[TEASER]
[MUSIC PLAYS UNDER DIALOGUE]
AKSHAY NAMBI: For me, research is just not about pushing the boundaries of the knowledge. It's
about ensuring that these advancements translate to meaningful impact on the ground. So, yes,
the big goals that guide most of my work is twofold. One, how do we build
technology that's scaled to benefit large populations? And two, at the same time,
I'm motivated by the challenge of tackling complex problems. That provides opportunity to explore,
learn, and also create something new, and that's what keeps me excited.
[TEASER ENDS]
CHRIS STETKIEWICZ: You're listening to Ideas, a Microsoft Research Podcast that dives deep into
the world of technology research and the profound questions behind the code. In this series, we'll
explore the technologies that are shaping our future and the big ideas that propel them forward.
[MUSIC FADES]
I'm your guest host, Chris Stetkiewicz. Today, I'm talking to Akshay Nambi. Akshay is a principal
researcher at Microsoft Research. His work lies at the intersection of systems, AI, and machine
learning with a focus on designing, deploying, and scaling AI systems to solve compelling
real-world problems. Akshay's research extends across education, agriculture, transportation,
and energy. He is currently working on enhancing the quality and reliability of AI systems by
addressing critical challenges such as reasoning, grounding, and managing complex queries.
Akshay, welcome to the podcast.
AKSHAY NAMBI: Thanks for having me.
STETKIEWICZ: I'd like to begin by asking you to tell us your origin
story. How did you get started on your path? Was there a big idea or
experience that captured your imagination or motivated you to do what you're doing today?
NAMBI: If I look back, my journey into research wasn't a straight line. It was more about
discovering my passion through some unexpected opportunities and also finding purpose along
the way. So before I started with my undergrad studies, I was very interested in electronics
and systems. My passion for electronics, kind of, started when I was in school. I was more
like an average student, not a nerd or not too curious, but I was always tinkering around,
doing things, building stuff, and playing with gadgets and that, kind of, made me very keen on
electronics and putting things together, and that was my passion. But sometimes things don't go as
planned. So I didn't get into the college which I had hoped to join for electronics, so I ended up
pursuing computer science, which wasn't too bad either. So during my final year of bachelor's,
I had to do a final semester project, which turned out to be a very pivotal moment. And
that's when I got to know this institute called Indian Institute of Science (IISc), which is a top
research institute in India and also globally. And I had a chance to work on a project there. And it
was my first real exposure to open-ended research, right, so I remember ... where we were trying to
build a solution that helped to efficiently construct an ontology for a specific domain,
which simply means that we were building systems to help users uncover relationships
in the data and allow them to query it more efficiently, right. And it was super exciting
for me to design and build something new. And that experience made me realize that I wanted to pursue
research further. And right after that project, I decided to explore research opportunities,
which led me to join Indian Institute of Science again as a research assistant.
STETKIEWICZ: So what made you want to take the
skills you were developing and apply them to a research career?
NAMBI: So interestingly when I joined IISc, the professor I worked with specialized in
electronics, so things come back, so something I had always been passionate about. And I was
the only computer science graduate in the lab at that time with others being electronic engineers,
and I didn't even know how to solder. But the lab environment was super encouraging, collaborative,
so I, kind of, caught up very quickly. In that lab, basically, I worked on several projects in
the emerging fields of embedded device and energy harvesting systems. Specifically,
we were designing systems that could harvest energy from sources like sun, hydro, and even
RF (radio frequency) signals. And my role was kind of twofold. One, I designed circuits and systems
to make energy harvesting more efficient so that you can store this energy. And then I also wrote
programs, software, to ensure that the harvested energy can be used efficiently. For instance,
as we harvest some of this energy, you want to have your programs run very quickly so that
you are able to sense the data, send it to the server in an efficient way. And one of the most
exciting projects I worked during that time was on data-driven agriculture. So this was back in 2008,
2009, right, where we developed an embedded system device with sensors to monitor the agricultural
fields, collecting data like soil moisture, soil temperature. And that was sent to the agronomists
who were able to analyze this data and provide feedback to farmers. In many remote areas, still
access to power is a huge challenge. So we used many of the technologies we were developing in the
lab, specifically energy harvesting techniques, to power these sensors and devices in the rural
farms, and that's when I really got to see firsthand how technology could help people's
lives, particularly in rural settings. And that's what, kind of, stood out in my experience at IISc,
right, was that it was [the] end-to-end nature of the work. And it was not just writing code or
designing circuits. It was about identifying the real-world problems, solving them efficiently,
and deploying solutions in the field. And this cemented my passion for creating
technology that solves real-world problems, and that's what keeps me driving even today.
