fairness and inclusion. Sriram also talks to us about a variety of things his own journey as a researcher, how the lab has changed from the time he joined it many years ago and his vision for the lab. Sridhar Vedantham: So today we have a very special guest on the podcast, and he is none other than Dr. Sriram Rajamani, who is the Managing Director of the Microsoft Research Lab in India. So Sriram welcome to the podcast. Sriram Rajamani: Yeah, thank you. Thank you for having me here, Sridhar. Sridhar Vedantham: OK, you've been around in Microsoft Research for quite a while, right? Can you give me a brief background as to how you joined and when you join and what's your journey been in MSR so far? Sriram Rajamani: Yeah, so I joined in 1999. And , oh man, it's now 22 years, I guess. I've been here for a while. Sridhar Vedantham: That's a long time. Sriram Rajamani: I joined in Microsoft Research in Redmond right after I finished my PhD in Berkeley and then I, you know, my PhD was in formal verification. So, my initial work in Microsoft in Redmond was in the area of formal verification and then at some point I moved to India around 2006 or something like that. So I think I spent about six or seven years in Redmond and my remaining time- another 15 years- in India. So that's been my journey, yeah. Sridhar Vedantham: OK, so this is interesting, right, because, you know, we constantly hear about India as being this great talent pool for software engineers, but we certainly don't hear as often that it is a great place for a computer science research lab. Why do you think a Microsoft Research lab in India works and what drew you to the lab here? Sriram Rajamani: I'm a scientist and I joined MSR because I wanted to do high quality science work that is also applicable in the real world, you know. That's why I joined MSR and the reason why I moved to India was because at some point. I just wanted to live here - I wanted to live here because I have family here and so on and then Anandan started the lab and so somehow things came together, and that's why I personally moved. But if you ask, you know, ask me why it makes sense for MSR to have a lab here, the reasons are quite clear. I think we are such a big country, we have enormous talent. I think talent is the number one reason I think we are here. Particularly unique to India is that we have really strong undergraduate talent, which is why we have programs like our Research Fellow program. But over the past, many years, right, the PhD talent is also getting better and better. As you know, initially when we started, you know, we recruited many PHDs you know from abroad, who had their PhD from abroad and then return just like me. But over the years we've also recruited many PhDs from Indian institutions as well. So, I think that talent is the number one reason. The second reason is you know the local tech ecosystem is very different. It started out as a service industry for the West- you know essentially all of the software we were doing, we were servicing companies in the western hemisphere. But over time, India has also become a local consumer of technology, right? Now, be it if you sort of think about, you know Ola or Flipkart, you know, the country is now using technology for its own local purposes. And because of the size and scale of the country, the amount the government and industry is pushing digitization, there's a huge opportunity there as well. And finally, I would say another reason to have a lab is in a place like India that it's a very unique testbed. You know, cost is a huge concern in a place like India, technology has to be really low cost for it to be adopted here. There are very severe resource constraints. Be it bandwidth…you know if you think about NLP, you know many of our languages don't have data resources. Very unreliable infrastructure- things fail all the time, and so you know, I've heard of saying that you know if you build something so that it works in India, it works anywhere. So it's a test bed to actually build something. If you can deploy it and make it work here, you can make it work anywhere. So in that sense actually it's also another reason. Sridhar Vedantham: OK, so basically it works here it's a good certification that it'll work anywhere in the world. Sriram Rajamani: Yeah, yeah. Sridhar Vedantham: All right. OK Sriram, so here's something I'm very curious about. How does a research scientist end up becoming the managing director of a lab? Sriram Rajamani: So the short answer is that it was rather unplanned, but maybe I can give a more longer answer. You know, I started out, you know, being a researcher like anyone else who joins MSR. My initial projects were all in the area of, you know, formal verification, you know, I built together with Tom Ball something called static driver verifier that used formal methods to improve windows reliability. Then I worked on verifiable design- how can you do better design so that you produce better systems? Then I worked on, you know, security, and now I work on machine learning and program synthesis. And you know, a common thread in my work has always been the use of programming languages and formal methods to sort of understand how to build various kinds of systems be it drivers, be it secure systems, be it machine learning systems. That has been sort of the theme underlying my research. But to answer your question as to how I sort of became lab director, you know, after some years after I moved back to MSR India, you know Anandan who was the lab director then, you know, he left. There was a leadership churn there, and at the time I was asked whether I would consider being the lab director. The first time I declined and because I had many other technical projects that are going on. But I got the opportunity the second time, you know, when Chandu and Jeanette really encouraged me when Chandu decided to move on. I had been in MSR maybe 15-16 years when that event happened. And one of the reasons why I decided to take this up was I felt very strongly for MSR, and I thought that MSR has given me a lot and I wanted to give back to MSR and MSR India. And MSR India is easily one of the best CS, computer science industrial labs in this part of the world. And, you know, it made sense that I actually devote my time to support my colleagues, grow their lab in ambition, impact and I sort of had a sense of p

Episode 007 | December 22, 2020 One of Microsoft Research India’s goals is to help strengthen the research ecosystem and encourage young students to look at research as a career. But it is not always easy for students to understand what research is all about and how to figure out if research is the right career for them. The Research Fellow program at Microsoft Research India enables bright young students to work on real-world research problems with top notch researchers across the research lifecycle, including ideation, implementation, evaluation, and deployment. Many of the students who have been part of the program have gone on to become researchers, engineers and entrepreneurs. Today, we speak to Shruti Rijhwani, a graduate of MSR India’s Research Fellow program who is currently doing her PhD at the Carnegie Mellon University, and joining us is Dr. Vivek Seshadri, a researcher at MSR India who also heads the Research Fellow program at the lab. Shruti was a research fellow at MSR India in 2016, working on natural language processing models for code-switched text. She is currently PhD student at the Language Technologies Institute at Carnegie Mellon University. Stemming from her work at MSR India, she has continued research in multilingual NLP, with a focus on low-resource and endangered languages. Vivek primarily works with the Technology for Emerging Markets group at Microsoft Research India. He received his bachelor’s degree in Computer Science from IIT Madras, and a Ph.D. in Computer Science from Carnegie Mellon University where he worked on problems related to Computer Architecture and Systems. After his Ph.D., Vivek decided to work on problems that directly impact people, particularly in developing economies like India. Vivek is also the Director for the Research Fellow program at MSR India. For more information about the Research Fellow program, click here. Related Microsoft Research India Podcast: More podcasts from MSR India iTunes: Subscribe and listen to new podcasts on iTunes Android RSS Feed Spotify Google Podcasts Email Transcript Shruti Rijhwani: I think I credit my whole graduate school decision-making process, the application process, and even the way I do research in grad school to my experience as a Research Fellow in MSR India. Of course, the first thing was that I wasn't even sure whether I wanted to go to grad school, but after going through the Research Fellow program and with my amazing mentors and collaborators at MSR India, I took the decision to apply to grad school. [Music] Sridhar Vedantham: Welcome to the Microsoft Research India podcast, where we explore cutting-edge research that’s impacting technology and society. I’m your host, Sridhar Vedantham. [Music] One of Microsoft Research India’s goals is to help strengthen the research ecosystem and encourage young students to look at research as a career. But it is not always easy for students to understand what research is all about and how to figure out if research is the right career for them. The Research Fellow program at Microsoft Research India enables bright young students to work on real-world research problems with top notch researchers across the research lifecycle, including ideation, implementation, evaluation, and deployment. Many of the students who have been part of the program have gone on to become researchers, engineers and entrepreneurs. Today, we speak to Shruti Rijhwani, a graduate of MSR India’s Research Fellow program who is currently doing her PhD at the Carnegie Mellon University, and joining us is Dr. Vivek Seshadri, a researcher at MSR India who also heads the Research Fellow program at the lab. [Music] Sridhar Vedantham: OK, so I'm looking forward to this podcast because it's going to be a little different from what we've done in the past, in the sense that this is not a podcast about research projects or technologies, but it's something much more human, and we're going to be talking about the Research Fellow program that we have at MSR India. And I'd like to welcome a special guest- Shruti, who used to be a Research Fellow at the lab and also Vivek Seshadri, who is a researcher at the lab and whom we've had on the podcast earlier in a different capacity. But today he's wearing the hat of the czar or the director of the Research Fellow program here. So welcome, Shruti and Vivek. Vivek Seshadri: Good evening, Sridhar, and very good morning Shruti. Shruti Rijhwani: Hi Sridhar and Vivek, it’s great to be here and great to be back to interacting with people from MSR India. It's been about four years since I left the RF program, so I'm really looking forward to talking about it and remembering some of my experiences. Sridhar Vedantham: Excellent, so let's lay a little bit of a groundwork before we jump into the whole thing. Vivek can you give us a bit of an overview of what the Research Fellow program is. Vivek Seshadri: Sridhar, the Research Fellow program has been around ever since the organization Microsoft Research India started itself. I think initially it was called the Assistant Researcher program and then it was called the Research Assistant Program and right now we're calling it the Research Fellow program. But the core of the program has been to enable recent undergraduate and Master’s students to spend one or two years at MSR India and get a taste for what research in computer science looks like, especially in an industrial setting. Sridhar Vedantham: So has the program evolved over time, or have there been any substantive changes or it's still the same at its core and its essence? Vivek Seshadri: I think the only thing that has changed significantly is the number of research fellows that we have had. I think in the program started in its first year, I think we had had three assistant researchers, and today as we speak, we have over 50 Research Fellows in the lab working on various projects. So, in that sense, the program has definitely grown in size along with the lab, but I think the core goal of the program has not changed at all. It is still to give Research Fellows a taste of what research looks like in computer science and enable them to build their profile and prepare them for a career in computer science research and engineering. Sridhar Vedantham: Right, so one thing that I've seen personally is that the Research Fellows add a huge amount of energy and life to the lab. And on that note, Shruti, what motivated you to come into MSR India to join the Research Fellow program? Shruti Rijhwani: That's a great question and something that I think my experience is probably what a lot of Research Fellows who apply and join the program actually go through before deciding to join the program. So, I did an undergrad degree in computer science from BITS PILANI and during those four years I took classes on machine learning, information retrieval and so on, and also did two internships where I got a taste of like how machine learning applications can be applied to products in the real world. But both of those internships were kind of focused on the engineering side and I was really interested in what a career in doing machine learning research or using machine learning for research-based applications would look like. And I knew that if I wanted to pursue a career in this field, I would probably have to go to graduate school to get a Master’s and a PhD, but I wasn't entirely sure whether this is what I wanted to do, so it was kind of like an exploratory phase for me to be in the Research Fellow program. I wanted to get some research experience, I wanted to see what established researchers in computer science do on a daily basis, and what the research process kind of is like when you're working in machine learning, and more specifically in natural language processing, which is what I was interested in. Sridhar Vedantham: Right, so, Vivek, what I'm getting from Shruti is that, uh, the Research Fellow program is something that she is looking at to form a base or a basis for a longer career in research itself. So do you have any specific insights or inputs into about what the program actually offers in a structured manner to the Research Fellows that we have? Vivek Seshadri: Yeah, so Microsoft Research now at its core is a research organization. All researchers at MSR do research in computer science and they're working on a variety of projects spanning all areas of computer science, you know, from theory, artificial Intelligence, machine learning, systems, security and MSR India also is known for its Technology for Emerging Markets (TEM) Group where we look at, you know, problems specifically affecting developing countries like India. So, essentially, Research Fellows join one of these projects and work with world class researchers on multiple phases of research, including ideations, building solutions, prototyping those solutions, deploying them in the field, working with large real industrial datasets to test out their solutions. So that experience essentially gives the perfect taste of what modern computer science research looks like for Research Fellows, and just like Shruti most of our Research Fellows after their stint at MSR India apply for grad school and you know, go to one of the top grad schools across the world, but there are others who decide, you know, research is not for them. Many of them join Microsoft and continue to work at Microsoft in some, you know some other role. And a few of them actually, you know, have taken the entrepreneurial route. You know, we have had CEOs of many, many big companies, including Ola and some of our own projects which have been converted to startups like Digital Green and Everwell. So, you know, some of them take that route as well. But primarily it's that experience of what computer science research looks like today. I think that’s the essence of what the research program offers students. Sridhar Vedantham: Great and uh, in terms of

Episode 006 | October 20, 2020 At Microsoft Research India, research focused on societal impact is typically a very interdisciplinary exercise that pulls together social scientists, technology experts and designers. But how does one evaluate or validate the actual impact of research in the real world? Today, we talk to Tanuja Ganu who manages the Societal Impact through Cloud and AI (or SCAI) group in MSR India. SCAI focuses on deploying research findings at scale in the real world to validate them, often working with a wide variety of collaborators including academia, social enterprises and startups. Tanuja is a Research SDE Manager at Microsoft Research, India. She is currently part of MSR’s new center for Societal impact through Cloud and Artificial Intelligence (SCAI). Prior to joining MSR, she was a Co-Founder and CTO of DataGlen Technologies, a B2B startup that focuses on AI for renewable energy and sustainability technologies. Prior to this, she has worked as Research Engineer at IBM Research, India. Tanuja has completed MS in Computer Science (Machine Learning) from Indian Institute of Science (IISc, Bangalore). She has been recognized as MIT Technology Review’s Innovator Under 35 (MIT TR 35) in 2014 and IEEE Bangalore Woman Technologist of the Year in 2018.  