STETKIEWICZ: And as you're thinking about those problems that you want to try and solve, where did
you look for, for inspiration? It sounds like some of these are happening right there in your home.
NAMBI: That's right. Growing up and living in India, I've been surrounded by these, kind of,
many challenges. And these are not distant problems. These are right in front of us.
And some of them are quite literally outside the door. So being here in India provides
a unique opportunity to tackle some of the pressing real-world challenges in agriculture,
education, or in road safety, where even small advancements can create significant impact.
STETKIEWICZ: So how would you describe your
research philosophy? Do you have some big goals that guide you?
NAMBI: Right, as I mentioned, right, my research philosophy is mainly rooted in solving real-world
problems through end-to-end innovation. For me, research is just not about pushing the boundaries
of the knowledge. It's about ensuring that these advancements translate to meaningful impact on
the ground, right. So, yes, the big goals that guide most of my work is twofold. One, how do
we build technology that's scaled to benefit large populations? And two, at the same time,
I'm motivated by the challenge of tackling complex problems. That provides opportunity to explore,
learn, and also create something new. And that's what keeps me excited.
STETKIEWICZ: So let's talk a little bit about your journey at Microsoft Research. I know you
began as an intern, and some of the initial work you did was focused on computer vision,
road safety, energy efficiency. Tell us about some of those projects.
NAMBI: As I was nearing the completion of my PhD, I was eager to look for opportunities
in industrial labs, and Microsoft Research obviously stood out as an exciting opportunity.
And additionally, the fact that Microsoft Research India was in my hometown, Bangalore, made it even
more appealing. So when I joined as an intern, I worked together with Venkat Padmanabhan,
who now leads the lab, and we started this project called HAMS, which stands for Harnessing
AutoMobiles for Safety. As you know, road safety is a major public health issue globally,
responsible for almost 1.35 million fatalities annually and with the situation being even more
severe in countries like India. For instance, there are estimates that there's a life lost
on the road every four minutes in India. When analyzing the factors which affect road safety,
we saw mainly three elements. One, the vehicle. Second, the infrastructure.
And then the driver. Among these, the driver plays the most critical role in many incidents,
whether it's over-speeding, driving without seat belts, drowsiness, fatigue, any of these,
right. And this realization motivated us to focus on driver monitoring, which led to the development
of HAMS. In a nutshell, HAMS is basically a smartphone-based system where you're mounting
your smartphone on a windshield of a vehicle to monitor both the driver and the driving in
real time with the goal of improving road safety. Basically, it observes key aspects such as where
the driver is looking, whether they are distracted or fatigued, while also considering the external
driving environment, because we truly believe to improve road safety, we need to understand
not just the driver's action but also the context in which they are driving. For example,
if the smartphone's accelerometer detects sharp braking, the system would automatically check the
distance to the vehicle in the front using the rear camera and whether the driver was
distracted or fatigued using the front camera. And this holistic approach ensures a more accurate
and comprehensive assessment of the driving behavior, enabling a more meaningful feedback.
STETKIEWICZ: So that sounds like a system that's got several moving parts to it. And
I imagine you had some technical challenges you had to deal with there. Can you talk about that?
NAMBI: One of our guiding principles in HAMS was to use commodity, off-the-shelf smartphone
devices, right. This should be affordable, in the range of $100 to $200, so that you can just take
out regular smartphones and enable this driver and driving monitoring. And that led to handling
several technical challenges. For instance, we had to develop efficient computer vision algorithms
that could run locally on the device with cheap smartphone processing units while still performing
very well at low-light conditions. We wrote multiple papers and developed many of the novel
algorithms which we implemented on very low-cost smartphones. And once we had such a monitoring
system, right, you can imagine there’s several deployment opportunities, starting from fleet
monitoring to even training new drivers, right. However, one application we hadn't originally
envisioned but turned out to be its most impactful use case even today is automated driver's
license testing. As you know, before you get a license, a driver is supposed to pass a test,
but what happens in many places, including India, is that licenses are issued with very
minimal or no actual testing, leading to unsafe and untrained drivers on the road. At the same
time as we were working on HAMS, Indian government were looking at introducing technology to make
testing more transparent and also automated. So we worked with the right set of partners, and we
demonstrated to the government that HAMS could actually completely automate the entire license
testing process. So we first deployed this system in Dehradun RTO (Regional Transport Office)—which
is the equivalent of a DMV in the US—in 2019, working very closely with RTO officials to define
what should be some of the evaluation criteria, right. Some of these would be very simple like,
oh, is it the same candidate who is taking the test who actually registered for the test,
right? And whether they are wearing seat belts. Did they scan their mirrors before taking a left
turn and how well they performed in tasks like reverse parking and things like that.
STETKIEWICZ: So what's been the government response to that? Have
they embraced it or deployed it in a wider extent?
NAMBI: Yes, yes. So after the deployment in Dehradun in 2019, we actually open sourced
the entire HAMS technology and our partners are now working with several state governments and
scaled HAMS to several states in India. And as of today, we have around 28 RTOs where HAMS is
actually being deployed. And the pass rate of such license test is just 60% as compared to
90-plus percent with manual testing. That's the extensive rigor the system brings in. And now what
excites me is after nearly five years later, we are now taking the next step in this project where
we are now evaluating the long-term impact of this intervention on driving behavior and road safety.
So we are collaborating with Professor Michael Kremer, who is a Nobel laureate and professor at
University of Chicago, and his team to study how this technology has influenced driving patterns
and accident rates over time. So this focus on closing the loop and moving beyond just deployment
in the field to actually measuring the real impact, right, is something that truly excites
me and that makes research at Microsoft is very unique. And that is actually one of the reasons
why I joined Microsoft Research as a full-time after my internship, and this unique flexibility
to work on real-world problems, develop novel research ideas, and actually collaborate with
partners both internally and externally to deploy at scale is something that is very unique here.
STETKIEWICZ: So have you actually received
any evidence that the project is working? Is driving getting safer?
NAMBI: Yes, these are very early analysis, and there are very positive insights we are getting
from that. Soon we will be releasing a white paper on our study on this long-term impact.
STETKIEWICZ: That’s great. I look forward to that one. So you've also done some interesting
work involving the Internet of Things, with an emphasis on making it more reliable and
practical. So for those in our audience who may not know, the Internet of Things,
or IoT, is a network that includes billions of devices and sensors in things like smart
thermostats and fitness trackers. So talk a little bit about your work in this area.
NAMBI: Right, so IoT, as you know, is already transforming several industries with billions
of sensors being deployed in areas like industrial monitoring, manufacturing,
agriculture, smart buildings, and also air pollution monitoring. And if you think about it,
these sensors provide critical data that businesses rely for decision making. However,
a fundamental challenge is ensuring that the data collected from these sensors is actually
reliable. If the data is faulty, it can lead to poor decisions and inefficiencies. And the
challenge is that these sensor failures are always not obvious. What I mean by that is
when a sensor stops working, it always doesn't stop sending data, but it often continues to
send some data which appear to be normal. And that's one of the biggest problems,
right. So detecting these errors is non-trivial because the faulty sensors can mimic real-world
working data, and traditional solutions like deploying redundant sensors or even manually
inspecting them are very expensive, labor intensive, and also sometimes infeasible,
especially for remote deployments. Our goal in this work was to develop a simple and efficient
way to remotely monitor the health of the IoT sensors. So what we did was we hypothesized
that most sensor failures occurred due to the electronic malfunctions. It could be either
due to short circuits or component degradation or due to environmental factors such as heat,
humidity, or pollution. Since these failures originate within the sensor hardware itself,
we saw an opportunity to leverage some of the basic electronic principles to create a novel
solution. The core idea was to develop a way to automatically generate a fingerprint for
each sensor. And by fingerprint, I mean the unique electrical characteristic exhibited by a properly
working sensor. We built a system that could devise these fingerprints for different types
of sensors, allowing us to detect failures purely based on the sensors internal characteristics,
that is the fingerprint, and even without looking at the data it produces. Essentially what it means
now is that we were able to tag each sensor data with a reliability score, ensuring verifiability.