Her work was covered by top technical media (IEEE Spectrum, MIT Technology Review, CISCO Women Rock IT TV series, IBM Research blog and Innovation 26X26: 26 innovations by 26 IBM women). Click here to go to the SCAI website. Related Microsoft Research India Podcast: More podcasts from MSR India iTunes: Subscribe and listen to new podcasts on iTunes Android RSS Feed Spotify Google Podcasts Email Transcript Tanuja Ganu: As the name suggests, SCAI, that is Societal Impact through Cloud and Artificial Intelligence, it is an incubation platform within MSR for us to ideate on such research ideas, work with our collaborators like academia, NGOs, social enterprises, startups, and to test or validate our hypothesis through very well defined real world deployments. At SCAI, it's an interdisciplinary team of social scientists, computer scientists, software engineers, designers, and program managers from the lab who come together for creating, nurturing and evaluating our research ideas through real world deployments and validations. [Music] Sridhar: Welcome to the Microsoft Research India podcast, where we explore cutting-edge research that’s impacting technology and society. I’m your host, Sridhar Vedantham. [Music] At Microsoft Research India, research focused on societal impact is typically a very interdisciplinary exercise that pulls together social scientists, technology experts and designers. But how does one evaluate or validate the actual impact of research in the real world? Today, we talk to Tanuja Ganu who manages the Societal Impact through Cloud and AI (or SCAI) group in MSR India. SCAI focuses on deploying research findings at scale in the real world to validate them, often working with a wide variety of collaborators including academia, social enterprises and startups. Tanuja has been recognized as one of MIT Technology Review’s Innovators Under 35 (MIT TR 35) in 2014 and by IEEE Bangalore as a Woman Technologist of the Year in 2018, and her work has been covered by top technical media. [Music] Sridhar Vedantham: Tanuja, welcome to the podcast. I'm really looking forward to this particular edition of what we do here. Because, I know that you manage SCAI and it's quite an intriguing part of the lab. Now before we get into that, tell us a little bit about yourself. Tanuja Ganu: First of all, thanks Sridhar for having me on the podcast today. And uh, yes, uh, I'm not a full-time researcher, but I'm engineer by training and I have done my Master’s in Computer Science. Over the last decade or so, my work is primarily at the intersection of research and engineering, and it's on the applied research side. So throughout my experience and journey, working at research labs and start up, I'm very much interested in taking a research idea through the entire incubation phase to validate its applicability in real world problem settings. Sridhar Vedantham: So, Tanuja, I know you manage this thing called SCAI within the lab and I think it's a very interesting part of the lab. Talk to us a little bit about that, and especially expand upon what SCAI- the term SCAI- itself stands for, because I myself keep tripping up on it whenever I try to explain it. Tanuja Ganu: Yes, Sridhar. So since the inception of our lab, the lab has been doing very interesting work in the societal impact space. Additionally, with the advances in artificial intelligence and cloud-based technologies in recent years there are increased opportunities to address some of these societal problems through technology and amplify its positive effect. So as the name suggests, SCAI, that is Societal Impact through Cloud and Artificial Intelligence, it is an incubation platform within MSR for us to ideate on such research ideas, work with our collaborators like academia, NGOs, social enterprises, startups, and to test or validate our hypothesis through very well defined real world deployments. Also our location in India allows us to witness and carefully analyze various socio-economic challenges. So the solutions that we ideate are inspired by Indian settings and in many cases equally applicable to different parts of the world. Sridhar Vedantham: Interesting, so it sounds like there's a fair amount of difference between the kind of work that SCAI does and between what the rest of the lab actually does in terms of research. Tanuja Ganu: So at MSR India, where research work is mainly along three different axes, firstly advancing the state of the art in science and technology, second is inspiring the direction for technology advances, and the third important axis is building the technology for driving societal impact. So SCAI is primarily focused on social impact access and many of our projects also do have very strong academic and technological impact. At SCAI, it's an interdisciplinary team of social scientists, computer scientists, software engineers, designers, and program managers from the lab who come together for creating, nurturing and evaluating our research ideas through real world deployments and validations. So that's really the difference in terms of the other type of research that we do at lab and what we do at SCAI. Sridhar Vedantham: So when you decide to take up a project or accept it under the SCAI umbrella, what do you actually look for? Tanuja Ganu: Yeah, we look for a few things for defining a SCAI project. So firstly, it should address a significant real-world problem and should have a potential to scale. The second thing is the problem should offer interesting research challenges for our team. The next thing is whether we have credible partners or collaborators with domain expertise to deploy, evaluate and validate of our research. We also look for how we can define rigorous impact evaluation plan for a project. And lastly, we look for what are the feasible graduation paths for the project within two to three years of time horizon. Sridhar Vedantham: What do you mean by graduation? Tanuja Ganu: So, um, there are different ways in which a particular project can complete its successful execution at SCAI center, and that's what we're really terming it as a graduation. And there could be really different types of graduation path depending upon each type of project. Sridhar Vedantham: OK, let's talk a little bit about some of the projects that you are currently doing under the SCAI umbrella. Because to me from what you've said so far, it sounds like there's probably going to be a fairly wide spread of types of projects, and quite a large variety in the type of things that you're doing there. Tanuja Ganu: So yes, Sridhar, that's very true. We are working on a very diverse set of projects right now. And, um, so to give a flavor of our work, I would discuss about two or three projects briefly. The first project is called HAMS that is Harnessing Automobiles for Safety. We all know that road safety is very critical issue and according to World Bank Report globally there are 1.25 million road traffic deaths every year. In India there is one death every 4 minutes. That happens due to road accidents. So, to understand and address this very critical issue of road safety, HAMS project was initiated by our team at MSR, including Venkat Padmanabhan, Akshay Nambi and Satish Sangameswaran. HAMS provides a low cost solution which is being evaluated for automated driver license testing. HAMS includes a smartphone with its associated sensors like camera, accelerometer, etc that is fitted inside a car. It monitors a driver and the driving environment and using AI and edge intelligence, it provides effective feedback on the safe driving practices. So at present, HAMS has been deployed at regional transport office in Dehradun, India for conducting dozens of driver license tests a day, and the feedback from this deployment is very encouraging, since it provides transparency and objectivity to the overall license testing and evaluation process. The second project is in the domain of natural language processing, called Interactive Neural Machine Translation, which was initiated by Kalika Bali and Monojit Choudhury in our NLP team. So, when we look at this problem, there are 7000 plus spoken languages worldwide, and for many many use cases, we often need to translate content from one language to another. Though there are many commercial machine translation tools available today, those are applicable to a very small subset of languages, say 100, which have sufficiently large digital datasets available to train machine learning models. So to aid human translation process as well as for creating digital data set for many low resource or underserved languages, we combine innovations f

Episode 005 | September 08, 2020 Podcast: Making cryptography accessible, efficient and scalable. With Dr. Divya Gupta and Dr. Rahul Sharma Ensuring security and privacy of data, both personal and institutional, is of paramount importance in today’s world where data itself is a highly precious commodity. Cryptography is a complex and specialized subject that not many people are familiar with, and developing and implementing cryptographic and security protocols such as Secure Multi-party Computation can be difficult and also add a lot of overhead to computational processes. But researchers at Microsoft Research have now been able to develop cryptographic protocols that are developer-friendly, efficient and that work at scale with acceptable impact on performance. Join us as we talk to Dr. Divya Gupta and Dr. Rahul Sharma about their work in making cryptography easy to use and deploy. Dr. Divya Gupta is a senior researcher at Microsoft Research Lab. Her primary research interests are cryptography and security. Currently, she is working on secure machine learning, using secure multi-party computation (MPC), and lightweight blockchains. Earlier she received her B.Tech and M.Tech in Computer Science from IIT Delhi and PhD in Computer Science from University of California at Los Angeles where she worked on secure computation, coding theory and program obfuscation. Dr. Rahul Sharma is a senior researcher in Microsoft Research Lab India since 2016. His research lies in the intersection of Machine Learning (ML) and Programming Languages (PL), which can be classified into the two broad themes of “ML for PL” and “PL for ML”. In the former, he has used ML to improve reliability and efficiency of software. Whereas, in the latter, he has built compilers to run ML on exotic hardware like tiny IoT devices and cryptographic protocols. Rahul holds a B.Tech in Computer Science from IIT Delhi and a PhD in Computer Science from Stanford University. Click here for more information in Microsoft Research’s work in Secure Multi-party Computation and here to go to the GitHub page for the project. Related Microsoft Research India Podcast: More podcasts from MSR India iTunes: Subscribe and listen to new podcasts on iTunes Android RSS Feed Spotify Google Podcasts Email Transcript Divya Gupta: We not only make existing Crypto out there more programmable and developer friendly, but we have developed super-duper efficient cryptographic protocols which are tailored to ML, like secure machine learning inference task and work for large machine learning benchmarks. So before our work, the prior work had three shortcomings I would say. They were slow. They only did small machine learning benchmarks and the accuracy of the secure implementations was lower than the original models. And we solved all three challenges. So our new protocols are at least 10 times faster than what existed out there. [Music] Sridhar: Welcome to the Microsoft Research India podcast, where we explore cutting-edge research that’s impacting technology and society. I’m your host, Sridhar Vedantham. [Music] Ensuring security and privacy of data, both personal and institutional, is of paramount importance in today’s world where data itself is a highly precious commodity. Cryptography is a complex and specialized subject that not many people are familiar with, and developing and implementing cryptographic and security protocols such as Secure Multi-party Computation can be difficult and also add a lot of overhead to computational processes. But researchers at Microsoft Research have now been able to develop cryptographic protocols that are developer-friendly, efficient and that work at scale with acceptable impact on performance. Join us as we talk to Dr. Divya Gupta and Dr. Rahul Sharma about their work in making cryptography easy to use and deploy. Sridhar Vedantham: Alright, so Divya and Rahul, welcome to the podcast. It's great to have you guys on the show and thank you so much. I know this is really late in the night so thank you so much for taking the time to do this. Divya Gupta: Thanks Sridhar for having us. Late is what works for everyone right now. So yeah, that's what it is. Rahul Sharma: Thanks Sridhar. Sridhar Vedantham: Alright, so this podcast, I think, is going to be interesting for a couple of reasons. One is that the topic is something I know next to nothing about, but it seems to me from everything I've heard that it's quite critical to computing today, and the second reason is that the two of you come from very different backgrounds in terms of your academics, in terms of your research interests and specialities, but you're working together on this particular project or on this particular field of research. So let me jump into this. We're going to be talking today about something called Secure Multi-party Computation or MPC. What exactly is that and why is it important? Divya Gupta: Right, so Secure Multi-party Computation and as you said, popularly known as MPC, is a cryptographic primitive, which at first seems completely magical. So let me just explain with an example. So let's say you, Sridhar, and Rahul are two millionaires and you want to know who has more money or who's richer. And you want to do this without revealing your net worth to each other, because this is private information. So at first this seems almost impossible. As in how can you compute a function without revealing the inputs of the function? But MPC makes this possible. What MPC gives you is an interactive protocol in which you and Rahul will talk to each other back and forth, exchanging some random looking messages. And at the end of this interaction you will learn the output, which is that who is richer and you will only learn the output alone. So this object MPC comes with the strong mathematical guarantees which say that at the end of this interaction only the output is revealed, and anything which can be deduced from output, but nothing else about the input is revealed. So in this example, Sridhar, you and Rahul both will learn who is richer. And let's say you turn out to be richer. Then of course from this output you would know that your net worth is more than Rahul’s and that's it. Nothing else you will learn about Rahul’s net worth. So this is what MPC is. This example is called the Millionaire’s Problem, where the function is very simple. You're just trying to compare two values, which is the net worth. But MPC is much more general. So just going into a bit of history, I would say that MPC can compute any function of your choice on secret inputs. And this result in fact was shown as early as 1980s and this power of MPC, of being able to compute any function securely, got many people interested in this problem. So a lot of work happened and people kept coming up with better and better protocols which were more efficient. So when I say efficient, some of the parameters of interest are the data being sent in the messages back and forth. The number of messages you want to exchange, and also the end to end latency of this protocol, like how much time does it take to compute the function itself, And people kept coming with better and better protocols. And finally, the first implementations came out in 2008 and since then, people have evaluated a few real world examples using MPC and one example which I found particularly interesting is the following, which was a social study which was done in a privacy preserving manner using MPC in Estonia in 2015. So, the situation was as follows. Along with the boom in information and communication technology, it was observed that more and more students were dropping out of college without finishing their degree. And the hypothesis going around was that the students, when they are studying in the University, get employed in IT jobs and they start to value their salaries more than their University degree and hence drop out. But a counter hypothesis was that it is because IT courses are gaining popularity, more and more students are enrolling into it and find it hard and drop out. So the question was, is working during studies in IT jobs correlated with high dropout rate? And to answer to answer this question, a study was proposed to understand the correlation between early employment of students in IT jobs while being enrolled in University and high dropout rate. Now this study can be done by taking in data from employment records in the tax department and also the enrollment records in the education department and just cross referencing this data. So even though all of this data is there with the government, it could not be shared in the clear between the two departments because of legal regulations and the way they solve this problem is by doing this Secure Multi-party Computation between Ministry of Education and tax board. So this, I feel, is an excellent example which shows that MPC can help solve real problems where data sharing is important but cannot be done in the clear. Sridhar Vedantham: OK. Rahul was there something you wanted to add to that? Rahul Sharma: Yes, Sridhar. So if you realized what is happening today, the data is being digitized. Financial documents, medical records. Everything is being digitized, so we are getting, you can say, flood of data which is being available and the other thing which has happened in computer science is that we have now very, very powerful machine learning algorithms and very powerful hardware which can crunch these machine learning algorithms on this huge amount of data. And so machine learning people have created, for example, machine learning models which can beat human accuracy on tasks in computer vision. Computer vision is basically you have an image and you want to find some pattern in that image. For example, does the image belong to a cat or a dog? And now we have classifiers which will beat humans on such tasks. And the way these machine learning classifiers work is

  Episode 004 | August 04, 2020 Podcast: Can we make better software by using ML and AI techniques? With Chandra Maddila and Chetan Bansal The process of software development is dramatically different today compared to even a few years ago. The shift to cloud computing has meant that companies need to develop and deploy software in ever shrinking timeframes while maintaining high quality of code. At the same time, developers can now get access to large amounts of data and telemetry from users. Is it possible for companies to use Machine Learning and Artificial Intelligence techniques to shorten the Software Development Life Cycle while ensuring production of robust, cloud-scale software? We talk about this and more with Chandra Maddila and Chetan Bansal, who are Research Software Development Engineers at Microsoft Research India. Click here for more information on Project Sankie. Related Microsoft Research India Podcast: More podcasts from MSR India iTunes: Subscribe and listen to new podcasts on iTunes Android RSS Feed Spotify Google Podcasts Email Transcript Chandra Maddila: One of the biggest disconnects we used to have in boxed product world where we used to ship software as a standalone product and give it to customers is, once customer takes the product, it is in their environment, we don’t have any idea about how it is being used and what kind of issues people are facing unless they come back to Microsoft support and say, “Hey, we are using this product, we get into these issues, can you please help us?”