STETKIEWICZ: So how does that technology get deployed in the
real world? Is there an application where it's being put to work today?
NAMBI: Yes, this technology, we worked together with Azure IoT and open-sourced it
where there were several opportunities and several companies took the solution into their systems,
including air pollution monitoring, smart buildings, industrial monitoring. The one
which I would like to talk about today is about air pollution monitoring. As you know, air
pollution is a major challenge in many parts of the world, especially in India. And traditionally,
air quality monitoring relies on these expensive fixed sensors, which provide limited coverage.
On the other hand, there is a rich body of work on low-cost sensors, which can offer
wider deployment. Like, you can put these sensors on a bus or a vehicle and have it move around
the entire city, where you can get much more fine-grained, accurate picture on the ground.
But these are often unreliable because these are low-cost sensors and have reliability issues. So
we collaborated with several startups who were developing these low-cost air pollution sensors
who were finding it very challenging to gain trust because one of the main concerns was the
accuracy of the data from low-cost sensors. So our solution seamlessly integrated with these sensors,
which enabled verification of the data quality coming out from these low-cost
air pollution sensors. So this bridged the trust gap, allowing government agencies
to initiate large-scale pilots using low-cost sensors for fine-grain air-quality monitoring.
STETKIEWICZ: So as we're talking about evolving technology, large language models,
or LLMs, are also enabling big changes, and they're not theoretical. They're happening
today. And you've been working on LLMs and their applicability to real-world
problems. Can you talk about your work there and some of the latest releases?
NAMBI: So when ChatGPT was first released, I, like many people, was very skeptical. However,
I was also curious both of how it worked and, more importantly, whether it could accelerate
solutions to real-world problems. That led to the exploration of LLMs in education, where
we fundamentally asked this question, can AI help improve educational outcomes? And this was one of
the key questions which led to the development of Shiksha copilot, which is a genAI-powered
assistant designed to support teachers in their daily work, starting from helping them to create
personalized learning experience, design assignments, generate hands-on activities,
and even more. Teachers today universally face several challenges, from time management to lesson
planning. And our goal with Shiksha was to empower them to significantly reduce the time spent on
this task. For instance, lesson planning, which traditionally took about 60 minutes,
can now be completed in just five minutes using the Shiksha copilot. And what makes Shiksha unique
is that it's completely grounded in the local curriculum and the learning objectives, ensuring
that the AI-generated content aligns very well with the pedagogical best practices. The system
actually supports multilingual interactions, multimodal capabilities, and also integration
with external knowledge base, making it very highly adaptable for different curriculums.
Initially, many teachers were skeptical. Some feared this would limit their creativity.
However, as they began starting to use Shiksha, they realized that it didn't replace their
expertise, but rather amplified it, enabling them to do work faster and more efficiently.
STETKIEWICZ: So, Akshay, the last time you and I talked about Shiksha copilot,
it was very much in the pilot phase and the teachers were just getting their hands on it.
So it sounds like, though, you've gotten some pretty good feedback from them since then.
NAMBI: Yes, so when we were discussing, we were doing this six-month pilot with 50-plus
teachers where we gathered overwhelming positive feedback on how technologies are helping teachers
to reduce time in their lesson planning. And in fact, they were using the system so
much that they really enjoyed working with Shiksha copilot where they were able to do
more things with much less time, right. And with a lot of feedback from teachers,
we have improved Shiksha copilot over the past few months. And starting this academic year, we have
already deployed Shiksha to 1,000-plus teachers in Karnataka. This is with close collaboration with
our partners in … with the Sikshana Foundation and also with the government of Karnataka.
And the response has been already incredibly encouraging. And looking ahead, we are actually
focusing on again, closing this loop, right, and measuring the impact on the ground, where we are
doing a lot of studies with the teachers to understand not just improving efficiency of
the teachers but also measuring how AI-generated content enriched by teachers is actually enhancing
student learning objectives. So that's the study we are conducting, which hopefully will close this
loop and understand our original question that, can AI actually help improve educational outcomes?