. But with the advent of services, one of the beautiful things that happened is, now we have the ability to collect telemetry about various issues that are happening in the service. So, this helps us pro-actively fix issues and help customers mitigate outages and also join the telemetry data from deployment side of the world all the way into coding phase, which is the first phase of software development life cycle. [Music] Sridhar: Welcome to the Microsoft Research India podcast, where we explore cutting-edge research that’s impacting technology and society. I’m your host, Sridhar Vedantham. [Music] Sridhar: Chandra and Chetan, welcome to the podcast. And thank you for making the time for this. Chetan: Thanks, Sridhar, for hosting this. Chandra: Thanks Sridhar, thanks for having us. Sridhar: Great! Now, there is something that’s interested me when I decided to host this podcast with you guys. You are both research software development engineers and Microsoft research is known for being this hardcore computer science research lab. So, what does it mean to be a software developer in a research org like MSR? And how is it different than being a software developer in say, a product organization, if there is a difference? Chetan: Yeah, that’s a great question, Sridhar about the difference between the RSDE role which is research software developer engineer at MSR vs. the product groups at Microsoft. In my experience the RSDE role is sort of open ended. Because often times, research teams work on open ended research problems. So, the RSDE engineers often work on things like prototypes and building products from the ground up which are deployed internally and which are the pre-cursor for products which are shipped to our customers, so there’s a lot of flexibility and openness in terms of what the RSDEs work on, and it can range from open ended research to actually building products which are shipped to our customers. So, there’s a wide spectrum of things and roles which RSDE plays. Sridhar: Chandra, what’s your take on that? Chandra: I think Chetan summarized it pretty well. RSDE in general is much more flexible compared to a typical software engineer role in products groups. You can switch from areas to areas and products to products. I, for example was working on NLP for some time, then web applications, learning platforms for some time. Then, I switched to software engineering. So, we have this flexibility to move across different areas and also, one thing we I think do as RSDEs is working on long-term problems, problems from ground up which takes some time to incubate and productize, whereas software engineers and product groups have well defined scope and well defined problems which are aligned to their product’s vision. So, that way they have slightly more constraint in terms of what kind of problems they work on. But, at the same time of the greatest advantages people in product groups have is the accessibility to customers. They are very close to customers and they really work on customer problems and ship things quite faster, whereas RSDEs in MSR don’t have access to direct customers. Sridhar: Interesting, so it sounds like it’s kind of a play between customer access and freedom as far as RSDEs are concerned. Chandra: Yeah, as RSDEs in Microsoft research, we have lot more flexibility and provision to explore more interesting areas in research, new and upcoming areas like probably, quantum computing or block chain or advances in AI/ ML etc and do more exploratory things. Chetan: Just wanted to add another thing here. A lot of times, people have misconceptions that in Microsoft Research or in other research organizations, a doctorate or Ph.D is required to get a job or to work for these organizations. But there are roles such as RSDEs, and product managers, program managers or even designers which people can take on without the need to have a Ph.D or a doctorate and they can still contribute to the research happening in companies like Microsoft. Sridhar: Great. Now, we keep hearing now-a-days that the process of software development has changed tremendously over the last few years. So, what’s actually caused these changes? Chetan: I think, to start with there are two things which in my opinion have caused this sort of revolution in the software development industry. One of them is the move to the services-oriented world, so we are no longer shipping boxed products in a CD or a DVD. But we are actually shipping services, we are actually selling services which are used by our customers unlike before where you ship a software and that’s used by our customers for couple of years and then they update it. So, I think that’s one key change which has happened in the last decade and the other major paradigm shift which has happened is the move to cloud. So, even in terms of software deployment, today it’s being done on cloud instead of on-prem, which is within the premises of a customer or a company. So, that has brought in a whole range of changes in terms of how a software is developed, deployed, and maintained within small and big companies like even Microsoft. And today startups and any new company doesn’t have to actually spend a lot of money in capex, capital expenditure on buying servers or hiring people to maintain the servers, but they can basically ship and operate out of cloud which saves a lot of money and time. So, in my opinion, these are the two major paradigm shifts which has happened and which has positively impacted the software industry. Chandra: Compared to 90’s, when we used to for instance, ship boxed products, now everything is becoming a service, that is also primarily driven by customer expectation. So, these days customers are expecting companies to actually ship services more faster, make the new features available at a much faster pace which is also accelerated by the development and growth in cloud computing technologies which makes software companies or software developers to scale the services really fast and serve more people and ship things much faster. Sridhar: So, I know for a fact that earlier there used to be these long ship cycles where somebody would develop some software, and there would be a bunch of people testing it and after which it would reach the customer, whether it would be the retail customer or the enterprise customer, right. I think, a lot of these processes have either disappeared or been extremely compressed. So, what kind of challenges and opportunities do these changes provide you guys as software developers? Chandra: So, these rapid development models where people are expected to ship really fast brought down the overall ship cycles, the duration of the ship cycles down, to even like days, or in a single day, you experience the entire software development life cycle, all the steps of the development life cycle starting from coding, to testing to deployment in a single day. This definitely poses lot of challenges because you have to make sure, you are shipping fast, but at the same time you are making sure your service is stable and customers are not experiencing any interruptions. So, you need to build tools, and services that aid developers to achieve this. So, the tools and services has to be pretty robust and make sure they catch all the catastrophic bugs early on and developers to achieve this feat of shipping their services much faster. So, the duration between someone writing the code and the code hitting the customer has come down significantly, which is what we all need to make sure we support. Chetan: I just want to add two more things- two more changes which have helped evolve the software development life cycle and processes. First is the possibility of collecting telemetry and data from our users. So, basically, we are able to observe how our features or our code is been behaving or being used in near real time which allows us to see if there is any regression or if there are any changes or if there are any bugs which needs to be fixed. This wasn’t possible in the past within the boxed software world because we didn’t have access to the telemetry. The second aspect is having a set of users which are helping you test your features and services at the same time. So, now, we can sort of do software development in parallel as we roll out our current set of features. Sridhar: Cool. So, it sounds like yo