STETKIEWICZ: And is the deployment primarily in rural areas, or does
it include urban centers, or what's the target?
NAMBI: So the current deployment with 1,000 teachers is a combination of
both rural and urban public schools. These are covering both English medium
and Kannada medium teaching schools with grades from Class 5 to Class 10.
STETKIEWICZ: Great. So Shiksha was focused on helping teachers and making their jobs easier,
but I understand you're also working on some opportunities
to use AI to help students succeed. Can you talk about that?
NAMBI: So as you know, LLMs are still evolving and inherently they are fragile, and deploying them in
real-world settings, especially in education, presents a lot of challenges. With Shiksha,
if you think about it, teachers remain in control throughout the interaction,
making the final decision on whether to use the AI-generated content in the classroom
or not. However, when it comes to AI tutors for students, the stakes are slightly higher,
where we need to ensure the AI doesn't produce incorrect answers, misrepresent concepts,
or even mislead explanations. Currently, we are developing solutions to enhance accuracy and also
the reasoning capabilities of these foundational models, particularly solving math problems.
This represents a major step towards building AI systems that's much more holistic personal tutors,
which help student understanding and create more engaging, effective learning experience.
STETKIEWICZ: So you've talked about working in computer vision and IoT and LLMs. What do
those areas have in common? Is there some thread that weaves through the work that you're doing?
NAMBI: That's a great question. As a systems researcher, I'm quite interested in this
end-to-end systems development, which means that my focus is not just about improving a
particular algorithm but also thinking about the end-to-end system, which means that I,
kind of, think about computer vision, IoT, and even LLMs as tools, where we
would want to improve them for a particular application. It could be agriculture, education,
or road safety. And then how do you think this holistically to come up with the best efficient
system that can be deployed at population scale, right. I think that's the connecting story here,
that how do you have this systemic thinking which kind of takes the existing tools,
improves them, makes it more efficient, and takes it out from the lab to real world.
STETKIEWICZ: So you're working on some very powerful technology that is creating
tangible benefits for society, which is your goal. At the same time, we're still in the
very early stages of the development of AI and machine learning. Have you ever thought about
unintended consequences? Are there some things that could go wrong, even if we
get the technology right? And does that kind of thinking ever influence the development process?
NAMBI: Absolutely. Unintended consequences are something I think about deeply. Even
the most well-designed technology can have these ripple effects that we may not fully anticipate,
especially when we are deploying it at population scale. For me, being proactive
is one of the key important aspects. This means not only designing the technology at
the lab but actually also carefully deploying them in real world, measuring its impact,
and working with the stakeholders to minimize the harm. In most of my work, I try to work
very closely with the partner team on the ground to monitor, analyze, how the technology is being
used and what are some of the risks and how can we eliminate that. At the same time, I also remain
very optimistic. It's also about responsibility. If we are able to embed societal values, ethics,
into the design of the system and involve diverse perspectives, especially from people
on the ground, we can remain vigilant as the technology evolves and we can create systems
that can truly deliver immense societal benefits while addressing many of the potential risks.
STETKIEWICZ: So we've heard a lot of great examples today about building technology to
solve real-world problems and your motivation to keep doing that. So as you look ahead,
where do you see your research going next? How will people be better off
because of the technology you develop and the advances that they support?
NAMBI: Yeah, I'm deeply interested in advancing AI systems that can truly assist anyone in their
daily tasks, whether it's providing personalized guidance to a farmer in a rural village, helping
a student get instant 24 by 7 support for their learning doubts, or even empowering professionals
to work more efficiently. And to achieve this, my research is focusing on tackling some of the
fundamental challenges in AI with respect to reasoning and reliability and also making sure
that AI is more context aware and responsive to evolving user needs. And looking ahead,
I envision AI as not just an assistant but also as an intelligent and equitable copilot
seamlessly integrated into our everyday life, empowering individuals across various domains.
STETKIEWICZ: Great. Well, Akshay, thank you for joining us on Ideas. It's been a pleasure.
[MUSIC]
NAMBI: Yeah, I really enjoyed talking to you, Chris. Thank you.
STETKIEWICZ: Till next time.
[MUSIC FADES]