Episode 003 | June 02, 2020 Many of us who speak multiple languages switch seamlessly between them in conversations and even mix multiple languages in one sentence. For us humans, this is something we do naturally, but it’s a nightmare for computing systems to understand mixed languages. On this podcast with Kalika Bali and Dr. Monojit Choudhury, we discuss codemixing and the challenges it poses, what makes codemixing so natural to people, some insights into the future  of human-computer interaction and more. Kalika Bali is a Principal Researcher at Microsoft Research India working broadly in the area of Speech and Language Technology especially in the use of linguistic models for building technology that offers a more natural Human-Computer as well as Computer-Mediated interactions, and technology for Low Resource Languages.  She has studied linguistics and acoustic phonetics at JNU, New Delhi and the University of York, UK and believes that local language technology especially with speech interfaces, can help millions of people gain entry into a world that is till now almost inaccessible to them. Dr. Monojit Choudhury is a Principal Researcher in Microsoft Research Lab India since 2007. His research spans many areas of Artificial Intelligence, cognitive science and linguistics. In particular, Dr. Choudhury has been working on technologies for low resource languages, code-switching (mixing of multiple languages in a single conversation), computational sociolinguistics and conversational AI. He has more than 100 publications in international conferences and refereed journals. Dr. Choudhury is an adjunct faculty at International Institute of Technology Hyderabad and Ashoka University. He also organizes the Panini Linguistics Olympiad for high school children in India and is the founding chair of the Asia-Pacific Linguistics Olympiad. Dr. Choudhury holds a B.Tech and PhD degree in Computer Science and Engineering from IIT Kharagpur. Related Microsoft Research India Podcast: More podcasts from MSR India iTunes: Subscribe and listen to new podcasts on iTunes Android RSS Feed Spotify Google Podcasts Email Transcript Monojit Choudhury: It is quite fascinating that when people become really familiar with a technology, and search engine is an excellent example of such a technology, people really don’t think of it as technology, people think of it as a fellow human and they try to interact with the technology as they would have done in natural circumstances with a fellow human. [Music plays] Host: Welcome to the Microsoft Research India podcast, where we explore cutting-edge research that’s impacting technology and society. I’m your host, Sridhar Vedantham. [Music plays] Host: Many of us who speak multiple languages switch seamlessly between them in conversations and even mix multiple languages in one sentence. For us humans, this is something we do naturally, but it’s a nightmare for computing systems to understand mixed languages. On this podcast with Kalika Bali and Monojit Choudhury, we discuss codemixing and the challenges it poses, what makes codemixing so natural to people, some insights into the future  of human-computer interaction and more. [Music plays] Host: Kalika and Monojit, welcome to the podcast. And thank you so much. I know we’ve had  trouble getting this thing together given the COVID-19 situation, we’re all in different spots. So, thank you so much for the effort and the time. Monojit: Thank you, Sridhar. Kalika: Thank you.   Host: Ok, so, to kick this off, let me ask this question. How did the two of you get into linguistics? It’s a subject that interests me a lot because I just naturally like languages and I find the evolution of languages and anything to do with linguistics quite fascinating. How was it that both of you got into this field? Monojit: So, meri kahani mein twist hai (In Hindi- “there is a twist in my story”). I was in school, quite a geeky kind of a kid and my interests were the usual Mathematics, Science, Physics and I wanted to be a scientist or an engineer and so on. And, I did study language, so I know English and Hindi which I studied in school. Bangla is my mother tongue, so, of course I know. And I also studied Sanskrit in great detail, and I was interested in the grammar of these languages. Literature was not something which would pull me, but language was still in the backbench right, what I really loved was Science and Mathematics. And naturally I ended up in IIT, I studied in IIT Kharagpur for 4 years doing Computer Science, and everything was lovely. And then one day there was a project when we were in final year where my supervisor was working on what is called a text to speech system. So, in this system, it takes a Hindi text and the system would automatically speak it out and there was a slight problem that he was facing. And he asked me if I could solve that problem. I was in my final year- undergrad year at that time. And the problem was how to pronounce Hindi words correctly. At that time, it sounded like a very simple problem, because in Hindi the way we write is the way we pronounce unlike English, where you know, you have to really learn the pronunciations. And turns out, it isn’t. If you think of the words, ‘Dhadkane’ and ‘Dhadakne’, you pretty much write them in exactly the same way, but one you pronounce as ‘Dhadkane’ and the other one is pronounced as ‘Dhadakne’. So, this was the issue. So, my friend, of course, who was also working with me was all for machine learning. And I was saying, there must be a pattern here and I went through lots and lots of examples myself and turned out that there is this very short, simple, elegant rule which can explain most of Hindi words- the pronunciation of those words perfectly. So, I was excited. I went to my professor, showed him the thing, he was saying, “Oh! This is fantastic!”, let’s write a paper and we got a paper and all this was great. But then, somebody, when I was presenting the paper said, “Hey, you know what the problem you solved!” It’s called ‘schwa deletion’ in Hindi. Of course, I wasn’t in linguistics, neither my professor was, so he had no clue what was ‘schwa’ and what was ‘schwa deletion’. I dug a little deeper and found out that people had written entire books on ‘schwa deletion’. And, actually what I really found out was in line with what people had done their research on. And this got me really excited about linguistics. And more interestingly, you know, what I saw is, like you said, language evolution, if you think of why this is there. So, Hindi uses exactly the same style of writing that we use for Sanskrit. But in Sanskrit, there is no ‘schwa deletion’. But if you look at all the modern Indian languages which came from Sanskrit, like Hindi, Bengali or Oriya, they have different degree of pronunciation different from Sanskrit. I am not getting into the detail of what exactly is ‘schwa deletion’, that’s besides the point. But the pronunciations evolve from the original language. The question I then eventually got interested in is, how this happens and why this happens. And then I ended up doing a Ph.D. with the same professor on, language evolution and how sound change happens across languages. And of course, being a computer scientist, I tried modelling all these things computationally. And then there was no looking back, I went, more and more deeper into language, linguistics and natural language processing.   Host: That's fascinating. And I know for sure that Kalika has got an equally interesting story, right? Kalika, you have a undergrad degree in chemistry? Kalika: I do.   Host: Linguistics doesn’t seem very much like a natural career progression from there. Kalika: Yes, it doesn’t. But before I start my story, I have one more interesting thing to say. When Monojit was presenting his ‘schwa deletion’ paper, I was in the audience. I was working somewhere else and I looked at my colleague at that time and said, “We should get this guy to come and work with us.” So, I actually was there when he was presenting that particular ‘schwa deletion’ paper. So, yes, I was a Science student, I was studying Chemistry, and after Chemistry, the thing in my family was everybody goes for higher studies, I rebelled. I was one of those difficult children that we now are very unhappy about. But I said that I didn’t want to study anymore. I definitely didn’t want to do Chemistry and I was going to be a journalist, like my dad. I had already got a job to work in a newspaper. And I went to the Jawaharlal Nehru University to pick up a form for my younger sister. And I looked at the university and said, “This is a nice place, I want to study here.” And then I looked at the prospectus, kind of flicked through it and said, “what’s interesting?”. And I looked at this thing called Linguistics, and it seemed very fascinating. I had no idea what linguistics was about. And then, there was also ancient history which I did know what it was about and it seemed interesting. So, I filled in forms and sat for the entrance exam, after having read like a thin, layman’s guide to linguistics I borrowed from the British Council Library. And I got through. And the interesting thing is that the linguistic entrance exam was in the morning, the ancient history exam was in the afternoon. This was peak summer in Delhi. There were no fans in the place where the exam was being held. So, after taking the linguistic exam, I thought I can’t sit for another exam in this heat and I left. So, I only took the linguistic exam. I got through, no one was more surprised than I was. And I saw it as a sign that I should be going. So, I started a course without having any idea what linguistics was and completely fell in love with the subject within the first month. And coming from a science background, I was very naturally attracted towards phonetics, which I think is, to really understand phonetics and speech science part of linguistics, you do need to

  Episode 002 | March 20, 2020 Enabling Rural Communities to Participate in Crowdsourcing, with Dr. Vivek Seshadri Crowdsourcing platforms and the gig economy have been around for a while. But are they equally accessible to all communities? Dr. Vivek Seshadri, a researcher at Microsoft Research India, doesn’t think so, and is trying to change this. On this podcast, Vivek talks about what motivated him to focus on research that can help underserved communities, and in particular, about Project Karya, a new platform to provide digital work to rural communities. The word “Karya” literally means “work” in a number of India languages. Vivek primarily works with the Technology for Emerging Markets group at Microsoft Research India. He received his bachelor's degree in Computer Science from IIT Madras, and a Ph.D. in Computer Science from Carnegie Mellon University where he worked on problems related to Computer Architecture and Systems. After his Ph.D., Vivek decided to work on problems that directly impact people, particularly in developing economies like India. Related · Microsoft Research India Podcast: More podcasts from MSR India · iTunes: Subscribe and listen to new podcasts on iTunes · Android · RSS Feed · Spotify · Google Podcasts · Email Transcript Vivek Seshadri: If you look at crowdsourcing platforms today, there are a number of challenges that actually prevent them from being accessible to people from rural communities. The first one is, most of these platforms contain tasks only in English. And all their task descriptions, everything, is in English which is completely inaccessible to rural communities. Secondly, if you go to rural India today, the notion of digital work is completely alien to them. And finally, there is a logistical challenge here. Most crowdsourcing platforms will assume that the end-user has a computer and constant access to internet. This is actually a luxury in many rural communities in India even today. (Music plays) Host: Welcome to the Microsoft Research India podcast, where we explore cutting-edge research that’s impacting technology and society. I’m your host, Sridhar Vedantham. Crowdsourcing platforms and the gig economy have been around for a while. But are they equally accessible to all communities? Dr. Vivek Seshadri, a researcher at Microsoft Research India, doesn’t think so, and is trying to change this. On this podcast, Vivek talks about what motivated him to focus on research that can help underserved communities, and in particular, about Project Karya, a new platform to provide digital work to rural communities. The word “Karya” literally means “work” in a number of India languages. Vivek primarily works with the Technology for Emerging Markets group at Microsoft Research India. He received his bachelor's degree in Computer Science from IIT Madras, and a Ph.D. in Computer Science from Carnegie Mellon University where he worked on problems related to Computer Architecture and Systems. After his Ph.D., Vivek decided to work on problems that directly impact people, particularly in developing economies like India. (Music plays) HOST: Vivek, welcome to the podcast. Vivek: Thanks, Sridhar. This is the first time I am doing anything like this, so I am really excited and a little bit nervous. Host: Oh, I don't think there's anything to be nervous about really here. You guys are used to speaking in public all the time. So, I'm sure it'll be fine. Vivek, you are a computer scientist and you did your PhD in Computer Science in Systems, right? What made you gravitate towards research that helps underserved communities, typically the kind of research that one associates with the ICTD space? Vivek: So, Sridhar, when I finished my PhD in 2016, I sort of had two decisions to make- should I stay in the US or should I move back to India? Should I stay in the same area that I am doing research in or should I move to a different field? Both these questions were sort of answered when I visited MSR and had interactions with people like Bill Thies. The kind of research that they were doing impressed me and also influenced me to make the decision to come back to India and work on similar problems that directly impact people. Host: That's interesting. So this is something that was brought upon by meeting people in the lab here rather than something that was there in your mind all along. Vivek: Absolutely. Actually, when I started my PhD, I wanted to come back and become professor in places like IIT or IISc. And when I moved back, I was actually introduced to MSR by one of my friends who actually visited MSR before me. And I just thought I'll pay a visit. And the conversations that I had with people here, sort of made my decision absolutely easy. Host: And the rest is history, as they say. Vivek: Absolutely. It’s been three years since I moved here and I couldn't be happier. Host: Great. So Vivek, walk us through this project called Karya, which I know you have been associated with for quite a while. What exactly is project Karya and what are your goals with that project? Vivek: So, there are two trends that enables or motivates the need for a project like Karya. The first trend is that there is a digital revolution in the world today, where improvements in technologies like Machine Learning are allowing people to interact with devices using natural language. The second trend is specific to India where we are trying to push towards a digital future which is creating a lot of tasks like audio transcription, document digitization, etc. Both these trends are going to result in a huge amount of what we call digital work. And the goal for project Karya is to take this digital work and make it accessible to people from rural communities who typically have very low incomes today and are predominantly stuck with physical labor. We believe completing these digital tasks and getting paid for them will be a valuable source of supplemental income for people from rural communities. Host: Crowdsourcing and crowdsourcing platforms have been around for quite a while now. And they are also well-established methods of gig work. So what's the need for another approach or a different framework like Karya? Vivek: That's a great question. If you look at crowdsourcing platforms today, there are a number of challenges that actually prevent them from being accessible to people from rural communities. Specifically, let me describe to you three challenges. The first one is, most of these platforms contain tasks only in English. And all their task descriptions, everything, is in English which is completely inaccessible to rural communities. Secondly, if you go to rural India today, the notion of digital work is completely alien to them. In fact, when we went to rural communities in our first visit and told them we will actually pay some money for completing some set of digital tasks, they looked at us in disbelief. Like they actually didn't believe that we are going to pay them until we actually did. So, there is this huge issue of awareness. And finally, there is a logistical challenge here. Most crowdsourcing platforms will assume that the end-user has a computer and constant access to internet. This is actually a luxury in many rural communities in India even today. Host: So, does Karya enable people to use their existing skillsets and knowledge to earn supplemental or extra income? Vivek: So, Sridhar, like I mentioned, there are two sources of digital work that we are looking at currently. One is creating label data sets for models like automatic speech recognition, and other language-based machine learning models. The second source of digital work that we are looking at is things like speech transcription or document digitization, which the government is very extremely interested in. Now depending on what type of task we are going to do, people may have to be able to read in their regional language or type in their regional language. Now, when it comes to reading, we find that most people from rural communities are adept at reading in their regional language. When it comes to typing, as you can imagine there are not many good keyboards that will allow you to type in your local language. This is something that most people in rural communities have never done before. In fact, even though, most people in rural communities are not familiar with English, they actually use a very crude form of transliteration to actually communicate in their regional languages. That's what we observed- most people used WhatsApp and when communicating with each other they actually use transliteration in English and not type in their native language. Host: So, you are saying that there is a large number of people who are actually typing in the English script, but the language that they are representing is their own vernacular. Vivek: Exactly. And the transliteration is very crude. They know what sounds each English alphabet corresponds to and they just put together a bunch of characters next to each other and it's almost like they have created a whole new script for their local language. Host: Right. Vivek: But something like that wouldn't actually be useful for us. We would want them to type in their local language. For instance, let’s take an example of document digitization. The idea there is, the government has a whole of government records which contain hand-written words in their local language. It could be names of people, it could be addresses, etc. When I want to digitize these documents, I may actually want someone to type out the names that they see in the document in the local language. Now, there, I would actually want them to use the native script. And not, some crude form of transliteration. Host: Sure. Vivek: So, in this particular case, we actually used a keyboard that was developed by IIT Bombay called Swarachakra. And our users actually learnt to use that keyboard within

Episode 001 | March 06, 2020 Dr. Eric Horvitz is a technical fellow at Microsoft, and is director of Microsoft Research Labs, including research centers in Redmond, Washington, Cambridge, Massachusetts, New York, New York, Montreal, Canada, Cambridge, UK, and Bengaluru, India. He is one of the world’s leaders in AI, and a thought leader in the use of AI in the complexity of the real world. On this podcast, we talk to Dr. Horvitz about a wide range of topics, including his thought leadership in AI, his study of AI and its influence on society, the potential and pitfalls of AI, and how useful AI can be in a country like India. Transcript Eric Horvitz: Humans will always want to make connection with humans, sociologists, social workers, physicians, teachers, we’re always going to want to make human connections and have human contacts. I think they’ll be amplified in a world of richer automation so much so that even when machines can generate art and write music, even music with lyrics that might put tear in someone’s eye if they didn’t know it was a machine, that will lead us to say, “Is that written by a human. I want to hear a song sung by a human who experienced something, the way I would experience something, not a machine.” And so I think human touch, human experience, human connection will grow even more important in a world of rising automation and those kinds of tasks and abilities will be even more compensated than they are today. (music plays) Host: Welcome to the Microsoft Research India podcast, where we explore cutting-edge research that’s impacting technology and society. I’m your host, Sridhar Vedantham. Host: Our guest today is Dr. Eric Horvitz, Technical Fellow and director of the Microsoft Research Labs. It’s tremendously exciting to have him as the first guest on the MSR India podcast because of his stature as a leader in research and his deep understanding of the technical and societal impact of AI. Among the many honors and recognitions Eric has received over the course of his career are the Feigenbaum Prize and the Allen Newell Prize for contributions to AI, and the CHI Academy honor for his work at the intersection of AI and human-computer interaction. He has been elected fellow of the National Academy of Engineering (NAE), the Association of Computing Machinery (ACM) and the Association for the Advancement of AI , where he also served as president. Eric is also a fellow of the American Association for the Advancement of Science (AAAS), the American Academy of Arts and Sciences, and the American Philosophical Society. He has served on advisory committees for the National Science Foundation, National Institutes of Health, President’s Council of Advisors on Science and Technology, DARPA, and the Allen Institute for AI. Eric has been deeply involved in studying the influences of AI on people and society, including issues around ethics, law, and safety. He chairs Microsoft’s Aether committee on AI, effects, and ethics in engineering and research. He established the One Hundred Year Study on AI at Stanford University and co-founded the Partnership on AI. Eric received his PhD and MD degrees at Stanford University. On this podcast, we talk to Eric about his journey in Microsoft Research, his own research, the potential and pitfalls he sees in AI, how AI can help in countries like India, and much more.   Host: Eric, welcome to the podcast. Eric Horvitz: It’s an honor to be here. I just heard I am the first interviewee for this new series. Host: Yes, you are, and we are really excited about that. I can’t think of anyone better to do the first podcast of the series with! There’s something I’ve been curious about for a long time. Researchers at Microsoft Research come with extremely impressive academic credentials. It’s always intrigued me that you have a medical degree and also a degree in computer science. What was the thinking behind this and how does one complement the other in the work that you do? Eric Horvitz: One of the deep shared attributes of folks at Microsoft Research and so many of our colleagues doing research in computer science is deep curiosity, and I’ve always been one of these folks that’s said “why” to everything. I’m sure my parents were frustrated with my sequence of whys starting with one question going to another. So I’ve been very curious as an undergraduate. I did deep dives into physics and chemistry. Of course, math to support it all – biology and by the time I was getting ready to go to grad school I really was exploring so many sciences, but the big “why” for me that I could not figure out was the why of human minds, the why of cognition. I just had no intuition as to how the cells, these tangles of the cells that we learn about in biology and neuroscience could have anything to do with my second to second experience as being a human being, and so you know what I have to just spend my graduate years diving into the unknowns about this from the scientific side of things. Of course, many people have provided answers over the centuries- some of the answers are the foundations of religious beliefs of various kinds and religious systems. So I decided to go get an MD-PhD, just why not understand humans deeply and human minds as well as the scientific side of nervous systems, but I was still an arc of learning as I hit grad school at Stanford and it was great to be at Stanford because the medical school was right next to the computer science department. You can literally walk over and I found myself sitting in computer science classes, philosophy classes, the philosophy of mind-oriented classes and cognitive psychology classes and so there to the side of that kind of grad school life and MD-PhD program, there are anatomy classes that’s being socialized into the medical school class, but I was delighted by the pursuit of- you might call it the philosophical and computational side of mind- and eventually I made the jump, the leap. I said “You know what, my pursuit is principles, I think that’s the best hope for building insights about what’s going on” and I turned around those principles into real world problems in particular since that was, had a foot in the medical school, how do we apply these systems in time-critical settings to help emergency room, physicians and trauma surgeons? Time critical action where computer systems had to act quickly, but had to really also act precisely when they maybe didn’t have enough time to think all the way and this led me to what I think is an interesting direction which is models of bounded-rationality which I think describes us all. Host: Let’s jump into a topic that seems to be on everybody’s mind today – AI. Everyone seems to have a different idea about what AI actually is and what it means to them. I also constantly keep coming across people who use AI and the term ML or machine learning as synonyms. What does AI mean to you and do you think there’s a difference between AI and ML? Eric Horvitz: The scientists and engineers that first used the phrase artificial intelligence did so in a beautiful document that’s so well written in terms of the questions it asks that it could be a proposal today to the National Science Foundation, and it would seem modern given that so many the problems have not been solved, but they laid out the vision including the pillars of artificial intelligence. This notion of perception building systems that could recognize or perceive sense in the world. This idea of reasoning with logic or other methods to reason about problems, solve problems, learning how can they become better at what they did with experience with other kinds of sources of information and this final notion they focused on as being very much in the realm of human intelligence language, understanding how to manipulate symbols in streams or sequences to express concepts and use of language. So, learning has always been an important part of artificial intelligence, it’s one of several pillars of work, it’s grown in importance of late so much so that people often write AI/ML to refer to machine learning but it’s one piece and it’s an always been an important piece of artificial intelligence. Host: I think that clarifies the difference between AI and ML. Today, we see AI all around us. What about AI really excites you and what do you think the potential pitfalls of AI could be? Eric Horvitz: So let me first say that AI is a constellation of technologies. It’s not a single technology. Although, these days there’s quite a bit of focus on the ability to learn how to predict or move or solve problems via machine learning analyzing large amounts of data which has become available over the last several decades, when it used to be scarce. I’m most excited about my initial goals to understand human minds. So, whenever I read it a paper on AI or see a talk or see a new theorem being proved my first reaction is, how does it grow my understanding, how does it help to answer the questions that have been long-standing in my mind about the foundations of human cognition? I don’t often say that to anybody but that’s what I’m thinking. Secondly, my sense is what a great endeavor to be pushing your whole life to better understand and comprehend human minds. It’s been a slow slog. However, insights have come about advances and how they relate to those questions but along the way what a fabulous opportunity to apply the latest advances to enhancing the lives of people, to empowering people in new ways and to create new kinds of automation that can lead to new kinds of value, new kinds of experiences for people. The whole notion of augmenting human intellect with machines has been something that’s fascinated me for many decades. So I love the fact that we can now leverage these technologies and apply them even though we’re still very early on in how these ideas relate to what’s going on in our minds. Applications include healthcare. There’s so much to do in healthca